Tatiana Matveeva

Víctor González

Florent Lassalle

Kasthuri Venkateswaran

Nitin K Singh

Céline Lavire

Joseph Nesme

Ludovic Vial

Xavier Nesme

Christopher E Mason

Comparative Genomics of Novel Agrobacterium G3 Strains Isolated From the International Space Station and Description of Agrobacterium tomkonis sp. nov

Keywords: Agrobacterium tomkonis, genomovar G3, ISS, metagenomics, phylogenomics, MLSA

Strains of Agrobacterium genomospecies 3 (i.e., genomovar G3 of the Agrobacterium tumefaciens species complex) have been previously isolated from diverse environments, including in association with plant roots, with algae, as part of a lignocellulose degrading community, from a hospital environment, as a human opportunistic pathogen, or as reported in this study, from a surface within the International Space Station. Polyphasic taxonomic methods revealed the relationship of Agrobacterium G3 strains to other Agrobacterium spp., which supports the description of a novel species. The G3 strains tested (n = 9) were phenotypically distinguishable among the strains from other genomospecies of the genus Agrobacterium. Phylogenetic analyses of the 16S rRNA gene, gyrB gene, multi-locus sequence analysis, and 1,089-gene core-genome gene concatenate concur that tested G3 strains belong to the Agrobacterium genus and they form a clade distinct from other validly described Agrobacterium species. The distinctiveness of this clade was confirmed by average nucleotide identity (ANI) and in silico digital DNA-DNA hybridization (dDDH) comparisons between the G3 tested strains and all known Agrobacterium species type strains, since obtained values were considerably below the 95% (ANI) and 70% (dDDH) thresholds used for the species delineation. According to the core-genome phylogeny and ANI comparisons, the closest relatives of G3 strains were Agrobacterium sp. strains UGM030330-04 and K599, members of a novel genomospecies we propose to call genomovar G21. Using this polyphasic approach, we characterized the phenotypic and genotypic synapomorphies of Agrobacterium G3, showing it is a bona fide bacterial species, well separated from previously named Agrobacterium species or other recognized genomic species. We thus propose the name Agrobacterium tomkonis for this species previously referred to as Agrobacterium genomospecies 3. The type strain of A. tomkonis is IIF1SW-B1 T (= LMG

INTRODUCTION

Agrobacterium species are firstly known for their pathogenic potential on plant, causing either crown gall or hairy root disease, depending on the presence of a tumor-inducing (Ti) or rootinducing (Ri) megaplasmid in their genome; however, most strains lack such plasmids and are not pathogenic. Agrobacterium species are common members of soil communities, and are efficient colonizers of the rhizospheres of a wide variety of plant hosts [START_REF] Macrae | Colonization of Tomato Plants by Two Agrocin-Producing Strains of Agrobacterium tumefaciens[END_REF][START_REF] Mafakheri | Two Novel Genomospecies in the Agrobacterium tumefaciens Species Complex Associated with Rose Crown Gall[END_REF], with which they have a commensal relationship [START_REF] Sanguin | Potential of a 16S rRNA-Based Taxonomic Microarray for Analyzing the Rhizosphere Effects of Maize on Agrobacterium spp. and Bacterial Communities[END_REF][START_REF] Dessaux | Niche Construction and Exploitation by Agrobacterium: How to Survive and Face Competition in Soil and Plant Habitats[END_REF]. Agrobacterium is a polyphyletic group, which taxonomy, originally aimed at reflecting the pathogenicity status of strains [START_REF] Smith | A Plant-Tumor of Bacterial Origin[END_REF], was revised multiple times, in the light of growing phenotypic and genotypic evidence, notably showing that the pathogenicity status was not correlated to the diversity of agrobacteria. Based on biochemical testing and DNA-DNA hybridization (DDH), Agrobacterium species were initially classified into three biovars [START_REF] Keane | Crown Gall of Stone Fruit II. Identification and Nomenclature of Agrobacterium Isolates[END_REF][START_REF] Kersters | Numerical Taxonomic Analysis of Agrobacterium[END_REF][START_REF] Ophel | Molecular genetics of plant-microbe interactions[END_REF]. Agrobacterium species belonging to biovar 1, as well as closely related species such as Agrobacterium rubi and Agrobacterium larrymoorei can be recognized by the presence of a linear chromid as the second major molecule of the genome, a synapomorphic trait enabled by the landmark acquisition of the telA gene [START_REF] Ramírez-Bahena | Single acquisition of protelomerase gave rise to speciation of a large and diverse clade within the Agrobacterium/Rhizobium supercluster characterized by the presence of a linear chromid[END_REF]. The strains that belong to biovar 2 and biovar 3 are only distantly related to Agrobacterium biovar 1, which later led to their reclassification into the genera Rhizobium and Allorhizobium, respectively [START_REF] Young | A revision of Rhizobium Frank 1889, with an emended description of the genus, and the inclusion of all species of Agrobacterium Conn 1942 and Allorhizobium undicola de Lajudie et al. 1998 as new combinations: Rhizobium radiobacter, R. rhizogenes[END_REF][START_REF] Mousavi | Revised phylogeny of Rhizobiaceae: proposal of the delineation of Pararhizobium gen. nov., and 13 new species combinations[END_REF]. Further refinement of the taxonomy of Agrobacterium biovar 1, also known as the Agrobacterium tumefaciens species complex, was conducted based on whole-genome information using DDH, leading to the classification of strains into species-level units called genomic species or genomovars [START_REF] Popoff | Taxonomic position of Agrobacterium strains of hospital origin[END_REF]. This classification framework was later validated and enriched by studies using amplified fragment-length polymorphism (AFLP), marker gene phylogeny, multi-locus sequence analysis (MLSA), comparative genome hybridization (CGH) and core-genome phylogeny [START_REF] Portier | Identification of genomic species in Agrobacterium biovar 1 by AFLP genomic markers[END_REF][START_REF] Costechareyre | Rapid and efficient identification of Agrobacterium species by recA allele analysis: Agrobacterium recA diversity[END_REF][START_REF] Lassalle | Genomic species are ecological species as revealed by comparative genomics in Agrobacterium tumefaciens[END_REF][START_REF] Lassalle | Ancestral Genome Estimation Reveals the History of Ecological Diversification in Agrobacterium[END_REF][START_REF] Mousavi | Revised phylogeny of Rhizobiaceae: proposal of the delineation of Pararhizobium gen. nov., and 13 new species combinations[END_REF], as recently reviewed by [START_REF] Flores-Felix | History and current taxonomic status of genus Agrobacterium[END_REF]. At the time of writing this communication, the Agrobacterium genus contained 15 named species, of which 11 had validly published names. Moreover, nine out of 15 described genomic species within the Agrobacterium biovar 1 are not yet formally named: G3 (this study), G5, G6, G7 ("Agrobacterium deltaense"), G8 ("Agrobacterium fabrum"), G13, G15 ("Agrobacterium viscosum"), G19 and G20 [START_REF] Mafakheri | Two Novel Genomospecies in the Agrobacterium tumefaciens Species Complex Associated with Rose Crown Gall[END_REF].

Several hundred microbial strains were isolated and identified in an ongoing investigation to map microbial diversity of the International Space Station (ISS;[START_REF] Checinska Sielaff | Characterization of the total and viable bacterial and fungal communities associated with the International Space Station surfaces[END_REF]. The ISS is a hermetically sealed closed system and their modules are at least 20-year-old, depending on their time of addition. The source of microorganisms is mainly through the human traffic, cargo transport and also associated with other experimental components. The air of the ISS is recirculated via an advanced environmental control system, and surfaces are maintained by implementing periodic cleaning. Previous attempts to isolate microorganisms from ISS environmental surfaces revealed that they generally consist of predominantly benign and commensal microorganisms [START_REF] Mhatre | Description of Chloramphenicol Resistant Kineococcus rubinsiae sp. nov. Isolated from a Spacecraft Assembly Facility[END_REF][START_REF] Bijlani | Methylobacterium ajmalii sp. nov., Isolated From the International Space Station[END_REF], but potentially pathogenic microorganisms were also sporadically isolated [START_REF] Knox | Characterization of Aspergillus fumigatus Isolates from Air and Surfaces of the International Space Station[END_REF]Singh et al., 2018a). In 2015, several bacterial strains were collected from the observation deck (Cupola) of the ISS, and whole-genome sequences (WGS) were generated for these isolates (Bharadwaj et al., 2020a,b;Bijlani et al., 2020a,b;Daudu et al., 2020a,b;[START_REF] Solomon | Draft Genome Sequences of Klebsiella Species Isolated from the International Space Station[END_REF][START_REF] Simpson | Draft Genome Sequences of Various Bacterial Phyla Isolated from the International Space Station[END_REF]. Based on preliminary genomic analyses, three ISS strains were identified as belonging to the Agrobacterium genomospecies 3 (also known as A. tumefaciens genomovar G3 or Agrobacterium G3).

The first objective of this study is to describe the phylogenomic novelty and characterize the taxonomic affiliation of the three strains isolated from the ISS environment. We, therefore, assembled a dataset of complete genomes of Agrobacterium G3, including previously released sequences from five strains isolated from various geographical regions and environments: an eosin flask from a hospital environment in France (CFBP 6623, sequenced twice), a cave in Lechuguilla, United States (LC34), algae in the United Kingdom (SUL3), Arabidopsis roots in Germany (Root651), and bioprospecting for lignocellulolytic microbes and enzymes from natural, highly evolved plant biomass-degrading systems in the United States (UGM030330-04). We added two novel sequences from Agrobacterium G3 strains from our own collection, including an opportunistic pathogen isolated from cerebrospinal fluid (CFBP 6624) and one isolated from a tobacco plant rhizosphere (RTP8). In addition to traditional phenotype testing, molecular taxonomy utilizing 16S rRNA gene, gyrB, MLSA (gyrB, parE, recA, and rpoB genes), and core-gene-based phylogenic analyses were carried out to describe Agrobacterium G3 strains.

Second objective of this study is to perform a comparative genomic analysis of Agrobacterium G3 strains together with representatives of other Agrobacterium species (n = 41 genomes) to elucidate genomic complexity. Subsequently, experimental characterization of the plasmid content was carried out on the set of Agrobacterium strains available to us in culture, i.e., G3 strains IIF1SW-B1 T , IIF1SW-B3, IIF1SW-B4, CFBP 6623, CFBP 6624, and RTP8 and reference strains "A. fabrum C58, " Agrobacterium radiobacter B6 T , and Agrobacterium pusense CFBP 5494. Thirdly, a pangenome analysis was implemented to identify core homologous gene clusters specific to ISS isolates and other Agrobacterium G3 strains. Finally, the metagenome reads of ISS environmental surfaces [START_REF] Singh | Succession and persistence of microbial communities and antimicrobial resistance genes associated with International Space Station environmental surfaces[END_REF] were mined for the presence of Agrobacterium G3 species to understand the prevalence of these novel species in ISS.

RESULTS AND DISCUSSION

The three strains collected from ISS belonging to Agrobacterium G3 along with six other G3 strains were subjected to a polyphasic characterization to determine the variable, conserved or distinctive traits of this genomic species.

Genome Characteristics of Novel Genome Sequences

Table 1 summarizes assembly statistics of all five novel ISS strains sequenced in this study. As an example, for strain IIF1SW-B1 T , the Illumina NovaSeq platform yielded 1.8 × 10 7 paired-end (PE) reads were reduced to 1.79 × 10 7 PE reads after performing trimming and quality filtering. The assembled draft genome of ISS strains consisted of 76-81 contigs with a genome size of 6.25-6.29 × 10 6 bp; the assembled genomes of CFBP 6624 and RTP8 had 35 and 38 contigs, for a size of 5.48 × 10 6 bp and 5.44 × 10 6 bp, respectively. The contig N50 size was 3.21-3.78 × 10 5 -bp, with a mean coverage of 100x for ISS strains, and 18x and 19x for strains CFBP 6624 and RTP8, respectively. G + C% of the five genomes was 59.12-59.21.

Phylogenetic Relationship of G3 Strains With Other Agrobacterium Species

A Maximum Likelihood (ML) tree of 16S ribosomal RNA (rRNA) gene sequences shows that this marker is not able to resolve and distinguish all Agrobacterium species (Figure 1A). All strains from genomospecies G3 and G4; G7 and G13; as well as G2 and G9 were found to have identical 16S rRNA gene sequences. In addition, the support for the topology of the 16S tree is generally low, with only branches leading to species A. rubi, Agrobacterium bohemicum and the clade formed by Agrobacterium genomospecies G6 and G8 being well supported. In addition, the type strains of non-biovar 1 species A. larrymoorei and "Agrobacterium albertimagni" are positioned within the clade containing otherwise only Agrobacterium biovar 1 strains, indicating that these 16S alleles are likely the result of horizontal gene transfer events. Similar results showing high similarities or identical 16S rDNA sequences for Agrobacterium rosae NCPPB-1650 T , Agrobacterium skierniewicense Di1472 (99.7%), and A. rubi NBRC 13261 T (99.5%) were reported [START_REF] Kuzmanović | Agrobacterium rosae sp. nov., isolated from galls on different agricultural crops[END_REF]. This altogether suggests that the 16S rRNA gene is not a good marker to identify Agrobacterium species or to study their relationships, as observed in other taxa [START_REF] Duc | gyrB as a phylogenetic discriminator for members of the Bacillus anthracis-cereusthuringiensis group[END_REF].

The gene gyrB provides a much better phylogenetic resolution of relationship among Agrobacterium species, with all except genomospecies G7 forming highly supported clades (Figure 1B). All nine strains belonging to G3 were grouped into one clade. The shallow grouping of species into clades is highly supported except G4, G7, and G9 strains, but deeper relationships within the Agrobacterium biovar 1 are not. This indicate that gyrB is a bona fide marker gene to identify and study the diversity of Agrobacterium species, even though its use as a marker for amplicon-based surveys has been shown to be impractical due to the presence of the paralogous gene parE in the genome rendering selective amplification difficult [START_REF] Puławska | Phylogenetic relationship and genetic diversity of Agrobacterium spp. isolated in Poland based on gyrB gene sequence analysis and RAPD[END_REF].

The MLSA tree provides further resolution for Agrobacterium species, where strains belonging to various genomospecies were placed in a tight clade with high bootstrap values (Figure 1C). The relationships between species according to this MLSA tree are mostly in agreement with previous reports based on the concatenation of multiple loci or core-genome genes [START_REF] Mousavi | Phylogeny of the Rhizobium-Allorhizobium-Agrobacterium clade supports the delineation of Neorhizobium gen[END_REF][START_REF] Mousavi | Revised phylogeny of Rhizobiaceae: proposal of the delineation of Pararhizobium gen. nov., and 13 new species combinations[END_REF][START_REF] Lassalle | Ancestral Genome Estimation Reveals the History of Ecological Diversification in Agrobacterium[END_REF], indicating that this fourloci MLSA scheme (including gyrB, parE, recA, and rpoB genes) provides an efficient way to affiliate phylogenetic relationships among Agrobacterium species. All nine strains of Agrobacterium G3 formed a clade with a bootstrap value >88%.

Finally, a tree based on the concatenation of 1,089 coregenome genes is fully resolved, with almost all branches having the highest support (Figure 2, bootstrap supports indicated if not 100%), and thus constitutes the gold standard for depicting the phylogenetic relationships of Agrobacterium species.

According to the gyrB and MLSA trees, the closest species to Agrobacterium G3 strains is the group formed of "A. fabrum" (G8) and Agrobacterium G6 strains, and these results were in accordance with previous reports [START_REF] Lassalle | Ancestral Genome Estimation Reveals the History of Ecological Diversification in Agrobacterium[END_REF]. However, the core-genome tree revealed that the closest relatives were strains Agrobacterium sp. (Rhizobium sp.) UGM030330-04 and Agrobacterium sp. (Agrobacterium rhizogenes) K599 whereas strains belonging to genomovars G8 and G6 formed a different and distant clade (Figure 2). We note that strains UGM030330-04 and K599 are misnamed given these phylogenies clearly show that they are members of the Agrobacterium genus and thus not part of Rhizobium, nor of A. rhizogenes, which is a synonym of R. rhizogenes; they are thus more correctly referred to as Agrobacterium sp.

Overall Genome Relatedness Indexes

All average nucleotide index (ANI) values among Agrobacterium G3 strains were over 97.5% whereas when comparing G3 strains to non-G3 strains, the ANI values were below 90.68% (Table 2 andSupplementary Table 1). The ANI values of Agrobacterium G3 strains and most closely related (as per core-genome) Agrobacterium sp. strains UGM030330-04 and K599 were ∼90.5% similarity. Similarly, the ANI values of Agrobacterium G3 strains with other closely related -according to gyrB and MLSA trees -Agrobacterium G6 (NCPPB 925) and G8 strains (J-07 and C58), exhibited ∼87.6% relatedness. Furthermore, average amino-acid identity (AAI) values among Agrobacterium G3 strains were over 97.6% while values between G3 and other agrobacteria were below 94%. Likewise, the digital DNA:DNA hybridization (dDDH) values among Agrobacterium G3 strains were over 83.6% while values between Agrobacterium G3 and other agrobacteria were 41.7% or below. More specifically the dDDH values were ∼41.7% for comparisons with strains UGM030330-04 and K599, 34.5% with G6 strains, and <33.9% with G8 strains. The ANI and dDDH values obtained for all the Agrobacterium G3 strains -including the ISS isolates -with other Agrobacterium species were below the threshold of 95% ANI [START_REF] Yoon | A large-scale evaluation of algorithms to calculate average nucleotide identity[END_REF] and 70% dDDH values [START_REF] Auch | Digital DNA-DNA hybridization for microbial species delineation by means of genome-to-genome sequence comparison[END_REF], which were established as standard for prokaryotic species delineation. We thus propose the name Agrobacterium tomkonis for the genomospecies 3 (= genomovar G3) of the Agrobacterium biovar 1 (i.e., A. tumefaciens species complex). Agrobacterium sp. strains UGM030330-04 and K599 consistently form a distinct group in the gyrB, MLSA and core-genome tree; these two strains share an ANI of 98.11% and have <90.68% ANI with any other Agrobacterium strain. We therefore propose to group them into a new genomospecies of the Agrobacterium biovar 1, to be named genomovar G21, following the recently described genomovars G19 and G20 [START_REF] Mafakheri | Two Novel Genomospecies in the Agrobacterium tumefaciens Species Complex Associated with Rose Crown Gall[END_REF].

Phenotypic Characterization

Differential phenotypic characteristics of A. tomkonis strains with six other Agrobacterium species are given in Table 3 and Supplementary Table 2. The biochemical tests were highly variable among Agrobacterium species tested, in accordance with previous reports [START_REF] Kuzmanović | Agrobacterium rosae sp. nov., isolated from galls on different agricultural crops[END_REF]. However, we found a distinctive trait in that all Agrobacterium biovar 1 strains except those belonging to the A. tomkonis assimilated D-galacturonic acid. In addition, A. tomkonis strains tested assimilated the following carbon sources (Supplementary Table 2). D-maltose, D-trehalose, D-cellobiose, gentiobiose, sucrose, D-turanose, stachyose, D-raffinose, α-D-lactose, D-melibiose, β-methyl-D-glucoside, D-salicin, N-acetyl-D-glucosamine, N-acetyl-D-galactosamine, α-D-glucose, D-mannose, D-fructose, D-galactose, D-fucose, L-fucose, L-rhamnose, D-sorbitol, D-mannitol, D-arabitol, myo-inositol, glycerol, D-glucose-6-PO4, D-fructose-6-PO4, L-alanine, L-arginine, L-aspartic acid, L-glutamic acid, L-histidine, L-pyroglutamic acid, L-serine, pectin, D-gluconic acid, D-Glucuronic acid, quinic acid, methyl pyruvate, L-malic acid, γ-amino-butyric acid, propionic acid, acetic acid. Moreover, L-lactic acid, D-malic acid, bromosuccinic acid, β-hydroxy-D, L-butyric acid and acetoacetic acid support at least a weak metabolic activity. However, no metabolic activity was observed for A. tomkonis strains on N-acetylβ-D-mannosamine, N-acetyl neuraminic acid, 3-Methyl Glucose, fusidic acid, D-serine, D-aspartic acid, D-galacturonic acid, L-galactonic acid lactone, mucic acid, D-saccharic acid, p-hydroxyphenylacetic acid, and sodium butyrate.

Among various compounds tested, ISS A. tomkonis strains did not metabolize citric acid and Niaproof 4 (an anionic surfactant) whereas other A. tomkonis strains utilized them. Most of the compounds tested were metabolized unvaryingly by all ISS strains whereas strain IIF1SW-B3 was able to utilize lithium chloride as sole carbon source as that of other A. tomkonis strains tested. Similarly, strain IIF1SW-B3 was metabolizing Tween 40 (polysorbate, surfactant) but other A. tomkonis strains did not assimilate it. However, all six tested A. tomkonis strains are tolerant to troleandomycin, rifamycin SV, lincomycin, guanidine HCl, vancomycin, potassium tellurite, aztreonam, 1% sodium lactate, tetrazolium violet, and tetrazolium blue, but not to minocycline. Moreover, they all grew at pH 6 but not or only weakly at pH 5. All six tested A. tomkonis strains did not grow at 4% NaCl and beyond. The strains IIF1SW-B1 T , IIF1SW-B4, and RTP8 tested on tomato produced no tumor (results not shown) and are therefore considered not pathogenic on tomato plant.

Comparative Genomic Analysis and A. tomkonis-Specific Gene Content

Forty-nine genes were specifically found in A. tomkonis genomes. These genes were mostly interspersed in the genome and coded a range of functions (Supplementary Table 3), but a fraction clustered into three genomic islands, two on the linear chromosome and one on the largest of the two megaplasmids present in strain CFBP 6623 (pCFBP6623a). The first cluster of seven A. tomkonis specific genes clearly encodes the biosynthesis of a surface polysaccharide; the second encodes mostly proteins of unknown function, with one potentially related to type 6 secretion systems; the last A. tomkonis specific gene cluster on the plasmid encodes resistance to copper. Other A. tomkonis specific genes are scattered in the genome, but have related functions. There are notably two genes encoding ankyrin-repeat domain-containing proteins, as well as a protein containing an extracellular HAF repeat domain (InterPro domain IPR014262) Puławska et al. (2012); **Data from [START_REF] Bouzar | Agrobacterium larrymoorei sp. nov., a pathogen isolated from aerial tumours of Ficus benjamina[END_REF], [START_REF] Panday | Rhizobium pusense sp. nov., isolated from the rhizosphere of chickpea (Cicer arietinum L.)[END_REF]. +, positive; -, negative; w, weak reactions; nd, not determined.

D-Raffinose

+ + + + + + - α-D-Lactose + + + + + + - Acetic Acid + + + + + + - L-Arginine + + - - - + nd Methyl Pyruvate + w + - w w + N-Acetyl-D-Galactosamine + + - w w + - D-Galacturonic Acid - + + + + + - D-Saccharic Acid - - - - - - + D-Glucuronic Acid w w + + + + - L-Lactic Acid w + - + + + + D-Malic Acid w + - w w + nd Bromosuccinic Acid w w - - - - + Acetoacetic Acid w w + - - - nd Pectin w + - w - - nd *Data from
Frontiers in Microbiology | www.frontiersin.org and an autotransporter domain, all three exported to the cell surface and likely mediating attachment to an extracellular substrate. Together with the biosynthesis of a likely extracellular polysaccharide, these species-specific genes present a convergent function related to surface attachment and potentially resistance to stress from the external medium. This enrichment in the specific core-genome of A. tomkonis suggests natural selection was at work and that surface attachment may have a strong role in A. tomkonis ecology. Indeed, the first reported A. tomkonis strains CFBP 6623 and CFBP 6624 were isolated from within an eosin flask and from a human clinical sample (likely as an opportunist taking advantage of another pathogen infection), respectively [START_REF] Popoff | Taxonomic position of Agrobacterium strains of hospital origin[END_REF], showing already unusual colonization abilities for agrobacteria. Moreover, the strains recovered from the ISS were isolated from the surface of the observation dome (Cupola), which panel is made of aluminum with a polyurethane topcoat (e.g., Aeroglaze A276 or BMS10-60), an ultra-clean and smooth surface that carries very little microbial life. The independent isolation of G3 strains in such locations suggest that attachment to inhospitable surfaces may be a way for A. tomkonis to colonize new habitats where it can evade competition with other microbes. Similarly, the gene clustering analysis between the genomes of 40 different Agrobacterium strains allowed the identification of ISS strains-specific gene clusters (Figure 3 and Supplementary Table 4). We found 556 genes specifically present in the three ISS strains; we identified these genes based on their presence in all ISS strains and none of the 17 genomes in the background clade comprising the other A. tomkonis strains, all strains of Agrobacterium arsenijevicii, Agrobacterium nepotum, Agrobacterium genomospecies G1, G5, G13, and G2 (Supplementary Data online) 1 . Using a more relaxed criterion allowing presence of the genes in a maximum of 2 out the 17 genomes in the background clade, to allow the detection of genes specifically gained by the ancestor of the ISS strains but that may have independently introduced in close relatives via horizontal gene transfer, we found 838 genes (Supplementary Table 4), most of them located in large contiguous regions.

Among this wider set of ISS clone-specific genes, some were found to cover 10 entire contigs of the genome assembly of strain IIF1SW-B1 T (Supplementary Table 5), totaling to 347 genes over 613 kb of ISS clone-specific contigs, which possibly represent (parts of) extrachromosomal elements. Indeed, four of these ISS clone-specific contigs carried genes with functions associated to type 4 secretion systems (T4SS), with two seemingly complete clusters, indicating they may constitute conjugative elements such as plasmids or integrative and conjugative elements (ICE). Most of the other ISS clone-specific contigs carried transposase or genetic mobility-associated genes, suggesting they also constitute mobile genetic elements (MGEs). In addition, large contiguous portions of other contigs that were specific to the ISS strains carried transposase genes -often located at the extremity of the ISS clone-specific region -suggesting they may represent inserted MGEs such as transposons or genomic islands (Supplementary Tables 4,5). In particular, the ISS clone-specific region of 1 https://10.6084/m9.figshare.16782964 contigs #27 of strain IIF1SW-B1 T genome assembly clearly corresponds to a prophage.

Beyond genetic mobility, the ISS clone-specific genes displayed a range of other functions, which were enriched for a range of functions, some forming coherent cellular processes (Supplementary Table 6). Among the top enriched functions, we found a multi-copper oxidase, accompanied by a chaperone for its maturation and transporters that might allow the assimilation of copper ions from the environment; these functions may form a pathway for biosynthesis of a redox enzyme that could be involved in a respiratory chain. Another set of coherent functions were found to be involved in scavenging and uptake of a ferric ion-siderophore complex, as well as a non-ribosomal peptide synthases-polyketide synthases (NRPS/PKS) biosynthetic cluster, which might be involved in the biosynthesis of the siderophore.

Plasmid Profiling

Based on migration of genomic DNA on Eckhardt gels [START_REF] Vaudequin-Dransart | Novel Ti plasmids in Agrobacterium strains isolated from fig tree and chrysanthemum tumors and their opinelike molecules[END_REF], all A. tomkonis strains were evidenced to carry plasmids, although differences in size and number were observed between strains (Supplementary Figure 1). Indeed, strain CFBP 6624 presented a single plasmid of approximately 500 kb, whereas CFBP 6623, RTP8 and the three ISS strains showed one to three plasmids, with a common largest band around 260 kb. RTP8 and the three ISS strains have in common a 130-kb plasmid, and a 40-kb plasmid is present exclusively in the three ISS strains. The ISS strains do not harbor a pTi plasmid, as no classical vir genes were detected in their genomes (data not shown). Accordingly, no symptoms of crowngall diseases were observed on injured tomato plants inoculated with any of the ISS strains (data not shown).

Agrobacterium tomkonis Reads in International Space Station Metagenomes

In order to evaluate the relative abundance of A. tomkonis onboard the ISS, we performed metagenomic read recruitment obtained from ISS environmental DNA shotgun datasets [START_REF] Singh | Succession and persistence of microbial communities and antimicrobial resistance genes associated with International Space Station environmental surfaces[END_REF] with strain IIF1SW-B1 T genome as a reference. Visual inspection of the mapped read profiles showed that most recruited reads were located on conserved bacterial genes encoding products such as ribosomal RNA operons, ribosomal proteins, DNA-directed RNA polymerase rpoB or elongation factor Tu. Considering the high degree of conservation of these genes across distant bacterial lineages and the non-uniform read coverage of G3 contigs we conclude that despite being isolated from the same environment, A. tomkonis is not present in any detectable abundance in the ISS metagenomes.

In summary, A. tomkonis genomes are diverse, and notably carry a variety of plasmids, even though none of the G3 strain in our dataset carried a tumorigenic plasmid. As previously shown for other Agrobacterium genomic species [START_REF] Lassalle | Ancestral Genome Estimation Reveals the History of Ecological Diversification in Agrobacterium[END_REF], A. tomkonis strain genomes share a number of speciesspecific genes. A. tomkonis specific gene functions notably relate to surface adhesion and could be involved in the ecological specificity of A. tomkonis suggested by their varied source of isolation [START_REF] Popoff | Taxonomic position of Agrobacterium strains of hospital origin[END_REF]. Indeed, while most species of the Agrobacterium biovar 1 are usually isolated from soils, plant rhizospheres or as plant pathogens inducing tumors [START_REF] Barton | Ecological and evolutionary dynamics of a model facultative pathogen: Agrobacterium and crown gall disease of plants[END_REF][START_REF] Dodueva | Plant tumors: a hundred years of study[END_REF][START_REF] Weisberg | Unexpected conservation and global transmission of agrobacterial virulence plasmids[END_REF], A. tomkonis strains never have been isolated as a plant pathogen, nor a tumor-inducing plasmid was observed in their genome, which we confirmed by negative infection tests on tomato plants. Even though the isolation of A. tomkonis strains RTP8 from a tobacco plant rhizosphere and Root651 from Arabidopsis roots prove that A. tomkonis strains are able to survive in plant rhizospheres, A. tomkonis strains were otherwise all isolated from a variety of unhospitable environments, including from an antiseptic flask, as an opportunistic pathogen of the human central nervous system, from a cave wall, and lastly from the inert surface of the ISS cupola. The ability to colonize these substrates, unusual for most agrobacteria, resonate with the finding that A. tomkonis genomes specifically carry genes involved in mediating attachment to surfaces, including production of putative adhesins and biofilm. This suggests a particular ability of A. tomkonis to colonize a different kind of habitats, that are even poorer in nutrients and harsher than soil, thus possibly escaping competition with other agrobacteria that are better at growing in richer environments such as plant rhizospheres.

The genomes of A. tomkonis strains isolated from the ISS, appear to be highly similar, suggesting they are clonally related. As such, they carry further specific genomic traits, including the presence of a 40-kbp plasmid observed via electrophoresis, as well as several other potential MGEs detected in their specific genomic content, with several ISS clone-specific contigs carrying conjugation genes (cumulated length 346 kbp) and more contigs that could also be part of conjugative elements or other types of MGEs such as integrated prophages. These ISS clone-specific genes also include many potentially adaptive genes, including putative pathways for respiratory chain biosynthesis and iron scavenging, but it is unclear what would be the selective advantage those genes could bring to the ISS strains in the context they were isolated from.

CONCLUSION

Using a polyphasic approach, we characterized the phenotypic and genotypic synapomorphies of Agrobacterium G3 (Agrobacterium genomospecies 3), showing it is a bona fide bacterial species, well separated from previously named Agrobacterium species or other recognized genomic species. Based on this evidence, we propose to name this species Agrobacterium tomkonis (formal description below). In addition, we performed an in-depth investigation of the pangenome, notably identifying species-specific genes, allowing us to relate the description of this new species to hypotheses on its ecology. Within this novel species, we further characterized the clonal group of strain isolated from the ISS, showing it possesses its own set of specific genes and MGEs that could carry adaptive traits linked to its survival in the ISS environment.

Description of Agrobacterium tomkonis sp. nov.

Agrobacterium tomkonis (tom. ko'ni.s. N.L gen. n. tomkonis referring to David Tomko, a well-known NASA Space Biology scientist who advanced space research in the United States). Cells are Gram-stain-negative, non-spore-forming, aerobic rods, 0.5-0.6 µm in width and 0.8-1.6 µm in length. Colonies are translucent and cream to white, with a diameter of 2-3 mm on R2A medium after incubation for 3 days at 25 • C. Growth occurs at 20 • C through 35 • C, with an optimum temperature of 25 • C. A. tomkonis strains grew at pH 6 but not or only weakly at pH 5 with a pH optimum at 7.5. All tested A. tomkonis strains failed to grow at 4% NaCl and beyond.

A. tomkonis strains metabolize D-maltose, D-trehalose, D-cellobiose, gentiobiose, sucrose, D-turanose, stachyose, D-raffinose, α-D-lactose, D-melibiose, β-methyl-D-glucoside, D-salicin, N-acetyl-D-glucosamine, N-acetyl-D-galactosamine, α-D-glucose, D-mannose, D-fructose, D-galactose, D-fucose, L-fucose, L-rhamnose, D-sorbitol, D-mannitol, D-arabitol, myo-inositol, glycerol, D-glucose-6-PO4, D-fructose-6-PO4, L-alanine, L-arginine, L-aspartic acid, L-glutamic acid, L-histidine, L-pyroglutamic acid, L-serine, pectin, D-gluconic acid, D-Glucuronic acid, quinic acid, methyl pyruvate, L-malic acid, γ-amino-butyric acid, propionic acid, acetic acid. However, did not utilize N-acetyl-β-D-mannosamine, N-acetyl neuraminic acid, 3-Methyl Glucose, fusidic acid, D-serine, D-aspartic acid, D-galacturonic acid, L-galactonic acid lactone, mucic acid, D-saccharic acid, p-hydroxyphenylacetic acid, and sodium butyrate.

A. tomkonis strains tested are tolerant to troleandomycin, rifamycin SV, lincomycin, guanidine HCl, vancomycin, potassium tellurite, aztreonam, 1% sodium lactate, tetrazolium violet, and tetrazolium blue, but not to minocycline.

The delineation of A. tomkonis from other Agrobacterium species was based on overall genome relatedness indexes (all A. tomkonis share ANI > 98% and dDDH > 84%), as well as based on well-supported clades in gyrB gene, MLSA (with gyrB, parE, recA, and rpoB genes) or core-genome phylogenies. The type strain, IIF1SW-B1 T (= LMG 32164 = NRRL B-65602), was isolated from the ISS Port panel of the Cupola, which is the observation deck for the crew. The type strain DNA G + C content is 59.18 mol% and its genome sequence is available from GenBank under WGS accession JABXYF000000000.

MATERIALS AND METHODS

Sample Collection and Isolation of Bacteria

The sampling of ISS surfaces performed for this study took place within the United States on-orbit segments. Samples collected during this study were: Node 3 (Locations #1, #2, and #3), Node 1 (Locations #4 and #5), Permanent Multipurpose Module (Location #6), U.S. Laboratory (Location #7), and Node 2 (Locations #8 and control). A detailed description of the various locations sampled was published elsewhere [START_REF] Singh | Succession and persistence of microbial communities and antimicrobial resistance genes associated with International Space Station environmental surfaces[END_REF]. Sample collection from ISS environmental surfaces, processing, and cultivation of bacteria have been previously reported [START_REF] Checinska Sielaff | Characterization of the total and viable bacterial and fungal communities associated with the International Space Station surfaces[END_REF]. Three strains isolated during the second flight from Location #1 (Cupola) surfaces, were later identified as belonging to the genus Agrobacterium via 16S rRNA gene sequencing of the cultivated strains; these were designated: IIF1SW-B1 T , IIF1SW-B3, and IIF1SW-B4.

Phenotypic Characterization

Strains IIF1SW-B1 T , IIF1SW-B3, IIF1SW-B4, CFBP 6623, CFBP 6624, and RTP8 strains were subjected for the phenotypic characterization. All strains are grown on yeast extract-peptoneglucose (YPG) medium and collected from the surface of the agar plates [START_REF] Scortichini | Detection and identification methods and new tests as developed and used in the framework of cost 873 for bacteria pathogenic to stone fruits and nuts Pseudomonas avellanae[END_REF] before inoculation and screened for various organic substrate utilization using Biolog GEN III system (Biolog, Inc., Hayward, CA, United States) following manufacturer's recommendations. Concurrently, two replicates of Biolog tests were carried out, and Biolog plates were incubated at 28 • C for 48 h. After incubation, the OmniLog TM system was used to measure carbon substrate utilization.

Molecular Characterization of International Space Station Strains

A loopful of purified microbial culture (only ISS strains) was subjected to DNA extraction with the UltraClean DNA kit (MO BIO, Carlsbad, CA, United States) or Maxwell Automated System (Promega, Madison, WI, United States) as per manufacturer instructions. The extracted DNA was eluted in 50-µL of molecular grade water and stored at -20 • C until further analysis. The 16S rRNA gene was amplified using the forward primer, 27F (5 -AGA GTT TGA TCC TGG CTC AG-3 ) and the reverse primer, 1492R (5 -GGT TAC CTT GTT ACG ACT T-3 ) [START_REF] Checinska Sielaff | Characterization of the total and viable bacterial and fungal communities associated with the International Space Station surfaces[END_REF] and PCR was performed with the following conditions: denaturation at 95 • C for 5 min, followed by 35 cycles consisting of denaturation at 95 • C for 50 s, annealing at 55 • C for 50 s, and extension at 72 • C for 1.5 min, and finalized by extension at 72 • C for 10 min. The amplified products were treated with Antarctic phosphatase and exonuclease (New England Biolabs, Ipswich, MA, United States) to remove 5 -and 3 -phosphates from unused dNTPs before sequencing. Sequencing was performed by Macrogen (Rockville, MD, United States) using 27F and 1492R primers for Bacteria. The resulting sequences were assembled using SeqMan Pro from the DNASTAR Lasergene package (DNASTAR Inc., Madison, WI, United States). Bacterial sequences were searched against the EzTaxon-e database [START_REF] Kim | Introducing EzTaxon-e: a prokaryotic 16S rRNA gene sequence database with phylotypes that represent uncultured species[END_REF] and identified based on the closest percentage similarity (>98.6%) to previously identified microbial-type strains.

Analysis of Plasmid Content

Plasmid profiles were determined by a modified Eckhardt agarose gel electrophoresis technique as previously described [START_REF] Vial | N-acyl-homoserine lactone-mediated quorum-sensing in Azospirillum: an exception rather than a rule[END_REF]. Isolates were grown overnight in YPG medium at 28 • C until the optical density at 600 nm reached 0.4. Electrophoresis was carried out at 5 V for 30 min and 90 V for 5 h at 4 • C on a 0.75% agarose gel containing 1% sodium dodecyl sulfate. Plasmids sizes were estimated by comparison with those of "A. fabrum" C58 and Allorhizobium vitis S4 [START_REF] Wood | The genome of the natural genetic engineer Agrobacterium tumefaciens C58[END_REF][START_REF] Slater | Genome sequences of three agrobacterium biovars help elucidate the evolution of multichromosome genomes in bacteria[END_REF].

Analysis of International Space Station Strains Pathogenicity on Tomato Plant

To test if the ISS G3 strains were plant-pathogen strains, 3-weekold tomato plants, cultivated with a photoperiod of 16 h/8 h light/dark in a greenhouse were stem-injured and inoculated with 10 µL of bacterial overnight culture (10 8 CFU/ml). The plants were then incubated for 21 days, and the presence or absence of crown gall symptoms was established by visual inspection. "A. fabrum" C58 strain was used as the positive control.

Whole Genome Sequencing of New Agrobacterium Strains

The WGS sequencing of the three ISS G3 strains was carried out as per established procedures (Singh et al., 2018a). Shotgun libraries were prepared using the Illumina Nextera Flex protocol [START_REF] Singh | Succession and persistence of microbial communities and antimicrobial resistance genes associated with International Space Station environmental surfaces[END_REF] and sequenced using NovaSeq 6000 S4 flow cell 2 × 150 PE sequencing kit. Verification of the quality of the raw sequencing data was carried out using FastQC v0.11.7 using default parameters2 . Quality control for adapter trimming and quality filtering were performed using fastp v0.20.0 [START_REF] Chen | fastp: an ultra-fast all-in-one FASTQ preprocessor[END_REF], and then SPAdes v3.11.1 [START_REF] Bankevich | SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing[END_REF] was used to assemble all the cleaned sequences. Fastp quality control was based on the following three parameters: (i) correction of mismatches in overlapped regions of pairedend reads, (ii) trimming of autodetected adapter sequences, and (iii) quality trimming at the 59 and 39 ends. To determine the quality of the assembled sequences, the number of contigs, the N50 value, and the total length were calculated using QUAST v5.0.2 [START_REF] Gurevich | QUAST: quality assessment tool for genome assemblies[END_REF]. Default parameters were used for all software. The WGS sequencing of strains RTP8 and CFBP 6624 was carried out by Beckman Coulter Genomics (Takeley, Essex, United Kingdom). SPAdes v3.8.1 [START_REF] Bankevich | SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing[END_REF] was used to assemble all the cleaned sequences. All five new genomes were annotated using Prokka v1.14.5 [START_REF] Seemann | Prokka: rapid prokaryotic genome annotation[END_REF] with the following options: "-force -addgenes -compliantusegenus." Protein function was annotated by performing a blastp similarity search against a reference proteome database made of 405 high-quality genome assemblies obtained from the NCBI RefSeq, selecting all genomes belonging to the Rhizobiaceae family available as on 14th August 2018 that had a contig N50 greater than 98 kbp (Supplementary Table 7).

Overall Genome Relatedness Index Calculations

The ANI [START_REF] Yoon | A large-scale evaluation of algorithms to calculate average nucleotide identity[END_REF] between each pair of the 40 Agrobacterium genomes (41 genomes including the two published versions of strain CFBP 6623 genome) was calculated using pyANI [START_REF] Pritchard | Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens[END_REF]. The dDDH analysis was performed using the Genome-to-Genome Distance Calculator 2.0 (GGDC 2.0) [START_REF] Auch | Digital DNA-DNA hybridization for microbial species delineation by means of genome-to-genome sequence comparison[END_REF].

Pangenome Analysis

We applied two separate pangenome analysis pipelines to obtain robust estimates of the sets of genes present and absent in each genome and to compute core and clade-specific gene sets for the various taxonomic groups represented in our dataset, including Agrobacterium biovar 1, A. tomkonis strains including ISS isolates.

(1) On one hand, a pangenomic Snakemake workflow [START_REF] Köster | Snakemake-a scalable bioinformatics workflow engine[END_REF] was run in anvi'o v6.2 [START_REF] Eren | Anvi'o: an advanced analysis and visualization platform for 'omics data[END_REF]. Briefly, gene clusters (GC) were defined by clustering allversus-all open-reading frames BLAST similarity scores using the Markov Clustering (MCL) algorithm [START_REF] Enright | An efficient algorithm for large-scale detection of protein families[END_REF] with an inflation score of 8. Open reading frames were identified with Prodigal 2.6.3 in single mode [START_REF] Hyatt | Prodigal: prokaryotic gene recognition and translation initiation site identification[END_REF]. Open reading frames were annotated using COG and KOfam databases [START_REF] Eren | Anvi'o: an advanced analysis and visualization platform for 'omics data[END_REF][START_REF] Delmont | Linking pangenomes and metagenomes: the Prochlorococcus metapangenome[END_REF]. Results were displayed using anvi-display-pan function [START_REF] Delmont | Linking pangenomes and metagenomes: the Prochlorococcus metapangenome[END_REF]. Functional enrichment in the ISS and G3 strain sets was obtained using anvi-compute-functional-enrichment function, as described in [START_REF] Shaiber | Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome[END_REF], based on the COG annotation of open reading frames. Only enrichment for which FDR adjusted p-value < 0.05 are reported in Supplementary Table 5.

(2) On the other hand, a phylogenomic database was built with the Pantagruel pipeline [START_REF] Lassalle | Automated reconstruction of all gene histories in large bacterial pangenome datasets and search for co-evolved gene modules with Pantagruel[END_REF], using the "usingGeneRax" branch of the code 3 version "df79fee577b72389926a0138353fa657c11f3368." The following pipeline tasks were performed: init, 00-03 and 05, leading to the clustering of homologous protein families with MMseqs2 [START_REF] Steinegger | MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets[END_REF], the alignment of their reversetranslated coding sequences with Clustal Omega [START_REF] Sievers | Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega[END_REF] and Python scripts using Biopython [START_REF] Weisberg | Unexpected conservation and global transmission of agrobacterial virulence plasmids[END_REF], and the computation of a maximum-likelihood (ML) phylogenetic tree with RAxML 8.1.2 [START_REF] Stamatakis | RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees[END_REF] based on the concatenation of 1,089 core-genome gene alignments, as previously described [START_REF] Lassalle | Phylogenomics reveals the basis of adaptation of Pseudorhizobium species to extreme environments and supports a taxonomic revision of the genus[END_REF] (Supplementary Data File doi: 10.6084/m9.figshare.14792178). In addition, clade-specific sets of homologous gene clusters were determined using the "get_clade_specific_genes.r" script from Pantagruel package using the homologous gene family presence/absence matrix as input.

Metagenome Sequence Reads Recruitment to Strain IIF1SW-B1

Metagenomic datasets (n = 42) previously obtained from environmental swabbing of the ISS [START_REF] Singh | Succession and persistence of microbial communities and antimicrobial resistance genes associated with International Space Station environmental surfaces[END_REF] were downloaded from NCBI-SRA BioProject PRJNA438545, quality filtered and mapped using BWA-MEM [START_REF] Li | Fast and accurate short read alignment with Burrows-Wheeler transform[END_REF] against the genome assembly of strain IIF1SW-B1 T . These steps were performed using anvi'o v6.2 [START_REF] Eren | Anvi'o: an advanced analysis and visualization platform for 'omics data[END_REF] using anvi'o metagenomic workflow in reference mode and default parameters. Visual inspection of read mapping alignment was carried out using Artemis BamView [START_REF] Carver | BamView: visualizing and interpretation of next-generation sequencing read alignments[END_REF].

Phylogenetic Analysis of Marker Genes

For the 16S rRNA gene phylogenetic analysis, we extracted the 16S rRNA sequences from the genomes and added the reference 16S gene sequence from Rhizobium leguminosarum type strain USDA 2370 T (GenBank accession U29386.1) and then aligned all sequences with Clustal Omega [START_REF] Sievers | Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega[END_REF] (Supplementary Data File doi: 10.6084/m9.figshare. 14792148). For the gyrB single-gene phylogenetic analyses, as well as multi-locus sequence analysis (MLSA), we used the coding sequence (CDS) alignments for the marker genes parE, gyrB, recA and rpoB from the Pantagruel database described above (see Supplementary Data Files available at doi: 10.6084/m9.figshare.14792169 and doi: 10.6084/m9. figshare.14792100). For the MLSA, we concatenated the CDS alignments of parE, gyrB, recA, and rpoB into a multilocus alignment, totaling 9,968 positions. ML Phylogenies were then inferred based on these alignments with RAxML-NG 1.0.0 [START_REF] Stamatakis | RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees[END_REF] with the options "model GTR + G4 -all, " implying the use of the model GTR + FO + G4m, and that 20 independent inferences 3 https://github.com/flass/pantagruel/tree/usingGeneRax were run, starting with 10 random trees and 10 parsimonyoptimized tree, and retaining the best ML tree from these 20 inferences. Branch supports were estimated with 200 Felsenstein bootstrap trees. The 16S tree was rooted using R. leguminosarum USDA 2370 T sequence, whereas the gyrB and MLSA trees were rooted using the sequence from "A. albertimagni" AOL15 -a distant relative incorrectly classified in the Agrobacterium genus for which a classification in the newly proposed genus "Peteryoungia" was proposed [START_REF] Kuzmanović | Taxonomy of Rhizobiaceae revisited: proposal of a new framework for genus delimitation[END_REF].

Task Order NNN13D111T award to KV, which also funded the post-doctoral fellowships for NS. This research was funded in part to FL by the Wellcome Trust Grant (206194). For the purpose of Open Access, the author has applied a CC-BY public copyright license to any author accepted manuscript version arising from this submission.

FIGURE 2 |

 2 FIGURE 2 | Phylogeny of 40 Agrobacterium spp. strains based on the concatenation of 1,089 core-genome genes. Maximum-likelihood phylogenetic tree of 40 Agrobacterium distinct strains (41 when including the two published genome versions for strain CFBP 6623) obtained using the bioinformatic pipeline Pantagruel (data available at https://doi.org/10.6084/m9.figshare.14792178). The tree was computed with RAxML 8.1.2 based on the concatenation of 1,089 core-genome gene alignments under the GTRCATX model, with 200 rapid bootstraps and rooted with RAxML tree-balance algorithm. All branch supports are 100%, unless displayed.

FIGURE 3 |

 3 FIGURE 3 | Visualization of the Agrobacterium pangenome. Genomes are ordered as layers using a tree based on average nucleotide identity (ANI) values matrix (Euclidean distance, Ward clustering). Color hue of ANI values are ranging from 70% ANI (white) to 100% ANI (red) and displayed as a heatmap on the right-side panel. Gene clusters are represented as vertical black bars indicating their presence in each genome layer. The central tree is ordering gene clusters based on their presence/absence in genomes using Euclidean distance and Ward clustering. The "Core GC" highlight indicates the gene clusters identified in all genomes. Gene clusters that are found in a single genome are collapsed as "Singletons GC." The "A. tomkonis ISS strains GC" highlight indicates gene clusters found exclusively on the three strains isolated from the International Space Station. The figure was created in Anvi'o v6.

TABLE 1 |

 1 Summary of the draft whole-genome sequences of several Agrobacterium tomkonis strains.

	Genome characteristics	IIF1SW-B1 T	IIF1SW-B3	IIF1SW-B4	CFBP 6624	RTP8
	NCBI WGS accession	JABXYF000000000	JABXYG000000000	JABXYG000000000	JAFIRL000000000	JAFIRM000000000
	# contigs (> = 0 bp)	137	154	150	44	55
	# contigs (> = 1000 bp)	76	81	80	35	38
	Total length (> = 1000 bp)	6,234,019	6,273,081	6,273,787	5,480,209	5,440,309
	Largest contig	644,466	644,466	726,576	1,342,491	928,660
	Total length	6,240,486	6,282,603	6,282,746	5,483,735	5,445,056
	GC (%)	59.18	59.17	59.17	59.12	59.21
	N50	350,004	350,004	354,854	378,069	320,860
	# of genes	5,917	5,962	5,964	5,223	5,147
	# CDS	5,863	5,908	5,910	5,175	5,098
	Frontiers in Microbiology | www.frontiersin.org

Phylogeny of 40 Agrobacterium spp. strains based on 16S rRNA gene, gyrB, and MLSA. Conserved marker gene phylogenies of 40 Agrobacterium distinct strains (41 when including the two published genome versions for strain CFBP 6623. (A) 16S rRNA gene phylogeny, based on 1,602 aligned positions), rooted with sequence from Rhizobium leguminosarum USDA 2370 T ; (B) gyrB gene phylogeny, based on 2,477 aligned positions; (C) multi-locus sequence analysis phylogeny, based on the concatenated alignments of genes parE, gyrB, recA, and rpoB, resulting into 9,968 aligned positions. All gene sequences were extracted from the 41 studied and then aligned with Clustal Omega (for coding genes, alignment was performed at the protein level and then reverse-translated into codons

  ). Maximum-likelihood trees were inferred with RAxML-NG 1.0.0 under the model GTR + FO + G4m, taking the best of 20 independent inferences, started with 10 random trees and 10 parsimony-optimized tree. Branch supports were estimated with 200 Felsenstein bootstrap trees. Only branch support over 70% are displayed; full information on the trees are available at https://doi.org/10.6084/m9.figshare.14792148; https://doi.org/10.6084/m9.figshare.14792169; https://doi.org/10.6084/m9.figshare.14792100.

TABLE 2 |

 2 List of Agrobacterium genomes used in this study and Average Nucleotide Identity (ANI), Average Amino acid Identity (AAI), and digital DNA-DNA Hybridization (dDDH) values of A. tomkonis IIF1SW-B1 T compared with all other tested Agrobacterium strains.

	Organism	GenBank #	OGRI values in comparison to A. tomkonis	Genomospecies
				IIF1SW-B1 T
			ANI	AAI	dDDH
	Agrobacterium tomkonis IIF1SW-B1 T	JABXYF000000000	100.00	100.00	100.00
	Agrobacterium tomkonis IIF1SW-B3	JABXYG000000000	100.00	100.00	100.00
	Agrobacterium tomkonis IIF1SW-B4	JABXYH000000000	100.00	100.00	100.00
	Agrobacterium genomospecies 3 CFBP 6623	GCF_900013535.1	98.08	97.62	84.90
	Agrobacterium sp. (Rhizobium sp.) Root651	GCF_001427625.1	97.74	97.25	83.60
	Agrobacterium genomospecies 3 LC34	GCF_001005815.1	97.82	97.77	83.50
	Agrobacterium genomospecies 3 SUL3	GCF_001263295.1	97.64	97.73	83.90
	Agrobacterium genomospecies 3 CFBP 6624	JAFIRL000000000	98.00	98.09	85.20
	Agrobacterium genomospecies 3 RTP8	JAFIRM000000000	98.08	98.15	85.40
	Agrobacterium genomospecies 3 CFBP 6623	GCA_005221385.1	98.12	97.61	84.80
	Agrobacterium sp. (Rhizobium sp.) UGM030330-04	GCF_003208455.1	90.58	93.91	41.70	21
	Agrobacterium sp. (A. rhizogenes) K599	GCF_002005205.3	90.51	93.39	41.50	21
	Agrobacterium genomospecies 7 Zutra 3/1	GCF_900013515.1	88.63	91.71	36.70
	Agrobacterium genomospecies 13 CFBP 6927	GCF_900012615.1	88.59	92.49	36.20	13
	Agrobacterium deltaense CNPSo 3391	GCF_003931535.1	88.66	92.25	36.30
	Agrobacterium genomospecies 7 RV3	GCF_900013505.1	88.56	91.77	36.30
	Agrobacterium genomospecies 5 CFBP 6626	GCF_900012595.1	88.11	91.62	35.40
	Agrobacterium radiobacter B6	GCF_900045375.1	87.97	90.95	35.40
	Agrobacterium radiobacter Kerr 14	GCF_900011755.1	87.79	91.06	35.30
	Agrobacterium genomospecies 6 NCPPB 925	GCF_900012625.1	87.54	90.09	34.50
	Agrobacterium fabrum J-07	GCF_900013525.1	87.39	90.88	33.90
	Agrobacterium fabrum C58	GCF_000092025.1	87.41	90.68	33.70
	Agrobacterium genomospecies 9 Hayward 0363	GCF_900012565.1	87.76	91.98	34.20
	Agrobacterium salinitolerans YIC 5082	GCF_002008225.1	87.55	91.18	33.90
	Agrobacterium arsenijevicii KFB 330	GCF_000949895.1	87.04	89.42	33.70	*
	Agrobacterium nepotum 39 7	GCF_000949865.1	87.21	89.99	33.60	14
	Agrobacterium tumefaciens CFBP 5771	GCF_900039255.1	86.72	90.34	32.30
	Agrobacterium fabacearum CNPSo 675	GCF_009649785.1	86.66	89.97	32.20
	Agrobacterium genomospecies 1 TT111	GCF_900012575.1	86.60	89.84	32.30
	Agrobacterium genomospecies 2 CFBP 5494	GCF_900013495.1	86.37	90.58	31.90
	Agrobacterium genomospecies 1 S56	GCF_900014385.1	86.51	89.89	32.10
	Agrobacterium pusense LMG 25623	GCF_900102105.1	86.51	90.59	31.80
	Agrobacterium larrymoorei AF3 10 ATCC 51759	GCF_000518585.1	78.42	77.11	21.80	-
	Agrobacterium rubi W2 73	GCF_001692345.1	77.93	75.37	21.30	-
	Agrobacterium rosae B20 3	GCF_002915175.1	77.90	76.96	21.30	-
	Agrobacterium rubi TR3 NBRC 13261	GCF_000739935.1	77.96	75.98	21.10	-
	Agrobacterium skierniewicense Di1472	GCF_013320815.1	77.42	75.84	21.00	-
	Agrobacterium rosae NCPPB 1650	GCF_002915195.1	77.81	76.35	21.20	-
	Agrobacterium bohemicum R89-1	GCF_001562555.1	77.67	76.62	21.10	-
	Agrobacterium bohemicum R90	GCF_002896715.1	77.51	76.47	21.00	-
	Agrobacterium albertimagni AOL15	GCF_000300855.1	76.04	66.57	20.60	-

*A. arsenijevicii is part of the A. tumefaciens species complex but has not received a genomospecies number.

TABLE 3 |

 3 Differential biochemical characteristics between A. tomkonis and closely related species.

	Biochemical tests	A. tomkonis	A. tumefaciens	A. nepotum*	"A. fabrum"	A. pusense	A. radiobacter	A. larrymoorei**
		IIF1SW-B1 T	CFBP 5771	39/7 T	C58	CFBP 5494	B6	AF3.10 T

Frontiers in Microbiology | www.frontiersin.org

December 2021 | Volume 12 | Article 765943

http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Frontiers in Microbiology | www.frontiersin.org

Frontiers in Microbiology | www.frontiersin.org 12 December 2021 | Volume 12 | Article 765943

ACKNOWLEDGMENTS

We would like to thank Aleksandra Checinska-Sielaff for isolating the strain and Achintya Bharathwaj for initial phylogenetic analyses, astronauts Captain Terry Virts for collecting samples aboard the ISS and the Implementation Team at NASA Ames Research Center (Fathi Karouia) for coordinating this effort, and Ryan Kemp (Zymo Research Corp.) for extracting DNA and Dan Butler (Weill Cornell Medicine) for performing shotgun sequencing using the NovaSeq platform. We acknowledged the Jet Propulsion Laboratory supercomputing facility staff, notably Narendra J. Patel (Jimmy) and Edward Villanueva, for their continuous support in providing the best possible infrastructure for BIGDATA analysis. © 2021 California Institute of Technology, Government sponsorship acknowledged. We would also like to thank Elise Lacroix for the plant cultures at the "Serre et Chambres Climatiques" platform of the FR BioEEnViS (UCBL) and L. Loiseau of PARMIC/CRB-EML platform of Ecologie Microbienne UMR CNRS 5557, UMR INRAE 1418.

DATA AVAILABILITY STATEMENT

The draft genome sequences of three ISS strains including type strain IIF1SW-B1 T , as well as strains RTP8 and CFBP 6624, were deposited in NCBI. The genome sequences of isolates A. tomknonis IIF1SW-B1 T , RTP8, and CFBP 6624 have been deposited to GenBank under the BioSample accessions SAMN15333190, SAMN17917486, and SAMN17918142, respectively; the WGS project accession numbers are also given in Table 1. The genome versions described in this paper are the first versions. Analysis intermediary data and result tables are available under the FigShare project available at https://figshare. com/account/home#/projects/115866 with individual items available at the following DOIs: 10.6084/m9.figshare.14792100, 10.6084/m9.figshare.14792148, 10.6084/m9.figshare.14792169, 10.6084/m9.figshare.14792178, 10.6084/m9.figshare.14883546, 10.6084/m9.figshare.14883516, 10.6084/m9.figshare.14798289, and 10.6084/m9.figshare.16782964.

AUTHOR CONTRIBUTIONS

CL, LV, XN, KV, and NS conceived and designed the microbiological experiments. NS, CL, and LV performed the microbiological experiments. CL and LV carried out the phenotypic assays Biolog based biochemical characterization. CL performed the plant pathogenicity experiments. LV performed plasmid content analyses. XN managed the Agrobacterium strain collection and the non-ISS strain genome sequencing project. NS and KV managed the ISS strain collection and the ISS strain genome sequencing project. FL, JN, and NS analyzed the data. NS analyzed the de novo assemblies, including contig alignment, and annotation checks. FL performed phylogenetic and phylogenomic analyses. JN performed metagenomic analysis. FL and JN performed pangenome analyses. FL and KV wrote the manuscript. All authors read and approved the final manuscript. Supplementary Table 4 | List of Agrobacterium clade-specific homologous gene families. Clade-specific genes are listed per clade; with each list starting by a hashtag and the clade id: "# cladeXX". A. tumefaciens species complex/Agrobacterium biovar 1 is clade1; A. tomkonis is clade20; the clonal group of ISS strains is clade27. Genomes are listed using short codes, with the correspondence to strain names provided at the top.

FUNDING

Supplementary Table 5 | List of contigs specific to the clonal group of A. tomkonis strains isolated from the ISS and their characteristics. The count of genes with genetic mobility-related functions is based on the Prokka annotation of proteins.

Supplementary Table 6 | COG function enriched in 3 Agrobacterium tomkonis ISS strains, compared to the 37 other Agrobacterium genomes in the dataset. Supplementary Table 7 | List of genome assemblies used as reference to annotate the new Agrobacterium genomes with Prokka.

Conflict of Interest:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Olivier Gimenez
email: olivier.gimenez@cefe.cnrs.fr

Vivien Rossi

Rémi Choquet

Camille Dehais

Blaise Doris

Hubert Varella

Jean-Pierre Vila

Roger Pradel

STATE-SPACE MODELLING OF DATA ON MARKED INDIVIDUALS

State-space models have recently been proposed as a convenient and flexible framework for specifying stochastic models for the dynamics of wild animal populations. Here we focus on the modelling of data on marked individuals which is * Manuscript frequently used in order to estimate demographic parameters while accounting for imperfect detectability. We show how usual models to deal with capture-recapture and ring-recovery data can be fruitfully written as state-space models. An illustration is given using real data and a Bayesian approach using MCMC methods is implemented to estimate the parameters. We give important future developments facilitated by the SSM formulation. Overall, the general state-space modelling framework is shown to have a great potential in population ecology modelling.

Introduction

The estimation of animal survival is essential in population biology to investigate population dynamics, with important applications in the understanding of ecological, evolutionary, conservation and management issues for wild populations [START_REF] Pollock | Modeling capture, recapture, and removal statistics for estimation of demographic parameters for fish and wildlife populations -past, present, and future[END_REF][START_REF] Williams | Analysis and management of animal populations[END_REF]. While the time to event is known in medical, social or engineering sciences (death, marriage and failure respectively), models for estimating wild animal survival must incorporate nuisance parameters to account for incomplete detectability in monitoring individuals [START_REF] Schwarz | Estimating animal abundance: review III[END_REF]. Typically, individuals are captured, marked and can be resighted or recaptured (encountered thereafter) to construct encounter histories which consist of sequences of 1's and 0's according to whether a detection occurs or not. The likelihood for such data arises from products of multinomial distributions whose cell probabilities are complex functions of survival probabilities -parameters of primary interest -and encounter probabilities -nuisance parameters [START_REF] Cormack | Estimates of survival from the sighting of marked animals[END_REF][START_REF] Jolly | Explicit estimates from capture-recapture data with both death and immigration-stochastic model[END_REF]Seber, 1965 -CJS thereafter).

In this note, we show how the population process can be fruitfully disentangled, by distinguishing the underlying demographic process, i.e. the survival (as well as transitions between sites/states if needed), from its observation, i.e. the detectability. This leads us to consider a natural formulation for capture-recapture models using statespace models (SSMs). Our contribution is in line with a recent paper by Buckland et al. (2004) who have proposed to adopt SSMs as a convenient and flexible framework for specifying stochastic models for the dynamics of wild animal populations.

Thus far, SSMs have been mainly used for time series of animal counts [START_REF] De Valpine | Monte Carlo state-space likelihoods by weighted posterior kernel density estimation[END_REF][START_REF] Millar | Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling[END_REF] or animal locations (Anderson-Sprecher and Ledolter, 1991) to allow true but unobservable states (the population size or trajectory) to be inferred from observed but noisy data (see [START_REF] Clark | Hierarchical Bayes for structured, variable populations: From recapture data to life-history prediction[END_REF]Wang, 2006 for reviews). The novelty of our approach lies in the use of SSMs to fit capture-recapture models to encounter histories.

In Section 2, we discuss how to express the CJS model under the form of a SSM. The implementation details are provided, and real data are presented to compare parameter estimates as obtained using the standard product-multinomial and the SSM approaches.

In Section 3, the flexibility of the state-space modeling approach is demonstrated by considering two widely used alternative schemes for collecting data on marked animals.

Finally, Section 4 discusses important developments of capture-recapture models facilitated by the SSM formulation. We emphasize that this general framework has a great potential in population ecology modelling.

2 State-space modelling of capture-recapture data

We focus here on the CJS model for estimating animal survival based on capturerecapture data, as this model is widely used in the ecological and evolutionary litterature (e.g. [START_REF] Lebreton | Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies[END_REF].

Likelihood

We first define the observations and then the states of the system. We assume that n individuals are involved in the study with T encounter occasions. Let X i,t be the binary random variable taking values 1 if individual i is alive at time t and 0 if it is dead at time t. Let Y i,t be the binary random variable taking values 1 if individual i is encountered at time t and 0 otherwise. Note that we consider the encounter event as being physically captured or barely observed. The parameters involved in the likelihood are φ i,t , the probability that an animal i survives to time t + 1 given that it is alive at time t (t = 1, . . . , T -1), and p i,t the probability of detecting individual i at time t (t = 2, . . . , T ). Let finally e i be the occasion where individual i is encountered for the first time. A general state-space formulation of the CJS model is therefore given by:

Y i,t |X i,t ∼ Bernoulli(X i,t p i,t ), t > e i
(1)

X i,t+1 |X i,t ∼ Bernoulli(X i,t φ i,t ), t ≥ e i (2)
where Equation (1) and Equation (2) are the observation and the state equations respectively. This formulation naturally separates the nuisance parameters (the encounter probabilities) from the parameters of actual interest i.e. the survival probabilities, the latter being involved exclusively in the state Equation (2). Such a clear distinction between a demographic process and its observation makes the description of a biological dynamic system much simpler and allows complex models to be fitted [START_REF] Pradel | Multievent: An extension of multistate capture-recapture models to uncertain states[END_REF][START_REF] Clark | Hierarchical Bayes for structured, variable populations: From recapture data to life-history prediction[END_REF]. We will refer to this formulation as the individual state-space CJS model (individual SSM CJS hereafter). The rationale behind the above formulation is as follows. We give the full details for the observation Equation (1) only, as a similar reasoning easily leads to Equation (2). If individual i is alive at time t, then it has prob-ability p i,t of being encountered and probability 1p i,t otherwise, which translates into

Y i,t is distributed as Bernoulli(p i,t ) given X i,t = 1. Now if individual i is dead at time t,
then it cannot be encountered, which translates into Y i,t is distributed as Bernoulli(0)

given X i,t = 0. Putting together those two pieces of reasoning, the distribution of the observation Y i,t conditional on the state X i,t is given by Equation (1).

Statistical inference then requires the likelihood of the state-space model specified above. Assuming independence of individuals, the likelihood is given by the product of all individual likelihood components. The likelihood component for individual i is the probability of the vector of observations Y T i = (Y i,e i , . . . , Y i,T ) which gathers the information set up to time T for this particular individual. The likelihood component corresponding to individual i is therefore given by (e.g. [START_REF] Harvey | Forecasting, structural time series models and the Kalman Filter[END_REF])

X i,0 , . . . , X i,T    T t=e i +1 [Y i,t |X i,t ][X i,t |X i,t-1 ]dX i,t    [X i,e i ]dX i,e i (3)
where [X] denotes the distribution of X and X i,e i the initial state of individual i which is assumed to be alive. Because we deal with binary random vectors, we used the counting measure instead of the Lebesgue measure.

In its original formulation, the CJS makes important assumptions regarding individuals. First, all individuals share the same parameters, which means that the survival and detection probabilities depend on the time index only. In mathematical notation, we have φ it = φ t and p it = p t for all i = 1, . . . , n, so Equation (1) and Equation ( 2)

become X i,t+1 |X i,t ∼ Bernoulli(X i,t φ t ) and Y i,t |X i,t ∼ Bernoulli(X i,t p t ) respectively.
Second, the CJS model also assumes independence between individuals. By using simple relationships between Bernoulli and Binomial distributions, one can finally fruitfully formulates the original CJS model under the following state-space model:

Y t |X - t ∼ Bin(X - t , p t ) (4) X - t+1 |X + t ∼ Bin(X + t , φ t ) (5)
where X + t is the number of survivors of marked individuals from time t-1, X - t , plus the newly marked individuals at time t, and Y t is the total number of marked individuals encountered at time t. We will refer to this formulation as the population state-space CJS model (population SSM CJS hereafter). Interestingly, specifying the system under a state-space formulation now requires much less equations than the individual SSM CJS model, which may avoid the computational burden. Nevertheless, while the individual SSM CJS involves parameters for every single individual and sampling occasion, the population SSM CJS model makes the strong assumptions that all individuals behave the same as well as independently, which may be of little relevance from the biological point of view. To cope with this issue, in-between modelling can be achieved by considering age effects or groups classes in the population SSM model [START_REF] Lebreton | Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies[END_REF].

Finally, covariates can be incorporated in order to assess the effect of environment such as climate change, most conveniently by writing the relationship between the target probabilities and the predictors on the logit scale [START_REF] Pollock | The use of auxiliary variables in capture-recapture modelling: an overview[END_REF].

Implementation

Fitting capture-recapture models in a state-space framework is complicated due to the high-dimensional integral involved in the individual likelihood Equation (3). To circumvent this issue, several techniques have been proposed including Kalman filtering, Monte-Carlo particle filtering (such as sequential importance sampling) and MCMC (see [START_REF] Clark | Hierarchical Bayes for structured, variable populations: From recapture data to life-history prediction[END_REF][START_REF] Wang | On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models[END_REF] for reviews). Our objective here is not to discuss these different methods. For our purpose, we adopt the MCMC technique which is now widely used in biology [START_REF] Ellison | Bayesian inference in ecology[END_REF][START_REF] Clark | Why environmental scientists are becoming Bayesians[END_REF], in particular for estimating animal survival [START_REF] Schwarz | Estimating animal abundance: review III[END_REF]Brooks et al., 2000). Besides, this is to our knowledge the only methodology which comes with an efficient and flexible program to implement it, which, in our case, will allow biologists to easily and rapidly adopt our approach.

Illustration

We consider capture-recapture data on the European dipper (Cinclus cinclus) that were collected between 1981 and 1987 [START_REF] Lebreton | Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies[END_REF]. The data consists of marking and recaptures of 294 birds ringed as adults in eastern France. We applied standard maximum-likelihood estimation [START_REF] Lebreton | Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies[END_REF]) and MCMC techniques (Brooks et al. 2000) using the product-multinomial likelihood and the state-space likelihood of Equation ( 3) in combination with Equation (1) and Equation (2). We ran two overdispersed parallel MCMC chains to check whether convergence was reached [START_REF] Gelman | Inference and monitoring convergence[END_REF]. We used 10,000 iterations with 5,000 burned iterations for posterior summarization. We used uniform flat priors for both survival and detection probabilities. The simulations were performed using WinBUGS [START_REF] Spiegelhalter | WinBUGS user manual -Version 1[END_REF]. The R [START_REF] Ihaka | R: a language for data analysis and graphics[END_REF] package R2WinBUGS [START_REF] Sturtz | R2WinBUGS: a package for running Win-BUGS from R[END_REF] was used to call Win-BUGS and export results in R. This was especially helpful when converting the raw encounter histories into the appropriate format, generating initial values and calculate posterior modes. The programs are available in Appendix A. Posterior summaries for encounter and survival probabilities are given in Table 1, along with their posterior probability distributions that are displayed in Figure 1.

[Table 1 about here.]

[Figure 1 about here.]

Survival estimates were uniformally similar whatever the method used (Table 1). In particular, there is a clear decrease in survival 1982-1983 and 1983-1984, corresponding to a major flood during the breeding season in 1983 [START_REF] Lebreton | Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies[END_REF].

In contrast, posterior medians of detection probabilities using the CJS SSM approach are quite different from the classical maximum likelihood estimates, but more similar to the posterior medians obtained with the product-multinomial likelihood approach (Table 1). These discrepancies are no longer present when posterior modes are examined, as expected (recall that we use non-informative uniform distributions as priors for all parameters).

The last survival probability as well as the last detection probability are estimated with high variability (Table 1 and Figure 1). The fact that these two parameters cannot be separately estimated is not surprising since the CJS model is known to be parameter-redundant (Catchpole and Morgan, 1997). Also, the first survival probability and the first detection probability are weakly identifiable, due to the fact that very few individuals were marked at the first sampling occasion (approximately 7% of the full data set).

3 Further state-space modelling

Multistate capture-recapture models

Multistate capture-recapture models (Arnason, 1973;[START_REF] Schwarz | Estimating migration rates using tag-recovery data[END_REF]AS hereafter) are a natural generalization of the CJS model in that individuals can move between states, according to probabilities of transition between those states. States can be either geographical sites or states of categorical variables like reproductive status or size class [START_REF] Lebreton | Multistate recapture models: modelling incomplete individual histories[END_REF]. We provide here a state-space modelling formulation of the AS model [START_REF] Dupuis | Bayesian estimation of movement and survival probabilites from capture-recapture data[END_REF][START_REF] Newman | State-space modeling of animal movement and mortality with application to salmon[END_REF][START_REF] Clark | Hierarchical Bayes for structured, variable populations: From recapture data to life-history prediction[END_REF]. Without loss of generality, we consider 2 states. Let X i,t be the random state vector taking values

(1, 0, 0), (0, 1, 0) and (0, 0, 1) if, at time t, individual i is alive in state 1, 2 or dead respectively. Let Y i,t be the random observation vector taking values (1, 0, 0), (0, 1, 0) and (0, 0, 1) if, at time t, individual i is encountered in state 1, 2 or not encountered.

Parameters involved in the modelling include φ rs i,t , the probability that an animal i survives to time t + 1 given that it is alive at time t (t = 1, . . . , T -1) and makes the transition between state r and state s over the same interval (r, s = 1, 2), as well as p r i,t the probability of detecting individual i at time t in state r (t = 2, . . . , T , r = 1, 2). A state-space formulation for the multistate AS model is given by:

Y i,t |X i,t ∼ Multinomial         1, X i,t         p 1 i,t 0 1 -p 1 i,t 0 p 2 i,t 1 -p 2 i,t 0 0 1                 (6)
X i,t+1 |X i,t ∼ Multinomial         1, X i,t         φ 11 i,t φ 12 i,t 1 -φ 11 i,t -φ 12 i,t φ 21 i,t φ 22 i,t 1 -φ 21 i,t -φ 22 i,t 0 0 1                 (7)
where Equation ( 6) and Equation ( 7) are the observation and the state equations respectively. This formulation has similarities with that of [START_REF] Pradel | Multievent: An extension of multistate capture-recapture models to uncertain states[END_REF] who used hidden-Markov models to extend multistate models to cope with uncertainty in state assignment. Again, it should be noted that the state-space formulation allows the demographic parameters to be distinguished from the nuisance parameters. A similar reasoning to that adopted for the CJS model leads to Equations ( 6) and ( 7). As expected, Equation (6) and Equation ( 7) reduce to Equation (1) and Equation (2) if one considers a single state. Making similar assumptions as in the CJS model leads to the population AS SSM.

Ring-recovery models

The capture-recapture models presented above deals with apparent survival, which turns out to be true survival if emigration is negligeable. When marks of individuals (or individuals themselves) are actually recovered, true survival probabilities can be estimated using ring-recovery models (Brownie et al., 1985

Y i,t |X i,t , X i,t-1 ∼ Bernoulli ((X i,t-1 -X i,t )λ i,t ) (8) X i,t+1 |X i,t ∼ Bernoulli(X i,t φ i,t ) (9)
where Equation ( 8) and Equation ( 9) are the observation and the state equations respectively. While the state Equation ( 9) is the same as that in the individual SSM CJS, the observation Equation ( 8) deserves further explanation. If individual i, alive at time t, does not survive to time t + 1, then its mark has probability λ i,t of being recovered and probability 1λ i,t otherwise, which translates into Y i,t is distributed as Bernoulli(λ i,t ) given X i,t-1 = 1 and X i,t = 0 i.e. X i,t-1 -X i,t = 1. Now if individual i is in a given state (dead or alive) at time t and remains in this state till time t + 1, then its mark cannot be recovered, which translates into Y i,t is distributed as Bernoulli(0)

given X i,t-1 = 0 and X i,t = 0 or X i,t-1 = 1 and X i,t = 1 i.e. X i,t-1 -X i,t = 0. The distribution of the observation Y i,t conditional on the combination of states X i,t-1 -X i,t is thus given by Equation ( 8). Similar comments to that of previous sections can be made here as well. Finally, we note that because the probability distribution of the current observation does not only depend on the current state variable, the model defined by Equation ( 8) and Equation ( 9) does not exactly matches the definition of a state-space model but can be rewritten as such (see Appendix B).

Discussion

We have shown that, by separating the demographic process from its observation, CR models for estimating survival can be expressed as SSMs. In particular, the SSM formulation of the CJS model competes well with the standard method when applied to a real data set. Bearing this in mind, we see at least two further promising developments to our approach.

First, by scaling down from the population to the individual level while modelling the survival probabilities, random effects can readily be incorporated to cope with heterogeneity in the detection probability [START_REF] Huggins | A note on the difficulties associated with the analysis of capturerecapture experiments with heterogeneous capture probabilities[END_REF] and deal with a frailty in the survival probability [START_REF] Vaupel | Heterogeneity ruses -some surprising effects of selection on population dynamics[END_REF]. Second, the combination of various sources of information which has recently received a growing interest, (e.g. recovery and recapture data, [START_REF] Catchpole | Integrated recovery/recapture data analysis[END_REF]recovery and census data, Besbeas et al., 2002;Besbeas et al., 2003) can now be operated/conducted in a unique SSM framework and hence benefits from the corpus of related methods. Of particular importance, we are currently investigating the robust detection of density-dependence by accounting for error in the measurement of population size using the combination of census data and data on marked individuals.

Because most often, data collected in population dynamics studies give only a noisy output of the demographic process under investigation, the SSM framework provides a flexible and integrated framework for fitting a wide range of models which, with

widespread adoption, has the potential to advance significantly ecological statistics (Buckland et al., 2004;[START_REF] Thomas | A unified framework for modelling wildlife population dynamics[END_REF].

  's research was supported by a Marie-Curie Intra-European Fellowship within the Sixth European Community Framework Programme. This project was funded by the Action Incitative Régionale BioSTIC-LR 'Modélisation intégrée en dynamique des populations : applications à la gestion et à la conservation'. 6 Literature Cited Anderson-Sprecher, R., Ledolter, J., 1991. State-space analysis of wildlife telemetry data. Journal of the American Statistical Association. 86, 596-602. Arnason, A.N., 1973. The estimation of population size, migration rates and survival in a stratified population. Research in Population Ecology. 15, 1-8. Besbeas, P., Freeman, S.N., Morgan, B.J.T., Catchpole, E.A., 2002. Integrating markrecapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics. 58, 540-547. Besbeas, P., Lebreton, J.D., Morgan, B.J.T., 2003. The efficient integration of abundance and demographic data. Journal of the Royal Statistical Society Series C -Applied Statistics. 52, 95-102. Brooks, S. P., Catchpole, E. A., Morgan, B. J. T., 2000. Bayesian animal survival estimation. Statistical Science. 15, 357-376. Brownie, C., Anderson, D.R., Burnham, K.P., Robson, D.S., 1985. Statistical inference from band recovery data -a handbook. United States Fish and Wildlife Service, Washington.Buckland, S.T.,Newman, K.B., Thomas, L., Koesters, N.B., 2004. State-space models for the dynamics of wild animal populations. EcologicalModelling. 171,[157][158][159][160][161][162][163][164][165][166][167][168][169][170][171][172][173][174][175]

  ; RR models hereafter). Let X i,t be the binary random variable taking values 1 if individual i is alive at time t and 0 if it is dead at time t. Let Y i,t be the binary random variable taking values 1 if mark of individual i is recovered at time t and 0 otherwise. The parameters involved in the likelihood are φ i,t , the probability that an animal i survives to time t + 1 given that it is alive at time t (t = 1, . . . , T -1), and λ i,t the probability of recovering the mark of individual i at time t (t = 2, . . . , T ). A general state-space formulation of the RR model is therefore given by:

Appendix A: WinBUGS code for fitting the CJS model using the SSM formulation

# State-space formulation of the Cormack-Jolly-Seber model # # observations = 0 (non-encountered) and 1 (encountered) # # states = 0 (dead) and 1 (alive) #

# Define the priors for survival phi and detectability p

} # fullfil the remaining cells with zeros for (j in 1:(e[i]-1))

{

# 'e' is the vector of first encounters ('n' components)

# 'data' is a matrix with dimensions 'nx' times 3 where # the first column gives the current individual (1,...,'n'), # the second column gives the current encounter occasion (1,...,'K'), # the third column gives the observation (= 1 if detection, = 0 otherwise) # corresponding to the current individual and current encounter occasion

] be a bivariate random vector where its two components are denoted Z 1 i,t and Z 2 i,t . Equation ( 8) becomes

and Equation ( 9) becomes

The system defined by Equation (10) and Equation ( 11) is a state-space model and it is equivalent to the model defined by Equation ( 8) and Equation ( 9).

Note that an alternative state-space formulation can be adopted using a multistate formulation of the RR model [START_REF] Lebreton | Competing events, mixtures of information and multistrata recapture models[END_REF] and Section 3.1. Table 1: Estimated survival and detection probabilities for the Dipper data using the CJS model and three different methods, the state-space model (SSM) using a Monte Carlo Markov Chain (MCMC) method, the product-multinomial model (PMM) using a MCMC method and the PMM using a maximum-likelihood (ML) method. The two first methods were implemented using program WinBUGS [START_REF] Spiegelhalter | WinBUGS user manual -Version 1[END_REF], while program M-SURGE [START_REF] Choquet | M-SURGE: new software specifically designed for multistate capture-recapture models[END_REF]