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How, when, and where relic DNA biases estimates of microbial diversity

Extracellular or “relic” DNA is one of the largest pools of nucleic acids in the mbiosphere 1,2. Relic DNA can influence a number of important ecological and evolutionary processes, but it may also bias estimates of microbial abundance and diversity, which has implications for understanding environmental, engineered, and host-associated ecosystems. We developed models capturing the fundamental processes that regulate the size and composition of the relic DNA pools to identify scenarios leading to biased estimates of biodiversity. Our models predict that bias increases with relic DNA pool size, but only when the species abundance distributions (SAD) of relic and intact DNA are distinct from one another. We evaluated our model predictions by quantifying relic DNA and assessing its contribution to bacterial diversity using 16S rRNA gene sequences collected from different ecosystem types, including soil, sediment, water, and the mammalian gut. On average, relic DNA made up 33 % of the total bacterial DNA pool, but exceeded 80 % in some samples. Despite its abundance, relic DNA had no effect on estimates of taxonomic and phylogenetic diversity, even in ecosystems where processes such as the physical protection of relic DNA are common and predicted by our models to generate bias. Rather, our findings are consistent with the expectation that relic DNA sequences degrade in proportion to their abundance and therefore may contribute minimally to estimates of microbial diversity.


48
The processes that regulate relic DNA dynamics vary among ecosystems. In well-mixed microbial habitats, the residence time of relic DNA can be short owing to rapid rates of 50 hydrolysis, oxidation, and UV-mediated damage 2 . For example, in surface waters of freshwater and marine environments, the extracellular DNA pool turns over in less than one day 10,11 while 52 plasmid DNA begins to degrade in just minutes 12 . As a result, the size distribution of relic DNA is skewed towards small fragments ranging between 100 and 500 base pairs 13, 14 . In more 54 structured microbial habitats, other factors contribute to the size and turnover of the relic DNA pool. For example, binding with inorganic and organic substances in soils and sediments reduces 56 the rate of relic DNA degradation 9 . Likewise, biofilms, aggregates, and outer membrane vesicles can protect relic DNA from hydrolytic enzymes 8,15,16 . Alternatively, the heterogeneous 58 distribution of microorganisms in structured habitats creates "hot spots" of metabolic activity 17 that may increase relic DNA turnover. Collectively, these processes may help explain the 60 accumulation of relic DNA in a range of ecosystems including ocean sediments 1 , the built environment 18 , and the mammalian gastrointestinal tract 19 . 62 To date, the documented effects of relic DNA on estimates of diversity are idiosyncratic.
Even in samples with large amounts of relic DNA, bias can be non-existent or substantial, and 64 can lead to the overestimation or underestimation of diversity 20,21 . Such observations reflect the need for a more mechanistic understanding of relic DNA dynamics so that microbial 66 communities from different ecosystems can be consistently and accurately characterized. We address this issue by developing a theoretical framework that considers the processes regulating 68 the size and turnover of the relic DNA pool. We emphasize that the direction and magnitude of bias is influenced by sampling from a joint species abundance distribution (SAD) consisting of 70 sequences from the relic and intact DNA pools. We evaluated our models by quantifying the contribution of relic DNA to the abundance and diversity of bacterial communities in ecosystem 72 types that are known to have contrasting relic DNA dynamics.

RESULTS
We developed an interrelated set of models to identify conditions where relic DNA leads 76 to biased estimates of microbial diversity. We began with a conceptual model representing the dynamics of intact and relic DNA (Fig. 1a). The size and composition of the intact DNA pool 78 reflects sequences contained in viable bacteria belonging to different species, which are influenced by births, deaths, and immigration. The size and composition of the relic DNA pool is 80 4 determined by the input of sequences associated with the death of bacteria from the intact DNA pool and losses associated with the degradation of relic DNA sequences. With this framework 82 established, we developed a sampling model to explore how diversity estimates are affected when draws come from a joint distribution of sequences derived from the intact and relic pools 84 (see Methods). We found that a sufficiently large relic DNA pool is required, but not sufficient to create bias in estimates of species richness, the number of taxa in a sample (Fig. 1b). Critically, 86 there must also be differences between the species abundance distributions (SAD) of the intact and relic DNA pools for bias to arise. When the relic DNA pool has a more even SAD, sampling 88 from the total pool (intact + relic) leads to overestimation of true richness (Fig. 1b). Conversely, when the relic DNA pool has a less even SAD, sampling from the total pool leads to 90 underestimation of true richness (Fig. 1b). Last, we developed a process-based model to identify conditions that lead to relic DNA bias. We determined that relic DNA pool size reaches a stable 92 equilibrium when R = m I / d, where R is the size (number of sequences) of the relic DNA pool, m is the per capita mortality rate of bacteria in the intact DNA pool, I is the size (number of 94 sequences) of the intact DNA pool, and d is the per capita degradation rate of the relic DNA sequences (see Methods). From this, it can be shown that R increases with the residence time 96 (τ = R / d) of the relic DNA pool. We also used the process-based model to explore different scenarios that influence the SAD of the relic DNA pool. First, we assumed that the rate at which 98 relic DNA sequences degrade is equivalent among species. Under this neutral expectation, the shapes of relic SAD and intact SAD are nearly identical. No bias arises under these conditions, 100 regardless of the relic DNA pool size (Fig. 1c). Next, we simulated protection by reducing the degradation rates of relic DNA sequences belonging to randomly selected species (see Methods). 102 This created a less even relic SAD, resulting in the underestimation of richness in the total DNA 5 pool (Fig. 1c). Finally, we considered the scenario where there are "hot spots" of relic DNA 104 degradation. When simulating accelerated degradation rates for abundant relic DNA sequences, there was a more even relic SAD that resulted in the overestimation of richness in the total DNA 106 pool (Fig. 1c).

108
Variation in relic DNA pool size -We collected samples from different ecosystem types (soil, sediment, water, and mammalian guts) that are known to vary in relic DNA turnover rates 13,22 . 110 Using quantitative PCR with 16S rRNA primers, we quantified intact DNA after treating an aliquot of sample with DNase to remove relic DNA. In addition, we quantified total DNA (intact 112 + relic) pool size from a control aliquot of sample that was not treated with DNase (see Methods and Fig. S2). Consistent with previous reports 20,21,23 , relic DNA accounted for a substantial but 114 variable portion of the total bacterial DNA pool. The proportion of relic DNA was normally distributed (mean ± sd: 0.33 ± 0.218) and ranged from 0 to 0.83 across 34 samples obtained from 116 different ecosystem types (Fig. 2). Even though host and environmental features associated with different habitats are thought to influence relic DNA dynamics, there was only a marginally 118 significant effect of ecosystem type on the proportion of relic DNA (one-way ANOVA, F 3, 30 = 2.43, P = 0.084) 2,20 which likely reflected the contrast between soil and water samples (Tukey's 120 HSD, adjusted P = 0.058). Nevertheless, the large average pool size and range in relic DNA across samples provided us with the opportunity to explore features of our model, specifically 122 the magnitude and direction of bias that relic DNA should have on estimates of microbial diversity. 124 from the intact and total DNA pools to test whether or not relic DNA biases estimates of withinsample (alpha) bacterial diversity. Despite accounting for a substantial portion of the total DNA 128 ( Fig. 2), relic DNA had no effect on estimates of richness, evenness, or phylogenetic diversity (PD) (Table S1). We expressed each of these alpha-diversity metrics on a per-sample basis as a 130 ratio (total / intact), where values > 1 represent overestimation bias and values < 1 represent underestimation bias. Relic DNA had no effect on the diversity ratios based on the observation 132 that the 95 % confidence intervals overlapped with 1.0. Furthermore, the 95 % confidence intervals of the diversity ratios overlapped across ecosystem types, indicating that the 134 contribution of relic DNA to all measures of diversity was generally low irrespective of the microbial habitat. Finally, using indicator variable multiple regression, we evaluated whether the 136 magnitude of bias increased with relic DNA pool size, a predicted outcome from some of our simulations (Fig. 1b, c). For all diversity ratios (richness, evenness, and PD), the slopes were not 138 different from zero (P ≥ 0.65), the intercepts were not different from 1.0 (P ≥ 0.65), and the estimates were not affected by ecosystem type (P ≥ 0.26) (Fig. S5). Moreover, only a very small 140 amount of variation (R 2 ≤ 0.01) was explained by relic DNA and ecosystem type in the regression models. 142 Contribution of relic DNA to community composition -Based on 16S rRNA gene 144 sequencing of the intact and total DNA pools, relic DNA had no effect on the compositional (beta) diversity of bacterial communities within or across ecosystem types. First, the intact and 146 total DNA pools were significantly and very highly correlated with one another when we performed a taxonomically based (Bray-Curtis distances) Mantel test (P = 0.001, r = 0.959) and 7 a phylogenetically based (UniFrac distances) Mantel text (P = 0.001, r = 0.996). Second, we tested for the effect of relic DNA on bacterial community composition for the intact and total 150 DNA pools using a modified beta-dispersion metric 24 . Specifically, we calculated centroid distance ratios, which directly compared the dispersion between pairs of DNase-treated and 152 DNase-control subsamples (see Methods and Fig. S7). With this approach, if the centroid distance ratio was > 1, we concluded that relic DNA inflated beta-diversity; if the distance ratio 154 was < 1, we concluded that relic DNA homogenized beta-diversity. We found that relic DNA had no effect on the centroid distance ratios based on the observation that the 95 % confidence 156 intervals overlapped with 1.0 (Figs. 4, S8). Furthermore, the 95 % confidence intervals for the centroid distance ratios overlapped across ecosystem types, indicating that the contribution of 158 relic DNA to taxonomic and phylogenetic beta-diversity was low irrespective of the microbial habitat. Last, we used indicator variable multiple regression to test the prediction that bias in 160 estimates of community composition would increase with increasing relic DNA pool size (see Fig. S1). The proportion of relic DNA in a sample had no effect on the slopes for centroid 162 distance ratios regardless of whether they were calculated using taxonomic (P = 0.63) or phylogenetic (P = 0.59) distance matrices. Moreover, the intercepts for these regression 164 relationships were not different from 1.0 (P ≥ 0.74) and the amount of explained variation was low (R 2 ≤ 0.09) suggesting that the overall effect of relic DNA on beta diversity was negligible 166 (Fig. S8). The parameters estimates from the regression analyses were not affected by ecosystems type (P ≥ 0.29). 168

DISCUSSION 170
Rarely, if ever, are biological communities completely censused. As a result, estimates of diversity are often based on incomplete sampling, which introduces uncertainty and potential 172 bias 25 . For example, microbiologists using cultivation-independent approaches commonly estimate the diversity of a community using hundreds of thousands of sequences from samples 174 that contain in excess of a billion individuals. Diversity estimation of microbial communities is further complicated by the accumulation and persistence of relic DNA, a pool of sequences that 176 may not accurately reflect the composition of viable microorganisms. We developed a set of models that capture the fundamental processes regulating relic DNA dynamics along with the 178 effects of sampling from a joint species abundance distribution (SAD) that contain sequences from both the relic and intact DNA pools. Our models reveal that two criteria are required in 180 order for relic DNA bias to emerge. First, the relic DNA pool must be large enough so that it diminishes the probability of sampling sequences from the intact DNA pool. Second, the relic 182 SAD must be distinct from the intact SAD. If these conditions are not satisfied, relic DNA has minimal effect on estimates of biodiversity. We then explored the model's expectations by 184 analyzing the intact and total (relic + intact) DNA in ecosystems where relic DNA dynamics are known to vary. Despite making up a large portion of the total DNA, we found that relic DNA 186 had minimal to no effect on estimates taxonomic and phylogenetic diversity, consistent with model scenarios where relic DNA degradation rates are equivalent among species. 188 Despite sampling a range of habitats with attributes that affect relic DNA turnover, our data suggest that estimates of taxonomic and phylogenetic diversity were unbiased by relic DNA 190 Our simulations identify other ecological scenarios where relic DNA bias may arise. It 204 has been hypothesized that relic DNA can be protected from degradation inside of aggregates, biofilms, or other complexes that reduce contact between nucleic acids and extracellular 206 enzymes. Importantly, protection can only increase bias if it alters relic DNA degradation in a species-specific manner. While most studies have emphasized the potential for protection to 208 overestimate diversity, our model predicts the opposite (Fig. 1b). Protection created a less even relic SAD that resulted in the increased dominance of relic sequences associated with a few 210 species. Assuming a large relic DNA pool, preferential sampling of these protected sequences will lead to the underestimation or richness, which has been observed in some instances 20 . In 212 contrast, we identified at least one ecological scenario where relic DNA creates overestimation bias. Microscale heterogeneity in structured habitats can lead to non-uniform distributions of 214 microorganisms and their metabolic activities. Our simulations indicate that the resulting "hot spots" can degrade relic DNA sequences in a density-dependent matter resulting in a more even 216 relic SAD. Although not significant, sediment samples trended towards positive bias for richness and PD (Fig. 3), which could potentially reflect "hot spots" of relic DNA degradation. Inflation 218 bias can also arise when species in the regional pool disperse into habitats for which they are poorly adapted. When these immigrants die, they can enrich the relic DNA pool with sequences 220 that are dissimilar to sequences found in the local community. A similar effect could arise when dead bacteria are transported across ecosystem boundaries, a phenomenon that occurs, for 222 example, when marine snow is exported from surface waters to marine sediments 26  which should lead to biased estimates of microbial diversity, but sampling from a range of ecosystems suggests that such conditions are not prevalent in nature. 234 Our understanding of the microbial biosphere has been transformed by the development and application of molecular-based cultivation-independent techniques. The ability to rapidly 236 obtain millions of gene sequences and transcripts from a range of environments has yielded valuable insight into the processes that regulate community assembly and function 28,29 , and has 238 also paved the way for the discovery of new metabolisms 30 , tests for unifying patterns of culture-independent techniques, which include inefficient nucleic-acid extraction methods 32 and "universal" primers that over-represent some taxonomic groups while overlooking others 33 . 242 Sequencing of relic DNA is another important concern, which can potentially lead to the overestimation or underestimation of microbial diversity. However, this bias requires the 244 decoupling of processes that regulate the compositional turnover of the relic and intact DNA pools. While some recent evidence suggests this can arise 20 , our findings suggest that at least in 246 some ecosystems, relic DNA appears to contribute minimally to the characterization of microbial community structure. 248

METHODS 250
Sampling Model -We used a set of sampling-based simulations to explore the effects of 252 mixing intact and relic DNA on estimates of diversity. For each simulation, we defined a regional species pool consisting of 10,000 taxa with a lognormal abundance distribution 34 . The 254 intact community consisted of 1,000,000 individuals sampled from this regional pool. We then combined this intact community with a relic community at proportions ranging from 0.01 to 256 0.96. We altered the evenness of the regional pool from which the relic community was sampled by changing the scale parameter of the lognormal distribution. To decrease the evenness of the 258 relic DNA pool, we increased the scale parameter from 0.98 to 1.8. To increase the evenness of the relic DNA pool, we decreased the parameter from 0.98 to 0.25. After mixing the intact and 260 relic communities at the defined proportions, we rarified the total community to 10,000 observations and calculated richness and Bray-Curtis distances to estimate compositional 262 differences between the intact and total DNA pools. To estimate contribution of relic DNA to the `vegan` package as well as custom functions. 266 Process-Based Model -We developed a set of stochastic simulations to explore how the 268 processes regulating relic DNA dynamics can give rise to bias in the estimation of microbial diversity. For each simulation, we defined a regional species pool consisting of 4,000 taxa with a 270 lognormal abundance distribution 34 . We initiated an intact community by randomly sampling 20,000 individuals from the regional species pool. Subsequent dynamics were controlled by the 272 processes depicted in our conceptual model (Fig. 1a). Specifically, we simulated immigration by sampling j individuals from the regional species pool and adding them to the intact community. We simulated birth by randomly selecting I × r individuals from the intact community and adding them to the intact community again, where I is the size of the intact community and r is 276 the per capita birth rate. We simulated death by selecting (I × m) + j individuals from the intact community and moving them to the relic community (R), where m is the per capita mortality 278 rate. We simulated degradation by selecting (R × d) + j individuals from the relic community and removing them, where d is the per capita decay rate of relic DNA. 280 We simulated three scenarios with the process-based model. First, we considered a neutral scenario. At each time step, we simulated degradation by randomly selecting relic DNA 282 sequences and removing them. Second, we considered the scenario where some relic DNA sequences have lower rates of degradation owing to protection. Each species was randomly 284 assigned a degradation susceptibility probability from a beta distribution. The beta distribution is a continuous probability distribution bound by 0 and 1 with two shape parameters. We chose 286 13 shape parameters (α = 0.7, β = 0.7) to provide a wide range in susceptibility probability but also have increased probabilities of low (near 0) and high (near 1) susceptibility, which created a 288 wide U-shaped distribution. Susceptibility probabilities were randomly assigned to species for each model iteration and were used to weight the probability of selecting relic DNA sequences 290 for degradation at each time step. Last, we considered the scenario where there are "hot spots" of relic DNA degradation, reflecting the clumped distribution of microbial taxa and their associated 292 metabolic activities. At each time step, we calculated density-dependent selection probabilities by weighting the probability of selecting an individual from the relic community by its species 294 abundance. Therefore, relic DNA sequencing belonging to more abundant species would be more likely to be selected for degradation. For each of the three scenarios, we ran each 296 simulation for 10,000 time-steps. We used a constant immigration rate (j = 2,000) to maintain intact community diversity, and used equal and constant birth and mortality rates (b and m = 0.1) 298 to maintain intact community size. Likewise, we accounted for immigration during death and degradation to prevent uncontrolled growth. For each set of simulations, we used a range of 300 decay rates (d), which yielded relic DNA proportions between 0.05 and 0.95 according to the following equation: d = m/p -m, where p is the target proportion and m is the fixed mortality 302 rate (see Supplement for derivation). At the end of each simulation, we determined the effect of relic DNA on diversity estimates by comparing the total community (intact + relic) with the 304 intact community. All simulations were performed in the R statistic environment.

306
Sample collection and DNA pools -We collected samples from a diverse set of environmental and host-associated ecosystems. First, we sampled sediments and surface water from lakes near 308 the Michigan State University, W.K. Kellogg Biological Station (KBS) in Hickory Corners, from cows, dogs, horses, rabbits, and humans. In each of these ecosystem types, we obtained 312 samples from 6 -8 independent sites. In the laboratory, we applied a DNase treatment to each sample to remove relic DNA. The procedure is based on methods that have previously been used 314 to quantify relic DNA in marine sediments 37,38 , host tissue 37,38 , and drinking-water biofilms 37,38 . The DNase-based method has proven to be effective not only for removing extracellular 316 DNA, but also DNA that is contained inside of dead cells 37,38 while not compromising the integrity of living cells 37,38 . We determined that the DNase method is efficient (98%) at 318 removing relic DNA that was experimentally added to an environmental sample (see Supplementary Methods; Fig. S4). Thus, we refer to the DNA remaining in a subsample 320 following enzymatic treatment as "intact DNA" and assume it is derived from viable cells. "Total DNA" refers to the DNA in subsample that was not treated with DNase (negative control), which 322 reflects the sum of intact DNA and relic DNA.
For aquatic samples, we filtered 250 mL lake water through a 47 mm 0.2 µm Supor PES 324 membrane filter using 10 mm Hg vacuum. We cut the filter in half and randomly assigned one half of the filter to a DNase treatment and used the other half as the control. We then rolled up 326 each piece of filter using sterile forceps and inserted it into a 2 mL centrifuge tube containing 1.5 mL of pH 7.3 phosphate buffered saline (PBS). After vortexing at room temperature for 5 min, 328 we removed the filter from the tube and centrifuged for 5 min at 10,000 x g. We then discarded the supernatant and resuspended the pellet in 375 µL of PBS, which was transferred to a 2 mL 330 centrifuge tube. For non-aqueous samples (soil, sediments, and feces), we directly added 0.25 g of material to a 2 mL centrifuge tube. At this stage in the procedure, aquatic and non-aquatic 332 samples were identically processed. For each sample in its 2 mL centrifuge tube, we added sample, we measured pH using a micro pH probe (Orion 9110DJWP, Thermo Scientific) and adjusted to 7.3 -7.7, which is in the optimum range for DNase. We then incubated the samples 342 horizontally on a shaker table at 37 ºC for 60 min. Following this, we transferred each sample to a 15 mL Falcon tube containing 1 mL of 1X hexadecyltrimethylammonium bromide (CTAB) 344 buffer, which consisted of one part solution 1 (1 g CTAB + 0.58 g NaCl in 10 mL of nanopoure water) and one part solution 2 (0.58 g NaCl + 0.82 K 2 HPO 4 + 0.04 KH 2 PO 4 in 10 mL of 346 nanopoure water). We then added 25 µL of 0.5 M EDTA to each tube and vortexed. We stopped the DNase reaction by incubating the tubes at 75 ºC for 10 min. We began DNA extraction by 348 adding 1 mL of phenol:chloroform:isoamylalcohol (25:24:1) to the 2 mL tube, which was then vortexed for 10 min. This was followed by centrifuging at 7,100 x g for 10 min. We transferred 350 the top aqueous layer to a 15 mL Falcon tube and combined it with an equal volume of chloroform:isoamylalcohol (24:1) and then vortexed. After centrifuging the Falcon tubes at 352 7,100 x g for 5 min, we transferred 400 µL of the top aqueous layer to a new 2 mL centrifuge tube. For clean up, we used the MO BIO PowerLyzer PowerSoil DNA Isolation Kit starting from 354 Step 9. At Step 21 of the DNA Isolation Kit, we eluted in 50 µL of Solution C6 and centrifuged for 1 min at 10,000 x g. We stored extracted DNA at -20 °C for short-term storage or at -80 °C 356 for long-term storage.

358
Contribution of relic DNA to bacterial abundance -We used 16S rRNA gene copy numbers and Eub518 (reverse) primers 40 . PCR assays were performed with an Eppendorf Mastercycler Realplex 2 system using previously reported thermal cycle conditions 40 . We generated qPCR 366 standards from bacterial genomic DNA (Micrococcus sp.) using the TOPO TA Cloning Kit (Invitrogen). We extracted plasmids from transformed cells 41 , and used the M13 forward and 368 reverse primers to generate PCR products. The PCR products were quantified and used to generate a standard curve capturing a range of 10 2 -10 7 gene copies/µL. We used a noiseband 370 threshold for quantification. The coefficients of determination (r 2 ) for our assays ranged from 0.96 and 0.99, while amplification efficiencies fell between 0.93 and 0.99. Based on melting 372 curve analyses, we found no evidence for primer dimers. All unknown samples, no template controls, and standards were run in triplicate on every plate. The mean coefficient of variation 374 (standard deviation / mean) for our 16S rRNA qPCR assay was 0.16.

Contribution of relic DNA to bacterial diversity
Community sequencing -We estimated the contribution of relic DNA to bacterial diversity 378 using high-throughput sequencing of the 16S rRNA gene. Specifically, we amplified the V4 region of the 16S rRNA gene from the intact and total DNA pools of each sample using 380 barcoded primers (515F and 806R) designed to work with the Illumina MiSeq platform 42 . We cleaned the sequence libraries using the AMPure XP purification kit, quantified the resulting 382 products using the QuantIt PicoGreen kit (Invitrogen), and pooled libraries at equal molar ratios (final concentration: 20 ng per library). We then sequenced the pooled libraries with the Illumina 384 MiSeq platform using paired end reads (Illumina Reagent Kit v2, 500 reaction kit) at the Indiana University Center for Genomics and Bioinformatics Sequencing Facility. Paired-end raw 16S 386 rRNA sequence reads were assembled into contigs using the Needleman algorithm 43 . We obtained a total of 12,916,632 16S rRNA sequences. After quality trimming with a moving 388 average quality score (window 50 bp, minimum quality score 35), we aligned the sequences to the Silva Database (version 123) using the Needleman algorithm. Chimeric sequences were 390 removed using the UCHIME algorithm 44 . After this filtering, there was an average (± SEM) of 222,701 ± 9,560 sequences per site. We created operational taxonomic units (OTUs) by first 392 splitting the sequences based on taxonomic class (using the RDP taxonomy) and then binning sequences into OTUs based on 97% sequence similarity. Our depth of sequencing led to a high 394 degree of coverage across samples (minimum Good's Coverage = 0.98). For phylogenetic analysis, we picked representative sequences for each OTU by using the most abundant unique 396 sequence. We used FastTree 45 to generate a phylogenetic tree from the representative sequences using the generalized time-reversible model of nucleotide evolution. We calculated phylogenetic 398 distances using weighted UniFrac distances 46 . All initial sequence processing was completed using the software package mothur (version 1.38.1) 47 . 400 Alpha Diversity -We estimated the effects of relic DNA on richness, evenness, and 402 phylogenetic diversity for the intact and total DNA pools with a sample. To estimate the number of OTUs (richness), we used a resampling approach that subsampled each sample to an equal 404 number of sequences per sample and summed the number of OTUs that were represented 48 .
Briefly, we subsampled to 30,000 observations, resampled 999 additional times, and then 406 calculated the average richness estimates (± SEM) for each sample. To estimate the equitability in abundance among taxa in a sample (evenness), we used the same resampling approach and 408 calculated average evenness estimates (± SEM) using Simpson's Evenness index 49 . To test whether relic DNA affected the phylogenetic diversity within a sample, we subsampled 410 communities to 30,000 observations and then calculated Faith's D statistic, which sums the branch lengths for each species found in a sample from the root to the tip of the phylogenetic tree 412 50 . All estimations were performed in the R statistic environment (v 3.3.2) 35 using the vegan, ape, ade4, picante, and plyr packages, along with custom functions. 414 Beta Diversity -We estimated the effects of relic DNA on between-sample (beta) diversity by 416 comparing the taxonomic and phylogenetic diversity of bacterial communities in the intact and total DNA pools. First, we conducted a Principal Coordinates Analysis (PCoA) on log 10 -418 transformed relative abundance data to visualize the effects of relic DNA removal (via DNase treatment) on bacterial community composition within and among ecosystem types. The PCoA 420 was performed with Bray-Curtis and UniFrac distances to assess taxonomic and phylogenetic effects, respectively. In addition, we used PERMANOVA to test for differences in taxonomic 422 and phylogenetic composition based on ecosystem type for the total DNA pool. Second, we conducted a Mantel test to assess the correlation between the community resemblance matrices 424 (either Bray-Curtis or UniFrac) represented by the intact and total DNA pools. Last, we tested whether relic DNA altered beta-diversity within an ecosystem type by comparing centroid 426 distances. To calculate this metric of sample dispersion, we determined the centroid from a given ecosystem type. We then measured the Euclidean distances between the centroid and all samples (total and intact) to determine the centroid distances (see Fig. S7). 430

ACKNOWLEDGMENTS 432
We acknowledge constructive feedback from KD Webster, RZ Moger-Reischer, EK Hall, NI Wisnoski, and WR Shoemaker. We thank S. Kuenzel and B.K. Lehmkuhl for technical support.  We then sampled and mixed the intact and relic communities so that the relic contribution to 572 total community ranged from 0.01 to 0.96.c To gain mechanistic insight into how bias arises, we created a stochastic process-based model that captures features that influence relic DNA 574 dynamics, including: immigration, birth, death, degradation (a). We simulated a range of degradation rates to achieve relic DNA pool sizes with proportions ranging between 0.05 and 576 0.95. To explore how degradation alters the SAD of the relic community, we explored three scenarios. First, we simulated a neutral scenario where relic DNA sequences produced by 578 different species degrade at the same rate. Second, we simulated conditions where physical, chemical, or biological processes slow the degradation rate of relic DNA belonging to some 580 species via protection. Third, we simulated "hot spots" where there more abundant relic DNA sequences experience faster rates of relic DNA degradation, a condition that may arise in 582 structured habitats where there are patchy distributions of individuals and their metabolic products (i.e., enzymes). We ran simulations for 10,000 time steps and then sampled the intact 584 and relic communities. To quantify bias in diversity (b, c), we calculated "richness ratios" which reflect the number of species in the total DNA pool (intact + relic) divided by the number of 586 species in the intact DNA pool in a simulation. When values richness ratios = 1, relic DNA has no effect on estimates of diversity; when richness ratios > 1, relic DNA overestimates true 588 diversity; and when richness ratios < 1, relic DNA underestimates true diversity. portion of the total DNA pool, but was not affected by the ecosystem type from which the sample was collected (gut, soil, sediment, and water). Grey symbols are the observed data and 596 black symbols represent means ± 95% confidence intervals. distance metrics (Bray-Curtis and UniFrac, respectively). The centroid distance ratio was calculated on each sample within an ecosystem type and reflects the composition of the total DNA pool (intact + relic) relative to the intact DNA pool. Relic DNA had no effect on betadiversity for any of the ecosystem types sampled. Grey symbols are the observed data and black symbols represent means ± 95% confidence intervals. Modeling relic DNA dynamics. a The amount of relic DNA in a microbial environment is determined by inputs associated with the mortality of viable individuals with intact DNA and by losses associated with the degradation of relic DNA. If the diversity of sequences contained in the relic DNA pool is sufficiently different from that in the intact DNA pool, then relic DNA may bias estimates of microbial biodiversity (as indicated by different colored boxes) when sampling from the total (intact + relic) DNA pool. b We developed a sampling-based simulation model to explore the effects of mixing intact and relic DNA on estimates of diversity. We populated intact and relic communities with individuals from a lognormal species abundance distribution (SAD). We altered the diversity of the relic community by changing the scale parameter of the lognormal distribution describing the SAD. We then sampled and mixed the intact and relic communities so that the relic contribution to total community ranged from 0.01 to 0.96.c To gain mechanistic insight into how bias arises, we created a stochastic processbased model that captures features that influence relic DNA dynamics, including: immigration, birth, death, degradation (a). We simulated a range of degradation rates to achieve relic DNA pool sizes with proportions ranging between 0.05 and 0.95. To explore how degradation alters the SAD of the relic community, we explored three scenarios. First, we simulated a neutral scenario where relic DNA sequences produced by different species degrade at the same rate. Second, we simulated conditions where physical, chemical, or biological processes slow the degradation rate of relic DNA belonging to some species via protection. Third, we simulated "hot spots" where there more abundant relic DNA sequences experience faster rates of relic DNA degradation, a condition that may arise in structured habitats where there are patchy distributions of individuals and their metabolic products (i.e., enzymes). We ran simulations for 10,000 time steps and then sampled the intact and relic communities. To quantify bias in diversity (b, c), we calculated "richness ratios" which reflect the number of species in the total DNA pool (intact + relic) divided by the number of species in the intact DNA pool in a simulation. When values richness ratios = 1, relic DNA has no effect on estimates of diversity; when richness ratios > 1, relic DNA overestimates true diversity; and when richness ratios < 1, relic DNA underestimates true diversity. We quantified the amount of intact DNA in a sample after removing relic DNA with a DNase treatment. We then estimated the proportion of relic DNA as 1 -(intact DNA / total DNA), where the total DNA concentration was quantified without DNase treatment. Relic DNA constituted an appreciable portion of the total DNA pool, but was not affected by the ecosystem type from which the sample was collected (gut, soil, sediment, and water). Grey symbols are the observed data and black symbols represent means ± 95% confidence intervals. We tested for the effects of bias caused by relic DNA by calculating diversity ratios for (a) richness, (b) evenness, and (c) phylogenetic diversity. The ratios reflect the diversity of the total DNA pool (intact + relic) divided by the diversity of the intact DNA pool. Relic DNA did not bias any measures of diversity in any of the ecosystem types. Richness was calculated as the number of operational taxonomic units (97% sequence similarity of the 16S rRNA gene), evenness was calculated using Simpson's evenness index, and phylogenetic diversity was calculated using Faith's D index. Grey symbols are the observed data and black symbols represent means ± 95% confidence intervals. We tested for the effects of bias caused by relic DNA by calculating a beta-diversity ratio based on centroid distances. Centroid distances were estimated after performing Principle Coordinates Analyses (PCoA) using taxonomic (a) and phylogenetic (b) distance metrics (Bray-Curtis and UniFrac, respectively). The centroid distance ratio was calculated on each sample within an ecosystem type and reflects the composition of the total DNA pool (intact + relic) relative to the intact DNA pool. Relic DNA had no effect on beta-diversity for any of the ecosystem types sampled. Grey symbols are the observed data and black symbols represent means ± 95% confidence intervals.