Diversity and distribution of sediment bacteria across an ecological and trophic gradient.

The microbial communities of lake sediments have the potential to serve as valuable bioindicators and integrators of watershed land-use and water quality; however, the relative sensitivity of these communities to physio-chemical and geographical parameters must be demonstrated at taxonomic resolutions that are feasible by current sequencing and bioinformatic approaches. The geologically diverse and lake-rich state of Minnesota (USA) is uniquely situated to address this potential because of its variability in ecological region, lake type, and watershed land-use. In this study, we selected twenty lakes with varying physio-chemical properties across four ecological regions of Minnesota. Our objectives were to (i) evaluate the diversity and composition of the bacterial community at the sediment-water interface and (ii) determine how lake location and watershed land-use impact aqueous chemistry and influence bacterial community structure. Our 16S rRNA amplicon data from lake sediment cores, at two depth intervals, data indicate that sediment communities are more likely to cluster by ecological region rather than any individual lake properties (e.g., trophic status, total phosphorous concentration, lake depth). However, composition is tied to a given lake, wherein samples from the same core were more alike than samples collected at similar depths across lakes. Our results illustrate the diversity within lake sediment microbial communities and provide insight into relationships between taxonomy, physicochemical, and geographic properties of north temperate lakes.

Introduction

A community of microorganisms living together in a particular environment or habitat are referred to as a microbiome. In the past decade, studies into the microbiomes of human organs, plants, soils, waters, and even space station astronauts have enhanced our understanding of how microbiota protect us from pathogens, increase agricultural production, and ultimately cycle nutrients in and throughout the natural environment [1-4]. Microbiomes are connected to the biogeochemical cycling of nutrients at both local and global levels, and as a result, disturbances or variations in their community composition can result in gains or losses of functional attributes, changes in nutrient availability, and shifts in ecosystem adaptability [5-7]. Understanding the selective pressures on microbiome community composition can provide insight into an environment’s ability to support higher trophic levels and respond to anthropogenic change. Freshwater lakes are an ideal system to explore these insights as they provide a variety of regulating and cultural services, which hinge on the composition of their microbiome [8].

Despite their relatively small surface area, lakes contribute disproportionately to biogeochemical cycles, including the essential macronutrients carbon, nitrogen, and phosphorus [9-13]. Nutrients in lakes are recycled through several biotic and abiotic processes, but ultimately a significant proportion end up in sediments, where bacterial abundance and diversity typically exceeds that of the water column [14-16]. In the sediments, bacteria and archaea degrade organic matter—consuming oxygen and proceeding with anaerobic respiration processes. These respiratory processes occur along a redox gradient, eventually leading to the transformation of nitrogen, iron, and sulfur compounds. The complementary metabolisms of sediment microbiomes make sediments a global biogeochemical hotspot, one in which there has been a concerted effort to understand the environmental factors that regulate composition and function [17-20].

Chemical and physical characteristics of the lake such as salinity, pH, temperature, and nutrient concentrations select for specific bacteria, a process commonly referred to as species sorting [21]. The physicochemical characteristics of the system are partly based on the external inputs of both organic matter and nutrients, such as phosphorus and nitrogen, from the surrounding watershed [22]. Different land uses (e.g., agricultural, urban, forested, etc.) in the watershed strongly influence the amount and types of terrestrial organic matter and nutrients that enter the water, and therefore land use may subsequently affect community composition [23]. While there have been several studies that address the effects that local environmental factors have on microbiome species selection (e.g., eutrophic reservoirs, alpine lakes), few have examined the effects of the land-use of the watershed on bacterial community assembly [22-30].

In this study we selected twenty lakes with varying physio-chemical properties across four ecological regions with varying land use in Minnesota (U.S.A.) (Fig 1). We sought to (i) evaluate the diversity and spatial variation of the bacterial community at the sediment-water interface and (ii) determine how lake location and watershed land-use impact aqueous chemistry and influence bacterial community structure. We hypothesize that community composition of lake sediments will appear homogeneous across ecological regions and land use at higher taxonomic levels; however, we hypothesize increased structure by eco-region at lower taxonomic levels. To test this, we compare the alpha and beta diversity of bacteria across taxonomic scales (Phylum to Order) and ecological regions, and we highlight important regional and local factors that influence community composition.

Fig 1

Map of sampling locations.

Location of the twenty study lakes that were cored across the state of Minnesota (U.S.A.) between the summer of 2018 and 2019, shaded by ecoregion. Map was created using QGIS and data were made available by the MN Geospatial Commons (public domain) https://gisdata.mn.gov/.

Materials & methods

Site description

For this study, we selected twenty lakes within Minnesota’s Sentinel Lakes in a Changing Environment (SLICE) program. SLICE is a collaborative research initiative providing long-term data on a representative sub-sampling of Minnesota’s lakes that span the diverse geographic, land-use, and climatic gradients present in Minnesota (Fig 1). The lakes span four of the seven Environmental Protection Agency/Commission for Environmental Cooperation’s (level III) ecological regions. These regions can be characterized by their underlying geology, soils, vegetation, and land use (S1 Table). This is the first comprehensive sediment bacterial survey of these lakes.

Water sample collection & analysis

From each site we collected water profile measures for temperature, pH, conductivity, turbidity, and dissolved oxygen using a YSI XO2 multi-parameter sonde (YSI, Inc.). We also collected an integrated (0-2m) epilimnetic water sample, and a hypolimnetic (maximum lake depth–1m) water sample when thermal stratification was present. All samples were stored on ice in the field and at 4°C or -20°C in the laboratory, depending on methodology, until processed. Samples for soluble reactive phosphorus (SRP), dissolved organic carbon (DOC), and dissolved inorganic carbon (DIC) were filtered, processed, and analyzed within 36 hours of sampling using standard methods for SRP (4500-P) on a SmartChem 170 (Unity Scientific, Inc.) and DIC/DOC Method 5310-C using a Torch Combustion TOC Analyzer (Teledyne Tekmar, Inc.) [31]. Samples for total nitrogen (TN) and total phosphorus were frozen and analyzed using standard methods for TN (4500-N), and TP (4500-P). Samples for ammonia (NH3) and nitrate (NO3) were filtered and frozen prior to analysis following methods NH3 (4500-NH3) and NO3 (4500-NO3). All TP, TN, NH3, NO3 samples were analyzed within six months of sampling on a SmartChem 170 (Unity Scientific, Inc.) discrete analyzer (APHA 2012). Additionally, we filtered, froze, and analyzed samples for chlorophyll-a concentrations via fluorometry following the EPA method 445.0 [32]. We provided a complete summary of aqueous chemistry results, including sampling dates, in the S2 Table.

Sediment sample collection & DNA isolation

We collected sediment cores from July 2018 through June 2019 using a rod-driven piston corer with a 7cm diameter polycarbonate tube [33]. We determined coring locations (i.e., flat areas near the deepest basin) using publicly available bathymetric maps (https://www.dnr.state.mn.us/lakefind/index.html), avoiding steep-sided “holes” where sediment-focusing may be high. After sediment core retrieval, we stabilized core tops in the field using a gelling agent (e.g., Zorbitrol) and returned intact cores to the laboratory where we stored them vertically at 4°C for no more than seven days until processing. In cases where the upper sediments were extremely flocculent, we immediately sectioned the upper most sections (~0–30 cm) in the field to prevent mixing during transport.

We vertically extruded the cores in the lab in 1 to 2 cm intervals, depending on lake productivity, and took subsamples from two intervals for DNA analysis. The subsamples collected were from the 0-2cm (hereafter referred to as shallow) and either the 3-4cm or 4-6cm interval (hereafter referred to as deep). Subsamples were frozen under nitrogen for up to three months before DNA was extracted (S3 Table). We extracted DNA from 0.25g of wet sediment from each subsample using a PowerSoil DNA Isolation Kit (Qiagen, Inc.) following the manufacturer’s protocols. We performed negative controls by carrying out extractions on blanks, using only reagents without sample. We determined final bulk DNA concentrations using a Qubit™ dsDNA HS Assay kit (Molecular Probes, Eugene, OR, USA) and Qubit™ Fluorometer (Invitrogen, Carlsbad, CA, USA). The detection limit for the Qubit™ dsDNA HS Assay Kit is 10 pg/μL. All samples that yielded detectable amounts of DNA were sent for sequencing (S3 Table). Despite not detecting DNA in our negative controls, these were submitted for sequencing where they failed to pass quality control performed by the University of Minnesota Genomic Center (UMGC) and no sequencing information was obtained.

Nucleic acid preparation, amplification, and sequencing

We submitted the DNA samples to the UMGC where they performed library preparation for Illumina high-throughput sequencing using a Nextera XT workflow and a 2x300 bp chemistry. The workflow utilizes transposome-based shearing which fragments the DNA and adds adapter sequences in one step. The DNA was amplified and dual-indexed with adapter sequences through PCR, using primers 515F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGGTWTCTAAT-3′) to target the V4 hypervariable region of bacterial 16S SSU rRNA gene sequences. The amplicon library preparation methods created and employed by the UMGC have been shown to be more quantitatively accurate and qualitatively complete—detecting taxonomic groups that often go undetected with existing methods [34]. The indexed samples were then sequenced once using an Illumina MiSeq at the UMGC. A total of 3.29 million (3,290,170) raw reads were obtained from 40 samples.

Data processing

We conducted post-sequence processing in Mothur (v1.43.0) following the MiSeq SOP [35, 36]. Briefly, we merged forward and reverse reads, and screened, trimmed and removed ambiguous bases. We then aligned the reads to references in the SILVA database (v.132), and identified and removed chimeras using vsearch (v2.13.3) [37, 38]. Finally, given the nature of the study (i.e., broad scale patterns of diversity), we classified the sequences as operational taxonomic units (OTUs) using a 97% similarity threshold and assigned taxonomy using the SILVA database [39, 40].

Community analysis & statistics

Unless otherwise stated, we conducted all statistical analyses in R (v4.0.0) [41, 42]. We loaded both the environmental and community data into R using Phyloseq (v1.32.0) [43] and removed any reads classified as mitochondrial or chloroplast. Our final dataset after all post-processing contained 2,181,132 reads assigned to 53,854 taxa across 40 (two sediment depths/lake) samples.

Alpha diversity

We removed all singletons (OTUs observed only once across all 40 samples) from the data before calculating alpha diversity statistics. Given the observed correlation of richness based on sample read depth across sequencing batches (S1 Fig), we chose to rarefy the data to 90% the read depth of the lowest samples (15,771 reads; S2 Fig and S3 Table). Our final dataset for alpha diversity included 630,840 read counts of 25,563 taxa across 40 samples. We calculated alpha diversity measures using the Phyloseq package in R (S3 Fig and S4 Table) [43]. We compared the richness (observed number of OTUs) and evenness (Shannon) of the samples based on sample depth (shallow n = 20, deep n = 19) using a Wilcox test, and trophic status (hypereutrophic n = 4, eutrophic n = 16, mesotrophic n = 16, and oligotrophic n = 3) and ecological status (Western Cornbelt Plains n = 12, North Central Hardwood Forests n = 14, Northern Lakes & Forests n = 8, Canadian Shield n = 5) using a Kruskal-Wallis test with a Dunn Post Hoc test and Bonferroni correction. In all tests, one outlying sample (Trout, Deep) was removed due to uncharacteristically low diversity. Finally, we assessed the predictive capabilities of the environmental parameters, collected at the time of sampling (S2 Table), on the alpha diversity of the sample using multiple regression and determined the significance and variance partitioned by each regressor using the relaimpo (v.2.2.3) and vegan (v.2.5–6) packages in R [44, 45]. We selected the final models based on AIC scores for both richness (observed) and evenness (Shannon).

Beta diversity

Prior to beta diversity analysis we filtered the samples by removing any OTU that did not have 2 or more counts and occur in at least 10% of the samples. Post filtering, the average number of reads per sample was reduced to 47,605, the minimum read depth was 15,150, and the maximum read depth was 99,561. Since OTU data have a strong positive skew, we attempted to diminish the effects using a variance stabilizing transformation (VST) [46]. Log-like transformations, like VST, have been shown to transform count data to near-normal distributions and produce larger eigengap values, ultimately leading to more consistent correlation estimates which influence downstream analyses [47]. After filtering and transformation, the final dataset for beta diversity analysis included 5,512 taxa across 40 samples.

We visualized the sample dissimilarity using principal component analysis (PCA) and the ordinate function in Phyloseq [43]. After ordination, we further analyzed the distribution of taxa based on the ecological regions using permutational analysis of variance (PERMANOVA) and the “adonis” function in vegan [45]. We used a Bray-Curtis dissimilarity to test for group differences and assessed dispersion within groups using permutations and vegan’s “betadisp” and “permutest” functions. Prior to creating the dissimilarity matrix, we converted negative VST values to zero because negative values after transformation likely represent zero counts or very few counts and for the distances and hypothesis in future tests these values would be negligible. We performed a cluster analysis using Ward’s (D2) method and the same dissimilarity matrix generated for the PERMANOVA analysis.

Results and discussion

Alpha diversity

We used alpha diversity metrics to summarize the structure of the bacterial communities in terms of the number of OTUs (richness) and the distribution of their abundances (evenness) for all samples. We then compared the observed diversity (a measure of richness) and Shannon diversity (a measure of evenness) across sampling locations and ecological regions (S4 Fig). Sample richness varied from ~2000–4000 OTUs and sample evenness varied from 5.5 to 7.5. Sediments, both shallow and deep, from Carrie Lake were the most diverse in terms of richness (4116 OTUs and 4100 OTUs; shallow and deep respectively) and evenness (7.41, 7.39; shallow and deep respectively). The least diverse shallow sample in terms of richness was Pearl Lake (2371 OTUs) and evenness was Greenwood Lake (6.44). Trout Lake was the least diverse deep sample in both the total number of OTUs (552 observed) and Shannon diversity (5.49). There was no significant difference within lake diversity between shallow and deep lake sediments across all samples (Wilcoxon test p >0.05); however, the deeper interval sample was more diverse in both richness and evenness in a majority of lakes. The exception to that pattern were the samples from lakes in the Canadian Shield (CS) where all of the shallow interval samples were more diverse.

While all samples were highly diverse when compared to the bacterial diversity of the overlying water column or the number diatom species found in the sediments (Observed Richness > 2250 OTUs; Shannon 5.5–7.5), there were differences in the levels of richness and evenness when comparing samples across the ecological region (Fig 2 and S5 Fig) [48-50]. Shannon diversity (evenness) levels were statistically different across the ecological regions (Kruskal Wallis p = 0.008). Samples from lakes in the Western Cornbelt Plains (CB) were more diverse than both the Northern Lakes and Forests (NLF) (Dunn’s test p = 0.0206) and CS (Dunn’s test p = 0.0099) samples. Observed diversity (richness) was also statistically significant across the ecological regions (Kruskal Wallis p = 0.003). Again, there were statistical differences in the diversity levels of the CB and NLF (Dunn’s test p = 0.007) and CS (Dunn’s test p = 0.006) wherein the CB samples had greater richness.

Fig 2

Bacterial alpha diversity by ecological region.

Box plots show mean alpha level diversity of the observed Operational Taxonomic Units (OTUs) and Shannon indices of the four distinct ecological regions present within the study area: Western Cornbelt Plains (CB), North Central Hardwood Forests (NCHF), Northern Lakes and Forests (NLF), and Canadian Shield (CS). Open (shallow) and closed (deep) circles indicate unique samples and color indicates the ecological region. One sample was removed from both plots for due to uncharacteristically low diversity. Significance between regions was calculated nonparametrically using a Kruskal Wallis H test followed by a Dunn post hoc test with a Bonferroni correction. Reported p values indicate significant differences in Observed and Shannon diversity (respectively) across ecological regions, specifically the diversity of CB lake sediments when compared to NLF (p = 0.007 & p = 0.0206) and CS (p = 0.006 & p = 0.0099) sediments.

The lakes in the CB are highly impacted by agricultural activity and have cultivated or pastureland comprising approximately 50–90% of their total watershed. This land use contrasts with other ecological regions like the NLF (2–24% agriculture) and the CS (<3% agriculture) where species richness was statistically less rich. Agricultural runoff and drainage can carry a variety of contaminants (e.g., herbicides, pesticides) which can stimulate the growth of certain bacteria (e.g., Planktothrix) [7, 51]. The selecting pressure of land-use on microbial taxa and food webs varies within and across ecosystems from highly selective to uninformative, and often land use is described as having indirect effects on microbial composition and diversity (i.e., differing land covers lead to differing nutrient loads in runoff) [22, 52, 53]. Ultimately, our data confirm patterns observed by others: that eco-regional or eco-zone concepts can affect alpha diversity and species richness [53]. We explored the eco-regional-diversity relationship further by examining within phyla richness across the four ecoregions (S5 Table). From this we found 15 phyla which were statistically different in terms of richness in one ecoregion. Several of these phyla (e.g., Bacteroidetes, Spirochaetes and Lentisphaerae) exhibited patterns of richness across ecological regions like that seen in the entire microbiome (i.e., a decreasing richness from CB to CS) while only one phylum, Armatimonadetes, showed an opposite richness pattern. Other phyla like Epsilonbacteraeota and Modulibacteria were more diverse in the CB, while Kiritimatiellaeota were more diverse in CS samples. The identification of specific phyla that display varying richness depending on the broader ecological region of their environment may provide insight into the more nuanced indirect effects that geography plays in microbial assembly. For example, a shift in Bacteroidetes richness across these ecological regions may be indicative of the changing land use and subsequent nutrient regimes which could lead to increased algal biomass within the lake (as discussed below). However, given the low resolution of 16S rRNA gene sequencing (particularly partial gene amplicons) and an inability to confidently determine unique species and potential functional differences we could not specifically address the mechanisms that lead to increased richness across ecological regions.

Because lakes in these ecological regions also tend to vary based on their trophic status, we compare the differences in alpha diversity based on proxies for lake productivity. Using previously reported (yearly average values) of chlorophyll-a and total phosphorous (Chl-a, TP), and Secchi depth provided by the MN Department of Natural Resources, we classified the lakes as hypereutrophic, eutrophic, mesotrophic, and oligotrophic (S1 Table). We found that both Shannon diversity and observed OTUs were greater in hypereutrophic systems compared to oligotrophic systems, and eutrophic systems were also statistically richer than oligotrophic systems (S6 Fig). Since most of the lakes were classified as eutrophic or mesotrophic and the values of TP and Chl-a vary seasonally depending on sampling times, we further assessed the effects of local water chemistry, measured at time of sampling, on alpha diversity. Using an exhaustive search with AIC selection criteria we modeled Shannon and Observed diversity using all aqueous chemistry measures, lake latitude, depth, trophic status, ecoregion, and land cover use in the watershed. The lake’s latitude, temperature, and specific conductivity as well as the concentrations of total phosphorus (TP), total nitrogen (TN), Chlorophyll-a best predicted Shannon diversity (adj R2 = 0.586). Whereas the lake’s latitude as well as the concentrations of TP, TN, DOC, specific conductivity, pH, turbidity and ecological region best predicted observed diversity (adj R2 = 0.7229).

The results of our alpha diversity analysis indicate that sediments in more eutrophic systems, like those of the CB, are more diverse. This is in contrast to unimodal diversity-productivity relationships seen among other freshwater communities such as phytoplankton, zooplankton, and fish but similar to other studies on freshwater bacterial communities along a trophic gradient [54-58]. Previous work addressing the diversity-productivity relationship of bacterial communities highlights the importance of rare or dormant taxa, in that as trophic status increases the diversity of rare/dormant taxa increases [59]. In our study we deemed rare taxa at the phylum level as those not comprising more than 1% relative abundance of the sample. We then compared the richness of these phyla individually across ecological region and trophic status (S4 Table). From this, we found that no rare taxa (at the phylum level) exhibited a statistically significant (Kruskal Wallis p<0.01) increase in richness due to increased trophic status. Nevertheless, three common phyla (Chloroflexi, Spirochaetes, and TA06) did increase as a function of trophic status. Among these three phyla only Chloroflexi, which play an important role in the degradation of labile carbon and secretion of organic acids in subsurface sediments, have previously been shown to increase in abundance and diversity with eutrophication in aquatic environments [60, 61]. By examining the trends in richness across trophic status, like ecological region, we may begin to uncover indicator taxa for nutrient pollution and eutrophication; however, coarser species and function relationships need to be considered.

Beta diversity

To further distinguish trends in the data based on ecoregion and other environmental measures, we examined beta diversity (diversity between samples). Using our variance stabilized data, we conducted a principal component analysis (PCA) to explore the differences in community composition of samples across sites and sediment depths. The first two components explained ~30% of the variation in the samples (Fig 3). In addition to PCA we performed a cluster analysis to determine which samples were most similar (Fig 4). Using both approaches, we concluded that there was a clear distinction between community composition based on ecological region and lake depth.

Fig 3

Beta-diversity: Principal component analysis of samples.

Principal component analysis (PCA) of surface sediment microbiome samples where color represents ecological region. Components one and two explained 31.4% of the total variance. Environmental variables were fit using linear regressions where each component was plotted as a function of an environmental vector and those with p<0.01 were plotted. Solid line ellipses are the outer sample bounds for each region and the shaded ellipses are the standard error of the weighted centroids for the data. Abbreviations: Dissolved Inorganic Carbon (DIC), Total Nitrogen (TN), Total Phosphorus (TP), Chlorophyll-a (Chl-a), Specific Conductance (SPC).

Fig 4

Beta-diversity: Hierarchical clustering of samples.

Hierarchical clustering analysis of sediment bacterial communities using Ward’s D linkages. Clusters reflect the dissimilarities (Bray-Curtis) between variance stabilized 16S rRNA OTUs within each sediment sample where shape indicates depth of samples and color ecological region. Bars along the bottom highlight the first four clusters. These clusters highlight differences in ecological region and depth, where bars 1 & 4 respectively separate Canadian Shield (CS) and Western Cornbelt Plains (CB) samples and bar 2 clusters samples from lakes ~20m or deeper. Bar 3 represents the remaining samples from the Northern Lakes and Forests (NLF) and Northcentral Hardwood Forest (NCHF) regions. Map was created using QGIS and data were made available by the MN Geospatial Commons (public domain) https://gisdata.mn.gov/.

In terms of ecological region, samples from the CS were the first to cluster out. The second cluster consists of six samples from three lakes that are best characterized as deep (max depth > ~20m) meso-eutrophic systems. After depth, the CB samples cluster and the remaining cluster consists of samples from both the NLF and NCHF. Because the most predominant clusters were based on ecological region, we used a non-parametric multivariate analysis of variance (PERMANOVA) with the four regions as our independent variable and tested for differences in community dissimilarities at the OTU level using the same Bray-Curtis dissimilarity matrix as in the cluster analysis. The results of the PERMANOVA indicate there was an ecoregional difference in composition. To ensure these results reflected distinct groups and not over dispersion within groups, we used vegan’s “betadisp” function to test for homogeneity of variance. The resulting insignificant values led us to conclude that community composition is a function of ecoregion and sample sites within these regions are not over dispersed.

Because ecoregion was only a consistent explanatory variable for the more geographically distant ecological regions (CB, CS), we wanted to determine if any of the local physicochemical or watershed land-use factors were also driving community composition. We passively fit these factors to the PCA, treating them as dependent variables explained by the scores from the ordination. Each variable was analyzed separately and added to the plot where the direction of the arrow indicates the gradient direction, and the length indicates the strength of the correlation. After correcting the p-values for multiple comparisons (i.e., Bonferroni), the concentrations for DIC, TP, TN, and Chl-a and lake depth, temperature, specific conductance, and latitude were the most significant local physicochemical factors differentiating the samples. The four land uses include pastured, cultivated, developed, and forested of which only developed, forested, and cultivated lands were significant sorting factors. Forested land use was correlated in the direction of the CS whereas cultivated and urban land-uses were correlated in the opposite direction. Beyond land use, the concentration of DIC and SPC was negatively correlated with samples from lakes in the CS. Finally, the cluster of deep lakes was best explained by a combination of lake depth, temperature, and TP. These are likely related, as the greater depths of these lakes can lead to stronger and prolonged periods of thermal stratification wherein temperatures are around 4°C and redox conditions of the sediment change to release phosphorus bound to reducible forms of iron.

The results from our study indicate that beta diversity among lake sediment microbiomes is determined by a combination of land use and productivity. These results are consistent with previous studies examining inter-lake microbiome variability across a variety of spatial scales [22, 26, 53, 58, 62]. More specifically, our work parallels findings that nitrogen is a selective variable for community composition and that it covaries with urban and agricultural land use [52]. Beyond nutrients, lake depth is commonly identified as a partitioning factor for microbial communities, as it was in our system [53]. DOC concentrations have also been strongly associated with beta diversity of microbial communities; however, our data do not reflect this trend, potentially highlighting the importance of other micronutrients and abiotic factors for explaining community variation from lake to lake [63]. Importantly, while our findings suggest a combination of productivity measures and land use are drivers of lake sediment microbiome structure, the abiotic measures used to assess these relationships were taken from water column measurements. While surface sediments are suitable for estimating site diversity, the specific vertical abiotic properties of sediments may provide deeper context to the microbiome as a whole [20, 26, 64].

Community composition

From the 40 samples we recovered 55 unique phyla—22 of which were dominant (~90% of the total sample relative abundance) (S7 and S8 Figs). In all samples Proteobacteria were the most abundant, comprising approximately 5–20% of the total population of a given sample. Proteobacteria are often the most abundant phylum in sediment and soil ecosystems given their diverse metabolisms and role in the degradation of organic matter [65-67]. At the class level, samples were predominated largely by Deltaproteobacteria and Gammaproteobacteria and there was no clear pattern in their distribution across ecological or trophic gradients. One exception was the presence and/or high abundance of the order MBNT15 (class Deltaproteobacteria) in CS sediments (Fig 5). While there is little known about the ecological significance of MBNT15, these organisms are obligate anaerobes commonly found in stable sediments and known to reduce nitrate; as such, their presence correlates negatively with rates of nitrogen cycling [68, 69]. Additionally, they have been found to be minor constituents in sediments overburdened with organic matter [70]. Given the physicochemical properties of lakes in the CS region (low DOC, low DOM, and TN) our observations of greater MBNT15 abundance in the CS are not unexpected. Additionally, the presence of orders Syntrophobacterales and Desulfobacterales in several of our shallow (0-2cm) samples indicate active sulfur cycling and the depletion of oxygen in the surface sediment, as the genera within these orders are strict anaerobes [71].

Fig 5

Deltaproteobacteria abundance across samples.

Abundance comparison of sediment deltaproteobacterial communities where shape indicates depth of samples, color ecological region, and size the relative abundance in percent. Bars along the left group the OTUs by order. OTUs were selected if they comprised >0.05% of the total relative abundance of the sample. The OTUs bolded are mentioned in the text.

Beyond Proteobacteria, other phyla like Acidobacteria, Actinobacteria, Bacteroidetes, Latescibacteria, Nitrospirae, and Spirochaetes exhibit shifts in abundance based on ecological region (S5 Fig). Latescibacteria, for example, show a subtle increase in abundance moving from the CB to the CS across the NLF and NCHF. The presence of Latescibacteria in all systems is likely due to their ability to degrade several different polymers (proteins, lipids, polysaccharides, fatty acids) and their presence in freshwater sediments supports their proposed role in algal detritus turnover [72]. The increase in abundance as a function of ecoregion may be related to the number of stratified lakes within the CS region, especially given the increase in abundance with sediment depth for most lakes.

Other phyla like Acidobacteria and Bacteroidetes display greater changes in abundance across ecological regions (S5 Fig). For example, Acidobacteria comprise <1% of the total population in CB sediments; however, in CS sediments they can make up as much as 6% of the total population. Acidobacteria, like Proteobacteria, are well distributed across environments and metabolically diverse [73]. In soil systems these bacteria have been shown to partition based on regional land use. Notably, the order subgroup 6 (phylum Acidobacteria) has previously been observed more frequently in pastured regions compared to forested region; however, in our sediment samples these trends do not exist [74]. Subgroup 6 predominates the Acidobacteria population, is found throughout all geographic regions, and is more abundant in White Iron lake—which has one of the most heavily forested watersheds (83%). Bacteroidetes populations exhibit an opposite trend to Acidobacteria—decreased abundance in the CS sediments compared to the CB. Bacteroidetes species specialize in the degradation of organic polymers (e.g., Bacteroides in the gut microbiome) [75]. Given their proclivity for polymers over monomers, members of Bacteroidetes are often more abundant in aquatic systems during and shortly after algal blooms [76, 77]. Our data confirm the relationship between Bacteroidetes and productivity, as lakes in the CB region receive excess nutrient loads and often experience algal blooms during the open water season.

In addition to shifts in composition across ecological regions, there were certain phyla that were not observed at our filtering level (OTUs comprising >0.5% of the total abundance within a sample). Most notably were the absence of Cyanobacteria in CS lake sediments. Land use models have previously been used to partially estimate total cyanobacterial biomass and examine cyanobacterial community structure and genes related to the production of the cyanotoxin microcystin [78, 79]. Our data show similar findings to these previous works in which the populations of toxin-forming members like those found in the order Nostocales (S9 Fig) were more abundant in heavily agriculture and urban settings where they are well adapted to handle higher nutrient levels in the water [80].

Caveats and conclusion

The data obtained during this study provide unique insight into the structure, diversity, and distribution of sediment bacteria across both a trophic and ecological region gradient. A primary caveat is the limited explanation of variance (~30%). While we were able to explain a similar amount of the variability to that observed in other studies, there is still a large amount unexplained [21, 52, 81]. This could be in part due to measurements that were not collected. For example, specific parameters like pH and redox potential at the sediment water interface within each lake would provide greater context for the sediment microbiome structure. Our analysis was also limited to abiotic factors as explanatory variables of alpha and beta diversity for bacterial communities. However, biotic interactions exert a selective force on community structure through a variety of control methods (e.g., grazing, phage infection) [82]. Moreover, the horizontal structuring of sediment communities as well as the overall food web dynamics especially given the differing productivity-diversity relationships could be considered in future studies.

In summary, we examined the lake sediment bacterial communities of 20 lakes to determine the influence of land-use and large-scale land classifications on community structure and diversity. We observed that ecological region with more agricultural land use and greater eutrophication exhibited higher diversity. Likewise, we found that toxin-forming community members were more abundant in heavily agriculture and urban settings. While the ability to connect changes in taxonomic composition using physio-chemical and geographical patterns is possible for some organisms, the limited resolution of short read 16S rRNA data prevent us from detecting specific taxa differences across ecological or trophic gradients.

Many land managers have access to land use maps, and remote sensing is improving our means of evaluating land use in poorly accessible parts of the world. Our results, along with future studies, offer opportunities to connect land use with sediment microbial structure and ultimately to understand lakes’ abilities to adapt to anthropogenic changes.

Batch effect on richness.

Observed richness (total number of OTUs) based on the total number of reads recovered per sample where color indicates the sequencing batch. There was a statistically significant Pearson’s correlation between the number of total reads and the observed richness; p < 0.001 and R2 = 0.63.

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Rarefaction curves.

Rarefaction curves for all forty samples in the dataset. Where each curve indicates a different sample and the vertical line is the sampling depth of 15,771 reads.

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Alpha diversity measures by sample.

Alpha diversity measures across samples, where shape indicates depth of sample and color indicates ecological regions. All measures were calculated using Phyloseq and exhibit similar patterns in diversity; decreasing diversity across a northeasterly transect.

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Click here for additional data file.

Bacterial alpha diversity across samples.

Observed diversity, a measure of richness, and Shannon diversity, measure of evenness, for all samples where shape indicates the sediment depth, color indicates the ecological region, and sites are ordered based on ecological region then latitude. One sample (Trout, Deep—CS), with lower diversity, was removed for visualization.

(TIF)

Click here for additional data file.

Bacterial alpha diversity across ecological region and depth.

Boxplots show mean alpha level diversity of the Observed Operational Taxonomic Units (OTUs) and Shannon indices for the four ecological regions within the study area: Western Cornbelt Plains (CB), North Central Hardwood Forests (NCHF), Northern Lakes and Forests (NLF), and Canadian Shield (CS). Samples are faceted by their sediment depth where Shallow is 0-2cm deep and Deep is 3–4 or 4-6cm deep. One deep, CS sample was removed from alpha diversity metric both plots for due to uncharacteristically low diversity.

(TIF)

Click here for additional data file.

Bacterial alpha diversity by trophic status.

Box plots show mean alpha level diversity of the observed Operational Taxonomic Units (OTUs) and Shannon indices of the four trophic status classifications within the study area: Oligotrophic, Mesotrophic, Eutrophic, and Hypereutrophic. Each point represents a given sample where shape indicates depth of sample and color indicates the trophic status. One sample with extremely low richness and diversity was removed from both plots for visualization. Significance between regions was calculated nonparametrically using a Kruskal Wallis H test followed by a Dunn post hoc test with a Bonferroni correction. Reported p-values indicate significant differences in Observed and Shannon diversity, respectively, across trophic status, specifically the diversity of Oligotrophic lake sediments when compared to Eutrophic (p = 0.0224) and Hypereutrophic (p = 0.0038 & p = 0.013) sediments.

(TIF)

Click here for additional data file.

Bacterial relative abundance.

Bar plots of phyla that comprise >1% of the total relative abundance of a given sample. Samples are sorted along the X axis by ecological region.

(TIF)

Click here for additional data file.

Bacterial abundance at the phylum level across ecological regions.

Box plots show mean relative abundance for the phyla across ecological region. Each point is a sample. Abbreviations: Western Cornbelt Plains (CB), North Central Hardwood Forests (NCHF), Northern Lakes and Forests (NLF), and Canadian Shield (CS).

(TIF)

Click here for additional data file.

Cyanobacterial abundance across samples.

Abundance comparison of sediment cyanobacterial communities where shape indicates depth of samples, color ecological region, and size the relative abundance in percent. Bars along the left group the OTUs by order. OTUs were selected if they comprised >0.01% of the total relative abundance of the sample.

(TIF)

Click here for additional data file.

Description of ecological regions.

(XLSX)

Click here for additional data file.

Aqueous chemistry data.

(XLSX)

Click here for additional data file.

Sediment extraction, DNA quality, and read depth data.

(XLSX)

Click here for additional data file.

Values for all alpha diversity measure calculated by sample.

(XLSX)

Click here for additional data file.

Kruskal Wallis P-values for phyla richness across ecological regions and trophic status.

Table of Kruskal Wallis p-values for individual phyla within the alpha diversity dataset (rarefied). P-values for both the significance based on ecological region and trophic status are reported and bolded values are significant at p = 0.001. Dunn post hoc test with Bonferroni correction p-values are reported for those KW p<0.001. Phyla below the bolded line are phyla that comprise of <1% of the total relative abundance of a given sample (see S4 Fig).

(XLSX)

Click here for additional data file.

2 Nov 2021

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Reviewer #1: Please find below the comments to the manuscript “Diversity and distribution of sediment bacteria across an ecological and trophic gradient” by Hailey M. Sauer, Trinity L. Hamilton, Rika E. Anderson, Charles E. Umbanhowar Jr. and Adam J. Heathcote. The authors present in their manuscript an amplicon sequencing study of bacterial 16S rRNA genes from sediments of lakes in Minnesota. The aim of the study is to determine the influence of geography, land cover and physicochemical properties of the lakes on the bacterial sediment community and their diversity. Their findings include a clustering of samples due to ecological regions, trophy and lake depth.

In general, the manuscript is well written in all parts and easy to read and comprehend. The analyses they perform are sound. My biggest point of criticism is the potential lack of data that might better describe the distribution patterns.

Major:

1. The sequencing depth seems to be very shallow with only 3.3 million raw reads for 40 samples, especially when complex communities are expected in the sediment samples. Maybe the diversity in some lakes sediments is underestimated because rare taxa were missed. Please at least provide the read numbers for each lake and rarefaction curves.

2. As explanatory variables mainly the area type and measures for carbon, phosphorous and nitrogen were used. But some simple to determine variables are missing, such as water temperature, pH, conductivity, (oxygen concentration in the sediment), which are known to strongly effect some of the detected bacterial phyla, such as Proteobacteria. If these variables are neglected, the “true” causal effect might not be detectable, e.g. the effect attributed to the regions could simply be a temperature effect. Is there no additional information on the lakes available that could be included? Such as annual temperature and pH?

Minor:

1. Line 76: There are more studies available that should be cited which study communities and distribution patterns related to environmental factors, especially across Europe: e.g. https://sfamjournals.onlinelibrary.wiley.com/doi/full/10.1111/1462-2920.14992

2. The provided images have a low quality, but that might be due to the incorporation into the pdf

3. Line 99: “These regions can be characterized by their underlying geology, soils, vegetation, and land use.” Could you provide this information.

4. Line 120: How long were the samples stored before processing?

5. Nucleic Acid Preparation, Amplification, and Sequencing: Was the quality/integrity of the DNA controlled before library prep? Please provide the measures.

6. Nucleic Acid Preparation, Amplification, and Sequencing: Was the PCR performed by the core facility? What kind of PCR protocol was used?

7. Line 136: Please provide a reference for the used primers.

8. Methods: Please provide versions for all tools, programs are R packages used.

9. Methods: Were any of the environmental/physicochemical variables standardized or log transformed for any of the analyses, e.g. in the PERMANOVA? Do any of these factors covary?

10. Line 155: There is no Figure S1 in the supplement.

11. Line 158 onwards. It is not clear from the methods how the samples were grouped for the statistical tests, how many groups there were and which environmental parameters were used.

12. Line 163: Multiple (linear?) regression was used for prediction. Please have a look at https://onlinelibrary.wiley.com/doi/full/10.1111/mec.15872 were it is shown that linear predictors do not perform well on such data. Since I did not find a prediction in the results, maybe just the fitting of environmental data to the PCA axes is meant here. Please clarify.

13. Line 211: “> 2250 OTUs” is stated as diverse, but there are no references provided that compare it to other studies.

14. Fig. 2 and Fig. 3 are more or less redundant. Fig. 2 could be put into the Supplement. Further, it would be good to split the boxplots for deep and shallow sediment samples which would better show that there is not difference between these. Again, it would be good to know the sequencing depth per sample and saturation before drawing conclusion about the richness/evenness.

15. Line 255: It would be good to have some examples about functions from analysed species. Most result/discussion points provided later are on the level of phyla, classed etc. Did you not determine genera or species that perform specific function which could be attributed to ecological functions linked the different trophies?

16. Figure S3 does not indicate significant differences.

17. Line 275: What could be the reason for the more diverse community here? Are different metabolic processes involved?

18. Line 279: Rare taxa are mentioned here as important. Were these captured at this sequencing depth?

19. Line 353: You state that nitrogen is a selective variable also in your data, did you find more/less taxa for nitrogen cycling in these samples?

Reviewer #2: The manuscript hypothesizes that community composition of lakes sediments will appear homogeneous across ecological regions and land use at higher taxonomic levels but will vary at lower taxonomic levels.

The manuscript is written in an intelligible fashion and written in standard english.

I would suggest the following changes:

1. Line 31, replace morphological and chemical properties with Physico-chemical properties.

2. Line 35, add 'bacterial' community structure

3. I suggest to mention brief methods used in the study to reach the conclusion in the abstract as well.

4. Line 37, What is TP?

5. I could not find the knowledge gap or significance of the study in the whole manuscript. I suggest adding a significance statement both in the abstract and the introduction.

6. Line 61-62, needs restructuring

7. Line 76, add 'microbial/bacterial' community assembly.

8. Line 81-84, the sentence is too long and have a lot of jargon. please re-structure

9. Line 103, what year was the water sample collected.

10. How was the water sample collected and stored? Was there any treatment done to the water prior to any tests conducted? were these samples collected every-time the sediment samples were collected? why was the water analysis done only once? This is not clear.

11. How ofter were the samples collected. Details of the time the sediment samples were collected?

12. Line 126, how many samples were collected in total? You have said there were total 40 samples. How are they distributed?

13. Was there any data collected for sediment samples? e.g, pH, temperature, salinity etc?

14. Line 128, replace recommendations with protocol.

15. Line 128, How were the extraction carried out? Were they extracted in duplicates? was a certain number of samples repeated if not done in repeats? What were your controls? Both positive and negative. How can you determine the efficiency of your extraction? and how did you control for contaminations?

16. Line 136, references for your primers?

17. how were the sequences indexed?

18. What were your controls for PCR? Did you sequence your controls as well? Having negative controls is extremely important.

19. What is the distribution of these 40 samples?

20. Line 146, what version of SILVA database did you use?

21. How did you deal with controls? Did you remove any contaminant taxa?

22. Line 158, what do you mean by all available measures for alpha diversity? please mention names? Why did you choose to analyse all of them? I suggest you choose only one for each measure.

23. What metric did you choose to measure sample richness?

23. Line 168, denoising does not mean removing rare OTUs, replace word with 'filtered'.

24. Line 211, richness is not equal to diversity. What did you mean here?

25. Line 214, add name of the test used to all p values.

26. Line 283. These taxa are extremely tricky to analyse if you do not have controls. I would like you to mention the contaminants found in the sequences and then analyse this. Otherwise rare taxa data cannot be trusted.

27. Line 373, add 'relative' abundance.

28. I would suggest to draw conclusions from previous data. Did you get the same findings as from other studies?

29. Please revisit your hypothesis in the conclusion and explain in relevance to your findings.

30. Figure 3, add the name of metric to y-axis labels.

**********

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Reviewer #1: No

Reviewer #2: No

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16 Dec 2021

Responses to the Academic Editor

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We have removed our funding information from the main manuscript text and wish our Funding Statement to read as follows:

Funding for this project was provided by a grant to AJH from the Minnesota Environment and Natural Resources Trust fund as recommended by Legislative-Citizen Commission on Minnesota Resources (LCCMR). HMS and TLH were also supported by NSF grant #1948058.

Please include your full ethics statement in the ‘Methods’ section of your manuscript file. In your statement, please include the full name of the IRB or ethics committee who approved or waived your study, as well as whether or not you obtained informed written or verbal consent. If consent was waived for your study, please include this information in your statement as well.

No IRB or ethics committee was required for the sample collection as we did not use human or animal subjects as part of this study.

We note that Figures 1 and 5 in your submission contain [map/satellite] images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

All the data used to compile the maps in Figures 1 and 4 (was Figure 5) were public domain. We have added sourcing information to each figure legend for the data. Lines 96-97 and 351.

—————————————————————————————————————————————

\fResponses to Reviewer #1

Major:
1. The sequencing depth seems to be very shallow with only 3.3 million raw reads for 40 samples, especially when complex communities are expected in the sediment samples. Maybe the diversity in some lakes sediments is underestimated because rare taxa were missed. Please at least provide the read numbers for each lake and rarefaction curves.

We understand and appreciate this concern. We felt given our goals of examining broad scale changes in higher taxonomic level distributions that we had sufficient read depth and did not chose to resequence the samples. We have included more information on individual sample read depth in a supplemental table (S2 Table) and have added a supplemental figure (S2 Fig.) illustrating rarefaction curves.

2. As explanatory variables mainly the area type and measures for carbon, phosphorous and nitrogen were used. But some simple to determine variables are missing, such as water temperature, pH, conductivity, (oxygen concentration in the sediment), which are known to strongly effect some of the detected bacterial phyla, such as Proteobacteria. If these variables are neglected, the “true” causal effect might not be detectable, e.g. the effect attributed to the regions could simply be a temperature effect. Is there no additional information on the lakes available that could be included? Such as annual temperature and pH?

We appreciate this review and have since added more water quality parameters to our analysis, including dissolved oxygen, temperature, pH, conductivity, and turbidity. However, as mentioned in our conclusions/caveats we do not have any sediment specific parameters. We agree with the reviewer that by neglecting some of these variables we may not be able to detect and "true" causal effect. Nevertheless, with the addition of the previously mentioned variables to our analysis there were changes in our alpha diversity models as well as the vector fitting of the PCA. Specifically, specific conductance and temperature were the two variables that correlated the strongest with the PCA axis scores and were informative to the alpha diversity models.

Minor:
1. Line 76: There are more studies available that should be cited which study communities and distribution patterns related to environmental factors, especially across Europe: e.g. https://sfamjournals.onlinelibrary.wiley.com/doi/full/10.1111/1462-2920.14992

We appreciate the reviewer’s suggestion to add further sources to this statement, and greatly appreciate the inclusion of a specific source. We have added an additional four citations to this section. We recognize that there are many more studies than the nine cited here but feel these best highlight the argument that we were making. Lines 81-82

2. The provided images have a low quality, but that might be due to the incorporation into the pdf

We apologize for the low quality of the images provided. We did adhere to the journal requirements for exporting but have re-exported all figures in the hopes of correcting this issue.

3. Line 99: “These regions can be characterized by their underlying geology, soils, vegetation, and land use.” Could you provide this information.

We appreciate this suggestion as it provides a greater context to the overall area of the study. We have included a new table for the supplement. Line 107

4. Line 120: How long were the samples stored before processing?

We thank the reviewer for this comment and have since added storage times for both the sediment cores (up to 7 days) and frozen subsamples (up to three months). We have also included this information (the collection and extraction dates) in a supplemental table (S2 Table). Lines 134 & 140-141

5. Nucleic Acid Preparation, Amplification, and Sequencing: Was the quality/integrity of the DNA controlled before library prep? Please provide the measures.

We appreciate the reviewer’s concerns about DNA quality/integrity and have added a new supplemental table (S2 Table) which includes Qubit fluorimeter readings for all DNA extractions. We've also added reference to the S2 Table in the section regarding DNA isolation where we previously commented on our final DNA concentrations. Lines 144-150

6. Nucleic Acid Preparation, Amplification, and Sequencing: Was the PCR performed by the core facility? What kind of PCR protocol was used?

We send genomic DNA to the core facility and they perform all library prep and sequencing. We have added additional reference to the methods used by the University of Minnesota Genomic Center for further clarification. Line 162

7. Line 136: Please provide a reference for the used primers.

This was an oversight on our part, and we have rectified it by including references for both primers. Lines 156-158

8. Methods: Please provide versions for all tools, programs are R packages used.

Thanks for catching this! We've updated the text to include all version notes at the first appearance of the tool/program/package in the text. Lines 166, 168, 169, 174-175

9. Methods: Were any of the environmental/physicochemical variables standardized or log transformed for any of the analyses, e.g. in the PERMANOVA? Do any of these factors covary?

We appreciate this question and apologize for not making this clear in the text. There were two circumstances where we were comparing our environmental data, first to the alpha diversity scores in the linear model and second to the axis scores of our PCA. In both circumstances we log transformed our right skewed data (e.g., TP, TN). However, in the case of the PERMANOVA we were looking at the categorical variable of ecological region, so there was no transformation necessary. Physio-chemical variables such as nutrient concentrations and chlorophyll a that describe enrichment and productivity will naturally covary along trophic gradients. This covariation is reflected in the directionality of the vectors in the PCA. Additionally, model selection using AIC penalizes for additional complexity and thus selects against explanatory variables whose explained variance is shared among 1 or more variables already included in the model.

10. Line 155: There is no Figure S1 in the supplement.

We apologize for this and have made sure to include all supplemental and intext figures on the resubmission.

11. Line 158 onwards. It is not clear from the methods how the samples were grouped for the statistical tests, how many groups there were and which environmental parameters were used.

We apologize for the lack of clarity and have added details on the number of samples per group to the methods section of the text. Lines 187-190

12. Line 163: Multiple (linear?) regression was used for prediction. Please have a look at https://onlinelibrary.wiley.com/doi/full/10.1111/mec.15872 were it is shown that linear predictors do not perform well on such data. Since I did not find a prediction in the results, maybe just the fitting of environmental data to the PCA axes is meant here. Please clarify.

First, we'd like to sincerely thank the reviewer for including this source as it was incredibly informative. However, we believe there may be confusion with its relationship to this work. We used a multiple regression to model Shannon diversity and the Observed number of OTUs using our environmental data. In this case, we still had more samples than regressors and found this approach to fit the data. The results of these models were included with their R2 values in the alpha diversity subsection of the results. We did additionally fit environmental data to the PCA axes and those results are in the beta diversity subsection of the results. We regret for the lack of clarity in this approach and have refined the text to make this clearer. Lines 298-305

13. Line 211: “> 2250 OTUs” is stated as diverse, but there are no references provided that compare it to other studies.

We thank the reviewer for this comment and have since clarified the language with this statement. We were indicating that with regard to other locations in the lake microbiome (e.g., the water column) and with regard to other sediment organisms (e.g., diatoms) these levels of diversity are great. However, our levels of diversity are similar to other studies looking at the bacterial microbiome of sediments. Lines 239-242

14. Fig. 2 and Fig. 3 are more or less redundant. Fig. 2 could be put into the Supplement. Further, it would be good to split the boxplots for deep and shallow sediment samples which would better show that there is not difference between these. Again, it would be good to know the sequencing depth per sample and saturation before drawing conclusion about the richness/evenness.

We have taken the reviewer's advice and moved Fig. 2 to the supplemental information. We have left Fig. 3 in the text as it reflects the samples per group for the statistical testing present in the results; however, we made an additional figure illustrating the separation of the metrics by depth (S5 Fig.). We also provide details regarding the sequencing depth per sample as mentioned in a previous comment/response.

15. Line 255: It would be good to have some examples about functions from analysed species. Most result/discussion points provided later are on the level of phyla, classed etc. Did you not determine genera or species that perform specific function which could be attributed to ecological functions linked the different trophies?

We appreciate this comment but hesitate to attempt to assign function from a small portion of the 16S rRNA gene. We did initially perform a Tax4Fun analysis which was able to assign functions for around 1% of the data; however, the top hits were all related to housekeeping functions. In addition, at increasing taxonomic resolution, the confidence in OTU assignment decreased. A number of our OTUs were only classified (at high confidence) to the Order level. Due to the limitations of connecting function to 16S rRNA genes (e.g. https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-020-00815-y reports limited success of functional assignments to 16S rRNA amplicons outside the human microbiome), the inability of Tax4Fun to assign distinct function, and the lack of higher resolution taxonomy, we cannot determine specific functions or genera or species let alone their ecological functions across trophic levels.

16. Figure S3 does not indicate significant differences.

We have added significant differences to what was figure S3 and is now figure S6.

17. Line 275: What could be the reason for the more diverse community here? Are different metabolic processes involved?

See response to item 15 for context into differing metabolic processes/functions with regard to the dataset.

18. Line 279: Rare taxa are mentioned here as important. Were these captured at this sequencing depth?

When we mention rare taxa, we're using our definition of rare in which "... we deemed rare taxa at the phylum level as those not comprising more than 1% relative abundance of the sample". Lines 312-315 While there's always the possibility that greater sequencing depth may have captured more taxa, when we discuss the importance of rare taxa we are doing so with this definition in mind.

19. Line 353: You state that nitrogen is a selective variable also in your data, did you find more/less taxa for nitrogen cycling in these samples?

We appreciate this question and have elaborated in the text to the best of our ability about nitrogen cycling taxa. We discussed some nitrogen cycling taxa as members of the order MBNT15 and in our supplemental table 3 we do note that taxa in the phylum Nitrospirae statistically varies across the ecological regions. However, we did not discuss any specific Nitrospirae as there is only three classes, each of which contains only one order, and one family. Below the family level, our taxonomic resolution was uncultured, 6unclassified class members, or Nitrospira. Given the lack of taxonomic resolution we felt there was no substantial discussion to have surrounding the potential roles of nitrifying bacteria across these systems.

—————————————————————————————————————————————

Responses to Reviewer #2

1. Line 31, replace morphological and chemical properties with Physico-chemical properties.

We have refined this terminology and the sentence now reads: "In this study, we selected twenty lakes with varying physio-chemical properties across four ecological regions of Minnesota." Lines 33-34 & 83-84

2. Line 35, add 'bacterial' community structure

We have clarified this and the statement now reads: (ii) determine how lake location and watershed land-use impact aqueous chemistry and influence bacterial community structure. Lines 36-37 & 86-87

3. I suggest to mention brief methods used in the study to reach the conclusion in the abstract as well.

We appreciate this suggestion and have added a statement regarding the use of 16S rRNA amplicon data to our abstract. Line 37

4. Line 37, What is TP?

We apologize for the addition of an undefined acronym here. We've corrected this to read total phosphorus instead of TP. Line 40

5. I could not find the knowledge gap or significance of the study in the whole manuscript. I suggest adding a significance statement both in the abstract and the introduction.

We appreciate this suggestion and have added a statement of significance to the manuscript in the abstract/introduction and we revisit this significance in our conclusion. Lines 27-31 & 477-480

6. Line 61-62, needs restructuring

We were unsure of what exactly to restructure with these lines; however, we do acknowledge that the statements were written passively in an otherwise active narrative. We've revised them to keep the same tense throughout the paragraph. Lines 65-67

7. Line 76, add 'microbial/bacterial' community assembly.

We have clarified this sentence ensure the community assembly in question is specific to bacterial community assembly. Line 81

8. Line 81-84, the sentence is too long and have a lot of jargon. please re-structure

We thank the reviewer for this suggestion; however, we feel that we have provided the details necessary in the preceding introduction and thus feel this wording accurately and best describes the specific aims of our study. We are also careful throughout to define terms, avoid jargon, and translate our results for both specialists and a broader audience.

9. Line 103, what year was the water sample collected.

We apologize for not including this information in our tables. We've since updated supplemental table 2 and have included a statement about dates in the text.

10. How was the water sample collected and stored? Was there any treatment done to the water prior to any tests conducted? were these samples collected every-time the sediment samples were collected? why was the water analysis done only once? This is not clear.

We apologize for a lack of clarity in the water sampling methodology. We have since rewritten this section to incorporate sample storage as well as sample hold times/temperatures for all measured parameters. All samples, including sediment core samples, were only taken at a single timepoint. We have also included sampling dates in our supplemental table which were accidentally left out in the first version. Lines 110-126

11. How ofter were the samples collected. Details of the time the sediment samples were collected?

We thank the reviewer for this comment and have since added a supplemental table (S3 Table) with the exact dates of sediment sampling -- moving beyond the existing text which only clarifies the range of dates for sampling.

12. Line 126, how many samples were collected in total? You have said there were total 40 samples. How are they distributed?

We apologize for the confusion surrounding the total number of samples and their distribution. To clarify we selected twenty lakes, each was cored and subsampled at two depth intervals resulting in the forty total samples. Following a previous comment from this reviewer, we have added a statement regarding methodology to the abstract as well as throughout the text. Lines 37-38, 137-140 & 177-178

13. Was there any data collected for sediment samples? e.g, pH, temperature, salinity etc?

We appreciate this review and have acknowledged in our Caveats and Conclusions that our dataset is limited to water quality data only. Nevertheless, we have added water measures of pH, temperature, specific conductance, turbidity, and dissolved oxygen in our analysis. These measures, like our nutrient measures, are from a location of depth minus one meter. When added, to our analysis there were changes in our alpha diversity models as well as the vector fitting of the PCA. Specifically, specific conductance and temperature were the two variables that correlated the strongest with the PCA axis scores and were informative to the alpha diversity models.

14. Line 128, replace recommendations with protocol.

We have changed this text to read: "We extracted DNA from 0.25g of wet sediment from each subsample using a PowerSoil DNA Isolation Kit (Qiagen, Inc.) following the manufacturer’s protocols." Lines 141-143

15. Line 128, How were the extraction carried out? Were they extracted in duplicates? was a certain number of samples repeated if not done in repeats? What were your controls? Both positive and negative. How can you determine the efficiency of your extraction? and how did you control for contaminations?

We thank the reviewer for these comments and have addressed our use of negative controls in the text. Additionally, we provide reference to methodologies used by the University of Minnesota Genomic Center who performed negative controls for sequencing. Given the complexity of the samples we did not perform any positive control, as no mock community would adequately ensure that we sufficiently extracted the contained organisms of our samples. To address the final questions of the reviewer, extractions were only carried out once per sample. Lines 143-150 & 159-162

16. Line 136, references for your primers?

This was an oversight on our part, and we have rectified it by including references for both primers. Lines 156-158

17. how were the sequences indexed?

We send genomic DNA to the core facility and they perform all library prep and sequencing. We have added additional reference to the methods used by the University of Minnesota Genomic Center for further clarification. We have included reference to their procedure in the text. Line 162 -- 1. Gohl DM, Vangay P, Garbe J, MacLean A, Hauge A, Becker A, et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat Biotechnol. 2016 Sep;34(9):942–9.

18. What were your controls for PCR? Did you sequence your controls as well? Having negative controls is extremely important.

We send genomic DNA to the core facility and they perform all library prep and sequencing, including controls. We have added additional reference to the methods used by the University of Minnesota Genomic Center for further clarification. Line 162 -- 1. Gohl DM, Vangay P, Garbe J, MacLean A, Hauge A, Becker A, et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat Biotechnol. 2016 Sep;34(9):942–9.

19. What is the distribution of these 40 samples?

We clarify the distribution of the 40 samples in a previous comment, and in turn we've added (n=#) for all group statistical testing to clarify the distribution of samples across ecological regions. Lines 187-190

20. Line 146, what version of SILVA database did you use?

We thank the reviewer for catching this and have since included all version notes for tools, programs, and packages at the first appearance in the text. Lines 166, 168, 169, 174-175

21. How did you deal with controls? Did you remove any contaminant taxa?

We clarified our use of controls in the initial comment (#15); however, negative controls were sent for sequencing where they failed quality control by the core facility and were not sequenced. This information has been added to the methods section of the text. Lines 143-150 & 159-162

22. Line 158, what do you mean by all available measures for alpha diversity? please mention names? Why did you choose to analyse all of them? I suggest you choose only one for each measure.

We thank the reviewer for this question and have included multiple measures of alpha diversity in the supplemental information because we are aware that researchers may exhibit a preference for a given metric. We chose to include them because under all examples the patterns in diversity are the same. However, in the main text we exclusively discuss the observed number of OTUs as a measure of species richness and Shannon index scores as a measure of species evenness.

23. What metric did you choose to measure sample richness?

As stated in the text Line 186, we use the observed number of OTUs as a measure of sample richness.

23. Line 168, denoising does not mean removing rare OTUs, replace word with 'filtered'.

We have clarified this language in two locations in the text. Lines 200 & 207

24. Line 211, richness is not equal to diversity. What did you mean here?

We appreciate the question surrounding our language with this statement and have since clarified this comment to reflect a comparison in richness and diversity especially with regards to the water column microbiome and diversity of diatoms in the sediments. Lines 239-242

25. Line 214, add name of the test used to all p values.

We appreciate the reviewer’s suggestion and have added test names to all p-values throughout the text.

26. Line 283. These taxa are extremely tricky to analyse if you do not have controls. I would like you to mention the contaminants found in the sequences and then analyse this. Otherwise rare taxa data cannot be trusted.

We understand the reviewer’s concerns and we feel we have adequately addressed the issue of controls in previous comments (15, 18, & 21)

27. Line 373, add 'relative' abundance.

We have made this change.

28. I would suggest to draw conclusions from previous data. Did you get the same findings as from other studies?

We have included several discussions linking the findings in our data to those found in previous data. Lines 306-310 compared trends in alpha diversity, 385-387 contextualized our beta diversity analysis to several other studies, and 408-412, 445-449, and several others discussed specific taxonomic relationships with trophic and/or ecological status. These discussions and comparisons to existing studies served as the basis for our conclusions.

29. Please revisit your hypothesis in the conclusion and explain in relevance to your findings.

While we don't explicitly restate our initial aims, we do paraphrase the objectives of the study "we examined the lake sediment bacterial communities of 20 lakes to determine the influence of land-use and large-scale land classifications on community structure and diversity." We then proceed to discuss the patterns we found in alpha diversity and with regards to specific taxa. However, we did not include a statement regarding our findings about the drivers of community of composition, so we have added that to this section. We have also added additional language about the findings in relation to our study’s significance to the conclusion section. Lines 473-480

30. Figure 3, add the name of metric to y-axis labels.

We have added the metrics to the y-axis in addition to the facets. Please note however, this figure is no longer figure 3 and is now figure 2.

Submitted filename: Sauer_etal_ResponseToReviewers_Dec2021.pdf

Click here for additional data file.

7 Mar 2022

Diversity and distribution of sediment bacteria across an ecological and trophic gradient

PONE-D-21-30070R1

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11 Mar 2022

PONE-D-21-30070R1

Diversity and distribution of sediment bacteria across an ecological and trophic gradient

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