The online database MaarjAM reveals global and ecosystemic distribution patterns in arbuscular mycorrhizal fungi (Glomeromycota)

Transcription

1 Research The online database MaarjAM reveals global and ecosystemic distribution patterns in arbuscular mycorrhizal fungi (Glomeromycota) M. Öpik 1, A. Vanatoa 2, E. Vanatoa 1, M. Moora 1, J. Davison 1, J. M. Kalwij 1,Ü. Reier 1 and M. Zobel 1 1 Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, 40 Lai St., Tartu, Estonia; 2 Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Fr. R. Kreutzwaldi 5, Tartu 51014, Estonia Summary Author for correspondence: M. Öpik Tel: maarja.opik@ut.ee Received: 22 February 2010 Accepted: 11 May 2010 (2010) 188: doi: /j x Key words: arbuscular mycorrhizal (AM) fungi, distribution, diversity, global, host range, metadata, sequence database, SSU rdna. Here, we describe a new database, MaarjAM, that summarizes publicly available Glomeromycota DNA sequence data and associated metadata. The goal of the database is to facilitate the description of distribution and richness patterns in this group of fungi. Small subunit (SSU) rrna gene sequences and available metadata were collated from all suitable taxonomic and ecological publications. These data have been made accessible in an open-access database ( Two hundred and eighty-two SSU rrna gene virtual taxa (VT) were described based on a comprehensive phylogenetic analysis of all collated Glomeromycota sequences. Two-thirds of VT showed limited distribution ranges, occurring in single current or historic continents or climatic zones. Those VT that associated with a taxonomically wide range of host plants also tended to have a wide geographical distribution, and vice versa. No relationships were detected between VT richness and latitude, elevation or vascular plant richness. The collated Glomeromycota molecular diversity data suggest limited distribution ranges in most Glomeromycota taxa and a positive relationship between the width of a taxon s geographical range and its host taxonomic range. Inconsistencies between molecular and traditional taxonomy of Glomeromycota, and shortage of data from major continents and ecosystems, are highlighted. Introduction Arbuscular mycorrhizal (AM) fungi (phylum Glomeromycota; Schüßler et al., 2001b) are among the world s commonest soil microorganisms and associate with > 80% of all plant species (Smith & Read, 2008). To understand the role of Glomeromycota in ecosystems, information about their ecology and biogeography is of primary importance (Fitter, 2005; Chaudhary et al., 2008). During the past two decades, since the work by Simon et al. (1992), such information has increasingly been sought using molecular tools. However, despite the gradual accumulation of data indicating the molecular diversity and distribution of Glomeromycota, a broader ecological understanding remains elusive. Analysis of Glomeromycota molecular diversity is hampered by the lack of a universal system of nomenclature applicable to taxa that are identified only by their DNA sequences. Thus, the same phylogroups (sequence groups, species, OTUs) are often referred to differently by individual case studies. Furthermore, there is no consensus as to which criteria are suitable for delimiting phylogroups. Therefore, sequence groups identified by different authors contain different degrees of within-group variability. Meanwhile, reference data describing intra- and interspecific genetic variability in described Glomeromycota species are contradictory and limited to relatively few taxa (Nilsson et al., 2008; Rosendahl, 2008; Gamper et al., 2009; Stockinger et al., 2009). Sequences of cultured reference species are not yet available for most of the phylogroups detected in natural ecosystems, meaning that relationships with known species are unclear for a large proportion of phylogroups (van der Heijden et al., 2008; Rosendahl, 2008). For these reasons, arbitrarily named phylogroups rather than species assignment have Journal compilation Ó Trust (2010) (2010) 188:

2 224 Research typically been used when describing the natural diversity of Glomeromycota (Helgason et al., 1998; Vandenkoornhuyse et al., 2003). If original sequence data from case studies are made available in public databases, phylogenetic analysis can be performed on a metadataset comprising all such sequences in order to delimit standard phylogroups. Such phylogroups, hereafter referred to as virtual taxa (VT; cf. Öpik et al., 2009), can be named or coded to provide a standard taxonomy (see Horton et al., 2009). As well as allowing future studies to be comparable, a standardized taxonomy can be unequivocally associated with metadata, such as geographical location, host plant species and ecosystem, to provide an overview of Glomeromycota distribution, habitat preferences and other fundamental ecological characteristics. Currently, the quantity of comparable sequence data from Glomeromycota is reaching the level where ecologically meaningful questions can be answered. Indeed, metadata linked to sequence datasets of Glomeromycota has already provided insights into AM fungal community composition among ecosystems (Öpik et al., 2006), and in relation to host range (Helgason et al., 2007) and dominance patterns (Dumbrell et al., 2010). DNA sequence data in public databases can be poorly and or discontinuously annotated, misidentified or suffer from limited taxon sampling (Bridge et al., 2003; Bidartondo et al., 2008; Ryberg et al., 2008; Brock et al., 2009). In order to overcome these limitations, specialized curated databases are increasingly being compiled to provide high-quality sequences with complete and correct annotations, including a verified taxonomic identification (Kõljalg et al., 2005; Abarenkov et al., 2010). The importance of fully annotated datasets is likely to increase further still as they can serve as reference data for en masse identification of sequences in the large datasets produced by nextgeneration sequencing technologies (Buée et al., 2009; Horton et al., 2009; Nilsson et al., 2009; Öpik et al., 2009; Ryberg et al., 2009; Martin & Martin, 2010). In this paper we describe an open-access database, called MaarjAM ( and outline some of its potential applications. MaarjAM stores publicly available Glomeromycota sequence data and associated metadata such as location, host plant, biome, climatic zone etc. The information contained in MaarjAM is carefully checked by specialists and is presented in a format to facilitate downstream data analyses. Currently, the database primarily contains small subunit rrna gene sequences, but the range of molecular markers will likely be increased in the future. Earlier versions of this database have already been used by our study group for en masse identification of large-sequence datasets using the BLAST algorithm (Öpik et al., 2009; M. Moora et al., unpublished). MaarjAM additionally provides an overview of existing information about the ecology and biogeography of Glomeromycota. As an example, we investigate several of the many potential questions that MaarjAM could help to answer: what is the global distribution of individual Glomeromycota VT and of VT richness; and which abiotic and biotic factors explain the observed VT distribution patterns? Materials and Methods Data sources The MaarjAM database aims to provide a quality-controlled repository for published sequence data from Glomeromycota (the name of the database is an amalgamation of Maarja (from Maarja Öpik, the creator of the database) and arbuscular mycorrhiza (AM)). Therefore, data from both ecological and taxonomic studies are included. For inclusion in MaarjAM, sequence data must be both submitted to public sequence databases and published in scientific papers. An overview of the principles and procedure guiding data input is provided in Supporting Information, Fig. S1. Particular care has been taken to include all publications using the small subunit ribosomal RNA gene between PCR primers NS31 and AM1 because the majority of natural Glomeromycota diversity data have been obtained using this marker region (Lee et al., 2008). However, the number of studies using other marker regions, including nuclear internal transcribed spacer (ITS), large subunit (LSU) rrna gene, beta-tubulin, actin, elongation factor 1-alpha and mitochondrial LSU rrna gene, is increasing rapidly. These data will be included in future versions of the database. DNA sequence data were obtained from the GenBank EMBL DDBJ nucleotide sequence databases (hereafter INSD, for International Nucleotide Sequence Databases). The origin of cultured fungi was taken from the Glomeromycota culture collections (BEG, INVAM, GINCO) if missing or insufficiently described in the original publication. Where necessary, authors were contacted for clarification. Glomeromycota nomenclature follows A. Schüßler s Glomeromycota phylogeny ( ~schuessler/amphylo/; 29 April 2010). Host plant nomenclature follows APG III (Bremer et al., 2009; Chase & Reveal, 2009). Data structure The MaarjAM database contains individual records of phylogroup occurrences organized by location and host plant species. Several records per phylogroup with different DNA sequences were included if available in order to register within-group sequence variability in the database. When a specific location or host species was recorded in a publication, but no DNA sequences were made available, a dummy accession without sequence data was created in order to allow fungal distribution to be fully registered. Thus, the (2010) 188: Journal compilation Ó Trust (2010)

3 Research 225 following data are stored for each accession: DNA sequence (DNA sequence and INSD accession number if present, PCR primers, marker region), sample origin (four categories: plant roots, soil, cultured spores, other), host plant data (species, higher taxonomic classifications), location data (site name, geographical coordinates, administrative units, continent) and original publication reference. In particular, the trait sample origin allows different kinds of data, such as ecological surveys based on plant roots and purely taxonomic surveys based on cultured fungi, to be separated in downstream analyses. The category cultured spores is used for single-spore cultures (i.e. isolates) and single-species multispored cultures, but spores from natural soil, pot experiments and trap cultures are assigned to the category other. If possible, additional data were provided on the ecology and biogeography of the glomeromycotan taxa, either based on information available in the paper or inferred from the location of the study site. The ecological and biogeographical data are linked to individual accessions, not species names or other categories, in order to maintain independence of data for analyses. A brief description of these categories follows (for a full specification, see Supporting Information, Methods S1): Ecological categories The habitat classification implemented in the MaarjAM database uses three hierarchical levels (from general to specific): specific ecosystem features, biome, habitat (Table 1). The category specific ecosystem features, which currently contains six levels, is a categorization that allows researchers to address particular ecological patterns of interest (Table 1). For the questions addressed in this paper, it is useful for distinguishing between structurally and functionally different ecosystems, such as forests, shrublands and grasslands (cf. McNaughton et al., 1989), and to characterize the dynamic status of the ecosystem by distinguishing early successional natural ecosystems and anthropogenic ecosystems, the latter containing cultivated and other disturbed ecosystems. The sixth level culture is used for records originating from cultures of Glomeromycota if no information about the ecosystem of origin was available. Further specific categorizations may be created later to address future questions. The category biome represents the highest ecological classification unit that is convenient to recognize globally. Habitats within a biome function in a broadly similar way. The WWF classification of terrestrial ecoregions within 14 biomes and eight biogeographical realms was used as a starting point (Olson et al., 2001). However, in order to interpret the distribution of Glomeromycota in a more precise ecological context, the original classification was further refined to include 27 biomes (Supporting Information, Methods S1). Currently, the MaarjAM database contains records from 17 biomes (Table 1). The category habitat refers to the vegetation ecosystem ecoregion habitat type where sampling was Table 1 Hierarchical habitat classifications used in the MaarjAM database: specific ecosystem features, biome and habitat Specific ecosystem features Biome Examples of habitats Anthropogenic Anthropogenic ecosystem Abandoned mine, arable field, agricultural ecosystem, contaminated land, glasshouse, park, olive plantation, vineyard Culture a Habitat not known Culture a Forest Boreal forest Boreal forest Subtropical dry broadleaf forest Broadleaved forest Subtropical moist broadleaf forest Deciduous broadleaved second-growth forest Temperate broadleaf and mixed forest Boreo-nemoral forest, broadleaved forest, mixed deciduous woodland Temperate coniferous forest Temperate forest Tropical dry broadleaf forest Dry afromontane forest Tropical moist broadleaf forest Rainforest, tropical montane cloud forest Grassland Subtropical grasslands and savannahs Savannah, serpentine soil, semi-arid grassland Temperate cultivated grassland Agricultural grassland Temperate natural grassland Ancient meadow, tallgrass prairie Temperate semi-natural grassland Temperate grassland Tropical grasslands and savannas Tropical tussock grassland, savannah Shrubland Deserts and xeric shrublands Dolomitic shrubland, gypsophilous vegetation Subtropical shrubland Garrigue Successional Azonal and successional Boreal forest border, coastal vegetation, lahar area, saltern, sand dune Other wetlands Salt marsh, wetland Habitat is a description provided by original publications, of which just a few examples are shown in this table. Please see the Materials and Methods section and Supporting Information, Methods S1, for further details. a Records from cultures from known biomes and ecosystems are classified into the respective categories. Journal compilation Ó Trust (2010) (2010) 188:

4 226 Research conducted. If present, a description from the original publication was usually retained; however, if it was lacking or insufficient, wherever possible a specification was provided on the basis of other information available in the publication or elsewhere. Environmental categories A slightly modified version of Walter s (1994) system with five broad climatic zones corresponding to those of Holdridge (1967) is used in the MaarjAM database: tropical s.l. (equatorial and tropical); subtropical s.l. (subtropical, Mediterranean and warm temperate); temperate s.l. (temperate, nemoral and continental); boreal; and polar. There are currently few records in MaarjAM from the boreal climatic zone and no records from the polar climatic zone. Geographical categories The continents are defined as: Africa, North America (with an artificial southern boundary in Panama), South America, Oceania (including biogeographical realms Australasia and Oceania; see the next paragraph), Europe and Asia (with a boundary formed by the Ural and Caucasus mountain ranges, and Caspian and Black seas). The ancient supercontinents Laurasia and Gondwana, which developed following the break-up of Pangaea, were interpreted as suggested by Scotese (2004); that is, Gondwana consisting of present-day Africa, South America, Oceania and India; and Laurasia consisting of present-day North America and Eurasia, but excluding India, Indonesia and the Philippines. Biogeographical realms are large geographical regions where ecosystems share broadly similar biota. We distinguished eight biogeographical realms following Olson et al. (2001): Palearctic, Nearctic, Afrotropic, Neotropic, Indo- Malay, Australasia, Oceania and Antarctic. There are currently no data in MaarjAM from the Antarctic realm. Database design The MaarjAM database runs on a quad-core 64bit Linux server (CentOS 5.2, kernel x, webserver Apache ver. 2.x). Data are stored in the relational tables of a MySQL ver. 5.x database. A graphical user interface (GUI) for accessing and editing data has been built using PHP 5.x scripting language and Ajax technology. The GUI is accessible through all class A browsers regardless of operating system, though it has been most intensively tested using Mozilla Firefox (ver. 2.x and 3.x) and Google Chrome. Phylogenetic analysis Glomeromycota sequence groups were identified following automatic alignment of all MaarjAM sequences using the MAFFT multiple sequence alignment web service implemented in JALVIEW 2.4 (Clamp et al., 2004) and neighbor-joining analysis with TOPALi (Milne et al., 2004). Sequence groups (referred to as VT) were defined on the basis of bootstrap support and sequence similarity 97%. These criteria produced groupings with sequence variability (i.e. within-group variation) similar to those used by some authors (Helgason et al., 1998; Öpik et al., 2008). Phylogenetic analysis was also conducted using a Bayesian approach in BEAST (version 1.5.3; Drummond & Rambaut, 2007). The GTR + I + G nucleotide substitution model was chosen on the basis of AIC (jmodeltest; Posada, 2008). Posterior parameter estimates were drawn every 1000 steps from three separate step Markov chain Monte Carlo (MCMC) runs, with the first 10 15% of steps discarded as burn-in. Posterior clade probabilities were summarized on a maximum clade credibility tree. Statistical data analysis Coleman rarefaction analysis using 50 randomizations without replacement was performed in EstimateS (Colwell, 2006) to produce accumulation curves showing VT richness in relation to sampling intensity (number of accessions in MaarjAM). Two-way log-linear analysis and the Freeman Tukey deviation test (Legendre & Legendre, 1998) were performed to determine whether the frequencies of VT belonging to the five most abundant Glomeromycota families are represented differently among the continents or among climatic zones; and whether the continental distribution pattern of VT (found in one, two, three or four continents) is related to their distribution pattern across host plant lineages (colonizing plant species from one, two or three of the plant superorders; different host plant groups are unevenly represented in MaarjAM; therefore this analysis only included data from plant superorders Asteranae, Lilianae and Rosanae). To determine whether there was a relationship between spatial or environmental variables and VT richness, we first tested whether the sample size (number of accessions) and geographical distribution of the accessions held in MaarjAM provided sufficient power. This was done using the global map of vascular plants from Kier et al. (2005), which shows that plant species richness is negatively correlated with latitude, as a proxy. Analysis was restricted to MaarjAM records from plant roots, excluding those from soil, cultured spores or other, in order to ensure methodological consistency. Furthermore, because of the low sample size from the southern hemisphere, we only used MaarjAM data from the northern hemisphere in this analysis. A subset of the Kier et al. (2005) vascular plant dataset was extracted by taking plant richness estimates only from the geographical positions corresponding to northern hemisphere VT observations in MaarjAM. (2010) 188: Journal compilation Ó Trust (2010)

5 Research 227 Correlation between latitude and vascular plant richness in this reduced dataset was weak (r 2 adj = 0.305) but highly significant (F = , df = 80, P < ) indicating that our sample size and spatial distribution were sufficient to reveal a latitudinal effect of the magnitude exhibited among vascular plants. To determine whether elevation was a contributing environmental variable explaining VT richness, the elevation (m above sea level) at the geographical position of each VT observation was obtained from a digital elevation model (DEM) with a spatial resolution of c. 30 arcsec ( All MaarjAM records with geographical position data (n = 1983 accessions) were imported into a geographical information system (GIS) using ArcMap 9.2 (ESRI, 2006). For analysing the distribution of global VT richness we only used MaarjAM accessions from plant roots (n = 1801; 99 locations). Richness values were interpolated using the inverse distance weighting function, which interpolates values within a circular range of observations. To correct VT richness for sampling intensity, the number of observations (number of MaarjAM accessions) was determined for each location. Subsequently a linear regression model was fitted to determine the relationship between log e -transformed number of observations and log e -transformed VT richness, and the standardized residuals recorded. These residuals were interpolated using the same technique as described earlier to determine the global distribution of VT richness residuals. For ease of interpretation, interpolated richness data are only presented for terrestrial zones. Results MaarjAM database size and phylogenetic delimitation of VT As of 20 April 2010 the MaarjAM database of glomeromycotan environmental and culture-originating sequences contained 2447 records of small subunit (SSU) rrna gene sequence occurrences, derived from 105 publications. Sequences covering the NS31-AM1 amplicon of the SSU rrna gene (1844 in total) were used in phylogenetic analysis in order to delimit VT. One hundred and two publications provided data that could be assigned to VT (2238 records; Methods S2). The remaining 209 accessions were not assigned to VT because they were dummy accessions with an original phylogroup identity that was linked to several VT or because the sequence covered a different SSU rrna gene fragment. Based on clade support and sequence similarity 97%, 282 virtual taxa were delimited (Figs 1, S2). VT delimited on the basis of the neighborjoining phylogeny generally were supported by the Bayesian phylogenetic approach (Fig. S2). Two-thirds of the identified VT belonged to Glomeraceae (i.e. Glomus group A; 186 VT, Tables 2, S1). Sequences of cultured fungi ascribed to 71 species with Latin binomials were found in 53 VT (Tables 2, S1); some VT contained several named species, while some named species occurred in more than one VT. A list of VT showing the inclusion of previously named species and cultures not identified to species level is provided in Table S1. A total of 57 VT were represented by 10 records, while 54 VT were represented by only one record. VT richness Glomeromycota VT accumulation curves did not reach an asymptote, at either the global or the continental scale, indicating that there probably remain many unrecorded taxa. No marked differences in terms of taxon richness accumulation curves existed between the continents (Fig. 2). However, sampling effort has been extremely uneven (Table 3). The global distribution of VT richness, based on data from plant roots, is shown in Fig. 3(a). There was a significant linear relationship between VT richness per location and sample size (radj 2 = 0.818, df = 97, P < 0.001). When the effect of sample size (number of MaarjAM accessions from a location) was removed by using standardized residual VT richness per location, richness hotspots identified in the uncorrected analysis were no longer apparent (Fig. 3b). Distribution maps are shown in Fig. 4 for the 12 VT recorded from the greatest number of different locations. There were no significant relationships (P always > 0.05) between either log e -transformed VT richness or the standardized residual VT richness and any of the explanatory environmental variables (northern hemisphere latitude, log e -transformed elevation, log e -transformed vascular plant richness). Biogeographical and ecological patterns in VT distribution The distribution of VT across continents indicated a high prevalence of endemic VT (recorded from only one continent) and very few ubiquitous VT. Only one VT, Glomus VT166, has been recorded on all six continents (Table 3), while five VT have been recorded on five continents: Gigaspora VT39 (incl. G. albida, G. decipiens, G. gigantea, G. margarita, G. rosea), Scutellospora VT49 (incl. S. aurigloba, S. dipurpurescens), Glomus VT67 (incl. G. mosseae), Glomus VT191, Glomus VT219. By contrast, 168 VT have been recorded exclusively on one continent (Table 3) and 95 VT in one location. When the data from Europe and Asia, and North and South America were collapsed into two categories Eurasia and America the Journal compilation Ó Trust (2010) (2010) 188:

6 228 Research (a) (b) Fig. 1 Maximum clade credibility tree of glomeromycotan small subunit (SSU) rrna gene virtual taxa (VT) in the MaarjAM database, inferred using Bayesian phylogenetic analysis. Posterior probabilities (when > 0.5) for nodes are shown for higher-order clades. The numbers of VT in each lineage are indicated on this outline phylogeny. Refer to Supporting Information Fig. S2 for detailed placement of VT and described species. (a) Glomeromycota; (b) subtree showing Archaeosporales and Paraglomerales. Note: no sequences from described species are available for the majority of Glomus group A lineages and for the putatively named Archaeosporaceae? and Paraglomeraceae? clades. distinctness of VT pools remained (Fig. 5a). These two well-studied continents shared only 69 VT out of their respective totals of 189 and 151 VT (Fig. 5a). We compared the distribution of VT on land masses that historically belonged to the ancient supercontinents Laurasia and Gondwana (Fig. 5b). These historical (2010) 188: Journal compilation Ó Trust (2010)

7 Research 229 Table 2 Number of virtual taxa (VT) in each Glomeromycota family in the MaarjAM database Family Continent Africa Asia Europe North America Oceania Climatic zone South America Boreal Subtropical Temperate Tropical Total VT Known species in MaarjAM Total known species Acaulosporaceae Ambisporaceae Archaeosporaceae Diversisporaceae Geosiphonaceae Gigasporaceae Glomeraceae Glomus group B Pacisporaceae Paraglomeraceae Total Data are subdivided by continent and climatic zone. The final column provides the total number of known morphospecies following the nomenclature in A. Schüßler s Glomeromycota phylogeny ( and the number of known morphospecies represented in MaarjAM. Note that Glomeraceae includes Glomus group A; Glomus group B is treated as Fam. ined. (cf. Krüger et al., 2009). Entrophosporaceae with two species is not yet represented in MaarjAM and therefore is not in the table. Fig. 2 Global and continental taxon accumulation curves (Coleman rarefaction) showing glomeromycotan virtual taxon (VT) richness in relation to sampling intensity (the number of accessions in MaarjAM). Curves are presented up to a maximum of 500 accessions. See Table 2 for the total number of accessions in each category. supercontinents shared 67 VT out of totals of 221 and 122 VT in Laurasia and Gondwana, respectively. Virtual taxa were also characterized by small distribution ranges when other biogeographical and ecological units were compared: 61, 61, 53 and 51% of VT occurred in only one biogeographical realm, climatic zone, ecosystem and biome, respectively (Table 3, Fig. 5); 70, 70, 80 and 85% of such VT are known from only one location. MaarjAM contains records from 168 host plant species from seven vascular plant superorders and six nonvascular plant subclasses (Table 3). Ninety-six VT have been recorded from the roots of single host species. We compared the occurrence of VT in the three plant superorders with the largest number of records: Asteranae, Lilianae and Rosanae (Table 3, Fig. 5d); 112 VT (49%) were associated with host plants from only one of these plant superorders, including 53 VT that were represented by a single accession in the database. The distribution pattern of VT among host plant superorders contrasted with the pattern found in relation to geographical distribution, where relatively few records came from ubiquitous VT. VT detected in the roots of all three plant superorders were represented by large numbers of records and may thus represent abundant VT (average of 17.5, a maximum of 62 records per VT). By contrast, VT that were restricted to single plant superorders were represented by relatively few records and may represent less abundant VT (Table 3; average of 2.0, a maximum of 11 records per VT). However, as these include 53 VT with a single record among the three plant superorders, the effect of sample size cannot be excluded. For comparison, the global average across all VT in the database was 7.9 records per VT. Journal compilation Ó Trust (2010) (2010) 188:

8 230 Research Table 3 Numbers of Glomeromycota SSU rrna gene virtual taxa (VT), publications, and accessions in the MaarjAM database from different continents, biogeographical realms, climatic zones, ecosystems and biomes Accessions VT Papers Total From roots From soil From cultures Other Continents Africa Asia Europe North America Oceania a South America Occurs on one continent out of six Occurs on two continents out of six Occurs on three continents out of six Occurs on four continents out of six Occurs on five continents out of six Occurs on six continents out of six Laurasia Gondwana Biogeographical realms Afrotropic Australasia Indo-Malay Nearctic Neotropic Oceania a Palearctic Occurs in one realm out of seven Occurs in two realms out of seven Occurs in three realms out of seven Occurs in four realms out of seven Occurs in five realms out of seven Occurs in six realms out of seven 0 0 Occurs in seven realms out of seven 0 0 Climatic zones Boreal Subtropical Temperate Tropical Occurs in one climatic zone out of four Occurs in two climatic zones out of four Occurs in three climatic zones out of four Occurs in four climatic zones out of four 3 67 Ecosystems Anthropogenic Culture Forest Grassland Shrubland Successional Occurs in one ecosystem out of six (5) b 151 (141) b 399 Occurs in two ecosystems out of six (5) 62 (65) 383 Occurs in three ecosystems out of six (5) 37 (39) 478 Occurs in four ecosystems out of six (5) 15 (10) 283 Occurs in five ecosystems out of six (5) 9 (11) 320 Occurs in six ecosystems out of six Biomes Anthropogenic ecosystem Azonal and succesional Boreal forest Deserts and xeric shrublands Other wetlands Subtropical dry broadleaf forest Subtropical grasslands and savannas Subtropical moist broadleaf forest Subtropical shrubland Temperate broadleaf and mixed forest Temperate coniferous forest (2010) 188: Journal compilation Ó Trust (2010)

9 Research 231 Table 3 (Continued) Accessions VT Papers Total From roots From soil From cultures Other Temperate cultivated grassland Temperate natural grassland Temperate semi-natural grassland Tropical dry broadleaf forest Tropical grasslands and savannas Tropical moist broadleaf forest Occurs in one biome out of Occurs in two biomes out of Occurs in three biomes out of Occurs in four biomes out of Occurs in five biomes out of Occurs in six biomes out of Occurs in seven biomes out of Occurs in eight biomes out of Occurs in nine biomes out of Occurs in 10 biomes out of Occurs in 11 biomes out of Occurs in 12 biomes out of Occurs in biomes out of Host species Vascular plants (superorder level) Asteranae Austrobaileyanae Caryophyllanae Lilianae Magnolianae Ranunculanae Rosanae Occurrence among Asteranae, Lilianae, Rosanae Occurs in one superorder Occurs in two superorders Occurs in three superorders Nonvascular plants (subclass level) Lycopodiidae Marchantiidae Ophioglossidae Pinidae Polypodiidae Psilotidae a Oceania as a continent includes the biogeographical realms Australasia and Oceania (sensu Olson et al., 2001). b Parentheses contain the value if accessions from cultures with no known ecosystem of origin are removed; accessions from roots, soil or other sources with no known ecosystem of origin are not counted here. The data originate from the following publications: Simon et al. (1992, 1993a,b), Gehrig et al. (1996), Simon (1996), Helgason et al. (1998), Sawaki et al. (1998), Vandenkoornhuyse & Leyval (1998), Helgason et al. (1999), Declerck et al. (2000), Kramadibrata et al. (2000), Redecker et al. (2000a,b), Daniell et al. (2001), Schüßler et al. (2001a,b), Schwarzott et al. (2001), Bidartondo et al. (2002), Helgason et al. (2002), Husband et al. (2002a,b), Kowalchuk et al. (2002), Vandenkoornhuyse et al. (2002a,b), Helgason et al. (2003), Öpik et al. (2003), Regvar et al. (2003), Calvente et al. (2004), Ferrol et al. (2004), Haug et al. (2004), Heinemeyer et al. (2004), Oba et al. (2004), Saito et al. (2004), Scheublin et al. (2004), Walker et al. (2004), Whitfield et al. (2004), Wirsel (2004), de Souza et al. (2005), Douhan et al. (2005), Jumpponen et al. (2005), Ma et al. (2005), O Brien et al. (2005), Rowe & Pringle (2005), Russell & Bulman (2005), Sato et al. (2005), Yamato & Iwase (2005), de la Peña et al. (2006), DeBellis & Widden (2006), Franke et al. (2006), James et al. (2006), Martynova-Van Kley et al. (2006), Rodríguez-Echeverría & Freitas (2006), Santos et al. (2006), Vallino et al. (2006), Wubet et al. (2006a,b), Beck et al. (2007), Helgason et al. (2007), Kovács et al. (2007), Ligrone et al. (2007), Porras-Alfaro et al. (2007), Redecker et al. (2007), Renker et al. (2007), Santos-González et al. (2007), Walker et al. (2007), Vandenkoornhuyse et al. (2007), Winther & Friedman (2007), Alguacil et al. (2008), Appelhans et al. (2008), Błaszkowski et al. (2008), Burke (2008), Kottke et al. (2008), Lee et al. (2008), Lesaulnier et al. (2008), Liang et al. (2008), Likar et al. (2008), Maki et al. (2008), Merckx & Bidartondo (2008), Öpik et al. (2008), Palenzuela et al. (2008), Schechter & Bruns (2008), Toljander et al. (2008), Turrini et al. (2008), Winther & Friedman (2008), Yamato et al. (2008), Alguacil et al. (2009a,b,c), Błaszkowski et al. (2009), Gamper et al. (2009), Hausmann & Hawkes (2009), Ipsilantis et al. (2009), Liu et al. (2009), Öpik et al. (2009), Schreiner & Mihara (2009), Sonjak et al. (2009a,b), West et al. (2009), Wilde et al. (2009), Winther & Friedman (2009), Yamato et al. (2009), Dumbrell et al. (2010). Global geographical distribution patterns of VT in MaarjAM were related to their occurrence in host plant superorders (v 2 = 106.7, df = 6, P < 0.001). VT detected from a wider range of host plants (i.e. host plant species belonging to more than one of the three well-represented superorders) were also geographically more widespread (i.e. Journal compilation Ó Trust (2010) (2010) 188:

10 232 Research recorded in more than two continents) and vice versa: VT recorded from only one host plant superorder were overrepresented among VT recorded from only one continent (Freeman Tukey test, P < 0.05). The occurrence of Glomeromycota families in continents and climatic zones is shown in Table 2. The distribution among continents of VT from the five most abundant Glomeromycota families was significantly different (v 2 = 30.24, df = 16, P = 0.02). Acaulosporaceae were relatively overrepresented in Europe, while Gigasporaceae were underrepresented in Africa (Freeman Tukey test, P < 0.05). The distribution of VT in the five most frequently recorded families of Glomeromycota did not differ between climatic zones (v 2 = 6.6, df = 8, P = 0.58). The results of all log-linear analyses were unchanged when the 54 VT represented by a single accession were excluded. Discussion One of the basic goals of taxonomy is to provide a means to identify organisms (Hibbett et al., 2009). The MaarjAM database introduced here is a repository of reference sequences for use in DNA sequence-based identification of AM fungi (Glomeromycota). It combines published DNA sequence information from taxonomic and ecological publications, and stores available metadata in an easily accessible format. The unique features of MaarjAM include a global taxonomy of Glomeromycota VT, pre-evaluation of data by specialists and organized storage of DNA sequence and metadata. Sequences deposited in the database are subjected to phylogenetic analyses, whereby phylogroups (VT) are defined anew, irrespective of original phylogroup or species identifications, on the basis of > 97% sequence identity and high branch support. This approach overcomes the problem of missing or erroneous identifications in public databases and ensures that data used for downstream analyses are delimited using the same principles. All VT are given numerical codes reflecting the chronological order in which they were identified. A type sequence is assigned to each VT, around which the VT would evolve if the taxon splits or merges with others following the inclusion of additional sequences. The history of VT assignment will be included in a future version of MaarjAM in order to keep track of the changing nomenclature of accessions. If desired, the stored sequences and linked metadata in MaarjAM can be downloaded by end users and used to create alternative taxonomies. The principles of sequence-based taxonomy deserve further research in order to find the most suitable criteria and methods for molecular species delineation as well as optimal approach to the naming of sequence-based taxa or species (Hibbett et al., 2009; Horton et al., 2009). Evolutionary methods such as the generalized mixed Yule-coalescent model (GMYC), based on coalescence within independently evolving populations, have been successfully used to delineate insect and bacterial molecular taxa (Pons et al., 2006; Barraclough et al., 2009) and have the potential to improve the sequence similarity-based VT taxonomy of MaarjAM in the future. Thorough attention is paid to the accuracy of data included in MaarjAM, which are all pre-evaluated by a specialist. Since one of the aims of the database is to allow an overview of glomeromycotan species (taxon) distribution, records of phylogroup occurrence associated with specific locations or host plants are included even if no representative DNA sequence is available. Such dummy accessions are assigned to VT only if this can be done unambiguously. These records allow a more complete ecological interpretation of data in comparison with sequence databases, where identical DNA sequences from different geographical locations or host plants are frequently not submitted by authors. The metadata are all linked to accessions, not to VT or species identifications, in order to avoid making erroneous links between taxa and their biogeographical and ecological information. Particular care was taken to include all available data relating to morphotaxa in order to ensure that VT can be linked to fully taxonomically identified sequences wherever possible. However, when investigating ecological questions, data from purely taxonomic studies can be excluded by limiting the dataset by the origin of the sample (plant roots, cultured spores etc.). Other databases that aim to provide controlled reference sequences for fungal taxon identification include the UNITE database for identification of ectomycorrhizal fungi based on ITS sequences from voucher specimens (Kõljalg et al., 2005; Abarenkov et al., 2010) and PHYMYCO, which collates available SSU rrna gene sequences from all fungi (Le Calvez et al., 2009). It is clear that collaboration between these databases and software tools such as ARB (Ludwig et al., 2004) has the potential to improve certain of their shared features. These could result in future developments of MaarjAM, such as the inclusion of an automated sequence identification service. MaarjAM sequences (and linked metadata) are available for download as a reference dataset for identification of next-generation sequencing data (and have already been used for this purpose in Öpik et al., 2009; M. Moora et al., unpublished). Moreover, next-generation sequences can be included in the MaarjAM database following the same principles as those obtained by Sanger sequencing: only representatives of each phylogroup for each location and host plant combination are included; assuming sequences are available in public databases and that a corresponding research paper has been published. Because of the higher error rate of next-generation sequencing compared with Sanger sequencing, the type of sequencing technology used will be recorded for each accession so that data obtained by different methods can be analysed separately, if required. (2010) 188: Journal compilation Ó Trust (2010)

11 Research (a) Richness (b) Residual s 3.87 to to 0.03 Fig. 3 The global distribution of glomeromycotan virtual taxon (VT) richness: (a) raw data; and (b) standardized residual richness with the effect of sample size removed to to to 1.57 Phylogeny and VT delimitation The SSU rrna gene region generally provides sufficient phylogenetic signal to allow the delimitation of sequence groupings that can be used to describe natural assemblages of Glomeromycota. These groupings correspond roughly to the species level (Lee et al., 2008) or slightly above (see the next paragraph). Clearly, the proposed taxonomy does not replace classical taxonomic identification and description. However, the delimitation of VT provides a means to capture the DNA sequence diversity of organisms as it occurs in nature. Ideally, if all morphospecies were sequenced in the target region, then all environmental sequences could be identified in relation to known species by sequence comparison. Currently, sequences attributed to 71 out of the total of 222 described morphospecies (A. Schu ßler s Glomeromycota phylogeny, ~schuessler/amphylo/; 29 April 2010) fall into 53 VT (Tables 2, S1). Although the genus Glomus contains the highest number of described species, it is the genus with the smallest proportion of species sequenced for the SSU rrna gene to date (Tables 2, S1). Thus, it appears that conditions are not yet favourable for successful integration of classical morphotaxonomy and sequence-based taxonomy. The data currently held in MaarjAM allow a total of 282 SSU rrna gene VT of Glomeromycota to be distinguished. While this number exceeds the number of known Glomeromycota morphospecies, there are several reasons to The Authors (2010) Journal compilation Trust (2010) suppose that actual richness within this group of fungi is still underestimated. First, analysis of associated metadata indicates a degree of specificity exhibited by VT in relation to geographical distribution, climatic zone and ecosystem type. Therefore, new taxa can be expected from the wide geographical areas and major ecosystems that remain undersampled. Second, the SSU rrna gene is considered to be incapable of resolving species in certain genera, for example Ambispora, Diversispora and Scutellospora (de Souza et al., 2004; Walker et al., 2007; Gamper et al., 2009). This view is supported by the data presented in this paper. Thus, VT classification based on the central region of the SSU rrna gene may represent a taxonomic rank higher than that of species (Gamper et al., 2009), although rdna sequence variability probably differs among phylogenetic lineages. More published information is certainly required about intra- and interspecific genetic variation in a phylogenetically wide range of Glomeromycota taxa. Data should originate from well identified isolates in order to provide guidelines for DNA-based species delimitation (Gamper et al., 2009). The coverage of described species and currently unidentified isolates in culture collections among published SSU rrna gene sequences could be much improved and would also increase the proportion of taxonomically identifiable VT (cf. Brock et al., 2009). Taxon accumulation curves further supported the hypothesis that there remains Glomeromycota richness to be uncovered. Moreover, the MaarjAM database records (2010) 188:

12 234 Research VT (N = 37) n = 35 n=8 n=1 n=1 VT (N = 28) VT (N = 24) n = 46 n=1 n=1 VT (N = 21) n = 45 n=3 VT (N = 19) n = 15 n=5 n=4 VT (N = 17) n = 16 n=9 n=2 VT (N = 17) n = 55 n=4 VT (N = 16) VT (N = 13) n = 37 n=1 VT (N = 13) VT (N = 13) n = 22 n=1 VT (N = 12) n = 124 n=7 n = 99 n=3 n = 59 n=2 n = 43 n=2 (2010) 188: The Authors (2010) Journal compilation Trust (2010)

13 Research 235 Fig. 4 The global distribution of the 12 glomeromycotan virtual taxa (VT) in the MaarjAM database recorded from the greatest number of different locations. Records were derived from plant root samples (circles), cultured spores (triangles, apex up), soil samples (squares) and other samples (triangles, apex down). Number of locations (N) and number of accessions (n) for each type of sample are indicated. Known species are included in the following VT: VT 113, Glomus fasciculatum; VT 67, Glomus mosseae; VT 115, Glomus intraradices, Glomus irregulare, Glomus vesiculiferum; VT 193, Glomus claroideum, Glomus etunicatum, Glomus lamellosum, Glomus luteum, Glomus viscosum; Glomus group B; VT 65, Glomus caledonium, Glomus clarum, Glomus fragilistratum, Glomus geosporum, Glomus verruculosum; VT 64, Glomus constrictum; VT 105, Glomus intraradices; VT 62, Glomus cf. etunicatum; Diversisporaceae. (a) (b) Laurasia Gondwana Eurasia America N (VT) = Africa N (VT) = 276 Fig. 5 Venn diagrams showing the number of Glomeromycota virtual taxa (VT) in the MaarjAM database that are unique to and shared between different continents (a), ancient supercontinents (b), climatic zones (c) and host plant superorders (d). Numbers in the lower left-hand corner of each panel indicate the total number of VT for which the respective metadata have been recorded. The number of records in MaarjAM for each displayed category can be found in Table 3. Note: in the case of climatic zones, the boreal zone with three VT and six accessions is not shown here. (c) N (VT) = 278 Temperate Tropical Subtropical (d) Rosanae N (VT) = Asteranae Lilianae occurrences of VT, resulting in species lists of VT per location, while VT abundances within locations are not recorded, as such data are rarely comprehensively provided in original papers. Therefore, it is likely that the abundance of rare taxa is overestimated and that of abundant taxa is underestimated, which can have the effect of producing asymptotic rarefaction estimates. The phylogenetic placement of some sequences into the families Archaeosporales and Paraglomerales (VT1, VT308) remained ambiguous. Archaeosporaceae as defined here remains unsupported, with only VT245 including a described species (Archaeospora trappei; Figs 1, S2), and a well supported subclade of seven VT showing unclear phylogenetic placement in relation to known families. Furthermore, the genera Kuklospora and Gigaspora render Acaulospora and Scutellospora paraphyletic, suggesting that further taxonomic investigation is required to resolve these groups. Glomeromycota species identifications as provided by sequence authors were not always consistent with VT assignment. Reasons for this could include misidentification, the presence of cryptic species within recognized morphospecies, unresolved taxonomy or insufficient sequence variation to enable species distinction. Misidentification or the presence of cryptic species within apparent morphospecies may be the reason why some sequences obtained from spores derived from natural soils (e.g. VT265, incl. Glomus constrictum, Glomus coronatum, G. mosseae) appeared in clades unrelated to sequences from cultures of the same species (G. constrictum VT64 and G. mosseae VT67; Fig. S2). An even more striking example is the appearance of sequences named as Glomus viscosum in different families: Glomus A (VT63, incl. BEG126, EEZ34) and B clades (VT193, incl. BEG27). The unresolved taxonomy of the Glomus intraradices species group (Stockinger et al., 2009) is manifested here as sequences named as G. intraradices being present in three VT: VT105 (isolates IMA6, BEG123); VT114 (DAOM197198, the isolate for which the genome is being sequenced (Martin et al., 2008; Stockinger et al., 2009), EEZ1); and VT115 (GINCO 4695rac-11G2). Unresolved taxonomy may also be a reason for multiple species grouping in VT193 which currently includes Glomus claroideum, Glomus etunicatum, Glomus lamellosum, Glomus luteum and Journal compilation Ó Trust (2010) (2010) 188: