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ABSTRACT Ecologists have long studied the temporal dynamics of plant and animal communities with much less attention paid to the temporal dynamics exhibited by microbial communities. As a

result, we do not know if overarching temporal trends exist for microbial communities or if changes in microbial communities are generally predictable with time. Using microbial time series

assessed via high-throughput sequencing, we conducted a meta-analysis of temporal dynamics in microbial communities, including 76 sites representing air, aquatic, soil, brewery wastewater

treatment, human- and plant-associated microbial biomes. We found that temporal variability in both within- and between-community diversity was consistent among microbial communities from

similar environments. Community structure changed systematically with time in less than half of the cases, and the highest rates of change were observed within ranges of 1 day to 1 month for

all communities examined. Microbial communities exhibited species–time relationships (STRs), which describe the accumulation of new taxa to a community, similar to those observed previously

for plant and animal communities, suggesting that STRs are remarkably consistent across a broad range of taxa. These results highlight that a continued integration of microbial ecology into

the broader field of ecology will provide new insight into the temporal patterns of microbial and ‘macro’-bial communities alike. SIMILAR CONTENT BEING VIEWED BY OTHERS PRIORITY EFFECTS IN

MICROBIOME ASSEMBLY Article 27 August 2021 INTEGRATION OF TIME-SERIES META-OMICS DATA REVEALS HOW MICROBIAL ECOSYSTEMS RESPOND TO DISTURBANCE Article Open access 19 October 2020

META-ANALYSIS OF MULTIDECADAL BIODIVERSITY TRENDS IN EUROPE Article Open access 13 July 2020 INTRODUCTION Understanding how communities are structured in time is a fundamental pursuit in

ecology. There is a long history of research on temporal dynamics in animal and plant communities (for example, Preston, 1960; Holling, 1973; McNaughton, 1977; Pimm, 1984; Ives et al., 2003;

Ives and Carpenter, 2007), and this research has been integral to the development of a wide variety of concepts in ecology. In particular, research on long-term changes in plant and animal

diversity have been instrumental in helping ecologists recognize successional dynamics (for example, Lockwood et al., 1997; Chase, 2003), identify relationships between community stability

and biodiversity (Cottingham et al., 2001; White et al., 2006) and predict how communities may respond to disturbances, including longer-term global changes (for example, Fraterrigo and

Rusak, 2008; Magurran et al., 2010). Though microorganisms are ubiquitous, abundant and have critical roles in ecosystems, far less is known about the temporal dynamics exhibited by

microbial communities relative to those exhibited by communities of larger organisms. A growing collection of site-specific studies in the microbial ecology literature suggests that

microbial communities exhibit a wide range of discernable temporal patterns. For example, patterns of primary succession in an infant gut (Koenig et al., 2011) and on leaf surfaces (Redford

and Fierer, 2009) as well as patterns of recurring seasonality in aquatic systems (Fuhrman et al., 2006; Shade et al., 2007; Eiler et al., 2012; Gilbert et al., 2012) have demonstrated that

some microbial communities change directionally, according to environmental conditions. By contrast, patterns of stability in wastewater treatment systems suggest that some microbial

communities are composed of core members that exhibit minimal temporal variability and rarer taxa that exhibit more pronounced fluctuations in abundance over time (Werner et al., 2011).

Further, in a range of systems, including gut microbiota challenged with an antibiotic (Dethlefsen et al., 2008), lake microbial communities after water column mixing (Jones et al., 2008;

Shade et al., 2012b) and soil communities of denitrifiers and nitrite oxidizers after exposure to increased temperature (Wertz et al., 2007), some microbial communities have the capacity to

recover quickly after disturbance events, either to the pre-disturbance state or to an alternative stable state (Shade et al., 2012a). Overall, these and other time series highlight that

microbial communities, like plant and animal communities, are dynamic and exhibit temporal patterns that can reflect underlying biotic and abiotic processes. Because changes in microbial

community composition are often associated with changes in the functional capabilities of those communities (for example, Bell et al., 2005; Fierer et al., 2007; Strickland et al., 2009),

understanding microbial temporal patterns can be critical for understanding ecosystem processes. Despite this importance, our ability to generalize microbial community dynamics has been

limited by a focus on site- or habitat-specific research. However, the ever-increasing accumulation of 16S rRNA gene sequence data allows for comparison of microbial communities across

habitats. Though microbial communities from different habitats clearly differ in composition (Lozupone and Knight, 2007; Nemergut et al., 2011), it is unknown whether there are common

patterns in the dynamics or variability of microbial communities through time and across habitats. To date, there has been no concerted analysis of microbial temporal dynamics across biomes,

and thus understanding these dynamics and determining their commonalities are key challenges in microbial ecology. Aquatic systems are perhaps the most frequently studied microbial

communities through time, due, in part, to the relatively large number of long-term ecological studies conducted in aquatic systems. As a result, published time series of aquatic microbial

communities have yielded key insights into the drivers of microbial dynamics in marine and freshwater systems. For instance, we know that physicochemical changes in the environment often

drive shifts in aquatic microbial communities, as evident from the observation that marine, lake and river communities frequently exhibit pronounced seasonality (Hofle et al., 1999; Fuhrman

et al., 2006; Shade et al., 2007; Nelson, 2008; Crump et al., 2009; Eiler et al., 2012). Furthermore, there is evidence that phytoplankton and bacterioplankton communities appear to be

synchronous in some systems (Kent et al., 2007), potentially linked by the consumption of phytoplankton-specific exudates by heterotrophic bacterioplankton (Paver and Kent, 2010). These

select examples of aquatic microbial time series (culled from hundreds in the literature), demonstrate the importance of time series analysis for identifying the important biotic and abiotic

drivers of microbial community structure. There are clear challenges to studying microbial communities through time. First, it is often difficult to characterize the micron-scale niches for

microorganisms, and therefore the immediate environment experienced by many microbial communities remains unknown (Brock, 1987). Additionally, the timescales over which the greatest

microbial community changes occur are typically unknown (Shade and Peter et al., 2013). Depending on the habitat, survey efforts may lack the temporal resolution to capture rapid community

changes, particularly in systems with actively growing microbial populations, in which generation times may be on the order of minutes. Finally, surveying microbial diversity can be a

daunting task. Individual samples often harbor hundreds to thousands of individual microbial taxa (Curtis and Sloan, 2004; Schloss and Handelsman, 2007; Quince et al., 2008), and the

majority of microorganisms cannot be identified using traditional culture-based techniques (Pace, 1997; Schloss and Handelsman, 2007). Fortunately, the ongoing development of

culture-independent tools and high-throughput sequencing technologies has made it feasible to describe the temporal dynamics of microbial communities at time scales and resolutions that were

previously unattainable (Gonzalez et al., 2012). We conducted a meta-analysis of newly available time series of microbial communities assessed via high-throughput sequencing of the 16S rRNA

gene, which permits the detailed analysis of microbial community changes through time. Our objective was to characterize temporal dynamics of microbial communities from a suite of habitats,

and when possible, to compare these dynamics with communities of larger organisms. To assess whether temporal patterns were common across both microbial and ‘macro’-bial organisms, we

applied analyses previously applied to temporal patterns in plant and animal communities. We address the following outstanding questions: How variable are microbial communities over time,

and how does this variability compare within habitats? What kinds of temporal patterns are often exhibited by microbial communities, and at what scales are these patterns most apparent? Do

microbial communities exhibit species–time relationships (STRs), and if so, are those relationships similar to those for larger organisms? MATERIALS AND METHODS DATA SETS We compiled

bacterial and archaeal time series from 76 sites, spanning a wide range of study durations and habitats, including aquatic, air, brewery wastewater treatment, soil, plant- and

human-associated communities. Fungal and other eukaryotic communities were not included in the meta-analysis. Each site had a minimum of five observations through time, and sites having a

destructive sampling regime (for example, soils) were concatenated into a single time series. The compiled time series included eight seasonally sampled temperate bog lakes in Wisconsin,

USA, each monitored at two locations: one sample from the upper, mixed layer (epilimnion) and one sample from the lower stratified layer (hypolimnion). These time series included between 1

and 3 years of approximately once or twice weekly observations during the ice-free period (Shade et al., 2008). Additional lake microbial communities were sampled intensively over the month

of a whole-ecosystem disturbance experiment at three depths in the lake (Shade et al., 2012b). A second data set included air from near-surface troposphere samples from a mountaintop

location in Colorado, USA (Bowers et al., 2012). The air communities were sampled continuously for hours over a few days, with samplings occurring approximately every month for about a year.

Nine brewery wastewater treatment communities were sampled once per month for a year, and two of those sites had been previously sampled a few years before (Werner et al., 2011). The

six-year English Channel time series, a coastal marine system, provided our longest series of monthly samples (Caporaso et al., 2012; Gilbert et al., 2012). Flower-associated microbial

communities were sampled from six apple trees over the lifespan of the flowers (1 week), including five time points from before flowers opened until petal fall (Shade et al., 2013). In a

study of human-associated microbiota, the palm, oral and gut microbial communities from two human subjects were collected approximately daily for a year for a male subject, and daily for 6

months for a female subject (Caporaso et al., 2011). Gut microbiota were also sampled from one infant across dietary shifts for the first 2.5 years of life (Koenig et al., 2011).

Agricultural soils at the Kellogg Biological Station, Michigan, USA maintained under different management regimes were sampled monthly for 6 months (Lauber _et al._, 2013) and soils from

National Ecological Observatory Network (NEON) sites in Hawaii and Florida, USA (www.neon.org) were sampled once per month for 3 months. Finally, six freshwater streams in Colorado, USA were

sampled every 1–2 weeks for approximately 1 year (Portillo et al., 2012). Additional details about each data set are provided in Supplementary Table S1 and in the associated references.

Clearly, these sample sets vary widely with respect to their sampling intensity and study duration. However, this is unavoidable if we want to assess temporal dynamics for microbial

communities from diverse environments, and, as has been demonstrated for meta-analyses of plant and animal community dynamics (Nekola and White, 1999; White et al., 2006; Soininen et al.,

2007; Korhonen et al., 2010; White et al., 2010), we can still describe patterns that would not be evident if we were to restrict our analyses to a far more limited set of sample types.

SEQUENCE ANALYSES The microbial communities in each of the 3431 individual samples were characterized by sequencing a portion of the 16S rRNA gene on either the Illumina (San Diego, CA, USA)

or 454 (Branford, CT, USA) platforms. The 16S rRNA gene is widely used for determining the phylogenetic and taxonomic composition of bacterial communities and, in all the cases, the data

were derived from PCR amplification of environmental DNA using primer pairs designed to amplify the gene region from all, or nearly all, known bacterial taxa. A closed reference operational

taxonomic unit (OTU) picking protocol was applied to each data set separately (Caporaso et al., 2012). Briefly, OTUs were assigned based on 97% sequence identity to sequences in the

Greengenes reference database (McDonald et al., 2012) preclustered at 97% identity (http://qiime.org/home_static/dataFiles.html). As we used the same reference-based OTU picking strategy for

all samples, we could directly compare the relative abundances of taxa across samples. Furthermore, we sub-sampled each individual data set such that all samples from a given data set were

compared at an equivalent sequencing depth (Supplementary Table S1). All analyses were performed on the rarefied OTU tables to permit comparisons of patterns in within- and between-community

diversity. Our goal was not to quantify the absolute diversity found in any of the samples: this task is difficult, if not impossible, because individual samples may harbor thousands of

rare taxa (Sogin et al., 2006). However, as recent work has demonstrated (Shaw et al., 2008; Kuczynski et al., 2010a), it is not necessary to characterize absolute diversity in order to

accurately describe changes in within-sample and between-sample diversity within and between habitat types. Pielou’s evenness (Pielou, 1969), richness (number of OTUs) and Faith’s

phylogenetic diversity (Faith, 1992) were used as within-sample (alpha) diversity metrics. Bray–Curtis was used as a taxon-based metric of differences in community composition (beta

diversity), and the dissimilarities were calculated from the rarefied OTU tables in R using the vegan package (Oksanen et al., 2011; R Development Core Team, 2011). QIIME (version 1.2.1,

(Caporaso et al., 2010) was used for constructing weighted and unweighted UniFrac distances. UniFrac is a commonly used phylogenetic distance metric to assess pairwise dissimilarity in

community composition and incorporates information about differences in phylogenetic composition of community members (Lozupone and Knight, 2005; Lozupone et al., 2011), with weighted

UniFrac accounting for differences in the relative abundances of community members. STATISTICAL ANALYSES All analyses were performed using the R environment for statistical computing (R

Development Core Team, 2011), with the aid of the vegan and ggplot2 packages (Wickham, 2009; Oksanen et al., 2011). To compare temporal variability in diversity across habitats having

inherently different diversities, we calculated the coefficient of variation (CV) in within-sample diversity for each community (Equation 1) where σ is the s.d. and _μ_ is the mean. We

calculated median absolute deviation (MAD) to compare variability in between-sample diversity (Equation 2). Finally, we calculated _z_-scores of richness to examine the step-wise variability

of richness through time, across data sets (Equation 3). where _χ_ is the raw value of richness, _μ_ is the mean of the sample, and _σ_ is the s.d. around the mean. In using the coefficient

of variation, median absolute deviation and _z_-scores of richness over time, we compared variability in diversity rather than absolute measures of diversity, which was most appropriate for

comparing communities from different habitats that were assessed using different protocols for 16S rRNA short-read sequencing. To assess whether there were patterns in community structure

that could be described by time between observations, we related community similarity/distance to time elapsed using Mantel tests with Pearson’s correlation on 999 permutations. We evaluated

the decay of community similarity over time (time-decay) using the same methods for calculating decay of community similarity over space (distance-decay; Nekola and White, 1999; Soininen et

al., 2007). Though assessment of distance-decay is common in the literature, it is less common to assess time-decay. To assess time-decay in microbial communities, we used a similar

approach adopted by Korhonen et al. (2010) to the meta-analysis of aquatic community time-decay. A log-linear model was fitted between the change in community structure (assessed by

pair-wise similarities or distances, including Bray–Curtis, UniFrac and unweighted UniFrac) and days elapsed. Community dissimilarities were converted to similarities by subtracting from

one. Similarities were log-transformed. The slope of the log-linear model is a rate of community change, sometimes referred to as turnover (Nekola and White, 1999). For plotting time-decay

examples from each biome, we applied lowess smoothing over windows the length of 5% of the total series. Because time-decay can be sensitive to the duration of the study, we also performed a

simple analysis of how quickly microbial communities change at temporal scales from 1 week to 1 month, from 1 to 6 months, from 6 months to 1 year, from 1 to 2 years, from 2 to 3 years,

from 3 to 4 years, from 4 to 5 years and from 5 to 6 years. These windows encompass the breadth of time series durations included in the meta-analysis. For all pairs of observations within a

community’s time series, a rate of change was calculated by dividing Bray–Curtis dissimilarity by the time between observations. Next, all pairs of observations were partitioned into the

appropriate temporal window (determined by the time between observations) and an average rate of change for each window was calculated. The global average rate of change was summarized

across sites from the same habitat (reported in Supplementary Table S2). STRs for each site were constructed in R by calculating richness using the moving window approach of White et al.

(2006). This approach involves partitioning a time series into as many window subsets as possible given the number of observations and fitting the STR model (the power-law relationship

between time and richness) at each window. For example, a 250-time point series could divided into one 250-point window, two 249-point windows, and so on. The power function, rather than the

lognormal function, was used so that our results would be directly comparable with the results from communities of larger organisms reported in White et al. (2006). There is some debate

regarding which function is most appropriate for describing the STR (McGill, 2003; White et al., 2006), but White et al. (2006) found that the log and power functions produced identical

patterns. We compared STR patterns in microbial communities with patterns for communities of larger organisms. However, microbial communities are widely considered to be more diverse than

communities of larger organisms, and it remains difficult to make a direct comparison, as discussed at length previously (Fierer and Lennon, 2011). There are caveats to the data sets used

here that are worth highlighting. First, high-throughput sequencing of 16S rRNA genes can introduce biases in the determination of microbial diversity; these limitations have been described

elsewhere (for example, Kunin et al., 2010; Haas et al., 2011; Quince et al., 2011; Soergel et al., 2012). Second, because sequencing depth varied across the sample sets (Supplementary Table

S1), it is difficult to directly compare the temporal dynamics of individual taxa across the data sets (particularly taxa that are relatively rare). For this reason, we have focused our

meta-analyses entirely on the overarching patterns in within- and between-sample diversity, which should be reasonably robust to differences in sequencing depth (Kuczynski et al., 2010b);

however, we also tested whether inter-biome differences were a byproduct of differences in sequencing depth. Third, we picked OTUs using a closed-reference database protocol to compare data

sets generated using different primers that target different variable regions of the 16S rRNA gene (as done in Caporaso _et al._, 2011). Although this method allowed us to directly compare

data sets using the same reference phylogeny, any sequences not matching the well-curated database were discarded from the analyses. Therefore, communities that had better representation of

taxa in the database (for example, human-associated communities) had a higher proportion of taxon assignments than, for instance, flower communities, where a larger percentage of the

sequences lack a close match to those found in the database. However, we found no relationship between the proportion of sequences that matched the reference database and patterns of

temporal variability, suggesting that frequency of matches to the reference database did not bias overall patterns. RESULTS AND DISCUSSION TEMPORAL VARIABILITY IN WITHIN-SAMPLE DIVERSITY We

used coefficients of variation calculated over each site’s time series to compare the variability in community evenness (equitability of representation of taxa), richness (number of taxa)

and phylogenetic diversity (breadth of lineages). Variability in these diversity metrics was generally consistent across sites representing the same habitat (Figure 1a). Evenness exhibited

less variability than richness or phylogenetic diversity over time. This is likely due to the exceedingly uneven nature of microbial communities (for example, Dethlefsen et al., 2008; Quince

et al., 2008), where there are few very prevalent community members and a relatively large number of rare members (a ‘long tail’ distribution). Variability in community structure (beta

diversity), measured as the median absolute deviation in weighted UniFrac distances, exhibited similar patterns as the variability in other diversity metrics (Figure 1b). Supplementary

Figure S1 additionally provides for each time series a visualization of every time point’s deviation from the mean richness. Variability in evenness, richness, phylogenetic diversity and

community structure were all highly correlated (Figure 1c), demonstrating that both phylogenetic (phylogenetic diversity and UniFrac) and taxonomic (evenness and richness) approaches to

community diversity revealed similar overarching patterns in temporal variability across sites. This was surprising because we did not necessarily expect phylogenetic metrics, such as

phylogenetic diversity, and taxonomic metrics, such as richness, to vary consistently with one another in time. This result suggests that the diversity metric used to investigate temporal

variability may not matter, allowing robust comparisons of microbial diversity measured in different ways across different studies. Furthermore, differences in temporal variability across

sites and habitats were not likely an artifact of differences in survey effort (the number of sequences per sample) or an artifact of our ability to identify taxa against the reference

database (Figure 2, Pearson’s correlation, all _P_-values >0.10). These results suggest that there are often clear differences in the temporal variability in both within- and

between-sample diversity within bacterial communities from different environment types. Overall, soil and brewery wastewater treatment communities were consistently less variable than other

community types (Figure 1). Though we cannot know _a priori_ the drivers governing the observed rates of change, we can generate reasonable hypotheses based on knowledge of the biology and

ecology of a microbial habitat. For example, we may expect a relatively low degree of variability in soil communities for two reasons. First, soil environmental conditions may simply be less

variable at the timescale of the included studies (6 months) than some of the other habitats considered in this study. Second, soil communities contain a large proportion of dormant

organisms (Lennon and Jones, 2011) and because our sequencing approach could not discriminate between active and inactive members, the communities may appear to change relatively little over

the given time scale. Soils also have high spatial heterogeneity, which can mask changes in local communities with time because of high community variability across micro-sites. However,

brewery wastewater treatment communities may vary minimally for different reasons. Wastewater treatment facilities have been engineered to perform a function (for example, nutrient removal

Curtis and Sloan, 2006; Martín et al., 2006; Harris et al., 2012). Thus, a wastewater treatment system may represent an environmental filter for microorganisms that survive in wastewater

treatment processes while system performance is maintained. However, operating conditions, such as feeding rate, and other environmental variables (including temperature) change over time in

wastewater treatment facilities, as was true for the environmental conditions in the system studied here (Werner et al., 2011). Therefore, these communities may have low variability simply

because of an environmental filter, rather than because of dormancy or invariant environmental conditions. Stream bacterioplankton communities, the infant gut community, flower communities

and human palm communities were highly variable in diversity (Figure 1). Again, we can generate hypotheses as to the drivers of this variability based on the knowledge of the microbial

habitat. Streams in the sub-alpine region studied here are flow-through systems with pronounced physicochemical instability. They experience frequent and rapid shifts in environmental

conditions as well as shifts in bacterial inputs into the stream channel (from sediment, soil and biofilms) that are likely driven by pulse flashes in hydrological conditions (Portillo et

al., 2012). Human palm sites were previously noted to be highly variable (Fierer et al., 2008; Caporaso et al., 2011), and this was attributed to frequent exposure to bacterial inocula from

a diverse array of touched surfaces and a high degree of temporal heterogeneity in environmental conditions (likely driven, in part, by the disturbances associated with washing events). In

this way, palm and stream communities both experience environmental conditions that can vary considerably over time. The time series that represent cases of microbial primary succession, the

infant gut and flower surfaces, also exhibited a high degree of temporal variability in diversity. In both cases, there was microbial colonization of a sterile (or nearly sterile) habitat

and rapid replacement of members through time as the environment is altered (Fierer et al., 2010). Thus, just as primary succession in plant communities can often lead to rapid changes in

diversity and community composition over time, we would also expect a high degree of variability in diversity for microbial communities undergoing succession. PREDICTABILITY IN MICROBIAL

COMMUNITY CHANGES OVER TIME AND ACROSS TEMPORAL SCALES We next asked whether communities sampled close together in time were, on average, more similar in composition than communities sampled

further apart. Approximately 26–40% of the 76 sites had community changes that were correlated with time over the entirety of study duration (Figure 3). Specifically, there were 34

significant relationships using Bray–Curtis, 26 relationships for unweighted UniFrac and 21 relationships for weighted UniFrac out of the 76 sites. Thus, in less than half of the sites did

we find that samples collected at shorter intervals harbored communities that were consistently more similar than those collected at longer intervals. However, there was at least one site

from each biome that exhibited community changes that were significantly correlated with time (Figure 3). Overall, these results suggest that temporal variability is not necessarily

predictable solely by considering the time between sampling events. Those communities that consistently exhibited temporal dependence included air, marine, human and lake communities. The

air, marine and lake community time series were sampled over a duration that included seasonal changes, and thus temporal trends are expected for these sites (Shade et al., 2008; Bowers et

al., 2012; Gilbert et al., 2012). Further, human-associated microbial communities sampled consecutively over time have been shown to be more similar than those sampled more distantly in time

(Costello et al., 2009), which is consistent with our detection of a temporal trend for these communities. These results suggest that overall temporal signals exist for communities from

certain habitats and that, for communities from these habitats, the relationship between community structure and time is insensitive to the choice of metric used for evaluating community

structure (Figure 3). Several habitats had no or few communities that exhibited correlations between time and community structure (Figure 3). For example, depending on the metric used,

anywhere from five to none of the 25 soil time series exhibited significant temporal dependence (Figure 3). For soil communities, inter-annual changes would not be evident in this data set,

as the study durations were ⩽6 months. Thus, it may be that changes in soil communities are not correlated with time at the time scale included but that such relationships could become

evident if we had time series extending across full years or multiple years. It is also not surprising that the structure of brewery wastewater treatment communities was often not correlated

with time, as any changes in operation would not necessarily be related to time. For the flower communities, the taxonomic metric, Bray–Curtis, detected a significant temporal signal

(Figure 3a) where the phylogenetic metrics did not (Figures 3b and c), likely because closely related taxa were replacing one another (Shade et al., 2013), a change to which a phlyogenetic

metric would be less sensitive. Only one of the stream communities was correlated with time, and this correlation was evident only when using the weighted UniFrac distance metric (Figure

3c). Because environmental conditions in the sampled streams are flashy with irregular pulse distances (Portillo et al., 2012), changes in stream communities may not be correlated with time

but rather with the occurrence of these infrequent events. Plant and animal communities often (but not always) become less similar with increasing time or geographical distance, a phenomenon

known as similarity-decay (Nekola and White, 1999). To determine whether there was decay of microbial community structure over time (time-decay), we fit log-linear models to community

similarity over differences in time between sample collections (Nekola and White, 1999; Korhonen et al., 2010; Figure 4). The rate of temporal change in community structure is the slope of

the model, and this slope is a measure of community turnover. A community that is not changing over time would have a slope of zero. More negative, steeper slopes indicate a faster rate of

change than less negative slopes. In Figure 4, very gradual time-decay is evident, even for microbial communities that exhibit seasonality, such as the coastal marine and lake sites, as well

as for sites that have high temporal variability, such as the stream community. Overall, 31–40% of microbial communities exhibited significant time-decay in community similarity

(Supplementary Figure S2, _P_<0.05). Slope was not correlated with richness (all _P_>0.05 for all similarity metrics), suggesting that differences in the number of taxa detected in

individual samples did not influence outcomes. Slopes were barely negative in most cases. However, one flower (Gala 2), one soil (HI_R12) and one brewery wastewater treatment (E1) had slopes

between −0.02 and −0.03. Aside from these exceptions, the consistency of microbial time-decay suggests that community turnover is generally quite slow. Time-decay can be sensitive to a

study’s duration. Therefore, we asked at what temporal scale communities changed the most quickly for the study duration and sampling intensities of observations that were available to us.

To do this, we calculated a simple rate of change by dividing pair-wise Bray–Curtis dissimilarity by the time between observations and then partitioned observations into nine broad temporal

ranges, from 1 day to 6 years (Supplementary Figure S3), representing the breadth of the available time series. This analysis also showed a consistent trend of slower change with longer

durations of time, and additionally reveals gaps in the currently available time series, and points to the temporal resolutions that are yet unknown for certain habitats (Supplementary Table

S2). Notably, just because microbial community turnover is very gradual at long study durations does not mean that there is no change occurring in these communities. Rather, it suggests

that there are not drastic or new changes occurring, such as the addition of new or phylogenetically distinct taxa. Also, it is possible that some of the communities may be changing around a

‘baseline’ of normal variability, following the conceptual model of ball and urn for alternative stable states (Beisner et al., 2003). STRS STRs describe the accumulation of richness in a

community, over increasingly longer durations (for example, Preston, 1960). The exponent of the STR provides an indication of the rate at which new taxa are observed in a community over

time; the higher the exponent the more new taxa are introduced over time. Depending on the scale of the study and the community of interest, STRs can be explained by sampling, ecological or

evolutionary effects (Preston, 1960; White et al., 2006, 2010). Over short-time periods, incomplete sampling effort (duration or intensity) often drives STRs, but evolutionary processes such

as speciation and extinction become more important for STRs over longer-time periods. Between these extremes, ecological processes such as meta-community dynamics are important for STR,

including dispersal of transient tourists into a regional species pool (Magurran and Henderson, 2003). Though the STR spatial equivalent, the species–area relationship, remains actively

investigated in microbial ecology (for example, Horner-Devine et al., 2004; Fierer and Jackson, 2006; Green and Bohannan, 2006; Martiny et al., 2006; Woodcock et al., 2006; Bell, 2010), STRs

are less often documented for microorganisms. However, STRs have been reported for communities from diverse environments, such as on leaf surfaces (Redford and Fierer, 2009), in streams

(Portillo et al., 2012) and in bioreactors (Van Der Gast et al., 2008), hinting that STRs may generally apply to microbial communities. We found that all communities had significant STRs

(_P_<0.05, Figure 5a). Microbial STRs were not related to study duration or sequencing depth (Pearson’s correlation _P_=0.81 and 0.36, respectively), suggesting limited, if any, influence

of sampling. However, our analysis does not distinguish stochastic processes (for example, random presence and absence of taxa) from deterministic properties in driving the STR, and both of

these likely contribute. There was a very consistent taxa–time relationship across microbial communities, ranging from 0.24 to 0.61. Communities had comparable STR exponents within biomes,

again highlighting the within-biome consistency. Furthermore, differences in the STR exponents across biomes could be explained by known ecological attributes of the communities. For

example, the air and stream communities, with high flow-through and heterogeneity, had high STR. The infant gut and flower primary succession communities also had high STR. The soil

communities had the lowest STR exponents, demonstrating that despite their high levels of diversity (for example, Elshahed et al., 2008), the taxa present at a given location do not change

appreciably over time (a pattern also noted in Fierer and Jackson, 2006). These results are consistent with the general trends in diversity over time as seen in Figure 1 and suggest that

fluctuating abiotic conditions drive higher microbial STRs. These environmental fluctuations potentially allow for time series to effectively encompass a wider range of microbial niches

(Shurin, 2007). We next asked whether STRs could be distinguished at the phylum level. For each community, we calculated STRs for common phyla observed within each habitat (Actinobacteria,

Bacteroidetes, Firmicutes, Proteobacteria and Verrucomicrobia), at each site. We found a range of STRs at the phylum-level, but the exponents had comparable means across phyla (Figure 5b).

This suggests that microbial phyla do not have inherently different rates of replacement but that the local ecology drives the observed STR within a community. One exception to this may be

the Firmicutes, which had a slightly higher mean STR and wider upper range than the other phyla. We speculate that this pattern may be a product of many taxa affiliated with Firmicutes being

capable of sporulation (Onyenwoke et al., 2004) and may either have higher rates of dispersal into communities or are able to persist in communities in very low abundances when conditions

are unfavorable and bloom when conditions become favorable. However, taxa affiliated with other phyla are also capable of persisting in dormant states, and more evidence is needed to address

this hypothesis. It can be difficult to directly compare STR patterns of microbial communities with communities of larger organisms. First, most microbes probably have generation times

shorter than those of larger organisms, and the microbial time series investigated here likely included far more microbial generations than comparable study durations of plant and animal

taxa. In addition, species definitions as applied to microbes are distinct from how plant and animal species are typically defined (Stackebrandt et al., 2002; Gevers et al., 2005). Finally,

microbial communities are often more diverse than communities of larger organisms, with individual samples harboring hundreds to thousands of taxa as compared with the tens of taxa reported

in White et al., (2006). However, despite these caveats, the range of STRs for microbial communities was the same as that reported in White et al., (2006) meta-analysis of STR for larger

organisms (Figures 5a and c). On average, STR exponents are higher for microorganisms than for larger organisms, and the exponents were less variable within communities. Though White et al.

(2006) found a relationship between STRs and richness in their meta-analysis, we found no such relationship for microbial communities (_P_=0.19), suggesting that microbial community STRs are

better explained by changes in environmental conditions than by local richness. However, the slightly higher average STR for microbial communities over macrobial communities may be due to

the generally higher richness of microbial communities. Random changes in the occurrences of these community members may partly be driven by the stochastic process of drift (Vellend, 2010),

which could also contribute to the STR. Nonetheless, the overall consistency in STRs across microbial and macrobial communities is striking, suggesting that this may be an ecological pattern

that is ubiquitous across scales, phyla and habitats. CONCLUSIONS AND FUTURE DIRECTIONS From this meta-analysis, we gain a preliminary understanding of how temporal patterns in microbial

communities compare with each other and with those of communities of larger organisms. There was consistent variability in diversity and STRs across communities from similar habitats. This

consistency can be leveraged when making logistical decisions about sampling regimes and for understanding baseline levels of temporal variability. Within a habitat, these temporal patterns

may represent the equilibrium from which disturbance events may alter microbial dynamics. This information is important for identifying when a microbial community is experiencing a

disturbance, anticipating how quickly a community may recover from such events, and determining if and when a community has recovered post-disturbance (Allison and Martiny, 2008; Shade et

al., 2012a; Shade and Peter et al., 2013). A challenge in conducting this meta-analysis was the availability of directly comparable microbial time series collected across habitats. The same

protocols and standards of quality across data sets were necessary for this undertaking, and though there are 76 microbial communities that span 8 distinct microbial biomes, there are

obviously many habitats that were not represented in the meta-analysis simply because data were not available. Further, some of the available time series were limited in duration or sampling

intensity, and therefore, it cannot be determined whether temporal patterns were not discovered because they truly do not exist or because of a limitation in the sampling effort. For

example, we may observe seasonality in soil microbial communities sampled over 6 years instead of over 6 months. With the increased availability of high-throughput sequencing, the number of

higher-resolution, longer-term time series will only increase. For example, the Earth Microbiome Project is leading a concerted effort to collect and curate high-quality microbial community

sequencing data and corresponding contextual data from diverse environments (Gilbert et al., 2010). Placing microbial community dynamics within the rich context of environmental dynamics

will provide insight into the key drivers of those communities, help to explain discrepancies in patterns across communities from similar habitats, and allow ecologists to begin predicting

microbial dynamics. Efforts like these will help to build theory for microbial ecology, advancing beyond system-specific observations (Prosser et al., 2007). Additional discussion about

challenges with sampling microbial communities in time (including defining a microbial community, sampling the same community longitudinally and accounting for micro-scale spatial

heterogeneity) is available as a Supplementary Discussion. This meta-analysis highlights that microbial communities, like plant and animal communities, are variable with time; that microbial

temporal dynamics are dependent on the habitat type in question; and, furthermore, that microbial temporal dynamics are often predictable. Perhaps more importantly, this work demonstrates

the utility of time course analyses in microbial ecology. Likewise, this meta-analysis highlights that microbial communities represent a useful system for studying temporal dynamics in

communities, dynamics that would be very difficult to explore in plant and animal communities where generation times are often far longer. For example, it is often easier to execute

disturbance experiments with microbial communities than with plant and animal communities, and microbial responses can be detected over relatively short-time periods (for example, Dethlefsen

et al., 2008; Shade et al., 2012b). These kinds of directed, _in situ_ experiments with microbial communities may provide key empirical validation (or invalidation) of theoretical

paradigms. Just as comparing the spatial patterns exhibited by microorganisms versus larger organisms has yielded interesting findings that have allowed us test paradigms in biogeography

(for example, Horner-Devine et al., 2004; Fuhrman et al., 2008; King et al., 2010), paradigms almost wholly derived from research on plant and animal communities, we can use microbial

communities to build a more comprehensive understanding of time–biodiversity relationships. The continued integration of microorganisms into the broader field of ecology will clearly be

advantageous for both ‘macro’-bial and microbial ecologists, providing rich insight into the common forces that shape patterns of distribution and diversity for organisms of all sizes.

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ACKNOWLEDGEMENTS This work would not have been possible without the generosity and collaborative spirit of the many primary authors of the data sets included in the meta-analysis. AS is a

Gordon and Betty Moore Foundation Fellow of the Life Sciences Research Foundation. We thank Petr Keil for insightful discussions. We thank Jack Gilbert for the Western English Channel

dataset, and Jack Gilbert and Trina McMahon for the lakes dataset. NF was supported by funding from the National Science Foundation and the US Department of Agriculture. RK was supported, in

part, by the Howard Hughes Medical Institute and the National Institutes of Health. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Molecular, Cellular and Developmental

Biology, Yale University, New Haven, CT, USA Ashley Shade & Jo Handelsman * Department of Computer Science, Northern Arizona University, Flagstaff, AZ, USA J Gregory Caporaso * Argonne

National Laboratory, Argonne, IL, USA J Gregory Caporaso * Department of Chemistry and Biochemistry and Biofrontiers Institute, University of Colorado, Boulder, CO, USA Rob Knight * Howard

Hughes Medical Institute, Boulder, CO, USA Rob Knight * Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA Noah Fierer * Department of

Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA Noah Fierer Authors * Ashley Shade View author publications You can also search for this author inPubMed Google

Scholar * J Gregory Caporaso View author publications You can also search for this author inPubMed Google Scholar * Jo Handelsman View author publications You can also search for this author

inPubMed Google Scholar * Rob Knight View author publications You can also search for this author inPubMed Google Scholar * Noah Fierer View author publications You can also search for this

author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to Noah Fierer. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no conflict of interest. ADDITIONAL

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species–time relationship * beta diversity * 16S rRNA * turnover * high-throughput sequencing