Clovis Pawula

Dr Agnès

Dr Valéry Malécot

Dr Alix Pernet

Bernadette Julier

Patrice

Didier Peltier

Jérome Duminil

Leen Leus D'

Philippe Bardin

Jordane Cordier

Julien Mondion

Jasmin Grahić

Alexander Mrkvicka

Fuad Gasi

Nikola Tomljenovic

Ivan Pejic

Walter Rottensteiner

Karel Nepras

Pavel Dřevojan

Viktor Kereny-Nagy

Adriano Soldano

Pascale Marciano

Silvia Paola Assini

Fabrizio Bonali

Elena Tofan-Dorofeev

Adam Kapler

Renata Piwowarczyk

Karolina Ruraz

Grzegorz Łazarski

Anna Cwener

Ma- Teusz Wolanin

Zygmunt Dajdok

Veronica Sarateanu

Laszlo Bartha

Ladislav Bakay

Ivan Schanzer

Daniel Gomez

Segundo Rios

Halyna Olijar

Jérôme Garcia

Tatiana Thouroude

Gilles Michel

Jordan Marie-Magdelaine

Charlène Fy

Er- Win Hauser

Albin Lugmair

Christian Gilli

Emanuel Trimmer-Fink

Michael Hohla

Gerhard Kleesadl

Uwe Raabe

Georg Mrkvicka

Monika Fickel

Walter Mucher

Mar- Ica Rottensteiner

Jurgen Herbst

Franz G Dunkel

J Schach

Lenz Meierott

Anette Rosenbauer

Christof Nikolaus Shröder

Otto Elsner

Cristina Marie

Vanessa Martínez- Francés

David Pastor

Olesya Bezsmertha

Harald Per

Salvesen

Rosa gallica L. and other Gallic roses, origin(s) and role in the genesis of cultivated roses

Rosa gallica L., a perennial, tetraploid, heterozygous species that propagates naturally by seed and sucker, occurs in the wild, mainly in Europe, and as cultivars. In France, its wild genetic diversity is geographically structured. In addition, this species is thought to be at the origin of the first horticultural groups (Hybrid Gallica, Centifolia, Alba, Damas...) of roses from which modern roses are derived by hybridization. The aims of this thesis are (1) to identify the factors responsible for the structure of French populations by characterizing European wild diversity, and (2) to assess the contribution of R. gallica to the first horticultural groups. Nearly 1,600 individuals, both wild, sampled over almost the entire European range, and cultivated were genotyped by sequencing newly developed microsatellites. To analyze these data in polyploids, a method for predicting ploidy levels was developed. French diversity is particularly structured compared to the rest of Europe, suggesting multiple origins in France. As in the rest of Europe, populations on the eastern side of France seem to have originated by natural dispersal from a glacial refugium located further south. In contrast, populations on the western side of the country appear to be the result of more recent natural or human-mediated dispersal. Unlike the Hybrid Gallica, the other ancient horticultural groups are the result of interspecific hybridizations. These findings represent a step towards understanding the evolutionary history of R. gallica.

L'image du chercheur errant, seul dans son laboratoire obscur, est bien éloignée de l'atmosphère collaborative qui caractérise la recherche moderne. Ainsi, ce travail de thèse ne devrait pas porter qu'un seul nom d'auteur mais plutôt des dizaines. C'est pourquoi je vais m'attacher ici à remercier toutes les personnes qui ont rendu cette thèse réalisable par leur contribution aux diverses tâches mais aussi par leur soutien moral.

Je pense tout d'abord à mon encadrement sans qui cette thèse n'aurait jamais vu le jour.

Je voudrais donc remercier Agnès Grapin pour avoir endossé la fonction de directrice de thèse. De plus, je tiens à exprimer ma gratitude à Valéry Malécot pour avoir fait partie de l'encadrement et pour cette vision un peu différente qu'il a pu apporter. Merci à mon autre encadrante, Alix Pernet, nos très nombreux échanges m'ont permis d'avancer dans ma réflexion. J'espère avoir apporté ma pierre à ce projet sur la diversité de Rosa gallica que tu portes depuis tant d'années. Merci pour ton investissement sans faille tout au long de cette thèse, j'ai vraiment apprécié ces trois ans de collaboration. Enfin, j'aimerais inclure dans ce paragraphe dédié à l'encadrement Jérémy Clotault. Tu as été l'homme de l'ombre de cette thèse, bien plus investi que ton absence de l'encadrement officiel ne le suggère. Nos réflexions communes sur l'évolution des populations et sur d'autres sujets plus ou moins liés à la thèse ont toujours été prolifiques. Merci aussi d'avoir éveillé mon intérêt pour l'évolution à l'occasion d'un stage, effectué il y a bientôt cinq ans.

Je voudrais aussi remercier Florian Jabbour, Volker Wissemann et Roberto Bacilieri d'avoir fait partie de mon Comité de Suivi Individuel. Vos remarques m'ont permis de recentrer mon travail afin de gérer ce marathon qu'est la thèse.

Foreword

This thesis deals with the evolutionary history of the French rose, scientifically known as Rosa gallica, an important species in the history of rose breeding before the 19 th The present work benefits from the results obtained by previous doctoral researches done by Mathilde Liorzou (2013[START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF] and by Kevin Debray (2016-2019). In her project, Liorzou focused on the breeding history of roses during the 19 th century and revealed that the extensive use of Asian cultivars in breeding programs led to a shift from a European to an Asian genetic background of cultivars. Subsequently, Debray developed a stepwise procedure to unravel phylogenetic relationships among wild Rosa species.

Concentrating on R. gallica, the present project aims to bridge the gap between these two former projects (1) by unraveling the natural history at the species level and [START_REF] Koopman | AFLP markers as a tool to reconstruct complex relationships: A case study in Rosa (Rosaceae)[END_REF] by uncovering the genesis of European cultivated roses. This project is therefore at a narrower scale compared to Debray's and focuses on an earlier time frame in comparison to Liorzou's project.

The thesis project began with the design of the sampling scheme. Prior to the initiation of the project, the GDO team had collected samples from thousands of cultivated roses and hundreds of wild samples from 74 French wild sites. These samples were preserved by the Pome fruit and rose biological resources center. However, the existing samples needed to be supplemented to establish a suitable sampling scheme that would adequately address the research questions of the project. To achieve this, during the first 14 months, I identified, contacted, and collaborated with more than 40 people across 14 countries, establishing a network of collectors to acquire wild samples of R. gallica from abroad. This campaign was remarkably successful, with 121 sites across the fourteen countries sampled during the 2021 season. Furthermore, we improved the French sampling scheme by collecting samples from 32 previously unsampled sites. Finally, we also completed the cultivated sampling scheme. In parallel, we initiated a development project in collaboration with the Genome Transcriptome Facility of Bordeaux (PGTB), named "PolyRosa", to develop a panel of Simple Sequence Repeat markers genotyped by amplicon sequencing. The second year focused (i) on developing a DNA extraction protocol effective on the recalcitrant dried mature leaves of roses, the usual protocol adapted to young freeze-dried leaves having poor performance, (ii) on the extraction of most of the newly collected samples and (iii) on genotyping of the entire sample collection with the new method of SSR sequencing. During this year, I also had the opportunity to collaborate with Eduardo Ordoñez. In his 6-month MSc training period, Eduardo explored the relationship between wild R. gallica and cultivated roses. The third year was dedicated to data analysis and manuscript writing.

The present document begins with a general introduction to R. gallica and population genetics. This chapter first traces back the use of roses, particularly R. gallica, through history, then describes the diversity of roses and R. gallica, both wild and cultivated, and finally introduces key concepts and methods of population genetics. The second chapter presents the development of the molecular markers we used in this thesis. It also demonstrates how we developed a method to estimate the ploidy level based on the acquired molecular data. The third chapter presents the investigation we conducted on the wild diversity of R. gallica. The fourth one is dedicated to the study of the relationship between wild R. gallica and the first European cultivated roses. The dissertation concludes with a general discussion on the implications of our results for understanding the evolutionary history of R. gallica and its conservation. We also explain why the approach we used is relevant for orphan species when extensive sampling is needed.

Except for the introduction and the general discussion, the chapters are written following a scientific article structure. Therefore, they are readable almost independently from each other, although they contain some repetitions.

List of Figures

List of Supplementary Figures

Neolithic

Roses and humans share a long history that preceded the use of the modern Rosa classification that includes between 150 to 200 species [START_REF] Rehder | Rosa L." In: Manual of cultivated trees and shrubs hardy in North America[END_REF]. Ancient traces of rose use can be difficult to attribute to a specific Rosa species, at best to a group of species.

This issue is emphasized by the nature of the descriptions that were not botanical but rather spiritual, or decorative [START_REF] Tucker | Identification of the Rose, Sage, Iris, and Lily in the "Blue Bird Fresco" from Knossos, Crete (ca. 1450 B.C.E.)[END_REF]). The oldest records come from archaeobotanical studies. In a German archaeological site, well-preserved hips of a Rosa species were found in a wooden chamber built 7,000 years ago (Fig. 1.1). Moreover, seeds of a Rosa species were also uncovered at a Danish site dating back 5,000 years. As these seeds were found alongside remains of various edible species, it was suggested that the rose seeds were remnants of consumed rose hips [START_REF] Rengert | A Neolithic Treasure Chest[END_REF]. Remains of rose seeds have been discovered at several other sites dating back to the Neolithic era: a site in Britain, two distinct sites in Switzerland [START_REF] Steiner | Archaeobotanical and isotopic analyses of waterlogged remains from the Neolithic pile-dwelling site of Zug-Riedmatt (Switzerland): Resilience strategies of a plant economy in a changing local environment[END_REF][START_REF] Vandorpe | The importance of wild plant resources in the Neolithic: a case study of the Late Neolithic lakeshore settlement of Grandson-Corcelettes, Les Pins (Switzerland)[END_REF], and a site in Turkey [START_REF] Hatipoğlu | The importance of rose and rosehip species in past, present, and tomorrow[END_REF], suggesting that roses were a common part of the Neolithic diet.

Middle East during Antiquity

The first written record of a rose was identified on Mesopotamian (Middle East) cuneiform tablets, approx. 4200 Before Present (B.P.). A tablet relates the importation of a rose Introduction Figure 1.1: The well-preserved rose hip found in a German site dating back to 7000 years ago. Picture was taken from [START_REF] Rengert | A Neolithic Treasure Chest[END_REF].

by Sargon I, the king of Akkadia, at present-day Turkey, showing that human exchanges of roses begun far back in the history [START_REF] Baser | Turkish rose. A review of the history, ethnobotany, and modern uses of rose petals, rose oil, rose water, and other rose products[END_REF]. Additionally, other Assyrian tablets mention rose water and report details on how the rose fragrance was extracted.

Based on such use, [START_REF] Baser | Turkish rose. A review of the history, ethnobotany, and modern uses of rose petals, rose oil, rose water, and other rose products[END_REF]) hypothesized that the mentioned species would have been a fragrant species like R. moschata, R. gallica or its relatives R. centifolia and R. damascena. Another old trace of fragrant rose use is the findings of pieces of a distillation pot in the ancient Indian city of Harappa, dating back to 2600-1900 Before Common Era (B.C.E.). The city was located in the Punjab, present-day Pakistan [START_REF] Schmidt | Production of essential oils[END_REF]. Sammuramat (850-798 B.C.E.), a powerful queen of Assyria, had roses in her gardens [START_REF] Lehner | Folklore and symbolism of flowers, plants and trees[END_REF]. Later in the Middle East, Plutarch reports that the king of Persia, Artaxerxes II (405-359 B.C.E.), sent a wreath made of roses to the Greek Antalcidas [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. Additionally, in the Bundahishn, a collection of ancient texts dating back to 700 Common Era (C.E.), a rose with one hundred petals and a wild rose were mentioned. According to [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF], the Bundahishn reports some primitive traditions that may date back to the genesis of the Zoroastrianism religion (6th century B.C.E.). The Roman, Pliny the Elder (23-79 C.E.), indicated that perfumed oils and unguents were invented in Persia (Özcan 2012). The cultivation of highly-petaled roses in the Middle East in ancient times and their long history of fragrance extraction mastering, conducted [START_REF] Mattock | The Silk Road Hybrids': Cultural linkage facilitated the transmigration of the remontant gene in Rosa x damascena, the Damask rose, in circa 3,500 BCE from the river Amu Darya watershed in Central Asia, the river Oxus valley of the Classics, to Rome by 300 BCE[END_REF] to suppose that one of the roses was the fragrant Introduction R. damascena. [START_REF] Mattock | The Silk Road Hybrids': Cultural linkage facilitated the transmigration of the remontant gene in Rosa x damascena, the Damask rose, in circa 3,500 BCE from the river Amu Darya watershed in Central Asia, the river Oxus valley of the Classics, to Rome by 300 BCE[END_REF] argued this rose, after originating in the Middle East, spread to the west through the Greeks to the Romans. This plethora of old records on the use of roses in the Middle East highlights the key role played by these civilizations in the early development and spread of Humans love for roses, especially for their fragrance.

China during Antiquity

Chinese records on rose use were written by the philosopher Confucius (551-479 B.C.E.).

He reports the Chinese love for roses under the Zhou dynasty and the wide Chinese literature on the subject of roses [START_REF] Baser | Turkish rose. A review of the history, ethnobotany, and modern uses of rose petals, rose oil, rose water, and other rose products[END_REF]. It seems that the rose used for rose water production in China, at that time, was R. rugosa, a species that is not related to R. gallica [START_REF] Mattock | The Silk Road Hybrids': Cultural linkage facilitated the transmigration of the remontant gene in Rosa x damascena, the Damask rose, in circa 3,500 BCE from the river Amu Darya watershed in Central Asia, the river Oxus valley of the Classics, to Rome by 300 BCE[END_REF]. The roses used for rose water production in China have been named Mei-gui for centuries [START_REF] Ochir | Botanical origin of Mei-gui Hua (petal of a Rosa species)[END_REF]. In a recent study, [START_REF] Ochir | Botanical origin of Mei-gui Hua (petal of a Rosa species)[END_REF] investigated the botanical origin of Mei-gui samples collected in different Chinese regions. They found that samples from eastern China today were close to R.

rugosa while samples from western China were close to R. gallica. Therefore, it would be possible that R. gallica was used for such purpose in the most western part of the country, at that time [START_REF] Mattock | The Silk Road Hybrids': Cultural linkage facilitated the transmigration of the remontant gene in Rosa x damascena, the Damask rose, in circa 3,500 BCE from the river Amu Darya watershed in Central Asia, the river Oxus valley of the Classics, to Rome by 300 BCE[END_REF]. This highlights the difficulty of attributing a local word designating roses to a particular species in modern classification, even when the word is still used.

Introduction in its garden [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. Modern authors hypothesized this rose to be R. damascena, a relative of R. gallica [START_REF] Mattock | The Silk Road Hybrids': Cultural linkage facilitated the transmigration of the remontant gene in Rosa x damascena, the Damask rose, in circa 3,500 BCE from the river Amu Darya watershed in Central Asia, the river Oxus valley of the Classics, to Rome by 300 BCE[END_REF]. Theophrastus (371-288 B.C.E.) was the first Greek to give a detailed description of the roses known or used at that time. He mentioned five different types of roses that he called: the kynorodon (that might correspond to R. canina), the kynosbatos (that might correspond to R. sempervirens), the rhodon hekatonphyllon (that might correspond to R. centifolia), and the rhodon agrión or wild rose (that might correspond to another Caninae species, [START_REF] Osmun | Roses of Antiquity[END_REF]. Theophrastus also highlights the great diversity of traits harbored by the roses. He reported that roses had a various number of petals starting from five for the majority to twelve, twenty or even a hundred. Dioscorides, also known as "the father of pharmacology", gave a description of the wild roses. However, he did not mention different types of wild roses.

His description of the phenotypes of these wild roses can correspond either to R. sect.

Caninae or R. sempervirens [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. Concerning the cultivated roses, Dioscorides did not mention different types but attributed to all these roses the same medicinal properties [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. The hundred-petaled roses could be R. centifolia but Mattock (2017) suggested the R. centifolia of Greeks was different from the R. centifolia we know today and would rather be a variety of R. damascena. On the contrary, [START_REF] Osmun | Roses of Antiquity[END_REF] said that the probably most common rose in ancient Greece was R. gallica. According to the rose traits such as the fragrance, the variable number of petals and the medicinal properties mentioned in ancient Greek texts, both hypotheses seems plausible.

Roman Empire during Antiquity

Roses were also grown in Roman's gardens as attested by representations and numerous mentions in texts. The Roman's gardens composition was reported in a fresco of Livia's villa at Prima Porta in Rome that dates back to 30-20 B.C.E. (Fig 1.3A). Roses were represented along with other garden species. Roses were also depicted on a fresco from the House of the Golden Bracelet in Pompeii (79 C.E., Fig 1.3B). In the two frescoes (Fig 1 .3), the flowers are lighlty double or double and the leaves have five leaflets. On the fresco of the Livia's villa, the stem harbors many prickles. All these traits are compatible with the phenotype of R. gallica and its relatives (R. damascena and R. centifolia)

explaining why different modern authors debated the identity between R. damascena and R. centifolia [START_REF] Caneva | Botanic analysis of Livia's villa painted flora (Prima Porta, Roma)[END_REF][START_REF] Mattock | The Silk Road Hybrids': Cultural linkage facilitated the transmigration of the remontant gene in Rosa x damascena, the Damask rose, in circa 3,500 BCE from the river Amu Darya watershed in Central Asia, the river Oxus valley of the Classics, to Rome by 300 BCE[END_REF]. [START_REF] Mattock | The Silk Road Hybrids': Cultural linkage facilitated the transmigration of the remontant gene in Rosa x damascena, the Damask rose, in circa 3,500 BCE from the river Amu Darya watershed in Central Asia, the river Oxus valley of the Classics, to Rome by 300 BCE[END_REF] stated the painted rose on the fresco of the Livia's house is a R. damascena because, he wrote, "R. gallica is never very double and only very rarely lightly double". The affirmation is wrong as there exist R. gallica varieties that are effectively lightly double or double Introduction Figure 1.2: The blue bird fresco in Knossos (ca. 1450 B.C.E.). Note the differences between the original rose flower pictured (green square) and the flower after restoration (red square). Picture was taken from [START_REF] Caissard | Do we really know the scent of roses?[END_REF].

(e.g. some R. gallica var. 'officinalis' and var . 'versicolor'). However, this may not refute the hypothesis of [START_REF] Mattock | The Silk Road Hybrids': Cultural linkage facilitated the transmigration of the remontant gene in Rosa x damascena, the Damask rose, in circa 3,500 BCE from the river Amu Darya watershed in Central Asia, the river Oxus valley of the Classics, to Rome by 300 BCE[END_REF] as we do not know if such varieties of R. gallica existed at that time but rather highlights the uncertainty of the identification. Other Pliny attempted to characterize the cultivated roses, differentiating them by the number of petals, the relative number of prickles, the color and the scent [START_REF] Widrlechner | History and utilization of Rosa damascena[END_REF].

As also mentioned in old Middle East texts and by the Greek Theophrastus, Pliny told the existence of five-petaled to hundred-petaled roses. Additionally, he wrote the Introduction others a R. centifolia and others a double R. gallica [START_REF] Caissard | Do we really know the scent of roses?[END_REF][START_REF] Krüssmann | The rose in art and history[END_REF]. Given, the phenotype variability described by the Romans, is it also possible that all suspected species were simultaneously cultivated at the same time.

A B

Egypt during Antiquity

A hieroglyph representing a rose found in a tomb dating back to 1600 B.C.E. constitutes the earliest trace of roses in the Egyptian culture [START_REF] Flückiger | Pharmacographia: A History of the Principal Drugs of Vegetable Origin, Met with in Great Britain and British India[END_REF]. However, [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF] argued that this hieroglyph did not represent roses. In accordance with that, [START_REF] Widrlechner | History and utilization of Rosa damascena[END_REF] wrote that the roses were not known in the Egyptian culture before 613 B.C.E.. Another early record is a wreath made of roses discovered in a tomb 400-200 B.C.E. [START_REF] Flückiger | Pharmacographia: A History of the Principal Drugs of Vegetable Origin, Met with in Great Britain and British India[END_REF]. In addition, remnants of other wreaths made of R. x richardii dating back to 100 B.C.E. were found in the Egyptian cemetery of Hawara (Newberry 1889b). The author was able to identify, with a good accuracy, the rose species in the wreaths due to an exceptional dried conservation (Fig.

1 .4). However, it seems that this use of roses is not attributable to the Egyptians as Introduction wreaths made of roses were not a traditional Egyptian practice and a colony of Greeks had settled at Hawara at this time (Newberry 1889a). Moreover, according to Newberry (1889a), at this period, Greek workmen were in charge of the mummies decorations and might have mixed Greek and Egyptian traditions. This might also indicate that the roses grown in Egypt at that time might have been imported by the Greeks, and therefore be of the same species or varieties. Despite this importation, the Egyptians became important rose growers and sellers by taking advantage of the earliness of the flowering.

Indeed, as their roses flowered earlier than in other parts of the Roman's empire under Cesar (100-44 B.C.E.), rose fields were settled in Egypt by the Romans [START_REF] Widrlechner | History and utilization of Rosa damascena[END_REF]. Egyptian roses were a way for the Romans to extend the period of rose flowers and petals availability [START_REF] Widrlechner | History and utilization of Rosa damascena[END_REF]. Introduction old civilization, rarefying its cultivation and use in Europe [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. However, the rose dis-love did not spread to non Christian lands. Indeed, at the total opposite, in the Arab world, the roses were considered to be born of drops of Mohammed's sweat. The Muslim botanists described the roses grown at this time. Ishaq Ibn Imran (9th century C.E.) described two species of cultivated roses differentiating them by their colors, one white and one red. Other Muslim authors described these roses as harboring dozens of petals [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. At that time, the rose industry was flourishing in Persia. Fragrant rose products were used as bargaining chips [START_REF] Touw | Roses in the Middle Ages[END_REF]. The double-flowered trait, the red color and the fragrance are not incompatible with R. gallica and its relatives R.

damascena or R. centifolia. However, it is certain that other rose species were cultivated at this period, illustrated by the double white roses described by Ishaq Ibn Imran and other authors [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. The rose culture persisted in Persia to supply the demand during the Middle Ages and was at its height from 900 to 1600 C.E.. The Orient has been important in the rose history as they improved the method of fragrance extraction by the discovering of the essential oil we know today [START_REF] Touw | Roses in the Middle Ages[END_REF].

Occident during the Middle Age

Despite the rarefaction of the rose culture in the Christian world at the early beginning of the Middle Ages, roses were mentioned by few authors such as Avitus (Gaule, 5th century), Ennodius (Italy, 6th century), Grégoire de Tours and Fortunat (Gaule, 6th century) and in the 7th century by Isidore of Sevilla (Spain) and Aldhelm (England) [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. This shows that the roses were certainly not totally ignored but, perhaps, were scarcely cultivated. Paradoxically, that is the monks and nuns that conserved and grew the ancient Roman roses in their monasteries built throughout the Christian world.

By this, the clergy was an important vector for the diffusion of the cultivated roses to England and Germany [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. For instance, the plan of the garden of Saint Gall's monastery (present-day Switzerland) dating back to 840 C.E. contained roses [START_REF] Touw | Roses in the Middle Ages[END_REF]. [START_REF] Touw | Roses in the Middle Ages[END_REF] reported that garden roses were grown in hedges in southern France in the 7th century. The conserved roses in monasteries, rapidly spread to the burghs and castles. At the 6th century, the garden of the queen Ultragotha was remarked for its sweet rose perfumes. Charlemagne in its capitulare argued that roses should be included in gardens [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. According to [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF], almost all historians of botany agreed that the cultivated roses at this time was the hundred-petaled roses. Moreover, Joret argued the roses mentioned in text, poetry or medical preparation might be the hundredpetaled roses [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. In accordance, [START_REF] Touw | Roses in the Middle Ages[END_REF] indicated that at the beginning Introduction of the second millennium, two rose species were known, the hundred petaled one and the wild one (R. sect Caninae). However, Albertus Magnus, Bartholomaeus Anglicus and Pietro de' Crescenzi, three authors of the 13th century, described two types of hundredpetaled roses: the red and the white [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. As for the Antiquity and for the red rose of the Arab world, the red hundred petaled could be attributed to either R. gallica or its relatives (R. damascena and R. centifolia) [START_REF] Touw | Roses in the Middle Ages[END_REF]. The diversity of plants cultivated in the Occident has been increased by the Crusades. According to a legend, the red rose, cultivated in the Arab world, would have been brought back by Theobald IV of Champagne around 1238 C.E. [START_REF] Bourquelot | Histoire de Provins[END_REF]. Nevertheless, no written record of this fact exists and, as we saw above, the red rose would be the same rose that was cultivated by the Romans and thus already present in the Occident. [START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF] mentioned that this rose could also have been brought back by "Le Bon Roi René"

during the 15th century. This rose was cultivated largely in Provins, a town south-east of Paris, for medicinal purposes at the beginning and therefore called R. gallica var. Rockefeller Jr., 1938 (38.51.1, 2).

The tapestry image was extracted from [START_REF] Colburn | Three fragments of the" Mystic Capture of the Unicorn" tapestry[END_REF].

Linné in 1759 [START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF]. By the end of the 17th century, in the Netherlands, roses began to be reproduced by seed. As a result, the Dutch introduced new varieties with unique traits, which they exported to France in the latter half of the 18th century. However, by the end of that century, the number of rose varieties recorded in France was quite limited. This number began to increase significantly due to the creation of vast collections, like the one established by Joséphine de Beauharnais, which featured hundreds of different roses at the dawn of the 19th century [START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF]. As horticulture became economically viable and roses became fashionable, demand increased [START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF][START_REF] Oghina-Pavie | Représentations de la diversité des rosiers au XIXe siècle[END_REF]. French horticulturists and rose enthusiasts began breeding new varieties, many of which were attributed to R. gallica. These new cultivars were often grown adjacently, their seeds sown, and descendants with remarkable phenotypes were retained. This intensive cultivation led to the recording of 3,500 cultivars by 1840 Introduction in Parisian collections (Oghina-Pavie 2016). Breeding was also active in other countries, including England, Belgium, the Netherlands, Germany, and Crimea [START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF].

Given the abundance of rose cultivars, many enthusiasts attempted to classify them.

They were grouped by color, flowering period, and other attributes that horticulturists labeled as tribes, sub-species, or groups (Oghina-Pavie 2016). Additionally, these groups or varieties were identified either by a horticultural name, such as "Petite de Hollande", or by a Latin name, like Rosa borboniana. The usage of Latin names can lead to confusion, as it may imply that the cultivar is a distinct species. This ambiguity persists today for certain cultivars [START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF][START_REF] Oghina-Pavie | Représentations de la diversité des rosiers au XIXe siècle[END_REF]. Among the cultivars developed during the early 19th century, many were attributed to R. gallica, R. centifolia, R. damascena, or R. alba. Roses linked to the species R. gallica were termed the gallic roses, Hybrid Gallica, or simply French roses. The term "hybrid" in this context is ambiguous, as it might refer to a cross between two species or just between two cultivars (Oghina-Pavie 2016).

After 1830, the popularity of gallic roses waned. They were supplanted by the Hybrid Perpetual and subsequently by the Hybrid Tea cultivars, which offered continuous flowering [START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF]. This trait was introduced from Chinese varieties, inducing a shift in the genetic background of the cultivars from a European (R. gallica) to a Chinese background [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]. By the end of the 18th century, Hybrid Gallica were already considered as old roses and were more often preserved in rose gardens than sold.

In France, this conservation initiative was initiated by Jules Gravereaux who built a rose garden at l'Haÿ, with the objective to gather all the cultivars in one place [START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF]. The Hybrid Gallica roses were grown only for preservation until the end of the second world war. At that time, the English were the first to re-passionate for the old French roses and rose nurseries specialized in the trade of old French roses. Following the English, rose lovers regained interest for old French roses in Western Europe and in the United-States [START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF]. This trend was certainly a basis that led the English David Austin to breed and release cultivars of "English rose" from 1961. These roses were bred by crossing old roses (mainly Gallic roses) to modern roses in order to introduce recurrent blooming [START_REF] Austin | The english roses[END_REF]. Nowadays, the trend for old garden roses is still there, representing a niche market that allows some specialized nurseries to exist. For the perfume industry, the roses used today are mostly cultivars of R. damascena and R.

centifolia [START_REF] Caissard | Do we really know the scent of roses?[END_REF]).

Introduction

Roses and Rosa gallica description 1.2.1 Roses and Rosa gallica phylogenetic placement

The genus Rosa comprises 150 to 200 species occurring on all continents of the northern hemisphere. The current accepted classification is based on the one proposed by [START_REF] Rehder | Rosa L." In: Manual of cultivated trees and shrubs hardy in North America[END_REF], with modifications by Wissemann (2003) and Wissemann (2017). The classification mostly relies on morphological traits that include, but are not limited to, branches, shoots, prickles, bristles, leaf glands, branch fuzziness, pedicels, openings, stipules, foliage, leaf hue, shape of leaflets, flower structure, bracts, sepals, and the ovary [START_REF] Rehder | Rosa L." In: Manual of cultivated trees and shrubs hardy in North America[END_REF]. Under this classification, the genus is divided into four sub-genera: Rosa, Hulthemia, Platyrhodon, and Hesperhodos. The number of species included in these subgenera is unbalanced, as the last three are restricted to only one species, while the subgenus Rosa comprises all other species (Wissemann 2017). Given that, the sub-genus

Rosa is further divided into ten sections: Banksianae, Bracteatae, Caninae, Carolinae, Rosa, Gallicanae, Indicae, Laevigatae, Pimpinellifoliae, and Synstylae. Among them, the section Rosa (about 80 species), the section Caninae (about 50 species), and the section Synstylae contain a high number of species, while other sections are restricted to a few species (Wissemann 2017). Finally, the section Caninae is divided into six sub-sections:

Trachyphyllae, Rubrifoliae, Vestitae, Rubiginae, Tomentellae, and Caninae. The sections Rosa and Gallicanae were formerly called Cinnamomeae and Rosa, respectively.

Indeed, the lectotype of the genus Rosa was a specimen of the section Gallicanae until 2005, but was replaced by a specimen of the former section Cinnamomeae, justifying the renaming of the section Cinnamomeae to section Rosa. In the same manner, the former section Rosa was renamed Gallicanae (Wissemann 2017).

The classification was examined in light of molecular data multiple times using different types of markers, for a detailed analysis of these studies see Fougère-Danezan et al. (2020) suggested that these two groups could be considered as sub-sections if necessary.

Finally, they confirmed the existence of sections that originated from inter-sectional hybridization (sections Caninae and Gallicanae). Currently, the section Gallicanae comprises one wild species, R. gallica L., and five taxa of doubtfully wild origin, which are: R. alba L., R. centifolia L. 1753, R. damascena Mill., R. francofurtana Münchhausen, and R. polliniana Spreng (Wissemann 2017). However, for a long time, Eastern botanists considered up to 14 wild species belonging to this section. This incongruence was investigated by Fedorova (2014) using morphological and molecular markers. The study revealed that only R. gallica should be included in the section Gallicanae, while the other species, formerly considered as Gallicanae, were probably dwarf species of the section Caninae.

Introduction

Phenotypic description of Rosa gallica

The following description is based on [START_REF] Boulenger | Les roses d'Europe de l'herbier crépin. Vol. 2 (suite)[END_REF] and on my personal experience.

Rosa gallica is a small erect shrub that reaches 50-60 cm in height on average but can measure between less than 20 cm and approximately 1 meter. The stem is generally not branched, but a few branches may be present.

Genus diversification mechanism and Rosa gallica origin

Since the beginning of this thesis manuscript, I have been discussing the rose species and the species R. gallica. However, the notion of species can be difficult to conceptualize for this genus. A plethora of species definitions and concepts have been proposed to guide species delimitation [START_REF] Aldhebiani | Species concept and speciation[END_REF][START_REF] De Queiroz | Species Concepts and Species Delimitation[END_REF][START_REF] Freudenstein | Biodiversity and the Species Concept-Lineages are not Enough[END_REF]).

Among them, the biological species concept argues that individuals able to naturally reproduce, yielding a viable and fertile offspring, belong to the same species [START_REF] Dobzhansky | Mendelian populations and their evolution[END_REF][START_REF] Mayr | Systematics and the origin of species-Columbia Univ[END_REF]Wright 1940). Alternatively, the phylogenetic species concept asserts that a species must be a monophyletic lineage [START_REF] De Queiroz | Species Concepts and Species Delimitation[END_REF]. Another concept is the morphological species concept, under which "a species is a community, or a number of related communities, whose distinctive morphological characters are, in the opinion of a competent systematist, sufficiently definite to entitle it, or them, to a specific name" [START_REF] Regan | Organic evolution[END_REF]. This latter seems debatable, how do we define "distinctive morphological characteristics" or "a competent systematist" [START_REF] Aldhebiani | Species concept and speciation[END_REF])? Among others, these species concepts are, to some extent, incompatible. Broader concepts have emerged that might reconcile all the narrower ones proposed [START_REF] De Queiroz | Species Concepts and Species Delimitation[END_REF]. An initial broad concept is the one from [START_REF] Simpson | The species concept[END_REF], who developed the evolutionary species concept that defines a species as "a phyletic lineage evolving independently of others, with its own separate and unitary evolutionary role and tendencies." This closely corresponds to the modern unified species concept, which asserts that a species is a lineage that evolves separately from others [START_REF] De Queiroz | Species Concepts and Species Delimitation[END_REF]. In this case, species delimitation relies solely on the separation of lineages. The extent of separation is then evaluated using operational criteria arising from other narrower species concepts, such as reproductive isolation, monophyly, or morphological distinction (De Queiroz 2007). However, it is worth noting that today, all these different species concepts are still used by scientists to varying extents, with the biological species concept being the most used [START_REF] Stankowski | Quantifying the use of species concepts[END_REF]. In the genus Rosa, the species recognized today are largely inherited from old identifications by "competent systematists" that were mostly based on mor-Introduction phological attributes. Therefore, close species ambiguities are being investigated case by case [START_REF] Gao | Species Boundaries and Parapatric Speciation in the Complex of Alpine Shrubs, Rosa sericea (Rosaceae), Based on Population Genetics and Ecological Tolerances[END_REF][START_REF] Gao | Pleistocene glaciations, demographic expansion and subsequent isolation promoted morphological heterogeneity: A phylogeographic study of the alpine Rosa sericea complex (Rosaceae)[END_REF][START_REF] Joly | Delimiting Species Boundaries in Rosa Sect. Cinnamomeae (Rosaceae) in Eastern North America[END_REF][START_REF] Kellner | Low genetic and morphological differentiation in the European species complex of Rosa sherardii, R. mollis and R. villosa (Rosa section Caninae subsection Vestitae)[END_REF]. For instance, in a recent study addressing species delimitation between Rosa sericea Lindl.

and Rosa omeiensis Rolfe, the authors suggested that under the unified species concept, two distinct species exist, despite the significant gene flow observed [START_REF] Gao | Species Boundaries and Parapatric Speciation in the Complex of Alpine Shrubs, Rosa sericea (Rosaceae), Based on Population Genetics and Ecological Tolerances[END_REF].

Hybridization between individuals from different Rosa species commonly results in a fertile and viable progeny [START_REF] Henker | Rosa[END_REF][START_REF] Jičínská | The hybridization of some Rosa species of different levels of ploidy[END_REF][START_REF] Schanzer | ISSR (Inter Simple Sequence Repeat) markers reveal natural intersectional hybridization in wild roses [Rosa L., sect. Caninae (DC.)[END_REF]. Hybrids within the same section [START_REF] Gao | Species Boundaries and Parapatric Speciation in the Complex of Alpine Shrubs, Rosa sericea (Rosaceae), Based on Population Genetics and Ecological Tolerances[END_REF] or between sections have been described [START_REF] Andersen | Hybridization between the locally endangered Rosa spinosissima and Rosa mollis results in the pentaploid Rosa ×sabinii in western Norway[END_REF]Kellner et al. 2012;[START_REF] Schanzer | Is Rosa × archipelagica (Rosaceae, Rosoideae) really a spontaneous intersectional hybrid between R. rugosa and R. maximowicziana? Molecular data confirmation and evidence of paternal leakage[END_REF][START_REF] Zhang | Molecular and morphological evidence for hybrid origin and matroclinal inheritance of an endangered wild rose, Rosa��×�pseudobanksiae (Rosaceae) from China[END_REF]. As a part of this apparent hybridization might be due to an oversplitting of species, morphological, cytological, and genetic analyses have been performed to evaluate whether the parent species of a given hybrid can be considered as separated species. For example, [START_REF] Andersen | Hybridization between the locally endangered Rosa spinosissima and Rosa mollis results in the pentaploid Rosa ×sabinii in western Norway[END_REF]) investigated the inter-sectional hybrid Rosa x sabinii and revealed that the parent species, namely Rosa spinosissima (Pimpinellifoliae) and Rosa mollis (Caninae), can effectively be considered as separate species. The same characteristics were found for hybrids originating from Rosa rugosa (section Rosa) and Rosa mollis (Kellner et al. 2012). Interspecific hybridization occurs due to several different mechanisms, involving or not involving polyploidization (Fig. 1.10). Due to the low reproductive barrier between different Rosa species, some species of the same ploidy level can hybridize (i.e., homoploid hybridization, [START_REF] Mercure | Hybridization between the escaped Rosa rugosa (Rosaceae) and native R. blanda in eastern North America[END_REF]. An example of this, for genetically compatible diploid species, is shown in Figure 1.10A and can be applied to other homoploid hybridizations between genetically compatible species regardless of the level of ploidy. In hybridization between genetically incompatible species, polyploidization mechanisms may restore the fertility of the hybrid [START_REF] Alix | Polyploidy and interspecific hybridization: partners for adaptation, speciation and evolution in plants[END_REF].

The prevalent mechanism in the genus Rosa is the production of unreduced gametes [START_REF] Herklotz | Multiple and asymmetrical origin of polyploid dog rose hybrids (Rosa L. sect. Caninae (DC.) Ser.) involving unreduced gametes[END_REF][START_REF] Nybom | Unique genomic configuration revealed by microsatellite DNA in polyploid dogroses, Rosa sect. Caninae[END_REF][START_REF] Ritz | Microsatellite Analyses of Artificial and Spontaneous Dogrose Hybrids Reveal the Hybridogenic Origin of Rosa micrantha by the Contribution of Unreduced Gametes[END_REF]. In a cross between genetically incompatible diploid species, unreduced egg and sperm cells allow the production of a fertile allotetraploid (Fig. 1.10B). The same mechanism can occur regardless of the parent ploidy level. A third mechanism involves chromosome doubling in a sterile diploid, resulting in a fertile allotetraploid (Fig. 1.10C). Whatever the polyploidization mechanism, the allotetraploid obtained has then pairs of chromosomes that diverged by speciation and that were brought back together in its genome, such pairs are termed homeologuous chromosomes [START_REF] Glover | Homoeologs: What Are They and How Do We Infer Them?[END_REF]. The high genetic compatibility within the genus, combined with polyploidization mechanisms, allows for crosses between different ploidy levels. This has led to the wide range of ploidy levels recorded Introduction in the genus Rosa (from 2x to 10x) [START_REF] Fernandez-Romero | Cytological and molecular characterization of a collection of wild and cultivated roses[END_REF][START_REF] Jian | Decaploidy in Rosa praelucens Byhouwer (Rosaceae) Endemic to Zhongdian Plateau, Yunnan, China[END_REF][START_REF] Roberts | DNA amounts of roses (Rosa L.) and their use in attributing ploidy levels[END_REF]. Although the ploidy levels vary significantly, the base chromosome number remains seven for all Rosa species. For instance, triploids can be obtained by crossing two diploid species, with one producing unreduced gametes, or by crossing a tetraploid with a diploid undergoing regular meiosis. These triploid roses then maintain more or less fertility through the production of either haploid or diploid gametes [START_REF] Feng | Fertility and Progeny Ploidy of Triploids in China Rosa Germplasm[END_REF][START_REF] Zlesak | Pollen diameter and guard cell length as predictors of ploidy in diverse rose cultivars, species, and breeding lines[END_REF]). In the polyploid section Caninae (4x, 5x, 6x), the ability to reproduce sexually is preserved by asymmetric meiosis (Fig. 1.10D). In the illustrated case (5x, Fig. [START_REF] Boulenger | Les roses d'Europe de l'herbier Crépin[END_REF][START_REF] Fedorova | Systematics and geography of species of the genus Rosa L. sect. Gallicanae DC[END_REF][START_REF] Jičínská | The hybridization of some Rosa species of different levels of ploidy[END_REF][START_REF] Kerényi-Nagy | A történelmi Magyarország területén élő őshonos, idegenhonos és kultúrreliktum rózsák kismonográfiája = A small monograph of autochton, allochton and cultur-relict roses of historical Hungary[END_REF][START_REF] Ratsek | Crossing relations of some diploid and polyploid species of roses[END_REF][START_REF] Sołtys ; Lelek | Wild Rosa L. taxa of the National Nature Park 'Podilskyi Tovtry' (Podolian Hills, Western Ukraine)[END_REF]. Specifically, morphological evidences suggested that R. gallica can hybridize with R. arvensis and R. sempervirens, two European Synstylae [START_REF] Boulenger | Les roses d'Europe de l'herbier crépin. Vol. 2 (suite)[END_REF][START_REF] Boulenger | Sur une Rose hybride (Rosa gallica×sempervirens) rencontrée dans la Charente-Inférieure[END_REF][START_REF] Kerényi-Nagy | A történelmi Magyarország területén élő őshonos, idegenhonos és kultúrreliktum rózsák kismonográfiája = A small monograph of autochton, allochton and cultur-relict roses of historical Hungary[END_REF]. Experimental crosses between R. arvensis and R. gallica confirmed this potential [START_REF] Jičínská | The hybridization of some Rosa species of different levels of ploidy[END_REF]. Experimental crosses revealed that R. gallica can mate with various species from the Caninae section, albeit with varying success rates [START_REF] Fedorova | Systematics and geography of species of the genus Rosa L. sect. Gallicanae DC[END_REF]. In these crosses, hybridizations were more successful when R. gallica was the pollen donor [START_REF] Fedorova | Systematics and geography of species of the genus Rosa L. sect. Gallicanae DC[END_REF][START_REF] Ratsek | Crossing relations of some diploid and polyploid species of roses[END_REF]. In addition, molecular data indicated hybrids between R. gallica and R. canina [START_REF] Fedorova | Local differentiation and hybridization in wild rose populations in Western Ukraine[END_REF][START_REF] Wissemann | Genetic constitution of Rosa sect. Caninae (R-canina, R-jundzillii) and sect. Gallicanae (R-gallica)[END_REF]. [START_REF] Ratsek | Crossing relations of some diploid and polyploid species of roses[END_REF] The origin of a tetraploid affects chromosome pairing during meiosis, which in turn determines the mode of inheritance [START_REF] Zielinski | Meiosis in polyploid plants[END_REF]. Specifically, an autotetraploid possesses four highly similar homologous chromosomes, which allows the formation of tetravalents during the meiosis. This allows for recombination among all chromosomes and random chromosome segregation in gametes (Fig. 1.11). It is important to note that in autotetraploids, tetravalents can be formed at a low frequency, with bivalents remaining in the majority. In contrast, an allotetraploid comprises two subgenomes and has two sets of homologous chromosomes. These will preferentially pair during meiosis, producing two pairs of bivalents. Consequently, recombination occurs primarily within these bivalents, restricting recombination between subgenomes. Chromosome segregation is then not random, as each gamete carries one chromosome from each subgenome (Fig. 1.11, Zielinski and Mittelsten Scheid 2012). The exclusive formation of tetravalents leading to completely random chromosome segregation is termed tetrasomic inheritance, whereas the exclusive formation of bivalents is called disomic inheritance (Fig. 1.11). These inheritance patterns represent the extremes of a continuum [START_REF] Blischak | Demographic history inference and the polyploid continuum[END_REF][START_REF] Stift | Segregation Models for Disomic, Tetrasomic and Intermediate Inheritance in Tetraploids: A General Procedure Applied to Rorippa (Yellow Cress) Microsatellite Data[END_REF]. Indeed, homeologous pairing can occur in an allotetraploid depending on the level of similarity between the two subgenomes [START_REF] Gaeta | Homoeologous recombination in allopolyploids: the polyploid ratchet[END_REF], such organisms are designated as segmental allopolyploids. Moreover, the degree of similarity between subgenomes can differ between chromosomes, resulting in varying frequencies of homeologous pairing [START_REF] Chester | Extensive chromosomal variation in a recently formed natural allopolyploid species, Tragopogon miscellus (Asteraceae)[END_REF]). Fernández-Romero Introduction et al. (2001) studied the meiotic pairing of R. gallica var. 'versicolor'. They mainly found bivalents, but also observed a few univalents, trivalents, and tetravalents. Additionally, molecular studies suggested R. gallica as being an allotetraploid [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF][START_REF] Raymond | The Rosa genome provides new insights into the domestication of modern roses[END_REF]. Therefore, R. gallica might be a segmental allotetraploid with a limited yet effective degree of homeologous recombination. The species undergoes standard meiosis, resulting in 1n = 2x = 14 chromosomes egg and sperm cells [START_REF] Fernández-Romero | Physical mapping of ribosomal DNA on several species of the subgenus Rosa[END_REF].

Tetrasomic

P g

Disomic P g

Figure 1.11: Chromosome segregation in disomic and tetrasomic inheritance is illustrated for an individual locus with four alleles. Under tetrasomic inheritance, tetravalent formation allows alleles to segregate randomly during meiosis, leading to six possible gamete genotypes (g). On the contrary, in disomic inheritance, bivalent formation prevents the segregation of two chromosomes from the same subgenome to the same gamete, reducing the number of different gamete genotypes to four (g). Consequently, the number of possible offspring genotypes is lower under disomic inheritance than under tetrasomic inheritance.

Rosa gallica wild diversity

The natural range of R. gallica extends from west to east, spanning Western Europe (France) to the Caucasus. The northern limit of this range includes northern France, Germany, and Poland, while the southern limit reaches the Near East (Turkey, Iraq) [START_REF] Boulenger | Les roses d'Europe de l'herbier crépin. Vol. 2 (suite)[END_REF]. The native status of Spanish R. gallica populations is debatable [START_REF] Boixader | Notes sobre la flora al•lòctona a Catalunya. II[END_REF][START_REF] Ríos | Rosa gallica l. var. officinalis (hort. ex Andrews) Ser. en el norte de Alicante (Comunidad Valenciana)[END_REF]. The species has been introduced to North Africa (Tunisia, Libya, Dobignard and Chatelain 2010), Central and North America [START_REF] Boulenger | Les roses d'Europe de l'herbier crépin. Vol. 2 (suite)[END_REF], Oceania [START_REF] Webb | Naturalised Pteridophytes, Gymnosperms, Dicotyledons[END_REF], and Asia (Tajikistan, [START_REF] Nowak | Illustrated flora of Tajikistan and adjacent areas[END_REF]). The species is protected in France (https://www.legifrance.gouv.fr/loda/ id/LEGIARTI000006863076/2023-08-02). In Poland the species is also protected and classified as vulnerable in the 'Polish red list of pteridophytes and flowering plants' and in the 'Polish red data book of plants' [START_REF] Kaźmierczakowa | Polish red list of pteridophytes and flowering plants[END_REF][START_REF] Zieliński | Rosa gallica L. Róża francuska[END_REF].

The diversity of R. gallica has been investigated on various scales and locations. The largest study was conducted by [START_REF] Fedorova | Systematics and geography of species of the genus Rosa L. sect. Gallicanae DC[END_REF]. Rosa gallica samples were collected in eastern Europe. In detail, they gathered samples from one site in Greece, one site in Romania, two sites in Hungary, five sites in western Ukraine, and one site in Crimea (Fig. 1.12). Given the ability of R. gallica to produce suckers, they prevented the sampling of clones by collecting physically or morphologically distant individuals. Using morphological and genetic data (Inter Simple Sequence Repeat), they showed that the individuals were more similar within the sites than between the sites. Additionally, their results suggested isolation by distance since the most physically distant individuals (i.e.

the Greeks) were also the most genetically distant. Finally, they detected R. gallica hybrids with Caninae species. The structure of diversity was also studied on a local scale in western Ukraine near Lviv [START_REF] Fedorova | Local differentiation and hybridization in wild rose populations in Western Ukraine[END_REF]. The studied sites were the same as those studied by [START_REF] Fedorova | Systematics and geography of species of the genus Rosa L. sect. Gallicanae DC[END_REF], see Figure 1.12. They found no clear structure of diversity among their R. gallica samples, suggesting a continuous gene flow between those physically close sites.

In Poland, the species is found in the southern part of the country [START_REF] Nobis | New localities of Rosa gallica (Rosaceae) in Southern Poland[END_REF].

The distribution of R. gallica within the country is well documented due to numerous botanical studies [START_REF] Klichowska | Rzadkie, chronione i interesujące gatunki roślin naczyniowych ujściowego odcinka doliny Dunajca[END_REF][START_REF] Łazarski | Rosa gallica (Rosaceae) in the Świętokrzyskie Mountains and adjacent areas (Małopolska Upland)-occurrence and threats[END_REF][START_REF] Nobis | New localities of Rosa gallica (Rosaceae) in Southern Poland[END_REF][START_REF] Novák | Příspěvek ke květeně Litomyšlska -nálezy v roce 2014[END_REF][START_REF] Piwowarczyk | Róża francuska rosa gallica L. na przedgórzu iłżeckim (wyżyna małopolska)[END_REF][START_REF] Towpasz | Wystepowanie Rosa gallica [Rosaceae] na Plaskowyzu Proszowickim [Wyzyna Malopolska, poludniowa Polska][END_REF][START_REF] Wojcik | Current occurrence and diversity of vegetation patches with Rosa gallica in the Wroclaw area against the background of other regions of Poland[END_REF][START_REF] Wójcik | Nowe stanowiska roślin naczyniowych Polski, 2[END_REF][START_REF] Wolanin | The occurrence of Rosa gallica L. in the Polish Carpathians[END_REF]. Polish diversity was investigated using the morphological traits The sampling sites of five former studies are represented, these are : [START_REF] Bouillon | Geographical structuration of genetic diversity of wild populations of Rosa gallica and their relationship with the cultivated compartment[END_REF][START_REF] De Cock | Genetic diversity of wild roses (Rosa spp.) in Europe, with an in-depth morphological study of Flemish populations[END_REF][START_REF] Fedorova | Local differentiation and hybridization in wild rose populations in Western Ukraine[END_REF][START_REF] Fedorova | Systematics and geography of species of the genus Rosa L. sect. Gallicanae DC[END_REF] and [START_REF] Wrońska-Pilarek | Pollen morphology of Rosa gallica L. (Rosaceae) from Southern Poland[END_REF].

The diversity of R. gallica was also investigated in Germany and France using AFLP [START_REF] De Cock | Genetic diversity of wild roses (Rosa spp.) in Europe, with an in-depth morphological study of Flemish populations[END_REF]. A total of two individuals from one German site and 47 from eight sites located in south-eastern France were genotyped. Most of the differentiation was observed between individuals from the two countries. More precisely, the German site differed from all French sites. Based on these results, the authors hypothesized that the Alps act as a natural barrier, preventing gene flow between the populations on either side. They also found a high degree of similarity between geographically close sites.

Introduction

In France, studies on the diversity of R. gallica have been conducted on both national and local scales [START_REF] Ballerie | Étude de la variabilité génétique de Rosa gallica[END_REF][START_REF] Bouillon | Geographical structuration of genetic diversity of wild populations of Rosa gallica and their relationship with the cultivated compartment[END_REF][START_REF] Pernet | Genetic diversity exploration of rose at phenotypic and molecular levels: impact on the conservation of the Rosa gallica species[END_REF][START_REF] Reynders-Aloisi | Rosa gallica: The French rose. Its natural diversity within and between wild populations in the south-east of France[END_REF][START_REF] Thouroude | Diversité génétique dans le genre Rosa : méthodologies d'exploration et premiers résultats sur la conservation de l'espèce Rosa gallica[END_REF]. Most of these studies were carried out at the Institute of Research in Horticulture and Seeds (IRHS) by the Genetics and Diversity of Ornamentals (GDO) team [START_REF] Ballerie | Étude de la variabilité génétique de Rosa gallica[END_REF][START_REF] Bouillon | Geographical structuration of genetic diversity of wild populations of Rosa gallica and their relationship with the cultivated compartment[END_REF][START_REF] Pernet | Genetic diversity exploration of rose at phenotypic and molecular levels: impact on the conservation of the Rosa gallica species[END_REF][START_REF] Thouroude | Diversité génétique dans le genre Rosa : méthodologies d'exploration et premiers résultats sur la conservation de l'espèce Rosa gallica[END_REF]).

Among them, the most comprehensive study on R. gallica diversity was undertaken in [START_REF] Bouillon | Geographical structuration of genetic diversity of wild populations of Rosa gallica and their relationship with the cultivated compartment[END_REF] As a result, all the plants of the same cultivar belong to the same clonal lineage. A clonal lineage corresponds to "the asexual descendants of a given genotype differing from the originator only via mutation and mitotic recombination" [START_REF] Anderson | Clonality in Soilborne, Plant-Pathogenic Fungi[END_REF].

However, the reverse is not necessarily true, as individuals from the same clonal lineage may not belong to the same cultivar. In fact, a spontaneous (or induced) mutation can sometimes produce a desirable trait and be recognized as a new variety [START_REF] Smulders | In the name of the rose: a roadmap for rose research in the genome era[END_REF]. Cultivars obtained by this way are then said to be sports of the original cultivar.

In Section 1.2.1, we saw that the rose species are divided into Subgenera and sections in the botanical classification. However, cultivated roses are classified using another classification, proposed by the American Rose Society and recognized by the World Federation of Rose Societies. The classification first separates roses into wild species, Old

Garden Roses (OGR) and Modern roses. Then, OGR and modern roses are subdivided into horticultural groups that contain cultivars with more or less similar phenotypes.

OGR refer to horticultural groups obtained before 1867, the release date of the cultivar 'La France', the first Hybrid Tea. On the contrary, Modern roses are horticultural groups that were bred after this date (Fig. 1.13, Cairns 2003). The genetic basis of horticultural groups was deeply investigated by [START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF] and [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]. While [START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF] found genetic groups fairly well according to the horticultural classification, [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF] found a weak correlation between genetic and horticultural groups. In addition to the low genetic basis, a layer of complexity is added by differences among authors about the assignment of cultivars to a particular horticultural group [START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF].

Rose cultivars originated from two main breeding areas, Europe and Asia. This induced the establishment of two genetic pools of old rose cultivars, one European related to R. gallica [START_REF] Gardes | Molecular typing and history of the provins roses horticultural group[END_REF][START_REF] Koopman | AFLP markers as a tool to reconstruct complex relationships: A case study in Rosa (Rosaceae)[END_REF][START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF], and another Asian mostly related to R. chinensis [START_REF] Tan | Genetic relationships and evolution of old Chinese garden roses based on SSRs and chromosome diversity[END_REF]). These pools were extensively crossed, during the 19th century, after the introduction of Asian cultivars in Europe. (2000). Major horticultural group of OGR and modern roses are listed. Scheme reproduced from [START_REF] Cairns | Horticultural Classification Schemes[END_REF] from a R. gallica background to an Asian background. Furthermore, no clear separation between genetic groups was found in this study. This indicates that despite the use of horticultural groups to categorized rose cultivars, these are a continuum of cultivars with various degrees of introgression from different species.

The OGR groups related to R. gallica are Hybrid Gallica (HGal), Alba, Centifolia, Damask, Moss and to a lesser extent Portland, Bourbon and Hybrid Perpetual [START_REF] Cairns | Horticultural Classification Schemes[END_REF]Cui et al. 2022a;[START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF][START_REF] Hurst | Notes on the origin and evolution of our garden roses[END_REF][START_REF] Iwata | Triparental origin of Damask roses[END_REF][START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF][START_REF] Widrlechner | History and utilization of Rosa damascena[END_REF]. HGal are sometimes called the Gallic roses (e.g. [START_REF] Gardes | Molecular typing and history of the provins roses horticultural group[END_REF].

The name "Gallic rose" is also used to designate the wild species or very first cultivars such as R. gallica 'Officinalis' [START_REF] Caissard | Do we really know the scent of roses?[END_REF]. HGal cultivars are assumed to belong to R. gallica, or hybrid between R. gallica and other species. [START_REF] Gardes | Molecular typing and history of the provins roses horticultural group[END_REF] used RAPD markers to study the genetic structure of the HGal group. In their analyses, the HGal cultivars were separated from their wild relatives. However, the wild specimens Introduction included were collected in a narrow area (south western France) in comparison with the natural range of the species. Therefore, they might have sampled wild individual already differentiated from the wild ancestors of the HGal cultivars. They also remarked that as the 19th century progressed, cultivars were increasingly diverse and differentiated from the wild ones. The authors hypothesized that this increase of diversity and differentiation was caused by, in a first time, extensive crosses between HGal cultivars and, in a second time, by crosses between HGal and Asian cultivars.

HGal, Alba, Centifolia and Damask roses are also used in the perfume industry [START_REF] Pal | Evaluation, Genetic Diversity, Recent Development of Distillation Method, Challenges and Opportunities of Rosa damascena: A Review[END_REF]). The Damask are by far the most used for this purpose. Bulgaria and Turkey are the largest centers of production [START_REF] Pal | Evaluation, Genetic Diversity, Recent Development of Distillation Method, Challenges and Opportunities of Rosa damascena: A Review[END_REF]. In these countries, the few cultivars used in the industry are thought to be originated from the same plants [START_REF] Rusanov | Microsatellite analysis of Rosa damascena Mill. accessions reveals genetic similarity between genotypes used for rose oil production and old Damask rose varieties[END_REF].

Hurst (1941) hypothesized that Damask roses were interspecific hybrids between R. gallica and R. phoenicea. This was investigated using molecular markers by [START_REF] Iwata | Triparental origin of Damask roses[END_REF]. [START_REF] Iwata | Triparental origin of Damask roses[END_REF] and Cui et al. (2022a) were tetraploid while the accession used by [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF] was triploid, therefore the incongruence could be due to the use of different cultivars.

The Albas are taller than the other OGR, reaching up to 1.8 metre. They harbor double white (or pale pink) flowers with a sweet fragrance. Albas are the second most cultivated roses for perfumery in Bulgaria [START_REF] Rusanov | Flower phenotype variation, essential oil variation and genetic diversity among Rosa alba L. accessions used for rose oil production in Bulgaria[END_REF]. The chemical composition of their perfume is close to that of Damask roses [START_REF] Dobreva | Comparative Study of the Yield and Chemical Profile of Rose Oils and Hydrosols Obtained by Industrial Plantations of Oil-Bearing Roses in Bulgaria[END_REF]). These cultivars are believed to originate from a cross between R. canina and R. gallica or R. damascena [START_REF] Hurst | Notes on the origin and evolution of our garden roses[END_REF][START_REF] Maia | Cytotaxonomie du genre Rosa et origine des rosiers cultivés[END_REF]. Molecular investigations based on Internal Transcribed Spacer (ITS) sequences were in agreement with this hypothesis, as Alba roses had both 'Canina' and 'Gallica' types of ITS-1 sequences [START_REF] Ritz | Evolution by reticulation: European dogroses originated by multiple hybridization across the genus Rosa[END_REF]. In origin of Alba roses. They also supposed that a Caninae was the female parent of these hexaploid roses. In details, due to the asymmetric meiosis of Caninae, the cross would have involved a tetraploid female gamete (Caninae) and a diploid male gamete (Gallicanae, resulting in a hexaploid with more alleles inherited from the Caninae [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]). The study of [START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF] was not incompatible with that as they Introduction found Albas clustered with Caninae. The diversity of the Alba roses was investigated by [START_REF] Rusanov | Flower phenotype variation, essential oil variation and genetic diversity among Rosa alba L. accessions used for rose oil production in Bulgaria[END_REF]. A germplasm of 38 Alba accesions (representing the Alba grown for oil in Bulgaria) was evaluated using morphological and genetic markers. Two The origin of the Centifolia group has been less studied compared to that of the Damask or Alba. Centifolia is a group of roses that feature an extraordinary number of petals, which reduces their fertility. Many cultivars within this group are believed to be sports Introduction R. canina, and R. phoenicea. To achieve this genetic mixture, he theorized the most straightforward method would involve viewing Centifolia as hybrids between Damask and Alba roses. This hypothesis, not based on molecular markers, remains highly speculative.

Unfortunately, it has never been extensively explored. However, insights regarding this hypothesis can be gleaned from genetic diversity analyses of OGR. In the study by [START_REF] Gardes | Molecular typing and history of the provins roses horticultural group[END_REF], the included R. centifolia was somewhat different from most HGal cultivars, aligning with a possible interspecific hybrid origin. [START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF] [START_REF] Hurst | Notes on the origin and evolution of our garden roses[END_REF]. This remains speculative, since no study has explicitly been dedicated to this question.

According to [START_REF] Hurst | Notes on the origin and evolution of our garden roses[END_REF], Moss varieties are sports of Centifolia cultivars. However, study that included Moss revealed that Mosses are cultivars with a very variable genetic background [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF][START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF]. Finally, Moss are a group of roses that harbor mossy sepals, calyx and stems (Fig. 1.15). This trait alone would have been the criteria for the groups allowing the inclusion of very different roses in this horticultural group [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF][START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF]).

Theory and methods in genetic diversity characterization 1.3.1 Population genetic theory

Population genetics is central to evolutionary biology. In general, population genetics is concerned with the description of observed or inferred heritable traits in populations over time and space [START_REF] Amorim | Brenner's Encyclopedia of Genetics[END_REF]. More specifically, it aims to understand how and why allele and genotype frequencies evolve over time and space within and between pop-Introduction ulations by elucidating the microevolutionary forces that drive genetic variation [START_REF] Clark | Population Genetics[END_REF]. The basis for identifying what drives variation in a population is the state in which allelic and genotypic frequencies remain constant over generations. This model was defined separately in 1908 by the famous Godfrey Harold Hardy (1877Hardy ( -1947) ) and

Wilhelm Weinberg (1862Weinberg ( -1937) ) [START_REF] Hardy | Mendelian proportions in a mixed population[END_REF][START_REF] Weinberg | Uber den nachweis der vererbung beim menschen[END_REF]. The model postulates that for a diploid organism, with fully sexual reproduction, discrete generations, under panmixia (i.e. random mating) and in the absence of evolutionary forces (selection, genetic drift, mutation, migration), genotypic frequencies can be expressed as a simple function of allelic frequencies, which are then constant over time [START_REF] Waples | Testing for Hardy-Weinberg Proportions: Have We Lost the Plot?[END_REF]. In other words, the Hardy-Weinberg equilibrium will be reached in a single generation and will be maintained if: (1) mating is random (with discrete generations), [START_REF] Koopman | AFLP markers as a tool to reconstruct complex relationships: A case study in Rosa (Rosaceae)[END_REF] there is no natural selection, (3) the population size is extremely large, minimizing the effects of genetic drift, (4) there is no gene flow or migration, (5) mutations do not occur, and (6)

the locus is on an autosomal chromosome, [START_REF] Young | Modern Roses-12: The Comprehensive List of Roses in Cultivation or of Historical or Botanical Importance[END_REF] the organism is diploid [START_REF] Lachance | Hardy-Weinberg Equilibrium and Random Mating[END_REF][START_REF] Waples | Testing for Hardy-Weinberg Proportions: Have We Lost the Plot?[END_REF]. In this model, the expected heterozygosity, calculated from the allele frequencies, is equal to the observed heterozygosity, calculate from the observed number of heterozygotes [START_REF] Meirmans | The Analysis of Polyploid Genetic Data[END_REF]. Any deviation from this null model may be due to violations of at least one of these assumptions, providing a basis for investigating which microevolutionary forces are responsible for the deviation [START_REF] Waples | Testing for Hardy-Weinberg Proportions: Have We Lost the Plot?[END_REF].

A crucial term in population genetics is the word "population". Its meaning seems so intuitive that it is rarely defined in population genetic study. However, the word can have multiple senses depending on the field and the authors [START_REF] Waples | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF]). An obvious divergence of sense is the one between the statistical versus biological meaning of "a population". In statistics, it refers to a group of entities (individuals or statistical objects) for which we want to infer some properties based on a sample, while in biology, it refers to a group of individuals sharing biological features [START_REF] Waples | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF].

Then variability in the definition of a biological population is also found in literature (e.g. [START_REF] Hedrick | Genetics of populations, 2nd edn[END_REF][START_REF] Krebs | Ecology the experimental analysis of distribution and abundance[END_REF][START_REF] Lapedes | McGraw-Hill dictionary of scientific and technical terms[END_REF]. [START_REF] Waples | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF] compared the different definitions and identified two types of definitions under what they call the ecological and evolutionary paradigms. Under the ecological paradigm, a population is "a group of individuals of the same species that co-occur in space and time and have an opportunity to interact". Under the evolutionary paradigm, a population is "a group of individuals of the same species living in close enough proximity that any member of the group can potentially mate with any member". Comparing the two definitions, we can see that the ecological definition implies interaction between members. The interactions Introduction can be reproductive but also include, for example, social interaction and competitions for resources. The evolutionary definition narrows the interaction to reproduction under random mating [START_REF] Waples | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF]. Therefore, panmixia is the only criterion to call a group of individuals a population under the evolutionary paradigm. However, both definitions include the term "species", which has, as the term "population", several definitions adding a layer of complexity. In fact, these definitions are based on the assumption that the species are well delineated, something that may be difficult to achieve in the presence of gene flow between species, as identified for Rosa species (e.g. [START_REF] Gao | Species Boundaries and Parapatric Speciation in the Complex of Alpine Shrubs, Rosa sericea (Rosaceae), Based on Population Genetics and Ecological Tolerances[END_REF].

Departure from the Hardy-Weinberg equilibrium can result from non-random mating in a population, dividing the population into sub-units in panmixia (sometimes called subpopulations). Since the sub-units are in panmixia, they can be termed "populations," which explains why they are called subpopulations [START_REF] Chakraborty | Analysis of Genetic Structure of Populations: Meaning, Methods, and Implications[END_REF]. For a species, the term "metapopulation" can be used to refer to all the subpopulations and their pairwise gene flows [START_REF] Waples | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF]. This arrangement is known as the population structure, characterized by the number of sub-units, the distribution of allele frequencies in each sub-unit, and the extent of gene flow between them [START_REF] Chakraborty | Analysis of Genetic Structure of Populations: Meaning, Methods, and Implications[END_REF]. Non-random mating occurs in a population when the level of gene flow between potential subpopulations falls below the level needed to maintain panmixia. This decrease can result from geographic (e.g., mountain, river, highway, [START_REF] Tesfaye | Genetic diversity of Nile tilapia (Oreochromis niloticus) populations in Ethiopia: insights from nuclear DNA microsatellites and implications for conservation[END_REF]), ecological (e.g., habits, ecological niche, diet, predators, Melo et al. 2014;[START_REF] Pilot | Ecological factors influence population genetic structure of European grey wolves[END_REF], or biological factors (e.g., phenology, reproductive isolation, [START_REF] Baack | The origins of reproductive isolation in plants[END_REF].

The subpopulations have a reduced size compared to the entire population, introducing stochasticity in the sampling of alleles each generation. Consequently, allele frequencies shift randomly across generations. Since this process occurs independently in each subpopulation, it results in varying allele frequencies. This leads to a departure from the Hardy-Weinberg equilibrium at the overall population level. The subdivision of the population causes an apparent deficit of heterozygotes and an excess of homozygotes when examining a sample of individuals collected in all subpopulations. This phenomenon is known as the Wahlund effect (Garnier-Géré and Chikhi 2013).

At the basis, the Hardy-Weinberg equilibrium is stated for diploids but can be extended to polyploids [START_REF] Meirmans | The Analysis of Polyploid Genetic Data[END_REF]. In polyploids, if all the conditions of Hardy-Weinberg equilibrium are respected (except the diploidy), the equilibrium (expected heterozygosity equal to observed heterozygosity) will either be reached after several generations of random mating or will not be reached depending on the inheritance model Introduction of the species. For example, under fully polysomic inheritance, a tetraploid population containing 50% of AAAA genotype and 50% of BBBB genotypes will produced the gametes AA and BB resulting at the first generation to three possible genotypes that are AAAA, AABB and BBBB. Since the genotypes ABBB and AAAB are lacking, the population will surely not be in equilibrium at the first generation but will tend to over the generations [START_REF] Meirmans | The Analysis of Polyploid Genetic Data[END_REF]. Under disomic inheritance, the equilibrium will never be reach and the expected heterozygosity will be lower than the observed heterozygosity [START_REF] Meirmans | The effects of inheritance in tetraploids on genetic diversity and population divergence[END_REF]. The intensity of the heterosygote excess will vary in function of the rate of homeologous exchange [START_REF] Meirmans | The effects of inheritance in tetraploids on genetic diversity and population divergence[END_REF]. Double reduction (the fact that regions from two sister chromatids become included in the same gamete, only possible in polyploids) may result to the production, by a genotype ABCD, of AA gametes [START_REF] Zielinski | Meiosis in polyploid plants[END_REF].

When double reduction occurs in a population, the equilibrium will never be reached even in the presence of panmixia [START_REF] Meirmans | The Analysis of Polyploid Genetic Data[END_REF].

Deviation from the Hardy-Weinberg equilibrium corresponds to a change in allele frequencies, which is synonymous with evolution in population genetics. Evolutionary forces that influence allele frequencies in populations include genetic drift, migration, mutation, and natural selection [START_REF] Clark | Population Genetics[END_REF]. Genetic drift results from a finite population size. In each new generation, allele frequencies arise from a random sample of alleles from the previous generation. Given that the size of this sample is finite, repeated sampling will yield different allele frequencies between the preceding generation and the new generation. This phenomenon is termed genetic drift. This change occurs with each generation, introducing consistent stochasticity in the allele frequencies. The intensity of genetic drift is inversely proportional to the population size [START_REF] Amorim | Brenner's Encyclopedia of Genetics[END_REF][START_REF] Clark | Population Genetics[END_REF]. Over time, genetic drift can lead to some alleles becoming fixed (frequency equal to 1) or lost (frequency equal to 0), thus reducing both allele richness and heterozygosity [START_REF] Nei | The Bottleneck Effect and Genetic Variability in Populations[END_REF]. Furthermore, population size fluctuates over time, therefore, even if the population size is large at the time of study, the apparent number and frequencies of alleles may have been influenced by past size reduction. A significant reduction in population size that leads to substantial modifications in allele frequencies and loss of alleles is referred to as a "bottleneck effect" (Amorim 2013). Even if the population recovers in size, the genetic bottleneck can still be detected due to the persistent loss of allele diversity. When a species colonizes a new area, several founder individuals move to the unoccupied territory. Since these individuals represent a sample from the original population, they carry only a subset of the alleles of the original population, resulting in a loss Introduction of alleles (founder effect). The new population will then tend to be differentiated (i.e., have different allele frequencies) from the source population, depending on the number of founders and the level of gene flow with the source population [START_REF] Amorim | Brenner's Encyclopedia of Genetics[END_REF][START_REF] Nei | The Bottleneck Effect and Genetic Variability in Populations[END_REF]. Migration (also known as gene flow) refers to the exchange of alleles between populations (or subpopulations) and is different from the colonization of a new area by founder individuals [START_REF] Wright | Evolution in Mendelian populations[END_REF]. While genetic drift increases differentiation between populations, migration decreases it [START_REF] Lacy | Loss of Genetic Diversity from Managed Populations: Interacting Effects of Drift, Mutation, Immigration, Selection, and Population Subdivision[END_REF]. More specifically, gene flow is a crucial evolutionary force that counteracts genetic drift by maintaining similar allele frequencies between populations [START_REF] Wright | Evolution in Mendelian populations[END_REF]. Additionally, gene flow between new and source populations can mitigate the loss of alleles in the founder population [START_REF] Greenbaum | Allelic Richness following Population Founding Events -A Stochastic Modeling Framework Incorporating Gene Flow and Genetic Drift[END_REF]. Mutation, the sole process creating new alleles, encompasses all heritable changes in the genome, including SNP, Insertion/Deletion (InDel), transposable elements, copy number variations, and chromosome rearrangements. Although mutations generate new alleles, the fate of these alleles (whether they increase or decrease in frequency) depends on genetic drift and selection [START_REF] Clark | Population Genetics[END_REF]). In a infinite population, selection at a locus occurs when alleles are not neutral, meaning that different alleles have different reproductive values. In other words, some alleles have a relatively higher probability of being transmitted to the next generation than others [START_REF] Amorim | Brenner's Encyclopedia of Genetics[END_REF]. This probability varies depending on other competing alleles, genetic background, environment, and whether the allele is dominant or recessive. At a locus, selection results in an increase of the frequency of selected alleles [START_REF] Wright | Evolution in Mendelian populations[END_REF]. In natural population, each force may or may not influence the allele frequencies (i.e. evolution), therefore a practical population genetic study aims, by using molecular markers, at identifying which evolutionary forces are implicated in the evolution of the studied populations (Casillas and Barbadilla 2017).

Molecular population genetics

Molecular population genetic studies depend on both the sampling scheme and the molecular markers used to genotype samples. Pioneering molecular population genetic studies can be traced back to 1966 with the use of electrophoresis to characterize protein variations. However, this method allowed access only to mutations modifying the amino acid sequences of proteins and only when the change in amino acid affected the protein's mobility. Direct access to nucleotide diversity was first achieved using restriction enzymes in the early 1980s. The first study comparing sequences of orthologous loci to detect polymorphism was conducted in 1983 [START_REF] Casillas | Molecular Population Genetics[END_REF]. Since these early studies, molecular genetic markers have been categorized by the type of mutation they Introduction target and the lab method used to identify these mutations. The polymorphism types used include SNPs, InDels, and SSRs [START_REF] Vignal | A review on SNP and other types of molecular markers and their use in animal genetics[END_REF]. However, other types have been more recently explored, such as transposable elements (e.g. [START_REF] Zhao | Transposable Elements: Distribution, Polymorphism, and Climate Adaptation in Populus[END_REF]) and copy number variations [START_REF] Xu | Population-genetic properties of differentiated copy number variations in cattle[END_REF]). Among these, SNPs and SSRs (also known as microsatellites or short tandem repeats) are the most commonly used. A variety of methods have been employed to uncover SNP variations, including Amplified Fragment Length Polymorphism (AFLP) fingerprinting, SNP chips [START_REF] Wang | Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome[END_REF], and Kompetitive Allele Specific Polymerase Chain Reaction [START_REF] Semagn | Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement[END_REF]). Additionally, SNP genotyping has been conducted using High-Throughput Sequencing (HTS).

These methods include direct whole-genome sequencing, complexity reduction methods such as Genotyping-By-Sequencing (GBS), Restriction site-associated DNA sequencing (RADseq), sequence capture and amplicon sequencing [START_REF] Harvey | Sequence Capture versus Restriction Site Associated DNA Sequencing for Shallow Systematics[END_REF]. SSRs are traditionally genotyped through Polymerase Chain Reaction (PCR) amplification and amplicon separation by electrophoresis on either acrylamide gel or capillary electrophoresis. Compared to SNP genotyping, the use of HTS for SSR genotyping has only recently been developed [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF][START_REF] Vartia | A novel method of microsatellite genotypingby-sequencing using individual combinatorial barcoding[END_REF].

SSRs, or microsatellites, consist of short repeated DNA motifs, typically one to six nucleotides in length, repeated several to dozens of times. The polymorphic nature of these markers stems from their high variability in the number of repetitions. Due to this variability, these loci can be multi-allelic [START_REF] Li | Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review[END_REF]. In contrast, SNPs are single point mutations. Although in theory a SNP can have up to four alleles, the low mutation rate typically restricts these loci to a biallelic state [START_REF] Vignal | A review on SNP and other types of molecular markers and their use in animal genetics[END_REF]. Microsatellites harbor a high mutation rate per generation, ranging between 10 -2 and 10 -6 , that contrasts to SNPs which exhibit mutation rates from 10 -8 to 10 -10 (Bhargava and Fuentes 2010; [START_REF] Sandler | Estimation of the SNP Mutation Rate in Two Vegetatively Propagating Species of Duckweed[END_REF]. The information content of a microsatellite locus is generally considered to be four to twelve times that of a SNP, attributed to the larger number of possible alleles per locus [START_REF] Liu | Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure[END_REF]. However, the cost-effectiveness of SNP genotyping has meant that, even if individual SNPs convey less information, this shortfall is compensated by analyzing thousands of SNPs concurrently [START_REF] Malenfant | Design of a 9K illumina BeadChip for polar bears (Ursus maritimus) from RAD and transcriptome sequencing[END_REF]. Both SSRs and SNPs have been compared in numerous studies concerning their capacity to elucidate population structure and calculate diversity estimates [START_REF] Biello | From STRs to SNPs via ddRAD-seq: Geographic assignment of confiscated tortoises at reduced costs[END_REF][START_REF] Camacho-Sanchez | Comparative assessment of range-wide patterns of genetic diversity and structure with SNPs and microsatellites: A case study with Iberian amphibians[END_REF][START_REF] Choudhury | SSR and SNP Marker-Based Investigation of Indian Rice Landraces in Relation to Their Genetic Diversity, Population Structure, and Geographical Isolation[END_REF][START_REF] Lemopoulos | Comparing RADseq and microsatellites for estimating genetic diversity and relatedness Implications for brown trout conservation[END_REF][START_REF] Liu | Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure[END_REF][START_REF] Malenfant | Design of a 9K illumina BeadChip for polar bears (Ursus maritimus) from RAD and transcriptome sequencing[END_REF][START_REF] Miller | Estimating genome-wide heterozygosity: effects of demographic history and marker type[END_REF][START_REF] Osborne | Transitioning from microsatellites to SNP-based microhaplotypes in genetic monitoring programmes: Lessons from paired data spanning 20 years[END_REF][START_REF] Prunier | Comparing the Utility of Microsatellites and Single Nucleotide Polymorphisms in Conservation Genetics: Insights from a Study on Two Freshwater Fish Species in France[END_REF][START_REF] Zimmerman | An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern[END_REF]. In general, these investigations have found that while both markers yield comparable estimates of population structure, a significant number Introduction of SNPs can provide insights into finer population subdivisions. Diversity metrics are also more precisely determined using an extensive number of SNPs as opposed to microsatellites [START_REF] Sunde | Comparing the Performance of Microsatellites and RADseq in Population Genetic Studies: Analysis of Data for Pike (Esox lucius) and a Synthesis of Previous Studies[END_REF][START_REF] Miller | Estimating genome-wide heterozygosity: effects of demographic history and marker type[END_REF] posited that, for both marker types, the accuracy of diversity estimation increased with the number of markers employed.

Thus, one plausible reason for the reduced efficacy of microsatellites relative to SNPs could be the very large number of SNPs typically used in these studies in comparison to

microsatellites. An alternative or complementary explanation might be the high length homoplasy rate of microsatellites when genotyped via electrophoresis, as done in all studies comparing the two marker types. Electrophoresis allows only to identify alleles by the amplicon length and not by the sequences thus two alleles with the same length can not be distinguished from each other, this is termed length homoplasy [START_REF] Anmarkrud | Microsatellite evolution: Mutations, sequence variation, and homoplasy in the hypervariable avian microsatellite locus HrU10[END_REF][START_REF] Estoup | Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis[END_REF].

The problem of size homoplasy is easily resolved when using HTS [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF][START_REF] Vartia | A novel method of microsatellite genotypingby-sequencing using individual combinatorial barcoding[END_REF]. Indeed, access to the sequences allows differentiation of alleles of the same length but with different sequences due to the presence of SNPs or small InDels in the flanking sequences or directly within the SSR motifs [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF][START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF]. HTS reveals a high homoplasy rate in microsatellites. For instance, Darby et al.

(2016) found 1.8 times more alleles when calling the alleles by considering the sequences compared to a calling based on amplicon length only. Similarly, Šarhanová et al. (2018) found that 44.7%, 45.9%, and 63.5% of the amplicon length classes harbored homoplasy in Donatia fascicularis, Mulguraea tridens, and Oreobolus obtusangulus, respectively. The reduction of homoplasy by HTS increased the polymorphism information content of the marker [START_REF] Tibihika | Application of microsatellite genotyping by sequencing (SSR-GBS) to measure genetic diversity of the East African Oreochromis niloticus[END_REF], leading to increased differentiation [START_REF] Curto | Application of a SSR-GBS marker system on investigation of European Hedgehog species and their hybrid zone dynamics[END_REF]). An improvement in fine-scale inferences of population structure and individual assignment accuracy was demonstrated in Salmo salar [START_REF] Bradbury | Genotyping-by-sequencing of genome-wide microsatellite loci reveals fine-scale harvest composition in a coastal Atlantic salmon fishery[END_REF], Oreochromis niloticus [START_REF] Tibihika | Application of microsatellite genotyping by sequencing (SSR-GBS) to measure genetic diversity of the East African Oreochromis niloticus[END_REF], Ondatra zibethicus [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF], and Ailanthus altissima [START_REF] Neophytou | Analysis of microsatellite loci in tree of heaven (Ailanthus altissima (Mill.) Swingle) using SSR-GBS[END_REF]. Diversity estimates are also increased by sequence-aware calling [START_REF] Neophytou | Analysis of microsatellite loci in tree of heaven (Ailanthus altissima (Mill.) Swingle) using SSR-GBS[END_REF][START_REF] Šarhanová | SSR-seq: Genotyping of microsatellites using next-generation sequencing reveals higher level of polymorphism as compared to traditional fragment size scoring[END_REF]. [START_REF] Layton | Resolving fine-scale population structure and fishery exploitation using sequenced microsatellites in a northern fish[END_REF] compared sequenced microsatellites to a panel of carefully selected SNPs in Arctic Charr. They found similar population assignment accuracy, although the SNP panel was better at identifying very weak divergence. However, as their SNP panel was designed for the population under study, the authors highlight that its applicability to other populations could be lower due to ascertainment bias, something from which sequenced microsatellites would suffer less.

The cost of sequencing microsatellites depends on the number of targeted loci, the level of multiplexing (both loci and individuals), and the number of samples [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF].

Introduction

Specifically, cost estimates performed in studies using microsatellite sequencing ranged from 6 dollars [START_REF] Neophytou | Analysis of microsatellite loci in tree of heaven (Ailanthus altissima (Mill.) Swingle) using SSR-GBS[END_REF]) to 14 dollars per sample [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF]. This price compares favorably to electrophoresis genotyping of SSRs [START_REF] Neophytou | Analysis of microsatellite loci in tree of heaven (Ailanthus altissima (Mill.) Swingle) using SSR-GBS[END_REF], but is also similar to a highly selected SNP panel [START_REF] Biello | From STRs to SNPs via ddRAD-seq: Geographic assignment of confiscated tortoises at reduced costs[END_REF]) and lower than high-number SNP genotyping techniques such as RAD-seq [START_REF] Inbar | Comparative study of population genomic approaches for mapping colony-level traits[END_REF]. Given this, sequenced microsatellites may represent an efficient balance between population structure and resolution of diversity estimates and cost-effectiveness for poorly studied species [START_REF] Layton | Resolving fine-scale population structure and fishery exploitation using sequenced microsatellites in a northern fish[END_REF]).

Research question and objectives

The long love history between R. gallica and Human societies that began thousands years ago contrasts with the poor understanding of the species we have. The wild genetic diversity has been only investigated at limited scales and the way this species participated to the genesis of roses cultivation and breeding was scarcely studied. All the studies focusing on wild R. gallica diversity did not investigate the question in a comprehensive context as their sampling schemes were only partly representative of the species range. Even when the diversity was investigated based on an extensive sampling over the focused territory as it was done for France, the lack of samples from other regions of the range had let question unresolved, notably about the origins of French populations. Beyond the natural diversity of R. gallica, there is its cultivated diversity.

Rosa gallica is known to be one of the major contributor to the European OGR that were so famous until the second part of the 19th century. The relationship between wild R.

gallica and the first European rose cultivars has only been investigated focusing on either a particular horticultural group or particular cultivars and never with a representative sampling of the wild diversity. Studying the diversity adds a layer of complexity due to the extensive interspecific hybridization from where originated roses cultivars that might have played role even at the early beginning of rose cultivation.

The overall objective of this thesis is to gain a deeper understanding of R. gallica evolutionary history, particularly in France. To do this, we focus on both the wild and cultivated forms of the species while considering polyploidy and inter-specific hybridizations. Therefore, the main objective can be divided into further sub-questions:

Related to the wild diversity of R. gallica:

1. Which factors, either natural or anthropic, are responsible for R. gallica expansion over its range?

2. What are the origins of the French populations?

Related to the cultivation of R. gallica:

3. How has R. gallica contributed to the breeding of the first cultivated roses in Europe?

Chapter 2

SSR sequencing, a new flexible tool for Roses Preamble

The foundations of population genetic studies are samples and genetic markers. These form the initial steps of my thesis. Having collected numerous samples pertinent to our research, we needed to choose an appropriate genetic marker system. While capillary electrophoresis-genotyped SSRs were commonly used for roses and by the GDO team, the system had low throughput and data accuracy limitations. Given our budget constraints and the need to genotype a large number of individuals, we embarked on a project with the Genome Transcriptome Facility of Bordeaux (PGTB) to pioneer amplicon sequencing on SSR loci in roses. The first section of this chapter describes how we designed and evaluated a new set of SSRseq loci adapted to the genus Rosa.

This section called "Putting rose microsatellites into orbit: development and assessment of an SSR sequencing method" corresponds to a work published in Acta Horticulturae [START_REF] Pawula | Putting rose microsatellites into orbit: development and assessment of an SSR sequencing method[END_REF]). In the second section, we demonstrate how we developed a machine learning-based method to infer the ploidy level on individuals based on the SSRseq data.

Introduction

Demands for food should increase until, at least, 2050. Cropland conversion dynamics may follow the same trend. Unfortunately, this represents a threat for biodiversity [START_REF] Masson-Delmotte | Climate change and land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and[END_REF]. In these conditions, conservation efforts will become central to maintain as high as possible biodiversity levels. In line, the Convention of Biological Diversity aims to maintain the intra-specific genetic diversity in cultivated and wild species (Convention on Biological Diversity COP 10 2011). Preserving the diversity level of wild crop relatives is of first interest as they represent resources in which interesting alleles or genes can be found.

Roses are mainly grown for an ornamental purpose. However, roses provide also perfumes, food and medicinal products [START_REF] Smulders | Rosa[END_REF]. Cultivated roses originated from seven to fifteen wild roses species and harbor various ploidy levels [START_REF] Smulders | In the name of the rose: a roadmap for rose research in the genome era[END_REF]. Rosa gallica L. (4x) was the main contributor of the old European varieties [START_REF] Gardes | Molecular typing and history of the provins roses horticultural group[END_REF]. Wild relatives of R. gallica cultivars still exist and are protected in France. Therefore, monitoring the genetic diversity of this species will not only aid to manage threatened wild populations but also to enhance the use of these genetic resources in breeding programs.

Roses genetic variations were explored many times using microsatellite markers, also known as Simple Sequence Repeats (SSR) and Short Tandem Repeats (STR) [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF][START_REF] Mayland-Quellhorst | Population structure of Rosa spinosissima L. on the Frisian Islands and introgression from cultivated material[END_REF][START_REF] Rusanov | Microsatellite analysis of Rosa damascena Mill. accessions reveals genetic similarity between genotypes used for rose oil production and old Damask rose varieties[END_REF][START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF][START_REF] Vukosavljev | Genetic diversity and differentiation in roses: A garden rose perspective[END_REF]. These particular sequences are repetitions of 1-6 bp DNA motifs in tandem exhibiting a high mutation rate [START_REF] Kelkar | What is a microsatellite: a computational and experimental definition based upon repeat mutational behavior at A/T and GT/AC repeats[END_REF]). Traditionally, these markers were genotyped using capillary electrophoresis (SSRce) to identify PCR products differing in length as different alleles [START_REF] Guichoux | Current trends in microsatellite genotyping[END_REF]). However, this technique suffers from low to medium throughput, length homoplasy (confusion between two different alleles of the same length) and moderate portability across laboratories [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF]).

Recently, it has been shown that high throughput sequencing technologies could help overcome traditional limitations in SSR genotyping by accessing to the DNA sequence.

Sequencing improves not only the identification of homoplasy but also gives more accurate sizing, allows a higher degree of multiplexing and unlocks data portability between laboratories [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF][START_REF] Vartia | A novel method of microsatellite genotypingby-sequencing using individual combinatorial barcoding[END_REF]. Additionally, a ready-to-implement workflow has been recently published, and is, theoretically, applicable to any species with low development costs [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF]). However, SSR sequencing (SSRseq) has rarely been applied to polyploids (Cui et al. 2022b).

Here we present the first development of SSR markers based on amplicon sequencing for roses. We detect SSR motifs and design primers based on existing genomic data. We assess the functionality and the reliability of sixty pre-selected markers by sequencing twice the same set of individuals. Reliable markers are then used to estimate the information gain.

Material and Methods

Marker design

We performed SSR motif detection and primer design from the Rosa chinensis 'Old Blush' reference genome (Hibrand Saint-Oyant et al. 2018) using QDD v3.1 software [START_REF] Meglécz | QDD version 3.1: a user-friendly computer program for microsatellite selection and primer design revisited: experimental validation of variables determining genotyping success rate[END_REF]). In addition, 17 SSRce markers routinely used on roses were specifically located to perform primer redesign in order to meet the requirement of the SSRseq. Detection step was performed with QDD default parameters. Primer design step was run with custom parameters to optimize PCR multiplexing. Briefly, the primer length was set to 25 bp (min = 21, max = 26), annealing temperature was set to 68°C

(min = 60°C, max = 75°C) and GC rate was set to 50% (min = 40%, max = 60%).

The amplicon length was constrained to the range 100-180 bp to match the length of the sequencing reads (paired-end reads 2 x 150 bp).

To maximize marker functionality, we checked their transferability on seven Rosa species that were selected to be representative of the genus diversity. Genome assemblies were available for six out of the seven, namely R. rugosa, R. minutifolia, R. moschata, R. laevigata, R. persica and R. xanthina [START_REF] Saint-Oyant | A highquality genome sequence of Rosa chinensis to elucidate ornamental traits[END_REF]assemblies unpublished). Primers were mapped on these genomes using Bowtie2 [START_REF] Langmead | Fast gapped-read alignment with Bowtie 2[END_REF]. Then a marker was classified as transferable on a species if both primers of the pair successfully mapped. For R. gallica, no assembly was available but sequencing reads [START_REF] Saint-Oyant | A highquality genome sequence of Rosa chinensis to elucidate ornamental traits[END_REF]. Therefore, we evaluated the transferability on this species by mapping the reads on the in silico amplicons obtained on the R.

chinensis 'Old Blush' reference genome using BWA-MEM [START_REF] Li | Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM[END_REF]. Additionally, we performed a variant calling using Naive Variant Caller [START_REF] Blankenberg | Dissemination of scientific software with Galaxy Tool-Shed[END_REF]. The sequencing reads of R. gallica were uploaded to the Galaxy web platform. We used the public server at https://usegalaxy.org/ to practice read mapping and variant calling [START_REF] Afgan | The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update[END_REF]. A marker was classified as transferable on R. gallica if reads mapped on the regions of the two primers on the amplicons and if there was no SNP with at least 90% read depth. We computed the transferability rate (TR) of each marker dividing the number of species on which the marker is transferable by the number of tested species (i.e. eight). Finally, we used reference annotations to manually check the position of each marker.

As we were not able to determine the intra-specific polymorphism level, we hypothesized that markers with a high TR were in conserved loci and then were less variable. Since we wanted to use first these markers to describe the diversity of R. gallica, we decided to select 30 markers transferable on R. gallica but not necessarily on the other species (called later "Gal" markers), 22 markers at least transferable on 5 species ("Trf" markers) and 8 successfully redesigned markers ("Lab" markers). We selected markers outside genes, except for 7 out the 8 "Lab" markers. Finally, markers were also chosen to obtain a good repartition on each chromosome.

Marker evaluation

1. Wet lab procedure.

We evaluated the markers on a test panel of 79 individuals that were chosen to be representative of the genus diversity. In details, the test panel was composed of 40 diploids and 39 tetraploids (among them 29 were R. gallica and relatives). Individuals were genotyped twice to check for allelic errors. DNA samples were gathered from the RosePom Biological Resources Center. DNA was quantified using Qubit 4 Fluorometer (Invitrogen). Samples were normalized to 10 ng.µL -1 with ultra-pure water in a final volume of 30 µL. PCR procedure and library preparation were then proceeded at the PGTB using the workflow described in [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF] except that locus specific PCR was performed in one step instead of two steps, and that the number of PCR cycles was decreased to 24 cycles instead of 35. After pooling the individuals, the sequencing step was conducted on an Illumina iSeq100 using 2x150 bp paired-end sequencing kit.

Bioinformatic analyses.

Sequences were demultiplexed and adaptors were trimmed using a sequencer platform built-in software. Paired-end reads were then merged using BBmerge with a minimum length overlap of 20 bp [START_REF] Bushnell | BBMerge -Accurate paired shotgun read merging via overlap[END_REF]. Additionally, merged reads shorter than 65 bp were removed. Allele calling was performed using the FDSTools v1.2 pipeline [START_REF] Hoogenboom | FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise[END_REF] as described in [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF].

The best FDSTools parameters to find the microsatellites alleles in either diploids or tetraploids were determined using a three-step procedure. First, we focused on diploids using the bash script SSRseq_DataAnalysis_ParametersComparison.sh [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF]. FDSTools can be used on either the whole amplicon sequence or only on the repeat motif. In line, we tested six sets of parameters that considered the whole amplicons and four sets that focused on the repeat motif. The parameter set that produced the lowest missing data rate and the lowest error rate was then selected for each marker. Second, we focused on tetraploids and tested six sets of parameters that considered the whole amplicon and four sets that focused on the repeat motif. The best parameter set was then selected for each marker. Markers for which it was not possible to identify a set of parameters that produced a reliable calling were discarded. Thirdly, we identified parameter sets that worked well with both diploids and tetraploids.

Marker validation.

Marker functionality was calculated through the missing data rate, estimated at two levels: the genus one and the R. gallica species one. First, we calculated the missing data rate of each marker on a subsample, called the "diversity" panel, of 49 individuals chosen to be representative of the genus without giving too much weight to one species.

Second, 23 individuals were retained for the "gallica" panel to calculate the missing data rate of each marker, the other ones being half-sib bringing thus little supplementary information for that question.

Allelic error rate per marker was calculated by comparing genotypes obtained with the two repeated experiments. The allelic error rate was calculated by dividing the number of mismatches between two replicates by the total number of alleles in the two replicates.

The total number of alleles was estimated by multiplying the number of replicates by the number of loci and by the ploidy level. We validated markers that produced less than 50% of missing data and with less than 10% of allelic error rate.

Information gain estimation

After SSRseq marker validation, we genotyped, without replicates, 61 additional R. gallica individuals collected in 14 French wild populations following the procedure described above. There were 2 to 8 individuals per population. After data curation, we retained the data of 31 SSRseq markers. SSRce data of 29 markers from the same populations with the same number of individuals per population were available and coded as allelic phenotypes. Markers and individuals with less than 50% missing data were retained.

Information gain was estimated through the comparison of an SSRce mimicking allele calling to a calling method that considers the sequence data. Briefly, to mimic the SSRce calling, we simply coded the alleles by their length in base pair. Oppositely, we also called the alleles by looking at the haplotype, one allele corresponding to one unique haplotype.

To obtain final genotype matrices, we either coded the alleles as allelic phenotype or as genotype with estimated allele dosage performed as described in Cui et al. (2022b). To sum up this part, at the end, each genotype was coded in four different manners (1) allele lengths with allelic phenotypes, (2) allele sequences with allelic phenotypes, (3) allele lengths with allele dosage, (4) allele sequences with allele dosage. Pairwise Euclidean distances between individuals were determined using the R environment (R Core Team 2022). These pairwise distances were also computed with SSRce data for comparison.

Results

In this study, efforts have been made to develop a set of SSRseq markers suitable to genotype rose individuals, with a particular focus on R. gallica. Following SSR motifs detection and primers design we estimated marker transferability. Sixty markers were selected for wet lab experiment in order to evaluate them and retain the best markers.

Finally, using different allele calling methods and SSRce data, we assessed the gain brought by sequencing in SSR genotyping.

Marker development

The sixty markers selected for the test were retained among 1389 designed primer pairs.

All chromosomes are covered by 6 to 13 markers. Twenty-four markers are in intergenic region (more than 2 kb distant from a gene), 29 are close to a gene (less than 2 kb) and 7 are inside a gene.

Out of these sixty markers, twenty were rejected because no effective FDSTools parameter sets have been found. Forty markers were validated as they harbor less than 50% of missing data at least in the "diversity" panel or in the "gallica" panel only (Table 2.1)

and less than 10% of allelic error rate. On the "diversity" panel, marker missing data rates range from 0% to 67% for an average value of 16.4%. Additionally, 20 markers harbor less than 15% of missing data. The allele number per marker ranges from 13 (Gal05_41) to 53 (Lab05_RMS034_37) for an average of 29. On the "gallica" panel, missing data rates range from 0% to 59% with a mean value of 13%. The allele number per marker ranges from 3 (Gal04_26) to 19 (Gal07_56, Table 2.1) with a mean value of 11%.

Allelic error rates extend from 0% to 7.8% with a median equal to 2.4% for diploids.

For tetraploids, these range from 0% to 7.0% with a median value at 2.2%. There is no significant difference between diploids and tetraploids on the allelic error rate (Figure 1 A). However, the absolute number of allelic errors is significantly higher for tetraploids, consistent with their larger number of alleles (Figure 2.1B).

Information gain estimation

We compared different allele calling methods and SSRce data obtained on two sets of 61 wild R. gallica individuals originating from the same 14 populations. One individual

Discussion

This study reports the development of forty sequence-based microsatellites markers adapted to the genus Rosa. Here we validated 40 markers out of the 60 with less than 50% of missing data. Considering that we chose a relatively high error rate threshold, the success rate (67%) is near from the ones obtained for other species [START_REF] Laurent | Microhaplotype genotyping-by-sequencing of 98 highly polymorphic markers in three chestnut tree species[END_REF][START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF][START_REF] Šarhanová | SSR-seq: Genotyping of microsatellites using next-generation sequencing reveals higher level of polymorphism as compared to traditional fragment size scoring[END_REF]). Missing data rates are comparable with rates found on two species of hedgehog (15.5%, Curto et al. 2019). Notably, our missing data rates are slightly higher than those determined on Alosa sp. and Meligua variegatipes in the study of [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF]. This could be due to a higher diver- Oppositely, our error rates seem to be near from the ones found on three other plant species but the calculation method is not detailed (Šarhanová et al. 2018). We can state that the SSRseq marker set developed here is of good quality.

SSRseq markers allow us to access the sequence. In an attempt to compare the information of SSRseq markers to SSRce markers, we used different calling methods and SSRce data. We revealed that, for R. gallica, much more alleles are detected when considering the sequence. The rate is of the same order as the one found on Quercus sp.

but is lower than rates calculated on Donatia fascicularis, Mulguraea tridens, Oreobolus obtusangulus [START_REF] Lepais | Joint analysis of microsatellites and flanking sequences enlightens complex demographic history of interspecific gene flow and vicariance in rear-edge oak populations[END_REF][START_REF] Šarhanová | SSR-seq: Genotyping of microsatellites using next-generation sequencing reveals higher level of polymorphism as compared to traditional fragment size scoring[END_REF]. Pairwise Euclidean distances between 61 R. gallica individuals show that the sequence permits to easier distinguish clones from distinct genotypes (Figure 2.2). SSRce data seem to give the same results than the SSRseq data when coded as allelic phenotypes. This means SSRce are enough variable to identify clones. However, it has been shown that, with SSRce data, an accurate allele dosage is only possible for certain markers on certain individuals [START_REF] Esselink | Assignment of allelic configuration in polyploids using the MAC-PR (microsatellite DNA allele counting-peak ratios) method[END_REF]). Allele quantification with SSRseq data was explored recently on Camellia oleifera (Cui et al. 2022b). Applying the same method, we increased the distance differences between clones and different individuals (Figure 2.2). However, we also increased the distances between clonal individuals, meaning that the allele dosage is not perfectly accurate. Indeed, the method proposed by Cui et al. (2022b) is very simple and does not really consider pairwise allele competition for amplification. Instead, they identified alleles that are preferentially amplified over all other alleles (Cui et al. 2022b).

The sequence not only permits to discover more alleles but is useful to interpret and remove the stutters as it is done by FDSTools [START_REF] Hoogenboom | FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise[END_REF]. Additionally, having access to the type of polymorphism enables to perform deeper analyses [START_REF] Lepais | Joint analysis of microsatellites and flanking sequences enlightens complex demographic history of interspecific gene flow and vicariance in rear-edge oak populations[END_REF]). On a technical point of view, the throughput of our marker set is higher than the former SSRce set. Indeed, we are able to genotype a maximum of 380 individuals with these forty markers (up to sixty) at the same time because all PCRs are multiplexed.

With the SSRce markers, only four PCRs were done at the same time [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]. Moreover, the SSRseq allele calling is very much faster because fully automated while a time-consuming human-checking step was needed with our former SSRce set.

The SSRseq markers tend to be cheaper than the SSRce markers when the number of individuals and markers increased [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF]. For instance, we can genotype a maximum of 60 markers on 380 individuals for a cost of less than 10 € per individuals.

Conclusion

The forty markers developed here will enhance SSR studies on roses. Numerous applications are now possible such as diversity and structure analyses of wild or cultivated individuals. For cultivated roses, these markers would be adapted to relatedness analysis. We focus the development on R. gallica and effectively this set of markers is more functionable on this species than on other Rosa. On R. gallica specimens, we showed that, for polyploids, the greatest advantage of SSRseq markers seems to rely on the ability to quantify the alleles. The access to the sequence leads also to a better understanding and treatment of stutters. We will first use this set of markers to genotype 1520 roses in order to decipher the origins of R. gallica French wild populations. This set could be used on any rose species directly or with few development efforts.

Acknowledgement

Microsatellites development and sequencing were performed at the PGTB (doi:10.

15454/1.5572396583599417E12) with the help of Benjamin Tyssandier and Adline Delcamp. We thank the Pome Fruits and Rose BRC for providing DNA and data associated to the tested accessions, as well as the "Conservatoires botaniques nationaux", especially the CBNPMP for their help in collecting wild Rosa gallica individuals. This work was conducted in the frame of the "Rosa gallica urbanisme" research program. We thank the University of Angers for funding the thesis work of Clovis Pawula.

Machine learning-based ploidy determination 2.2.1 Introduction

Population genetics aims to study the evolution of allele frequencies over time and space.

An accurate estimation of allele frequencies is a starting point for many population genetic analyses. This accurate estimation depends on both allele calling and allele dosage quality (Cui et al. 2022b). For instance, to run SSRseq calling pipelines [START_REF] Hoogenboom | FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise[END_REF][START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF]), the ploidy level is a prerequisite to reduce calling errors.

Moreover, most methods developed to estimate the allele dosage of polyploid organisms are based on either allele peak ratio [START_REF] Esselink | Assignment of allelic configuration in polyploids using the MAC-PR (microsatellite DNA allele counting-peak ratios) method[END_REF] Chromosome counting is the gold standard technique for accurately determining an individual's ploidy level. This method is typically performed on root tips, which are pretreated with various reagents, pressed flat onto a glass slide, and then observed under a microscope. The ploidy level is ascertained by counting the chromosomes in the obtained images. This method is time-consuming, making it infrequently used for large samples [START_REF] Lazarević | Patterns of cytotype distribution and genome size variation in the genus Sesleria Scop. (Poaceae)[END_REF]. Flow cytometry is another approach to estimate ploidy level by determining nuclear DNA contents. In this technique, nuclei are isolated, and DNA is stained using a fluorescent dye specific to DNA, the fluorescent intensity is then used to determine the quantity of DNA [START_REF] Doležel | Estimation of nuclear DNA content in plants using flow cytometry[END_REF]). However, flow cytometry measures only relative DNA content, which is then compared to the DNA content of a reference sample to estimate ploidy [START_REF] Temsch | Reference standards for flow cytometric estimation of absolute nuclear DNA content in plants[END_REF]. This comparison makes the ploidy determination highly dependent on the chosen reference sample. If the haploid genome size differs between the reference and the tested samples, ploidy determination can be inaccurate, as is the case within the genus Rosa [START_REF] Jian | Nuclear DNA content and 1Cx-value variations in genus Rosa L[END_REF][START_REF] Roberts | DNA amounts of roses (Rosa L.) and their use in attributing ploidy levels[END_REF]). Both flow cytometry and chromosome counting require fresh tissues, limiting their applicability for specific sample types, such as herbaria or silica dried samples.

Methods have been developed to infer the ploidy level from genetic or genomic data.

These use the number of alleles per locus or their intra-individual frequencies (based on read depth), depending on the multi-or bi-allelic nature of the loci studied. For example, [START_REF] Gompert | Detection of individual ploidy levels with genotypingby-sequencing (GBS) analysis[END_REF] The method is able to determine the ploidy level of an individual from a population, assuming that there is some degree of polymorphism and differentiation between the different ploidy levels in the population. The method uses an algorithm that iteratively estimates allele frequencies and assigns the ploidy level until convergence is reached. To estimate allele frequencies, the authors adapted the expectation maximization method of [START_REF] Kalinowski | Maximum likelihood estimation of the frequency of null alleles at microsatellite loci[END_REF] [START_REF] Goodfellow | Deep learning[END_REF]). These can be categorized into unsupervised or supervised methods. Unsupervised machine learning algorithms learn

(a) -2 0 2 -2 0 2 (b) -2 0 2 -2 0 2 (c) -2 0 2 -2 0 2 (d) -2 0 2 -2 0 2 (e) -2 0 2 -2 0 2 (f) -2 0 2 -2 0 2 (g) -2 0 2 -2 0 2 (h) -2 0 2 -2 0 2 (i) -2 0 2 -2 0 2 Figure 2.
3: Illustration of the k-means algorithm taken from [START_REF] Bishop | Pattern recognition and machine learning[END_REF]. (a) The data set in a two-dimensional Euclidean space is represented by green dots. The initial choices for cluster centres µ1 and µ2 are indicated by red and blue crosses, respectively.

(b) In the first step, each data point is assigned to either the red cluster or the blue cluster, depending on which cluster centre is closer. This assignment is determined by classifying the points according to which side of the perpendicular bisector of the two cluster centres they lie. The magenta line represents this bisector. (c) In the next step, each cluster centre is recalculated as the mean of the points assigned to its corresponding cluster. Steps (b) and (c) are then repeated iteratively until there is no further change in the cluster assignments, this iterative process is shown from (d) to (i). from a dataset without labeled entries, aiming to identify salient properties of the data. Such algorithms are suited for various tasks, including clustering. Numerous clustering algorithms exist, with k-means algorithms being among the most popular [START_REF] Ezugwu | A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects[END_REF]. Conversely, supervised machine learning algorithms utilize a labeled training dataset to learn how to predict labels from data [START_REF] Goodfellow | Deep learning[END_REF].

k-means is a set of algorithms widely used in data science to address the challenge of dividing a dataset into groups of similar individuals based on a set of features and a predefined number of k groups [START_REF] Goodfellow | Deep learning[END_REF]. For a given number k of groups, the algorithm searches for optimal groupings that minimize the distances within groups and maximize the distances between different groups [START_REF] Goodfellow | Deep learning[END_REF]. The optimal groupings are determined through an iterative process, as illustrated in Figure 2.3. In the initial step, a center is randomly defined for each group. Subsequently, each individual is assigned to the group with the nearest center. The center is then recalculated to minimize the squared distances between the individuals of its group.

Once the centers are updated, individuals are reassigned based on the minimized squared distances, and the centers are recalculated based on these new assignments. The iterative process stops when convergence is reached, i.e., the groupings do not change between two consecutive iterations [START_REF] Bishop | Pattern recognition and machine learning[END_REF].

A powerful supervised method used for classification is the Random Forest algorithm [START_REF] Breiman | Random Forests[END_REF]. The Random Forest is considered an ensemble method, it aggregates the results of numerous decision trees for training and prediction. In detail, multiple new datasets (called bags) are derived from the original dataset through bootstrapping.

A decision tree is then trained for each dataset using a random subset of the variables to split the decision tree at each node. This process of making multiple bootstrap resamplings and training a decision tree for each bootstrap dataset is referred as bagging (this means bootstrap aggregation, [START_REF] Breiman | Random Forests[END_REF]. During prediction, each decision tree provides a prediction (referred to as a "vote"). All the votes are tallied, and the class with the majority of votes becomes the final prediction made by the model, as illustrated in Figure 2.4.

Polyploidization has been an important factor in the diversification of the genus Rosa [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF]). Rose species are mostly diploid, triploid, tetraploid, pentaploid and hexaploid, but heptaploids, octoploids and decaploids have been described [START_REF] Jian | Decaploidy in Rosa praelucens Byhouwer (Rosaceae) Endemic to Zhongdian Plateau, Yunnan, China[END_REF]). In our sample, we collected individuals belonging to different species\sections of Rosa, including known polyploids. Ploidy was published for some of the samples [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]. But the determination methods varied and had some degree of uncertainty. Flow cytometry has a reduced accuracy for high ploidy levels and bibliography suffers from potential bias due to genotype identification. Sometimes, the highest number of different alleles was also considered, although it is greatly dependent on the quality of the capillary electrophoresis microsatellites. The FDSTools calling pipeline used on the SSRseq data requires a ploidy parameter [START_REF] Hoogenboom | FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise[END_REF]). Therefore, we needed to add a preliminary step to infer the ploidy level of our individuals. In this section, we developed a method to infer the ploidy level using the number of different identical-by-state alleles. To achieve this, we explored the use of machine learning methods to predict the ploidy level by answering the following questions: (1) Is the number of different identical-by-state alleles dependent on the ploidy level? (2) Can we regroup individuals based on these variables? (3) Does the use of supervised algorithms allow an accurate inference of ploidy level? We used a sequential approach to address these questions. First, we used the data from the diploid and tetraploid individuals used for marker development. Second, we increased the complexity by adding other individuals covering a wider range of ploidy levels to be predicted.

Material and Methods

Plant material and genotyping

We used a large dataset of 1586 unique individuals.

Bioinformatic allele calling

Allele calling was performed using the FDSTools v1.2 pipeline [START_REF] Hoogenboom | FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise[END_REF] as described in [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF]. As mentioned in the 2. S.2 and Table S.3. For this step of FDSTools parameter optimization, the ploidy parameter was set to 2 for diploids and 4 for tetraploids. We were unable to find sufficiently good parameters for 13 loci, which we removed from the analysis. After optimizing the calling parameters, we recalled the alleles using these parameters but setting the ploidy parameter to 20. This step allowed us to detect all alleles at a given locus. No allelic dosage was performed as we only used the number of different identical-by-state alleles. Therefore, at the end of the calling step, we obtained the allelic phenotype of each individual (i.e. alleles were recorded in presence/absence).

The number of different alleles per locus was determined using the allelic phenotype table to investigate ploidy prediction.

Data cleaning and paralogous detection

Out of the 45 tetraploids, six were synthetic tetraploids that were created by mixing DNA solutions obtained from two diploid individuals. These synthetic tetraploids were used, as other tetraploids, to optimize the calling parameters, but were not included in further analyses due to their possibility of having a different number of alleles per locus from true individuals. Furthermore, two diploid individuals and two loci were excluded from the analysis because they contained more than 50% missing data. Therefore, after the optimization step, the data of 45 loci on 40 diploids and 39 tetraploids were used for further analyses. Paralogous loci were detected by identifying loci with significantly more alleles than others. The Rosner outlier detection method implemented in the EnvStats R package was used to identify these loci [START_REF] Millard | EnvStats: An R package for environmental statistics[END_REF][START_REF] Rosner | On the Detection of Many Outliers[END_REF]. Loci with significantly more alleles than other loci, in at least one ploidy level, were excluded.

Differentiation of diploid and tetraploid

We conducted an initial investigation using only data from the blind-repeated diploid and tetraploid individuals to evaluate the suitability of the number of different alleles per locus to identify the ploidy level. We calculated the mean number of alleles per locus for each ploidy level. We used a paired t-test (α = 0.05) to determine the significance of the observed differences. We then calculated the percentage of increases in allele number for each locus between diploid and tetraploid individuals using the following formula:

R N a = N a 4x -N a 2x N a 2x × 100
R N a is the percentage of increases in number of alleles between the diploids and the tetraploids at the given locus. N a 4x is the average number of alleles among the tetraploids at the given locus. N a 2x is the average number of alleles among the diploid at the given locus. A bar plot was created using the ggplot2 R package to visualize the values of all loci [START_REF] Wickham | ggplot2: Elegant graphics for data analysis[END_REF]. We tested the significance of differences in the number of alleles between diploids and tetraploids for each locus, using an approximate twosample Fisher-Pitman permutation test. We corrected the p-value using the method of [START_REF] Benjamini | Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing[END_REF], since multiple tests were conducted. We calculated the average number of alleles per individual and plotted the distributions obtained for diploids and tetraploids. Unexpectedly, two individuals a priori diploid were found to harbor more than 2 alleles and several more alleles than other diploids. Hence, they were removed from the dataset.

A k-mean clustering was performed to assess the potential use of the number of alleles per locus for regrouping individuals based on their ploidy level. Specifically, missing data were replaced with median values for each locus. Following the data scaling and centering, the gap statistic was used with the factoextra R package (Kassambara and Mundt 2020) to determine the optimal number of clusters. The k-means algorithm by [START_REF] Hartigan | A K-Means Clustering Algorithm[END_REF] and the stats R package were used to conduct hard clustering. The fviz_cluster function from the R package factoextra (Kassambara and Mundt 2020) was used to represent the clustering results on a scatter plot. The first step of this function involves running PCA to plot individuals on a scatter plot using the first two components. To evaluate the effectiveness of the clustering method in regrouping individuals based on ploidy level, we constructed a confusion matrix and calculated the accuracy and kappa score using the caret R package [START_REF] Kuhn | Building predictive models in R using the caret package[END_REF]. The kappa score is used to assess the accuracy of the prediction like the accuracy statistic but the kappa is less sensitive to unbalanced dataset [START_REF] Cohen | A coefficient of agreement for nominal scales[END_REF].

Since supervised methods tend to be more accurate than unsupervised methods, provided they are well trained, we constructed a random forest model using the randomForest v4.7 R package [START_REF] Liaw | Classification and regression by randomForest[END_REF]. To train the model, we split the dataset into two parts: a train dataset comprising 80% of the individuals and a test dataset containing the remaining 20%. To evaluate the accuracy of the prediction, we constructed a confusion matrix and calculated the accuracy and kappa score on both the out-of-bag estimates and the test dataset.

Prediction on a large dataset with more ploidy levels

We investigated the possibility of accurately predicting the ploidy level in the sample of 1710 individuals. We removed individuals and loci with more than 50% missing data, including the paralogous loci. The final dataset comprised 1667 samples and 39 loci.

We were able to suspect the ploidy level for 1659 samples based on their metadata. The suspicions were highly putative for most of the samples, except for 202 of them. In fact, the ploidy levels of these 202 individuals were determined in previous study or in previous project in our lab using flux cytometry or NGS-based prediction [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF][START_REF] Fernandez-Romero | Cytological and molecular characterization of a collection of wild and cultivated roses[END_REF][START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF][START_REF] Roberts | DNA amounts of roses (Rosa L.) and their use in attributing ploidy levels[END_REF]). These individuals will be designated as "reference" samples. All the ploidy levels from 2x to 6x

were represented among the reference individuals. In detail, there were 57 diploids, 13 triploids, 108 tetraploids, 8 pentaploids and 16 hexaploids in the reference dataset. As the dataset of 1667 individuals was designed to study R. gallica, which is a tetraploid species, we expected most of the individuals to be tetraploid.

As a first exploration, we calculated the mean number of alleles per reference individual and plotted the distribution with respect to the ploidy levels. We used the unsupervised k-means clustering algorithm of [START_REF] Hartigan | A K-Means Clustering Algorithm[END_REF] to evaluate whether the number of groups and the grouping results fitted the ploidy level distribution. As done for the diploid-tetraploid dataset, we scaled and centered the data. We then used the R package factoextra and the gap statistic to determine the optimal number of clusters.

As the most probable number of k was lower than the actual number of different ploidy levels we run the clustering using two different k values, the inferred optimal number and the actual number of different ploidy levels (i.e. five). The clustering results were then visualized using the fviz_cluster function from the R package factoextra [START_REF] Kassambara | factoextra: Extract and visualize the results of multivariate data analyses[END_REF].

We As the four models aimed to predict more than two classes, the TPR and FPR were calculated with both macro and micro averaging. For macro averaging, the metrics (i.e. TPR and FPR) were calculated for each class and averaged over the classes. This gave the same weight to all classes without considering the number of individuals per class.

For micro averaging, the metrics were calculated over all classes directly without a first calculation per class, balancing the metrics values by the number of individual per class.

We retained the model with the highest TPR and the lowest FPR. The model was fine-tuned to identify optimal values for random forest hyperparameters that maximize accuracy. The optimal number of decision trees was determined by calculating accuracy for 1 to 3000 trees using out-of-bag estimates. The number of variables sampled at each node of the decision trees for splitting the branches was the second hyperparameter. The optimization was performed using the R package caret v 6.0.94 [START_REF] Kuhn | Building predictive models in R using the caret package[END_REF]. We tested 1 to 15 variables by using 10-fold cross-validation. We performed 10 repetitions of cross-validation for each number of sampled variables tested.

To calculate the performance of a specific number of sampled variables, we averaged the results of 10 repetitions. Finally, the ploidy level of the non-reference individuals was predicted using the tuned model. If available, the suspicious ploidy levels were compared with the predictions by constructing confusion matrices.

Results

Ploidy prediction on the diploid-tetraploid dataset

Four loci in the data set were found to have significantly more alleles than other loci.

These were assumed paralogous and removed, leaving a total of 41 loci in the data set. The number of alleles was lower in diploids than in tetraploids (Student's t-test, p < 0.05). The distribution of the mean number of alleles per locus for each sample indicated that the tetraploids had a noticeable increase in the allele count compared to diploids (Fig. 2.5). However, the mean allele count was more variable in tetraploids than in diploids. Interestingly, we observed two diploid individuals with an average of more than two alleles per locus, making them false diploids. Likewise, some tetraploids exhibited a notably low count of alleles per locus. Examining the variations in the allele count among each locus, we observed that the magnitude of the variation was highly dependent on the locus (Fig. 2.6). Out of the 41 loci studied, 24 exhibited significantly more alleles for tetraploids compared to diploids (Fig. 2.6).

The optimal number of clusters determined by the gap statistic was clearly two. On the scatter plot (Fig. both the out of bag estimates and the test dataset. The predictions were perfectly in accordance with the true ploidy level for the test dataset.

Extending the prediction to multiple ploidy levels

Distinguishing diploids from tetraploids served only as a proof of concept and was not an effective method to predict the ploidy levels in our dataset due to the existence of more 63 SSR sequencing, a new flexible tool for Roses

Discussion

Knowing the ploidy level is a prerequisite for many population genetics analyses. Currently, the methods aiming to infer the ploidy level based on codominant data rely on descriptive statistics or are dedicated to polysomic inherited species and based on assumptions [START_REF] Clark | polysat: an R package for polyploid microsatellite analysis[END_REF][START_REF] Huang | Thirteen Camellia chloroplast genome sequences determined by high-throughput sequencing: genome structure and phylogenetic relationships[END_REF][START_REF] Meirmans | genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids[END_REF]. However, the inheritance model may not be known or be different from polysomic for a particular polyploid species, preventing the use of such inferential methods. The development of a flexible method able to accurately determine the ploidy level without any particular assumption would represent a first step towards estimation of unbiased allele frequencies in polyploids.

The present study applied unsupervised and supervised machine learning algorithms to evaluate whether the number of different identical-by-state alleles can be used to predict ploidy level. First, the concept is proven by differentiating diploids from tetraploids.

Then, the method is applied to a large dataset containing a range of ploidy levels.

This demonstrates that the supervised random forest algorithm is a powerful tool for predicting the ploidy level. In other studies, it was proven that the number of identical by state alleles is useful for inferring ploidy levels based on descriptive statistics [START_REF] Clark | polysat: an R package for polyploid microsatellite analysis[END_REF][START_REF] Meirmans | genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids[END_REF]). We also observed differences in the mean number of different identical-by-state alleles between different ploidy levels. (2019)'s method is that it does not require individuals with known ploidy level, as our method does. However, their method is exclusively intended for polysomic inheritance, whereas our method is not dependent on such a requirement. As observed by Huang et al.

(2019), their method requires a significant number of individuals per ploidy level, here we had very few individuals for some ploidy levels. Having more individuals may increase the accuracy of the model. The assignment errors were mainly between neighboring ploidy levels, which is consistent with the findings of [START_REF] Huang | Inference of individual ploidy level using codominant markers[END_REF].

In certain studies, few ploidy levels are expected, especially in studies comparing tetraploids and their diploid ancestors [START_REF] Brugnoli | Diversity in Diploid, Tetraploid, and Mixed Diploid-Tetraploid Populations of Paspalum simplex[END_REF][START_REF] Guo | Sequence Divergence of Microsatellites and Phylogeny Analysis in Tetraploid Cotton Species and Their Putative Diploid Ancestors[END_REF][START_REF] Mairal | Multiple introductions, polyploidy and mixed reproductive strategies are linked to genetic diversity and structure in the most widespread invasive plant across Southern Ocean archipelagos[END_REF]). In the present study, we investigated whether the number of different identical-by-state alleles per locus could serve as a discriminator between diploid and tetraploid roses. Three methodologies were examined: raw allele count comparison across various ploidy levels, k-means clustering algorithm, and classification through the Random Forest algorithm.

Fundamental methods for distinguishing between different ploidy levels rely on the raw allele count per locus [START_REF] Clark | polysat: an R package for polyploid microsatellite analysis[END_REF][START_REF] Meirmans | genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids[END_REF]. Consistent with these methods, here tetraploid genotypes possessed more alleles than the diploid genotypes,

showing the usefulness of this statistic. However, some tetraploid individuals were challenging to differentiate from diploid individuals using this method. We identified two groups of individuals using k-means clustering. These groups were mostly consistent with the ploidy level, although a few tetraploids were still clustered with diploids. A random forest model accurately classified these problematic individuals. Thus, our results demonstrate the highly effective performance of this supervised algorithm when few ploidy levels are expected.

Due to the low sensitivity of the five-class models for triploids, pentaploids, and hexaploids, we grouped triploids with tetraploids and pentaploids with hexaploids. As a result, this decision appeared to increase accuracy. However, by doing that, we intentionally accepted that triploids were detected as tetraploids and pentaploids were treated as hexaploids, which means that some errors were not included in the model accuracy calculation. This choice helps to prevent the misclassification of tetraploids as triploids.

This three-class model was used to predict the ploidy level of the 1710 data set that will be used for further analyses in this thesis. Therefore, we preferred to be conservative at the tetraploid ploidy level because we anticipated that the dataset of 1710 individuals would consist mostly of tetraploids. Given the anticipated low numbers of triploid and pentaploid individuals, accepting some errors between adjacent ploidy levels would not significantly affect the results of our study. Considering triploids as tetraploids and pentaploids as hexaploids has certain implications for additional analysis. Allowing a greater number of alleles than the actual number at a locus during the calling step could slightly increase the number of allelic errors. Moreover, the allele dosage step can present a slight bias due to the addition of an additional dose. For example, if a triploid genotype containing three different identical-by-state alleles at a specific locus, encoded as A, B, and C, is mistakenly considered as a tetraploid, one allele gets erroneously counted twice. In reality, the ABC genotype will be considered to be AABC, ABBC, or ABCC.

This can lead to less accurate estimation of allele frequencies.

The foundation of our method is the number of alleles per locus. An individual with low heterozygosity may have fewer alleles. As a result, it may be incorrectly assigned to a lower ploidy level. In fact, some of the tetraploid individuals misclassified as diploid are modern varieties that are expected to be less heterozygous than wild or old cultivated individuals. This fact emphasizes the need for heterozygosity to accurately predict the ploidy level. For future studies, simulation-based evaluations would be necessary to determine how prediction accuracy varies with different levels of heterozygosity. Testing our model on one less heterozygous and autopolyploid species would aid in a better understanding of the limitations of the model. One future avenue of research could be to increase the number of triploid, pentaploid, and hexaploid reference individuals.

Retraining the random forest model with this increased dataset could improve sensitivity for these ploidy levels. These additions would require us to perform chromosome counting on individuals for which we have fresh material, such as specimens collected in rose gardens.

Conclusion

This study explores various methods for ploidy level estimation. The most accurate results were obtained with a supervised random forest algorithm. As the method does not depend on specific assumptions about the species biology, it can be used for any organism whose ploidy level is known. This flexible method can be used for various purposes ranging from distinguishing between two ploidy levels to assigning the ploidy level over a range of possibilities. Differentiating between two ploidy levels is highly effective. The accuracy is slightly reduced when dealing with multiple ploidy levels, with errors mainly occurring between adjacent levels alleviating the impact on downstream analyses. To increase the accuracy of the model, future studies should concentrate on introducing new variables, as random forest algorithms are robust to over-fitting [START_REF] Breiman | Random Forests[END_REF]. For example, new variables in the form of read depth per allele or the heterozygosity level at each locus could be introduced.

Chapter 3

R. gallica : European diversity and origins of the French populations 3.1 Introduction

The distribution of a plant species results from various biological, historical, and evolutionary factors such as dispersal dynamics, climate modifications, human activities, or hybridization [START_REF] Ali | A Conceptual Framework for Designing Phylogeography and Landscape Genetic Studies[END_REF][START_REF] Bullock | Human-Mediated Dispersal and the Rewiring of Spatial Networks[END_REF][START_REF] Bullock | A synthesis of empirical plant dispersal kernels[END_REF]. Assessing the diversity and population structure of a plant species using molecular approaches can aid in evaluating these factors [START_REF] Ali | A Conceptual Framework for Designing Phylogeography and Landscape Genetic Studies[END_REF]). Among these factors, human activities profoundly impact plant distribution patterns, primarily through species dispersal and environmental alterations [START_REF] Bullock | Human-Mediated Dispersal and the Rewiring of Spatial Networks[END_REF]. The dispersion of a plant species mediated by human activities can lead to an expansion of its range or to an increase in its density within the native range. Range expansion can enhance the resilience to habitat loss and dispersion within the native range promotes gene flow among populations [START_REF] Bullock | Human-Mediated Dispersal and the Rewiring of Spatial Networks[END_REF].

Horticultural ornamental plants have been extensively propagated by humans. These plants often possess specific traits, such as environmental compatibility, hardiness, or rapid vegetative growth, that enhance their survival in the wild [START_REF] Kleunen | The changing role of ornamental horticulture in alien plant invasions[END_REF][START_REF] Maurel | Introduction bias affects relationships between the characteristics of ornamental alien plants and their naturalization success[END_REF]. Therefore, perennial ornamental plants can persist for decades or even centuries, making them valuable subjects for studying historical events and the consequences of human-mediated dispersal. These plants offer a unique lens through which we can analyze the long-term effects of human actions on plant populations and biodiversity.

Roses have been cultivated and exchanged throughout the world for thousands of years.

Human love for roses began during the Neolithic period and has endured through various ages and across cultures [START_REF] Kleunen | The changing role of ornamental horticulture in alien plant invasions[END_REF][START_REF] Nybom | Introduction to Rosa[END_REF][START_REF] Tomljenovic | Taxonomic Review of the Genus Rosa[END_REF].

Roses are appreciated for their beauty, fragrance, nutritional qualities and medicinal properties [START_REF] Hegde | Edible rose flowers: A doorway to gastronomic and nutraceutical research[END_REF][START_REF] Nybom | Introduction to Rosa[END_REF] Europe, ranging from the Middle East to France [START_REF] Klastersky | Rosa L[END_REF]). Rosa gallica is a small shrub with the ability to produce root suckers [START_REF] Boulenger | Les roses d'Europe de l'herbier crépin. Vol. 2 (suite)[END_REF]. This generates clonal individuals and shapes the population pattern. As commonly described in the genus, R. gallica can naturally hybridize with other Rosa sp. [START_REF] Fedorova | Local differentiation and hybridization in wild rose populations in Western Ukraine[END_REF].

The origins of French and central European R. gallica populations are unclear. As R. gallica appeared before the last glacial maximum, one hypothesis would involve a natural recolonization of Europe after this period from one or several Southern refugia.

This has been described for other perennial species [START_REF] Ali | A Conceptual Framework for Designing Phylogeography and Landscape Genetic Studies[END_REF][START_REF] Tzedakis | Cryptic or mystic? Glacial tree refugia in northern Europe[END_REF].

Other hypotheses, implicating human-mediated dispersal, have been proposed on the basis of historical reports. et al. 2017;[START_REF] Vukosavljev | Genetic diversity and differentiation in roses: A garden rose perspective[END_REF]). These highly polymorphic markers are traditionally genotyped using capillary electrophoresis after PCR amplification to separate alleles by their length [START_REF] Guichoux | Current trends in microsatellite genotyping[END_REF]). However, this technique has drawbacks, such as relatively low to moderate throughput, susceptibility to length homoplasy (i.e. the po-tential confusion between two distinct alleles of similar length), and limited portability across different laboratory settings [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF]). To overcome these, the genotyping of SSR loci by amplicon sequencing (SSRseq) has been develop and proven to be an effective method that allows a direct access to allele sequences rather than just to allele lengths [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF][START_REF] Vartia | A novel method of microsatellite genotypingby-sequencing using individual combinatorial barcoding[END_REF]. SSRseq allows for the identification of repeat number variations, SNPs and InDel. Alleles can then be called considering the whole amplicon information instead of only considering allele length differences. Studies comparing these two types of calling demonstrated a resolution gain in population genetic analyses when using the whole amplicon information [START_REF] Bradbury | Genotyping-by-sequencing of genome-wide microsatellite loci reveals fine-scale harvest composition in a coastal Atlantic salmon fishery[END_REF][START_REF] Lepais | Joint analysis of microsatellites and flanking sequences enlightens complex demographic history of interspecific gene flow and vicariance in rear-edge oak populations[END_REF][START_REF] Šarhanová | SSR-seq: Genotyping of microsatellites using next-generation sequencing reveals higher level of polymorphism as compared to traditional fragment size scoring[END_REF][START_REF] Tibihika | Application of microsatellite genotyping by sequencing (SSR-GBS) to measure genetic diversity of the East African Oreochromis niloticus[END_REF][START_REF] Viruel | Advances in genotyping microsatellite markers through sequencing and consequences of scoring methods for Ceratonia siliqua (Leguminosae)[END_REF]).

In population genetics, the exploratory steps involve analyses that are based on calculation of dissimilarities between individuals. SSRseq data have been used for that purpose [START_REF] Curto | Application of a SSR-GBS marker system on investigation of European Hedgehog species and their hybrid zone dynamics[END_REF][START_REF] Layton | Resolving fine-scale population structure and fishery exploitation using sequenced microsatellites in a northern fish[END_REF]. Calculating dissimilarities between individuals required to set how the divergence between alleles are taken into account [START_REF] Kosman | Dissimilarity of individual microsatellite profiles under different mutation models: Empirical approach[END_REF]. One possibility is to assume an equivalent evolutionary distance between all allele pairs as done in Curto et al. ( 2019) and [START_REF] Layton | Resolving fine-scale population structure and fishery exploitation using sequenced microsatellites in a northern fish[END_REF]. Nevertheless, this assumption is far from the truth and attempts have been made to consider variable evolutionary distance between alleles for classical length calling [START_REF] Bruvo | A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level[END_REF]; Kosman and Jokela 2019). To do that, published methods used the difference in the number of repeats between alleles to approximate their pairwise evolutionary distances [START_REF] Bruvo | A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level[END_REF][START_REF] Kosman | Dissimilarity of individual microsatellite profiles under different mutation models: Empirical approach[END_REF]. These methods seem also applicable to SSRseq by counting the number of repeats and thus ignoring other type of polymorphism. However, to our knowledge, no attempt has been made to determine such pairwise allele dissimilarity while considering all types of polymorphism present in SSRseq microhaplotypes.

Dealing with several types of polymorphism can be challenging as they depend on different evolutionary processes. Therefore determining the evolutionary distance between alleles would require to make multiple assumptions about the evolution of each type of polymorphism. Alignment-free approaches are well known methods aiming to calculate dissimilarities between sequences without making assumptions on the evolutionary processes. Word-based methods are one of the categories of alignment-free approaches.

They focus on subsequences of a constant length (called k-mer) and evaluate pairwise similarities based on the number or frequency of shared subsequences [START_REF] Zielezinski | Alignment-free sequence comparison: benefits, applications, and tools[END_REF].

In this study, our main objective is to better understand the origins of R. gallica populations, particularly the French ones. In order to do this, we use a SSR sequence-based genotyping and we establish a method to measure dissimilarity between individuals considering all types of polymorphism. We undertake extensive genotyping of rose specimens, including R. gallica collected across a large part of its natural range, other Rosa species, and cultivated roses. Thanks to this empirical dataset, we bring answers to the following methodological and biological questions: (1) Do alignment-free methods offer a way to extract microhaplotype polymorphism information in dissimilarity-based exploratory analyses? (2) Do natural and human dispersal have both contributed to the expansion of this species? (3) Do the French populations and populations from other countries share the same natural history?

Materials and methods

Sampling

Due to the production of root suckers, wild sites of R. gallica are often composed of a few continuous plots of clonal individuals, each covering few to dozens square meters.

As each plot is generally composed of clonal individuals, these sites should regroup a low number of genets (i.e. different clonal lineages). In the present study, we use "site" instead of "population" to refer to the collecting place. Indeed, according to [START_REF] Waples | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF], "a population is group of individuals of the same species living in close enough proximity that any member of the group can potentially mate with any other member". The definition implies the notion of reproductive cohesion that is difficult to determine a priori, based on the sampling location and the geographical distances between individuals [START_REF] Waples | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF]. Therefore we preferred to use "site" that does not imply a reproductive cohesion but only a geographical co-occurrence.

Arbitrarily, individuals were considered belonging to two different sites when more than one kilometer separated all individuals from each site. Given the low expected number of genets per site, the number of collecting sites was maximized lowering the number of individuals collected per site. In total, leaf samples were collected on 905 R. gallica Samples metadata and details are available in supplementary data S.1.

individuals

DNA processing and genotyping

As the samples were either gathered from former projects, from the RosePom Biological Resources Center (Angers, France) or especially for this project by different collectors, there was variability on their nature. They were either freeze dried, silica dried or already in the form of extracted DNA. A few samples were collected on herbarium specimens.

Formerly extracted DNA were isolated using the Qiagen 96 Plant Kit (Qiagen, Hilden, Germany), the NucleoSpin® 96 Plant II Core Kit (Macherey-Nagel, Düren, Germany)

or the protocol described in [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF]. DNA extracted for this project were obtained using a custom protocol inspired from Keb-Llanes et al. ( 2002), Inglis et al.

(2018) and [START_REF] Anderson | Protocol: a versatile, inexpensive, highthroughput plant genomic DNA extraction method suitable for genotyping-by-sequencing[END_REF]. More precisely, 20-30 mg of freeze dried or silica dried samples were put in wells of a 2.2 ml 96 wells plate. Samples were ground in a ball mill with stainless steel balls for 3-6 minutes at 1500 Hz. Then, they were pre-washed following the sorbitol pre-wash step described in [START_REF] Inglis | Fast and inexpensive protocols for consistent extraction of high quality DNA and RNA from challenging plant and fungal samples for high-throughput SNP genotyping and sequencing applications[END_REF]. Lysis step was performed using the CTAB-SDS based methods described in Keb-Llanes et al. (2002) with modifications. These include addition of 0.5 µl per sample of RNAse A 20 mg.ml -1

to the EBA buffer and addition of 2 µl per sample of Proteinase K 20 mg.ml Germany). Subsequently, the DNA were normalized to 10 ng.µl -1 .

In total 1710 samples were genotyped, including 1586 unique individuals plus technical replicates (supplementary data S.2). The sequencing was done in five different runs including a pilot run where 95 samples (including diploids and tetraploids), chosen to represent the genus diversity, were blind-repeated genotyped. Individuals were genotyped using 60 primer pairs targeting nuclear SSRs. Primer pairs design is described in our previous work [START_REF] Pawula | Putting rose microsatellites into orbit: development and assessment of an SSR sequencing method[END_REF]. The polymerase chain reaction (PCR) process, library preparation and sequencing experiment were carried out at the Genome Transcriptome Facility of Bordeaux, following the method outlined in [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF], with a few modifications. Instead of performing the multiplexed locus-specific PCR in two separate steps, it was done in a single step. Additionally, the number of PCR cycles was reduced to 24 cycles from the original 35 cycles. Once the individuals were pooled, the sequencing step was performed on an Illumina iSeq100 using a 2 x 150 bp paired-end sequencing kit.

Allele calling

The raw read sequences were sorted based on their barcodes, and the adaptors were eliminated using the software provided by the sequencer platform. Paired-end reads were merged using BBmerge, with a minimum overlap length of 20 bp [START_REF] Bushnell | BBMerge -Accurate paired shotgun read merging via overlap[END_REF]. Additionally, merged reads shorter than 65 bp were excluded from further analyses. Raw read sequences were converted into the final microhaplotypes using the pipeline based on the FDSTools v1.2 analysis toolkit [START_REF] Hoogenboom | FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise[END_REF]) designed by [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF]. The detected alleles were coded using an arbitrary three-digit code.

Several parameters were needed to run the pipeline [START_REF] Hoogenboom | FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise[END_REF]). Values for these parameters have a direct impact on the calling success (allelic errors and missing data, [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF]. Therefore, several sets of parameters were tested for each locus.

To evaluate the parameter sets, we used the data of diploid and tetraploid individuals from the pilot sequencing run that were successfully sequenced twice (87 out of the 95 individuals). Since one of the parameters corresponded to the ploidy level, the allelic error rate and the missing data rate were separately calculated for the 42 diploid and 45 tetraploid individuals. The allelic error rate was calculated using the formula:

Error rate = N mismatch N Ind * P loidy
With N mismatch being the sum of allelic mismatches over all comparisons and N Ind being the number of individuals compared. The expression "N Ind * P loidy" is then equal to the number of alleles compared. The parameters included the analytical strategy. The two possible strategies were either the FullLength or the RepeatFocused methods. In the FullLength strategy, the calling considered all the variations located between the two primers whereas, in the RepeatFocused strategy, flanking sequences are ignored and only the sequence variations in the repeat motif were considered. Focusing on the repeat motif allowed to reduce the allelic error rates in the presence of error prone flanking sequences.

For each locus, the parameter set producing the lowest allelic error and missing data rates, for both diploids and tetraploids, was selected. Final parameters are available in Table S.2 andTable S.3. These parameters were then assumed to be optimized whatever the ploidy level. To remove loci that were obviously paraloguous, alleles were called by allowing up to 20 alleles at a locus per individual. Then, we implemented the outlier detection method of Rosner available in the EnvStats R package [START_REF] Millard | EnvStats: An R package for environmental statistics[END_REF][START_REF] Rosner | On the Detection of Many Outliers[END_REF]) to identify loci with significantly more alleles than others and thus potentially paraloguous. These were removed for further analyses.

The ploidy parameter is crucial in the calling process. Setting it lower than the actual ploidy level increases the number of loci with missing data. Inversely, setting the ploidy level higher than the true ploidy level could relax the error detection and then increase the number of errors [START_REF] Hoogenboom | FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise[END_REF]). Therefore we started by determining the ploidy level of the individuals. Given the nature of the samples, we were not able to perform chromosome counting. Several methods have been proposed to aid ploidy determination in polyploids using codominant data. Simplest tools used the maximum number of identical-by-state alleles found for an individual to set its ploidy level [START_REF] Clark | polysat: an R package for polyploid microsatellite analysis[END_REF][START_REF] Meirmans | genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids[END_REF]). However, this method requires a care-full examination of each sample and is sensible to allelic errors that can easily increase the number of different alleles at a locus. [START_REF] Huang | Inference of individual ploidy level using codominant markers[END_REF] developed a method based on an iterative process coupled with allele frequency estimation to determine the ploidy level of individuals having a polysomic inheritance in a population assuming panmixia within each ploidy level. Unfortunately, this method was not suitable for our data due to the likely absence of a polysomic inheritance mode [START_REF] Bourke | Partial preferential chromosome pairing is genotype dependent in tetraploid rose[END_REF][START_REF] Fernández-Romero | Physical mapping of ribosomal DNA on several species of the subgenus Rosa[END_REF][START_REF] Lunerová | Asymmetrical canina meiosis is accompanied by the expansion of a pericentromeric satellite in non-recombining univalent chromosomes in the genus Rosa[END_REF]. Additionally, we expected departure from panmixia in our sample given the high number of different species represented. Therefore, to solve this statistical classification problem, we implemented a supervised machine learning method known as Random Forest using the R package caret v 6.0.93 [START_REF] Kuhn | Building predictive models in R using the caret package[END_REF]. As a supervised algorithm, the Random Forest model requires a training set. This means that we needed a group of individuals with known ploidy levels that represent all potential ploidy levels in our sample. We established a group of 202 individuals with high confidence in their ploidy level based on measurements done in other studies or previously in our lab [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF][START_REF] Fernandez-Romero | Cytological and molecular characterization of a collection of wild and cultivated roses[END_REF][START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF][START_REF] Roberts | DNA amounts of roses (Rosa L.) and their use in attributing ploidy levels[END_REF]). The number of triploids and pentaploids included in the present study was anticipated to be low. In accordance, few of them were present in the training dataset, which limited high-quality predictions for these two ploidy levels. To mitigate this, we decided to limit the prediction to the three main ploidy classes anticipated in our sample: diploids, tetraploids, and hexaploids. To achieve this, we categorized the reference samples into these three classes by merging triploids with tetraploids and pentaploids with hexaploids. To conduct the training and prediction, individuals were called allowing up to 20 alleles at each locus. We calculated the number of alleles at each locus and trained the model using the reference groups. The model accuracy was estimated using the out of bags estimates. We then utilized this model to predict the ploidy level of the other individuals in our sample. The final allele calling was done using the predicted ploidy level value as the ploidy parameter of the FDSTools-based pipeline (supplementary data S.2).

Retrieving the allele dosage in diploids is straightforward as it can be deduced from the number of identical by state alleles. For polyploids, the number of alleles can only provide information about the allele dosage when either it is equal to one (only one allele is present at a locus) or the number of different alleles equals the ploidy level.

The non straightforward retrieval of allele dosage is known as genotype ambiguity. To address this ambiguity and estimate the allele dosage, we utilized the method published by Cui et al. (2022b). This method uses read depth and a correction for preferential amplification to infer the allele dosage. We performed the computation using our own R implementation of this method.

To minimize the impact of missing data on our sample, we first removed loci and samples with more than 40 % of missing data. After that, a custom R script (Eveilleau and Pernet pers.com. modified by Pawula) was used to optimize the handling of missing data by selectively removing either loci or individuals. The aim was to retain the maximum amount of data in the dataset while ensuring that each pair of individuals can be compared using at least half of the loci.

Clonal lineages detection

At this step, the Rosa sp. dataset included 1618 samples succesfully genotyped (including known repeated individuals) and 29 loci. Clonal lineages were detected using a thresholdbased method, which has been adapted for polyploids and is available in the R package Polysat v1.7.7 [START_REF] Clark | polysat: an R package for polyploid microsatellite analysis[END_REF]. In details, the pairwise Euclidean distances between individuals were computed using the intra-individual allele frequencies and the R package factoextra v 1.0.7 [START_REF] Kassambara | factoextra: Extract and visualize the results of multivariate data analyses[END_REF]. Using the twice-genotyped individuals, we established a threshold to categorize pair of individuals as belonging to the same clonal lineage (i.e. belonging to the same genet). In order to avoid any false-positive clonal relationships, the threshold was set to a conservative value. This corresponded to the maximal pairwise distance between the repeated individuals after the removal of outliers (i.e. distance values higher than the 1.5 * interquartile range + third quartile of all pairwise distance among repeated individuals). Doing that, a threshold of 1.603 was used. This threshold method has the advantage of permitting mutations and genotyping errors. However, it can also lead to a confusion between clonal and sibling individuals. To assess whether our resolution was strict enough to avoid this kind of confusion, we used 10 known full-sibling and 10 half-sibling individuals to verify whether they were incorrectly included in the same clonal lineage. These sib individuals were then removed from the dataset for further analyses. The clonal relationship between individuals from two sites or between wild and botanical or cultivated individuals was then visualized using the ggplot2 R package [START_REF] Wickham | ggplot2: Elegant graphics for data analysis[END_REF]. One individual per clonal lineage was retained for further analyses by selecting the most consensual with the less missing data as possible. This dataset is designated hereafter as the clone-corrected Rosa sp. dataset.

Sequence based inter-individual dissimilarity

To explore the structure of our sample by taking into account all the polymorphism types that could be retrieved from the marker sequences, we developed a new measure of pairwise dissimilarity. The method can be divided into three steps, inspired from [START_REF] Kosman | Dissimilarity of individual microsatellite profiles under different mutation models: Empirical approach[END_REF]. First, we estimated the evolutionary distance for each pair of alleles at each locus. Second, we calculated dissimilarities between each pair of genotypes at each locus. Finally, we computed the dissimilarities between pairs of multilocus genotypes using all shared loci between each pair of individuals. These three steps are detailed below.

The first step was achieved using Alfpy v1.0.6 [START_REF] Zielezinski | Alignment-free sequence comparison: benefits, applications, and tools[END_REF] to perform k-mer counting and to calculate the dissimilarity between alleles at each locus. After testing two k-mer lengths (two and six), the k-mer length was set to six. We chose to use the Bray-Curtis dissimilarity measure as it takes into account the abundances of each k-mer and was shown to perform well in a benchmarking study [START_REF] Zielezinski | Benchmarking of alignment-free sequence comparison methods[END_REF]. For the second step, the dissimilarity between two genotypes A and B at a given locus j was determined in a very similar way as the one proposed by [START_REF] Kosman | Dissimilarity of individual microsatellite profiles under different mutation models: Empirical approach[END_REF].

We kept the nomenclature of these authors for consistency. First case : let consider individuals A and B are q-ploid, meaning that they respectively harbored the alleles < A 1 A 2 ...A q > and < B 1 B 2 ...B q > at the locus j. At the beginning of this step, all possible configurations for matching alleles from A to B were retrieved. For a given configuration, each allele appeared once and all alleles were present. The configuration was then composed of q allele pairs. The number of possible configurations was equal to q!. For diploid organisms, the number of possible configurations was 2. If A and B were diploid (q = 2) and respectively harbored the alleles A 1 , A 2 and B 1 , B 2 at the locus j, the possible configurations were A 1 /B 1 ; A 2 /B 2 or A 1 /B 2 ; A 2 /B 1 . Then, the dissimilarity was calculated for each configuration using the allele Bray-Curtis pairwise dissimilarities computed at the previous step. The minimal value among these configurations was then taken as the dissimilarity between the two genotypes at the locus j. For A and B, this can be expressed as follows:

dAB(j) = min dA 1 /B 1 + dA 2 /B 2 q ; dA 1 /B 2 + dA 2 /B 1 q (3.1)
dAB(j) is the dissimilarity between genotypes A and B at the locus j. dA i /B k is the dissimilarity between allele A i and B k at the locus j.

Second case : let consider individuals with different ploidy levels. This requires additional steps. Our approach varied depending on the ploidy levels being compared. For a comparison between a diploid and a tetraploid, the diploid genotype was simply duplicated. For example, the heterozygote genotype A was considered as A 1 ; A 2 ; A 1 ; A 2 .

In the case of a diploid-hexaploid comparison, the diploid genotype was tripled. When comparing a tetraploid and a hexaploid, all possible hexaploid genotypes derived from the tetraploid genotype were determined. For instance, if a tetraploid genotype D had the genotype D 1 ; D 2 ; D 3 ; D 3 , then the six possible hexaploid genotypes were:

D 1 ; D 2 ; D 3 ; D 3 ; D 1 ; D 2 D 1 ; D 2 ; D 3 ; D 3 ; D 2 ; D 3 D 1 ; D 2 ; D 3 ; D 3 ; D 3 ; D 3 D 1 ; D 2 ; D 3 ; D 3 ; D 1 ; D 3 D 1 ; D 2 ; D 3 ; D 3 ; D 1 ; D 1 D 1 ; D 2 ; D 3 ; D 3 ; D 2 ; D 2
All the possible configurations were then determined based on these possible genotypes of D. As indicated in the equation (3.1), the smallest dissimilarity was retained as the dissimilarity between D and the hexaploid genotype at the locus j.

In the third step, to compute the final individual pairwise dissimilarities, the dissimilarities per locus were normalized by dividing all values by the maximum value in the dataset for each locus. Finally, dissimilarity between two individuals were computed by averaging their dissimilarities per locus.

visual attribution of sites to a region, as done in [START_REF] Bina | Evidence of an additional centre of apple domestication in Iran, with contributions from the Caucasian crab apple Malus orientalis Uglitzk. to the cultivated apple gene pool[END_REF] was an option, it would not have been entirely reproducible and might have induced inconsistencies in the criteria defining a regional group. To address this, we used a method driven solely by the GPS location of each site, using spatial graph construction and community detection.

In detail, we determined the pairwise geodesic distances between sites using the distm function of the R package geosphere v1.5.18 [START_REF] Hijmans | geosphere: Spherical trigonometry[END_REF]. We then constructed two spatial graphs, using the sites as nodes, with the R package igraph v1.4.1 [START_REF] Csardi | The igraph software package for complex network research[END_REF]. In order to compare French populations with other R. gallica populations, we independently created two graphs, one for France and one for other wild sites, using the function graph_from_adjacency_matrix. The construction of the graphs required setting a threshold above which two nodes are considered not connected. After testing various values, a threshold of 165 km was retained as it effectively distinguished the most expected regions. Sites co-occurring in the same region were then determined by performing community detection separately on the two graphs. The community detection was performed using the igraph function cluster_infomap with 500 trials and weighted edges [START_REF] Rosvall | Maps of random walks on complex networks reveal community structure[END_REF]. Each detected community was named according to its regional location. Finally, the community detection sorted the sites into 5 French and 14 European regions (see Fig. 3.2).

We investigated the population structure among wild, botanical and cultivated R. gallica using different methods. First, we ran two multivariate analyses, one based on k-mer dissimilarities (PCoA) and one based on intra-individual allele frequencies (PCA) using the R package ade4 [START_REF] Dray | The ade4 Package: Implementing the Duality Diagram for Ecologists[END_REF]. We ran these two multivariate analyses to not only investigate the R. gallica population structure but also to compare these methods at the species level. Second, we used the hypothesis-driven bayesian clustering procedure implemented in the program STRUCTURE v2.3.4 [START_REF] Pritchard | Inference of Population Structure Using Multilocus Genotype Data[END_REF].

This model aims to define genetic groups while maximizing Hardy-Weinberg equilibrium inside each group. STRUCTURE parameters were selected following the recommendations of [START_REF] Wang | The computer program structure for assigning individuals to populations: easy to use but easier to misuse[END_REF]. Specifically, we used the ancestry model with admixture, while inferring the ALPHA parameters for each population (i.e. each STRUCTURE group), starting with an initial ALPHA value of 0.1. The inference was conducted using the correlated allele frequency model with a constant LAMBDA parameter. Based on the recommendations in the STRUCTURE manual and on F st plots, the burn-in period was set to 50 000 and the number of repetitions after burn-in to 300 000. The probability of the data under the model was estimated to identify optimal values of K groups. We ran the model with K values ranging from 1 to 20, with 10 independent runs per K value. The method of [START_REF] Evanno | Detecting the number of clusters of individuals using the software structure: a simulation study[END_REF] was used to determine most probable K values.

However, while this method is able to reveal large population structure, it generally performs poorly at detecting fine structure [START_REF] Janes | The K = 2 conundrum[END_REF][START_REF] Kalinowski | The computer program STRUCTURE does not reliably identify the main genetic clusters within species: simulations and implications for human population structure[END_REF][START_REF] Puechmaille | The program structure does not reliably recover the correct population structure when sampling is uneven: subsampling and new estimators alleviate the problem[END_REF][START_REF] Schwartz | Why sampling scheme matters: the effect of sampling scheme on landscape genetic results[END_REF]Wang 2019). Recently, Wang (2019) developed a new parsimony estimator to detect optimal K values, implemented in KFinder v2.1.

Wang ( 2019) demonstrated the robust performance of this estimator across various scenarios, including different population structure models, levels of differentiation, numbers of loci, and unbalanced sampling. Therefore, we used KFinder v2.1 to determine optimal K values aiming at revealing fine structure level. STRUCTURE group inference can be stuck at local maxima, leading, for the same value of K, to independent runs sometimes returning different groupings. This situation may result in various modes of groupings, supported by more or fewer independent runs. We used Pong v1.5 [START_REF] Behr | pong: fast analysis and visualization of latent clusters in population genetic data[END_REF] to align the different runs, identify the modes, and visualize the membership probability matrices (known as Q-matrices) in bar plots. Individual bars were clustered according to regional grouping. At the K determined by KFinder, the major mode was selected to calculate the mean membership probabilities per site and to map these values at their GPS locations using the R packages pophelper v2.3.1 [START_REF] Francis | pophelper: an R package and web app to analyse and visualize population structure[END_REF])and ggplot2 v3.4.1 [START_REF] Wickham | ggplot2: Elegant graphics for data analysis[END_REF]. Given the distribution of ancestry proportion (Fig. S.6), we set 0.80 as the threshold to differentiate between admixed and non admixed individuals. Finally, we used the STRUCTURE inferred allele frequency divergences among the genetic clusters at the major mode to draw an unrooted splits network using the lpnet R package v0.1.0 [START_REF] Guo | lpnet: Consruct a circular network based on a tree by Linear Programming[END_REF]. The obtained nexus file was imported to SplitsTree v5 for visualization [START_REF] Huson | Application of Phylogenetic Networks in Evolutionary Studies[END_REF]. We also assessed the differentiation between the inferred groups using the non admixed individual of each group and the ρ statistics computation implemented in Genodive v3.0 (Meirmans 2020).

Differentiation across Europe was also determined using the geographical regions. The pairwise ρ statistics were calculated using Genodive v3.0 (Meirmans 2020). Significance was assessed using 10,000 permutations. Regions with fewer than five clonal lineages were excluded from the analysis (i.e., Spain, Moldova, North Czechia, West Romania).

A graph based on ρ values was then constructed using the R package igraph v1.4.1 [START_REF] Csardi | The igraph software package for complex network research[END_REF]. Regional groups were represented as nodes, and ρ values lower than 0.05 as edges. A community detection analysis was conducted on the graph using igraph. The cluster_infomap function was employed with 500 trials and weighted edges [START_REF] Rosvall | Maps of random walks on complex networks reveal community structure[END_REF].

Marker evaluation

Marker information was recorded using the Rosa sp. dataset. In detail, the missing data rates, the observed number of alleles considering whole amplicon information, observed number of alleles considering only the length, the number of rare alleles, and the polymorphism information content (PIC) were determined. The polymorphism information content was calculated using the function PIC available in the R package Polysat v1.7.7 [START_REF] Clark | polysat: an R package for polyploid microsatellite analysis[END_REF].

An important metric for assessing marker quality is the extent of null alleles. Null alleles occur when the genotyping method fails to identify the presence of true alleles.

For an individual, this makes the loci with a null allele to appear as missing data if it is homozygous for the null allele or to appear as homozygous in case of it has an heterozygous genotype with a detectable allele and a null allele. Consequently, null alleles induce an increase in F is statistics, suggesting a departure from panmixia due to an excess of homozygotes (De Meeûs 2018). However, null alleles are not the only cause of an apparent excess of homozygotes as some other causes are Wahlund effect and selfing [START_REF] Waples | Testing for Hardy-Weinberg Proportions: Have We Lost the Plot?[END_REF]. Various methods are available to estimate allele frequencies in the presence of null alleles for diploids under the assumption of Hardy-Weinberg equilibrium [START_REF] Dąbrowski | True' null allele detection in microsatellite loci: a comparison of methods, assessment of difficulties and survey of possible improvements[END_REF]. For polyploids, dosage uncertainty, inheritance mode and double reduction make null allele estimation even more complicated. [START_REF] Huang | polygene: Population genetics analyses for autopolyploids based on allelic phenotypes[END_REF] adapted the methods of [START_REF] Kalinowski | Maximum likelihood estimation of the frequency of null alleles at microsatellite loci[END_REF] to estimate allele frequencies in the presence of null alleles for autopolyploids. However, their method was unsuitable for our data because it requires groups of individuals in Hardy-Weinberg equilibrium and uses models of polysomic inheritance. As R. gallica would be a segmental allopolyploid [START_REF] Fernández-Romero | Physical mapping of ribosomal DNA on several species of the subgenus Rosa[END_REF], we did not use this method. De Meeûs (2018) proposed a method to determine if null alleles are impacting the data in nonequilibrium populations. Despite being tested only on diploids, as this method is based on F-statistics and missing data rates, it should be applicable to tetraploids. Therefore, this method was used to ascertain if null alleles were affecting our dataset. In detail, the STRUCTURE inferred clusters with more than 20 non-admixed individuals were selected to form groups with reduced Wahlund effects. Polygene v1.6 was used to calculate F is and F st without estimating allele frequencies but using direct calculations based on genotypic data [START_REF] Huang | polygene: Population genetics analyses for autopolyploids based on allelic phenotypes[END_REF]. Following the procedure proposed by De Meeûs (2018), the variation of F is and F st across loci, the Pearson's correlation between F is and F st , the Pearson's correlation between F is and missing data rates, and the differences between standard errors of F is and F st were examined. Pearson's correlations were calculated using the R package ggpubr (Kassambara 2023a), while standard errors were determined using jackknifing over loci as done in De Meeûs (2018).

Diversity assessment

The inferred genetic groups were compared for several diversity estimates using nonadmixed individuals. Then we calculated the observed gametic heterozygosity, the gene diversity and the inbreeding coefficient (G is ) for each genetic group using Genodive v3.0 [START_REF] Meirmans | genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids[END_REF][START_REF] Meirmans | The Analysis of Polyploid Genetic Data[END_REF]. We utilized ADZE v1.0 to estimate their allelic richness and the private allelic richness using a rarefaction procedure [START_REF] Szpiech | ADZE: a rarefaction approach for counting alleles private to combinations of populations[END_REF]. The maximum standardized sample size used for the calculation (i.e. the G ADZE parameter) was set to 12. This corresponded to the number of alleles present at each locus in the smallest groups (i.e. three individuals and four alleles per individual for a given locus). Additionally, using ADZE, we also calculated the private allelic richness per region. The G parameter was set to 16, and regions containing less than four clonal lineages were excluded from the analysis (i.e. Spain, Moldova, North Czechia).

Results

Ploidy and marker quality check

After the removal of paralogs, filtering for error rates and optimizing for missing data, 2). Concerning the marker quality, the average error rate and the missing rate were 2.17 % and 5.42 % respectively. A total of 2326 and 884 alleles were identified in the Rosa sp. (see Table 3.1) and R. gallica (Table S.4) datasets respectively. Length homoplasy was quite common, as the number of alleles increased by 212.31 % when considering whole amplicon information compared to solely considering allele length.

Length homoplasy occurred even for markers for which alleles were called by focusing on the repeat motif only. On the Rosa sp. dataset, the PIC was high, ranging from 0.59 to 0.95 (see Table 3.1), highlighting the good discriminatory power of the markers.

The investigation for null alleles revealed a significant correlation between Fis and Fst, a correlation between Fis and missing rates, and the standard deviation of Fis is four times that of F st (see Fig. S.7). This seems to indicate that null alleles may affect the data. However, as most of the loci showed an excess of homozygotes, removing the ones with this excess would have removed a large part of the loci. Additionally, such a high number of loci with an excess of homozygotes suggested that other factors than only the null alleles may be also responsible for the excess. Therefore, we decided to keep the 29 loci and to take caution during the interpretation of differentiation values as these can be inflated by the presence of null alleles.

Wild-cultivated clonal relationship

The distance based method of clonal lineage detection that we implemented detected a clonal relationship for 1045 out of the 1618 succesfully genotyped samples. The method has a good specificity as no clonal relationship was found among the half and 10 full-sib individuals. After removing the 20 genets corresponding to these sib, the clonecorrected Rosa sp. dataset was composed of 867 genets (supplementary data S.2). We

Mis-identification detection through k-mer based dissimilarities

To assess the reliability of our k-mer based method for dissimilarities calculation, we compared the PCoA based on these dissimilarities and the PCA based on allele frequencies performed on the clone corrected Rosa sp. dataset. Both analyses separated most of the R. gallica specimens from specimens belonging to other species (see Fig. 3.4). In details, on the PCoA plots, most of the R. gallica specimens were grouped at the left of the plot when visualizing both the axis one and two (Fig. 3.4.A1, dark red diamonds), and the axis one and three (Fig. 3.4.A2, dark red diamonds). For the PCA, most of the R. gallica were located at the right of the plot when visualizing the axis one and two and the axis one and three (see Fig. 3.4.B1-B2, dark red diamonds). Comparing the k-mer dissimilarity based PCoA and the PCA (see Fig. 3.4), we also saw that specimens tended to be clustered according to their botanical section. Indeed, on the PCoA, the European Synstylae were grouped apart at the bottom left of the Figure 3.4.A2 (purple circles). They also grouped together on the PCA, (Fig. 3 in the groups of Rosa-Carolinae, Synstylae-Chinenses and European Synstylae. The location of these individuals suggested a mis-identification. Finally, the grouping obtained using either a PCoA based on k-mer dissimilarities or PCA based on intra-individual allele frequencies were globally similar. Moreover, the percentages of explanation were of the same order for both methods. However, the groups seemed to be more separated from each other when using the PCoA (Fig. 3.4). The structure pattern of R. gallica individuals was also investigated using the inferential model-based STRUCTURE software. The ∆K and the parsimony-based methods, respectively, suggested K = 2 and K = 8 as probable optimal numbers of clusters (Fig. values exhibited minor variations among groups. However, G is was the lowest in the small cluster of individuals from South Western France (dark blue). The highest G is values were observed in the two largest groups (orange and grey, see Fig. 3.7E).

R. gallica structure and diversity

Discussion

In the present study, our aim was (1) to develop a method to consider different types of polymorphisms when implementing dissimilarity-based analysis, (2) to unravel the factors responsible for the range expansion of R. gallica and (3) to evaluate whether French populations have similar or particular natural history compared to populations of other countries. To achieve this, we genotyped a large panel of Rosa sp. individuals with a particular focus on the R. gallica using a recently developed SSRseq marker set [START_REF] Pawula | Putting rose microsatellites into orbit: development and assessment of an SSR sequencing method[END_REF]. We investigated our methodological objective taking advantages of word based alignment-free sequence comparisons. We then empirically compared results R. gallica : European diversity and origins of the French populations Inbreeding coefficient (G is ).

R. gallica : European diversity and origins of the French populations obtained with this method to results obtained with an allele frequencies based classical method. Furthermore, we defined an analysis framework adapted to analyze diversity and structure of a clonal species being part of a genus with high rate of heterozygosity, interspecific hybridization and polyploidization. Indeed, we sequentially selected high quality loci, retained one individual per clonal lineage and removed mis-identifications to focus on R. gallica. Then, analyzing the largest collection of specimens of this species, we revealed the species diversity and structure leading to hypotheses on the origins of the French populations. Detected communities are represented by node colors. Node size is proportional to the private allelic richness, calculated using a G parameter set at 16. Regions with fewer than four clonal lineages were excluded.

Valuable dissimilarity estimation based on k-mer counts

Individual pairwise dissimilarities are the fundamental basis for many population genetic analyses [START_REF] Bruvo | A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level[END_REF][START_REF] Kosman | Dissimilarity of individual microsatellite profiles under different mutation models: Empirical approach[END_REF]. In this work, we revealed a high rate of length homoplasy thanks to the presence of multiple type of polymorphism in the allele sequences. To account for all these polymorphisms, we proposed a new method based on k-mer counts to calculate pairwise allele distances. Our method considers these pairwise allele distances to determine individual pairwise dissimilarities. With the k-mer length set to six, our method successfully grouped Rosa sp. individuals according to their botanical section (see Fig. 3.4) and their clade in [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF]. At the species level, the PCoA based on the newly developed dissimilarities output similar results than the PCA based on intra-individual allele frequencies (see Fig. 3.5). These results suggest that our dissimilarity measure is effective for investigating population structure at the individual level. As various ploidy levels were present in the Rosa sp. dataset, our measure of dissimilarity seems to be valuable to compare individuals of a mixed-ploidy dataset. To confirm this first result, future simulation work could compare our new dissimilarity measure with other existing distances methods [START_REF] Bruvo | A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level[END_REF][START_REF] Kosman | Dissimilarity of individual microsatellite profiles under different mutation models: Empirical approach[END_REF][START_REF] Viruel | Advances in genotyping microsatellite markers through sequencing and consequences of scoring methods for Ceratonia siliqua (Leguminosae)[END_REF]. As is the case with most word-based methods, the choice of the k-mer length is crucial. It should not be too short to avoid nonhomologous comparisons, but not too long either to enable matching of words between sequences. Therefore, experimenting with different lengths is recommended to choose this parameter. Additionally, while we tested the Bray-Curtis distances, there are plenty of k-mer based distances [START_REF] Zielezinski | Benchmarking of alignment-free sequence comparison methods[END_REF], testing others could be interesting.

R. gallica present days structure pattern suggest recolonization from primary refugia

In the present study, we reveal the diversity structure pattern of R. gallica populations found in the wild across Europe. Of the eight genetic groups containing wild growing individuals, only three are present in the large area covering the South Alps and Central-Eastern Europe, while six are found in France. Among them, five genetic groups are exclusively found in France, located in the South Western, Central, and Central-Eastern parts of the country. These groups exhibit a lower allelic richness than the other three.

This suggests different or additional diversification factors for these French localities.

Therefore, we will examine the diversification factor hypotheses separately for the South Alps and Central-eastern Europe (including South East and North East France) and for the rest of the French regions (and Spain).

For the South Alps and Central-Eastern Europe, three hypotheses were formulated regarding the expansion of R. gallica. One suggests a natural recolonization from refugia after the last glacial maximum, while the other two invoke human-mediated dispersal at different ages (Antiquity and Middle Age). The natural recolonization from Southern refugia (i.e., the Balkans, Iberia, and/or Italy) has been described for several species [START_REF] Daneck | Glacial survival in northern refugia? Phylogeography of the temperate shrub Rosa pendulina L. (Rosaceae): AFLP vs. chloroplast DNA variation[END_REF][START_REF] Magri | A new scenario for the Quaternary history of European beech populations: palaeobotanical evidence and genetic consequences[END_REF]. Recolonization can occur from one or multiple refugia, leading to different patterns of diversity today [START_REF] Ali | A Conceptual Framework for Designing Phylogeography and Landscape Genetic Studies[END_REF]. For instance, when refugia contribute unevenly to the recolonization, a large part of the recolonized area is covered by related populations that appear to belong to the same genetic cluster or to minimally differentiated ones. This phenomenon has been described for the common beech [START_REF] Magri | A new scenario for the Quaternary history of European beech populations: palaeobotanical evidence and genetic consequences[END_REF]) and the meadow grasshopper [START_REF] Korkmaz | The contribution of Anatolia to European phylogeography: the centre of origin of the meadow grasshopper, Chorthippus parallelus[END_REF]). Additionally, the recolonization process may involve the loss of alleles in recolonized areas.

The level of differentiation and the intensity of allele loss depend on the number of founders and the extent of gene flow between the refugium and the recolonized area [START_REF] Ali | A Conceptual Framework for Designing Phylogeography and Landscape Genetic Studies[END_REF]). Here, two clusters cover most of the South Alps and Central-Eastern Europe (i.e., clusters orange and grey) and this predominance of two clusters was confirmed by the community detection performed on the ρ differentiation graph. Moreover, low latitude regions of this area exhibit more private allelic richness than high latitude regions.

Therefore, our findings on the present-day diversity pattern of R. gallica seem compatible with the hypothesis of a recolonization following the last glacial period. Determining from where the South Alps and Central-Eastern Europe were recolonized remains highly speculative. Here, the two clusters are only minimally differentiated and low differentiation is present between geographical regions occupied by the grey and orange cluster.

Moreover, the North of Italy exhibit a high private allelic richness while regions covered by the orange genetic cluster had low allelic richness. Therefore, the region of South Alps and Central-Eastern Europe could have been recolonised from one unique refugium (as described for the common beech [START_REF] Magri | A new scenario for the Quaternary history of European beech populations: palaeobotanical evidence and genetic consequences[END_REF]) located in the Italian peninsula.

In this scenario, the Alps could have acted as a reproductive barrier responsible for the minimal but significant differentiation between the grey and orange clusters. This would also explain the low differentiation found between Croatia/Slovenia and two regions from Central Europe. However, in this study, we did not detect any indication of a northern refugium for R. gallica, as no high-latitude region exhibited high private allelic richness.

Such refugia have been suggested to exist for another European Rosa species, R. pendulina [START_REF] Daneck | Glacial survival in northern refugia? Phylogeography of the temperate shrub Rosa pendulina L. (Rosaceae): AFLP vs. chloroplast DNA variation[END_REF]. Finally, the third cluster found in Eastern Europe (the dark red one) is located at the extreme eastern part of the studied range. This cluster shows high diversity and private diversity, suggesting that another refugium might be present in this area, but without a significant westward post-glacial expansion.

The two other hypotheses on the expansion argue that humans were important factors in the expansion process of R. gallica, either during the Romans age [START_REF] Debray | Phylogénomique du genre Rosa : hybridation et polyploïdie comme facteurs de diversification[END_REF] or during the Middle Age [START_REF] Fedorova | Systematics and geography of species of the genus Rosa L. sect. Gallicanae DC[END_REF]. The Romans cultivated few types of roses that might eventually be attributed to R. gallica. These would have been the rose of Praeneste, the rose of Milet and the rose of Trachyne [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. At this age, the roses were mainly multiplicated by the root suckers [START_REF] Mattock | The Silk Road Hybrids': Cultural linkage facilitated the transmigration of the remontant gene in Rosa x damascena, the Damask rose, in circa 3,500 BCE from the river Amu Darya watershed in Central Asia, the river Oxus valley of the Classics, to Rome by 300 BCE[END_REF]. During the Middle age, the propagation by root suckers was also known and there was only few varieties of R. gallica [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]. The propagation of R. gallica from a reduce number of distinct individuals by root suckers would have let trace on the actual diversity. Under these human-mediated dispersal hypotheses, few parental types of roses would have been propagated by humans across Europe by clonal reproduction. As thousands or hundreds years spent, the clonal relationship might be difficult to trace due to potential sexual reproduction generations since that time. However, a clonal propagation at a particular time would have represented a strong bottleneck leading to a low degree of diversity observed within the genetic groups. Additionally, the clustering pattern expected would be influenced by the number of varieties propagated by region and the potential hybridization between varieties happening after their human mediated dispersal. This could lead to a low spatial genetic structure. Here, we found both a high diversity in the largest clusters (grey and orange) and a clear spatial genetic structure with low differentiation values found predominantly between adjacent regions.

Therefore, our results do not suggest that humans, either during the Romans age or during the Middle age, were important factor of dispersion for R. gallica in South Alps and Central-Eastern Europe.

Only one of the five clusters found in South Western, Central and Central-Eastern France is widespread (i.e., the light blue cluster). This one is located in the most Western part of the species range. For several European species, an Iberian or Pyrenean refugium has been described [START_REF] Daneck | Phylogeography of Lonicera nigra in Central Europe inferred from molecular and pollen evidence[END_REF][START_REF] Daneck | Glacial survival in northern refugia? Phylogeography of the temperate shrub Rosa pendulina L. (Rosaceae): AFLP vs. chloroplast DNA variation[END_REF][START_REF] Magri | A new scenario for the Quaternary history of European beech populations: palaeobotanical evidence and genetic consequences[END_REF]). Therefore, it is possible that the Western part of the R. gallica range was recolonized from such a refugium. Another possibility is to consider the hypothesis of [START_REF] Klastersky | Rosa L[END_REF], which proposes that R. gallica specimens growing in Western France and Spain originated from a secondary introduction driven by human-mediated dispersal. Recolonization from an unsampled Iberian refugium would result in individuals from Western and Central France and Spain belonging to a different genetic cluster than those present in other parts of the range. Furthermore, this cluster would be highly differentiated from others, as the Iberian refugium would have been isolated during the last glacial period. Long-term isolation caused by the prolonged glacial period usually leads to the appearance or loss of different alleles in separated populations [START_REF] Daneck | Glacial survival in northern refugia? Phylogeography of the temperate shrub Rosa pendulina L. (Rosaceae): AFLP vs. chloroplast DNA variation[END_REF]. This phenomenon often results in high private allelic richness. Some of the private alleles are then introduced to the recolonized areas. Therefore, even if only the recolonized area is sampled and compared to areas recolonized from a different refugium, high private allelic richness would be observed. In our results, individuals from the most Western part of the range belong to the light blue cluster, which is well differentiated from the two largest clusters (South Alps -grey and Central Eastern Europe -orange). However, the low private allelic richness of this South Western and Central France cluster does not seem compatible with the hypothesis of an Iberian refugium origin or a with the hypothesis of French microrefugium.

The hypothesis of [START_REF] Klastersky | Rosa L[END_REF] suggests that several founders were imported to the Western areas by humans. These founders may or may not have a common origin with other individuals that were assigned to other genetic groups present in the dataset. As explained above, the low private allelic richness of the genetic cluster found in South

Western and Central France (light blue) seems incompatible with different origins for these individuals. Depending on the number of founders, a human-mediated secondary introduction could represent a genetic bottleneck, inducing genetic drift. Genetic drift increases population differentiation [START_REF] Wright | Evolution in Mendelian populations[END_REF]. It also affects allele frequencies, leading to allele fixation or loss, and results in decreased heterozygosity and, to a greater extent, decreased allelic richness [START_REF] Greenbaum | Allelic Richness following Population Founding Events -A Stochastic Modeling Framework Incorporating Gene Flow and Genetic Drift[END_REF]. Therefore, under the hypothesis of [START_REF] Klastersky | Rosa L[END_REF], we would expect individuals in the Western part of the range to be differentiated from those in the South Alps and Central-Eastern Europe. These

Western individuals would also exhibit relatively low heterozygosity and allelic richness.

The cluster found in this Western most part of the range (light blue) is well differentiated from the groups occurring in the more eastern parts of the range. Relative to these groups, the light blue one displays low allelic richness and low heterozygosity. Thus, the hypothesis of [START_REF] Klastersky | Rosa L[END_REF] cannot be dismissed based on our results. However, the establishment of the blue group could also be explained by the natural dispersal of a few founders. In this scenario, we would expect the same patterns of genetic diversity and structure as those anticipated under the [START_REF] Klastersky | Rosa L[END_REF] Some wild collected French samples belong to clusters that are locally spread (i.e., the yellow, green, dark blue, and pink clusters). Their restricted distribution complicates interpretations, but certain hypotheses regarding their origins can be proposed. The yellow cluster predominantly contains cultivated individuals. Thus, the three wild sites with a high membership probability to the yellow cluster might comprise formerly cultivated individuals that have persisted in the wild since their abandonment. The green cluster includes only three individuals that exhibit more than a membership probability of 80%. This cluster displays low allelic richness but a high private allelic richness. It is highly differentiated from all other clusters. On the PCoA plot that describes the Rosa sp. dataset, individuals from this cluster are situated not in the core of the R. gallica group but rather between the European Synstylae group and the R. gallica group (see Fig. 3.4A2). Moreover, among the 17 individuals with a 50-80% membership probability to this group, five are botanical varieties with suspected interspecific hybrid origins.

Thus, individuals from this group might exhibit introgression from other Rosa species.

The final two groups, dark blue and pink, are quite restricted and primarily consist of individuals from neighboring sites. Hence, they might have a high degree of relatedness.

Given that STRUCTURE inference is sensitive to relatedness, the identification of these groups at K = 8 could be an artifact. However, when examining the clustering results at K = 2 (Fig. S.13) and the split network partitions at K = 8, individuals in the pink group seem to have emerged from hybridization between Western and more eastern R. gallica.

Intriguingly, at K = 2, individuals from the dark blue group cluster with those found in the South Alps and Central-Eastern Europe, while others from South Western France cluster with cultivated individuals. This suggests that the dark blue group might have originated from a different introduction event (either natural or human-mediated) than individuals from the light blue group. [START_REF] Boulenger | Les roses d'Europe de l'herbier crépin. Vol. 2 (suite)[END_REF] stated that cultivated R. gallica specimens escaped from the cultivation in France. For a particular site, the indication of ancient cultivation could be the inclusion of an individual from the site in the same clonal lineage as a known botanical or cultivated variety. However, the clonal link could also be explained by the fact that an individual from the site, harboring interesting traits, has been extracted from wild to be cultivated. Therefore, when individuals from only one site are found in the same clonal lineage as one or several botanical or cultivated varieties, we can not confidently state the the site was a ancient cultivation place. When multiple sites belong to the same clonal lineage as one or several botanical or cultivated varieties, we can conclude that among the multiple sites, there are ancient cultivation areas. Nevertheless, these sites may or may not include a potential site of origin of the variety. Here, we found multiple sites with individuals belonging to the same clonal lineage than botanical or cultivated varieties. Some of the clonal lineages grouped individuals sampled in more than one site. This totally supports the hypothesis of [START_REF] Boulenger | Les roses d'Europe de l'herbier crépin. Vol. 2 (suite)[END_REF] as all or part of these sites are ancient cultivations. However, we can not state for each site if it is an ancient cultivation or if it is the wild site of origin of a variety. The botanical or cultivated varieties detected in this type of clonal lineages are often of unknown origins, reinforcing the doubt on their origins. Under the hypothesis of [START_REF] Boulenger | Les roses d'Europe de l'herbier crépin. Vol. 2 (suite)[END_REF], the detection of clonal lineages containing wild and non-wild samples is highly dependent on the presence of the clonal non wild specimens in the dataset. If the actual botanical or cultivated variety is not included in the dataset, finding clonal lineages grouping individuals from different sites would be an indication of ancient cultivation. This case was found in Spain, making the ancient cultivation hypothesis possible for the clonal group J. This would also be in accordance with the hypothesis of a secondary origin of Spanish populations as written by [START_REF] Klastersky | Rosa L[END_REF]. A similar grouping was found for three individuals collected from three different sites in Slovenia. These were sampled in the botanical garden of Ljubljana from three plants that were reported to be collected in three distant wild Slovenian sites. Therefore, an explanation for the clonal grouping would be plant identification loss or mistake during transplantation. Additionally, we found clonal lineages gathering botanical or cultivated variety and Bosnian individuals from two sites. These Bosnian individuals were considered as wild for the analysis but were in garden, therefore, the found clonality supports their non wild nature.

Evidence for human-mediated dispersal in France and Spain

Conclusion and perspectives

The empirical assessment of the alignment-free dissimilarity estimation method proposed here demonstrated its effectiveness in considering mixed types of polymorphisms. The method appears to be useful at both the inter-species and intra-species levels, making it a valuable supplement to other dissimilarity calculation methods. The characterization of the range-wide diversity of the tetraploid R. gallica revealed that natural expansion and human-mediated dispersal both played a role in the evolutionary history of the species. Our results suggest the natural expansion after the last glacial maximum was the primary factor of diversification for South Alps and Central-Eastern Europe R. gallica populations, while human-mediated dispersal might have been a major factor driving the colonization of the most Western part of the species range. Our study allows us to formulate hypotheses, but it leaves some parts of the question unresolved.

106

R. gallica : European diversity and origins of the French populations

Progressing to hypothesis-testing analyses would provide additional evidences to not only ascertain the origins of the Western France populations but also to evaluate the hypotheses of a Southern refugium. An effective and flexible framework to assess the reliability of various evolutionary scenarios is the Approximate Bayesian Computation (ABC) framework [START_REF] Beaumont | Approximate Bayesian Computation in population genetics[END_REF]. The mixed polymorphism present in SSRseq microhaplotypes (repeat number variation, SNP and InDel) has been demonstrated, on a diploid species, to be of interest in the ABC framework [START_REF] Lepais | Joint analysis of microsatellites and flanking sequences enlightens complex demographic history of interspecific gene flow and vicariance in rear-edge oak populations[END_REF]. The ABC approach necessitates the simulation of genetic data under the different tested scenarios.

However, simulations of polyploids population are not straightforward and a few authors who have applied the ABC framework to polyploids, either diploidized their data [START_REF] Lepais | High Genetic Diversity and Distinctiveness of Rear-Edge Climate Relicts Maintained by Ancient Tetraploidisation for Alnus glutinosa[END_REF] or simulated several diploid populations with a given pairwise gene flow to mimick the exchanges between sub-genomes [START_REF] Blischak | Demographic history inference and the polyploid continuum[END_REF][START_REF] Chapuis | A young age of subspecific divergence in the desert locust inferred by ABC random forest[END_REF].

Therefore, future work implementing the ABC framework to unravel polyploid population demographic history would require first to evaluate the diploidization methods or the test numerous demographic models with a range of homeologuous exchange levels.

Chapter 4

Genetic constitution of the horticultural groups of European

Old Garden Roses and their relationship with wild R. gallica

Introduction

Plant cultivation began approximately 12,000 years ago and produced a shift in the human way of life [START_REF] Diamond | Evolution, consequences and future of plant and animal domestication[END_REF]. At this time hunter-gatherer societies became sedentarised societies that continuously developed until today. Indeed, modern societies still heavily rely on the production of plants species for food, but also for energy, material, medicine, and even for art and pleasure [START_REF] Diamond | Evolution, consequences and future of plant and animal domestication[END_REF][START_REF] Schaal | Plants and people: Our shared history and future[END_REF]. All these needs are met thanks to the large number of types of cultivated plants such as cereal crops, legumes, fruit trees, forage and ornamental plants. Ornamental plants include more species than all other agricultural and horticultural crops together [START_REF] Weiss | Introduction of new cut flowers: domestication of new species and introduction of new traits not found in commercial varieties[END_REF]. These plants are used mostly for their aesthetic value but some ornamental plants are also cultivated for pharmacology, perfumery, or other purposes [START_REF] Altman | Ornamental plant domestication by aestheticsdriven human cultural niche construction[END_REF]). Human cultures have not only been affected by food crops, but also by ornamentals. Indeed, ornamental plants have been shown to improve human health, well-being, and creativity [START_REF] Husti | Psychological Benefits of Ornamental Plants Used in Office Environments[END_REF]. [START_REF] Doxey | The impact of interior plants in university classrooms on student course performance and on student perceptions of the course and instructor[END_REF] demonstrated that interior plants can improve levels of satisfaction with work and home life and can affect mood and cognition. In their relationship with wild R. gallica matter of creativity, ornamental serves for thousands years as a source of inspiration for art and philosophy [START_REF] Altman | Ornamental plant domestication by aestheticsdriven human cultural niche construction[END_REF]. Roses can be considered as an emblematic ornamental plant not only because they inspire artists since the Greek civilisation [START_REF] Joret | La rose dans l'antiquité et au moyen âge: Histoire, légendes et symbolisme[END_REF]) but also by the multiplicity of its uses. In detail, roses are today used mostly for their aesthetic quality but are also used for perfume, food, and medicinal properties [START_REF] Leus | Rose[END_REF].

Roses belong to the genus Rosa that includes between 150 and 200 species. The botanical classification regroups rose species into four sub-genera (i.e. Rosa, Hulthemia, Platyrhodon, and Hesperhodos), but almost all species are part of the sub-genus Rosa.

This sub-genus is divided into ten sections that are Banksianae, Bracteatae, Caninae, Carolinae, Rosa, Gallicanae, Indicae, Laevigatae, Pimpinellifoliae, and Synstylae (Wissemann 2017). [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF] examined the classification in the light of molecular data and proposed some modifications. Cultivated roses originate from 8 to 15 species [START_REF] Smulders | Rosa[END_REF][START_REF] Wylie | The history of garden roses[END_REF]. The rose cultivation and breeding started from two main areas, namely Asia and Europe. Globally, most Asian cultivars of Old Garden Roses (OGR) are relative to R. chinensis (section Indicae) and harbor continuous flowering [START_REF] Tan | Genetic relationships and evolution of old Chinese garden roses based on SSRs and chromosome diversity[END_REF]) while most European cultivars of OGR are relative to R. gallica (section Gallicanae) and are hardy, cold resistant but generally flower only once in the season [START_REF] Gardes | Molecular typing and history of the provins roses horticultural group[END_REF][START_REF] Koopman | AFLP markers as a tool to reconstruct complex relationships: A case study in Rosa (Rosaceae)[END_REF][START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]. Cultivars are classified using the horticultural classification that divides roses into three classes : wild roses, OGR and Modern roses [START_REF] Cairns | Horticultural Classification Schemes[END_REF]. Within OGR and Modern roses, the cultivars are grouped into different horticultural groups according to their phenotype and presumed genealogy. European OGR horticultural groups were bred before 1867, the date that separates OGR and Modern roses. Among the European OGR, the Hybrid Gallica (HGal), Alba, Centifolia, Damask, and Moss roses are the oldest groups and are related to the wild R. gallica.

Some authors [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF][START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF] have a European parent they might have also inherited alleles from R. gallica but to a lower degree than the oldest European OGR groups [START_REF] Cairns | Horticultural Classification Schemes[END_REF]Cui et al. 2022a;[START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF][START_REF] Gardes | Molecular typing and history of the provins roses horticultural group[END_REF][START_REF] Hurst | Notes on the origin and evolution of our garden roses[END_REF][START_REF] Iwata | Triparental origin of Damask roses[END_REF][START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF][START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF][START_REF] Widrlechner | History and utilization of Rosa damascena[END_REF].

The role played by R. gallica during the breeding process in Europe was investigated using molecular markers. [START_REF] Gardes | Molecular typing and history of the provins roses horticultural group[END_REF] evaluated the relationship between wild R. gallica and the HGal cultivars. In their study, the HGal were separated from wild R. gallica. However, the wild R. gallica they used were sampled only from South East

France, a limited geographical area that may not represent the whole wild diversity.

Therefore, the separation between HGal and wild R. gallica might be due to a sampling bias. Several other studies investigated the contribution of R. gallica to the Alba, Centifolia and Damask groups using phylogenetics (Cui et al. 2022a;[START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF][START_REF] Iwata | Triparental origin of Damask roses[END_REF][START_REF] Ritz | Evolution by reticulation: European dogroses originated by multiple hybridization across the genus Rosa[END_REF]. These studies revealed that the cultivars of these groups might be R. gallica hybrids. However, some of them (Cui et al. 2022a;[START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF][START_REF] Iwata | Triparental origin of Damask roses[END_REF] yielded different results. This might be explained by the low number of cultivars they included, making their conclusions valuable only for the cultivar they used and not for an entire horticultural group. Finally, it was shown that most the Damask and Alba used in the perfume industry belong to a few clonal lineages.

Therefore, it is possible that some groups contains many sports or synonymous cultivars, making them to be constituted of only a restricted number of clonal lineages.

In this paper, our objective is to understand the genetic constitution of European OGR groups and assess how wild R. gallica contributed to the establishment of various Euro-their relationship with wild R. gallica pean OGR groups, either directly or through the HGal. To achieve this, we used SSRseq data of an extensive rose collection, including cultivars of European OGR, cultivars of non-European OGR, wild R. gallica specimens, and specimens of other Rosa species. To evaluate the genetic constitution of the European OGR, we determined whether clonal relationships (i.e. sports or synonymous cultivars) are more predominant in certain groups than in others and we evaluated whether OGR groups correspond to separated genetic groups. Finally, we unraveled the contribution of R. gallica to the various OGR groups.

Material and Methods

We used the SSRseq data of some individuals that were genotyped in Chapter 3. Individuals were selected to build a sample adapted to the research questions of the present study.

Individual selection and genotyping

We selected 640 individuals, among the 1618 successfully genotyped samples in Chapter (2002). The samples were genotyped using 60 SSRseq markers [START_REF] Pawula | Putting rose microsatellites into orbit: development and assessment of an SSR sequencing method[END_REF]) following the procedure of [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF]. Allele calling was performed on raw read sequences using a pipeline based on FDSTools v1.2 [START_REF] Hoogenboom | FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise[END_REF]) developed by [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF]. FDSTools parameters and marker repeatability were optimized using 87 individuals genotyped independently twice. The ploidy classes of the individuals were inferred based on the number of alleles per locus using a machine learning procedure (see section 2.2). This method classified individuals as being either diploid, tetraploid or hexaploid.

Due to the low number of expected triploids and pentaploids in the whole dataset used in Chapter 3, these were classified as tetraploid or hexaploid, respectively (supplementary data S.2). Paraloguous loci and loci with more than 5% of errors were removed from the dataset. Allele dosage was recovered using the read depth based method developed by Cui et al. (2022b). Finally, the dataset was optimized, setting the number of missing data as low as possible and keeping the maximum of data with the constraint that all pairs of individuals shared at least 50% of data. This optimized dataset was obtained their relationship with wild R. gallica by using a custom R script (Eveilleau and Pernet pers.com. modified by Pawula) that eliminated according the case either SSRseq markers, either individuals. This led to the removal of 31 loci, keeping 29 loci with minimal missing data and good repeatability.

Clonal lineages analysis

Clonal lineages detection was performed on the whole dataset of 640 individuals. The genotypic data were used to compute pairwise Euclidean distances between individuals with the R package factoextra v 1.0.7 [START_REF] Kassambara | factoextra: Extract and visualize the results of multivariate data analyses[END_REF]. Subsequently, the pairwise distance threshold-based method implemented in the R package Polysat v1.7.7 [START_REF] Clark | polysat: an R package for polyploid microsatellite analysis[END_REF] was used to identify clonal lineages. This method allows mutations and genotypic errors. Nevertheless, it is sensible to aggregation. Namely, falsepositives may arise when an individual is detected as belonging to a large clonal lineage even though it harbors a pairwise distance lower than the threshold with only one other individual of the clonal lineage. To prevent this, we performed a two-step procedure.

First, we used the conservative threshold determined in chapter 3 (i.e. 1.603) to prevent the aggregation phenomenon. Secondly, we checked the presence of aggregation in clonal lineages containing more than two individuals by constructing genetic distance networks using the R package igraph v1.4.1 [START_REF] Csardi | The igraph software package for complex network research[END_REF]. The genetic distance networks were constructed using a less conservative threshold set to 2.2 as there was no risk of aggregation for this second step. We determined if there was a lot of aggregation in clonal lineages by calculating the completeness of each network (i.e. the actual number of edges divided by the theoretical maximum number of edges given the number of nodes).

To compare the proportion of individuals belonging to the same clonal lineages within each horticultural group, we estimated the clonal richness (R) per horticultural group using the formula :

R = (G -1) (N -1)
where G is the number of clonal lineages within the horticultural group and N the number of individuals in each horticultural group [START_REF] Arnaud-Haond | Standardizing methods to address clonality in population studies[END_REF]). To determine whether horticultural groups were composed of few large or of many small clonal lineages, D max and D min were estimated as :

D max = (G -1) G × N (N -1)
and To identify whether horticultural groups followed or not the population structure, we first conducted analyses without a priori assumptions. Specifically, the structure was investigated without considering the horticultural group or botanical section of the individuals. Group affiliations were used solely to describe the results. For this, we performed a PCA on the clone-corrected dataset. To run the PCA, individual genotypes were first converted into intra-individual allelic frequencies. All individuals contributed to computing the principal components. Additionally, we employed the model-driven STRUCTURE v2. 3.4 (Pritchard et al. 2000) tool on each of the clone-corrected dataset and the cultivated clone-corrected dataset to reveal the genetic structure within these datasets. For both, the admixture model with correlated allele frequencies was utilized, and the ALPHA parameter was inferred for each population (i.e., each STRUCTURE genetic group). The starting ALPHA values were set at 0.1 and 0.2 for the clone-corrected dataset and the cultivated clone-corrected dataset, respectively. The burn-in period was Genetic constitution of the horticultural groups of European Old Garden Roses and their relationship with wild R. gallica set to 50,000, followed by 300,000 MCMC repetitions for inference. Analysis was run on both datasets from K = 1 to K = 10, with 10 independent runs for each K value. We estimated the probability of the data under the model to determine optimal K values with both the method of [START_REF] Evanno | Detecting the number of clusters of individuals using the software structure: a simulation study[END_REF] and the one of Wang (2019) using KFinder v2.1 (Wang 2019). Pong v1.5 [START_REF] Behr | pong: fast analysis and visualization of latent clusters in population genetic data[END_REF]) was used to align the results of different runs within and among K values, to identify the major mode for each K and to visualize the Q-matrices (i.e., the membership probability matrices).

D min = (2N -G)(G -1) N 2
The relevance of the horticultural groups to describe the genetic structure was also investigated using the horticultural groups as a a priori grouping variable. For that, an Analysis of Molecular Variance (AMOVA) was run in Genodive v3.0 [START_REF] Meirmans | genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids[END_REF] using the infinite allele model. Standard deviations of the F statistics [START_REF] Weir | Estimating F-Statistics for the Analysis of Population Structure[END_REF] were determined by jackknifing over loci. The significance of each source of variation was calculated using 1000 permutations [START_REF] Meirmans | genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids[END_REF]. Furthermore, pairwise differentiation between horticultural groups (cultivated clone-corrected dataset)

were determined using Genodive [START_REF] Meirmans | genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids[END_REF]. The ρ statistics were calculated between each pair of horticultural groups. The significance of the differentiation was estimated using 1000 permutations.

Genetic contribution of wild R. gallica to cultivated groups

The contribution of wild R. gallica to the different cultivated groups was investigated by performing population assignment. To do that, we ran STRUCTURE using the Synstylae showing less than 80% of membership probability to their expected genetic group were removed. Inference was performed at K = 6 using the admixture model with correlated allele frequencies by inferring ALPHA for each population with a initial ALPHA value set at 0.1. The burn-in period was set to 50 000 and 300 000 MCMC repetitions after burn-in were done. The computation was repeated independently 20 times.

The membership probability to the cluster of wild R. gallica was compared between the OGR groups of interest by performing pairwise two sample Wilcoxon test using the rstatix R package [START_REF] Kassambara | ggpubr: 'ggplot2' based publication ready plots[END_REF]. The p-values were corrected for multiple tests using the Holm method [START_REF] Holm | A simple sequentially rejective multiple test procedure[END_REF].

Results

Clonality

The two-step procedure used to identify clonal lineages was highly effective in reducing the number of false positives, as all genetic distance-based networks showed the maximal number of edges. Among the 640 individuals included, we did not detect any clonal relationship for 383 of them. Furthermore, we identified 78 clonal lineages that contained, in total, 257 individuals. The median number of individuals per clonal lineage was 2, indicating that they mostly included only a few individuals (Fig. 4.2A). Among the 384 individuals who belonged to the horticultural groups of interest (i.e. HGal, Alba, Centifolia, Damask, Moss, Portland and Bourbon), 209 were found in 64 clonal lineages.

In other words, only 14 clonal lineages did not include individuals of horticultural groups in which we were interested, and these contained only a few individuals (Fig. The high value of this indice for HGal (0.94, Tab. 4.1) showed that the clonal lineages were of equivalent size within this group.

Genetic structure

The PCA of the clone-corrected dataset (N = 461) separated the R. gallica (left) from other species (right) on the first principal component (Fig. 4.4). The second prin-Genetic constitution of the horticultural groups of European Old Garden Roses and their relationship with wild R. gallica Among the horticultural groups of interest, the HGal, and Bourbon individuals clustered quite well according to their horticultural group and apart from other groups of interest.

In contrast, as indicated by their centroid, the Centifolia, Damask, Moss The optimal numbers of STRUCTURE clusters were estimated to K = 3 and K = 7, based on the Evanno's method [START_REF] Evanno | Detecting the number of clusters of individuals using the software structure: a simulation study[END_REF] Genetic constitution of the horticultural groups of European Old Garden Roses and their relationship with wild R. gallica non cultivated individuals were grouped by botanical sections (Fig. 4.5). Surprisingly, the wild R. gallica appeared admixed between European Synstylae and HGal. This may be an artifact due to both the proximity of R. gallica to the European Synstylae and HGal. In addition, the unbalanced number of HGal can bias the analysis and may be one of the reasons for the admixture of the wild R. gallica [START_REF] Lawson | A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots[END_REF]). Thus, the apparent admixture certainly does not signify that wild R. gallica are effectively admixed between those groups. As also identified at K = 3, HGal and Bourbon were in different genetic clusters (Fig. 4.5). Bourbon individuals clustered with cultivars from other horticultural groups (mainly Chinese OGR or hybrids of Chinese OGR). Centifolia, Damask, Moss, Alba and Portland showed admixture between the HGal and either the cluster that included the Bourbon (and cultivars of other horticultural groups) or a third pink cluster. As for wild R. gallica, the admixture could be an artifact, but it seems less probable since some of these admixed horticultural groups were described as being hybrids (Cui et al. 2022a;[START_REF] Iwata | Triparental origin of Damask roses[END_REF][START_REF] Ritz | Evolution by reticulation: European dogroses originated by multiple hybridization across the genus Rosa[END_REF]). The pink cluster included mainly European OGR but almost no individual showed high membership probability to this cluster. Few HGal, Centifolia, Moss, Alba and Portland cultivars were assigned to the same cluster as Caninae individuals (Fig.

0 -1 1 -2 2 0 -1 1 -2 2 0 -1 1 -2 2 0 -1 1 PC 1 (12. 2

Discussion

The aim of our study was to investigate the genetic constitution of European OGR groups and to understand how wild R. gallica are related to these horticultural groups. Bourbon. We hypothesized that some horticultural groups would harbor a high rate of clonality as found for Damask and Alba roses grown for the perfume industry [START_REF] Rusanov | Microsatellite analysis of Rosa damascena Mill. accessions reveals genetic similarity between genotypes used for rose oil production and old Damask rose varieties[END_REF][START_REF] Rusanov | Flower phenotype variation, essential oil variation and genetic diversity among Rosa alba L. accessions used for rose oil production in Bulgaria[END_REF]. Moreover, we expected that horticultural groups of OGR would more or less be in accordance with the genetic structure [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF][START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF]. Our analysis of a comprehensive collection of European OGR revealed that clonality is predominant within the Alba, Centifolia, Damask and Bourbon as many cultivars of these groups belong to the same clonal lineages. Moreover, we found no obvious matches between horticultural groups and genetic structure among the Centifolia, Damask, Alba, Moss and Portland while HGal and Bourbon groups tend to be well differentiated. As expected, the HGal were closer to the wild R. gallica than any other groups, the Bourbon being only slightly related to this species.

Unreliability of horticultural classification in describing genetic structures of European old garden roses

The horticultural group-based classification is not a reliable tool to describe the genetic structure of the European OGR, as demonstrated by several of our results. The horticultural grouping explains only a very low degree of the genetic variation. Even though certain groups (e.g. HGal and Bourbon) are well separated from other OGR, no evidence of differentiation was found between the other European OGR. This is generally in accordance with the findings of [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]. However, although pairwise differentiation was low, it was significant in the study of [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]. The explanation for this divergence might lie in our clone detection step, which was not performed by (2006), who found that individuals from the same horticultural group mostly regrouped together. However, their sampling scheme was significantly reduced compared to ours, as only a few cultivars per horticultural group were included. This might explain inconsistencies with our results. We found that all horticultural groups contain individuals with variable genetic backgrounds. This intra-group variability is present even within well-defined groups such as the HGal. Another strong argument against the use of the horticultural classification as an indication of genetically similar individuals is the fact that many clonal lineages include cultivars attributed to different horticultural groups.

Finally, even though horticultural groups are poor descriptors of the genetic variation among European OGR, they might be useful for particularly well-defined groups such as HGal and Bourbon. However, despite these two groups being clearly differentiated from other horticultural groups included in the present study, we were not able to determine if they were also well differentiated from horticultural groups not included in the study.

For instance, [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF] showed that Bourbons might be not differentiated from other horticultural groups.

Variable contribution of wild R. gallica among and within European Old Garden roses groups

At the group level, almost all European OGR are related to R. gallica, as shown by the results of the Bayesian genetic assignment. However, the contribution of R. gallica varies between groups. Overall, the HGal are the closest group to the wild R. gallica

Genetic constitution of the horticultural groups of European Old Garden Roses and their relationship with wild R. gallica and appear to be a group of bred R. gallica without much introgression from other species. This is consistent with the results of [START_REF] Gardes | Molecular typing and history of the provins roses horticultural group[END_REF] and the hypothesis of [START_REF] Hurst | Notes on the origin and evolution of our garden roses[END_REF]. Our study adds robustness to their hypotheses and conclusions, as it demonstrates the closeness of the HGal to the wild R. gallica in a comprehensive context, since we included a more representative sample from the wild and cultivated compartment. In addition, several HGal appear to be interspecific hybrids between R.

gallica and other species.These cultivars might be, according to our results, the most recent individuals of the group that could have originated from hybridization with other genetic pools, as was globally shown for all rose cultivars during the 19th century.

Bourbon is the most distant group to the wild R. gallica, in accordance with the results of [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]. At the group level, Centifolia, Damask, Moss, Alba and Portland appear to be hybrids between R. gallica and individuals belonging to the section Indicae (either wild or Chinese OGR) or to the section Caninae but this varies greatly from one individual to another within groups. For instance, within the Alba group, some individuals appear clearly to be hybrids between R. gallica and Caninae while other appear to be hybrids between R. gallica and individual of other cultivated groups or even to be Caninae or to be close to individual of other cultivated groups. Therefore, it would be misleading to assumes that group tendency are valuable to identify from which species a horticultural group originated. Rather, it seems more reliable to focus on key cultivars of each group. As horticultural groups are a human construction based on phenotypic trait and supposed genealogy, the term key cultivar needs to be defined.

Here, a key cultivar of a horticultural group, corresponds to the cultivar that might have been used to define the typical phenotype of the cultivar of the given group, and not necessarily the common ancestor of all cultivars belonging to the group.

Clonal detection identifies key cultivars of each Old Garden Roses group to unravel their origin

Numerous Centifolia, Alba, and Damask belong to the same three clonal lineages, highlighting the importance of sports breeding or synonymy for these groups. Moreover, we hypothesize that these clonal lineages might represent important cultivars of their respective groups. Our hypothesis is based on several lines of evidence. First, Centifolia, Damask, and Alba are among the groups with the lowest clonal diversity in our study, due to the overrepresentation of one clonal lineage in each of these three groups. Second, the oldest Centifolia, Damask, and Alba belong to these clonal lineages. Third, our finding aligns with the results of other studies. [START_REF] Rusanov | Microsatellite analysis of Rosa damascena Mill. accessions reveals genetic similarity between genotypes used for rose oil production and old Damask rose varieties[END_REF] also identified a Genetic constitution of the horticultural groups of European Old Garden Roses and their relationship with wild R. gallica large clonal lineage within the Damask that included some of the same cultivars as ours. [START_REF] Rusanov | Flower phenotype variation, essential oil variation and genetic diversity among Rosa alba L. accessions used for rose oil production in Bulgaria[END_REF] found that R. alba 'Semi-plena' and R. alba 'Maxima' belong to the same clonal lineage, along with other cultivars grown in Bulgaria; in our study, these two cultivars also belong to the largest clonal lineage of Alba. [START_REF] Hurst | Notes on the origin and evolution of our garden roses[END_REF] suggested that many Centifolia may be sports varieties. Although these cultivars may not be the first bred representatives of their groups, due to the possible loss of authentic one over time, they may be the closest to this first bred available today, providing a unique avenue for studying the origins of these horticultural groups.

We sought to identify the species/section that contributed to the breeding of the representative cultivars. Centifolia and Damask seem to have hybrid origins involving R.

gallica and another species that is challenging to pinpoint based on the STRUCTURE inference we conducted. From our data, the key Alba cultivar appears to be a hybrid between R. gallica, a species from the section Rosa-Carolinae-Pimpinellifoliae, and a species from the section Caninae. The involvement of R. gallica and a Caninae aligns with the hypotheses of [START_REF] Ritz | Evolution by reticulation: European dogroses originated by multiple hybridization across the genus Rosa[END_REF] and [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]. However, the involvement of a Section Rosa specimen is surprising and might be due to either the setup or design of the STRUCTURE inference we undertook, the unsuitability of the analysis we conducted considering the sample, or the limited efficacy of the markers we utilized for this part of the question. The fact that Caninae have an hybrid origin involving Rosa species [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF]) might also perturb the inference.

Conclusion and perspectives

European Chapter 5

General Discussion and Perspectives

This thesis deals with the impact of natural and anthropic factors on the evolutionary history of R. gallica. We wanted to understand how R. gallica thrived in France where this species is protected. This species being at the genesis of the European cultivated pool, we also aimed to investigate how the wild R. gallica specimens are genetically related to the different horticultural groups of European Old Garden Roses (OGR).

We treated these questions sequentially, starting by unraveling the wild diversity of R. gallica. This allowed us to draw a representative subsample of the wild diversity to investigate the relationship between wild R. gallica and European OGR. To answer these questions as comprehensively as possible, the first challenge was to obtain genetic data from a sample that was both representative of R. gallica diversity and adapted to take account of the frequent interspecific hybridization between Rosa species. To solve this, we built an extensive network of academic collaborators that sampled more than a hundred wild sites of R. gallica. In complement, we collected by our own many wild and cultivated individuals in France (cultivated and wild), Italy (wild) and Slovenia (wild).

In parallel, we developed an effective genetic marker set more high-throughput and more accurate than older ones, adapted to genotype our extensive sampling in a highly costeffective manner. The second challenge was to deal with the complexity layers added by the evolutionary mechanisms occurring within the genus Rosa, namely, polyploidy, interspecific hybridization, and clonality. We circumvent these in a stepwise procedure.

First, we took advantage of the high polymorphism level of the designed markers to develop a model-free ploidy prediction method. Second, we dealt with clonality by General Discussion and Perspectives performing clone detection. Third, we used non R. gallica samples and a clustering approach to estimate the extent of introgression from other species in the specimens identified as R. gallica. Once these technical constraints were overcome, we brought significant contribution to the understanding of the R. gallica evolutionary history. On the first hand, we obtained a clear overview of the range-wide wild diversity of R. gallica underlying the role of both natural and human mediated dispersal in the colonization of France by the French rose. On the other hand, we have demonstrated genetically that among the European OGR, the HGal are basically selected R. gallica, without introgression from other species, whereas all the other European horticultural groups are the result of interspecific hybridization involving R. gallica. In this part, we discuss the implications of our findings and methods for understanding the evolutionary history of R. gallica, the genesis of the rose cultivation and more largely on the study of the evolutionary history of orphan species.

A complete overview as a starting point towards a better understanding of R. gallica evolutionary history

In chapter 3, we aimed to unravel the origins of the French populations. In detail, we examined through population genetic analyses whether natural or human mediated dispersal could have played a role in the colonization of France by R. gallica. We found a highly structured diversity within the country that contrasted with the global European structure of diversity. By estimating the level of diversity within the structure units, we hypothesized that Western France populations might be somewhat more recent than Using 29 SSRseq loci, we were able to unravel the genetic diversity and structure of R.

gallica over its range. Since such a low number of loci, compared to thousands of SNPs from whole genome sequencing data, is not suitable for detecting loci under selection, we did not perform such analyses. However, the comprehensive view of the diversity we provided can be viewed as a first step towards the detection of loci responsible for adaptation in R. gallica. Indeed, the knowledge acquired in this thesis could be used to design a reduced subsample representative of the diversity, thus reducing the cost needed to genotype thousands of SNPs through any reduced representation sequencing method or whole genome sequencing. With such data, we would be able to identify SNPs showing significant associations with environmental variations and to detect selection signatures.

These analyses would not only unravel the genetic basis of local adaptation within the General Discussion and Perspectives wild compartments, but would also identify loci that were selected by human breeding.

Generally, post-glacial histories are reconstructed using multiple lines of evidence, combining paleobotany and genetics [START_REF] Magri | A new scenario for the Quaternary history of European beech populations: palaeobotanical evidence and genetic consequences[END_REF]. Therefore, the hypotheses we presented here, based on genetic data about the post-glacial recolonization process of R.

gallica, could be supported by fossil pollen analyses. As fossil pollens carry information on the presence of a plant species at a particular date, they could not only help in reconstructing the timeline of recolonization but also be used to test our hypotheses of a recent human-mediated introduction in Western France. For Europe, pollen data are accessible through the Neotoma Paleoecology Database (http://www.neotomadb.org). However, no pollen data are identified as R. gallica as all records are classified as Rosa without any identification of species. To be usable, further identifications are therefore needed.

Within the genus Rosa, species identification may be possible based on pollen morphology, since palynologists have unraveled differences between rose species (Wronska-Pilarek and Jagodzinski 2009; Wrońska-Pilarek and Jagodziński 2011). Moreover, the pollen of R. gallica seems distinguishable from others Rosa (Wronska-Pilarek and Jagodzinski 2009). Nevertheless, these studies were based on fresh pollen. Since fossil pollens can be more or less degraded [START_REF] Elsik | Biologic Degradation of Fossil Pollen Grains and Spores[END_REF], the distinction between closely related species would be more difficult. This problem may be alleviated by high-resolution microscopy and the advent of deep learning [START_REF] Romero | Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy[END_REF].

Implication for conservation

R. gallica is protected by national laws in France and in Poland. Our analysis provided key knowledge that may assist conservation. In practice, our results were used to guide a project aiming at defining conservation actions. This project was led by the National Botanical Conservatory of the Paris Basin (CBNBP) and focused on the R. gallica population of the Centre Val de Loire region (France). In this region, R. gallica is scarcely found, and some sites are restricted to a few square meters, motivating their project. We participated in this project by genotyping several individuals sampled in this region. We found that several sites only included individuals belonging to the same clonal lineages as cultivated individuals. Additionally, we found several sites with low clonal richness, as they were made up of a single clonal lineage. Finally, the individuals occurring in sites located in the Cher department belonged to a cluster well separated from the others (pink, chapter 3). This cluster harbors low allelic richness. Knowing this, we can make some recommendations. First, sites for which all individuals are in the General Discussion and Perspectives same clonal lineage as cultivated specimens should not be prioritized in the conservation program. Second, since the Cher sites are large but have relatively low allelic richness, we advise to focus on the environment preservation (i.e. in-situ conservation). Finally, for sites with low clonal richness, if the size is reduced, ex-situ conservation could be implemented. For ex-situ conservation, as low clonal richness per site was detected, it would be preferable to collect fewer individuals per site, maximizing the number of sites.

The comprehensive dataset we produced is highly valuable for those interested in the conservation of R. gallica. A low genetic diversity hinders the capacity of population or a species as a whole to adapt to environmental change [START_REF] Frankham | Genetics and extinction[END_REF]. Therefore, our results can aid to estimate the relative adaptive capacity of R. gallica populations.

For example, some of the genetic clusters we detected that included individuals from Western France, have a lower allelic richness than other clusters found in the rest of the range, making them more sensitive to climate change. In addition, the genetic data we obtained could directly be used to guide the implementation of ex-situ conservation through the constitution of a core-collection maximizing the diversity. Furthermore, the data we produced can also be used in various other ways to assist conservation. the present genomic composition and the predicted required genomic composition to face future climate conditions, this is called genomic offset. This would require the acquisition of genomic data, but would provide an estimate of the adaptation potential of R. gallica in a particular region [START_REF] Rellstab | Prospects and limitations of genomic offset in conservation management[END_REF].

Towards the reconstruction of the genesis of European roses

In addition to the comprehensive overview of the diversity of the wild compartment of R.

gallica, our study represents the most comprehensive work focusing on the relationship between the wild compartment of a rose species and the cultivated pool of roses. Our General Discussion and Perspectives aim was to unravel the relationship between wild R. gallica and the first horticultural groups. We confirmed that most of the horticultural groups do not correspond to genetic groups. Only the HGal and Bourbon appeared to be mostly separated from other OGR horticultural groups in our samples. Furthermore, we provided the first robust map of clonal relationships among OGR. Finally, despite the high variability within each horticultural group, we determined that the contribution of wild R. gallica was higher for HGal than for any other horticultural groups.

Our study represents a first step towards understanding the origins of the oldest cultivated roses in Europe. Indeed, the confirmation that horticultural groups are not genetically consistent suggests that investigation into the origins of rose selection cannot be studied by taking a horticultural group as a unit of study. Rather, our results highlight that such investigations should focus on key cultivars. This raises two questions:

(1) What is a key cultivar in the breeding history of a plant species? and (2) How to identify key cultivars? For the first question, we consider that a key cultivar would be one of the first bred cultivars in history or one that was extensively exchanged and used for both culture and breeding. For the second question, one has to determine the breeding date using historical data, as done in [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF], and to determine whether a cultivar was important in the breeding history. This might be evaluated using multiple lines of evidence; historical reports can aid in identifying the celebrity of a particular cultivar during a given period, and genetic data can be used to assess the importance of a cultivar in the cultivation history, by performing clonal lineages detection or parentage analysis. Genetic data are also of great importance to evaluate the correct identification of a cultivar, as long-term conservation of old cultivars in rose gardens can be errorprone. With this historical\genetic approach, we were able to identify key cultivars of three horticultural groups, namely the Damask, Alba, and Centifolia. Focusing on the HGal, we did not identify a large clonal lineage that included the oldest HGal. However, in chapter 3, we found a clonal lineage across multiple French sites that included the R. gallica 'Versicolor' (botanical), Rosier des Parfumeurs (HGal), and Belle Sultane (HGal). At least one of these should have been very famous, making this clonal lineage a key target for investigating R. gallica breeding history. It was more difficult to detect key cultivars within the Moss, Portland, and Bourbon based on our data.

The population assignment approach revealed the hybrid origins of these cultivars but was unable to accurately unravel the identity of the parent species. Such an investigation constitutes an other step of our approach, which is not presented in this thesis. We plan to answer this question using phylogenomics. By implementing amplicon sequencing of General Discussion and Perspectives the key cultivars, we revealed allele variation in 92 nuclear loci as well as 4 haploid loci of the chloroplast genome. We used highly-selected single-copy loci adapted to address infra-generic relationships in Rosa [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF]. This dataset will permit to identify the parental species of the key cultivars quantitatively, as we will determine the fraction inherited from each parent while accounting for incomplete lineage sorting through phylogenetic network reconstruction. A comparable methodology was used in [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF]), yet its scope was limited by the low number of individuals and reticulations allowed, a restriction imposed due to prohibitive computational demands when handling large datasets [START_REF] Yu | A maximum pseudo-likelihood approach for phylogenetic networks[END_REF]. We would be able to circumvent these limitations by taking advantage of less time-consuming methods developed recently [START_REF] Yan | Maximum Parsimony Inference of Phylogenetic Networks in the Presence of Polyploid Complexes[END_REF][START_REF] Zhu | A divide-and-conquer method for scalable phylogenetic network inference from multilocus data[END_REF].

This approach will represent the most comprehensive analysis ever done to investigate the origins of rose cultivation. On one hand, the step we conducted reduced the probability of sampling a non-representative cultivar of a certain group, protecting against incongruities between studies. For instance, [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF] identified slightly different parental lineages than [START_REF] Iwata | Triparental origin of Damask roses[END_REF] and Cui et al. (2022a). Our results show that the R. damascena included in [START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF] does not belong to the large Damask clonal lineages. In contrast, it seems probable that the four cultivars used by [START_REF] Iwata | Triparental origin of Damask roses[END_REF] correspond to this large clonal lineage, which explained the incongruities. On the other hand, the coming step (reconstruction of phylogenetic networks) will perform better than previous studies such as those by [START_REF] Iwata | Triparental origin of Damask roses[END_REF] and Cui et al. (2022a), which used only a small number of loci, making their inference sensitive to incomplete lineage sorting and non-quantitative.

Comprehensive sampling and cost-effective genotyping as a framework to study evolutionary history of orphans species

Orphan species are underutilized or neglected species [START_REF] Gahukar | Potential of Minor Food Crops and Wild Plants for Nutritional Security in the Developing World[END_REF] and are characterized by limited genomic data and financial resources. Studying the evolutionary history of a species requires both appropriate sampling and the acquisition of molecular data [START_REF] Ali | A Conceptual Framework for Designing Phylogeography and Landscape Genetic Studies[END_REF]. When examining the diversity of a species comprehensively, extensive sampling across its range is essential to avoid missing specific genetic units. For orphan species, the sampling task could be the primary constraint, as accurate occurrence data needed for sampling might be limited. Moreover, when the range spans multiple countries, oc-currence data availabiltiy and accuracy can vary among them. Thus, the sampling effort should involve collaboration with local researchers or citizens to ensure the successful collection of samples. The present work exemplifies such collaborative needs. The samples we obtained were collected by more than forty individuals across fifteen countries.

Collaboration not only reduced sampling costs but also facilitated access to samples in countries where occurrence data were unavailable. Notably, such collaboration strengthens ties between international researchers, potentially paving the way for larger, more funded future projects that would enhance the understanding of the orphan species in question.

For such studies, it would then result in a high number of samples to genotype. With often limited financial resources, genotyping such an extensive panel poses a second constraint. Thus, a cost-effective genotyping method must be employed to acquire the necessary molecular data. Here, we demonstrate how SSRseq can be implemented and used to investigate range-wide evolutionary history. This marker system possesses key attributes making it highly suitable for orphan species. The most evident is the cost, with a per-sample expense of around nine euros (personal estimation), enabling the genotyping of thousands of individuals, presenting a favorable comparison to the costs of traditional capillary electrophoresis SSR [START_REF] Darby | Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing[END_REF]. The second attribute is the feasibility of developing such a marker system even with scarce genomic resources.

In this instance, we employed a newly established workflow to create sequence-based microsatellites [START_REF] Lepais | Fast sequence-based microsatellite genotyping development workflow[END_REF], enhancing marker transferability among species using resequencing data of several individuals from different Rosa species. This workflow is adaptable for use in any other genus or species complex. The third feature is the short length of the targeted amplicon (averaging 150 bp), allowing the genotyping of lowquality DNA. This aspect renders our SSRseq system appropriate for orphan species, as some are resistant to DNA extraction using standard protocols [START_REF] Healey | Protocol: a simple method for extracting next-generation sequencing quality genomic DNA from recalcitrant plant species[END_REF].

The fourth characteristic lies in the ability to call alleles using a bioinformatics pipeline, significantly reducing the time compared to capillary electrophoresis. Lastly, access to the sequence addresses the issue of length homoplasy. This not only enhances the analytical resolution but also facilitates handling polyploids by allowing the estimation of ploidy levels and allele dosage.

By unraveling genetic data from comprehensive samples, such low-cost genotyping enables the creation of appropriate sub-samples tailored to address specific subsequent questions. Consequently, it enhances the resolution power for such inquiries by reducing sampling bias. In essence, for orphan species, collaboration first facilitates extensive sampling, then SSRseq permits broad genotyping, and thirdly, the knowledge acquired from these data can be employed, if necessary, to design highly adapted samples to unravel specific questions. Title : Rosa gallica L. and other Gallic roses, origin(s) and role in the genesis of cultivated roses Keywords : Population genetics, Genetic diversity, Polyploids, Evolutionary history Abstract: Rosa gallica L., a perennial, tetraploid, heterozygous species that propagates naturally by seed and sucker, occurs in the wild, mainly in Europe, and as cultivars. In France, its wild genetic diversity is geographically structured. In addition, this species is thought to be at the origin of the first horticultural groups (Hybrid Gallica, Centifolia, Alba, Damas...) of roses from which modern roses are derived by hybridization. The aims of this thesis are (1) to identify the factors responsible for the structure of French populations by characterizing European wild diversity, and (2) to assess the contribution of R. gallica to the first horticultural groups. Nearly 1,600 individuals, both wild, sampled over almost the entire European range, and cultivated were genotyped by sequencing newly developed microsatellites. To analyze these data in polyploids, a method for predicting ploidy levels was developed. French diversity is particularly structured compared to the rest of Europe, suggesting multiple origins in France. As in the rest of Europe, populations on the eastern side of France seem to have originated by natural dispersal from a glacial refugium located further south. In contrast, populations on the western side of the country appear to be the result of more recent human or natural dispersal. Unlike the Hybrid Gallica, the other ancient horticultural groups are the result of interspecific hybridizations. These findings represent a step towards understanding the evolutionary history of R. gallica.

  Century. This PhD project, named GALLO, started in October 2020 and finished in October 2023. The project was led in the mixed unity of research (UMR 1345) "Institute of Research in Horticulture and Seeds" (IRHS), located in Beaucouzé near the city of Angers, France. This research unit regroups the University of Angers, The Institut-Agro Rennes-Angers and the French national institute for research on agriculture, food, and the environment (INRAE). The research led within the unit are articulated around four axes: (1) Mechanisms of response and adaptation of plants to biotic and abiotic environments. (2) Mechanisms and strategies of biocontrol and other alternative methods to chemical inputs. (3) Evolutionary histories of plants and associated microorganisms. (4) Methodological research and development of innovative tools. This thesis research questions belong to the third and fourth axes. Within the unit, I was part of the "Genetics and Diversity of Ornamental Plants" (GDO) research team. The project was supervised by Alix Pernet (INRAE), Jérémy Clotault (University of Angers), Valéry Malécot (Institut-Agro) and Agnès Grapin (Institut-Agro). Personal salary was financed by the university of Angers through a Doctoral Contract. Operating costs were mostly funded in the frame of the "Rosa gallica urbanisme" research program. Moreover, I obtained, at the beginning of the second year of my PhD, an additional grant of the Institut-Agro.

1. 1

 1 The well preserved rose hip found in a German site dating back to 7000 years ago. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 The blue bird fresco in Knossos (ca. 1450 B.C.E.) . . . . . . . . . . . . . 1.3 Roses representation on the (A) fresco of Livia's villa and on the (B) fresco from the House of Golden Bracelet in Pompeii. . . . . . . . . . . . . 1.4 Remnants of a wreath made of R. x richardii dating back to 100 B.C.E. found in the Egyptian cemetery of Hawara . . . . . . . . . . . . . . . . . . 1.5 Cultivated roses representation made in Netherlands at the beginning of the 16th century. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Examination of the genus Rosa classification in the light of molecular data. 1.7 Rosa gallica colony shape, architecture and leaves . . . . . . . . . . . . . . 1.8 Picture illustrating R. gallica stem diversity . . . . . . . . . . . . . . . . . 1.9 Wild Rosa gallica flower shape and diversity . . . . . . . . . . . . . . . . . 1.10 Example of interspecific hybridization mechanisms and Caninae meiosis . 1.11 Chromosome segregation in disomic and tetrasomic inheritance . . . . . . 1.12 Sampling sites of former studies dealing with the diversity of R. gallica. . 1.13 Cultivated rose classification recognized by the World Federation of Rose Societies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.14 Flower of some cultivars belonging to the HGal, Damask, Alba and Centifolia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.15 The mossy trait . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Boxplot of the allelic error rate and allelic error number . . . . . . . . . . 2.2 Distribution of pairwise Euclidean distances between wild R. gallica individuals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Illustration of the k-means algorithm . . . . . . . . . . . . . . . . . . . . . LIST OF FIGURES 2.4 Schematic representation of the random forest algorithm training. . . . . . 56 2.5 Histogram of the mean number of alleles per individual over 41 loci . . . . 61 2.6 Percentage of increases in the number of alleles between the diploids and the tetraploids per locus . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 2.7 Differentiating tetraploids from diploids using k-means clustering. . . . . . 64 2.8 Confusion matrices of the random forest algorithm ploidy level predictions. 65 2.9 k-means clustering done on the reference dataset of 202 individuals. . . . 66 2.10 ROC curve comparing four RF models fitted to predict the five ploidy levels present on the dataset . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.1 Sampling sites location and clonal relationships between individuals from different sites and/or between wild and botanical/cultivated individuals. . 77 3.2 Spatial networks of collection sites of the individuals present in the R. gallica dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 3.3 Comparisons between flowers of 12 individuals belonging to clonal lineage B and flowers found in sites for which no individual presented any clonal relationship with cultivars or individuals from other sites. . . . . . . . . . 90 3.4 Multivariate analyses describing the Rosa sp. dataset. . . . . . . . . . . . 93 3.5 Multivariate analyses describing the R. gallica dataset. . . . . . . . . . . . 95 3.6 Population genetic structure of R. gallica. . . . . . . . . . . . . . . . . . . 96 3.7 Diversity indices and differentiation among STRUCTURE clusters at K = 8. 99 3.8 Pairwise ρ differentiation graph and private allelic richness by geographical region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.1 Breeding period of cultivars included in the study by groups of interest . . 112 4.2 Distribution of individuals across the detected clonal lineages. . . . . . . . 117 4.3 Composition in terms of horticultural groups of the largest clonal lineages found in the dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 4.4 PCA of intra-allele frequencies of the clone corrected dataset . . . . . . . 121 4.5 Genetic structure of the clone corrected dataset (N = 461) and of the cultivated clone corrected dataset (N = 216). . . . . . . . . . . . . . . . . 123 4.6 Pairwise ρ differentiation between horticultural groups of interest. . . . . 124 4.7 Genetic contribution of R. gallica to the cultivated group of interest. . . . 126 List of Tables 2.1 Characteristics of the 40 markers. . . . . . . . . . . . . . . . . . . . . . . . 3.1 Loci quality and polymorphism, assessed on the Rosa sp. dataset. . . . . 4.1 Clonal richness, heterogeneity and evenness within the horticultural groups of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 AMOVA analysis investigating the molecular variation when considering the horticultural groups as populations. . . . . . . . . . . . . . . . . . . .

S. 1

 1 Histogram of the mean number of alleles per individual over 39 loci in the reference dataset (N = 202). . . . . . . . . . . . . . . . . . . . . . . . . . . i S.2 Confusion matrices of the 3-class random forest model predictions against the suspected ploidy level. . . . . . . . . . . . . . . . . . . . . . . . . . . . ii S.3 Physical position on the 'Old Blush' reference genome of the 29 loci retain in the optimized dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii S.4 Determination of the number of k-means clusters in clone-corrected Rosa sp. dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv S.5 Distribution of the individual probability to be a non hybrid R. gallica. . v S.6 Distribution of the STRUCTURE ancestry proportions at K = 8 for the R. gallica dataset, chapter 3. . . . . . . . . . . . . . . . . . . . . . . . . . vi S.7 Exploration of null alleles. . . . . . . . . . . . . . . . . . . . . . . . . . . . vii S.8 Scatterplot of the two first DAPC discriminant functions . . . . . . . . . . viii S.9 DAPC clusters content in term of botanical sections and cultivated groups. ix S.10 DAPC compoplot of the 27 mis-identified R. gallica. . . . . . . . . . . . . x S.11 Multivariate analyses describing the R. gallica dataset with a focus on French wild collected individuals. . . . . . . . . . . . . . . . . . . . . . . . xi S.12 Estimation of the most probable number of K STRUCTURE clusters. . . xii S.13 STRUCTURE bar plots of the membership probabilities at K = 2 and K = 7 for the R. gallica dataset, chapter 3. . . . . . . . . . . . . . . . . . . . xiii S.14 Estimation of the most probable number of K STRUCTURE clusters for the chapter 4 clone-corrected dataset. . . . . . . . . . . . . . . . . . . . . xiv S.15 Estimation of the most probable number of K STRUCTURE clusters for the group of interest dataset, chapter 4. . . . . . . . . . . . . . . . . . . . xv S.16 STRUCTURE bar plots at K = 6 calculated by setting the individuals of groups of interest as supplementary individuals. . . . . . . . . . . . . . . xvi

  representations were done in mosaics of the Vatican Museum (100-200 C.E.). Pliny the Elder (23-79 C.E.) described the roses in its Naturalis Historia. He detailed the architecture of a rose and the flower development (Pline l'Ancien and Schmitt 2013).

Figure 1 . 3 :

 13 Figure 1.3: Roses representation on the (A) fresco of Livia's villa and on the (B) fresco from the House of Golden Bracelet in Pompeii. Note the lightly double nature of the flower on both frescoes. Pictures were extracted from Bowe (2004).

Figure 1 . 4 :

 14 Figure 1.4: Remnants of a wreath made of R. x richardii dating back to 100 B.C.E. found in the Egyptian cemetery of Hawara. ©The Trustees of the British Museum.

Figure 1 . 5 :

 15 Figure 1.5: Cultivated roses representation made in the Netherlands at the beginning of the 16th century. (A) Part of The Hunt of The Frail Stag. Southern Netherlands, around 1500-1525 C.E.. Gift of Mary Still man Harkness, 1950 (50.145.5). The present image was extracted from Larkin (2006). (B) Part of a fragment of the tapestry called The Mystic Capture of the Unicorn. Gift of John D.Rockefeller Jr., 1938 (38.51.1, 2).The tapestry image was extracted from Colburn (2010).

(

  2015). The most comprehensive analysis of the genus Rosa classification based on molecular data was conducted by Debray et al. (2022) using phylogenomics. They developed a framework that took into account the pattern of reticulation to unravel the phylogenetic relationships within the genus Rosa. Most of the Rosa species were included (126 species out of the 150-200), representing all sub-genera and sections. Based on the phylogenetic trees and networks produced using chloroplastic and nuclear sequences, they proposed some modifications to the classification (Fig 1.6). In detail, they suggested considering all sub-genera as sections, incorporating section Carolinae into the section Rosa, and embedding section Indicae into the section Synstylae[START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF]. This Introduction was also recommended by previous studies such as[START_REF] Fougère-Danezan | Phylogeny and biogeography of wild roses with specific attention to polyploids[END_REF] and[START_REF] Zhu | Phylogeny of Rosa sections Chinenses and Synstylae (Rosaceae) based on chloroplast and nuclear markers[END_REF]. Since European Synstylae species (R. arvensis, R. sempervirens, and R. leschenaultiana) were distinct from Asian and American Synstylae species, Debray

Figure 1 . 6 :

 16 Figure 1.6: Examination of the genus Rosa classification in the light of molecular data by Debray et al. (2022).

Figure 1 . 7 :

 17 Figure 1.7: Rosa gallica colony shape, shoot architecture and leaves. (A) Picture of a wild site showing the colony formed by the suckers, Credits: A. Mrkvicka. (B) General architecture of R. gallica, Credits: C. Pawula. (C) Adaxial side of a leaf, Credits: N. Tomljenović. (D) Abaxial side of a leaf , Credits: N. Tomljenović.

Figure 1 . 8 :Figure 1

 181 Figure 1.8: Picture illustrating R. gallica prickles diversity. See the diversity in term of prickles and glandular bristle density, prickles color and shape. Credits (from left to right): C. Pawula, C. Pawula, A. Mrkvicka, J. Mondion and J. Mondion-J. Cordier.

Figure 1 .

 1 Figure 1.10: Example of interspecific hybridization mechanisms and Caninae meiosis. (A) Hybridization between genetically compatible species. (B) Hybridization between genetically incompatible diploid species, unreduced egg and sperm cells produce a fertile hybrid. (C) Hybridization between genetically incompatible diploid species producing a fertile hybrid by chromosome doubling. (D) Pentaploid Caninae sexual reproduction mediated by asymmetric meiosis. The scheme is adapted from Simpson(2010) and[START_REF] Ritz | Microsatellite Analyses of Artificial and Spontaneous Dogrose Hybrids Reveal the Hybridogenic Origin of Rosa micrantha by the Contribution of Unreduced Gametes[END_REF].

  Figure 1.11: Chromosome segregation in disomic and tetrasomic inheritance is illustrated for an individual locus with four alleles. Under tetrasomic inheritance, tetravalent formation allows alleles to segregate randomly during meiosis, leading to six possible gamete genotypes (g). On the contrary, in disomic inheritance, bivalent formation prevents the segregation of two chromosomes from the same subgenome to the same gamete, reducing the number of different gamete genotypes to four (g). Consequently, the number of possible offspring genotypes is lower under disomic inheritance than under tetrasomic inheritance. Figure adapted from[START_REF] Zielinski | Meiosis in polyploid plants[END_REF].

Figure 1 . 12 :

 112 Figure1.12: Sampling sites of former studies dealing with the diversity of R. gallica. The sampling sites of five former studies are represented, these are :[START_REF] Bouillon | Geographical structuration of genetic diversity of wild populations of Rosa gallica and their relationship with the cultivated compartment[END_REF][START_REF] De Cock | Genetic diversity of wild roses (Rosa spp.) in Europe, with an in-depth morphological study of Flemish populations[END_REF][START_REF] Fedorova | Local differentiation and hybridization in wild rose populations in Western Ukraine[END_REF][START_REF] Fedorova | Systematics and geography of species of the genus Rosa L. sect. Gallicanae DC[END_REF] and[START_REF] Wrońska-Pilarek | Pollen morphology of Rosa gallica L. (Rosaceae) from Southern Poland[END_REF].

Figure 1 .

 1 Figure 1.14: Flower of some cultivars belonging to the HGal, Damask, Alba and Centifolia. The name of the cultivar is indicated below each picture. Credits : Genetic and Diversity of Ornamentals (GDO) research team, Institute of Research in Horticulture and Seeds, France.

  phenogroups but only one SSR multilocus genotype, were found, showing that all of these Albas belong to the same clonal lineage. The clonal lineage included the two famous Alba cultivars, R. alba 'Maxima' and R. alba 'Semiplena'. Unfortunately, no clone detection was performed by[START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]. However, on their DAPC, half of the Alba, including R. alba 'Maxima' and R. alba 'Semiplena' clustered together with a one hundred membership probability. This might be explained by a clonal relationship. The other half of the Alba mainly clustered with HGal, Damask, Moss, or Centifolia cultivars, but these Alba were often mainly cultivars that are sometimes included in the Alba or in other groups OGR according to the authors. Some are also described as hybrids between Alba and other OGR (https://www.helpmefind.com/gardening/roses.php).

Figure 1 . 15 :

 115 Figure 1.15: The mossy trait. Credits : Genetic and Diversity of Ornamentals research team, Institute of Research in Horticulture and Seeds, France.

Figure 2 . 1 :

 21 Figure 2.1: Boxplot of the allelic error rate (A) and allelic error number (B) calculated separately on 40 diploids and 38 tetraploids. Asterisk represents Wilcoxon's paired test significant difference (pvalue < 0.05).

Figure 2 . 2 :

 22 Figure 2.2: Distribution of pairwise Euclidean distances between wild R. gallica individuals. Values were calculated based on four SSRseq datasets, obtained with different allele calling methods, and one SSRce dataset

Figure 2 . 4 :

 24 Figure 2.4: Schematic representation of the random forest algorithm training. Point colors represent the individual decision tree prediction process.

  1.2 section, we tested several FDSTools parameter sets during the marker development step. After this development step, we tested additional parameter sets with a reduced minimum read depth (10 reads instead of 20) required to call the given loci. To explore the new parameter set, we used individuals from the pilot run. However, additional data checking in the pilot run allowed us to increase the number of blindly repeated diploids to 42 and tetraploids to 45, the genotyping of the last eight of out the 95 failed. The testing of additional parameters led to several modifications of the parameter sets used to call the 1710 samples, final calling parameters are available in Table

  transitioned from unsupervised to supervised learning by training a random forest model to predict the ploidy level. There were few reference samples for triploids, pentaploids, and hexaploids. Thus, splitting the dataset into a training and testing set would have drastically reduced the number of individuals in these ploidy classes, leaving only a few individuals to train the model. To avoid this, all the 202 reference individuals were included in the training dataset. We evaluated the model accuracy using out-ofbag estimates. Due to the low number of individuals for triploids, pentaploids, and hexaploids, we additionally trained a model with the aim of only distinguishing diploids from tetraploids and from hexaploids. To achieve that, we combined the 13 triploids and the 108 tetraploids into one class (called the 'tetraploid class'). Similarly, we combined the pentaploids and the hexaploids into one class (called the 'hexaploid class'). As previously mentioned in the introduction, the process of model training requires bagging. Random sampling can be performed with or without controlling the number of individuals per ploidy level in each bootstrap sample. We trained the models with and without balanced sampling. We tested four different random forest model parameterizations, (1) a model trained to predict the five ploidy levels, (2) a model trained to predict the five ploidy levels but without a large difference in the number of individuals per ploidy level in each bag, (3) a model trained to predict only three ploidy classes, and (4) a model trained to predict only three ploidy classes but without a large difference in the number of individuals per ploidy level in each bag. Confusion matrices were constructed to compare all the models with the R package caret (Kuhn and Max 2008). Additionally, we calculated the True Positive Rate (TPR) and the False Positive Rate (FPR) for each 60 SSR sequencing, a new flexible tool for Roses model to draw Receiver Operating Characteristic (ROC) curves. the TPR and the

Figure 2 . 5 :

 25 Figure 2.5: Histogram of the average number of alleles per individual over 41 loci. Colors indicate the ploidy level. One unique individual is represented by two data points corresponding to the two blind repeated genotyping.

Figure 2 . 6 :

 26 Figure 2.6: Percentage of increases in the number of alleles between the diploids and the tetraploids per locus. Error bars correspond to standard deviation calculated on 1000 bootstraps. Stars indicate significant increases for tetraploids compared to diploids. Significance was determined using an approximate two-sample Fisher-Pitman permutation test and a p-value correction for multiple tests (α = 0.05). One unique individual is represented by two data points corresponding to the two blind repeated genotyping.

Figure 2 . 7 :Figure 2 . 8 :Figure 2 Figure 2 .

 272822 Figure 2.7: Differentiating tetraploids from diploids using k-means clustering. (A) Scatter plot showing the two first component of a PCA run on the scaled number of alleles per loci (41 loci). Colors indicate the cluster membership, shapes indicate the true ploidy level. (B) Confusion matrix of the k-means group assignment. Values of the accuracy and the kappa score are shown.

  . The cultivated roses have been obtained from 8 to 15 species of the 150 to 200 recognized species in the genus Rosa[START_REF] Smulders | Rosa[END_REF] Wissemann 2003; Wissemann 2017;[START_REF] Wylie | The history of garden roses[END_REF]. Roses are classified using two different classifications, the botanical one(Wissemann 2017) and the horticultural one. The botanical classification distributes wild rose species into four subgenera and the largest subgenus (the subgenus Rosa) into 11 sections.[START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF] have deeply examined this classification in the light of molecular data and proposed to merge certain sections. The horticultural classification of the roses was proposed by the American Rose Society and then adopted by the World Federation of Rose Societies. It divides the roses into three classes, wild and botanical roses (i.e. supposed wild specimens grown in botanical gardens), Old Garden Roses (OGR) bred before 1867, and modern roses bred after this year. OGR and modern roses are subdivided into horticultural groups that include cultivars with similar traits. Rosa gallica L., also known as the 'French Rose', is one of the main ancestor of the European OGR and the oil-bearing roses. The OGR cultivars closely related to R. gallica belonged to the horticultural groups Hybrid Gallica, Damask, Centifolia, Alba, Portland, Moss and Bourbon[START_REF] Roberts | Encyclopedia of Rose Science[END_REF]. French rose breeders played a major role in the breeding of R. gallica cultivars by producing more than two thousand varieties in the first part of the 19th century[START_REF] Joyaux | La rose de France: Rosa Gallica et sa descendance[END_REF]. In France, the wild populations of this tetraploid species are protected (https://www.legifrance.gouv.fr/loda/id/ LEGIARTI000006863076/2023-08-02). According to[START_REF] Fougère-Danezan | Phylogeny and biogeography of wild roses with specific attention to polyploids[END_REF], R. gallica have originated few millions year ago at the end of the pliocene or at the beginning of the pleistocene. Currently, the species is found predominantly in central

Figure 3 . 1 :

 31 Figure 3.1: Sampling sites location and clonal relationships between individuals from different sites and/or between wild and botanical or cultivated individuals. The colors indicate the detected clonal lineage. Only wild sites are presented on the map. A site is colored as belonging to clonal lineages A to K, if at least one individual from that site belongs to one of these clonal lineages. Botanical or cultivated varieties belonging to the different clonal lineages are: (A) 'Agathe Rose' -'Rose de Rescht', (B) R. gallica 'Versicolor' -'Bouquet de Vénus' -'Rosier des parfumeurs', (C) 'Frankfurt' -R. gallica 'Splendens', (D) 'La belle Sultane' -R. centifolia 'Simplex' -'Rosa violacea', (E) 'Parure des vierges', (F) 'Roi des Pays-Bas' -R. gallica 'Agatha' -'Rosier d'amour', (G) R. centifolia 'Parvifolia' -R. centifolia 'pomponia', (H) Unnamed cultivars, (I) a Botanical R. gallica. The clonal lineages J and K contain individuals from three Spanish or three Slovenian sites, respectively. The label (No inter clone) indicates that, for the site, no clonal relationship was detected with botanical or cultivated individuals or with individuals for other wild sites.

Figure 3 . 2 :

 32 Figure 3.2: Spatial networks of collection sites for the individuals present in the R. gallica dataset. Nodes of the network done with French sites are represented as squares. Nodes of the network done with non French sites are represented by circles. Colors indicate regional groups inferred by community detection. Red edges represent a pairwise geographic distance between two sites that is below the threshold of 165 kilometers.

the

  Rosa sp. dataset was composed of 29 loci and 1618 individuals. Each of the seven chromosomes were covered by at least three loci (Fig. S.3). Wild collected R. gallica in this dataset represented 219 out of the 223 collection sites, meaning that we failed to genotype individuals from four sites. The four missing sites were represented each by only one herbarium specimen. Our ploidy prediction model achieved an accuracy of 89.6 % according to the out-of-bag estimates. Of the 1618 individuals, 140 were considered diploid, 1381 were classified as tetraploid, and 97 as hexaploid (supplementary data S.

Figure 3 . 3 :

 33 Figure 3.3: Comparisons between flowers of 12 individuals belonging to clonal lineage B (left) and flowers found in sites for which no individual presented any clonal relationship with cultivars or individuals from other sites (right). The name of the cultivars, of the site (S) (left) or of the country of the site (right) are indicated above each picture. Credits: Genetic and Diversity of Ornamentals (GDO) research team, Institute of Research in Horticulture and Seeds, France, Clovis Pawula, Alexander Mrkvicka, Jordane Cordier, Renata Piwowarczyk, Karolina Ruraz and Fabrizio Bonali

Figure 3

 3 Figure 3.4: (Figure on previous page.) Multivariate analyses describing the Rosa sp. dataset. (A) Principal coordinates analysis based on pairwise k-mer dissimilarities. (B) Principal component analysis of intra-individual allele frequencies. A1 and B1 correspond show individual values for the first and the second principal components while A2 and B2 the first and the third principal components whatever the multivariate analysis used. Cultivated individuals are represented as triangles, whereas wild R. gallica are shown as diamonds, and other Rosa sp. individuals are represented by circles. Colors correspond to the horticultural or botanical groups. Botanical sections are regrouped according to the results of Debray et al. (2022).

A

  DAPC was used to statistically determine which individuals were mis-identified R. gallica or putative inter-specific hybrids. The probability of being a R. gallica, determined based on the plots of the first two discriminant functions at K = 12 (Fig. S.8) and on the membership values of the DAPC group (Fig. S.9), revealed 27 individuals sampled as R. gallica specimens that are not belonging to this species or are hybrids (Fig. S.10). Among these, 19 were grouped with Caninae, two with European Synstylae, and one with Synstylae-Indicae. Three others were distributed among other non-R. gallica groups that regrouped individuals from different botanical sections or horticultural groups. The remaining two were part of R. gallica groups but with membership probabilities below the threshold suggesting a hybrid origin (Fig. S.10). In total, 519 individuals were identified as belonging to the R. gallica species. These included 417 wild-collected R. gallica individuals and 112 botanical or cultivated individuals. These 519 individuals constituted the R. gallica dataset and were used to investigate the European diversity of the species and the origins of French populations.

Figure 3

 3 Figure 3.5: (Figure on previous page.) Multivariate analyses describing the R. gallica dataset. (A) PCoA based on pairwise k-mer dissimilarities. (B) PCA based on intraindividual allele frequencies. A1 and B1 correspond show individual values for the first and the second principal components while A2 and B2 the first and the third principal components whatever the multivariate analysis used. Cultivated or botanical individuals are depicted as triangles, whereas French wild-collected ones are shown as diamonds, and other wild-collected individuals are represented by circles. Colors correspond to the regional groupings inferred from community detection analysis (see Fig. 3.2).

S. 12 )

 12 . The major mode was well supported at K = 2 (Fig. S.13) and K = 8 (see Fig. 3.6). At K = 2, almost all cultivated individuals, half of the botanical ones, and wild individuals from South Western and Central France were grouped together, along with the clonal lineage from Spain (Fig. S.13). Additionally, some individuals from Central East France appeared admixed between the two groups. Rosa gallica individuals from other countries were grouped with individuals from the East of France and the remaining half of botanical ones (Fig. S.13). The finest structure level (i.e., K = 8) revealed a structure driven by regional collection sites (see Fig. 3.6). Indeed, all inferred clusters regrouped individuals from one or several adjacent regions. The widest cluster (orange), located in major mode 9/10 runs

Figure 3 . 6 :Fig. 3 .

 363 Figure 3.6: Population genetic structure of R. gallica. (A) Mapping of mean membership probabilities per site of collection at K = 8 (N = 519). Membership probabilities of the representative individual of the clonal lineages A, B, D, G, H and I, are shown on the left of the map. Mean membership probabilities of cultivars and botanical are also shown on the left of the map. Some variability was added to the site locations to increase map readability. (B) STRUCTURE barplot representing the major mode found using Pong v1.5 (Behr et al. 2016). (C) Unrooted split network showing allele frequency divergence among STRUCTURE clusters.

Figure 3 . 7 :

 37 Figure 3.7: Diversity indices and differentiation among STRUCTURE clusters at K = 8. (A) Sample size independent mean allelic richness per locus. (B) Sample size independent mean private allelic richness per locus. (C) Same as B but determined without the yellow cluster. Error bars reflect standard error of the mean across loci. (D) Pairwise ρ differentiation values. (E) Expected and observed heterozygosity. (F)Inbreeding coefficient (G is ).

Figure 3 . 8 :

 38 Figure 3.8: Pairwise ρ differentiation graph and private allelic richness by geographical region. Edges indicate a ρ lower than 0.05 and nodes represent geographical regions.Detected communities are represented by node colors. Node size is proportional to the private allelic richness, calculated using a G parameter set at 16. Regions with fewer than four clonal lineages were excluded.

  hypothesis. The discovery of clonal lineages containing wild-collected individuals at distant sites and in the Western part of the study range is an evidence of human dispersal as only humans can clonally reproduced R. gallica. Nevertheless, this evidence of ferality is valuable only for sites where we found such clonal lineages. Their dispersal could have taken place either before (as per the Klastersky (1968) hypothesis) or after the establishment of R. gallica in the Western most part of the range.

  evaluated whether the diversity structure of cultivated individuals revealed by molecular markers was in accordance to the horticultural classification.[START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF] found that the genetic structure matched pretty well with the horticultural classification while[START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF] revealed a weak correlation. These incongruities might be due either to the low number of cultivars included in[START_REF] Scariot | Characterization and Genetic Relationships of Wild Species and Old Garden Roses Based on Microsatellite Analysis[END_REF]. Additionally, in both studies no investigation of sports relationship was performed. This might have bias the comparison between the genetic structure and the classification. Genetic constitution of the horticultural groups of European Old Garden Roses and their relationship with wild R. gallica Between the end of the 18th century and the half of the 19th century, cultivars were bred following two strategies. On the first hand, breeders grew cultivars with interesting traits next to each other and let the plants be free pollinated. On the other hand, when a spontaneous mutation produced a desirable trait, the breeders vegetatively propagated the part of the plant with the interesting phenotype and released it as a new cultivar (Oghina-Pavie 2016). The breeders started to perform control crosses only during the second half of the 19th century (Oghina-Pavie 2016). During the 19th century, cultivars from the two genetic pools (European and Asian OGR) were crossed in Europe to introduce the Asian continuous flowering trait into European cultivars (Smulders et al. 2011). Globally, the introduction of alleles from the Asiatic pool into the European one induced a shift in the genetic background of European cultivars during the 19th century (Liorzou et al. 2016). The Portland, Bourbon, and Hybrid Perpetual groups might have originated from crosses between European and Asian cultivars. As these groups should

Figure 4 . 1 :

 41 Figure 4.1: Breeding period of cultivars included in the study by groups of interest.Botanical Gallica are also shown.

  we estimated the clonal heterogeneity(Simpson's complement index, D) and evenness (V , Arnaud-Haond et al. 2007) for each horticultural group following the formulas: D min ) D max -D min Genetic constitution of the horticultural groups of European Old Garden Roses and their relationship with wild R. gallica where p i is the frequency of the clonal lineage i in the horticultural group (this frequency corresponds to the number of individuals in the clonal lineage i divided by the total number of individuals in the horticultural group), and D max and D min are the approximate maximum and minimum values of D given the number of samples in the horticultural group (N ) and the number of clonal lineages detected for that horticultural group (G).

4. 2 . 3

 23 Genetic structure among cultivated groups At this step, one representative individual was selected per clonal lineage, reducing the dataset to 461 genotypes. The representative individual was the most consensual one with the lowest missing data. This dataset is designated as the clone-corrected dataset. The clone-corrected dataset was also sub-sampled to remove non-cultivated individuals while retaining cultivated individuals belonging to the HGal, Alba, Centifolia, Damask, Moss, Portland, and Bourbon. This dataset of 216 individuals is designated as the cultivated clone-corrected dataset. Some clonal lineages contained individuals belonging to different horticultural groups. The apparent horticultural group of each clonal lineage was that of the majority of the individuals in each clonal lineage. When several horticultural groups were equally represented, the horticultural group of the oldest individual was taken as the one of the clonal lineage.

  USEPOPINFO model. Under this model, the allele frequencies of each cluster are updated using only selected individuals. This allows us to assist the ancestry estimation of individuals of unknown origins. Individuals belonging to the Botanical gallica, HGal, Alba, Centifolia, Damask, Moss, Portland and Bourbon were classified as individuals of unknown origin by setting the POPFLAG argument to 0. Based on the STRUCTURE results at K =7 and on the PCA done on the clone-corrected dataset (N = 461), we built six possible populations of origin, setting the POPFLAG argument to 1 for the individuals included in these populations. The first population contained all wild R. gallica present in the clone corrected dataset. The second was made of individuals belonging to other cultivated groups showing more than 80% of membership probability to the genetic groups that contain most of the cultivated individual from other horticultural groups. The same rule was used to build the populations corresponding to Caninae, Rosa-Carolinae-Pimpinellifoliae, non european Synstylae. The European Synstylae clusterised with Gallicanae at K = 7 but were clearly differentiated from them their relationship with wild R. gallica in chapter 3, on the PCA and belonged to another cluster at K = 3. Therefore, we considered them to be the sixth population for inference. Individuals belonging to Caninae, Rosa-Carolinae, Pimpinellifoliae, non european Synstylae-Indicae and European

Figure 4 . 2 :Figure 4 . 3 :

 4243 Figure 4.2: Distribution of individuals across the detected clonal lineages. (A) Size distribution of clonal lineages, indicating the proportion of clonal lineages that include at least one individual from a horticultural group of interest (blue). (B) Number of clonal lineages per horticultural group or per combination of horticultural groups

  , and Portland individuals appeared to regroup altogether in the bottom left part of the scatter plot without clear distinction between them (Fig. 4.4). When examining each horticultural group of interest individually, we observed that they all harbored individuals located at very different positions on the PCA scatterplot (Fig. 4.4). For example, although the HGal were generally positioned on the left side of the plot, some HGal were located on the right, close to individuals from other botanical sections (Fig. 4.4, HGal subplot). In proportion to the number of individuals they included, this phenomenon was particularly pronounced for the Centifolia, Moss, and Alba groups.

  and the method of Wang (2019), respectively (Fig. S.14). At K = 3, a genetic cluster encompassed most of the non cultivated specimens of other species than R. gallica (blue, Fig. 4.5). Wild R. gallica and most of the HGal were grouped together (orange, Fig. 4.5), in accordance with the PCA results (Fig. 4.4). The last cluster (green) regrouped half of the Non European Synstylae-Indicae, cultivars of other horticultural groups than those of interest (mostly Chinese OGR) and the Bourbon. Centifolia, Damask, Moss, Alba and Portland were, for most of them, admixed between the orange and the green cluster. This results were in accordance with the location of Centifolia, Damask, Moss, Alba and Portland on the PCA (Fig. 4.4). At K = 7, most of the genetic clusters present at K = 3 were sub-separated. In general, Genetic constitution of the horticultural groups of European Old Garden Roses and their relationship with wild R. gallica Class of individuals not belonging to groups part of the figure on next page.) Genetic constitution of the horticultural groups of European Old Garden Roses and their relationship with wild R. gallica (See first part of the figure on previous page.)

Figure 4

 4 Figure 4.4: PCA of intra-allele frequencies of the clone corrected dataset. All individuals were used to compute the principal components. Individuals who did not belong to groups of interest are represented in all subplots. The point shapes indicate the class of these individuals. For visualization, a focus is done in one particular group on each subplot (colored circle), individuals of other horticultural group than the focused ones are shown in grey circle. Circle sizes are proportional to the number of individuals in each clonal lineages.

Figure 4

 4 Figure 4.5: Genetic structure of the clone corrected dataset (N = 461) and of the cultivated clone corrected dataset (N = 216). STRUCTURE results at K = 3 and K = 7 are shown for the clone corrected dataset. These were, respectively, determined by Evanno's method (Evanno et al. 2005) and the parsimony method of Wang (2019). Results at K = 4 are shown for the cultivated clone-corrected dataset (K value was chosen based on the parsimony method).

Figure 4 . 6 :4. 3 . 3

 4633 Figure 4.6: Pairwise ρ differentiation between horticultural groups of interest (i.e the cultivated clone-corrected dataset). The significances of the differences were calculated using permutation tests based on 1000 permutations.

EuropeanRFigure 4 . 7 :

 47 Figure 4.7: Genetic contribution of R. gallica to the cultivated group of interest. (A) Boxplot of individual membership probability to the STRUCTURE R. gallica population at the major mode (7/20 independent runs) by horticultural groups. The letters above each boxplot indicate if the horticultural groups differed significantly in their R. gallica ancestry proportion. (B) Membership probability to the six reference populations of the four largest clonal lineage found in the dataset.

  At the range scale, conservation geneticist could use GPS locations of our sampling sites and occurrence data available in open databases (after a careful filtering step to remove the numerous cultivated occurrences present in these databases) such as the Global Biodiversity Information Facility (GBIF), available at https://www.gbif.org/, to predict habitat suitability in the future under climate change models. Such Ecological Niche Modelling[START_REF] Rinnan | Climate-niche factor analysis: a spatial approach to quantifying species vulnerability to climate change[END_REF] would guide conservation initiatives toward regions predicted to become unsuitable for R. gallica. In a finer way, the exhaustive sample we gathered could serve as basic material to evaluate the genetic distance between

  1.10D), during meiosis, 2 of the 5 homologous chromosomes form bivalents, while the other 3 remain unpaired. During microsporogenesis, these univalents are eliminated, but they are retained in megasporogenesis, resulting in the formation of

haploid sperm cells and tetraploid egg cells in pentaploid Caninae

[START_REF] Ritz | Microsatellite Analyses of Artificial and Spontaneous Dogrose Hybrids Reveal the Hybridogenic Origin of Rosa micrantha by the Contribution of Unreduced Gametes[END_REF]

).

As mentioned above, rose species readily hybridize, and R. gallica does not escape to the rule. Based on morphological data and experimental crosses, it has been demonstrated that R. gallica can hybridize and give fertile progeny with species from almost all major sections of the subgenus Rosa

1.2.5 Cultivated pool description and its relationship with Rosa gallica

	Introduction
	arvensis (european Synstylae) and R. rubiginosa (Caninae, Wylie 1954 according to Leus
	et al. 2018). Additional rose species recently used in breeding include, for example, R.
	palustris (Carolinae), R. setigera (non european Synstylae, Young et al. 2019), R. persica
	(Subgenus Hulthemia, Leus et al. 2018), and R. arkansana (section Rosa, Vukosavljev
	et al. 2013). Rose cultivars are vegetatively propagated , making them clonal varieties.
	, a master's thesis. The study encompassed samples from 27 sites in
	Eastern France (from Alsace-Lorrain to Provence-Alpes-Côte d'Azur regions), six sites
	in central France, three sites in Western France (Pays-de-la-Loire region), and 15 sites
	around the city of Toulouse. The samples were genotyped using 32 Simple Sequence
	Repeat (SSR) markers. These genetic data highlighted a clear structure of the diver-
	sity driven by geographical location. Specifically, individuals growing near the city of
	Toulouse (South West France) were differentiated from those located in eastern France.
	Individuals from central France were intermediate between eastern and southwestern
	individuals. Based on these results, the author hypothesized that the current structure
	was influenced by different introductions, whether natural or human-mediated. Finally,
	individuals from a few sites were distinct from all other R. gallica , which, according
	to the author, suggests introgression of cultivated roses at these sites. Another study,
	focused on South East France, used both morphological markers and Random Ampli-
	fied Polymorphic DNA (RAPD) to reveal a large morphological and genetic diversity
	(Reynders-Aloisi et al. 2000).
	Roses are cultivated and traded worldwide as garden plants, cut flowers, dwarf pot
	plants, for the perfume industry, and as medicinal or food plants. The rose market is
	among the most robust in the ornamental plant sector. Indeed, in France in 2020, roses
	represented 17% (cut flowers) and 2.7% (garden and dwarf pot plants) of the trading
	value of plants (including trees, plants and flowers) purchased by consumers (VAL'HOR
	2022). The large diversity of color, perfume, architecture, size offered by the more than
	30 000 cultivars may explain this success (Young et al. 2007). Cultivated roses originated
	from only 8 to 15 species (Smulders et al. 2011). Important species in roses breeding
	history are R. chinensis and R. gigantea (Indicae), R. multiflora, R. lucieae and R.
	moschata (non european Synstylae), R. gallica (and its presumed hybrid R. damascena,
	Gallicanae) and the Pimpinellifoliae: R. foetida. Other notable contributions were
	from R. rugosa and R. pendulina (section Rosa), R. phoenicia, R. sempervirens, and R.

Table 2 .

 2 1: Characteristics of the 40 markers. Number of alleles (Na) and missing data rate (Md) calculated on the R. gallica subsample ('gal') and on the "diversity" subsample ('div').

	Locus	Chr Gene prox	Na	Md	Na	Md
				'gal'	'gal'	'Div'	'Div'
	Gal01_01	1	Close	7	27.60%	15	52.10%
	Gal01_02	1	Close	13	44.80%	41	27.10%
	Gal01_03	1	Intergenic	8	6.90%	34	27.10%
	Gal01_06	1	Close	11	10.30%	43	4.20%
	Gal02_11	2	Close	10	20.70%	23	22.90%
	Gal02_13	2	Intergenic	15	17.20%	29	41.70%
	Gal02_14	2	Close	19	10.30%	42	16.70%
	Gal03_19	3	Intergenic	10	6.90%	23	16.70%
	Gal03_25	3	Close	4	0.00%	31	8.30%
	Gal04_26	4	Intergenic	3	34.50%	17	35.40%
	Gal04_32	4	Close	8	24.10%	17	66.70%
	Gal04_33	4	Intergenic	6	13.80%	13	64.60%
	Gal04_34	4	Close	10	0.00%	23	2.10%
	Gal05_38	5	Intergenic	8	6.90%	26	27.10%
	Gal06_42	6	Close	10	17.20%	26	27.10%
	Gal06_43	6	Intergenic	8	44.80%	29	33.30%
	Gal07_48	7	Close	8	0.00%	18	20.80%
	Gal07_51	7	Close	6	0.00%	21	12.50%
	Gal07_55	7	Close	13	6.90%	24	35.40%
	Gal07_56	7	Intergenic	19	0.00%	37	10.40%
	Lab01_H9B07_07	1	Inside	13	10.30%	25	6.30%
	Lab03_Rh58_24	3	Inside	6	0.00%	26	27.10%
	Lab05_RMS034_37	5	Inside	12	31.00%	53	8.30%
	Lab07_Rw5G14_54	7	Inside	10	0.00%	29	4.20%
	Trf01_04	1	Close	6	0.00%	23	2.10%
	Trf01_05	1	Intergenic	5	0.00%	18	0.00%
	Trf02_08	2	Close	9	0.00%	26	0.00%
	Trf02_12	2	Intergenic	13	58.60%	45	31.30%
	Trf03_21	3	Close	13	0.00%	32	2.10%
	Trf03_23	3	Intergenic	5	3.40%	28	4.20%
	Trf04_27	4	Intergenic	14	6.90%	41	8.30%
	Trf04_30	4	Intergenic	13	3.40%	23	33.30%
	Trf05_35	5	Close	13	10.30%	38	12.50%
	Trf05_39	5	Close	6	3.40%	20	14.60%
	Trf06_44	6	Close	13	34.50%	36	18.80%
	Trf06_47	6	Intergenic	12	0.00%	30	2.10%
	Trf07_50	7	Close	14	0.00%	22	12.50%
	Trf07_52	7	Intergenic	18	10.30%	42	4.20%
	Trf07_53	7	Intergenic	15	0.00%	44	2.10%
	Trf07_60	7	Close	17	41.40%	40	27.10%

  or read depth ratio (Augusto Corrêa dos Santos et al. 2017; Cui et al. 2022b) and thus require the ploidy levels as in-

put. As the ploidy level is central to these steps, an accurate ploidy level is a requirement for population genetic studies.

  released the gbs2ploidy R package, which allows low-level ploidy determination based on thousands of heterozygous bi-allelic SNPs. Augusto Corrêa dosSantos et al. (2017) developed a tool to visualize allele frequency distributions that can be used on multiallelic data. Ploidy is then determined by statistically comparing observed distributions with theoretical ones. SSR alleles are multi-allelic data, but the read depth can be influenced by preferential amplification, thus preventing the use of such a method. As SSR loci are generally highly variable, methods have been developed to estimate the ploidy level based on the maximum number of different identicalby-state alleles[START_REF] Clark | polysat: an R package for polyploid microsatellite analysis[END_REF][START_REF] Meirmans | genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids[END_REF]). However, these methods require careful examination of each sample and are highly sensitive to allelic error, which can easily increase the number of different alleles at a locus[START_REF] Clark | polysat: an R package for polyploid microsatellite analysis[END_REF][START_REF] Meirmans | genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids[END_REF]. Individual heterozygosity and locus polymorphism are also important factors that can lead to underestimation of ploidy levels[START_REF] Huang | Inference of individual ploidy level using codominant markers[END_REF].[START_REF] Huang | Inference of individual ploidy level using codominant markers[END_REF] have recently published a more refined method for assigning ploidy levels.

  The 1586 individuals included 905 wild R. gallica, 412 rosebushes known to be related to R. gallica, and 269 specimens belonging to other species of the genus Rosa. Further details can be found in the section 3.2 and in supplementary data S.1.In total 1710 samples, including the 1586 unique individuals plus technical replicates, were genotyped, using 60 SSRseq markers designed in the section 2.1.2 (see primer information in TableS.1). The sequencing was carried out in five different runs, including a pilot run where 95 samples, selected to represent the diversity of the genus, were blindly genotyped twice. The 40 diploids and 39 tetraploids used in the 2.1.2 section were part of the pilot run. Details of the wet lab work can be found in sections 2.1.2 and 3.2.

  [START_REF] Huang | Inference of individual ploidy level using codominant markers[END_REF] constructed a model to infer the ploidy level based on allelic phenotypes for polysomic inherited samples. The authors tested the model using an empirical dataset of Oxalis pes-caprae comprising 42 diploids, 242 tetraploids, and 96 pentaploids, and achieved a 91% accuracy. This accuracy is similar to that of our best model (89.6%) and only slightly higher than our best five-class model (83.7%). This comparison demonstrates the effectiveness of random forest in predicting ploidy based on codominant data. The slightly higher accuracy might be attributed to a different dataset, lower number of ploidy classes, and lower number of individuals per class. The main advantage ofHuang et al.

  One suggested that R. gallica might have colonized Europe after escaping from Roman fields[START_REF] Debray | Phylogénomique du genre Rosa : hybridation et polyploïdie comme facteurs de diversification[END_REF]. Other authors hypothesized that the species may have been introduced to Europe from the Middle East since the 13th century(Saakov, 1965 according to Fedorova 2014). For the most Western part of the range (West of France and Spain),[START_REF] Klastersky | Rosa L[END_REF] hypothesized that R. gallica populations in these areas may have originated from secondary introductions. In the same manner,[START_REF] Boulenger | Les roses d'Europe de l'herbier crépin. Vol. 2 (suite)[END_REF] indicates that R. gallica has been widely cultivated in France and has escaped from the gardens in many localities.

	2000).
	Microsatellites, simple sequence repeats (SSRs), or short tandem repeats, are short DNA
	motifs (one to six bases) tandem repeated throughout eukaryotic genomes (Hodel et al.
	2016). Roses diversity questions were addressed using microsatellites (Liorzou et al. 2016;
	Mayland-Quellhorst and Wissemann 2021; Rusanov et al. 2005; Scariot et al. 2006; Tan

Explorations of R. gallica diversity have been performed only at limited scales, using either morphological or genetic markers. In Poland, palynologists have identified two different groups of pollen shapes among Polish R. gallica

[START_REF] Wrońska-Pilarek | Pollen morphology of Rosa gallica L. (Rosaceae) from Southern Poland[END_REF]

. The most extensive study, in terms of geographic area, was limited to around forty samples from Ukraine, Hungary, Romania, and Greece. The authors used Inter Simple Sequence Repeat and revealed a clear geographical pattern of diversity structure as the individuals clustered by country of origins

[START_REF] Fedorova | Systematics and geography of species of the genus Rosa L. sect. Gallicanae DC[END_REF][START_REF] Fedorova | Local differentiation and hybridization in wild rose populations in Western Ukraine[END_REF]

.

De Cock (2008) compared populations from South East France to a German one using Amplified Fragment Length Polymorphism, and found the German individuals to be differentiated from the French ones. At the France scale, preliminary works revealed a structure between wild and cultivated R. gallica. Additionally, wild French specimens that were collected in geographically close sites were genetically similar

[START_REF] Pernet | Genetic diversity exploration of rose at phenotypic and molecular levels: impact on the conservation of the Rosa gallica species[END_REF][START_REF] Thouroude | Diversité génétique dans le genre Rosa : méthodologies d'exploration et premiers résultats sur la conservation de l'espèce Rosa gallica[END_REF]

). Another study focused on South East France and used morphological and RAPD data to reveal a high genetic diversity (Reynders-Aloisi et al.

  from 223 sites in 15 countries (see Fig.3.1). Among them, 485 individuals were collected from 102 French sites. The number of individuals collected from each site ranged from one to thirteen, with a median of four. When possible, we tried to collect at least four individuals per site by collecting at least one individual from each continuous plot of plants present on the site. Therefore more individuals were collected on sites harboring many plots. In addition, samples of 412 rosebushes suspected to be related to R. gallica were gathered. These were either botanical varieties attributed to R. gallica (e.g. R. gallica var 'Versicolor') or cultivated varieties belonging to the Hybrid gallica and other OGR groups related to R. gallica: the Damask, Centifolia, Portland, Alba, Bourbon or Moss groups (e.g. 'Belle Virginie'). Botanical individuals of R. gallica are plants preserved in rose gardens identified with a species binomial name while cultivated varieties are cultivars belonging to horticultural groups of old gardens roses. To be able to detect mis-identification or R. gallica interspecific hybrids with other Rosa sp. and to select only non hybrid cultivated R. gallica, we gathered 269 more individuals belonging to other species of the genus. These were wild specimens, botanical specimens, or cultivars non related to R. gallica. These individuals were chosen to represent the diversity of the genus Rosa according to findings of[START_REF] Debray | Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the Genus Rosa[END_REF].

Table 3 .

 3 1: Loci quality and polymorphism, assessed on the Rosa sp. dataset. N_WAI: Number of alleles considering the Whole Amplicon Information. N_length: Number of alleles considering only allele length. N_rare: Number of alleles, detected by considering the Whole Amplicon information, with a frequency lower than 1%. PIC: Polymorphism Information Content.

	Locus	Calling strategy Missing rate Error rate N_WAI N_length N_rare PIC
	Gal01_03	RepeatFocused	10.14	2.84	80	65	0.61
	Gal02_11	FullLength	10.07	3.10	109	87	0.92
	Gal02_14	FullLength	5.75	2.21	233	194	0.95
	Gal03_19	FullLength	2.41	0.94	50	28	0.86
	Gal03_25	RepeatFocused	1.79	1.40	45	36	0.60
	Gal04_34	RepeatFocused	0.74	2.42	50	34	0.80
	Gal05_38	FullLength	14.52	2.84	96	76	0.70
	Gal06_42	RepeatFocused	15.45	2.17	86	56	0.91
	Gal07_48	RepeatFocused	4.88	1.69	49	33	0.85
	Gal07_51	RepeatFocused	0.93	0.51	44	33	0.73
	Gal07_55	FullLength	8.47	2.10	97	75	0.77
	Gal07_56	FullLength	9.02	2.05	97	66	0.89
	Lab01_H9B07_07	RepeatFocused	0.19	2.16	31	22	0.79
	Lab03_Rh58_24	FullLength	6.12	0.84	88	79	0.62
	Lab07_Rw5G14_54 RepeatFocused	0.74	2.17	66	45	0.89
	Trf01_04	RepeatFocused	0.06	1.34	45	29	0.82
	Trf01_05	RepeatFocused	0.12	0.61	28	19	0.59
	Trf02_08	RepeatFocused	0.68	1.68	60	34	0.90
	Trf03_21	RepeatFocused	0.37	1.13	93	72	0.86
	Trf03_23	FullLength	0.87	1.37	62	46	0.64
	Trf04_27	FullLength	14.52	3.67	108	82	0.93
	Trf04_30	RepeatFocused	11.37	3.26	52	31	0.86
	Trf05_35	FullLength	11.37	2.78	116	95	0.85
	Trf05_39	RepeatFocused	1.55	2.76	36	16	0.75
	Trf06_44	RepeatFocused	9.46	4.46	124	85	0.95
	Trf06_47	FullLength	0.62	1.49	107	79	0.84
	Trf07_50	RepeatFocused	8.22	4.60	41	26	0.83
	Trf07_52	RepeatFocused	4.82	3.65	147	105	0.95
	Trf07_53	RepeatFocused	1.85	0.74	86	52	0.91

  3 (supplementary data S.2). To make the selection, we first retained all cultivated individuals. Then we selected all individuals from other species than R. gallica.

		Genetic constitution of the horticultural groups of European Old Garden Roses and
					their relationship with wild R. gallica
		50	Botanical Gallica	HGal	Centifolia	Damask
		40
		30
	Number of Individuals	0 10 20 30 40 50	Moss	Alba	Portland	Bourbon
		20
		10
		0
			Before 1750 1750-1800 1801-1810 1811-1820 1821-1830 1831-1840 1841-1850 1851-1860 1861-1880 1881-1950 Botanical Unknown	Before 1750 1750-1800 1801-1810 1811-1820 1821-1830 1831-1840 1841-1850 1851-1860 1861-1880 1881-1950 Botanical Unknown	Before 1750 1750-1800 1801-1810 1811-1820 1821-1830 1831-1840 1841-1850 1851-1860 1861-1880 1881-1950 Botanical Unknown	Before 1750 1750-1800 1801-1810 1811-1820 1821-1830 1831-1840 1841-1850 1851-1860 1861-1880 1881-1950 Botanical Unknown
				Breeding Period
						As an
	unbalanced sample can bias genetic structure inference, we sub-selected 42 wild collected
	R. gallica from the 905 successfully genotyped ones. The 42 individuals were selected
	to represent the wild diversity of R. gallica by proportionally selecting individuals in
	each of the genetic groups inferred in the Chapter 3. Finally, the dataset contained 27
	Botanical gallica, 32 Alba, 39 Bourbon, 42 Centifolia, 27 Damask, 159 HGal, 40 Moss, 18
	Portland and 82 cultivars from other cultivated groups. Furthermore, there were 42 wild
	R. gallica, 15 European Synstylae, 51 Caninae, 27 non European Synstylae -Indicae, 30
	Rosa -Carolinae and nine Pimpinellifoliae. Cultivars belonging to horticultural groups
	of interest (i.e. HGal, Alba, Centifolia, Damask, Moss, Portland and Bourbon) included,
	as far as possible, the oldest of each group. The distribution of the breeding period by
	horticultural groups is shown in Figure 4.1, breeding periods were extracted from Liorzou
	et al. (2016) and https://www.helpmefind.com/gardening/roses.php. The samples were
	either gathered from former projects, from the RosePom Biological Resources Center
	(Angers, France) or especially gathered for this project from wild or rose gardens.
	Wet lab procedure is described in Chapter 3. Briefly, due to the variability in the origin
	of the samples, DNA were either already extracted before the project or extracted for the

Table 4 .

 4 1: Clonal richness, heterogeneity and evenness within the horticultural groups of interest. Number of individual (N), number of clonal lineages (MLL), clonal richness (R), heterogeneity (D) and eveness (V).

	Horticultural group	N MLL	R	D	V
	Alba	32	17 0.52 0.86 0.46
	Bourbon	39	28 0.71 0.95 0.46
	Centifolia	42	27 0.63 0.88 0.13
	Damask	27	18 0.65 0.91 0.39
	HGal	159	123 0.77 1.00 0.94
	Moss	40	33 0.82 0.99 0.72
	Portland	18	18 1.00 1.00
	cipal component separated Caninae (top) from non-European Synstylae-Indicae and
	cultivated individuals from other horticultural groups (bottom, mostly Chinese OGR).

Table 4 .

 4 2: AMOVA analysis investigating the molecular variation when considering the horticultural groups as populations. The AMOVA was run using the infinite allele model on the cultivated clone-corrected dataset (N = 216). All sources contributed significantly to the molecular variation (p-value > 0.05).

	Source of Variation	Percentage of variation	F-statistic	Standard deviation
	Within Individuals		71.8 %	Fit = 0.282	0.032
	Among Individuals within Horticultural group		22.2 %	Fis = 0.236	0.033
	Among Horticultural groups		6.0 %		Fst = 0.060	0.006
		A lb a	B o u r b o n	C e n t if o li a D a m a s k	H G a l	M o s s
	Portland	0.032 ns	0.175 **	0.001 ns	0 ns	0.059 **	0.001 ns
	Moss	0.034 *	0.152 **	0 ns	0.006 ns	0.082 **
	HGal	0.149 **	0.309 **	0.075 **	0.058 **
	Damask	0.063 **	0.229 **	0 ns
	Centifolia	0.035 *	0.189 **
	Bourbon	0.144 **

  [START_REF] Liorzou | Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background[END_REF]. Having clonal individuals in a sample can increase or decrease differentiation between groups. In detail, if the clonal lineages include individuals from different groups, it would decrease the differentiation, if they include individuals from the same groups, it would increase the differentiation. Our results contradict Scariot et al.

  OGR are traditionally classified into horticultural groups and are believed to originate from R. gallica. However, it is unclear how wild R. gallica are related to the different horticultural groups of European OGR, a question that first requires a better understanding of the genetic relationships among and within horticultural groups to evaluate whether cultivars from a given group have a common origin. By genotyping a representative collection of European OGR, wild R. gallica, and other rose species, we confirmed that horticultural groupings poorly reflect the complex genetic structures of European OGR, with only HGal and Bourbon showing notable differentiation. We found a variable contribution of R. gallica across and within groups. At the group level, we confirmed that HGal corresponds to bred R. gallica while the most of the other European OGR appear as hybrids of R. gallica. However, it remain unclear whether these groups originated directly from the wild R. gallica by a cross between wild R. gallica and other species or indirectly by a cross involving HGal and wild or cultivated individual Genetic constitution of the horticultural groups of European Old Garden Roses and their relationship with wild R. gallica belonging to another species. Using multiple lines of evidence, we identified key cultivars of the Centifolia, Alba, and Damask groups as being interspecific hybrids of R. gallica.

	However, we were unable to accurately identify the other species involved in these hy-
	bridizations. Future research should focus on such key cultivars to unravel the origins
	of the Centifolia, Alba, and Damask. Genotyping them with a marker set designed
	to resolve infra-generic relationships would enable the application of phylogenomics to
	address this question.

  Eastern ones and might originate from natural or human-mediated dispersal. We also demonstrated by detection of clonal lineages that human-mediated dispersal has occurred in Central and Western France but not in Central and Eastern Europe. Eastern France was not differentiated from South Alps and Central-Eastern Europe but harbored a slight reduction of diversity. Such pattern of diversity in Eastern France might be explained by a natural expansion after the last glacial maximum from a refugium located in the Rosa, our study is one of the largest on a particular rose species ever done in Europe. Indeed, only two other studies targeting roses carried out such a comprehensive sampling[START_REF] Daneck | Glacial survival in northern refugia? Phylogeography of the temperate shrub Rosa pendulina L. (Rosaceae): AFLP vs. chloroplast DNA variation[END_REF][START_REF] Reichel | Untangling the hedge: Genetic diversity in clonally and sexually transmitted genomes of European wild roses, Rosa L[END_REF].[START_REF] Daneck | Glacial survival in northern refugia? Phylogeography of the temperate shrub Rosa pendulina L. (Rosaceae): AFLP vs. chloroplast DNA variation[END_REF] sampled only 34 sites of Rosa pendulina over its range[START_REF] Reichel | Untangling the hedge: Genetic diversity in clonally and sexually transmitted genomes of European wild roses, Rosa L[END_REF] did not focus on a species but rather a section (Caninea). Our exhaustive sampling allows us to give insight on the evolutionary history of the wild R. gallica in Europe. We suggested that Central and Eastern Europe (excluding Crimea) might have been recolonized from a unique Italian refugium, as we also hypothesized for Eastern French populations. We also revealed a putative secondary differentiation between population from South Alps and populations of Central and Eastern Europe driven by the Alps acting as a reproductive barrier. However, as our hypotheses are mostly based on descriptive analyses of the actual diversity, it would be interesting to test them. To do that, we could use an Approximate Bayesian Computation framework[START_REF] Beaumont | Approximate Bayesian Computation in population genetics[END_REF]. This would involve the building of alternative demographic models, simulations of genetic data under these models, and comparison of their summary statistics with the ones calculated on observed data. Al-

	the genus
	Italian peninsula.
	At a broader scale, our range-wide analysis provides the first clear and comprehensive
	overview of the wild diversity of R. gallica in Europe. Indeed, while its genetic diversity
	was sometimes investigated by different research groups on various scales (Bouillon 2020;
	De Cock 2008; Fedorova 2014; Fedorova et al. 2010; Pernet et al. 2009; Reynders-Aloisi
	et al. 2000), we unified the investigation thanks to our almost exhaustive sampling. For

though the data we produced should be usable in such a framework

[START_REF] Lepais | Joint analysis of microsatellites and flanking sequences enlightens complex demographic history of interspecific gene flow and vicariance in rear-edge oak populations[END_REF]

), ABC has only been scarcely implemented in polyploid species and therefore requires additional data formatting and tests. In any case, making such demographic inferences could help answering the questions that arise from our study. (

1

) At what time did populations from Southern and Northern Alps differentiate? What is the extent of gene flow between these populations? Did Northern Alps populations experience a bottleneck, in which intensity? (2) For France, did the population of Western France originate from Northern or Southern Alps? Recently? To what extent was the population size reduced at the time of Western France colonization?

Table S

 S Supplementary TableS.2: Calling parameters of the first 30 loci out of the 60 SSRseq loci used to call the alleles of the 1710 samples. .3: Calling parameters of the last 30 loci out of the 60 SSRseq loci used to call the alleles of the 1710 samples. Loci quality and polymorphism assessed, on the R. gallica dataset (chapter 3). N_WAI: Number of alleles considering the Whole Amplicon Information. N_length: Number of alleles considering only allele length. N_rare: Number of alleles with a frequency lower than 1%. PIC: Polymorphism Information Content. Rosa gallica L. et autres roses Galliques, origine(s) et rôle dans la genèse des roses cultivées Mots clés : Génétique des populations, Diversité génétique, Polyploïdes, Histoire évolutive Europe, et sous forme de cultivars. En France, sa diversité génétique sauvage est structurée géographiquement. De plus, cette espèce est supposée être à l'origine des premiers groupes horticoles (Hybrides Gallica, Centifolia, Alba, Damas…) de rosiers dont sont issus par hybridation les rosiers actuels. Les objectifs de cette thèse sont (1) d'identifier les facteurs à l'origine de la structuration des populations françaises par la caractérisation de la diversité sauvage européenne, et (2) d'évaluer la contribution de R. gallica aux premiers groupes horticoles. Pour analyser ces données chez des polyploïdes, une méthode de prédiction du niveau de ploïdie a été mise au point. La diversité française est particulièrement structurée comparée au reste de l'Europe, ce qui suggère des origines multiples en France. Les populations de la façade Est française semblent être issues, comme pour le reste de l'Europe, d'une dispersion naturelle depuis un refuge glaciaire situé plus au sud. A l'inverse, les populations de la façade Ouest seraient plus récentes et issues d'une dispersion naturelle ou humaine. Contrairement aux Hybrides Galliques, les autres groupes horticoles anciens sont issus d'hybridations interspécifiques. Ces résultats représentent une étape vers la compréhension de l'histoire évolutive de R. gallica.

	Loci Lab01-H9B07_07 Supplementary Tables Supplementary Tables Supplementary Tables	Chrom Start Chr01 55192600 55192759 AAG End Motif PRIMER_LEFT TGTGAGGGTGTGCTCTTGTTCGTGT	PRIMER_RIGHT ACCCTTCAAACATCATAAGGAGCAC
	Lab02-RMS132_10	Chr02 Individual ID	23430172 23430350 AG Species	TGGAGTTTCCCTTTAGTAACACAGAG Collect year Classif global Classif fine	CCCACTGTAGAGACCTATGGCTGTT Country Site
	Lab03-Rh58_24	Chr03 dona_sol	37823459 37823590 AG Cultivated	ACCAACACCATACAACATGAAACTG 2012 B or C R. gallica Hybrid Gallica	GCCATCTCTGCCATTACCGGAAAGC
	Lab04-Rw55E12_29 double_white Chr04	35709013 35709190 AG Cultivated	GGACTCAGAAGCGGTGGGTGGACAT 2007 other Rosa sp. C other Rosa sp.	TGGAGGCAACAACACACTCTCTCTT
	Lab05-RMS034_37 duc_d_angouleme Chr05	23393824 23393958 AG Cultivated	TGCTCCCTGCTCCCGCTCAAATCAA B or C R. gallica Centifolia	CGAAGCTTCTCGGTCTCGTGCTCTC
	Lab06-CLROW2980_46 Chr06 duc_de_bordeaux	65197522 65197677 AG Cultivated	CGCGTGCGCCATAACAGATCGTCTT 2021 B or C R. gallica Hybrid Gallica	ATCGAATCCAACTTCAGCGTGCTCA
	1 0.2 0.3 0.4 28966389 28966546 AG F st Chr07 Chr07 49951798 49951937 AG Calling strategy s-1 s+1 m n Lab07-Rw5G14_54 Lab07-RMS003_58 Loci ID duc_de_brabant Cultivated duc_de_crillon Cultivated -0.2 0.0 Trf01_04 Chr01 36627580 36627714 AG Trf01_05 Chr01 51548208 51548355 AG Trf02_08 Chr02 898365 898533 AG Trf02_12 Chr02 30879421 30879600 AG Trf03_18 Chr03 22020483 22020634 AC Trf03_20 Chr03 25921217 25921346 AG Trf03_21 Chr03 32107691 32107800 AG Trf03_22 Chr03 35147440 35147576 AG Trf03_23 Chr03 36666637 36666789 AG Trf04_27 Chr04 15003927 15004096 AG Trf04_30 Chr04 39222314 39222473 AG Trf04_31 Chr04 43470463 43470632 AG Trf05_35 Chr05 5271462 5271634 AG Trf05_39 Chr05 56028192 56028368 AG Trf06_44 Chr06 41386178 41386355 AG Trf06_47 Chr06 65625988 65626160 AG Trf07_49 Chr07 1941088 1941244 AG Trf07_50 Chr07 4222878 4223030 AG Trf07_52 Chr07 19814447 19814578 AG Trf07_53 Chr07 22244547 22244659 AG Trf07_57 Chr07 36090655 36090834 AG Trf07_60 Chr07 55475334 55475488 AG Gal01_01 RepeatFocused 85 15 25 10 33 Ploidy F is CCCTCTCTCTCAACAACACTCCACCA F is AAAGGTAGGCAGAAGTGACAGACAT M x A 2009 B or C R. gallica Centifolia 2007 B or C R. gallica Bourbon F st 0.2 0.4 AGACACAGGCTTTGAGGGTTTGGCT ACTGCTGGAGTTAGACTTGACTGAGT TGGGTTGGGTGTGGTGTGGACTCTT TCGAGGTCGGTGAGAGAATCGGAGA GGAACATGTCTCTTGTCATACCCGA GTGCACGTCACAGGCTTTGCAGTGA ATTTGCACAGAATGATGCTTATGGG TCTGTCGTGTTGTTCTGTTGCCACG CATTAACCAACCCTCTTTCCCTCCG TTGGGTTGGCCACGTGAAACCATCC ATGCAGGAATCCAACACCAGCAACA GCCAGTCCCAAACCGATTCCTCCCT GCGTCGTCGTCTCGTCTTCGCTTTC TGCCTTTACCCTTGTTTGTGGACCA GGCTATTGAATTTGGCCGAGTTGGG CGACGACCACCGATCGATCTGAGCT AGATTGAAATTTGAGGGAGCTAGCT GCAAACAACTACTACTAGTCTAGCCA CATTGGGCGTGCTCTCATCGACTCA ACGTCTTCACCGATATTGAGAGCCT CCCGTCATTGACCTTCTTAAATTTCC GGACCCAAGTCTCGATCGATCAGCCA Gal01_02 RepeatFocused 85 15 25 20 33 Ploidy Gal01_03 RepeatFocused 85 15 25 10 33 Ploidy Gal01_06 RepeatFocused 85 15 25 10 33 Ploidy Gal02_09 failed Gal02_11 FullLength 85 15 25 20 33 Ploidy Gal02_13 FullLength 70 10 25 10 33 Ploidy Gal02_14 FullLength 85 15 25 10 33 Ploidy Gal03_15 FullLength 70 10 25 10 33 Ploidy Gal03_16 RepeatFocused 85 15 25 20 33 Ploidy Gal03_17 failed Gal03_19 FullLength 85 15 25 20 33 Ploidy Gal03_25 RepeatFocused 70 10 25 10 33 Ploidy Gal04_26 RepeatFocused 85 15 25 20 33 Ploidy Gal04_28 failed Gal04_32 RepeatFocused 85 15 25 10 33 Ploidy Gal04_33 RepeatFocused 70 10 25 20 33 Ploidy Gal04_34 RepeatFocused 85 15 25 20 33 Ploidy Gal05_36 failed Gal05_38 FullLength 85 15 25 20 33 Ploidy Gal05_40 failed Gal05_41 RepeatFocused 85 15 25 20 33 Ploidy Gal06_42 RepeatFocused 85 15 25 20 33 Ploidy Gal06_43 RepeatFocused 85 15 25 10 33 Ploidy Gal06_45 failed Gal07_48 RepeatFocused 85 15 25 20 33 Ploidy Gal07_51 RepeatFocused 85 15 25 20 33 Ploidy Gal07_55 FullLength 85 15 25 20 33 Ploidy Gal07_56 FullLength 85 15 25 10 33 Ploidy Gal07_59 failed Supplementary Loci ID xix Calling strategy s-1 s+1 m n M x A Lab01-H9B07_07 RepeatFocused 70 10 25 10 33 Ploidy Lab02-RMS132_10 failed Lab03-Rh58_24 FullLength 85 15 25 20 33 Ploidy Lab04-Rw55E12_29 RepeatFocused 70 10 25 10 33 Ploidy Lab05-RMS034_37 RepeatFocused 85 15 25 10 33 Ploidy Lab06-CLROW2980_46 FullLength 85 15 25 10 33 Ploidy Lab07-RMS003_58 failed Lab07-Rw5G14_54 RepeatFocused 85 15 25 20 33 Ploidy Trf01_04 RepeatFocused 85 15 25 20 33 Ploidy Trf01_05 RepeatFocused 85 15 25 10 33 Ploidy Trf02_08 RepeatFocused 70 10 25 20 33 Ploidy Trf02_12 FullLength 70 10 25 10 33 Ploidy Trf03_18 FullLength 70 10 25 10 33 Ploidy Trf03_20 failed Trf03_21 RepeatFocused 70 10 25 20 33 Ploidy Trf03_22 failed Trf03_23 FullLength 85 15 25 10 33 Ploidy Trf04_27 FullLength 70 10 25 20 33 Ploidy Trf04_30 RepeatFocused 85 15 25 10 33 Ploidy Trf04_31 RepeatFocused 85 15 Trf05_35 FullLength 70 10 Trf05_39 RepeatFocused 85 15 Trf06_44 RepeatFocused 70 10 Trf06_47 FullLength 70 10 Trf07_49 Trf07_50 RepeatFocused 85 15 Trf07_52 RepeatFocused 85 15 Trf07_53 RepeatFocused 70 10 Trf07_57 Trf07_60 FullLength 85 15 Trf07_53 1.16% 43 18 30 0.872 25 20 33 Ploidy Trf07_52 4.82% 76 34 58 0.932 failed Trf07_50 4.82% 23 18 13 0.778 25 20 33 Ploidy Trf06_47 0.39% 40 15 26 0.788 25 20 33 Ploidy Trf06_44 10.4% 59 23 38 0.931 25 10 33 Ploidy Trf05_39 0% 16 15 8 0.656 failed Trf05_35 7.51% 32 20 19 0.796 25 10 33 Ploidy Trf04_30 5.59% 27 14 18 0.828 25 20 33 Ploidy Trf04_27 12.72% 39 32 22 0.906 25 20 33 Ploidy Trf03_23 0% 15 13 9 0.451 25 10 33 Ploidy Trf03_21 0% 19 10 9 0.797 25 20 33 Ploidy duc_de_guiche Cultivated 2021 B or C R. gallica Hybrid Gallica duchess_of_sutherland Cultivated 2008 other Rosa sp. C other Rosa sp. duchesse_d_angouleme Cultivated 2021 B or C R. gallica Hybrid Gallica duchesse_d_istrie Cultivated 2009 B or C R. gallica Moss duchesse_de_berry Cultivated 2012 B or C R. gallica Hybrid Gallica duchesse_de_buccleugh Cultivated 2021 B or C R. gallica Hybrid Gallica duchesse_de_montebello Cultivated 2012 B or C R. gallica Hybrid Gallica Supplementary Table S.4: Loci Missing N_WAI N_length N_rare PIC Gal01_03 1.54% 23 15 19 0.404 Gal02_11 5.59% 49 28 38 0.875 Gal02_14 5.39% 75 31 61 0.932 Gal03_19 1.93% 30 26 21 0.817 Gal03_25 0% 14 9 9 0.52 Gal04_34 0.19% 18 14 10 0.68 Gal05_38 8.48% 32 16 23 0.612 Gal06_42 12.52% 38 25 27 0.858 Gal07_48 0.77% 27 20 13 0.867 Gal07_51 0% 11 8 7 0.606 Gal07_55 2.12% 51 21 44 0.711 Gal07_56 5.01% 36 16 22 0.794 Lab01-H9B07_07 0.58% 10 7 3 0.75 Lab03-Rh58_24 0.58% 7 3 4 0.455 Lab07-Rw5G14_54 0% 24 15 14 0.836 Trf01_04 0% 13 8 6 Trf01_05 0% 5 5 3 Trf02_08 0.58% 32 22 18 0.863 l'aire de répartition européenne, et cultivés ont 0.379 échantillonnés sur quasiment l'ensemble de 0.745 duchesse_de_portland Cultivated 2012 B or C R. gallica Portland DUM_01 Rosa dumalis Bechst. 2017 other Rosa sp. Caninae DUM_02 Rosa dumalis Bechst. NA other Rosa sp. Caninae edith_de_murat Cultivated 2021 B or C R. gallica Bourbon ELI_01 Rosa elliptica Tausch 2018 other Rosa sp. Caninae elisabeth_d_angleterre Cultivated 2010 other Rosa sp. C other Rosa sp. enfant_de_france Cultivated 2013 other Rosa sp. C other Rosa sp. erik Cultivated 2017 B or C R. gallica Hybrid Gallica eucharis Cultivated 2007 B or C R. gallica Hybrid Gallica eugenie_de_guinoisseau Cultivated 2009 B or C R. gallica Moss eulalie_lebrun Cultivated 2007 B or C R. gallica Hybrid Gallica eveque Cultivated 2007 Rosa gallica L., une espèce propage naturellement par graines et drageons, existe à l'état sauvage, de 1600 individus, sauvages, principalement en Près été génotypés par séquençage de B or C R. gallica Résumé : pérenne, tétraploïde, hétérozygote, qui se microsatellites, nouvellement développés.	GCTGCAAGTGTGTAAGGTGAGGTCA AGCAACACTGTCATTTGGTTGCCCT GGGAGAGGGAGAGAGGAGACAAAGCT TGCGTGCAGTTCGAGGCATAATTCG GGCAGACCCATAATCTTCAATGCCT CCGTCGACCTCAACAACTAGGCCTC TGAATTGGCTACCACTAACCTCCCT AGTTTCCATGCGAATGAACCTAGCT GGCTGCCAGGGACAGCTTATTTGCA CCCAACCTCATCAGCAACCGACAACC ACCAACAACAAATTCCGCAACTGTT TGACAGCATCCTAATCTCCAAGAAA TGGGCCTCTCTTCTTTCCCACCATT AGGTGAAAGCTCCTGACCTTCCCGC ACCCTGATTTCTTCTCTCCCACCTCC ATGTACGTCTGTAGGAGAAGGGCAT ACATGGCCGTTCTCTCCGTGCCTAA GTACTAGTAGCGCGCATCTTTCGGG TGGTACTGACTACGGAGATGGCCCA TCCTGTGTTAGCTAGGTTTCAGTCA ACGAGTTCATTTCCCAAGCAAGCAT GCGTGGTTTATGCCTTGTAGAATCCC TGGTGTTATTCCGTCCATCATGCAG AGACCCTGGCAGGTTCCTTGTATGCT
				xx xxi xxii

Titre :

Acknowledgements

Percentage of allele number increases between tetraploids and diploids

List of Supplementary Tables

Mis-identification detection

We used the clone-corrected Rosa sp. dataset to select only on R. gallica individuals for further analyses by detecting mis-identifications or putative hybrids. To do that, we first visualized the genetic structure within this dataset by performing a Principal Coordinate Analysis (PCoA) based on the k-mer pairwise dissimilarities matrix using the dudi.pco function from the R package ade4 v1.7.22 [START_REF] Dray | The ade4 Package: Implementing the Duality Diagram for Ecologists[END_REF]. To compare this k-mer dissimilarity based method to a more conventional one, we also performed a PCA based on intra-individual allele frequencies using the dudi.pca function from the R package ade4 [START_REF] Dray | The ade4 Package: Implementing the Duality Diagram for Ecologists[END_REF]. For the PCA, the missing data were turned as zero values.

After this step of visual comparison, a K-means clustering (R package adegenet v2. 1.10, Jombart et al. 2010) was used to define groups of genetically similar individuals based on all the principal components of the PCoA. The clustering and determination of the most probable number of groups (K ) were achieved by independently running the adegenet function find.clusters 100 times for each K from K = 1 to K = 30. We selected K = 12 as the most probable number of groups based on the Bayesian Information Criterion (Fig. S.4). We then performed, on these principal components, a Discriminant Analysis of Principal Components (DAPC) to describe the groups and assign each individual a probability of belonging to each group. The function dapc.dudi was run on the first 11 principal components chosen using the K -1 criterion, as suggested by [START_REF] Thia | Guidelines for standardizing the application of discriminant analysis of principal components to genotype data[END_REF]. We retained the first eight discriminant functions. Based on the scatter plot of the first three discriminant functions and on group memberships, we selected the four groups out of the 12 that contained almost only individual attributed to R. gallica during the sampling process based on their phenotypes. By summing the membership probabilities for these four groups, we determined a probability for each individual to be a R. gallica. We set a highly restrictive threshold of 0.99 for membership probability to select R. gallica individuals. This threshold was selected after reviewing the distribution of membership probabilities (Fig. S.5). This dataset with only R. gallica individuals is designated hereafter as the R. gallica dataset.

Population structure of R. gallica

The R. gallica dataset was composed of wild-collected and botanical or cultivated individuals. Concerning the wild ones, site-based analysis was impossible as very few clonal lineages were identified per site. Therefore, to investigate the factors that shaped the French diversity, we chose to group the wild clonal lineages into regional groups. While ii Supplementary Figures Supplementary Figure S.12: Estimation of the most probable number of K STRUCTURE clusters for the . The DeltaK method of [START_REF] Evanno | Detecting the number of clusters of individuals using the software structure: a simulation study[END_REF] (left) and the parsimony method of Wang (2019)

Supplementary Figures

Chr05 Chr07 Chr02

Supplementary Data

Supplementary Data S.1: Metadata for all individuals collected (N = 1586). The data are available on the next pages. The description of columns content is available below.

Column descriptions