Non-cyanobacterial diazotrophs mediate dinitrogen fixation in biological soil crusts during early crust formation

Abstract

Abstract

Biological soil crusts (BSC) are key components of ecosystem productivity in arid lands and they cover a substantial fraction of the terrestrial surface. In particular, BSC N\(_{2}\)-fixation contributes significantly to the nitrogen (N) budget of arid land ecosystems. In mature crusts, N\(_{2}\)-fixation is largely attributed to heterocystous cyanobacteria, however, early successional crusts possess few N\(_{2}\)-fixing cyanobacteria and this suggests that microorganisms other than cyanobacteria mediate N\(_{2}\)-fixation during the critical early stages of BSC development. DNA stable isotope probing (DNA-SIP) with \(^{15}\)N\(_{2}\) revealed that Clostridiaceae and Proteobacteria are the most common microorganisms that assimilate \(^{15}\)N\(_{2}\) in early successional crusts. The Clostridiaceae identified are divergent from previously characterized isolates, though N\(_{2}\)-fixation has previously been observed in this family. The Proteobacteria identified share \(>\)98.5 %SSU rRNA gene sequence identity with isolates from genera known to possess diazotrophs (e.g. Pseudomonas, Klebsiella, Shigella, and Ideonella). The low abundance of these heterotrophic diazotrophs in BSC may explain why they have not been characterized previously. Diazotrophs play a critical role in BSC formation and characterization of these organisms represents a crucial step towards understanding how anthropogenic change will affect the formation and ecological function of BSC in arid ecosystems.
keywords: microbial ecology / stable isotope probing / nitrogen fixation / biological soil crusts

Introduction

Biological soil crusts (BSC) are specialized microbial communities that form at the soil surface in arid environments and they fill a variety of important ecological functions. BSCs occupy plant interspaces and cover a wide, global geographic range (Garcia-Pichel et al., 2003). For example, in some regions on the Colorado Plateau BSCs cover 80% of the ground (Karnieli et al., 2003). The global biomass of BSC cyanobacteria alone is estimated at 54 x 10\(^{12}\) g C (Garcia-Pichel et al., 2003). BSD nitrogen fixation (N\(_{2}\)-fixation) is responsible for significant input of nitrogen (N) to arid environments (Evans et al., 1999; Belnap, 2003). Interestingly, much of this fixed N is exported from the crusts in dissolved form through percolation or runoff and little is lost to volatilization (Johnson et al., 2007). The presence of BSC is positively correlated with vascular plant survival due in part to N inputs from BSC (for review of BSC-vascular plant interactions see Belnap et al. 2003). These microbial ecosystems are not immune to climate change and changes in precipitation and temperature could alter BSC microbial community structure/membership and possibly BSC diazotroph diversity and N\(_{2}\)-fixation (Garcia-Pichel et al., 2013).

BSC are highly susceptible to natural and anthropogenic disturbance (Garcia-Pichel et al., 2013). Succession in BSC communities is characterized by transition from early successional “light” crusts to mature “dark” crusts (Yeager et al., 2004; Belnap, 2002). Motile non-heterocystous cyanobacteria(e.g.Microcoleus vaginatus or M. steenstrupii), which cannot fix N\(_{2}\) are pioneer colonizers of early successional crusts and are abundant in all types of BSCs (Garcia-Pichel et al., 2013). Successional development of mature crust is accompanied by a change in color produced by secondary colonization with non-motil N\(_{2}\)-fixing heterocystous cyanobacteria which produce sunscreen compounds that reduce soil albedo . These heterocystous cyanobacteria (e.g. Scytonema, Spirirestis, and Nostoc) increase in abundance during crust development and are more abundant in mature crusts (Yeager et al., 2007; Yeager et al., 2012). Heterocystous cyanobacteria are numerically dominant in surveys of BSC nifH gene diversity . For example, 89 percent of 693 nifH sequences derived from Colorado Plateau and New Mexico BSC were attributed to heterocystous cyanobacteria (Yeager et al., 2007). Other BSC nifH sequences are attributed to Alpha-, Beta-, and Gammaproteobacteria, as well as a nifH clade (nifH cluster III) that includes diverse anaerobes such as clostridia, sulfate reducing bacteria, and anoxygenic phototrophs (Steppe et al., 1996; Yeager et al., 2007).

Two lines of evidence suggest that nitrogen fixers other than phototrophs are important in early-successional crusts. First, the contributions of early successional BSC to N\(_{2}\)-fixation in arid ecosystems may have been systematically underestimated. The high abundance of heterocystous cyanobacteria at the surface of mature crusts, where acetylene reduction assay rates are often maximal, is generally taken as evidence that BSC N\(_{2}\)-fixation occurs primarily in mature crusts and is dominated by heterocystous cyanobacteria. However, rates of BSC N\(_{2}\)-fixation are typically determined by areal measurements made at the crust surface with the acetylene reduction assay and vary significantly across samples and studies (Evans et al., 2001). The reasons for inter-site and inter-study variability are complex and likely include the spatial heterogeneity of BSC (Evans et al., 2001). The acetylene reduction assay is also subject to methodological artifacts that can complicate comparisons between samples that differ in their physical and biological characteristics (see Belnap 2001 for review). In particular, N\(_{2}\)-fixation in early successional BSC is maximal below the crust surface (Johnson et al., 2005) and hence diffusional limitation (of both acetylene and ethylene) across the crust surface can cause severe underestimates if they do not allow for sufficiently long incubation times (Johnson et al., 2005). If BSC N\(_{2}\)-fixation is instead estimated by integrating rates across a depth profile (which eliminates constraints from diffusional limitation), then total rates of N\(_{2}\)-fixation do not differ significantly between early successional and mature BSC (Johnson et al., 2005). This result suggests that diazotrophs other than heterocystous cyanobacteria may be important contributors to N\(_{2}\)-fixation in early successional BSC communities as early successional BSC possess few heterocystous cyanobacteria and these are present near the crust surface. Second, the bare soils that are colonized during the process of early crust formation are unconsolidated and oligotrophic in many respects, with much lower N content than adjacent crusts (Beraldi-Campesi et al., 2009), and the cyanobacteria that are typical colonization pioneers (Microcoleus spp., Garcia-Pichel et al. 2009), are unable to fix nitrogen as they lack that genetic capacity (Starkenburg et al., 2011; Rajeev et al., 2013).

To determine the agency of nitrogen fixation in early developmental crusts, we conducted \(^{15}\)N\(_{2}\) DNA stable isotope probing (DNA-SIP) experiments with early successional Colorado Plateau BSC conspicuously devoid of significant surface populations of heterocystous cyanobacteria. DNA-SIP with \(^{15}\)N\(_{2}\) has not been previously attempted with BSC. DNA-SIP provides an accounting of active diazotrophs on the basis of \(^{15}\)N\(_{2}\) assimilation into DNA whereas nifH clone libraries merely account for microbes with the genomic potential for N\(_{2}\)-fixation. Further, we investigate the distribution of these active diazotrophs in surveys of microbial diversity conducted on BSC over a range of spatial scales and soil types (Garcia-Pichel et al., 2013; Steven et al., 2013).

Results

DNA buoyant density changes in response to \(^{15}\)N\(_{2}\)

BSCs were wetted and incubated for 4 days in transparent chambers with headspace containing N\(_{2}\) either from air or from 100 percent atom enriched \(^{15}\)N\(_{2}\). The chambers were illuminated with 16 h on / 8 h off cycles at an intensity of 200 \(\mu\)mol photons m\(^{-2}\) s\(^{-1}\), which is the equivalent of an overcast/rainy day. N\(_{2}\)-fixation as measured by acetylene reduction increased from 4.8 \(\mu\)moles m\(^{-2}\) d\(^{-1}\) on day 2 to 14.5 \(\mu\)moles m\(^{-2}\) d\(^{-1}\) on day 4. Amplicon sequences from \(^{15}\)N\(_{2}\)-labeled samples and their corresponding unlabeled controls diverged specifically in heavy gradient fractions (Figure [fig:ord_heavy] and Figure [fig:ordination]) as assessed by Bray-Curtis dissimilarity (Bray et al., 1957), and this result was significant (Adonis test (Anderson, 2001); p-value: 0.001, r\(^{2}\): 0.18).

OTUs responsive to \(^{15}\)N\(_{2}\) are primarily Proteobacteria and Clostridiaceae

OTUs that incorporated \(^{15}\)N into their DNA were detected by a differential change in their abundance within heavy gradient fractions of \(^{15}\)N\(_{2}\)-labeled samples relative to corresponding controls. A total of 2,127 and 2,160 OTUs were detected in days 2 and 4, respectively, and these OTUs were interrogated for evidence of \(^{15}\)N\(_{2}\)-labelling. Of these OTUs, only 499 and 563, respectively, passed a sparsity threshold applied to filter out OTUs with insufficient data for statistical analysis (see Love et al. (2014) for discussion of independent filtering). Of OTUs passing the sparsity criterion, 34 were enriched significantly in heavy fractions relative to control and this result is specifically expected for OTUs that have \(^{15}\)N-labeled DNA (i.e. \(^{15}\)N\(_{2}\) “responders”). Of these, 19 are annotated as Firmicutes, 12 as Proteobacteria, 2 as Actinobacteria and 1 as Gemmatimonadetes (Figure [fig:l2fc], Figure [fig:trees]). If the responder OTUs are ranked by descending enrichment in heavy gradient fractions versus control, 8 the top 10 responders (i.e. those most enriched in the heavy fractions of labeled gradients) are either Firmicutes (3 OTUs) or Proteobacteria (5 OTUs) (Figure [fig:scatter_heavy]). Centroids (seed sequences) for strongly responding Proteobacteria OTUs all share high SSU rRNA gene sequence identity (\(>\)98.48%, Table [tab:LTP_blast]) with isolates from genera known to possess diazotrophs including Pseudomonas, Klebsiella, Shigella, and Ideonella. None of the Firmicutes OTU centroids in the top 10 responders share greater than 97% SSU rRNA gene sequence identity with sequences in the Living Tree Project (LTP) database of 16S rRNA gene sequences from type strains (release 115) (see Table [tab:LTP_blast]). OTUs that passed the sparsity threshold but were not classified as \(^{15}\)N-responsive were subsequently tested with the null hypothesis that the OTU fold enrichment in labeled gradient heavy fractions versus control was above the selected threshold. Rejecting the second null indicates that an OTU did not incorporate \(^{15}\)N into biomass. There were 86 and 89 “non-responders” at days 2 and 4, respectively. The \(^{15}\)N labelling of OTUs that did not pass sparsity or could not be classified as either a responder or non-responder cannot be determined conclusively.

Although we did not take replicate samples within a time point we can assess the consistency of each OTUs response across the two time points. OTU fold enrichment at day 2 and 4 was consistent (Figure [fig:2v4]). There was a significant correlation between OTU fold enrichment at day 2 versus day 4 (P-value 4.35e\(^{-8}\)). When the enrichment at day 2 is compared to day 4 via an interaction term (day \(*\) label/control, see methods), we found only two OTUs had significantly different enrichment between time points (“OTU.227” and “OTU.4037”, Table [tab:LTP_blast]). In addition, when day 2 and day 4 samples are treated as replicates (see methods) only five of the OTUs we identified as responders OTUs were not significantly enriched in labeled gradient heavy fractions versus control (“OTU.140”, “OTU.4037”, “OTU.227”, “OTU.137”, and “OTU.263”, Table [tab:LTP_blast]). The labeling of these OTUs should be interpreted with caution. None of the top 10 strongest responding OTUs showed inconsistent enrichment across time points based on the above analyses. Further, confidence in enrichment (i.e. lowest enrichment P-values between Day 2 and Day 4) appears to be correlated with consistency in response across both days (Figure [fig:2v4]).

\(^{15}\)N-responsive OTUs are found in low abundance in available environmental BSC SSU rRNA gene surveys

In total 13 of the 34 \(^{15}\)N-responsive OTUs have been observed previously in SSU rRNA gene surveys of BSC communities (Figure [fig:trees], Figure [fig:rspndr_dist]). Eleven of the 19 \(^{15}\)N-responsive Firmicutes OTUs are members of the Clostridiaceae. Three \(^{15}\)N-responsive Clostridiaceae have been observed in previous BSC SSU rRNA gene surveys. Two \(^{15}\)N-responsive Clostridiaceae were found in “light” (i.e. early successional) crust during SSU rRNA gene sequence analysis of BSC (Garcia-Pichel et al., 2013), and one \(^{15}\)N-responsive Clostridiaceae OTU was found among the “below crust” BSC SSU rRNA gene sequences described by Steven et al. (2013) (Figure [fig:trees]). Five \(^{15}\)N-responsive proteobacterial OTUs (Table [tab:LTP_blast]) were detected previously in BSC samples (Garcia-Pichel et al., 2013; Steven et al., 2013). The \(^{15}\)N-responsive Gemmatimonadetes OTU was observed in four Steven et al. (2013) samples and one \(^{15}\)N-responsive Actinobacteria OTU was found in three Steven et al. (2013) samples. Gemmatimonadetes and Actinobacteria \(^{15}\)N-responsive OTUs were not observed in samples collected by Garcia-Pichel et al. (2013).

Comparison of SSU rRNA gene sequences from different BSC samples

We compared the SSU rRNA gene sequences determined in this DNA-SIP experiment with two previous surveys of SSU rRNA gene amplicons from BSC communities (Garcia-Pichel et al., 2013; Steven et al., 2013). There were 3,079 OTUs (209,354 total sequences after quality control) in the DNA-SIP data, 3,203 OTUs (129,033 total sequences after quality control) in the Garcia-Pichel et al. (2013) study, and 2,481 OTUs (129,358 total sequences after quality control) in the Steven et al. (2013) study with a total of 4,340 OTUs across all three datasets. Of the total 4,340 OTU centroids established for this study, 445 have matches in the Living Tree Project (LTP) (a collection of SSU rRNA gene sequences for all sequenced type strains (Yarza et al., 2008)) at or above a threshold of 97% sequence identity (LTP version 115). That is, 445 of 4,340 OTUs are closely related to known isolates. The DNA-SIP data shares 56% OTUs with the Steven et al. (2013) data and 46% of OTUs with the Garcia-Pichel et al. (2013) data, while these latter two studies share 46% of their OTUs. This result suggests that low frequency OTUs likely remain undersampled in all datasets.

Sequencing of DNA subjected to CsCl fractionation is expected to sample a different subset of diversity than that sampled by sequencing of unfractionated bulk DNA. For example, SIP enhances detection of OTUs that incorporate \(^{15}\)N into their DNA, and these OTUs will be overrepresented in the overall DNA-SIP sequence pool relative to their relative abundance in unfractionated bulk community samples. In addition, the DNA-SIP sequencing effort was directed at a relatively small number of “light” crust samples (n = 4), while previous sequencing efforts (Garcia-Pichel et al., 2013; Steven et al., 2013) were spread across hundreds of samples from both “light” and “dark” crusts. Hence, it is likely that the current study will be more likely to detect rare OTUs present in early successional “light” crust communities, particularly those that incorporate \(^{15}\)N into DNA. In all three BSC studies, most sequences were annotated as either cyanobacteria or Proteobacteria, though only in the DNA-SIP data did the sequences of Proteobacteria outnumber those of cyanobacteria. Proteobacteria represented 29.8% of sequence annotations in DNA-SIP data as opposed to 17.8% and 19.2% for the Garcia-Pichel et al. (2013) and Steven et al. (2013) data, respectively. In addition, sequences annotated as Firmicutes were more abundant in the DNA-SIP data (19%) than in the data from Steven et al. (2013) and Garcia-Pichel et al. (2013) (0.21% and 0.23%, respectively) (Figure [fig:study_phy_dist]). Finally, and congruently with sampling design sequences annotated to “Subsection IV” of cyanobacteria, which encompasses the heterocystous cyanobacteria in the Silva taxonomic nomenclature (Pruesse et al., 2007), comprised only 0.29% of cyanobacteria sequences in the DNA-SIP data while representing 15% and 23% of cyanobacteria sequences from the Steven et al. (2013) and Garcia-Pichel et al. (2013) data, respectively.

Discussion

BSC N-fixation has long been attributed to heterocystous cyanobacteria and the preponderance of cyanobacterial nifH genes observed in molecular surveys of BSCs have generally supported this hypothesis (Yeager et al., 2007; Yeager et al., 2004; Yeager et al., 2012). However, in this study \(^{15}\)N\(_{2}\)-DNA-SIP reveals that non-cyanobacterial microorganisms fix N\(_{2}\) in early successional BSC samples. Proteobacteria and Clostridiaceae were most abundant among \(^{15}\)N\(_{2}\)-responsive OTUs as revealed by a robust statistical framework for quantifying and evaluating differential OTU abundance in microbiome studies (McMurdie et al., 2014; Love et al., 2014). Many of these OTUs (about 40%) have been observed previously in BSC communities. Rarefaction curves of data from Steven et al. (2013) and Garcia-Pichel et al. (2013) are still sharply increasing especially for sub-crust samples (Figure [fig:rarefaction]) suggesting the communities remain undersampled. Parametric richness estimates of BSC diversity indicate that the Steven et al. (2013) and Garcia-Pichel et al. (2013) sequencing efforts recovered on average 40.5% (s.d. 9.99%) and 45.5% (s.d. 11.6%) of predicted SSU rRNA gene OTUs from crust samples (inset Figure [fig:rarefaction]), respectively. Therefore, it would have been surprising if all of the \(^{15}\)N-responsive OTUs had been observed in prior environmental surveys of BSCs. Nitrogenase nifH gene sequences related to both Proteobacteria and Clostridiaceae have been previously observed in BSC samples, though typically at relative abundance that is much lower than nifH gene sequences from heterocystous cyanobacteria.

We propose three mechanisms that could bias nifH clone libraries against heterotrophic diazotrophs. First, extreme polyploidy in cyanobacteria (up to 58x ploidy in stationary phase,(Griese et al., 2011)) can be expected to inflate the representation of cyanobacteria nifH gene sequences in community DNA relative to the frequency of N\(_{2}\)-fixing heterocysts. Although, cyanobacteria often have relatively large cells so ploidy per cell is probably greater than ploidy per unit volume. Second, heterocysts make up a small fraction of total cells along a trichome, though all cells in the trichome possess the nifH gene. As a result of polyploidy and heterocyst frequency in a cyanobacterial filament, the ratio of cyanobacterial nifH gene copies to heterotrophic nifH gene copies may be inflated as much as 10\(^{3}\) times relative to the corresponding ratio of N\(_{2}\)-fixing cells (i.e. the ratio of heterocyst number to the cell number of heterotrophic diazotrophs). Third, nifH PCR primers, which are highly degenerate, could be biased against heterotrophic diazotrophs. For example, the nifH PCR primers used in the second round of a widely used nested PCR protocol (Yeager et al., 2007; Yeager et al., 2004; Yeager et al., 2012) have fairly low coverage for Proteobacteria and Clostridiales (Gaby et al., ???). Primer “nifH11” is biased against “Cluster III” nifH gene sequences which includes those of the Clostridiales (50% in silico coverage of reference nifH sequences). In addition, primer “nifH22” has low coverage of reference sequences from Proteobacteria, cyanobacteria and “Cluster III” nifH gene sequences (16%, 23% and 21% in silico coverage, respectively) (Gaby et al., ???). Hence, it is reasonable to assume that heterotrophic diazotrophs may have been underestimated in previous analyses of early successional BSC communities. Our DNA-SIP results, which do not require PCR of functional genes, suggest that BSC N-fixation in early successional BSC may include a large non-cyanobacterial component. This is consistent with small-scale, spatially resolved functional measurements of nitrogen fixation in BSCs (Johnson et al., 2005) that show a subsurface maximum that does not coincide spatially with maxima in chlorophyll a (a proxy for phototrophic biomass) in early-successional crusts, and a surface maximum of N\(_{2}\)-fixation in mature crust that coincides with the maximum in chlorophyll a.

We did not observe incorporation of \(^{15}\)N\(_{2}\) into the DNA of heterocystous cyanobacteria in the early successional BSC samples used in this study. It is possible that \(^{15}\)N\(_{2}\)-fixation by heterocystous cyanobacteria could go undetected in DNA-SIP. One possible explanation for this result is that the early successional BSC samples used in this study possessed too few heterocystous cyanobacteria to statistically evaluate their \(^{15}\)N-incorporation. Indeed, heterocystous cyanobacteria represented only 0.29% of sequences from the DNA-SIP data (see results) as opposed to 15% and 23% of total sequences in the Steven et al. (2013) and Garcia-Pichel et al. (2013) data, respectively. OTUs that correspond to heterocystous cyanobacteria (as defined by Yeager et al. (2007)), all fall below the sparsity threshold used in our analysis (see methods). Given the sparsity of heterocystous cyanobacteria sequences in the light crust DNA-SIP data, it is not possible to conclusively determine whether heterocystous cyanobacteria incorporated \(^{15}\)N during the incubation. Our results show that heterotrophic diazotrophs can contribute to N\(_{2}\)-fixation in early successional BSC but they do not exclude the potential for fixation by heterocystous cyanobacteria. Indeed, heterocystous cyanobacteria if present, active, and limited for nitrogen would be expected to form heterocysts and fix N\(_{2}\). It is likely that scarcity limits their contribution to N\(_{2}\)-fixation in early successional crusts. Heterocystous cyanobacteria form sessile colonies and they require stabilization of the crust environment before they can successfully colonize soil; and this stabilization is performed by other pioneering members of the crust community (Castenholz et al., 2002). N\(_{2}\)-DNA-SIP would also fail to identify N\(_{2}\)-fixing bacteria if N\(_{2}\)-fixation were uncoupled from DNA replication over the time frame of the experiment (i.e. 4 days), that is \(^{15}\)N\(_{2}\)-DNA-SIP will not detect bacteria that fix N\(_{2}\) but do not incorporate the \(^{15}\)N-label into DNA. Therefore, the contribution of heterocystous cyanobacteria (or any other microbe) to N\(_{2}\) would be underestimated if their cell division is uncoupled from N\(_{2}\)-fixation at time frames of up to 4 days. We should also note that \(^{15}\)N can be incorporated into biomass from trophic interactions although in this case the \(^{15}\)N labeling would likely be weaker than that for a N\(_{2}\)-fixer as a result of label dilution.

The OTUs with significant evidence of \(^{15}\)N-incorporation during the incubation were predominantly Proteobacteria and Firmicutes. The Proteobacteria OTUs with the strongest signal of \(^{15}\)N-incorporation all shared high sequence identity (\(>\)98.5%) with SSU rRNA gene sequences from genera known to contain diazotrophs (Table [tab:LTP_blast]). In contrast the Firmicutes that displayed signal for \(^{15}\)N-incorporation (predominantly Clostridiaceae) were not closely related to any known cultivars (Table [tab:LTP_blast]). Hence, we have little knowledge of the ecology of these organisms. Assessing the physiological characteristics of these diazotrophic Clostridiaceae may be useful for predicting how environmental change will affect the development and stability of BSC. Prior intense cultivation efforts from these crusts in separate studies did not yield any members of the Clostridiaceae (Gundlapally et al., 2006). Although under sampled in environmental data sets, \(^{15}\)N-responsive OTUs were indeed more abundant in sub-crust or in early successional BSC samples relative to crust surface or mature crust samples (Figure [fig:trees] and Figure [fig:rspndr_dist]). While members of Clostridiaceae have been found in low abundance in molecular surveys of BSC, most surveys are carried out on desicated crust samples, where thick-walled spores would predominate relative to vegetative cells, thus increasing the likelihood for their underrepresentation in DNA surveys. It should also be noted that crusts were incubated in an atmosphere of He and N\(_{2}\) rather than O\(_{2}\) and N\(_{2}\). While cyanobacteria in the presence of light rapidly produce oxygen super saturation in BSC relative to air (Garcia-Pichel et al., 1996), and whereas heterotrophic N\(_{2}\)-fixation by many microorganisms is inhibited in the presence of atmospheric levels of O\(_{2}\), it remains possible that the conditions present in microcosm are not representative of field conditions and may have favored N\(_{2}\)-fixation by crust organisms that are less active in situ. Further experiments will need to be performed to verify that these heterotrophic diazotrophs are contributing to the N budgets of early successional crusts in the field.

Our results generate more refined hypotheses pertaining to the contribution of diazotrophs during the development of BSC communities. Specifically, N\(_{2}\)-fixation in BSC may not be tied solely to the climax of heterocystous cyanobacteria in mature crusts. Rather, N\(_{2}\)-fixation may occur throughout crust development with the transition between early successional and mature crusts marked by a transition between heterotrophic and phototrophic N\(_{2}\)-fixation in the crust community. Therefore, sub-biocrust soil may contribute significantly to the arid ecosystem N budget and may be of considerable importance in the early phases of BSC establishment. We propose that interactions between fast-growing heterotrophic diazotrophs such as members of the Clostridiaceae and filamentous (non-heterocystous) cyanobacteria are important in the early establishment of BSC communities. During progressive desication, cyanobacteria, such as M. vaginatus, accumulate compatible solutes such as trehalose and sucrose (Rajeev et al., 2013). Upon wetting, microorganisms rapidly excrete compatible solutes to prevent cell lysis due to osmotic shock (Poolman et al., 1998). Among them are dihexoses (such as sucrose and trehalose), which are observed in natural crusts upon wetting and then are rapidly depleted in the soil solution (Northen, 2014). Many Clostridiaceae have a saccharolytic metabolism with the potential for rapid growth rates on substrates such as trehalose and/or sucrose (Wiegel et al., 2006). Wetting of crust may allow for rapid germination and growth of these organisms as the time required for germination of clostridial spores can be less than 30 minutes (Stringer et al., 2005). Indeed, intense blooms of clostridia have been detected in crusts within tens of hours of wetting (Karaoz et al., 2014). N\(_{2}\)-fixing clostridia are common in soils (Wiegel et al., 2006) and it is notable that C. pasteurianum, isolated from soil, was the first N\(_{2}\) fixing bacterium ever described (Winogradsky, 1895). C. pasteurianum, though an anaerobe, grows readily in the presence of oxygen when co-cultured with aerobic organisms that reduce oxygen tension (Chester, 1903). We propose that during a typical precipitation event, water saturation and heterotrophic activity rapidly render the interior of the crusts anoxic (Garcia-Pichel et al., 1996), presenting optimal conditions for growth of anaerobic, dihexose-fermenting, N\(_{2}\) fixing clostridia. Clostridial organic nitrogen would then become available to other members of the community, including the primary producers, when carbon limitation induces sporulation and mother cell lysis. Mother cell lysis, the last step in sporulation, releases rich sources of P and N into the environment in the form of nucleotides and peptides (Hoch et al., 2002).

Conclusion

The abundance of \(^{15}\)N-responsive OTUs from Clostrideaceae and Proteobacteria found in this study, the nifH gene sequences of Clostrideaceae and Proteobacteria observed previously in BSC (Steppe et al., 1996), and the evidence for subsurface N\(_{2}\)-fixation in early successional BSC (Johnson et al., 2005), taken together, suggest that heterotrophic diazotrophs may be important contributors to N\(_{2}\)-fixation in the subsurface of early successional BSC. Heterocystous cyanobacteria are also key contributors to the BSC N-budget, however and it is clear that heterocystous cyanobacteria increase in abundance with BSC age (Yeager et al., 2004). It is less clear if the transition to mature crust is marked mainly by a change in the abundance and activity of heterocystous cyanobacteria, or rather represents a succession within the diazotroph community from early crusts where N\(_{2}\)-fixation is dominated by Clostridiaceae and Proteobacteria to mature crusts where it is dominated by heterocystous cyanobacteria. Predicting the ecological response of BSC to climate change, altered precipitation regimes, and physical disturbance requires an understanding of crust establishment, stability, and succession. Diazotrophs are critical contributors to all of these phenomena and their activities make critical contributions to the N-budget of arid ecosystems worldwide.

Materials and Methods

BSC sampling and incubation conditions

BSC samples were taken from the Green Butte site near Moab, Utah as previously described (site “CP3”; latitude N 38\(^{\circ}\)44’55.1“, longitude W 109\(^{\circ}\)44’37.1”; Beraldi-Campesi et al. 2009). All samples were from early successional ’light’ crusts as described by (Johnson et al., 2005). Early successional BSC samples (37.5 cm\(^{2}\), average mass 35 g) were incubated in sealed chambers under controlled atmosphere and in 16 h light / 8 h dark for 4 days. Crusts were sampled and transported while dry and wetted at initiation of the experiment. Water was added to each sample to fully saturate the soil, but avoid visible ponding. The samples were then placed in air-tight sealed incubation containers for the rest of the experiment, so that soil and atmosphere remained saturated through the incubation period. The water was amended with calcium bicarbonate to yield a final concentration of 3 mM, so that autotrophy could proceed unimpeded. The control treatment received a headspace of air and the experimental treatment received a headspace containing \(^{15}\)N\(_{2}\) (\(>\)98% atom \(^{15}\)N\(_{2}\)). \(^{15}\)N\(_{2}\) (100%) gas was purchased from Sigma-Aldrich (St. Louis, MO). We used a composition of 75% \(^{15}\)N\(_{2}\) in helium for the initial incubation headspace. Four crust samples were treated and incubated (two control and two experimental). One control/experimental crust pair was collected at day 2 and the other at day 4. Acetylene reduction rates were measured daily. Acetylene reduction rates increased over the course of the experiment (0.8, 4.8, 8.8, and 14.5 \(\mu\)moles m\(^{-2}\) hr\(^{-1}\) ethylene for days 1 through 4, respectively).

DNA extraction

DNA was extracted for DNA-SIP at 2 and 4 days. DNA was extracted from 1 g of BSC. DNA from each sample was extracted using a MoBio (Carlsbad, CA) UltraClean Mega Soil DNA Isolation Kit (following manufacturer’s protocol, but lysis was done as previously described (Strauss et al., 2011)), and then gel purified to select high molecular weight DNA (\(>\)4 kb) using a 1% low melt agarose gel and \(\beta\)-agarase I for digestion (manufacturer’s protocol, New England Biolabs, M0392S). Extracts were quantified using PicoGreen nucleic acid quantification dyes (Molecular Probes).

Formation of CsCl equilibrium density gradients

CsCl gradient fractionation was used to separate the DNA into 36 gradient fractions on the basis of buoyant density. CsCl density gradients were formed in 4.7 mL polyallomer centrifuge tubes filled with gradient buffer (15 mM Tris-HCl, pH 8; 15 mM EDTA; 15 mM KCl) which contained 1.725 g mL\(^{-1}\) CsCl. CsCl density was checked with a digital refractometer as described below. A total of 2.5-5.0 \(\mu\)g of DNA was added to each tube, and the tubes mixed, prior to centrifugation. Centrifugation was performed in a TLA-110 fixed angle rotor (Beckman Coulter) at 20\(^{\circ}\)C for 67 hours at 55,000 rpm. (Buckley et al., 2007). Centrifuged gradients were fractionated from bottom to top in 36 equal fractions of 100 \(\mu\)L, using a syringe pump as described previously (Buckley et al., 2007). The density of each fraction was determined using an AR200 refractometer modified to accommodate 5 \(\mu\)L samples as described previously (Buckley et al., 2007). DNA in each fraction was desalted on a filter plate (PALL, AcroPrep Advance 96 Filter Plate, Product Number 8035), using four washes with 300 \(\mu\)L TE per fraction. After each wash, the filter plate was centrifuged at 500 x g for 10 minutes, with a final spin of 20 minutes. Purified DNA from each fraction was resuspended in 50 \(\mu\)L of TE buffer.

PCR, library normalization and DNA sequencing

To characterize the distribution of SSU rRNA genes across density gradients, SSU rRNA gene amplicons were generated from 20 gradient fractions per gradient for both unlabeled controls and \(^{15}\)N\(_{2}\) labeled samples. The 20 fractions analyzed are those expected to contain DNA (both labeled and unlabeled) having buoyant density in the range of 1.66 g mL\(^{-1}\) to 1.77 g mL\(^{-1}\). Barcoded PCR of bacterial and archaeal SSU rRNA genes was carried out using primer set 515F/806R (Walters et al., 2011) (primers purchased from Integrated DNA Technologies). The primer 806R contained an 8 bp barcode sequence, a “TC” linker, and a Roche 454 B sequencing adapter, while the primer 515F contained the Roche 454 A sequencing adapter. Each 25 \(\mu\)L reaction contained 1x PCR Gold Buffer (Roche), 2.5 mM MgCl\(_{2}\), 200 \(\mu\)M of each of the four dNTPs (Promega), 0.5 mg mL\(^{-1}\) BSA (New England Biolabs), 0.3 \(\mu\)M of each primers, 1.25 U of Amplitaq Gold (Roche), and 8 \(\mu\)L of template. Each sample was amplified in triplicate. Thermal cycling occurred with an initial denaturation step of 5 minutes at 95\(^{\circ}\)C, followed by 40 cycles of amplification (20 s at 95\(^{\circ}\), 20 s at 53\(^{\circ}\), 30 s at 72\(^{\circ}\)), and a final extension step of 5 min at 72°C. Triplicate amplicons were pooled and purified using Agencourt AMPure PCR purification beads, following manufacturer’s protocol. Once purified, amplicons were quantified using PicoGreen nucleic acid quantification dyes (Molecular Probes) and pooled together in equimolar amounts. Samples were sent to the Environmental Genomics Core Facility at the University of South Carolina (now Selah Genomics) where they were run on a Roche FLX 454 pyrosequencing machine (FLX-Titanium platform).

Data analysis

Sequence quality control

Sequences were initially screened by maximum expected errors at a specific read length threshold (Edgar, 2013) and this has been shown to be as effective as denoising with respect to removing pyrosequencing errors. Specifically, reads were first truncated to 230 nucleotides (nt) (all reads shorter than 230 nt were discarded) and any read that exceeded a maximum expected error threshold of 1.0 was removed. After truncation and max expected error trimming, 91% of original reads remained. Forward primer and barcode were then removed from the high quality, truncated reads. Remaining reads were taxonomically annotated using the “UClust” taxonomic annotation framework in the QIIME software package (Caporaso et al., 2010; Edgar, 2010) with cluster seeds from Silva SSU rRNA database (Pruesse et al., 2007) 97% sequence identity OTUs as reference (release SSU Ref 111). Reads annotated as “Chloroplast”, “Eukaryota”, “Archaea”, “Unassigned” or “mitochondria” were removed from the dataset. Finally, reads were aligned to the Silva reference alignment provided by the Mothur software package (Schloss et al., 2009) using the Mothur NAST aligner (DeSantis et al., 2006). All reads that did not align to the expected amplicon region of the SSU rRNA gene were discarded. Quality control parameters removed 34,716 of 258,763 raw reads. Raw sequences have been uploaded to MG-RAST (MG-RAST ID 4603397.3).

Sequence clustering

Sequences were distributed into OTUs using the UPARSE methodology (Edgar, 2013). Specifically, OTU centroids (i.e. seeds) were identified using USEARCH on non-redundant reads sorted by count. The sequence identity threshold for establishing a new OTU centroid was 97%. After initial OTU centroid selection, select SSU rRNA gene sequences from Yeager et al. (2007) were added to the centroid collection. Specifically, Yeager et al. (2007) Colorado Plateau or Moab, Utah sequences were added which included the SSU rRNA gene sequences for Calothrix MCC-3A (accession DQ531700.1), Nostoc commune MCT-1 (accession DQ531903), Nostoc commune MFG-1 (accession DQ531699.1), Scytonema hyalinum DC-A (accession DQ531701.1), Scytonema hyalinum FGP-7A (accession DQ531697.1), Spirirestis rafaelensis LQ-10 (accession DQ531696.1). Original centroid sequences that matched selected Yeager et al. (2007) (above) sequences with greater than to 97% sequence identity were subsequently removed from the centroid collection. With USEARCH/UPARSE, potential chimeras are identified during OTU centroid selection and are not allowed to become cluster centroids effectively removing chimeras from the read pool. All quality controlled reads were then mapped to cluster centroids at an identity threshold of 97% again using USEARCH. A total of 95.6% of quality controlled reads could be mapped to centroids. Unmapped reads do not count towards sample counts and were removed from downstream analyses. The USEARCH software version for cluster generation was 7.0.1090. Garcia-Pichel et al. (2013) and Steven et al. (2013) sequences were quality screened by alignment coordinates (described above) and included as input to USEARCH for OTU centroid selection and subsequent mapping to OTU centroids.

Phylogenetic analysis

Alignment of SSU rRNA genes was done with SSU-Align which is based on Infernal (Nawrocki et al., 2013; Nawrocki et al., 2009). Columns in the alignment that were not included in the SSU-Align covariance models or were aligned with poor confidence (less than 95% of characters in a position had posterior probability alignment scores of at least 95%) were masked for phylogenetic reconstruction. Additionally, the alignment was trimmed to coordinates such that all sequences in the alignment began and ended at the same positions. FastTree (Price et al., 2010) was used to build the tree.

Identifying OTUs that incorporated \(^{15}\)N into their DNA

DNA-SIP is a culture-independent approach towards defining identity-function connections in microbial communities . Microbes are identified on the basis of isotope assimilation into DNA. As the buoyant density of a macromolecule is dependent on many factors in addition to stable isotope incorporation (e.g. G+C-content in nucleic acids (Youngblut et al., 2014)), labeled nucleic acids from one microbial population may have the same buoyant density as unlabeled nucleic acids from another. Therefore, it is imperative to compare results of isotopic labelling to results obtained with unlabeled controls where everything mimics the experimental conditions except that unlabeled substrates are used. By contrasting heavy gradient fractions from isotopically labeled samples relative to corresponding fractions from controls, the identities of microbes with labeled nucleic acids can be determined

We used an RNA-Seq differential expression statistical framework (Love et al., 2014) to find OTUs enriched in heavy fractions of labeled gradients relative to corresponding density fractions in control gradients (for review of RNA-Seq differential expression statistics applied to microbiome OTU count data see McMurdie et al. 2014). We use the term “differential abundance” (coined by McMurdie et al. 2014) to denote OTUs that have different proportion means across sample classes (in this case the only sample class is labeled:control). CsCl gradient fractions were categorized as “heavy” or “light”. The heavy category denotes fractions with density values above 1.725 g mL\(^{-1}\). Since we are only interested in enriched OTUs (labeled versus control), we used a one-sided Wald-test to test the statistical significance of regression coefficients (the null hypothesis is that the labeled:control fold enrichment for an OTU is less than a selected threshold). We independently filtered out sparse OTUs prior to P-value correction for multiple comparisons. The sparsity threshold was set to the value which maximized the number of p-values under a false discovery rate (FDR) the specific sparsity threshold was 0.3 meaning that an OTU not found in at least 30% of heavy fractions (control and labeled gradients) in a given day were not considered further and not included in P-value adjustment for multiple comparisons. P-values were corrected with the Benjamini-Hochberg method (Benjamini et al., 1995) and a FDR of 0.10 was applied (this rate is the typical FDR threshold adopted during RNASeq analysis). We selected a log\(_{2}\) fold change null threshold of 0.25 (or a labeled:control fold enrichment of 1.19). DESeq2 was used to calculate the moderated log\(_{2}\) fold change of labeled:control proportion means and corresponding standard errors for the Wald-test (above). Fold change moderation allows for reliable ranking such that high variance and likely statistically insignificant fold changes are appropriately shrunk and subsequently ranked lower than they would be as unmoderated values. Those OTUs that exhibit a statistically significant increase in proportion in heavy fractions from \(^{15}\)N\(_{2}\)-labeled samples relative to corresponding controls have increased significantly in buoyant density in response to \(^{15}\)N\(_{2}\) treatment; a response that is expected for N\(_{2}\)-fixing organisms.

We also assessed the consistency of enrichment between time points by including the interaction of day and label:control in a DESeq2 generalized linear model. The interpretation of the interaction coefficient is the change in OTU enrichment per unit time. P-values for the interaction coefficient were adjusted for all OTUs that passed the sparsity threshold in the label versus control comparison (above) and we used the default null model that the coefficient equaled zero. Additionally, we assessed fold change between labeled and control gradient heavy fractions after pooling day 2 and day 4 data when treating the different time points as replicates. The same null model as the label versus control comparison (above) was used in this replicate analysis (log\(_{2}\) fold change in abundance between label and control is less than or equal to 0.25). We included all OTUs that passed sparsity based independent filtering at either day (above) for p-value adjustment in the replicate analysis.

Community and Sequence Analysis

BLAST searches were done with the “blastn” program from BLAST+ toolkit (Camacho et al., 2009) version 2.2.29+. Default parameters were always employed and the BioPython (Cock et al., 2009) BLAST+ wrapper was used to invoke the blastn program. Pandas (McKinney, 2012) and dplyr (Wickham et al., 2014) were used to parse and manipulate BLAST output tables.

Principal coordinate ordinations depict the relationship between samples at each time point (day 2 and 4). Bray-Curtis distances were used as the sample distance metric for ordination. The Phyloseq (McMurdie et al., 2014) wrapper for Vegan (Oksanen et al., 2013) (both R packages) was used to compute sample values along principal coordinate axes. GGplot2 (Wickham, 2009) was used to display sample points along the first and second principal axes. Adonis tests (Anderson, 2001) were done with default number of permutations (1000).

Rarefaction curves were created using bioinformatics modules in the PyCogent Python package (Knight et al., 2007). Parametric richness estimates were made with CatchAll using only the best model for total OTU estimates (Bunge, 2010).

All code to take raw sequencing data through the presented figures (including download and processing of literature environmental datasets) can be found at:

https://github.com/chuckpr/NSIP_data_analysis

Acknowledgements

We would like to thank T Whitman, CHD Williamson, AN Campbell and EK Hall for helpful comments in the preparation of this manuscript. This material is based upon work supported by the Department of Energy Office of Science, Office of Biological & Environmental Research Genomic Science Program under Award Numbers DE-SC0004486 and DE-SC0010558. This project was also supported by Agriculture and Food Research Initiative Competitive Grant no. 2007-35107-18299 from the USDA National Institute of Food and Agriculture. This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Ordination of heavy gradient fractions by Bray-Curtis distances on the basis of OTU content. Each point represents a gradient fraction OTU profile. Points closer together have more similar OTU content than those further apart.

Distribution of sequences into top 9 phyla (phyla ranked by sum of all sequence annotations).

Ordination of Bray-Curtis sample pairwise distances for each incubation time. Point area is proportional to the density of the CsCl gradient fraction for each sequence library, and color/shape reflects control (red triangles) or labeled (blue circles) treatment. Each point represents the OTU profile for a single gradient fraction. Points closer together are more similar in OTU content than those further apart.

Moderated log\(_{2}\) fold change of OTUs proportions for labeled versus control gradients (heavy fractions only, densities \(>\)1.725 g/mL). All OTUs passing the sparsity treshold (see methods) at a specific incubation day are shown. Red color denotes a proportion fold change that has a corresponding adjusted p-value below a false discovery rate of 10% (ratio is significantly greater than 0.25, black line.)

Relative abundance values in heavy fractions (density greater or equal to 1.725 g/mL) for the top 10 \(^{15}\)N “responders” (putative diazotrophs, see results for selection criteria of top 10) at each incubation day. Each point is a relative abundance value for the indicated OTU in a CsCl gradient fraction SSU rRNA gene collection. See Table [tab:LTP_blast] for BLAST results against the LTP database (release 115). Point area is proportional to CsCl gradient fraction density, and color signifies control (red) or labeled (blue) treatment.

Rarefaction curves for all samples presented by Garcia-Pichel et al. (2013) and Steven et al. (2013). Inset is boxplot of estimated sampling effort for all samples in Garcia-Pichel et al. (2013) and Steven et al. (2013) (number of observed OTUs divided by number of CatchAll (Bunge 2010) estimated total OTUs).

Counts of “responder” OTU occurrences in samples from Steven et al. (2013) and Garcia-Pichel et al. (2013). Steven et al. (2013) collected BSC samples (25 samples total) and samples from soil beneath BSC (17 samples total, “below” column in figure). Garcia-Pichel et al. (2013) collected samples from “dark” (9 samples total) and “light” (12 samples total) crusts in addition to “Lichen” (2 samples total) dominated crusts.

Phylogenetic trees of OTUs passing sparsity threshold for Proteobacteria A and Firmicutes B. \(^{15}\)N-responders are identified by dots present in column i. Log\(_{2}\) of OTU proportion fold change (labeled:control samples) for each OTU are presented as a heatmap in column ii with results from days 2 and 4 on the left and right sides of the column respectively. High fold change values indicate \(^{15}\)N incorporation. Presence/absence of OTUs (black indicates presence) in lichen, light, or dark environmental samples (Garcia-Pichel et al., 2013) is shown in column iii. Presence/absence of OTUs (black indicates presence) in crust and below crust samples (Steven et al., 2013) is shown in column iv.

Scatter plot of fold enrichment values (label versus control heavy fractions) for OTUs passing sparsity criteria in day 2 and 4. P-value is the minimum value from day 2 or 4. Blue line has slope of one.

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