Guillaume Touya

Finding the Oasis in the Desert Fog? Understanding Multi-Scale Map Reading

Keywords: multi-scale maps, mental maps, landmarks, fluid interaction, smooth zooming, continuous generalisation

Fluid interactions of complex information visualizations promote flow by giving "a sense of control" and a loss of selfconsciousness" during the interaction [START_REF] Elmqvist | Fluid interaction for information visualization[END_REF]. In the context of multi-scale geovisualization with different maps rendered at different zoom levels, fluid interactions mainly consist in smoothly zooming in the scales/zoom levels, while keeping a constant sense of place. User experience shows that current multi-scale maps are not provided with fluid interactions. Despite recent improvements, zooming in and out of multi-scale maps such as GoogleMaps, OSM, or National Mapping Agencies' geoportals still causes a desert fog effect [START_REF] Jul | Critical Zones in Desert Fog: Aids to Multiscale Navigation[END_REF] when the map changes. Desert fog can be defined as "a condition wherein a view of an information world contains no information on which to base navigational decisions." In a multi-scale map, it means that the abstraction, content and style changes between two maps at different scales make the user lose his sense of place for a short moment wondering where his previous position (i.e. where he was looking at, or where his cursor was) is in the newly rendered map [START_REF] Dumont | A Comparative Study of Existing Multi-Scale Maps: What Content at Which Scale?[END_REF]. Contributions on continuous generalisation (van [START_REF] Oosterom | Data Structures for Continuous Generalisation: tGAP and SSC[END_REF], or on progressive generalisation [START_REF] Touya | Progressive Block Graying and Landmarks Enhancing as Intermediate Representations between Buildings and Urban Areas[END_REF][START_REF] Touya | Progressive Block Graying and Landmarks Enhancing as Intermediate Representations between Buildings and Urban Areas[END_REF] try to provide more continuous or progressive abstractions of map features across scales.

But to improve the fluidity of zooming in multi-scale maps, we first need to understand how map readers relocate after a scale change, then we would understand more easily why map readers have difficulties with current multi-scale maps and how we could improve these multi-scale maps to enable more fluidity. Past research provided many explanations on how map users read and understand a map but what happens when scale changes is still an issue [START_REF] Mark | Cognitive models of geographical space[END_REF]. For instance, do multi-scale map readers use a multi-scale mental map?

Although multi-scale map reading has not been the core topic of past research in geographical information science and spatial cognition, the analysis of related research gives us hints on how it might work. Particularly, we tried to find clues on how relocation works after a zooming interaction, and on the factors that help or limit this relocation by the map user. From research in spatial cognition, we can see that there are different types of cognitive models for spatial cognition [START_REF] Mark | Cognitive models of geographical space[END_REF]: the concept of cognitive atlas from [START_REF] Kuipers | The "Map in the Head" Metaphor[END_REF] is really interesting because it states that geographic knowledge is a set of cognitive maps at different scales, with gaps, so the mental representation of map users is also composed of maps at different scales. [START_REF] Tversky | Cognitive maps, cognitive collages, and spatial mental models[END_REF] proposed a similar theory calling the geographic knowledge more a cognitive collage than a cognitive map or atlas. The Anchor theory [START_REF] Couclelis | Exploring the anchor-point hypothesis of spatial cognition[END_REF] is also apposite as it claims that mental maps are more topological than metrical, and places are located in relation to a set of hierarchical anchors, or landmarks: for instance, we know where a specific place is its near to a restaurant that is itself close to our workplace, a higher level anchor compared to the restaurant.

Research on wayfinding is also interesting as it involves space representation at different scales: spatial chunking is a common task in wayfinding [START_REF] Klippel | Urban granularities-a data structure for cognitively ergonomic route directions[END_REF] and the mental representation of a route often contains parts at different abstraction levels or different scales [START_REF] Godfrey | The bounds of distortion: truth, meaning and efficacy in digital geographic representation[END_REF]. Landmarks where the route changes scale (e.g. changing from subway to bus) are keys to link the representations at different scales. We have seen that one problem with the multi-scale maps is that the potential landmarks are not represented similarly when scale changes, and they may even disappear. One solution to this problem could be to model vertical spatial relations for the important landmarks [START_REF] Steiniger | Relations among Map Objects in Cartographic Generalization[END_REF][START_REF] Burghardt | Relation Modelling within Multiple Representation Databases and Generalisation Services[END_REF], or hierarchical relations (Mustière & Moulin 2002[START_REF] Chaudhry | Utilising Partonomic Information in the Creation of Hierarchical Geographies[END_REF] to better abstract the landmarks when scale decreases. The importance of text in zooming fluidity should also be considered (Dumont et al., 2015): it should be consistent across scales [START_REF] Schwartges | Optimizing Active Ranges for Point Selection in Dynamic Maps[END_REF] and could also be used to abstract the important landmarks.

One final element to note is that research on spatial cognition showed that geographical elements are remembered more regular than they really are [START_REF] Mark | Cognitive models of geographical space[END_REF]: we remembered alignments, orthogonality or parallelism even when these properties are not exact in the map. So, progressive generalisation that tends to enhance such properties by making spatial relations more regular can play a key role for zooming fluidity.

To summarize this literature review, we can make the following hypotheses: multi-scale scale mental maps (i.e. cognitive collages) are used by map readers, and multi-scale anchors are key features to enable multi-scale map reading; these multi-scale anchors can be visually salient features, but can also derive for the user spatial knowledge of the space depicted in the map.

To better understand the implications of such hypotheses, I tried a little exercise with the standard OSM multi-scale map, centred on my workplace, IGN near Paris (Figure 1). I captured all consecutive maps when zooming out and I later listed the visual cues in each map, with a generic and a personal point of view. For four of the maps, the ones reproduced in Figure 1, Table 1 lists these visual cues. The table shows that visual landmarks change when scale decreases, even if some are rather visible at several consecutive scales. We can also note that the personal visual cues are not always the same as the visually salient features.

Figure 1. Zooming out from a large scale map centred on IGN France. Some visual cues are quite preserved through scales but some disappear generating some desert fog.

Scale

Generic visual cues Personal visual cues Highways, "Bois de Vincennes" Paris ring road, "Bois de Vincennes" Table 1. List of generic and personal visual cues for the four maps from Figure 1.

For now, the hypotheses proposed in this abstract are rather vague and only backed by intuitions. The next step is clearly to set up user experiments to test these hypotheses. Research collaborations are welcome to face this huge task. A first experiment is planned at the time of the workshop to generalise the exercise presented in this abstract. Then, the next step will be to use this knowledge on multi-scale map reading to improve both multi-scale map design (with multiscale styling and progressive generalisation?), and interactions (new zooming interactions specific to multi-scale maps?). Controlled and staged animations [START_REF] Tversky | Animation: can it facilitate?[END_REF][START_REF] Harrower | The Cognitive Limits of Animated Maps[END_REF][START_REF] Bach | GraphDiaries: Animated Transitions and Temporal Navigation for Dynamic Networks[END_REF] can also be interesting methods to improve the fluidity of the zooming interaction.


Noppol Kobmoo
email: noppol.kob@biotec.or.th

Suchada Mongkolsamrit

Nuntanat Arnamnart

Janet Jennifer Luangsa-Ard

Tatiana Giraud

Population genomics revealed cryptic species within host-specific zombie-ant fungi (Ophiocordyceps unilateralis)

Keywords: Ophiocordyceps unilateralis, cryptic species, phylogenetic species, recombination, population genomics

The identification and delimitation of species boundaries are essential for understanding speciation and adaptation processes and for the management of biodiversity as well as development for applications. Ophiocordyceps unilateralis sensu lato is a complex of fungal pathogens parasitizing Formicine ants, inducing zombie behaviors in their hosts. Previous taxonomic works with limited numbers of samples and markers led to the "one ant-one fungus" paradigm, resulting in the use of ant species as a proxy for fungal identification. Here, a population genomic study with sampling on three ant species across Thailand supported the existence of host-specific species in O. unilateralis s.l. with no footprints of long term introgression despite occasional host shifts and first-generation hybrids. We further detected genetic clusters within the previously delimited fungal species, with each little footprints of recombination, suggesting high levels of inbreeding. The clusters within each of O. camponoti-leonardi and O. camponoti-saundersi were supported by differentiation throughout the genome, suggesting they may constitute further cryptic species parasitizing the same host, challenging the one ant-one fungus paradigm. These genetic clusters had different geographical ranges, supporting different biogeographic influences between the north/center and the south of Thailand, reinforcing the scenario in which Thailand endured compartmentation during the latest Pleistocene glacial cycles.

Introduction

The identification and delimitation of species boundaries are essential for understanding speciation and adaptation processes and for the management of biodiversity and conservation. The existence of cryptic species, i.e. distinct species with hardly distinguishable morphologies, have been increasingly documented in all phyla and constitutes a challenge for the study of speciation, the assessment of biodiversity and the implementation of conservation plans [START_REF] Fišer | Cryptic species as a window into the paradigm shift of the species concept[END_REF][START_REF] Struck | Finding Evolutionary Processes Hidden in Cryptic Species[END_REF]. Particularly for fungi, the recognition of species can be difficult as they are versatile organisms, with variable morphology and life styles [START_REF] Khonsanit | Cryptic species within Ophiocordyceps myrmecophila complex on formicine ants from Thailand[END_REF][START_REF] Molina | Another example of cryptic diversity in lichen-forming fungi: The new species Parmelia mayi (Ascomycota: Parmeliaceae)[END_REF][START_REF] Mongkolsamrit | Disentangling cryptic species with isaria-like morphs in Cordycipitaceae[END_REF][START_REF] Montarry | Spatio-temporal distribution of Erysiphe necator genetic groups and their relationship with disease levels in vineyards[END_REF][START_REF] Vincenot | Out of Asia: Biogeography of fungal populations reveals Asian origin of diversification of the Laccaria amethystina complex, and two new species of violet Laccaria[END_REF]. Fungi are frequently symbionts to other organisms and often evolve host specialization [START_REF] Fournier | Sympatric genetic differentiation of a generalist pathogenic fungus, Botrytis cinerea, on two different host plants, grapevine and bramble[END_REF][START_REF] Gerardo | Ancient host-pathogen associations maintained by specificity of chemotaxis and antibiosis[END_REF][START_REF] Gerardo | Exploiting a mutualism: Parasite specialization on cultivars within the fungus-growing ant symbiosis[END_REF], which is a fertile background for host-specific, cryptic species. The use of the phylogenetic species criterion based on concordance between gene genealogies has been highly useful for delimiting fungal cryptic species [START_REF] Taylor | Phylogenetic Species Recognition and Species Concepts in Fungi[END_REF]. With the advent of nextgeneration sequencing, the concordance among gene genealogies can be assessed throughout the genome and new phylogenetic species can be revealed with even more power and reliability [START_REF] Matute | Fungal species boundaries in the genomics era[END_REF][START_REF] Weiss | Genome-wide single-nucleotide polymorphism data reveal cryptic species within cryptic freshwater snail species-The case of the Ancylus fluviatilis species complex[END_REF] or well-established species can be questioned [START_REF] Lifjeld | When taxonomy meets genomiocs: lessons from a common songbird[END_REF]. Furthermore, a population genomics approach can identify gene flow and introgression at fine scale along the genome [START_REF] Chan | Species delimitation with gene flow: A methodological comparison and population genomics approach to elucidate cryptic species boundaries in Malaysian Torrent Frogs[END_REF][START_REF] Savary | A population genomics approach shows widespread geographical distribution of cryptic genomic forms of the symbiotic fungus Rhizophagus irregularis[END_REF].

Ophiocordyceps unilateralis sensu lato is a ubiquitous species complex of ant-pathogenic fungi with a pan-tropical distribution [START_REF] Araújo | Zombie-ant fungi across continents: 15 new species and new combinations within Ophiocordyceps. I. Myrmecophilous hirsutelloid species[END_REF]) and an outstanding biology; these fungi modify the behavior of their ant hosts to promote their own dispersal. Infected ants are called zombie ants because they develop erratic behaviors, excluding themselves from their nest before climbing into vegetation and biting vegetal substrates until death [START_REF] Hughes | Behavioral mechanisms and morphological symptoms of zombie ants dying from fungal infection[END_REF]. The taxon called O. unilateralis has historically been characterized by the presence of fungal stalks arising from dead ants with lateral cushions containing perithecia and whole ascospores. Recent studies based on morphology and molecular markers have shown that O. unilateralis represented a complex of cryptic species, with allopatric species from distinct continents [START_REF] Araújo | Zombie-ant fungi across continents: 15 new species and new combinations within Ophiocordyceps. I. Myrmecophilous hirsutelloid species[END_REF] as well as host-specific sympatric species [START_REF] Kobmoo | Molecular phylogenies reveal host-specific divergence of Ophiocordyceps unilateralis sensu lato following its host ants[END_REF]. A single and host-specific fungal species appeared to be associated to each ant species, leading to the "one ant-one fungus" paradigm and to the use of ant host identity as proxy for fungal species identification. However, little attention has been paid to intra-specific diversity and to the possibility of differentiated populations or species occurring on a given ant species. Species in the O. unilateralis complex are closely related and many are sympatric, which could lead to introgression and hybridization, although these questions have never been comprehensively studied.

In Thailand, six cryptic species of the O. unilateralis s.l. complex have been identified based on molecular phylogenies and morphological traits [START_REF] Kobmoo | New species of Ophiocordyceps unilateralis, an ubiquitous pathogen of ants from Thailand[END_REF][START_REF] Luangsa-Ard | Ophiocordyceps halabalaensis: A new species of Ophiocordyceps pathogenic to Camponotus gigas in Hala Bala Wildlife Sanctuary, Southern Thailand[END_REF]:

O. polyrhachis-furcata, O. camponoti-leonardi, O. camponoti-saundersi, O. septa and O. rami and O. halabalaensis. At the macro-morphological level, only O. rami and O. halabalaensis can be easily distinguished from the others, having one stalk arising from the junction of the ant head and thorax, and two others arising from the junction of the forelegs and thorax; O. rami has sinuous purple stromata while O. halabalaensis has bigger but less sinuous blackish-brown stromata. At the micro-morphological level, O. septa has distinctive swollen and multi-septate ascospores; O. polyrhachis-furcata, O. camponoti-leonardi and O. camponoti-saundersi are the most similar genetically and morphologically and can be found on the ants Polyrhachis furcata, Colobopsis leonardi and Colobopsis saundersi, respectively [START_REF] Kobmoo | Molecular phylogenies reveal host-specific divergence of Ophiocordyceps unilateralis sensu lato following its host ants[END_REF]. Although morphological traits can distinguish these species, host species is the most convenient proxy for species identification and appears reliable [START_REF] Araújo | Zombie-ant fungi across continents: 15 new species and new combinations within Ophiocordyceps. I. Myrmecophilous hirsutelloid species[END_REF][START_REF] Kobmoo | New species of Ophiocordyceps unilateralis, an ubiquitous pathogen of ants from Thailand[END_REF][START_REF] Kobmoo | Molecular phylogenies reveal host-specific divergence of Ophiocordyceps unilateralis sensu lato following its host ants[END_REF]. The criterion of congruence among gene genealogies was consistent with the separation of these host-specialized cryptic species in O. unilateralis s.l. [START_REF] Kobmoo | Molecular phylogenies reveal host-specific divergence of Ophiocordyceps unilateralis sensu lato following its host ants[END_REF]; however, they were based on only two gene sequences (Elongation factor 1-alpha and Beta tubulin).

Due to frequent expansion and contraction events of the forest during the Pleistocene glacial cycles, Southeast Asia shows strong biogeographic compartmentation [START_REF] Woodruff | Biogeography and conservation in Southeast Asia: How 2.7 million years of repeated environmental fluctuations affect today's patterns and the future of the remaining[END_REF]. Genetic differentiation between the north and the peninsular south have been reported in many organisms, including insects such as bees [START_REF] Insuan | Genetic Differentiation of the Giant Honey Bee (Apis dorsata) in Thailand Analyzed by Mitochondrial Genes and Microsatellites[END_REF][START_REF] Sittipraneed | Genetic differentiation of the honey bee (Apis cerana) in Thailand revealed by polymorphism of a large subunit of mitochondrial ribosomal DNA[END_REF] and flies [START_REF] Pramual | Phylogeography of the black fly Simulium tani (Diptera: Simuliidae) from Thailand as inferred from mtDNA sequences[END_REF], trees (Yu et al., 2019), vertebrates including frogs [START_REF] Chen | Large-scale phylogenetic analyses provide insights into unrecognized diversity and historical biogeography of Asian leaf-litter frogs, genus Leptolalax (Anura: Megophryidae)[END_REF], monkeys [START_REF] Karanth | Molecular phylogeny and biogeography of langurs and leaf monkeys of South Asia (Primates: Colobinae)[END_REF] and bats [START_REF] Tu | Comparative phylogeography of bamboo bats of the genus Tylonycteris (Chiroptera, Vespertilionidae) in Southeast Asia[END_REF]; so far this geographical pattern has not been studied in fungi.

Furthermore, an increase in genetic diversity from North to South has been reported in various organisms in the Northern hemisphere, including fungi [START_REF] Miraldo | An Anthropocene map of genetic diversity[END_REF][START_REF] Smith | A latitudinal phylogeographic diversity gradient in birds[END_REF][START_REF] Vercken | Glacial refugia in pathogens: European genetic structure of anther smut pathogens on Silene latifolia and Silene dioica[END_REF] although investigations in Southeast Asia are still lacking.

Furthermore, genetic diversity in fungi is also influenced by reproductive modes (sexual vs clonal) and mating systems (selfing vs outcrossing) [START_REF] Nieuwenhuis | The frequency of sex in fungi[END_REF]. The sexual reproductive structures of O. unilateralis has been widely observed in nature; the fungi develop ascomata from diseased ants, which release ascospores into the environment. However, additional transmission via conidia (asexual spores) cannot be ruled out and sexual reproduction can occur between closely related individuals, and even between clonemates in case these fungi are homothallic, which is unknown. Which reproductive mode and mating system are predominant, and whether there are differences between species, are still open questions which can have major implications for genetic diversity, reproductive barrier and adaptation in these fungi.

In the present study, we therefore used a population genomics approach to investigate: i) whether the existence of host-specific cryptic species was supported by genome-wide genetic differentiation in O. unilateralis, ii) in this case, whether introgression occurred between species, and in which genomic locations, iii) whether cryptic genetic subdivision could be identified beyond host specific species, e.g. according to geographic regions, iv) whether genetic diversity increased towards the South within each species, and v) what the reproductive mode and mating system of the different species were. For these goals, we collected samples of O. polyrhachis-furcata, O. camponoti-leonardi and O. camponoti-saundersi (identified based on host identity as a first proxy) across Thailand, and sequenced their genomes. We investigated their species status using the criterion of concordance between genealogies performed using multiple genomic regions. We also studied the genetic subdivision within each species, possibly related to geography, as well as possible clines in diversity, and the possibility of introgression between species through analyses of Bayesian clustering and allelic spectrum-based models. Finally, we also searched for footprint of recombination to gain insight into the mode of reproduction.

Materials and Methods

Sampling, isolation and sequencing

A total of 59 samples of O. unilateralis s.l. were collected in Thailand, 29 on C. leonardi, 16 on C. saundersi and 14 on P. furcata (Fig. 1a and Table 1S); fungal samples were named according to their host species (i.e., O. polyrhachis-furcata, O. camponoti-leonardi and O. camponotisaundersi, respectively). To conform to the Nagoya protocols on access and benefit-sharing, we obtained an authorization from the Department of National Parks, Wildlife and Plant Conservation (DNP) at the Ministry of Natural Resources and Environment of Thailand for all strain collections.

Isolations and extractions of fungal DNA were conducted following [START_REF] Kobmoo | A genome scan of diversifying selection in Ophiocordyceps zombie-ant fungi suggests a role for enterotoxins in co-evolution and host specificity[END_REF].

Basically, ascospores were harvested after being discharged from the sexual reproductive structures (perithecia) of each sample on potato dextrose agar; whenever possible, random single ascospores were picked up with a needle and transferred to a liquid medium for culturing haploid strains. A single monospore strain per sample was retained for analyses. However, initiating cultures from single spores was not consistently successful; some strains were thus obtained by pooling the discharged ascospores together and thus result from a mixture of products of meiosis from a diploid individual; the corresponding DNAs thus correspond to diploid genotypes. Ascospores are thought to constitute the principal mode of infection for O. unilateralis [START_REF] Araújo | Diversity of Entomopathogenic Fungi. Which Groups Conquered the Insect Body?[END_REF] but it is still unknown whether infections by two mating-type compatible haploid individuals are required to allow for sexual reproduction or whether these fungi are homothallic.

DNA quality and quantity were checked with gel electrophoresis using 0.8 % agarose gel and a Nanodrop (ThermoFisher). Paired-end genomic libraries (150 bp) were constructed and sequenced with an Illumina HiSeq3000 system at the GenoToul platform (Toulouse, France).

Mapping and detection of single nucleotide polymorphisms

The raw reads were trimmed for adaptors and low-quality bases on both ends (q < 20) with Trim Galore! (Krueger, 2015). Reads shorter than 50 bp were discarded. Processed reads were then initially mapped to the latest version of O. polyrhachis-furcata reference [START_REF] Kobmoo | A genome scan of diversifying selection in Ophiocordyceps zombie-ant fungi suggests a role for enterotoxins in co-evolution and host specificity[END_REF], thanks to its higher quality (43.25 Mb, 68 scaffolds, N50 ~ 2.9 Mb) compared to those of the two other species 531 scaffolds,N50 ~ 140 Kb;O camponotisaundersi: 49.26 Mb, 1,700 scaffolds, N50 ~ 102 Kb), using bwa-mem [START_REF] Li | Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM[END_REF] with the optionsk 15 -d 150 -c 2000 -D 0.3 -B 1 -O 3 -Y -M. SNPs were detected using GATK [START_REF] Mckenna | The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data[END_REF] through the GATK HaplotypeCaller function. The ploidy parameter was set as haploid for single-spore strains, and as diploid for multiple-spore strains as, for a given strain, a bulk of spores from the sample represents a mix of products of multiple meioses from the same diploid individual.

As there were no reference variants, the variant recalibration step was dismissed but the variants were filtered a posteriori with total depth between 700x and 5000x, depth for individual samples > 10, mapping quality > 10, quality normalized by depth > 20, genotyping quality > 50; various corrections for biases related to the difference of quality between reference and variant alleles were also applied. Any SNP overlapping with regions including repeats identified beforehand with RepeatMasker (Smit et al., http://www.repeatmasker.org) was excluded. Finally, only biallelic SNPs without indels and with at least 80% samples with reliable inferred variants were retained.

For the within-species analyses, the reads from O. camponoti-leonardi and O. camponotisaundersi were mapped to their respective reference [START_REF] Kobmoo | A genome scan of diversifying selection in Ophiocordyceps zombie-ant fungi suggests a role for enterotoxins in co-evolution and host specificity[END_REF]. The same procedures of mapping and variant calling were applied as in O. polyrhachis-furcata above with slightly different filtering (for O. camponoti-leonardi, final SNPs with total depth between 50x and 3000x, mapping quality > 10, quality normalized by depth > 20; for O. camponoti-saundersi, final SNPs with total depth between 50x and 1000x, depth for individual samples > 20, mapping quality > 10, quality normalized by depth > 20).

Phylogenetic reconstruction and genealogical concordance phylogenetic species recognition (GCPSR)

Whole scaffold sequences for each sample were obtained using the FastaAlternateReferenceMaker tool in GATK. Sequences of a given scaffold from different samples altered at the SNPs positions were aligned using an in-house awk script. The alignments of all scaffolds were concatenated to generate a whole-genome alignment. The alignments were used to build maximum-likelihood phylogenetic trees with RaxML based on the model GTRCAT with 1000 bootstrap replicates. A tree for each scaffold with variants was inferred with the same parameters.

All trees were rooted with O. kimflemingiae [START_REF] De Bekker | Ant-infecting Ophiocordyceps genomes reveal a high diversity of potential behavioral manipulation genes and a possible major role for enterotoxins[END_REF], by aligning this latest genome to that of O. polyrhachis-furcata using the nucmer command from MUMmer 3 [START_REF] Delcher | Fast algorithms for large-scale genome alignment and comparison[END_REF][START_REF] Kurtz | Versatile and open software for comparing large genomes[END_REF]. The concordance between different genomic regions was also evaluated under the framework of Bayesian concordance analyses using BUCKy v. 1.4.4 (Larget et al., 2010), after Bayesian inferences over all scaffolds with SNPs bigger than 100 Kb chunked by 100 Kb separately using MrBayes 3.2.2 [START_REF] Ronquist | MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space[END_REF]. The MCMC chains were allowed to sample across the substitution model space. Chunks of 100 Kb were used due to the high linkage disequilibrium found (see the section 3.7). The posterior tree distributions from the Bayesian inferences were fed into BUCKy to estimate the concordance of the nodes among the tree topologies, given as a concordance factor (CF) ranging from 0 to 1 (absence of concordance to complete concordance).

The concordance factor thus represents the genome-wide genealogical support for the monophyly of clades.

Linkage disequilibrium, recombination footprints

The correlation coefficient r² between SNPs of less than 1M bp with minor allele frequency of at least 0.1 and per-sample inbreeding coefficients for diploid samples were calculated with plink [START_REF] Purcell | PLINK: a tool set for whole-genome association and population-based linkage analyses[END_REF]; r 2 is a correlation coefficient representing the association and thus linkage between SNPs. In case of regular recombination, physically close SNPs are expected to be linked thus to have higher r² than farther SNPs. In case of low frequency of sexual reproduction, linkage disequilibrium, and thus r², will be low across the whole genome. The r² coefficient was averaged over 100 bp intervals and plotted against the pairwise physical distance to obtain linkage disequilibrium (LD) decay plots. Pairwise Homoplasy Index (PHI) tests of recombination [START_REF] Bruen | A simple and robust statistical test for detecting the presence of recombination[END_REF] were performed for the whole dataset and for each species on each scaffold. The test is based on the detection of incompatibility between pairs of sites regarding whether there is a genealogical history that can be inferred parsimoniously that does not involve any convergent mutations; if any incompatibility exists, under an infinite-site model of evolution, it must come from recombination. PHI-test returns the probability of observing the data under the null hypothesis of no recombination. Bonferroni corrections were applied to the p-values. Population-scaled recombination rates (rho = 2Ne × r; Ne = effective population size, r = recombination per site per generation) were calculated for each species using the R package FastEPRR [START_REF] Gao | New Software for the Fast Estimation of Population Recombination Rates (FastEPRR) in the Genomic Era[END_REF]. The per-sample inbreeding coefficients were calculated for diploid individuals as method-of-moments estimate F coefficients based on observed and expected homozygosity for each diploid sample.

Neighbor-net networks were inferred using SplitsTree [START_REF] Huson | Application of phylogenetic networks in evolutionary studies[END_REF] for the whole dataset and for each species to gain insights into the level of recombination between and within the various O. unilateralis species.

Principal component analysis (PCA), molecular diversity and genetic differentiation

The nucleotide diversity (Pi) of all species/populations and inter-specific/inter-population F ST and d xy values were calculated using PopGenome package in R [START_REF] Pfeifer | PopGenome: An efficient swiss army knife for population genomic analyses in R[END_REF]. These calculations were performed based on SNPs called using the O. polyrhachis-furcata reference to use a common set of SNPs. The correlation between Pi and the latitude of the populations was tested with a Spearman's correlation test. We investigated the pattern of isolation by distance (IBD) by testing the existence of a correlation between F ST /(1-F ST ) and the log of geographic distance [START_REF] Rousset | Genetic Differentiation and Estimation of Gene Flow from F-Statistics Under Isolation by Distance[END_REF], using a Mantel test with 10,000 permutations.

Genetic clustering, inference of gene flow and introgression

A Bayesian clustering was conducted via FastStructure [START_REF] Raj | fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets[END_REF] with admixture model for the whole dataset to infer genetic structures between species. At the intra-specific level, we conducted the same analysis using the SNPs generated from each species reference. The most relevant number of clusters (K) were determined based on the plots of marginal likelihood and visual assessment of the barplots. The introgression footprints along the genome were investigated with a clustering analysis of genetic similarity on 500 SNPs sliding windows among the samples based on Gower's distance, using Mclust package in R [START_REF] Scrucca | mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models[END_REF]. Neighbor-joining trees based on the F84 distance [START_REF] Kishino | Evaluation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in hominoidea[END_REF] [START_REF] Kishino | Evaluation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in hominoidea[END_REF] were also constructed to illustrate the relationships among the different clusters.

To infer events of gene flow between species, we used TreeMix [START_REF] Pickrell | Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data[END_REF] which estimates the most likely topology representing the evolutionary history between populations and mixing based on genome-wide allele frequency data. TreeMix compares the likelihood of models with different numbers of migration events between populations.

Results

Detection of single nucleotide polymorphisms (SNPs)

By using O. polyrhachis-furcata BCC54312 as the reference for mapping all genomes, we retained 688,264 SNPs in the whole dataset. Only a small proportion of them was shared between the three host species (1144 SNP ~ 0.166 %, Fig. S1). Based on the phylogenetic species recognition analysis (see the subsection 3.2), we excluded a sample from the host C. leonardi with an unclear placement (NK122ss). There were 443,670 SNPs in the remaining dataset. The samples classified as O. camponoti-leonardi had more SNPs (219,017) than the other species (92,133 for O. camponoti-saundersi and 53,552 for O. polyrhachis-furcata). By using the O. polyrhachis-furcata reference genome to detect SNPs in all species, we could expect to find higher diversity in O.

polyrhachis-furcata due to a bias favoring detection of SNPs from this species. However, we observed in contrast fewer SNPs in O. polyrhachis-furcata, which is likely due to genuine lower genetic diversity in this species (see section 3.4). When using their respective references to call SNPs within each species, we found as expected more SNPs than when using the O. polyrhachisfurcata reference in the two other species [START_REF] Taylor | Clonal reproduction in fungi[END_REF]301,449 SNPs for O. camponoti-saundersi).

Species delimitation using the phylogenetic criterion of species recognition

The maximum likelihood (ML) tree based on whole genomes supported the existence of the three host-specific species. Indeed, we found three clades supported by 100% bootstrap support corresponding to the three hosts sampled (Fig. 2a). The only exceptions were NK604, isolated from a C. saundersi ant but placed within the O. camponoti-leonardi clade, and NK122ss, isolated from C. leonardi but placed as a sister group to the O. camponoti-saundersi clade with full bootstrap support (Fig. 2a). The trees inferred from the different scaffolds displayed congruent topologies (Fig. 2b) except for the scaffolds 6. 2,16,18,20,21,25,29,36,41,53,105 (Fig.S2). Some of these scaffolds however suffered from a lack of polymorphism and/or yielded trees with low bootstrap supports (Table 2S).

The Bayesian concordance analysis comparing topologies between genealogies built from 100 kb fragments showed high concordance factors (CFs above 0.9; Fig. 3a), further indicating that most genomic regions supported the three host-specific clades and thus the existence of distinct, host-specialized species. The Bayesian concordance analysis also confirmed the position of NK122ss as the sister group of O. camponoti-saundersi (Fig. 3a).

Inter and intra-specific genetic differentiation

Based on the SNPs inferred using O. polyrhachis-furcata as the reference, the Bayesian clustering also yielded clusters grouping samples according to host species, except again for NK604 (Fig. 1c). The sample NK122ss, from the host C. leonardi, had admixed membership up to K=4 but gained a total membership to a distinct cluster from K=5, where two other clusters were distinguished in this host (Fig. S3a), a pattern corroborated by PCA (Fig. 1b). The sample NK122ss may belong to a species different from O. camponoti-leonardi, as also suggested by the phylogenies, and was thus excluded from further analyses. No additional cluster was found at higher Ks (Fig. S3a). The marginal likelihood curve in fact reached a plateau at K = 5 (Fig. S3b). The sample NK604, isolated from the host C. saundersi, clustered with those from the host C. leonardi in the PCA (Fig. 1b), in agreement with the results on species delimitation, and was thus considered to belong to O. camponoti-leonardi in the following analyses. It is likely a spill over, i.e. an occasional infection from a non-endemic species without sustainable persistence. Furthermore, two genetic clusters could be clearly distinguished within O. camponoti-leonardi (OCL1 and OCL2 in Fig. S1b and1c). The genetic differentiation between the three main species was high (F ST > 0.5, Table 1). We inferred genetic clusters within each of the three main species based on SNPs called using specific reference per species. For O. camponoti-leonardi, the results were slightly different from those when the O. polyrhachis-furcata reference was used (see above), as we retrieved three clusters (OCL1, OCL2, OCL3) for most K values (Fig. 4a and Fig. S4a). Only at K=6 were four clusters observed but the fourth disappeared at higher K values and the marginal likelihood was lowest at K=6 (Fig. S4b). We therefore considered three genetic clusters in O. camponoti-leonardi; two clusters were parapatric-one in the extreme North (OCL1) and another one extended to the center (OCL2). The samples NK140 and NK561, which appeared to belong to OCL2 using SNPs called with the O. polyrhachis-furcata reference (Fig. 1b and1c), were assigned differently when using SNPs called with the O. camponoti-leonardi reference; the sample NK140 displayed an admixed genetic membership but still mostly belonged to OCL2 while the sample NK561 from the south of Thailand was assigned to another cluster (OCL3).

Regarding O. camponoti-saundersi, three sympatric clusters (OCS1, OCS2, OCS3) were found from K=3 and above, with a plateau reached at K=3 for the marginal likelihood (Fig. S5); the three clusters were localized mainly in the north-east of Thailand (Fig. 4b). For O. polyrhachis-furcata, the marginal likelihood oscillated between K=2 and K=10 (Fig. S6b); we identified three main genetic clusters, one in the north-east of Thailand (OPF1) and two in the South (OPF2 and OPF3),

OPF3 being however represented by a single strain (NK157) (Fig. 4c and Fig. S6a). Another individual from the South of Thailand (NK151) had different cluster assignments across the various K values (Fig. 4c and Fig. S6a). NJ trees and PCAs retrieved the same patterns of genetic subdivisions within each of the three species (Fig. S7 -S9). The intra-specific differentiation level (between clusters of a given named species) was overall slightly lower than inter-specific differentiation level (Table 2).

Nucleotide diversity and isolation by distance

We used the SNPs inferred using the best assembled reference genome (O. polyrhachisfurcata) to compute nucleotide diversity (Pi) to compare between species (Table 1) as well as between intra-specific clusters (Table 2). The nucleotide diversity was highest for O. camponotileonardi (0.026), followed by O. camponoti-saundersi (0.015) and O. polyrhachis-furcata (0.007).

Based on the Bayesian clustering, we considered O. camponoti-leonardi to be composed of two genetic clusters, OCL1 and OCL2 (excluding the strain NK561 that appeared to belong to a distinct cluster; Fig. S4a andS7), O. camponoti-saundersi to be composed of three genetic clusters (OCS1, OCS2 and OCS3), and O. polyrhachis-furcata to be composed of two clusters (OPF1 and OPF2), excluding the two genetically distant individuals NK151 and NK157. The genetic diversity was variable among genetic clusters within each species (Pi values in Table 2). OCS1 had much lower diversity compared to OCS2 and OCS3, as shown by the Pi values and the shorter branches on the NJ tree (Fig. S8a). The correlation between genetic diversity (Pi) and latitude for O. camponotileonardi was not significant (Spearman's correlation test: r = -0.3, p-value = 0.683; Table 3S and Fig. 10Sa). Isolation by distance was not significant in O. camponoti-leonardi (Mantel's test: r = 0.236, p-value = 0.25; Fig. 10Sb). Not enough sampling sites were available for the other species to test for such correlations.

Concordance among genealogies for within-species clusters

We then investigated whether the different clusters found within each of the identified cryptic species so far could be considered as distinct species based on the criterion of concordance among multiple genomic genealogies. The Bayesian concordance analysis based on 100-kb DNA fragments showed that the three clusters in O. camponoti-saundersi formed respectively clades with high CFs (above 0.85; Fig. 3b). For O. camponoti-leonardi, the clade OCL1 was strongly supported with a high CF (0.993) while the clade OCL2 as defined previously was poorly supported (CF = 0.477; OCL2 sensu lato in Fig. 3b). However, when excluding NK140 from OCL2, the clade became strongly supported (CF = 0.901), while NK140, like NK561 which was previously shown to be admixed, had unclear status. For O. polyrhachis-furcata, the clusters OPF1 and OPF2 formed each a monophyletic clade but with low CFs (0.433 and 0.485 respectively: Fig. 3b). Neighbor-net networks using SplitsTree also recovered different species (Fig. 5) and the same well differentiated genetic clusters within species (Fig. S11).

Introgression and gene flow between species

Previous analyses showed that a few individuals had admixed genetic membership. We therefore performed genetic clustering analyses along the genome to identify possible footprints of localized introgression. The general pattern of host-specific clusters was found throughout the genome (Fig. 6). However, introgression footprints was found along the genome for two individuals, NK151 and NK157, that showed admixed or distinct membership in comparison to the other O.

polyrhachis-furcata samples in Bayesian clustering. The O. camponoti-leonardi individual NK561 appeared here highly admixed between OCL1 and OCL2. We did not recover in this analysis the different genetic clusters in O. camponoti-saundersi (Fig. 6). The Gower's genetic distance on which this clustering is based may not be able to detect finer genetic differences. Splitstree analyses recovered the same well differentiated genetic clusters within species and without footprints of extensive gene flow, except perhaps between OPF1 and OPF2 (Fig. S11).

Gene flow events were inferred using Treemix based on the allelic frequency spectra of the different species and clusters retrieved from the SNPs called on the O. polyrhachis-furcata reference genome. We excluded the individual with unclear status, i.e., NK122ss. To account for linkage disequilibrium, blocks of 500 SNPs were used. Setting the number of migration events to one, we inferred one gene flow event between OCL1 and OCS (Fig. 7). However, the model with gene flow was not significantly more likely than the model without migration (log likelihood ratio test: p-value 0.235).

Linkage disequilibrium and recombination footprints

We investigated recombination footprints within species and genetic clusters by looking at linkage disequilibrium (LD) decay (Fig. 8). None of the species had their LD decaying to less than 0.2 at the distance of 1 Mb, and the LD of both O. camponoti-leonardi and O. camponoti-saundersi remained flat after an initial rapid decrease, resembling patterns in clonal species [START_REF] Sepúlveda | Genome Sequences Reveal Cryptic Speciation in the Human Pathogen Histoplasma capsulatum[END_REF]. Using half of the maximum LD as the criteria for linkage distance [START_REF] Taylor | Clonal reproduction in fungi[END_REF], O.

unilateralis species seemed to maintain linkage over more than 1 Mb. Nevertheless, the PHI-tests revealed significant deviations from the expectation of no recombination for most scaffolds in O.

camponoti-leonardi and O. saundersi, and their respective intra-specific genetic clusters. Evidence of recombination was found on fewer scaffolds for O. polyrhachis-furcata, both when considering the whole species and considering separately its intra-specific clusters (Table S4). For all species and their clusters, no footprints of recombination could be recovered from small scaffolds with little polymorphism (Table S4). The population-scaled recombination rates (rho) were all of similar order of magnitude (1.88e-5 for O. polyrhachis-furcata, 1.49e-5 for O. camponoti-leonardi and 5.95e-5

for O. camponoti-saundersi). Splitstree analyses also suggested low levels of recombination within species (Fig. 4) and within genetic clusters (Fig. S11); reticulations were observed but seemed overall limited compared to the whole network except for O. polyrhachis-furcata, in which the reticulations appeared to be restricted to the two admixed samples (NK151 and NK157) (Fig. S11b).

The per-sample inbreeding coefficients of the diploid samples were extremely high in all species, approaching 1; this means that most sites were homozygous (Table .S5), suggesting a very high level of inbreeding.

Discussion

Host-specific species with occasional host-jump and gene flow

Our population genomics study on Thai populations of O. unilateralis sensu lato from three ant hosts showed that, apart from a few exceptions, fungal species matched ant species. The Occasional spill-over or hybridization thus did not seem to lead to long term introgression among species, potentially due to selection against hybrids.

Possible additional cryptic species and the one ant-one fungus paradigm

Our population genomics study further revealed the existence of differentiated genetic clusters within each host-specific species. The clusters within O. camponoti-leonardi and O.

camponoti-saundersi were particularly well differentiated and supported through different analyses, with little footprints of gene flow. Multiple genealogies were congruent in separating the genetic clusters within camponoti-saundersi and O. camponoti-leonardi indicating they should be considered as distinct species according to the GCPSR criterion [START_REF] Taylor | Phylogenetic Species Recognition and Species Concepts in Fungi[END_REF], and not just as different populations, and they would deserve further morphological examinations in future studies.

The clusters within O. polyrhachis-furcata in contrast did not appear supported as distinct species.

This study thus revealed hidden diversity beyond well described species, which could not be detected previously [START_REF] Kobmoo | New species of Ophiocordyceps unilateralis, an ubiquitous pathogen of ants from Thailand[END_REF][START_REF] Kobmoo | Molecular phylogenies reveal host-specific divergence of Ophiocordyceps unilateralis sensu lato following its host ants[END_REF], probably due to the limited quantity of markers and examined samples [START_REF] Balasundaram | How many DNA markers are needed to reveal cryptic fungal species?[END_REF][START_REF] Taylor | Phylogenetic Species Recognition and Species Concepts in Fungi[END_REF]. In addition, a sample (NK122ss) did not cluster with other individuals parasitizing the same host species and was instead placed as a sister taxon to O. camponoti-saundersi, without presenting admixture footprints, suggesting that it could belong to a different species or be admixed with another species not present in the sample.

The finding that there could be more than one Ophiocordyceps species parasitizing a given host challenges the well-accepted "one ant-one fungus" paradigm [START_REF] Araújo | Zombie-ant fungi across continents: 15 new species and new combinations within Ophiocordyceps. I. Myrmecophilous hirsutelloid species[END_REF]. It should however be noted that the ant taxonomy is itself still debated. The ants Colobopsis leonardi and C.

saundersi were previously classified in the genus Camponotus before a recent resurrection of the genus Colobopsis [START_REF] Ward | A revised phylogenetic classification of the ant subfamily Formicinae (Hymenoptera: Formicidae), with resurrection of the genera Colobopsis and Dinomyrmex[END_REF] and both are cryptic species with unclear taxonomy [START_REF] Laciny | Colobopsis explodens sp. n., model species for studies on "exploding ants" (Hymenoptera, Formicidae), with biological notes and first illustrations of males of the Colobopsis cylindrica group[END_REF]. The genetic clusters found within described Ophiocordyceps species in our study could actually be associated to different ant species in agreement with the one ant-one fungus hypothesis.

A taxonomic work with a strong population genomics aspect on both the hosts and the parasites will be necessary to elucidate species delimitation and cophylogeny analyses to assess whether codivergence occurred [START_REF] De Vienne | Cospeciation vs host-shift speciation: Methods for testing, evidence from natural associations and relation to coevolution[END_REF]Morand et al., 2015).

Reproductive mode and mating system in Ophicordyceps unilateralis

Recombination likely occurs in O. unilateralis sensu lato, but probably very rarely. The rho values were of the same order of magnitude (-5) for all three host-specific species and much lower than in fungi with regular outcrossing such as the heterothallic Zymoseptoria tritici and Z. ardabilae (rho values being respectively 0.0217 and 0.0045: [START_REF] Stukenbrock | Fine-Scale Recombination Maps of Fungal Plant 208[END_REF], or other fungi with diploid-selfing obligate sexual reproduction like Microbotryum species (order of magnitude -3 to -2: [START_REF] Badouin | Widespread selective sweeps throughout the genome of model plant pathogenic fungi and identification of effector candidates[END_REF] and Saccharomyces paradoxus (0.001 -0.003: [START_REF] Tsai | Population genomics of the wild yeast Saccharomyces paradoxus: Quantifying the life cycle[END_REF]. The per-sample inbreeding coefficients for the diploid individuals were actually all very high in all species and clusters, indicating that most SNPs were homozygous. As all the strains used in this study were isolated from sexual spores resulting from meiosis, O. unilateralis regularly undergoes sexual reproduction. Altogether, these findings suggest for the whole species complex a high level of inbreeding or even frequent intra-haploid mating, i.e. mating between genetically identical haploid cells [START_REF] Billiard | Sex, outcrossing and mating types: Unsolved questions in fungi and beyond[END_REF][START_REF] Giraud | Mating system of the anther smut fungus Microbotryum violaceum: Selfing under heterothallism[END_REF]. Intra-haploid mating is only possible in homothallic fungi as the existence of mating types in heterothallic fungi prevents such mating.

Whether O. unilateralis is homothallic or heterothallic is still unclear; the genome of O.

polyrhachis-furcata contains at least a mating type locus (MAT-1-2) and a putative mating typeswitching gene (Wichadakul, unpublished data) which suggests that it may be homothallic but no experimental observation has been done.

Such high inbreeding is consistent with the life history traits of these fungi. Fungal development into the transmission stage inside ant nest was not observed and infection outside the nest is likely favored to avoid social immunity [START_REF] Loreto | Long-term disease dynamics for a specialized parasite of ant societies: A field study[END_REF]. Field observations and a theoretical model established that effective infections were rare; only a weak proportion of samples observed on field went through sexual reproduction [START_REF] Andersen | Disease dynamics in a specialized parasite of ant societies[END_REF][START_REF] Mongkolsamrit | Life cycle, host range and temporal variation of Ophiocordyceps unilateralis/Hirsutella formicarum on Formicine ants[END_REF]. The probability to encounter compatible spores from distinct diploid fungal individuals may be low, thus promoting selfing in the fungi. Low probability of outcrossing can be found in other pathogenic fungi such as the plant castrating anther-smut fungi Microbotryum in which mating occurs between cells resulting from the same meiosis issued from a dispersing diploid spore [START_REF] Giraud | Mating system of the anther smut fungus Microbotryum violaceum: Selfing under heterothallism[END_REF][START_REF] Vercken | Glacial refugia in pathogens: European genetic structure of anther smut pathogens on Silene latifolia and Silene dioica[END_REF]. The infective spores of O. unilateralis are haploid which could render the probability to find a compatible mate in an insect even lower, favoring intra-haploid mating.

The biogeography of Ophiocordyceps unilateralis in Thailand

While the genetic clusters detected within O. camponoti-saundersi were sympatric and all restricted to the Northeast of Thailand, O. camponoti-leonardi and O. polyrhachis-furcata had widely distributed genetic clusters, with different geographical ranges, mainly reflecting the separation between the north/center and the south of Thailand. Such genetic differentiation between the North and the South of Thailand has been documented in many organisms [START_REF] Chen | Large-scale phylogenetic analyses provide insights into unrecognized diversity and historical biogeography of Asian leaf-litter frogs, genus Leptolalax (Anura: Megophryidae)[END_REF][START_REF] Karanth | Molecular phylogeny and biogeography of langurs and leaf monkeys of South Asia (Primates: Colobinae)[END_REF][START_REF] Pramual | Phylogeography of the black fly Simulium tani (Diptera: Simuliidae) from Thailand as inferred from mtDNA sequences[END_REF][START_REF] Sittipraneed | Genetic differentiation of the honey bee (Apis cerana) in Thailand revealed by polymorphism of a large subunit of mitochondrial ribosomal DNA[END_REF][START_REF] Tu | Comparative phylogeography of bamboo bats of the genus Tylonycteris (Chiroptera, Vespertilionidae) in Southeast Asia[END_REF]Yu et al., 2019), which was suggested to reflect different biogeographic entities, one from the Indochina and another from the Malay peninsula [START_REF] Woodruff | Biogeography and conservation in Southeast Asia: How 2.7 million years of repeated environmental fluctuations affect today's patterns and the future of the remaining[END_REF]. The existence of northern and southern distinct biogeographic compartments also explains that the genetic diversity does not increase toward south, contrary to what is found in the northern hemisphere for many organisms [START_REF] Hewitt | Quaternary Genetic consequences of climatic oscillations in the Genetic consequences of climatic oscillations in the Quaternary[END_REF]. Enlarging the sampling of both the hosts and the pathogens across Southeast Asia would give novel insights into the host-pathogen phylogeography of this region.

Conclusion

In this study, population genomics supported the host-specificity of previously described cryptic species in ant pathogenic fungi and further revealed genetic clusters sharing the same host but with contrasting geographical ranges as candidates to be erected as new species in the zombie ant fungal complex O. unilateralis sensu lato. Whole genome analyses also revealed individuals with admixed genetic membership but not leading to persistent gene flow among species or genetic clusters. The recognition of cryptic species has direct impacts on species diversity assessment and ecological research and population genomics can be powerful to reveal hidden diversity, as shown in our study. More generally, population genomics can reveal the existence of cryptic species or different populations of pathogens, contributing to our understanding of host-parasite/pathogen specificity and phylogeography. Furthermore, we studied for the first time recombination footprints in O. unilateralis sensu lato which suggested limited recombinations and a high level of inbreeding.

  previous separation of host-specific species within O. unilateralis s.l. from Thailand (O. camponotileonardi, O. camponoti-saundersi and O. polyrhachis-furcata) had strong whole-genome support with different methods, i.e., Bayesian clustering, PCA, maximum likelihood bootstrap and Bayesian concordance factors (BCF). In particular, the tree topologies obtained based on different genomic regions were congruent and supported the monophyly of individuals parasitizing a given host species, indicating that the three species O. camponoti-leonardi, O. camponoti-saundersi and O.polyrhachis-furcata form distinct lineages under the phylogenetic species criterion.Nevertheless, one individual was found in an ant species different from the typical host of its species, as previously documented[START_REF] Kobmoo | Molecular phylogenies reveal host-specific divergence of Ophiocordyceps unilateralis sensu lato following its host ants[END_REF], and two others showed signals of genetic admixture in Bayesian clustering which were also reflected into ambiguous or poorly supported placements in phylogenetic trees. Using an allelic frequency spectrum-based inference, a single event of gene flow from O. camponoti-leonardi to O. camponoti-saundersi was detected; however, the model with gene flow was not statistically more likely than the model without gene flow.

  Figure 1. a) Sampling sites of Ophiocordyceps unilateralis sensu lato across Thailand (1 = Ban

Figure 2 .

 2 Figure 2. Maximum likelihood-based tree inferred using a) the whole genome altered at the SNP

Figure 3 .

 3 Figure 3. Bayesian inference-based trees with genome-wide concordance factors and their

Figure 4 .

 4 Figure 4. Barplots of Bayesian clustering using single nucleotide polymorphisms (SNPs) inferred

Figure 5 .

 5 Figure 5. A neighbor-net phylogenetic network of Ophiocordyceps unilateralis (OCL = O.

Figure 6 .

 6 Figure 6. Clustering of Ophiocordyceps unilateralis sensu lato, based on Gower's distance, along

Figure 7 .

 7 Figure 7. Results of TreeMix. a) Best-fitting genealogy without admixture (m=0) for different

Figure 8 .

 8 Figure 8. Linkage disequilibrium (LD) decay of Ophiocordyceps camponoti-leonardi (red), O.

Fig. S3

 S3 Fig.S3 Bayesian clustering based on whole-genome single nucleotide polymorphisms (SNPs). a) Barplots of assignment probabilities to different clusters from K = 1 to K = 7. b) The marginal likelihood for different K values. 1 = Ban Huai Baba, 2 = Doi Inthanon National Park, 3 = Nam Nao National Park, 4 = Phu Khiao Wildlife Sanctuary, 5 = Khao Yai National Park, 6 = Ko Chang Island, 7 = Khlong Nakha Wildlife Sanctuary, 8 = Khao Luang National Park, 9 = Hala Bala Wildlife Sanctuary.

Fig. S4

 S4 Fig.S4 Bayesian clustering based on single nucleotide polymorphisms (SNPs) inferred from the reference genome of Ophiocordyceps camponoti-leonardi for the samples belonging to this species. a) Barplots of assignment probabilities to different clusters from K = 1 to K = 8 (The labels on the x axis correspond to the sampling sites). b) The marginal likelihood for different K values. 1 = Ban Huai Baba, 2 = Doi Inthanon National Park, 3 = Nam Nao National Park, 4 = Phu Khiao Wildlife Sanctuary, 5 = Khao Yai National Park, 6 = Ko Chang Island, 8 = Khao Luang National Park.

Fig. S5

 S5 Fig.S5 Bayesian clustering based on single nucleotide polymorphisms (SNPs) inferred from the reference genome of Ophiocordyceps camponoti-saundersi for the samples belonging to this species. a) Barplots of assignment probabilities to different clusters from K = 1 to K = 8 (The labels on the x axis correspond to the sampling sites). b) The marginal likelihood for different K values. 3 = Nam Nao National Park, 4 = Phu Khiao Wildlife Sanctuary, 5 = Khao Yai National Park.

Fig. S6

 S6 Fig.S6 Bayesian clustering based on single nucleotide polymorphisms (SNPs) inferred from the reference genome of Ophiocordycep polyrhachis-furcata for the samples belonging to this species. a) Barplots of assignment probabilities to different clusters from K = 1 to K = 8 (The labels on the x axis correspond to the sampling sites). b) The marginal likelihood for different K values. 5 = Khao Yai National Park, 7 = Khlong Nakha Wildlife Sanctuary, 8 = Khao Luang National Park.

Fig. S7

 S7 Fig.S7 Population structure among the samples belonging to Ophiocordyceps camponoti-leonardi. a) Neighbor-joining tree based on an alignment of single nucleotide polymorphisms (SNPs) and using the F84 distance. b) -d) Principal component analyses (PCA) based on SNPs.

Fig. S8

 S8 Fig.S8 Population structure among the samples belonging to Ophiocordyceps camponoti-saundersi. a) Neighbor-joining tree based on an alignment of single nucleotide polymorphisms (SNPs) using the F84 distance. b) A principal component analysis (PCA) based on SNPs.

Fig. S9

 S9 Fig.S9 Population structure among the samples belonging to Ophiocordyceps polyrhachis-furcata. a) Neighbor-joining tree from based on an alignment of single nucleotide polymorphisms (SNPs) and using the F84 distance. b) A principal component analysis (PCA) based on SNPs.

Fig.

  Fig.S10 a) Correlation between nucleotide diversity (Pi) per site and per species and the sampling site latitude (OCL = Ophiocordyceps camponoti-leonardi, OCS = O. camponoti-saundersi, OPF = O. polyrhachis-furcata). b) Correlation between genetic dissimilarity between sampling sites of O. camponoti-leonardi (F st /1-F st ) and the log distance between sites.

Fig. S11

 S11 Fig.S11 Neighbor-net phylogenetic networks for a) Ophiocordyceps camponoti-leonardi, b) O. polyrhachis-furcata and c) O. camponoti-saundersi.

Table 1 .

 1 Genetic differentiation between species (F ST : above the diagonal, d xy : below the diagonal; OCL = Ophiocordyceps camponoti-leonardi, OCS = O. camponoti-saundersi, OPF = O.polyrhachis-furcata). The nucleotide diversity (Pi) within species is given in the diagonal in bold; dxy = absolute divergence between two species; F ST = fixation index measuring relative divergence.

	F ST	OCL	OCS	OPF	Ophiocordyceps sp. nov.
	d xy				(NK122ss)
	OCL	0.026	0.696	0.588	0.865
	OCS	0.224	0.015	0.771	0.902
	OPF	0.127	0.162	0.007	0.959
	Ophiocordyceps	0.309	0.269	0.257	0
	sp. nov.
	(NK122ss)

Table 2 .

 2 Genetic differentiation between genetic clusters (F ST : above the diagonal, d

	Fst	OCL1	OCL2	OCL3	OCS1	OCS2	OCS3	OPF1	OPF2
	d xy
	OCL1	0.011	0.711	0.907	0.934	0.915	0.904	0.884	0.870
	OCL2	0.144	0.016	0.781	0.877	0.853	0.841	0.749	0.736
	OCL3	0.175	0.117	0	0.996	0.976	0.962	0.985	0.966
	OCS1	0.262	0.216	0.246	0.001	0.913	0.834	0.979	0.962
	OCS2	0.261	0.214	0.244	0.079	0.010	0.813	0.947	0.931
	OCS3	0.268	0.221	0.250	0.063	0.081	0.006	0.927	0.912
	OPF1	0.159	0.110	0.144	0.155	0.152	0.159	0.001	0.425
	OPF2	0.164	0.116	0.149	0.161	0.158	0.165	0.013	0.003

xy : below the diagonal). The nucleotide diversity (Pi) within clusters are given in the diagonal in bold. OCL3 includes a single individual (NK561).

  refugial-phase biodiversity. Biodivers. Conserv. 19, 919-941. https://doi.org/10.1007/s10531-010-9783-3 Yu, H., Tian, E., Zheng, L., Deng, X., Cheng, Y., Chen, L., Wu, W., Tanming, W., Zhang, D., Compton, S.G., Kjellberg, F., 2019. Multiple parapatric pollinators have radiated across a

	continental	fig	tree	displaying	clinal	genetic	variation.	Mol.	Ecol.
	https://doi.org/10.1111/mec.15046
	Figures Captions
	Figure 1

Table S1 :

 S1 Fungal isolates used in this study (BCC = Biotec Culture Collection Accession Number, BBH = Biotec Bangkok Herbarium accession number).

Isolate Isolation method Ant host Site Province BCC BBH BioSample

Table S5 : The observed, expected number of homozygous sites, total number of SNPs and inbreeding coefficient for the diploid samples.

 S5

	Samples	Species	Observed Expected	Total	F coefficient
	NK134	O. polyrhachis-furcata	687858	596600		1
	NK140	O. camponoti-leonardi	640930	560000		0.9855
	NK270	O. camponoti-saundersi	645429	561400		0.9998
	NK378	O. polyrhachis-furcata	687619	596400		1
	NK526	O. camponoti-leonardi	663027	575500		0.9998
	NK561	O. camponoti-leonardi	663418	576800		1
	NK316	O. camponoti-saundersi	646450	563200		0.9998
	NK461	O. camponoti-saundersi	640107	558300		0.9995
	NK453	O. camponoti-saundersi	561257	487900		0.9995
	NK482	O. camponoti-saundersi	453036	391900		0.9995
	NK483	O. camponoti-saundersi	643876	560500		0.9998
	NK573	O. camponoti-saundersi	647183	562900		0.9998
	NK583	O. camponoti-saundersi	646163	562200		0.9999
	NK586	O. camponoti-saundersi	646587	562700		0.9995
	NK597	O. camponoti-saundersi	644848	561400		0.9998
	NK604	O. camponoti-leonardi	670466	582300		0.9998
	NK151	O. polyrhachis-furcata	686833	595700		1
	NK157	O. polyrhachis-furcata	685814	594800		1

Acknowledgements

We would like to thank Alodie Snirc for advice concerning DNA extraction, Antoine Branca for help with bioinformatic protocols; Kanoksri Tasanathai, Donnaya Thanakitpipattana, Wasana Noisripoom and Artit Khonsanit for help in samples collection; Navavit Ponganan for making the maps in this publication. We also thank the Department of National Parks, Wildlife and Plant Conservation of Thailand for their cooperation and support of our project. Funding: This work was supported by a Marie-Skłodowska Curie Action [grant number 655278] to NK, a Biotec Fellow's Research Grant [grant number P1950231] to JJL, and by the National Science and Technology Development Agency, Cluster and Program Management Office [grant number P1300854] to SM.

Supplementary Materials

Supplementary Figures.

Table S1: Fungal isolates used in this study.

Table S2: Genetic diversity per scaffold in Ophiocordyceps unilateralis sensu lato.

Table S3: Genetic diversity by site and by species.

Research Data

The raw reads in this study were deposited at the NCBI Sequence Read Archive (SRA) under the accession PRJNA530395. The information regarding the fungal isolates used in this study were deposited at the NCBI BioSample under the accessions reported in the table S1 of the Supplementary Materials. The final sets of SNPs are available at… Appendix A: Supplementary Figures

Cryptic species within


Djamel- Eddine Henni
email: hennidjamel@yahoo.fr

Abdelghani Ghomari
email: ghomari65@yahoo.fr

Yassine Hadjadj-Aoul

Videoconferencing Over OpenFlow Networks: An Optimization Framework for QoS Routing

Keywords: SDN, QoS, Routing, Visioconference

The recent emergence of OpenFlow, in the field of networks' control, marks a major evolution in networking. Indeed, Openflow comes up with a centralized control plane (real or virtual) and a remote control of the data plan, which enable network's evolution with unprecedented rapidity. This evolution, come, however, with new challenges and open issues. The data path portion still resides on networking devices, while high-level routing decisions are moved to a separate remote server, which may impact the QoS of the supported services. Particularly, there is a need to guarantee at least the QoS that was formally provided using older and classical distributed protocols. This paper focuses on an improvement of the traditional OpenFlow controllers by providing QoS Routing support for Videoconferencing over OpenFlow networks. The simulation results under Mininet clearly demonstrate the effectiveness of the proposed approach.

I. INTRODUCTION

The forecasted dramatic growth of the number of connected devices and the volume of traffic originating particularly from multimedia application, such as videoconferencing and video streaming, calls for shifting from the current distributed management of the network to a centralized control 1 . Indeed, the network control centralization provides an efficient and attractive way to tackle the challenge of increased management complexity.

OpenFlow is certainly the more significant and popular open standard, which is initially proposed in [START_REF]OpenFlow[END_REF] with the objective to centralize (even partially) the management and the control of the network's nodes. It is, indeed, the most widely deployed Software Defined Networking (SDN) technology. The main idea behind SDN consists in decoupling the control and data plans of a network. A software-based controller is, thus, responsible for managing the forwarding information of one or more network devices that handle the forwarding of traffic according to the rules set by the controller.

Delivering services with guaranteed quality of service (QoS) has been historically considered as one of the main issues for network operators (NO). Nowadays, MPLS represents the most advanced mechanism for QoS support [START_REF] Rosen | Multiprotocol Label Switching Architecture[END_REF], which provides an ultra-fast switching capability. However, MPLS lacks real-time re-configuration and adaptation to network conditions. In the context of OpenFlow, QoS is at its infancy. Indeed, the OpenFlow 1.0 specifications only allow setting the network type of service for a particular flow by adding rules to enqueue its packets to a specific queue on a specific port. Even if the OpenFlow 1.3 specifications present more features, there is no models or architectures in terms of QoS deployment that has been implemented in the current Internet design.

In this paper, we propose a new controller design that enables QoS routing for videoconferencing flow delivery over OpenFlow networks in order to achieve the required QoS metrics. We propose a dynamical QoS routing for QoSenabled flows (i.e. videoconferencing traffic) which focus on protecting such traffic over non constrained flows (i.e. non prioritized). The dynamic QoS routing problem is formulated as a Constrained Shortest Path (CSP) problem, which is solved using the Delay-Constrained Least-Cost (DCLC) algorithm.

The rest of the paper is organized as follows: section II discusses the related works. Section III proposes an Openflow Based QoS Routing Architecture and the problem formulation. Simulation settings and results are discussed in section IV. Section V draws some conclusions and future work.

II. RELATED WORKS

Routing, generally, consists in two basic tasks or functions. The first one is to collect the state information and keep it up to date. The second task is to find a feasible path for a new connection based on the collected information. Thus, the performance of any routing algorithm depends, directly, on how well these two tasks were conducted.

QoS routing has been a major concern of both academia and network operators for many years. In fact, a very huge number of studies have been made in such topic to improve the users' quality of experience (QoE). In [START_REF] Chen | An overview of quality of service routing for next-generation high-speed networks: Problems and solutions[END_REF], the authors provide an overview of QoS routing techniques over next generation highspeed networks. They present different QoS routing problems, their challenges, the QoS routing strategies and a comparison of existing routing algorithms. Early, resources' reservation was considered as an efficient mean to protect QoS-constrained connections. In [START_REF] Ferrari | A Scheme for Real-Time Chonne Establishment in Wide-Area Networks[END_REF], [START_REF] Zhang | RSVP": A New Resource Reservation Protocol[END_REF] the authors have considered resource reservation (bandwidth, CPU time, buffer...etc.) at the connections' establishment to isolate concurrent connections sharing common links. In [START_REF] Wang | QoS Routing for Supporting Resource Reservation[END_REF] a new routing algorithm, named the Wang-Crowcroft algorithm, was proposed to find a bandwidthdelay-constrained path using Dijkstra shortest-path algorithm. At a first step of this algorithm, all the links with a bandwidth less than the requirement are eliminated, which allows any paths in the resulting graph to satisfy the constraint in terms of bandwidth. Then the shortest path in terms of delay is selected. In [START_REF] Zhao | Cotonnectionless Quality-of-Service Routing Framework[END_REF] the authors proposed a novel connectionless QoS routing framework, where QoS constraints are carried by every packet and in which the routing decisions are made hop by hop at the packet level improving the scalability of the network. In [START_REF] Xue | Polynomial time approximation algorithms for multi-constrained QoS routing[END_REF] the authors treat various approximation algorithms for MCP (multi constrained path) problems and propose an improved approximation scheme. A recent work of the authors, in [START_REF] Hilmi | An Optimization Framework for QoS-Enabled Adaptive Video Streaming Over OpenFlow Networks[END_REF], presented an optimization framework for the OpenFlow controller in order to provide QoS support for SVC (Scalable Video Coding) over an OpenFlow network where they provide lossless-QoS to the base layer of the SVC encoded video and a lossy-QoS or a best-effort to the enhancement layers of SVC video.

III. AN OPENFLOW-BASED QOS ROUTING ARCHITECTURE

A. Controller Design and QoS Routing Support in OpenFlow

SDN is characterized by a centralized control plane, which allows moving part of the decision-making logic of network devices to external controllers [START_REF] Sezer | Are we ready for sdn? implementation challenges for software-defined networks[END_REF]. This characteristic provides the controller device with the ability to have an overall view of the network infrastructure, thus becoming aware of network elements and network characteristics. SDN architecture can be viewed as having three following layers: the infrastructure layer, the control layer and the applications layer. These layers are illustrated in Fig 1 . The Openflow protocol is playing a key role in such architecture by providing a link and an interface between the SDN controllers using a network Operating System (OS) and the interconnection devices (i.e. the infrastructure elements).

As in the classical SDN-based architectures, the routing tasks, in the proposed architecture, are, also, performed at the control layer in which the controllers have a global abstracted view of the network topology. In fact, as mentioned above, the existing network architectures are not really suited with the critical applications. Indeed, when a packet arrives at a router, it checks the packet's source and destination address pair with the entries of the routing table, and forwards it according to predefined rules configured by the network operator. The springiness of Openflow allow us, however, to remedy this weakness by defining different routing rules associated with the data flows as mentioned later. The controller is the brain of the network, it runs customized programs to decide which rules and actions are going to be installed to control packet forwarding in each switch element through interfaces with these forwarders, which effectively apply the rules.

B. Defining Flows in Openflow Networks

In OpenFlow, we can define flows using different methods. Flows can contain similar or different types of packets. For In Openflow-based networks, the devices store the flows and their associated properties in flow tables, which are processed as a pipeline [START_REF]OpenFlow Switch Specification Version 1[END_REF].

Fig. 2. Identification of flow fields in Openflow

In order to differentiate between flows, we exploit the Openflow's flow-based forwarding paradigm, where flows may be determined using the following packet header fields or values [START_REF] Hilmi | An Optimization Framework for QoS-Enabled Adaptive Video Streaming Over OpenFlow Networks[END_REF]:

• TOS (Type of Service) field of IPv4.

• Traffic class header field in MPLS.

• Traffic class field in IPv6.

• IP addressing if the source IP address of the flow is known.

• Transport source and/or destination port numbers.

C. Optimization of QoS routing using an Openflow-based controller

In order to implement a QoS mechanism, we must define the QoS metrics that will be used, while defining the application's requirement in terms of QoS metrics.

Videoconferencing enables real time communication by allowing people at two or more sites to communicate with each other. In videoconferencing, in addition to voice-channels, as in the conventional public switched telephony network (PSTN), video channels are also exchanged from the various sites [START_REF] Vleeschauwer | Tuning an IPbased Network Transporting Telephony and Videophony[END_REF]. Each site has one or more cameras, microphones, loudspeakers and monitors, as well as a list of supported codecs. This type of conferencing aims to create an effect called Virtual Presence. The QoS metrics requirements for videoconferencing are given in Table I In the following, we formulate the dynamic QoS routing problem as a Delay-Constrained Least-Cost (DCLC). In fact, as explained above, videoconferencing is an interactive multimedia application which requires a strict end-to-end delay and packets loss requirement.

In our proposal, a network is formulated as a direct network graph G(N, L) where N is the set of nodes and L the set of all the links, so that link (i, j) is an ordered pair, which is outgoing from node i and ingoing to node j. We consider that R xy is the set of all possible routes from source x to destination y. For any route r ∈ R xy we define the cost function f C (r) and the delay function f D (r), which are calculated as follows:

f C (r) = (i,j)∈r c ij , f D (r) = (i,j)∈r d ij (1)
where the coefficients c ij and d ij represent, respectively, the cost in capacity and delay for the link (i, j). The best path is the one minimizing the cost function f C (r) subject to the delay function f D (r), which must be less than or equal to a specified value D max . We considered cost metric is calculated as the weighted sum of packet loss measure and delay as follows:

c ij = (1 -α)d ij + αp ij for 0 ≤ α ≤ 1, ∀(i, j) ∈ L (2)
where p ij represents the packet loss measure for the QoS constrained traffic and bets-effort flows on link (i,j), and α is the scale factor which determines the relative importance of the delay and the packet losses depending on network and traffic importance.The route selection would be more sensitive to packet losses on the QoS route for large network and more sensitive to delay for a small one.The formula for p ij is defined as below:

p ij = Qij +Tij -Bij Qij+Tij , B ij < Qij + T ij (3) 0, B ij ≥ Q ij + T ij
where B ij is the bandwidth of the link (i, j), Q ij and T ij are the amounts of QoS-cosntrained traffic and best-effort traffic observed on the link (i, j), respectively. The route management function of the controller collects data from forwarders (p ij , d ij ) and passes them to the route calculation. At the forwarder layer, the considered parameters are estimated as follows:

• Packet loss measure (p ij ) is calculated using (3) where B ij , Q ij and T ij are required parameters for calculation.

Monitoring of the per-flow traffic amounts (Q ij and T ij ) on each link is done using the Openflow protocol which maintains the per-flow counters in the forwarders [START_REF]OpenFlow Switch Specification Version 1[END_REF]. • Delay measure can be obtained by averaging the observed delay using time stamping as in RTP [START_REF] Schulzrinne | RTP: A Transport Protocol for Real-Time Applications[END_REF]. The problem proposed in this work is an example of minimum cost flow optimization problems in network optimization theory. In the literature, there are many algorithms available in order to solve min-cost flow problems. We solved the proposed problem by using the algorithm discussed in [START_REF] Mahmoud | A Proposal Algorithm to Solve Delay Constraint Least Cost Optimization Problem[END_REF] and illustrated in Algorithm 1.

When the route management function updates the QoS indicating parameters or the topology management function detects a topology change, the route calculation function runs the DCLC algorithm to solve the CSP problem (finding the best path which minimize the cost function f C (r) subject to the delay function f D (r), which must be less than or equal to a specified value D max .). Then, the controller updates the forwarders flow tables accordingly and the QoS routes are dynamically set for videoconferencing traffic.

D. The DCLC Algorithm

Step 0: Start.

Step 1: If there is more than one path with minimum cost, we find the least cost path between s (source) and d (destination) with minimum delay, and let this path named q.

Step 2: Check the delay of the selected path in (step1) is less than or equal the delay constrained.

Step 3: If the result of (step 2) is true then the suitable path is q.

Step 4: If the result of step 2 is false then let p be the shortest path in corresponding to delay (i.e. the path that has minimum delay).

Step 5: check if the delay of the path p is greater than delay constrained.

Step 6: If the result of (step 5) is true then there is no feasible path (return NULL).

Step 7: Create a flag variable named continue with Boolean type having initial value = True, Continue= True.

Step 8: Building a close loop with a condition continue=true and exit the loop just when continue =false. While continue do

Step 9: If the result of step 5 is false then calculate the parameter of constructing the mixed weight (α), where: α = Cd-d(p) c(p)-c(q) . Cd is the delay constrained, d(p) is the delay of the path, c(p) is the cost of the path and c(q) is the cost of the path q.

Step 10: Convert each path to one parameter (w), where w = delay + α * cost for each path, Then let r is the minimum path corresponding to (w) value.

Step 11: Check: If cost of the path(r) equal the cost of path(q) or the cost of the path p. If (c(r) = c(q) or c(r) = c(p) then

Step 12: If the result of the condition in (step 11) is true then let continue = false to exit loop (step 15), Continue=False

Step 13: If the result of the condition in (step 11) is false then (step 14) Else

Step 14: Let p = the minimum path(r).

Step 15: p is the algorithm choice of most suitable path. Return p .

Step 16: End. Having described the details of our proposal, we now direct our attention on evaluating its performance in realistic use cases. We particularly focus, in the following, on studying the impact of the use of QoS controller on multimedia flows when the network is overloaded.

Algorithm DCLC (G,s,t,c,d,Cd) q ← M odiDijk(c, d) if d(q) ≤ Cd then return q else p ← Dijk(d) end if d(p) >

A. Experimental Environnement

In order to simulate the OpenFlow architecture and test the performances of the proposed routing formulations, we used the mininet emulator [START_REF] Lantz | A network in a laptop: rapid prototyping for software-defined networks[END_REF]. The proposed application is integrated within the floodlight controllers [START_REF]Floodlight[END_REF]. A simple network topology, shown in Fig 3, was created to compare the efficiency of the proposed mechanism against the former one. The two hosts h1 and h2 are separated by five Open virtual switches and controlled by the floodlight controller. We have simulated three scenarios in which udp and tcp flows are generated between h1 and h2 at different rates in order to simulate different communication quality. Table II The udp flows represent a videoconferencing communication between h1 and h2 (QoS flow) while tcp flows are here in order to create a network congestion between h1 and h2 (best effort flow). Flows are generated using [START_REF]DITG[END_REF]. In the first scenario we generate udp flows at a rate of 384 Kbps between h1 and h2 without congesting the network with tcp flows and then we measure the packet loss, the jitter and the delay, then after, we congest the network with tcp flows without the use of the QoS controller and we measure the same parameters (packet loss, jitter and delay). After that, we implement the QoS controller using eclipse then we run the simulation with the same parameters (udp flows at a rate of 384Kbps with presence of tcp flows). Table IV Now, We simulate a real videoconference between h1 and h2 in two scenario using the same above topology where in the first scenario we use a video with 320x240 resolution with a frame frequency of 25 fps and coded with MPEG 2 codec. We generate a TCP flow between h1 and h2 in order to overload the network while they are in conference then we measure the QoE by calculation the PSNR for the received video with and without the application of the QoS mecanism. The obtained graph is shown in

B. Results Discussion

When we generate udp flows without tcp, the network is less congested where the communication between h1 and h2 follows the shortest path R1-R5, in this case and whatever the used udp rate is, it is quite clear that we obtain an acceptable packet loss, jitter and delay since there is no congestion in the network. When we generate udp flows under a congested network without using a QoS controller, the udp communication (QoS flow) between h1 et h2 follows and share the shortest path R1-R5 with the best effort flow, therefore more packet loss occurs and we measure a significant delay and jitter due to the congested shortest path R1-R5. When we generate udp flows under a congested network with the use of QoS Controller, we obtain a significant reduction in terms of packet loss, jitter and delay as shown in the tables above whatever the quality of the udp communication between h1 and h2 which follows another path instead of the congested shortest path R1-R5.

V. CONCLUSIONS AND FUTURE WORK

QoS mechanisms allow network administrators to use existing resources efficiently and ensure the required level of service without the need of expanding or over provisioning their networks. In this paper, we advocate the use of QoS Routing in OpenFlow based networks and have showed through simulations that the shortest path is not always the best path to choose as it is done through classical networks. SDN and OpenFlow allow us to make less effort with more flexibility to pass new proposition and experimentation in terms of protocols and techniques. In this paper we have proposed a new controller design which takes into consideration path selection depending on criticality of the flow and provides a QoS routing for these applications. As part of our future work, we plan to study other case-studies to validate the broader applicability of our work on more larger networks.

Fig. 1 .

 1 Fig. 1. SDN architecture

← r end end Algorithm 1 :

 1 Cd then return Null else Continue=TRUE end while Continue=TRUE do α ← Cd-d(p) c(p)-c(q) w=delay+α*cost for each path r ← Dijk(d + αc); if (c(r) = c(q)) or (c(r) = c(p)) then Continue=False Return p else p The DCLC Algorithm IV. SIMULATION AND RESULTS DISCUSSION

Fig. 3 .

 3 Fig. 3. The topology used in the simulations

  Fig 4.

Fig. 4 .

 4 Fig. 4. Comparison between received video (320x240) with and without QoS

Fig. 5 .

 5 Fig. 5. Comparison between received video (352x288) with and without QoS

  [START_REF] Chen | QoS Requirements of Network Applications on the Internet[END_REF].

	Response time (ms)	delay (ms)	Jitter (ms)	Loss Rate	Error Rate
	< 100	< 200	< 400	< 0.01%	< 0.01%

  and TableIIIsummarize the simulation parameters (udp rate, tcp rate, simulation time...etc).

	Parametres	Values
	Architecture	Openflow
	Flows	TCP
		UDP
	Simulation Time	240s
	TCP Rate	800 Kbps
	Bandwidth (R1-R5)	1Mbps
	Bandwidth (R1-R2-R5)	2Mbps
	Bandwidth (R1-R3-R4-R5)	3Mbps

  summarizes the results of this first scenario.

	Simulation	TCP	Controller	loss (%)	Jitter (ms)	Delay (ms)
	1	No	w/o QoS	0.00	5	20
	2	Yes	w/o QoS	15.50	120	565
	3	Yes	w/QoS	0.01	8	26

TABLE IV OBTAINED

 IV RESULTS FOR AN UDP RATE AT 384 KBPSIn the second scenario, we take the same parameters of the simulation above but this time with an udp flow at a rate of 512 Kbps. The simulation results of this scenario are presented in Tablev.

	Simulation	TCP	Controller	loss (%)	Jitter (ms)	Delay (ms)
	1	No	w/o QoS	0.00	6	20
	2	Yes	w/o QoS	39.40	250	680
	3	Yes	w/QoS	0.02	7	21

TABLE V OBTAINED

 V RESULTS FOR AN RDP RATE AT 512 KBPSIn the third scenario, we take the same parameters of the simulation above but this time with an udp flow at a rate of 784 Kbps. The simulation results of this scenario are presented in TableVI.

	Simulation	TCP	Controller	loss (%)	Jitter (ms)	Delay (ms)
	1	No	w/o QoS	0.00	6	21
	2	Yes	w/o QoS	58.05	310	740
	3	Yes	w/QoS	0.02	6	22

TABLE VI OBTAINED

 VI RESULTS FOR AN UDP RATE AT 784 KBPS

It is meant by centralization, a logical or a physical centralization of the control plan.