The experiment was conducted at the University of California McLaughlin Reserve in the Northern Coast Ranges of California (N 38°52′, W122°26′), which has a Mediterranean climate. Rainfall averages 62 cm per year, falling mainly between November and March. Rainfall was below average in the year samples were collected and the preceding year (53.9 cm in 2012; 51.9 cm in 2013), with associated reductions in plant productivity (Copeland et al., 2016).

The ~1,000 × 500 m experimental site contains three distinct soil and grassland types (Eskelinen and Harrison, 2015c). The first two are underlain by ultramafic (serpentinite, peridotite) bedrock, which has a low Ca:Mg ratio and high levels of some heavy metals; these are generally referred to as “serpentine soils.” One type of serpentine soil (“harsh serpentine”) is shallow, coarse-textured, and low in organic matter and nutrients (Eskelinen and Harrison, 2014). This soil supports low-productivity plant communities of diverse native annual forbs and geophytes (Eskelinen and Harrison, 2015c). The second type of serpentine soil (“lush serpentine”) is deeper, finer-textured, and has higher levels of organic matter and nutrients—although its Ca:Mg ratio is comparable to harsh serpentine (Eskelinen and Harrison, 2014). It supports a higher-productivity plant community of native forbs and geophytes, scattered native perennial grasses, and exotic annual grasses and forbs (Eskelinen and Harrison, 2015c). The third type of soil (“non-serpentine”) is derived from sedimentary bedrock and is also deeper than the harsh serpentine, with a loamy texture and higher levels of organic matter and nutrients (Eskelinen and Harrison, 2014). Plant productivity in this soil is similar to that on the lush serpentine, and plant communities consist primarily of exotic annuals (grasses and forbs) (Eskelinen and Harrison, 2015c). Hereafter, these three different soil + associated grassland types will be referred to simply as “soil types.”

Environmental manipulations were imposed beginning in 2010. A “precipitation addition” treatment was designed to simulate a lengthening of the rainy season into the early summer, a regional scenario predicted by some climate change forecasts (National Assessment Synthesis Team, 2000). A “nutrient addition” treatment was designed to investigate the impact of this precipitation change under relaxed nutrient limitation. The full factorial treatment combination (precipitation added, nutrients added, both added, neither added) was applied to 10–12 replicate 2 × 2 m plots on each of the three soil types, for a total of 132 plots. In each year, precipitation addition began after the natural spring rains had ceased and was added to simulate one moderate storm event per week for 8 weeks. For nutrient addition, a slow-release granular NPK (10-10-10) fertilizer with micronutrients (Lilly Miller Ultra Green; Lilly Miller Brands, Walnut Creek, CA, USA) was broadcast in three equal applications in November, early February, and late March of each year, for a total of 10 g N/m², 10 g P/m², and 10 g K/m² per year. See Eskelinen and Harrison (2014, 2015a,b,c) for further details of treatment implementation.

Soil samples were collected for microbial community characterization on 28 and 29 May 2013. Sampling occurred 6 days after the last simulated rainfall of the year, capturing the season-long effect of the treatment. After brushing aside leaf litter to expose the mineral soil surface, soil cores (7 cm diameter) were taken to a depth of 7.5 cm. This was the deepest core that could be reliably collected in the harsh serpentine plots. In each plot, three cores were collected from a 1 × 1 m quadrat. Cores were composited and a subsample for DNA extraction was removed into a sterile tube, placed on ice, transported to the laboratory, and stored at −20°C. Remaining soil was placed on ice packs and stored at 4°C for <48 h before extraction with 0.5 M K₂SO₄ for measurement of ammonium (NH₄-N), nitrate (NO₃-N), and dissolved organic carbon (DOC). 10 g soil and 50 mL K₂SO₄ were shaken for 1 h at 175 rpm before filtering the extract through pre-leached Whatman #1 filter paper; from these extracts, NH4+ and NO3− were measured colorimetrically (Kempers and Kok, 1989; Doane and Horwath, 2003) and dissolved organic carbon was measured with a Shimadzu Total Organic Carbon Analyzer (TOC-V CSH). Soil moisture was measured by comparing mass of a subsample before and after drying to constant mass at 105°C. Remaining soil was air-dried and sent to the A&L Western Laboratory (Modesto, CA) for measurement of additional soil chemical parameters, including pH, organic matter, Olsen P, S, K, Mg, Ca, Na, and cation exchange capacity (Gavlak et al., 2005). For testing associations between soil properties and estimated trait values, we also calculated two ratios: “extractable N:P” [(NH₄-N + NO₃-N)/Olsen P] and Ca:Mg.

DNA was extracted using a MoBio PowerLyzer kit (MoBio Laboratories, Carlsbad, CA). Extracted DNA was sent to the Argonne National Laboratory (Lemont, IL) for amplification and sequencing of the V4 region of the 16S rRNA gene. Amplification was performed following Earth Microbiome Project protocols (www.earthmicrobiome.org/emp-standard-protocols/16s), with 515F/806R primers from Caporaso et al. (2012). Paired-end 250 bp sequencing of the amplicon was performed on a MiSeq, in a run including only these 132 samples. Sequences are deposited in the Sequence Read Archive under accession number SRP098483.

Raw forward and reverse reads were demultiplexed (but not quality filtered) using QIIME 1.9.1 (Caporaso et al., 2010b). OTUs were then derived using the UPARSE pipeline in USEARCH 8.1 (Edgar, 2013). After forward and reverse reads were merged with USEARCH, all reads that had more than 1 barcode difference during the demultiplexing process were removed. To generate the set of high-quality sequences that would be used for picking OTUs, remaining merged reads were then quality filtered using a maximum error threshold (maxee) of 0.5 and a minimum length of 100. These filtered sequences were dereplicated and then clustered into OTUs at 97% similarity, removing singletons and chimeras in the process. Finally, any OTU that could not be mapped to greengenes (version 13_8) (DeSantis et al., 2006) with an identity of 75% or greater was discarded (Leff et al., 2015), which eliminated 6.8% of OTUs. The product of these processes was a set of 18,023 OTUs.

The full set of merged reads was then mapped to this OTU set to make the raw OTU table. Taxonomy was assigned to OTUs using the RDP classifier (Wang et al., 2007) implemented in QIIME 1.9.1. The raw OTU table was then filtered to remove all OTUs identified as chloroplasts or mitochondria. Next, the OTU table was rarefied to 26,690 sequences per sample (retaining 129 of the 132 plots). 100 rarefactions at this depth were performed in QIIME 1.9.1, and the 100 resulting tables were averaged.

All statistical analyses were conducted in R (R Core Team, 2016). For bacterial and archaeal genomes, 16S rRNA gene sequences and annotated trait values were downloaded from the Joint Genome Institute's Integrated Microbial Genomes (IMG) database (Markowitz et al., 2012) on December 18, 2015. 16S rRNA sequences were aligned using PyNAST (Caporaso et al., 2010a) in QIIME 1.9.1, and trees were constructed with RAxML 8.2.4 (Stamatakis, 2014) using a GAMMA model of rate heterogeneity. Four candidate traits were initially selected for testing: rRNA gene copy number, genome size, oxygen requirement, and motility. Phylogenetic signal was assessed for each trait (via K and λ for continuous traits and D for binary traits) (Pagel, 1999; Blomberg et al., 2003; Fritz and Purvis, 2010) using the phytools and caper packages (Revell, 2012; Orme et al., 2013). To assess trait estimation accuracy, we used leave-one-out cross-validation (Kembel et al., 2012), as well as a more conservative “test species” method emphasizing taxa likely to be found in soil. We note that this trait estimation approach is inherently limited by the phylogenetic distribution of available fully-sequenced genomes (Table S1) vs. the phylogenetic distribution of soil microbes (e.g., Tables 1, 2). Bearing this caveat in mind, we believe that useful insight into the ecology of soil microbial communities can still be gained from trait estimation based on published genomes (e.g., Goberna et al., 2014; DeAngelis et al., 2015; Nemergut et al., 2016), and we encourage future studies on this topic to update our reference trees with newly-published genomes, just as we endeavored to update trees from previous studies here. See Supplementary Material for more details on tree construction and trait estimation testing.

Trait values were estimated for all experimental OTUs by placing them onto the reference tree using pplacer (Matsen et al., 2010), then using ancestral state estimation methods (Kembel et al., 2012) to calculate trait value estimates for each experimental OTU based on its phylogenetic position in relation to reference taxa (via picante:phyEstimate for continuous traits and picante:phyEstimateDisc for binary traits; Kembel et al., 2010). Using the rRNA gene copy number estimates, relative abundances in the OTU table were adjusted with the script from Kembel et al. (2012). Community weighted mean trait values were then calculated for each trait in each plot with the FD package (Laliberté and Legendre, 2010; Laliberté et al., 2014) using this adjusted OTU table. As a final test of the estimation procedure, we evaluated differences between community weighted mean estimated trait values calculated using the full set of OTUs vs. those calculated excluding OTUs with the least certain 20% of estimates. Considering all tests, estimation for two candidate traits was most robust: rRNA gene copy number and genome size. Only those traits were used in the analysis and inference below. Reference trees and associated trait values for reference taxa are available for download as Supplementary Material.

Both rRNA gene copy number and genome size had significant phylogenetic signal as measured by the K and λ metrics (p ≤ 0.001 in all cases, detailed in Supplementary Material 1.3). Across our two testing procedures, correlations between estimated and actual trait values were 0.87–0.90 for rRNA gene copy number and 0.90–0.92 for genome size. Comparing linear mixed effects model results for community-weighted means calculated using the full set of OTUs vs. for those calculated using only the 80% of OTUs with most certain trait estimates, the two methods had very similar results for both traits, suggesting that model fits were not driven by the least certain trait estimates (Table S2). See Supplementary Material for more detail on trait estimation performance, including actual vs. estimated trait values for 100 test species (Table S3).

Community-weighted means for both estimated rRNA gene copy and estimated genome size differed among soils. Specifically, harsh serpentine microbial communities had a higher proportion of taxa with few estimated rRNA gene copies and small estimated genome sizes, with 4.93 and 4.89% lower community-weighted mean estimated rRNA gene copy numbers and 4.92 and 8.83% lower community-weighted mean estimated genome sizes than communities from the lush serpentine and non-serpentine soils, respectively [rRNA gene copy number: F_{(2, 109)} = 17.3, p < 0.0001; genome size: F_{(2, 109)} = 70.4, p < 0.0001] (Figure 1). Communities from the lush serpentine and non-serpentine soils had similar community-weighted mean estimated rRNA gene copy numbers (p = 0.984), while the non-serpentine had a 4.28% greater community-weighted mean estimated genome size than the lush serpentine (p < 0.0001).

Nutrient addition increased the proportion of microbes with high estimated rRNA gene copy numbers and marginally increased the proportion of microbes with large estimated genome sizes, increasing community-weighted means by 3.04% for estimated rRNA gene copy number and by 1.09% for estimated genome size, compared to unfertilized plots [rRNA gene copy number: F_{(1, 109)} = 18.3, p < 0.0001; genome size: F_{(1, 109)} = 3.3, p = 0.071] (Figures 2, 3). In contrast, late-season precipitation addition favored microbes with lower estimated copy numbers and smaller estimated genome sizes, decreasing community-weighted means by 2.40% for estimated rRNA gene copy number and by 1.61% for estimated genome size, compared to unwatered plots [rRNA gene copy number: F_{(1, 109)} = 13.5, p = 0.0004; genome size: F_{(1, 109)} = 10.9, p = 0.001] (Figures 2, 3). Both effects were largely consistent across soil types, although precipitation addition led to a marginally stronger reduction in community-weighted mean estimated genome size in the lush serpentine (3.30%) compared to the other two soil types (0.93% in non-serpentine and 0.78% in harsh serpentine) [soil type × precipitation treatment interaction: F_{(2, 109)} = 2.53, p = 0.084; watered vs. unwatered: lush serpentine p = 0.0004, harsh serpentine p = 0.821, non-serpentine p = 0.661] (Figure 3B). There was no interaction between the two treatments for either trait, nor was there three-way interaction among the two treatments and soil type (all p > 0.10).

OTUs that increased in relative abundance in response to nutrient addition had higher estimated rRNA gene copy numbers [1.99 ± 0.03 vs. 1.86 ± 0.03, t₍₂₁₅₅₎ = −3.26, p = 0.001] and larger estimated genome sizes [4.47 ± 0.07 vs. 4.19 ± 0.06, t₍₂₁₄₂₎ = −3.06, p = 0.002] than OTUs that decreased, on average (Figures S1, S2). Conversely, OTUs that increased in relative abundance in response to precipitation addition had lower estimated rRNA gene copy numbers [1.87 ± 0.02 vs. 1.96 ± 0.03, t₍₂₁₉₄₎ = 2.23, p = 0.026] and smaller estimated genome sizes [4.07 ± 0.06 vs. 4.42 ± 0.06, t₍₂₂₄₃₎ = 3.89, p = 0.0001] than OTUs that decreased, on average (Figures S1, S2). These results, based on sorting OTUs by % change in relative abundance (control vs. treatment), dividing this sorted list into four equal sequential groups (quartiles), and then comparing trait values of the highest (“increasers”) and lowest (“decreasers”) groups, were robust to this group number choice, with all p < 0.10 when we divided the sorted list into 3–30 groups (results not shown).

Both nutrient and precipitation addition remained significant predictors of community-weighted estimated trait means when the majority of measured soil properties were (individually) added to the lme models as covariates (data not shown). However, including ammonium, P, or K as a covariate made the effects of nutrient addition on community-weighted estimated trait means non-significant (p > 0.10) for both traits, and for estimated genome size, adding organic matter, DOC, or pH to the model also rendered the nutrient addition effect non-significant (Tables S4, S5). Adding ammonium, extractable N:P, or DOC to the models made the effects of precipitation addition on both community-weighted estimated trait means non-significant (p > 0.10), and for estimated genome size, adding pH to the model also rendered the precipitation addition effect non-significant (Tables S4, S5). Considering the correlations between soil properties and community-weighted trait means across all plots, community-weighted mean rRNA gene copy number was significantly positively correlated with CEC, K, organic matter, ammonium, Mg, Ca, P, and extractable N:P and was significantly negatively correlated with pH (Figure 4). Community-weighted mean genome size was significantly positively correlated with Ca, organic matter, CEC, Ca:Mg, K, ammonium, and extractable N:P and was significantly negatively correlated with pH; it was not strongly related to P (Figure 5).

In their distribution of relative abundance across plots, most (23 of 36) phyla showed a soil type preference (Table 1). Relative abundance of approximately half of the phyla changed in response to each of the treatments (Table 1). Treatment response also showed some association with soil type preference: in response to nutrient addition, phyla preferring harsh serpentine tended to decrease and phyla preferring lush and non-serpentine tended to increase, whereas in response to precipitation addition, phyla preferring harsh serpentine tended to increase while those preferring non-serpentine tended to decrease (Table 1). After filtering to exclude very rare phyla (those with ≤0.01% relative abundance across all plots), phyla that increased in response to nutrient addition tended to be those with larger mean estimated genome sizes (r = 0.35, p = 0.057) but not necessarily greater mean estimated copy numbers (r = 0.07, p = 0.726), whereas phyla that increased in response to precipitation addition tended to be those with lower mean estimated rRNA gene copy numbers (r = −0.33, p = 0.066) and slightly smaller mean estimated genome sizes (r = −0.29, p = 0.109) (Table S6). Considering community-weighted mean estimated trait changes within individual phyla, some phyla exhibited significant changes commensurate with the total community change or there was no significant change, but others showed significant changes in the opposite direction (Table 2).

The proportion of microbes with many estimated rRNA gene copies was higher in the two more fertile soil types, which had more soil organic matter in addition to higher levels of some inorganic nutrients (Eskelinen and Harrison, 2014), and in the nutrient addition plots, where nutrient levels were elevated (Table S4, Figure 4). OTUs that increased in response to nutrient addition also had higher estimated rRNA gene copy numbers, on average, than OTUs that decreased. Ammonium, P, and K levels were all positively associated with estimated rRNA gene copy number (as well as correlated with one another), suggesting that higher levels of one or more of these nutrients may favor microbes with more rRNA gene copy numbers; however, a role for other nutrient-mediated effects—such as increased plant biomass (Eskelinen and Harrison, 2015c)—cannot be ruled out. Overall, our results are in line with other studies showing that resource-rich conditions are likely to favor “copiotrophic” microbes with higher potential growth rates, which are often correlated with higher rRNA gene copy numbers (Fierer et al., 2007; Vieira-Silva and Rocha, 2010; Roller et al., 2016). In addition to high overall resource levels, high rRNA gene copy numbers tend to be particularly associated with highly pulsed resources (Klappenbach et al., 2000). While we did not collect data on how nutrient addition affected temporal patterns of resource availability for microbes, investigation of that relationship would be a valuable next step for understanding microbial ecological strategies.

Relatively more microbes also had larger estimated genome sizes in the two more fertile soil types and marginally in nutrient addition plots, and OTUs that increased in response to nutrient addition had larger estimated genome sizes, on average, than OTUs that decreased. Higher N availability—both naturally-occurring and resulting from our nutrient addition (Table S4, Figure 5)—was associated with larger estimated genome size, suggesting that it may have favored the retention of additional genes by easing constraints on replication (Giovannoni et al., 2014). In addition to nutrient levels, fluctuations in oxygen supply and/or in the quantity and types of substrates delivered to individual microbes—which tend to favor generalists with larger genomes (Vieira-Silva and Rocha, 2010; Guieysse and Wuertz, 2012; Barberán et al., 2014; Fierer et al., 2014; Giovannoni et al., 2014; Krause et al., 2014)—may have been greater in resource-rich plots due to factors, such as greater root biomass and soil aggregation (Huenneke et al., 1990; Six et al., 2004; Dukes et al., 2005; Riggs et al., 2015; Bach and Hofmockel, 2016; but see Eviner and Chapin, 2002). Conversely, since the lush serpentine and non-serpentine soils have similar nutrient levels and plant biomass (Eskelinen and Harrison, 2015c), the difference in estimated genome size between those soils was likely driven by their differences in other soil chemical properties, such as pH or Ca:Mg (both strongly correlated with estimated genome size).

Interestingly, a recent metagenomic analysis of six grassland N + P addition experiments found a result opposite to ours: several years of N + P addition (at levels similar to those in our experiment) decreased the proportion of microbes with large genomes (Leff et al., 2015). This difference may derive from the different methods used to estimate genome sizes and/or from ecological differences between the study sites. Analysis of their published data combined with data from our three soil types suggests that the effect of N + P addition on community-weighted mean genome size may vary with site pH and/or mean annual precipitation (% genome size change from control vs. pH: r = 0.86, p = 0.013; vs. mean annual precipitation: r = −0.74, p = 0.022). However, further study would be needed to test this hypothesis given the small number of sites in the combined data set and the difference in trait estimation methods.

In both the community-weighted mean and increaser vs. decreaser analysis, we found that microbes with fewer estimated rRNA gene copies were favored by late-season precipitation addition, which could relate to their ability to thrive in resource-poor conditions (Fierer et al., 2007; Roller et al., 2016, our data). In our experiment, levels of ammonium were especially decreased by late-season precipitation (Table S4) and could account for the precipitation effect as a covariate (Table S5), suggesting that lower ammonium levels in precipitation addition plots may have contributed to selection for low copy number taxa. Nutrient loss in precipitation addition plots may have resulted from several mechanisms, including greater leaching to below our sampling zone, increased uptake by plants (especially in the plots also receiving nutrient addition, where plant biomass was highest Eskelinen and Harrison, 2015c), and higher nitrification (Gravuer, 2016) and denitrification rates.

The one California grassland study that measured precipitation's effect on microbes with high vs. low rRNA gene copy numbers found the opposite result to ours: measurements 0–72 h after rewetting summer-dry soils revealed increases in relative activity of several high rRNA gene copy number groups, apparently stimulated by the precipitation-mediated resource pulse (Placella et al., 2012). However, our samples were taken several days after a series of weekly precipitation events and suggest that precipitation can favor ecologically different groups of microbes at the end of a wet season than at its onset (Cruz-Martínez et al., 2009, 2012). Our results also likely reflect microbial responses to multiple biotic and abiotic changes accumulated over 3 years of field precipitation manipulations (such as changes in plant composition and productivity) (Li et al., 2017), responses which can substantially differ from those to short-term moisture additions (Evans et al., 2014). These considerations suggest an important caveat when evaluating how the trait responses to precipitation can be generalized: because we measured only a single time point and microbial community responses to precipitation are known to be temporally variable, further studies with temporal sampling will be needed to fully illuminate the temporal pattern of precipitation effects on these microbial traits.

As for rRNA gene copy number, the effect of precipitation addition on community-weighted mean estimated genome size—and the estimated genome sizes of increasers vs. decreasers—were opposite to that of nutrient addition. Ammonium levels, lowest in watered plots, were positively correlated with genome size, suggesting that lower available N levels in watered plots may have favored microbes with more streamlined genomes (Giovannoni et al., 2014). Soil pH was also higher in watered plots and associated with smaller estimated genomes. In addition, root biomass and soil aggregation may have decreased with precipitation addition (Huenneke et al., 1990; Dukes et al., 2005; Chenu and Cosentino, 2011; Bach and Hofmockel, 2015), thus potentially decreasing soil habitat complexity and favoring microbes with smaller genomes, opposite to their influence in nutrient addition plots postulated above. Finally, microbes that thrive at higher soil moisture contents may have narrower moisture niches, lacking genes for the production of exopolymeric substances (EPS) and other mechanisms of drought tolerance (Lennon et al., 2012) and thus possessing smaller genomes. The precipitation addition plots on the high water holding capacity lush serpentine soil may have been especially favorable for microbes with poor drought tolerance, possibly contributing to the stronger precipitation treatment effect on estimated genome size in that soil.

We found a moderate correlation between estimated rRNA gene copy number and estimated genome size at the OTU level (r = 0.31), suggesting that conditions favoring high rRNA gene copy number might also favor large genome size. Drawing on genome streamlining ideas (Giovannoni et al., 2014), Roller et al. (2016) suggested that high rRNA gene copy numbers and large genomes are part of a characteristic suite of adaptations to high resource conditions, whereas low rRNA gene copy numbers and small genomes characterize microbes adapted to low resource conditions, as also observed by Lauro et al. (2009). While our findings support this idea, we note that other dimensions of the microbial niche—such as temporal pattern of resource delivery, environmental variability, and resource complexity—may at least somewhat independently affect the relative advantage of high or low values of these traits. For example, low rRNA gene copy number with large genome size appears to be favored under some soil conditions (Barberán et al., 2014), such as those with few labile substrates (DeAngelis et al., 2015). Other soil conditions, such as N and P additions to some grassland soils, appear to favor high rRNA gene copy number and small genome size (Leff et al., 2015). Understanding the conditions and microhabitats that favor particular rRNA gene copy number—genome size combinations could advance the definition of ecological strategies for soil microbes.

Trait estimation provided a window into the diversity of responses among phyla that would have been difficult to appreciate with composition data alone. There were several phyla within which estimated trait values changed in the opposite direction to that of the community mean. This may be because microbes in different soil microhabitats can respond differently to fertilizer addition and to wet-dry cycles (Ranjard and Richaume, 2001; Neumann et al., 2013), and certain soil microhabitats tend to harbor certain phylogenetic groups (Ruamps et al., 2011; Davinic et al., 2012; Nadeem et al., 2013; Shi et al., 2015; Nuccio et al., 2016). In addition, phyla that tend to occur in densely populated microhabitats, such as the rhizosphere, may experience indirect effects of environmental change via their cooperators' or competitors' responses, which may have smaller effects on phyla that tend to inhabit sparsely populated microhabitats. Additionally, the degree to which trait values in a particular phylum are able to respond to change may depend on the degree of effective variation in that trait within the phylum. Like previous studies in California grasslands (Gutknecht et al., 2012; Matulich et al., 2015), we found substantial independence in the phyla that responded to nutrient vs. precipitation addition, suggesting that different life strategies may be favored by the addition of these two resources as opposed to a simple division between “responders” and “non-responders” (Barnard et al., 2013). Overall, although this variation among phyla indicates that microbial responses were not monolithic, the relative consistency of community-weighted mean estimated trait shifts that we observed across the different soil types suggests that the community-level trait shifts favored by a particular environmental change may ultimately be predictable.

We found correlations between observed and estimated trait values that compare favorably to previous studies and to simulations of likely results from microbial trait estimation procedures (Goberna and Verdú, 2016), likely due to our intentional selection of traits with relatively strong phylogenetic signals and our use of reference trees containing thousands of taxa. Summarizing traits with community-weighted means for DNA-based soil microbial samples almost certainly led to conservative estimates of treatment effect sizes, as DNA from dead and dormant microbes could create inertia masking some of the true change in response to treatments (Lennon and Jones, 2011; Barnard et al., 2014; Carini et al., 2016). Thus, while the effect sizes of soils and treatments on community-weighted mean estimated trait values were small in this study, especially within soils (Figures S3, S4), we believe they are likely to be ecologically relevant. Nevertheless, it will be important to continually evaluate prediction of microbial traits, such as growth strategies and resource specialization as additional fully-sequenced genomes from currently uncultured groups become available, such as genomes from the recently-identified candidate phyla radiation (Brown et al., 2015). Better insight into the ecology of these groups will undoubtedly improve our understanding of complex soil community responses.

Trait estimation provided complementary insight to related approaches. The approach of assigning of “copiotrophic” or “oligotrophic” strategies to phyla and other large taxonomic groups (e.g., Fierer et al., 2007) lends insight into our soil type and nutrient results, as groups previously suggested to be copiotrophs did increase in relative abundance in nutrient-rich plots. However, the relative abundance of these phyla did not respond similarly to the addition of a different resource—water—highlighting the need for complementary approaches to understand responses to some types of environmental change. Relative to summaries of gene abundances from metagenomic data sets, traits may provide a means of synthesizing across gene categories to form a more coherent ecological picture (e.g., Lauro et al., 2009; Le Roux et al., 2016) and may prove more useful than individual metabolic pathways for incorporation into biogeochemical models. Ultimately, simultaneous estimation of gene abundances and trait distributions (as outlined in Fierer et al., 2014) may provide the richest picture. Until more metagenomic data sets accumulate, however, estimation of traits hypothesized to be ecologically important from publically available 16S rRNA data sets could illuminate key patterns, as well as help to target metagenomic analysis.

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