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      Biodiversity across trophic levels drives multifunctionality in highly diverse forests

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          Human-induced biodiversity change impairs ecosystem functions crucial to human well-being. However, the consequences of this change for ecosystem multifunctionality are poorly understood beyond effects of plant species loss, particularly in regions with high biodiversity across trophic levels. Here we adopt a multitrophic perspective to analyze how biodiversity affects multifunctionality in biodiverse subtropical forests. We consider 22 independent measurements of nine ecosystem functions central to energy and nutrient flow across trophic levels. We find that individual functions and multifunctionality are more strongly affected by the diversity of heterotrophs promoting decomposition and nutrient cycling, and by plant functional-trait diversity and composition, than by tree species richness. Moreover, cascading effects of higher trophic-level diversity on functions originating from lower trophic-level processes highlight that multitrophic biodiversity is key to understanding drivers of multifunctionality. A broader perspective on biodiversity-multifunctionality relationships is crucial for sustainable ecosystem management in light of non-random species loss and intensified biotic disturbances under future environmental change.


          Biodiversity change can impact ecosystem functioning, though this is primarily studied at lower trophic levels. Here, Schuldt et al. find that biodiversity components other than tree species richness are particularly important, and higher trophic level diversity plays a role in multifunctionality.

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          Structural Equation Modeling and Natural Systems

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            Exploring Microbial Diversity and Taxonomy Using SSU rRNA Hypervariable Tag Sequencing

            Introduction The biosphere contains between 1030 and 1031 microbial genomes, at least 2–3 orders of magnitude more than the number of plant and animal cells combined [1]. Microbes control global utilization of nitrogen through nitrogen fixation, nitrification, and nitrate reduction, and drive the bulk of sulfur, iron and manganese biogeochemical cycles [2]. They regulate the composition of the atmosphere, influence climates, recycle nutrients, and decompose pollutants. Without microbes, multi-cellular life on earth would not have evolved and biology as we know it would not be sustainable. The diversity of microbial communities and their ecologic and metabolic functions are being explored across a great range of natural environments: in soils [3]–[5], air [6] and seas [7]–[10], on plants [11] and in animals [12],[13] and in extreme environments such as the arctic [14], deep-sea vents [15], uranium-contaminated soil [16], and waste-water treatment discharge [17]. In recognition of the role marine microbes play in the biogeochemical processes that are critical to life in all environments on Earth including carbon and nitrogen cycling, the International Census of Marine Microbes (ICoMM: http://icomm.mbl.edu) has launched an international effort to catalogue the diversity of microbial populations in the oceanic, coastal, and benthic waters. Microbes associated with human health will be intensely studied through two recent large-scale initiatives: the Human Microbiome Project sponsored by the NIH (http://nihroadmap.nih.gov/hmp/) and MetaHIT sponsored by the EU (http://www.metahit.eu), which seek to characterize the composition, diversity and distribution of human-associated microbial communities. Other recent human health studies include microbes in breast milk [18], chronic wounds [19], human gut [20], dental caries [21], and childcare facilities [22]. Microbes associated with the human body outnumber human cells by at least a factor of ten [23]. Some microbes cause disease, but the overwhelming majority are either innocuous or play a role in human physiology, including immune response, digestion and vitamin production. As recently as the late 1980's, descriptions of human-associated microbiota were constrained by cultivation technologies. Over the last twenty years, sequencing surveys of amplified regions of small subunit ribosomal RNA (SSU rRNA) genes have revealed that microbial diversity is much greater than the 5,000 microbial species described using phenotypic features in Bergey's taxonomic outline [24], and that microbial communities are far more complex than initially thought. For instance, E. coli, once thought to be a dominant species in the human gut, is clearly a minor member relative to various members of the phyla Bacteroidetes and Firmicutes. It is now evident that microbiologists have been successful in culturing fewer than one percent of the different kinds of single cell organisms from most microbial communities [25]. Even for well-studied communities, such as the human distal gut, only 20–40% of the microbes have been cultured. Deeper surveys with new approaches are revealing ever-greater diversity. Even these studies, with hundreds of thousands of microbes sampled, have not been extensive enough to provide a complete picture of the diversity (richness) and relative abundance (evenness) of microbial communities. To explore these questions, microbiologists must be able to compare microbial communities within and across individuals, in different states of health and disease, and over time. The first step in these community analyses is to develop detailed descriptions of each population, including low abundance taxa that comprise the rare biosphere [9]. Exploration of the human microbiome can leverage methods used to explore the microbiome of other environments such as soil, the deep sea, and other vertebrate microbiomes. By necessity, microbiologists have historically focused their efforts on the dominant components of microbial communities. Recognizing the importance of gathering information about high, medium and very low abundance taxa, Sogin et al. [9] introduced the use of massively-parallel DNA sequencing of short hypervariable regions of SSU rRNA to characterize microbial populations. In a subsequent study that collected nearly one million short hypervariable region tags, Huber et al. [15] demonstrated that there are over ∼40,000 different kinds of bacteria and archaea in a few liters of hydrothermal vent fluid. Rarefaction data from this study and others show that in many environments, even this level of sequencing is insufficient to fully describe microbial diversity [5],[26]. The lower cost and higher throughput of pyrosequencing employed in these studies allows for sampling efforts that are orders of magnitude greater than traditional capillary dideoxy sequencing of cloned SSU rRNA amplicons [27]. With the recently announced capability to sequence >400 nt, it will be possible to span most hypervariable regions, multiple adjacent hypervariable regions, or possibly combinations of non-adjacent hypervariable regions through paired-end sequencing strategies. However, comparisons of 400 nt reads from rapidly evolving rRNA regions do not contain sufficient evolutionary information to infer robust phylogenetic relationships and the hundreds of thousands of reads produced in a single experiment far exceeds the limitations of current phylogenetic software. Tags from the V6 region are also too short for current implementations of Bayesian classifiers such as the Ribosomal Database Project Classifier (RDP) [28]. However, each read represents a hypervariable region tag of an SSU rRNA gene present in the sample. We developed a tag search engine, Global Alignment for Sequence Taxonomy [9], GAST, which utilizes existing databases of full-length SSU rRNA genes and their pre-computed phylogeny for high-throughput taxonomic analysis of microbial communities using hypervariable region tag sequences. The use of a single, small hypervariable region tag for assigning taxonomy presents several challenges. The information content in a short hypervariable region sequence may not be sufficient for inferring taxonomic affinity. BLAST [29] alone is insufficient to consistently identify the sequences in molecular databases that are the closest matches to tag queries along their full length. Here we analyze the reliability of assigning taxonomic identifiers based solely on tags, specifically using the V3 and the V6 hypervariable regions of SSU rRNA. Using SSU rRNA genes from the human gut and deep-sea vents, we compare the taxonomic assignments of the full-length sequences with the taxonomic assignments of their V3 and V6 regions, excised in silico. We then examine microbial populations of the human gut in greater detail, using both massively-parallel pyrosequencing of hypervariable region tag and Sanger-generated full-length sequences, to determine if any differences in sampling and taxonomic assignment exist with these two sequencing strategies. In a companion paper [30], we used GAST to examine the impact of the antibiotic ciprofloxacin on population structures of the human microbiome. Results Assessment of Hypervariable Region Specificity in the RefSSU Database Tags from a hypervariable region must map to a full-length SSU rRNA with minimal ambiguity to serve as reliable phylogenetic markers; i.e. phylogenetically distinct lineages should not contain identical tags. Most of the redundant SSU rRNA sequences containing identical sequences for a particular hypervariable region in the database are from the same genus. This redundancy does not interfere with the use of the hypervariable region tags for taxonomy, but rather strengthens the assignment. For each unique V3 and V6 sequence, we examined the number and taxonomy of all the source full-length sequences. We treat each hypervariable region independently, since two full-length rRNA sequences with identical V3 regions may differ at the V6 region or another hypervariable region. Of the 59,830 unique V6 reference sequences, 74% mapped to one SSU rRNA sequence, 10% mapped to 2 sequences, and only 5% mapped to 7 or more. The V3 region, which is longer, showed slightly better resolution: 82% mapped to one SSU rRNA, 8% mapped to 2 sequences, and only 3% mapped to 7 or more (results not shown). Although only a small percentage of tags map to more than a few SSU rRNA sequences, this included some that mapped to a very large number of different SSU rRNA sequences. For example, 3 V6 reference sequences and 8 V3 reference sequences each mapped to more than 1000 different SSU rRNA sequences. In almost all cases where a hypervariable tag sequence maps to more than one full-length SSU rRNA sequence, the overwhelming majority of full-length sequences still map to only one genus (Table 1). Since RefSSU sequences that give rise to identical hypervariable region tags are generally from the same or highly similar organisms, V6 and V3 tags can be unambiguously mapped to the genus level 97% and 99% of the time, respectively. Even if we examine only the subset of V3 sequences that mapped to multiple SSU rRNA sequences, 95% map uniquely to genus, 98% to family, and 99% to order, class and phylum. Similarly for the V6 region, 91% of tags derived from multiple SSU rRNA entries map uniquely to genus, 96% to family, 97% to order and 99% to class and phylum. Even in those cases where reference tags mapped to more than 1,000 full-length sequences, in 9 of the 11 cases there was one dominant taxon. Of the other two cases, one had complete consensus to the order level and the other had all but two matching at family level. Not only do most of the reference tags in the database have only one SSU rRNA source, even those from multiple SSU rRNA sources still represent almost exclusively one taxon. 10.1371/journal.pgen.1000255.t001 Table 1 Percent of hypervariable region tags from the RefSSU database that map to one or more taxa. Hypervariable region V3 Number of Taxa 1 2 3 4 5+ Phylum 99.96% / 114328 0.04% / 42 0.00% / 42 0 0 Class 99.93% / 109352 0.07% / 77 0.00% / 2 0 0 Order 99.88% / 99682 0.12% / 113 0.00% / 4 0 0 Family 99.62% / 88015 0.34% / 297 0.04% / 34 0.00% / 3 0.00% / 3 Genus 99.11% / 69686 0.07% / 495 0.09% / 64 0.05% / 35 0.00% / 29 Hypervariable region V6 Number of Taxa 1 2 3 4 5+ Phylum 99.83% / 54728 0.17% / 94  = 0.15, insufficient data to analyze the accuracy of the GAST process for these larger distances. 10.1371/journal.pgen.1000255.t002 Table 2 Comparison of taxonomic assignments using full-length and in silico generated V3 and V6 hypervariable region tags. Human Gut Microbiome V3 region Deep-sea Vents Microbiome V3 region Count Same %Same Count Same %Same Superkingdom 7208 7208 100.00% / 100.00% 963 963 100.00% / 100.00% Phylum 7168 7145 99.68% / 99.82% 920 901 97.93% / 97.68% Class 7033 7002 99.56% / 99.52% 884 857 96.95% / 99.55% Order 7019 6987 99.54% / 99.57% 807 784 97.15% / 100.00% Family 5726 5688 99.34% / 98.93% 764 746 97.64% / 98.44% Genus 5178 5153 99.52% / 97.99% 701 686 97.86% / 99.10% Human Gut Microbiome V6 region Deep-sea Vents Microbiome V6 region Count Same %Same Count Same %Same Superkingdom 7215 7215 100.00%/ 100.00% 1058 1058 100.00% / 99.53% Phylum 7175 7152 99.68% / 99.82% 1008 986 97.82% / 90.00% Class 7040 7009 99.56% / 99.52% 970 939 96.80% / 90.08%% Order 7026 6994 99.54% / 99.56% 881 847 96.14% / 87.06% Family 5731 5693 99.34% / 98.93% 833 814 97.72% / 87.23% Genus 5183 5158 99.52% / 97.97% 766 749 97.78% / 88.52% Treating the V3 and the V6 regions independently, we counted the number of assignments the GAST process made at each taxonomic rank and the number and percent of times those assignments were the same as the assignment given to the full-length source sequence. The second percent value is the rate at which the top BLAST match predicted the same assignment as the full-length source. We compared the use of GAST to assign taxonomy with the use of the top BLAST match (Table 2). While the top BLAST match was consistent with GAST to the order level, it was less accurate for family and genus for the V6 region, especially for the deep-sea vent sequences. In addition, the top BLAST match was often not the best GAST match in cases where the taxonomic assignment was the same (Table 3). A comparison of the BLAST rank vs. GAST distance did not show any significant correlation (results not shown). 10.1371/journal.pgen.1000255.t003 Table 3 BLAST ranks for top GAST hits. Human Microbiome Deep-Sea Vents BLAST Rank V3 region V6 region V3 region V6 region 1 83.70% 94.90% 75.17% 81.89% 2 13.48% 4.34% 5.27% 7.16% >2 2.82% 0.76% 19.57% 10.95% The percentages reported represent the frequency with which the top GAST match corresponds to the top BLAST match, the second best BLAST match, or any other BLAST match. Comparison of Population Sampling Using Sanger-Generated Full-Length and Pyrosequencing-Generated V3 and V6 Tags for the Human Gut Microbiome Dataset We compared taxonomic assignments and their frequencies for the human gut microbiome data sampled with full-length SSU rRNA (n = 7215, length = 1300–1450 nt), V3 tags (n = 422,992, trimmed length = 100–200 nt) and V6 tags (n = 441,894, trimmed length = 50–70 nt) [30]. Full-length sampling detected a total of 43 genera, V3 pyrosequencing detected 116 genera and V6 pyrosequencing detected 103 genera (Figure 2). V3 sampling detected 74 genera that were not detected using the full-length sequencing and V6 sampling detected 60 genera (102 different genera combined). No genera were detected by the full-length sequencing alone. V3 sampling missed one taxon represented in the full-length sequences: Escherichia, detected 5 times with the full-length and once with the V6. V6 sampling missed three taxa, Hespellia, Klebsiella, and TM7 detected by the full-length with only one sequence each (detected 12, 16 and 4 times with V3, respectively). Only the TM7 sequences could be affected by a primer bias; the other two taxa have exact matches to forward and reverse primers as represented in the reference database of full-length SSU rRNA. The TM7 includes several different primer region sequences in the reference database each of which is one or two bases from its nearest primer, but only at the 5′ end, which should still detect abundant genera. The lack of TM7 in the V6 pyrosequencing data could be caused by a primer bias acting on a rare population, or could simply be the undersampling of rare organisms. In sum, less than 1% of the taxa identified by full-length sequences, representing 1) that were lost in the full-length sequencing (y = 0). The x-intercept comparing the two tag sequencing experiments (Figure 3C) is at the origin, implying that the two hypervariable regions were comparable in elucidating the rare biosphere. Primer bias was shown to be negligible in most cases, and undersampling is the most likely cause for the differences in small populations detected. Both tag sequencing experiments found similar numbers of taxa and substantially more than in the full-length sequencing. For tag sequencing of hypervariable regions to be effective for mapping taxonomy, specific sequences must match unambiguously to source organisms. If it were common for two divergent organisms to have the same or highly similar V3 or V6 regions, tag sequencing would not be an accurate means for assigning taxonomy. In our reference database of over 500,000 rRNA sequences, we found that hypervariable region tags map to individual taxa with high fidelity. In only a few cases for either the V3 or the V6 region did we find sequences that exactly match two or more distinct taxa (Table 1). The reference databases are replete with highly studied bacteria, and multiple copies of a hypervariable region for these organisms do not imply any taxonomic ambiguity. The V3 region was 99% accurate to the genus level. The V6 region was only slightly less resolved than the V3, still providing a 97% accuracy in assignment to the genus level, 98+% accuracy to the level of family, and 99% accuracy at the level of order. The V3 region is longer than the V6, which should have a positive effect on its specificity. The RefV6 database contains about half the number of reference sequences as the RefV3, and the accuracy of the V6 may be even greater as the reference databases grow. These levels of accuracy show that hypervariable region tags contain adequate information to accurately map taxonomy of both bacterial and archaeal organisms. To assign taxonomy to our long SSU rRNA gene sequences, both in our RefSSU database and in our experimental sequences, we used the Ribosomal Database Project classifier (RDP). RDP is not 100% accurate and some of the ambiguities in the reference database could be attributable to limitations with the RDP classifier. For tag sequencing with the GAST process, however, we are assessing the utility of a tag as a surrogate for the longer SSU rRNA sequences via a look-up and distance matrix. Can we consistently assign the correct taxonomy to both a SSU rRNA sequence and its constituent hypervariable regions independently? The RDP taxonomy provides a consistent and high-quality taxonomic classification (Bergey's taxonomy), facilitating our analysis. Slight inaccuracies in RDP are not an important factor in whether a tag sequence can be used as a surrogate for a full-length SSU rRNA sequence. We conducted an in silico experiment assessing taxonomic assignment using tags of hypervariable regions extracted from full-length sequences and compared the tags directly to the full-length sequences. We used two independent datasets, a human gut microbiome SSU rRNA dataset, which should be relatively well represented in the reference databases of SSU rRNA genes, and one of deep-sea vent microbes, which are less well studied and therefore less well represented in the reference databases. We examined the use of both the V3 and the V6 region tags to assign taxonomy for both groups of microbes. In all four cases, we found excellent correspondence between the use of the GAST process for assigning taxonomy to short hypervariable region tags and the use of RDP for assigning taxonomy to the full-length sequences. For both variable regions from the human microbiota, the taxonomic assignments of the tags agreed with the long sequences at a rate consistently greater than 99%. The deep-sea vent data agreed in over 97% of instances at the genus level. The two variable regions mapped taxonomy with virtually identical fidelity. The greater difference was not in choice of variable region, but in the environment examined. Of the human gut microbes sampled, 91% of all tags had exact matches in the reference database and virtually all had matches within a 10% sequence match. Of the deep-sea vent microbes, which have not historically been as well studied, only 51% had exact matches in the reference database and only 90% were within a 10% sequence match of a reference sequence. Despite the fact that as many as 10% of the deep-sea vent tags did not have a close match in the reference database, the GAST process only mis-assigned 3% of the tags. The GAST process accurately assigns taxonomy to tags diverging as much as 15% from their nearest reference match (Figure 1). Although our data included insufficient tags with GAST distances greater than 0.15 to fully assess their accuracy, the ability to transfer taxonomic information from the reference database is will certainly be less accurate at greater GAST distances. The deep-sea vent mismatch rate was similar for both hypervariable regions and at each taxonomic level from genus to phylum, and less than one-third of the mismatches were for tags >15% divergent. A possible explanation is the deep-sea vent environment may contain phyla that are not yet adequately described, and whose full-length SSU rRNA sequences are therefore not well classified by the RDP. These results imply that hypervariable region tag sequencing and the GAST process are excellent tools for assigning taxonomy, but they cannot overcome basic gaps in knowledge of the under-explored areas of the microbiome. As more is learned about these organisms, and their full-length SSU rRNA genes are added to the reference databases, hypervariable region tags sequencing projects will directly benefit, and taxonomy will improve. The top BLAST matches for the human gut microbiome mapped taxonomy better than the top BLAST matches for the deep-sea vents. This is likely because the human microbiome data are better represented in the reference database. Since 91% of the human microbiome tags had exact matches in the reference database these should be consistently identified by BLAST as the best match. For the deep-sea samples where only 51% had exact matches, a top BLAST hit may find only a local match within the sequence rather than a globally-weighted match as with GAST. Reliance on local alignments can be misleading: 80% of the deep-sea vent V6 tags that did not rank among the top two BLAST hits showed a BLAST alignment length shorter than the tag sequence for the top BLAST hit. BLAST failed to identify the correct genus for 11% of these V6 tag sequences, whereas GAST which failed for only 2%. An increase in distance from the tag to the nearest reference sequence using GAST did not correlate with a lower BLAST rank. The magnitude of divergence from the reference database does not explain the difference between V3 and V6 regions. The V3 tags were noticeably more divergent from their top BLAST hit than were the V6 tags, despite the larger dataset of reference V3 sequences. This did not adversely affect the ability to identify tags to the genus level. Since the RDP taxonomy is restricted to the genus level, we could not review the BLAST ranks for species-level. Full-length sequencing missed 63% of the genera identified by V3 and 58% of the genera identified by V6 (for more details, see Dethlefsen et al. [30]). The full-length sequencing uncovered only four rare taxa missed by one or the other hypervariable region, but no taxa missed by both of the hypervariable regions. Primer mismatches were minimal, but may be relevant when combined with a very low abundance. The hypervariable region tag sequencing did not introduce any strong biases against the discovery of common taxa or the relative abundance of these taxa in this experiment. As predicted, the hypervariable region tag sequencing provided a much greater breadth and depth of sampling. Although the level of sampling with tag sequencing is orders of magnitude greater than with traditional methods, a single pyrosequencing run (with >400,000 sequences) is still insufficient to fully sample the rare biota in the human distal gut. The sampling limitation of this experiment can be seen in the small but distinct number of taxa that appeared in one but not the other tag sequencing experiment. All were of low abundance and are dispersed throughout the microbial world rather than clustering in one specific taxon. Since no common taxa were omitted by sequencing of either hypervariable region, any effects of primer bias are limited to rare taxa and cannot be discerned from the effects of undersampling. Other methods such as Greengenes [31] and SeqMatch [32] use short tag sequences to determine phylogenetic affinity through comparisons to reference data sets of nearly full-length SSU rRNA sequences without requiring the inference of phylogenetic trees from the hypervariable regions. Greengenes (http://greengenes.lbl.gov) uses NAST [33] to align tags by inserting them into a pre-existing database of >10,000 aligned full-length sequences, and then assigns taxonomy based on the nearest neighbor in the database. Liu et al [34] used NAST on simulated tag sequences and found similar Unifrac clustering results from the tags as from their full-length source sequences. The Greengenes website is limited to 500 sequences and uses a database of only 10,000 sequences for comparison and does not perform a consensus of multiple taxonomy matches. SeqMatch uses a k-nearest-neighbor, word-matching algorithm rather than a multiple sequence alignment to display nearest matches in the RDP dataset, and uses the lowest common taxon for consensus (essentially a unanimous consensus rather than 66% used by GAST). The RDP dataset is smaller than SILVA database but more selective. The website tool is limited to 2,000 sequences. Conclusions/Significance Hypervariable region tag sequencing using either the V3 or the V6 region, and presumably other hypervariable regions, is an effective means for assigning taxonomy and provides great advantages over traditional sampling. A tag mapping process such as GAST with an extensive database of rRNA genes such as our RefSSU derived from SILVA can map tag sequences to the same taxonomy as their source genes at better than a 99% correlation rate for commonly studied environments such as the human microbiome and better than 96% for less commonly studied environments such as deep-sea vents. The V3 and V6 regions have only minimal ambiguity in mapping to the SSU rRNA gene all the way to the genus level. While tags can map to more than one SSU rRNA source, these cross-mappings between taxa are infrequent and do not compromise the overall methodology. We show that these short hypervariable region tags contain adequate information to uniquely and accurately map the phylogeny with a 98% or greater fidelity even without an exact match in the reference database or with potential multiple copies in the database. GAST is accurate for tags as much as 15% divergent from their nearest reference match, although there were very few tags that far from the current set of reference SSU rRNA sequences. The GAST distance, like BLAST scores or e-values, should be maintained and used as an assessment for the likely reliability of the GAST assignment of more divergent tags. The consistently high correspondence of the hypervariable region tags vs. long SSU rRNA taxonomies shows the robustness of the GAST process and the use of tags as surrogates for the full-length rRNA genes even for microbial environments that are not well-represented in the reference databases. Massively-parallel pyrosequencing of tags can be used to great advantage over traditional sequencing of full-length rRNA genes to explore both the diversity and relative abundance of microbial populations. Further research into hypervariable region tag sequencing may uncover advantages of one region over another, such as the relative levels of microvariation, length of sequence, density of homopolymers (which can lead to pyrosequencing errors), ability to identify to the species level, or the merits of different amplification primers. Tag sequencing yields similar taxonomy and relative abundance values as conventional sequencing of full-length SSU rRNA genes, but provides more reads, uncovers more organisms, avoids assembly, and costs less per read than conventional sequencing of full-length SSU rRNA genes. As the technology continues to improve, yielding greater read counts and longer sequences, pyrosequencing will provide even greater opportunities for tag sequencing, such as the use of longer hypervariable regions or combinations of variable regions, and ever-greater sampling depth. This process will also improve as reference databases of SSU rRNA genes continue to grow. The great advantage of hypervariable region tag sequencing is that it can take advantage of massively-parallel pyrosequencing, sampling to depths several orders of magnitude greater than previously achieved, facilitating the exploration of the vast diversity of microbial populations and the rare biosphere. Materials and Methods Creating the Reference Database of Full-Length, V3, and V6 SSU rRNA Sequences We downloaded 503,971 aligned small subunit rRNA sequences from the SILVA database, version 92 [35]. Using the SILVA quality assessments, we eliminated low-quality sequences (sequence quality  = 80%. If the bootstrap value was  = 80) and generated a consensus taxonomy. If two-thirds or more of the full-length sequences shared the same assigned genus, the tag was assigned to that genus. If there was no such agreement, we proceeded up one level to family. If there was a two-thirds or better consensus at the family level, we assigned this taxonomy to the tag, and if not, we continued to proceed up the tree. Occasionally, a tag could not be assigned taxonomic classification at the domain level. This was because the RDP Classifier could not assign a domain with an adequate bootstrap value, rather than a tag mapping to full-length sequences from different domains. These may represent novel organisms whose taxonomy has not yet been determined. Sample tags that did not have a single BLAST match in the RefSSU database also were not given a taxonomic assignment. We chose to use a 66% (two-thirds) majority although other values or a distributional vs. strict percentage approach can be implemented. We reviewed nearly 17 million tags in our sequencing database (primarily of the V6 region) from a wide range of studies using the 66% majority as the threshold for assignment. A distribution curve of voting majority did not show any obvious break points (graph not shown), although 95% of the tags had a voting majority of 75% or better, and 90% had a voting majority > = 83%.
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              Is There a Latitudinal Gradient in the Importance of Biotic Interactions?


                Author and article information

                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                31 July 2018
                31 July 2018
                : 9
                [1 ]German Centre for Integrative Biodiversity Research, (iDiv) Halle-Jena-Leipzigv, Deutscher Platz 5e, 04103 Leipzig, Germany
                [2 ]ISNI 0000 0001 0679 2801, GRID grid.9018.0, Institute of Biology/Geobotany and Botanical Garden, , Martin-Luther-University Halle-Wittenberg, ; Am Kirchtor 1, 06108 Halle (Saale), Germany
                [3 ]ISNI 0000 0000 9130 6144, GRID grid.10211.33, Institute of Ecology, , Leüphana University Lüneburg, ; Scharnhorststrasse 1, 21335 Lüneburg, Germany
                [4 ]ISNI 0000 0004 1937 0650, GRID grid.7400.3, Department of Evolutionary Biology and Environmental Studies, , University of Zurich, ; Winterthurerstrasse 190, 8057 Zurich, Switzerland
                [5 ]ISNI 0000 0001 2322 4988, GRID grid.8591.5, Institute of Global Health, , University of Geneva, ; 9 Chemin des Mines, 1202 Geneva, Switzerland
                [6 ]Department of Soil Ecology, UFZ-Hemholtz Centre for Environmental Research, Theodor-Lieser-Strasse 4, D-06120 Halle (Saale), Germany
                [7 ]ISNI 0000 0001 2230 9752, GRID grid.9647.c, Department of Systematic Botany and Biodiversity, , Leipzig University, ; Johannisallee 21-23, 04103 Leipzig, Germany
                [8 ]ISNI 0000000419368657, GRID grid.17635.36, Department of Soil, Water, and Climate, , University of Minnesota, Twin Cities, ; 1991 Upper Buford Circle, St. Paul, MN 55108 USA
                [9 ]ISNI 0000 0001 2256 9319, GRID grid.11135.37, Key Laboratory for Earth Surface Processes of the Ministry of Education, Department of Ecology, , College of Urban and Environmental Sciences, Peking University, ; 100871 Beijing, China
                [10 ]GRID grid.5963.9, Nature Conservation and Landscape Ecology, , University of Freiburg, ; Tennenbacher Strasse 4, 79106 Freiburg, Germany
                [11 ]ISNI 0000 0001 2190 1447, GRID grid.10392.39, Chair of Soil Science and Geomorphology, , Eberhard Karls-University of Tübingen, ; Rümelinstrasse 19-23, 72720 Tübingen, Germany
                [12 ]ISNI 0000000119573309, GRID grid.9227.e, Institure of Botany, , Chinese Academy of Sciences, ; Beijing, 100093 China
                [13 ]GRID grid.5963.9, Faculty of Biology, Geobotany, , University of Freiburg, ; Schaenzlestrasse 1, 79104 Freiburg, Germany
                [14 ]GRID grid.5963.9, Freiburg Institute of Advanced Studies (FRIAS), , University of Freiburg, ; Albertstrasse 19, 79104 Freiburg, Germany
                [15 ]ISNI 0000 0001 2111 7257, GRID grid.4488.0, Institute of General Ecology and Environmental Protection, , Technische Universität Dresden, ; Pienner Strasse 7, D-01737 Tharandt, Germany
                [16 ]ISNI 0000 0004 0492 3830, GRID grid.7492.8, Department of Community Ecology, , UFZ-Helmholtz Centre for Environmental Research, ; Theodor-Lieser-Strasse 4, D-06120 Halle (Saale), Germany
                [17 ]ISNI 0000 0004 1792 6416, GRID grid.458458.0, Key Laboratory of Zoological Systematics and Evolution, , Institute of Zoology, Chinese Academy of Sciences, ; Beijing, 100101 China
                © The Author(s) 2018

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