Flooding and ecological restoration promote wetland microbial communities and soil functions on former cranberry farmland.

Microbial communities are early responders to wetland degradation, and instrumental players in the reversal of this degradation. However, our understanding of soil microbial community structure and function throughout wetland development remains incomplete. We conducted a survey across cranberry farms, young retired farms, old retired farms, flooded former farms, ecologically restored former farms, and natural reference wetlands with no history of cranberry farming. We investigated the relationship between the microbial community and soil characteristics that restoration intends to maximize, such as soil organic matter, cation exchange capacity and denitrification potential. Among the five treatments considered, flooded and restored sites had the highest prokaryote and microeukaryote community similarity to natural wetlands. In contrast, young retired sites had similar communities to farms, and old retired sites failed to develop wetland microbial communities or functions. Canonical analysis of principal coordinates revealed that soil variables, in particular potassium base saturation, sodium, and denitrification potential, explained 45% of the variation in prokaryote communities and 44% of the variation in microeukaryote communities, segregating soil samples into two clouds in ordination space: farm, old retired and young retired sites on one side and restored, flooded, and natural sites on the other. Heat trees revealed possible prokaryotic (Gemmatimonadetes) and microeukaryotic (Rhizaria) indicators of wetland development, along with a drop in the dominance of Nucletmycea in restored sites, a class that includes suspected mycorrhizal symbionts of the cranberry crop. Flooded sites showed the strongest evidence of wetland development, with triple the soil organic matter accumulation, double the cation exchange capacity, and seventy times the denitrification potential compared to farms. However, given that flooding does not promote any of the watershed or habitat benefits as ecological restoration, we suggest that flooding can be used to stimulate beneficial microbial communities and soil functions during the restoration waiting period, or when restoration is not an option.

Introduction

A paradigm shift has occurred in society’s perspective on wetlands. The “drain the swamp” mentality of the early 1900’s contrasts with today’s rallying calls to preserve, restore, and rewild. In recognition of the role of wetlands for critical ecosystem services, such as carbon storage and nitrogen removal, the United Nations declared 2021–2030 the decade on ecological restoration. While progress is evident, science is still unclear on how to best restore wetlands because the return of plant and animal communities does not guarantee the return of desirable ecosystem functions [1, 2]. Soil microbial community surveys can be used to gauge whether treated sites are on track to meet restoration goals, particularly those that accrue slowly and are difficult to measure, such as soil organic matter accumulation and nitrogen removal.

The physical soil environment influences microbial community composition, which in turn influences the physical soil environment. Despite this established feedback loop, it is still unclear whether discrete community types align closely with soil biogeochemistry (indicator species; [3-5]), or whether community members are redundant to one another, their identity uncoupled from their biogeochemistry [6, 7]. Evidence to support these theories remains incomplete, with much of this research effort focusing on prokaryotes (bacteria and archaea). Microeukaryotes—the microscopic fungi, soil animals, and protists that occur in high numbers in soil and water, have received less attention. Characterizing prokaryotes and microeukaryotes together should yield a more complete picture of the relationships between land management, soil physicochemical gradients, and microbial community structure. Within this field of inquiry, restored peat wetlands are a particularly underexplored ecosystem, and evaluating how microbial communities align with soil biogeochemistry should also provide insights for managing these unique ecosystems.

Cranberries (Vaccinium macrocarpon) are native to New England and occur naturally in kettle hole bogs and marshes. Indigenous groups including the Wampanoag have been cultivating and harvesting cranberries for over 12,000 years before commercial cranberry farming spread rapidly across the region in the 1800’s. At least half of Massachusetts cranberry farms are built on existing peat wetlands [8], exploiting natural springs for irrigation and harvesting purposes. On modern cranberry farms, water is supplied through a system of dams, ditches, and culverts. Pesticides are applied regularly, and thin layers of sand, often accumulating up to a meter in depth, are applied to promote drainage and prevent root dieback [9]. Until the 1990’s, Massachusetts was the worldwide leader in total harvestable acres, but due to climate and economic factors, at least 40% of existing farmland will be retired in the next decade [8].

The cranberry retirement wave presents a widespread opportunity, both for wetland restoration and for research on ecosystem trajectories. Active ecological restoration on former cranberry farms facilitates ecosystem recovery through earth moving, ditch filling, surface grading, and channel or floodplain reconstruction. When no action is taken, retired farms are typically drained of existing water. Over time, it appears that retired sites will often transition to an upland maple or pine forest, never regaining their historical wetland status [10].

Prior to restoration, cranberry farms are typically placed into conservation easements, and it is not uncommon for farms to be retired for 15 years or longer before they can be restored. Rather than allowing retired farms to develop into upland forests, the USDA National Resources Conservation Service (NRCS) experimented with leaving retired farms permanently flooded, in the harvest condition. While flooding clearly maintains ponded water and wet soils, the soil characteristics and microbial communities of flooded sites have not yet been characterized. If flooded sites have comparable soil-based benefits as restored sites, then flooding could be used as a mitigation strategy for sites awaiting restoration, or as an alternative when restoration is not an option.

We conducted a survey of soil microbial communities and ecosystem functions related to soil development and nutrient cycling across 23 sites and five treatments (farm, young retired, old retired, flooded, and restored) in southeastern Massachusetts. Our research questions were: 1) Which of the five treatments had the most similar microbial communities to natural sites? 2) How much variation in community composition can be explained by soil variables, and which of these variables align most closely to microbial community composition? 3) Which taxa are indicators of wetland development? Given the results of our previous work [11], we hypothesized that restored sites would harbor similar microbial communities to natural sites. While we had not examined flooded sites before, we hypothesized that flooded sites would also have similar characteristics to natural wetlands, but to a lesser extent than restored sites, due to their permanently flooded (unnatural) hydrologic regime. We expected that denitrification potential and soil organic matter would be important for explaining variation in microbial communities across the six treatment categories, and that these variables would be highest at the wet sites (natural, restored, and flooded) and lowest at the drier sites (farm, young retired, and old retired).

Methods

Field sampling and site histories

We sampled twenty-three field sites across southeastern Massachusetts during July 2017, spanning a perimeter of 100 km and total area of 500 km2. Treatments included: cranberry farm (3 sites), young retired (5 sites), old retired (3 sites), flooded (3 sites), restored (6 sites), and natural sites (3 sites) (). Natural sites have no history of cranberry farming. Restored sites, flooded sites and young retired sites were roughly comparable in age; restored sites had been restored either 1 year (Tidmarsh) or 6 years (Eel River) prior to sampling, flooded sites were flooded 4 years prior to sampling, and young retired sites were retired 1–4 years prior to sampling. Old retired sites were retired 17–20 years prior to sampling, and farms had been continuously farmed for at least 50 years. In our study, “treatments” include multiple sites with varying site ages, and conclusions are drawn with this in mind.

Map of study area.

23 sites were surveyed in southeast Massachusetts, spanning five treatments. Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap (WGS-84), under ODbL.

The flooded sites known as “Grassi”, “Goldawitz” and “Pembroke” are NRCS conservation easement sites. Early in the Wetlands Reserve Program, the agency made use of existing water control structures (flumes and wooden boards) to flood the farms and create artificial wetlands (S1 Fig in ). Ecologically restored sites were manipulated according to a methodology developed by the Massachusetts Department of Environmental Restoration (DER). Procedures include removal of dams and water control structures, plugging irrigation ditches with sand, surface roughening and microtopography grading, and, when needed, channel and floodplain reconstruction. No seeding was performed at either the restored or the flooded sites, and previous work has shown that native plants recolonize these sites through activation of the belowground seedbank [12, 13].

Some sampling sites were clustered together whereas others were more spread out (). This sampling scheme was deployed to capture variation within particularly large restoration projects (Tidmarsh West and Eel River), or when sites co-occurred with other outside long-term monitoring objectives (Tidmarsh East). To validate that clustered sampling still captured more variation across these areas, we conducted a sensitivity test by systematically dropping all possible combinations of two sites from Tidmarsh West, Eel River and Tidmarsh East, thereby equalizing site replication across treatment categories (S2 Fig in ). For the majority of cases (8 cases out of 12), dropping two sites at a time from Tidmarsh East (1, 2, and 3), Tidmarsh West (1, 2, and 3) and Eel River (North, Northeast, and South) decreased variation rather than increased variation. Therefore, we retained all 23 sites throughout subsequent analyses.

Eight soil samples (dysic, mesic Typic Haplosaprists of the Freetown series), were randomly selected at each site, spaced 30 m apart. Plants and mosses were removed from the soil surface, and soils were collected using a 1.5-cm diameter soil corer to a depth of 10 cm, and flame sterilized between each sample. Soil samples were homogenized in their own bags and subsampled in the field: half of each sample was flash frozen in liquid nitrogen and stored at -80°C until DNA extraction, and the other half was stored at 5°C for the remaining 35 soil analyses.

DNA sequencing and bioinformatics

DNA was extracted using a DNeasy Powersoil Kit (QIAGEN Inc, Hilden DE) using 0.2 g of soil. For prokaryotic amplicons, libraries were prepared by amplifying the V4-V5 hypervariable region of the 16S rRNA gene using primers 515F (5- GTGYCAGCMGCCGCGGTAA-3), and 926R (5-CCGYCAATTYMTTTRAGTTT-3) [14] and sequenced at the Joint Genome Institute (Walnut Creek, CA, USA) on an Illumina MiSeq 96 cycle cartridge, across four runs. For eukaryotic amplicons, libraries were prepared by amplifying the V9 hypervariable region of the 18S rRNA gene using primers 1391F (5-GTACACACCGCCCGTC-3) and reverse primer EukBR (5-TGATCCTTCTGCAGGTTCACCTAC-3) [15, 16] and sequenced at Argonne National Lab on an Illumina MiSeq 300 cycle V2 cartridge, on a single run.

Raw sequences were processed using Quantitative Insights into Microbial Ecology 2 (QIIME2) [17]. Amplicon sequence variants (ASVs) were identified using the Divisive Amplicon Denoising Algorithm (DADA2), which merges sequences, removes chimeric sequences, removes reads with ambiguous bases, and corrects sequence errors so that amplicon sequence variants can be resolved to the level of single-nucleotide differences [18].

For 16S amplicons, reverse reads were low quality (this is common for this gene region), so only forward reads were used. Sequences were truncated to 277 base pairs, and denoising was performed separately on each run, resulting in 22,176,127 total sequences (an average of 129,857 sequences per sample), and 13,978 unique ASVs across 183 samples (an average of 1,579 ASV’s per sample). One sample, TE3.2, was removed at the denoising step due to low sequence count. Feature tables and representative sequence files from each of the four runs were merged, and a denovo phylogenetic tree was made using the align-to-tree-mafft-fastree pipeline. Reads from the V4-V5 hypervariable region were extracted from the SILVA ver. 132 database [19], and a feature classifier was trained on that gene region. Taxonomy was assigned at 99% similarity using the SILVA ver. 132 QIIME2 release. Following taxonomy assignment, sequences and feature tables were filtered to remove mitochondrial and chloroplast sequences, as well as sequences that were unassigned at the domain level.

For 18S amplicons, forward and reverse reads were trimmed to 144 bp and denoised, resulting in 11,430,013 total sequences (average of 36,859 sequences per sample) amounting to 70,836 unique ASVs across 183 samples (average of 407 ASVs per sample). One sample (noted as M.3 in the ) was removed during denoising due to low sequence count. Phylogenetic trees and feature classification were performed in a similar manner as the 16S analyses, this time using a feature classifier trained on the V9 hypervariable region of the 18S gene. Taxonomy was assigned at 99% similarity, and sequences and feature tables were filtered to remove plants and unassigned sequences at the domain level.

Soil variables

We measured 35 soil variables to characterize the soil physicochemical environment (). Soil analyses were performed by the University of Massachusetts Plant and Nutrient Testing Laboratory, using standard protocols. Macro and micronutrient concentrations were determined using the Modified Morgan extraction procedure [20]. Cation exchange capacity was determined using hydrochloric acid cation displacement method [21], and base saturation of calcium, magnesium, and potassium was determined using atomic absorption spectrometry. Soil organic matter was determined through loss on ignition at 360° C.

Denitrification potential was measured at the Cary Institute of Ecosystem Studies using the denitrification enzyme assay method [22]. This method adds an abundance of carbon and nitrate to each sample in an oxygen free container, and uses an acetylene inhibitor to prevent reduction of N2O to N2. The amount of N2O produced is proportional to the amount of denitrification enzyme present. Potential methane emissions were measured following ten-day soil incubations, and were quantified using a Shimadzu GC-8 gas chromatograph with a flame ionization detector. Detailed methods for all soil variables are available in .

Statistical analyses

QIIME2 feature tables, representative sequences, taxonomy tables, and phylogenetic trees were imported into R (v. 1.2.1335, R Core Team 2020) using the qiime2R package [23]. Following conversion to phyloseq objects, we removed samples with less than 1000 sequences, which dropped 14 additional samples from the prokaryote dataset and no additional samples from the microeukaryote dataset.

Spatial dependency is a familiar challenge in microbial studies, and occurs in two ways: 1) environmental gradients are spatially structured, causing spatial structuring of taxa, or 2) biological processes such as speciation, extinction, dispersal or species interactions are distance-related. To test for the first type of spatial dependency, we compared the Euclidean distance matrix of GPS coordinates against the Euclidean distance of soil variables (Wisconsin double-transformed to control for different measurement scales). To test for the second type of spatial dependency, we compared the Euclidean distance matrix of GPS coordinates against the weighted UniFrac microbial community distance matrix. We performed both analyses across the whole dataset, and within each treatment category. There was no relationship between geography and soil variables in either case. In comparing geography and community composition, there was no autocorrelation for prokaryotes, but there was significant autocorrelation for microeukaryotes. We controlled for spatial autocorrelation when possible in further analyses, but acknowledge that spatial relationships cannot be fully decoupled from the treatments, which were also spatially structured ().

To assess microbial community similarity to natural sites, we used a phylogenetically meaningful distance measure, weighted UniFrac, because microbial phylogeny is highly correlated with functional traits [24]. Weighted UniFrac incorporates the proportion of shared branch lengths amongst the total branch length, as well as the relative abundance of each taxon [25]. Prior to distance calculations, we resolved multichotomies to dichotomies [26] and used a proportional transformation to turn ASV counts into relative abundance, in lieu of rarefaction [27]. We compared unweighted and weighted UniFrac measures for the 16S and 18S datasets, and chose weighted UniFrac for the final presentation because it produced the best model fit (R2) for prokaryotes and microeukaryotes in the canonical analyses of principal coordinates (CAP; methods to follow).

We conducted a similar analysis for the 35 soil variables. For this analysis, we used a Canberra (weighted Manhattan) distance matrix, calculated from Wisconsin double transformed data to account for different measurement scales for each soil variable. We chose Canberra distance rather than the more commonly used Euclidean distance because our dataset had several zeros (values that were below instrument detection limits) and Canberra distance is less sensitive to shared zeros than Euclidean distance. In this particular case, all distance values happened to fall between 0 and 1, so we converted these numbers to similarity (1-dissimilarity) to ease interpretation. We caution that this can only be done when similarity scores fall between 0 and 1, and similarity scores from this study and should not be compared to similarity scores from other studies. For each of the three analyses described above (), we also conducted linear mixed effects models on similarity scores using the lme4 package [28], with treatment coded as fixed effect and site included as random effect to control for non-independence of technical replicates taken from the same site (a square-root transformation was applied when necessary to achieve homogeneity of residuals). When omnibus tests were significant (α = 0.05), pairwise comparisons between treatments were estimated using the glht function in the multcomp package [29].

Similarity between treatments and natural wetland sites.

Plots depict similarity scores for (A) prokaryote communities; (B) microeukaryote communities; and (C) Soil variables. Points indicate the mean similarity of each site to all samples drawn from natural sites, and vertical error bars are standard deviations of similarity scores (eight technical replicates are represented). Grey box plots indicate the median and interquartile range of all soil samples for each treatment category.

To visualize community differences and to identify the soil variables that relate most strongly to microbial community composition, we used a constrained ordination approach using all 183 technical replicates (canonical analysis of principal coordinates; capscale function in phyloseq) [30, 31]. Like canonical correlation analysis (CCA), CAP can be used with any distance measure and can also remove variance from nuisance variables (we removed latitude and longitude). Unlike CCA, which assumes a unimodal distribution of species along gradients, CAP assumes a linear distribution of species along gradients, which is well suited to environmental gradients at the regional scale (i.e. southeastern Massachusetts). Using weighted UniFrac distance measures, we used a model selection approach to maximize model fit (R2). Redundant covariates were removed, as were covariates that contributed a variance inflation factor of greater than 10. Forward selection was run on soil variables using the ordiR2step function with 200 permutations [32]. Through this process, we achieved a final model with the highest explanatory power and the least number of variables for each of the 16S and 18S datasets. To visualize how environmental gradients align with communities, the top six continuous predictors were plotted as directional vectors.

To visualize differences in within-sample dominance of prokaryotic and microeukaryotic taxa, we constructed differential heat trees using the metacoder package [33]. This method expands on the heat map concept by incorporating both the differential abundance and the hierarchical organization of each taxon. We used the log2 median ratio to compare the differential abundance of each taxon, using natural wetlands as the universal reference group to compare against the other treatments. For instance, the median abundance for a given bacterial class across all the natural wetland samples was divided by the median abundance of that same bacterial class across all the farm samples, and a log2 transformation was applied to normalize the data. Finally, a Wilcoxon Rank-Sum test was used, such that significant differences were shown in color, and non-significant comparisons were shown in grey.

Results

Question 1: Which treatments had the most similar microbial communities to natural wetlands?

For prokaryote communities, flooded sites and restored sites had the highest median phylogenetic similarity to natural sites, followed by old retired sites, young retired sites, and farms, which had the lowest similarity (). The same pattern held for microeukaryote communities; restored sites and flooded sites had the highest median phylogenetic similarity to natural wetlands, followed by young retired sites, old retired sites, and farm sites (). In a similar fashion, the multivariate analysis of all 35 soil variables revealed that flooded sites were the most similar to natural sites, followed by restored sites (). Young retired sites, old retired sites and farms had equally lower similarity to natural sites ().

Question 2: How much variation in community composition can be explained by soil variables, and which of these variables have the strongest association with microbial community structure?

Canonical analysis of principal coordinates revealed microbial community differences and the soil variables that explained the greatest variation. The six strongest soil predictors of prokaryote community composition were: potassium base saturation, sodium, zinc, cation exchange capacity, denitrification potential, and iron. Potassium base saturation and sodium structured communities along CAP axis 1 (), in which farm sites, young retired sites and old retired sites had a higher percent potassium base saturation, and restored and flooded sites had higher sodium. Cation exchange capacity was also a large predictor, structuring communities along CAP axis 1 and CAP axis 2. Together, soil variables explained 45% of the variation of the weighted UniFrac distance, whereas 52% of the variation was unconstrained. Conditional variables (latitude and longitude) explained the remaining 3% of the variation in this dataset.

Canonical analysis of principal coordinates.

(A) Prokaryotic communities were distinct (capscale, p < 0.01), and the six largest predictors of community composition were: potassium base saturation, sodium, zinc, cation exchange capacity, denitrification potential, and iron. Together, soil variables explained 45% of the variation of the weighted UniFrac distance, whereas 52% of the variation was unconstrained. Conditional variables (latitude and longitude) explained the remaining 3% of the variation in this dataset. (B) Microeukaryote communities were distinct (capscale, p<0.01), and the six largest predictors of community composition were: potassium base saturation, sodium, pH, denitrification potential, copper and phosphorus. Together, soil variables explained 44% of the variation in the weighted UniFrac distance, whereas 49% of the variation was unconstrained. Conditional variables (latitude and longitude) explained the remaining 7% of the variation in this dataset.

For microeukaryotes, the six largest predictors of community composition were: potassium base saturation, sodium, pH, denitrification potential, copper and phosphorus. Similar to the prokaryotes, microeukaryote communities in farmed, young retired, and old retired sites were correlated with potassium base saturation and sodium along CAP axis 1 (), whereas the higher pH at restored and passively restored sites was correlated with communities at restored sites. Together, soil variables explained 44% of the variation in the weighted UniFrac distance, whereas 49% of the variation was unconstrained. Conditional variables (latitude and longitude) explained the remaining 7% of the variation in this dataset.

The twelve soil variables that were identified as important in the CAP analysis also varied significantly across treatments (). Three variables important to wetland development—denitrification potential, cation exchange capacity, and soil organic matter—were highest in flooded sites compared to farm sites, young retired sites, and restored sites. However, we note that that the difference in denitrification potential between flooded sites and restored sites was not statistically significant, and all treatments had less than 12% of the soil organic matter found in natural sites.

Question 3: Which taxa are indicators of wetland development?

We used heat trees to visualize how the within-sample dominance of taxa differed between treatments and natural wetlands. For prokaryotes, Parcubacteria and Methanobacteria were more dominant in natural wetlands, shown in teal (), whereas Actinobacteria and Bacteroidia were typically less dominant in natural wetlands, shown in orange. We identified a possible indicator of wetland development in the phylum Gemmatimonadetes, which was more dominant in natural sites when compared to the farm and retired sites (shown in teal), but equally dominant when compared to flooded sites and restored sites (shown in grey). Another possible indicator was a decline in FCPU426, which was more dominant in farm sites and retired sites when compared to natural sites (shown in orange), but equally dominant when compared to flooded sites and restored sites (shown in grey). Generally speaking, heat trees for farm sites and retired sites were similar (top row), and heat trees for flooded sites and restored sites were similar (bottom row), corroborating our earlier findings from the CAP analysis.

Differences in prokaryotic taxa between treatments and natural wetlands.

Heat trees were constructed using the metacoder package [33], at the class taxonomic level. The size of the node in each cladogram is proportional to the number of unique classes within each phylum. Color intensity is proportional to the difference in abundance between natural sites and the other treatments, as calculated from the log2 ratio of median abundance. Young retired and old retired samples were pooled together for simplicity, since they had similar communities. Taxa that have a higher within-sample dominance in natural sites are shown in teal whereas taxa that have a higher within-sample dominance in farm, retired, flooded, and restored wetlands are shown in orange. Nonsignificant comparisons are shown in grey. Colored labels showcase example taxa that were consistently more (teal) or less (orange) abundant in natural sites or displayed variation across the four heat trees.

For microeukaryotes, natural sites consistently lacked Nucletmycea, a superclass that includes the classes Fungi and Cristidicoidea (). While Nucletmycea was a clear indicator of cranberry farming and its legacy, Rigifilida and Metamonada were consistently more dominant in natural wetlands compared to all other treatments. Rhizaria was a possible indicator of wetland development; it was more abundant in natural sites, but differences in abundance were much smaller when compared to flooded sites and restored sites (indicated by muted teal and orange shading). Generally speaking, heat trees for farm sites and retired sites were similar (top row), and heat trees for flooded sites and restored sites were similar (bottom row), corroborating our earlier findings from the CAP analysis.

Differences in eukaryotic taxa between management histories and natural wetlands.

Heat trees were constructed using the metacoder package, at the class level. The size of the nodes in each cladogram is proportional to the number of unique classes within each phylum, and color intensity is proportionate to the difference in abundance between natural sites and each of the other management categories, calculated from the log2 ratio of median abundance. Young retired and old retired samples were pooled together for simplicity, since they had similar communities. Taxa that have a higher within-sample dominance in natural sites are shown in teal whereas taxa that have a higher within-sample dominance in farm, retired, flooded, and restored wetlands are shown in orange. Nonsignificant comparisons are shown in grey. Colored labels showcase example taxa that were consistently more (teal) or less (orange) abundant in natural sites or displayed variation across the four heat trees.

We further examined which taxa contributed to the class level patterns detected above. While no prokaryotic genera showed any particular trends during data exploration, a strong pattern appeared within the eukaryote dataset: the fungal genera Archaeorhizomyces and Cairneyella were the two most abundant eukaryotic genera across the dataset; together, these genera occupied an average of 36% of the total microeukaryote gene abundance per sample, and up to 78% of the total microeukaryote community in one sample. These genera were dominant in cranberry farms, and decreased markedly in retired, restored and natural wetlands ().

Relative dominance of two most common eukaryotic genera.

The two most common eukaryotic genera were Archaeorhizomyces and Cairneyella, suspected mycorrhizal symbionts of the cranberry crop. Both genera had lower median within-sample dominance in young retired farms, old retired farms, flooded, restored, and natural sites than in cranberry farms. Points indicate mean abundance for each site and vertical lines are standard errors calculated from eight technical replicates. Grey box plots indicate the median and interquartile range of all soil samples within each treatment category.

Discussion

Restoration success is often defined by the convergence of biological and functional variables towards natural reference conditions. Ecological restoration aims to achieve this by filling in drainage ditches of retired cranberry farms and breaking up the sand layer through surface roughening, thereby retaining water, slowing decomposition, and facilitating soil organic matter accumulation on top of the sandy agricultural substrate. We compared ecological restoration to the retired condition and the permanently flooded condition, which is achieved by retaining the agricultural dams that were formerly used for the cranberry harvest. We found that flooding was remarkably effective at jumpstarting wetland biological and functional characteristics, even exceeding restoration in terms of soil organic matter accumulation and cation exchange capacity, after only four years. In contrast, retired sites failed to achieve natural wetland conditions, even after several decades following retirement. These results suggest that farms that are slated for retirement should be left in the flooded condition rather than the drained condition.

Spatial autocorrelation is often in inherent in observational studies, and should be considered in the interpretation of results. The Tidmarsh East (restored sites) were close in proximity to the natural sites so there is some uncertainty as to whether the patterns observed here are geographical artifacts or representative of restoration activities as a whole. Yet, while we found spatial autocorrelation (for the microeukaryote dataset only), it was unlikely to have affected our major conclusions: there was no relationship between geography and soil variables, but treatments clearly had a consistent effect on soil variables, suggesting that treatment effects overwhelmed intrinsic environmental gradients (, ). Likewise, the flooded sites were the furthest geographically from the natural sites, yet they often had the highest similarity to the natural sites, also indicating that the effect of treatments were much stronger than microbial dispersal limitation.

While some soil-based functions, such as respiration and soil organic matter formation are widespread among microbial taxa, other functions such methanogenesis and denitrification may be narrowly distributed. In our study we found a strong correlation between microbial communities and soil functions; half of the variation in prokaryotes and microeukaryote communities could be explained by soil variables, including cation exchange capacity and denitrification. It has been previously found that soils with high denitrification potential also have a discrete microbial community [11, 34, 35]. In our study, denitrification was among the top six predictors for both prokaryotes and microeukaryotes, suggesting that the microeukaryotes surveyed here are contributing to denitrification themselves [36], or that microeukaryote communities sort with specific denitrifying bacterial communities. Furthermore, high methane fluxes in natural sites were associated with the dominance of Methanobacteria, a class of archaea containing several methanogens. These results lend further support to the idea that microbial taxa (or taxonomic groups) play deterministic roles in soil functions that are important to restoration practitioners, rather than being redundant to one another.

Microbial communities sorted with denitrification and cation exchange capacity, variables that restoration seeks to maximize, as well as underlying chemical gradients; prokaryote communities aligned with potassium base saturation, sodium, zinc and iron, and microeukaryote communities aligned with potassium base saturation, sodium, pH and copper. The high potassium base saturation in farm sites and retired sites may reflect a legacy of fertilizer application, whereas higher amounts of iron and zinc in flooded, restored and natural sites may be a direct result of the greater amount of soil organic matter found in these soils [37]. Natural sites were also saltier and more acidic than the other treatments, both of which are known environmental filters of microbial communities [38, 39]. Taken together, these results suggest that the effects of restoration treatments on microbial communities can be mediated by indirect effects on soil chemistry.

Differences in taxa dominance across treatments provided context for interpreting differences in biogeochemical variables. For instance, the high within-sample dominance of the methanogen Methanobacteria in natural and flooded sites makes sense, given that these sites both had saturated soils and some standing water. It has been previously noted that Euryarchaeota, a phylum containing methanogens, dominates wet ecosystems whereas Thaumarchaeota, a phylum containing ammonia oxidizers, dominates in drier systems [40]. Our finding that Gemmatimonadetes is a possible indicator of wetland development was supported by [41], which found Gemmatimonadetes in vegetated, but not bare wetlands. Actinobacteria and Bacteroidia, which were consistently less dominant in natural sites compared with the other treatments, may be indicators of disturbance or early succession, as noted by [42]. There are fewer ecological data sets on wetland microeukaryotes to compare our results to, but we note that Rhizaria, a large group that includes predators of both bacteria and autotrophic protists [43], was also identified as possible indicator of wetland development in our study. Longitudinal studies over time could reveal early, mid, and late successional indicator communities, and complementary isotope labeling studies could provide insights into trophic interactions [44].

Cranberries are a unique agricultural crop because they are a native wetland perennial, grown in monocultures. We detected a belowground legacy of cranberry cultivation on the soil microbiome, findings which are echoed in investigations of Vaccinium patches in natural bogs [45, 46]. The two most abundant genera across the microeukaryote dataset, Archaeorhizomyces spp. and Cairneyella spp., are suspected ectomycorrhizal and ericoid mycorrhizal symbionts of the cranberry crop [47, 48]. The decline of these genera in restored sites in particular was likely due to soil disturbance, uprooting of cranberry vines, and loss of host plants. Previous vegetation surveys in ecologically restored, former farms indicate that native vegetation establishes quickly following restoration, replacing most cranberry plants [10, 12, 49]. While it appears that these fungal symbionts track with aboveground cranberry abundance, future work evaluating rhizosphere community assembly around controlled plantings of cranberry and native wetland plants could reveal whether soil microbes control aboveground plant succession, or vice versa.

In previous studies, we have described the desirable impacts that active ecological restoration of retired cranberry farms has on both biogeochemical characteristics [50, 51] and microbial community structure [11]. Here, we found that flooding also promotes beneficial wetland functions, even exceeding wetland restoration for soil organic matter formation and cation exchange capacity. While flooding clearly promotes wetland biogeochemistry, flooding does not meet other restoration goals, such as repairing stream connectivity for fish and other aquatic life. Flooding is achieved by leaving artificial dams and water control structures in place, which necessarily impedes the flow of any streams or rivers on site. Many coastal cranberry farms are located on historically important migration and spawning grounds for anadromous fish such as alewife, blueback herring, rainbow smelt, American shad, and white perch. Restoration of these migratory habitats is one of the explicit primary goals of ecological restoration, and these goals are definitively not met when impoundments are left in place.

In conclusion, flooded and restored sites approached soil attributes of natural wetland sites, including prokaryote communities, microeukaryote communities, and 35 key soil variables. Effects were surprisingly strong for flooded sites, likely due to wetter conditions that support soil organic matter accumulation. While these results underscore the foundational role that water retention plays on soil development, we do not suggest that flooding should be a substitute for active ecological restoration. Rather, retired farms should be flooded during the restoration waiting period, or when ecological restoration is not an option.

This file is an excel spreadsheet containing site metadata, the 35 soil variables, and sequence counts for each sample.

(XLSX)

Click here for additional data file.

This file contains S1 and S2 Figs as well as detailed methods for the soil variable analyses.

(DOCX)

Click here for additional data file.

10 Dec 2020

PONE-D-20-33816

If you flood it, they will come: prokaryote and microeukaryote communities in passively restored wetlands approach reference conditions

PLOS ONE

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I have received the response from the reviewers and they recommended this manuscript for revision. Please revise in track change carefully with point to point answers to their queries. I would like to see the revise manuscript.

Thanks

Sincerely

ashwani

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: PONE-D-2033816

The manuscript is generally written very clearly and concisely. I am very grateful to the authors for that! The subject matter is also very interesting. Unfortunately, as the manuscript currently reads, I cannot recommend publication because 1) the analyses reflect pseudoreplication, 2) the soil sampling may have been flawed, and 3) age differences among sites of different treatments may prohibit comparisons among treatments.

Pseudoreplication

Lines 118-123. As far as I can tell, there is a single reference wetland, 2 actively restored sites, 3 passively restored sites, an unknown number of retired sites and an unknown number of active farms. The authors need to be clearer about replication for each treatment. This information and site descriptions should probably be made available in a table. Despite the limited replication at the site level, each individual soil sample was apparently analyzed as if it were independent of the other samples. I do not see how the authors can justify this pseudoreplication. The unit of replication is the SITE, not the SOIL SAMPLE because the treatments were applied at the level of the site, not the level of the soil sample. Therefore, these analyses are incorrect.

Soil sampling

Line 140. If the ratio of muck to sand varies from site to site, then one will probably see corresponding variation in microbial community structure, SOM concentration, CEC, etc. that has more to do with the relative amounts of muck and sand rather than anything to do with restoration effort. In other words, the properties of the muck may be highly similar in ALL sites, and the differences and similarities among sites may be explained simply by the amount of sand vs. muck in the soil samples, not necessarily by the level of restoration.

Age effects

Lines 118 – 123. Obviously, it is difficult to find retired farms, passively restored sites, and actively restored sites that are similar to each other except in the way they have been managed following farm retirement. But clearly time may affect many of the variables that were assessed. There are 3 replicate passively restored sites of the same age (4 y), but the actively restored sites were 1 and 6 years old, and the retired farms were newly retired (1-4 y) or “old retired”: (17-20 y). Which of the active farms, which of the actively restored sites, which of the retired farms, and which of the passively restored sites can logically be compared to the reference wetland? The authors will have to take age into consideration in drawing conclusions. On line 244, the authors remove site age as a “nuisance variable”. I do not understand the justification for doing so. I do not see how you can remove the effect of age from among, for example, the actively restored sites, of which there are only two. Presumably every variable is modeled as a linear function of age, but how can that be justified if there are only two replicates of given treatment? Moreover, there is but a single reference wetland. Such a justification is possible only if each soil sample is considered an independent variable, and I have already indicated above why this is not so.

Smaller editorial comments:

Line 74. The word “ditches” is used twice.

Line 102. It is not immediately clear what the difference is between retired farms and flooded sites because flooded sites are presumably retired farms.

Lines 101 – 103. Are these really the only questions? Certainly, these are important but, judging from the rest of the introduction, an important underlying management question concerns the effectiveness of passive flooding as a restoration strategy. Shouldn’t that be included?

Line 106. Yes, but what basis is there for thinking that simply because “extensive action” was taken, active restoration would be the best strategy? Is there any evidence for this expectation? One could possibly propose another even more logical, opposing hypothesis, which is that active restoration would result in less SOM accumulation because of the greater level of disturbance.

Line 115. Describing the length of the perimeter is not helpful at all unless we know the shape.

Lines 128-129. Previously the authors used the term “flashboard”, but here they use the term “wooden board”. Keep it simple and use just one term.

Line 141. “…at a depth of 10 cm” is not clear. Do the authors mean “…to a depth of 10 cm”?

Line 141. What does “Soil samples (50 cm3)” mean? Does this mean that 50 cm3 samples were taken at 10 cm depth?

Line 142. Were all 8 samples homogenized together, or do you mean each of the 8, 50 cm3 samples was homogenized?

Reviewer #2: The manuscript presented by Rubin et al is an interesting example of how restoration processes have effects on microbial soil communities. The paper is well written, and I think it is easy to understand. I agree with the conclusions obtained here; however, I have some concerns about the data showed here and the data that the authors did not show.

Please, show at least in supplementary data, a table containing number of raw reads, reads per sample after filtering, taxonomy… those data showed in most of papers, that I believe is mandatory.

Authors refer ASVs correspond to unique taxonomic assignments to genus and species level for 16S (lines 173-175). Which genera within the identified Classes are the most abundant? Same question remains unanswered for microeukaryotic populations.

How the authors removed chimeras and sequences from plants/algae in the 16S analyses?

Some minor comments:

Line 74, remove one ditches

Line 149-154, please identify the regions for 16S and 18S (V3-V5, V4…)

The elimination of the R2 raw reads represents here an issue. Please, explain pros and cons of using only R1 or give more reasons/discussion about how this affects the data quality. This is a limitation of the study.

Data regarding to sequencing must be presented at least as supplementary data.

Line 181, Charophytes refers only to green algae, why the authors remove this taxon if they form part of the microbial communities as a whole?

Please, upload raw reads to SRA NCBI for better data accessibility. MG-RAST showed no results for the number provided.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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23 Jan 2021

Comments are provided in the packet, together with the cover letter.

Submitted filename: Response to Reviews_1.19.21.docx

Click here for additional data file.

7 Apr 2021

PONE-D-20-33816R1

Flooding and ecological restoration promote wetland microbial communities and soil functions on former cranberry farmland

PLOS ONE

Dear Dr. Rachel Rubin,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by 06.05.2021. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Luigimaria Borruso

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: PONE-D-20-33816R1

In general, this manuscript is very much improved over the first iteration. My main concern is Table 1, which appears to list the wrong values for n. I hope it is just an oversight and that the authors did not really perform incorrect analyses. In addition, the following are some other things to consider.

Line 68. “Data” is a plural word. Thus, “data that are…” is correct.

Lines 99-101. I do not understand the reasoning here. If flooding produces comparable results to restoration, why use flooding merely as an intermediate step, or as an alternative only when restoration is NOT an option? If it is comparable, why not use it ALL the time, particularly if it is easier, faster and cheaper than restoration?

Line 120. I do not seem to have access to the S1 file. I looked for this file because I do not see a good description of the natural sites. In fact, they are not described as wetlands in the text, only that “natural reference sites have no history of cranberry farming (Fig 1).”

Line 144. What does this mean: “For the MAJORITY of cases”? It might be better to state how many cases out of how many cases so the reader can make the judgement as to whether the procedure justifies what was done.

Lines 144-146. This sentence seems to be missing something.

Line 154. “spaced 30 m apart” is not clear. In a straight line? Often non-random sampling schemes like this are not easily justified. The point is for the sampling to best represent the kind of real variability likely to exist at the site.

Line 155. What kind of sampling device was used? It seems like a cylindrical soil probe was used, in which case the width should be given as the diameter.

Lines 233-234. How did you control for spatial autocorrelation?

Lines 234-235. It is probably more accurate to write “spatial correlation is unavoidable in many ecological studies”. But this still does not eliminate the potential problems interpreting this dataset. The interpretations have to take this potential confounding into consideration.

Line 242. Explain ASV.

Table 1. I do not understand the values for n. The unit of replication is the site, not the soil sample. Therefore, I am not confident that the statistical analyses were done properly.

Reviewer #3: The present article investigates the effects of retiring cranberry farms, and compares how retiring alone, flooding and active restoration are affecting soil microbial communities.

Although numbers of real replicates are low, appropriate efforts such as statistical power tests have been applied to compensate for this drawback. Moreover, individual soils have been sampled intensively to account for within-soil heterogeneity. Certainly, it is not easy to find and sample suitable sites for these five(!) treatments. The ecological conclusions are important for restoration politics and can contribute to a site melioration during the time leading up to restoration.

I do have some minor points that should be addressed before publication:

INTRODUCTION

Do soil variables explain microbial communities or vice versa? You mentioned this point that is especially true for denitrification in the discussion (l445) but I suggest to pronounce it more in the introduction.

l58: reference is missing

METHODS

l144: Unfortunately, FigS2 doesn’t show any data, at least in my file.

l151: please include the projection type that you used in this map.

l191: At this point, the reader is not familiar with the identifier M3. I suggest either to reference the position where these identifiers are explained, or to shortly state what kind of site it was. e.g. restored site M3

I 201: Being new to this method, I would appreciate some more information on how this extraction works in the main text.

l205: Please include a reference for your spectrometry.

l241: Paradis and Schleip 2018 is not included in the reference list

l263-264: this statement is not easy to understand. Above you stated that your environmental data didn’t show a relationship between geography and soil variables but here you mention short environmental gradients. Please explain more carefully.

RESULTS

l316 + l 337(+figure caption of fig 3): Adding up both percentages in fig. 3A/B I am not getting the 45% /44% mentioned in the text (36.5/28%). Whether this is due to the forward selection of paricular variables or due to other reasons, please explain more carefully.

Table S1 is missing many values for sample ER.2.5: It is not clear, however, if this is a real artefact, or if it is only missing by mistake. If values were not obtained for this sample, plese state something like n.d. in the excel file.

l350-352: In order to complete this information, I suggest to include two columns indicating the importance of each variable for microeucariotes/procaryotes, and to shorten the table caption accordingly.

Fig. 4: I really like those graphs, they are very informative! However, you should stay consistent in what you are labelling and what you don’t. Please include Methanobacteria labeling that is missing in the graph depicting differences between natural and restored areas. Other labels of significant differences are also missing (same in fig. 5) and I am wondering why. Were they not as important or did you choose to label only the once with a certain log2 ratio of median proportions?

l379: …whereas taxa that are more abundant in retired, flooded, and restored… here, farm sites are missing from the description.

DISCUSSION

l391-392: Wasn’t this class (kathablepharidae) MORE abundant in flooded and restored sites (orange) as compared to natural sites? If this is the case, they seem to represent a species that gains importance in succession stages such as flooded and restored sites, but is not as important in the natural reference sites. Please correct also in the discussion.

Fig. 5: Nucletmycea are labelled as Nucleomycea in some graphs, please correct.

l408: This procedere has not been described in the methods. How did you define “standouts”?

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: Yes: Magdalena Nagler

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

16 Jun 2021

Provided in the cover letter

Submitted filename: Response to Reviews PONE-D-20-33816.docx

Click here for additional data file.

30 Jun 2021

PONE-D-20-33816R2

Flooding and ecological restoration promote wetland microbial communities and soil functions on former cranberry farmland

PLOS ONE

Dear Dr. Rachel Rubin,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by 29.07.2021. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Luigimaria Borruso

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: PONE-D-20-33816R2

I believe we have a fundamental disagreement about the unit of replication in this study. The authors believe that the soil sample is the unit of replication. Thus, in Table 1, n = 24 or n = 48. On the other hand, I believe the unit of replication is the farm or the flooded site or the restored site, etc. The reason for this is that the unit of replication is the unit to which the treatment is applied. Individual soil samples from a single farm do not represent units of replication simply because the treatment was not individually applied to each soil sample. Therefore, I must insist that the authors perform their statistical analyses correctly.I mentioned this in my last review.

Reviewer #3: The authors addressed most of my comments. The only critcial point, which may be addressed, however, by the publishing team is that FIG S2 in file S2 still doesn’t display any data.

L62: I have the feeling that “are” needs to be removed

L449: The effect of treatments was stronger,

L452: isn’t it the S2 File?

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Reviewer #1: No

Reviewer #3: Yes: Magdalena Nagler, PhD

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24 Sep 2021

.

Submitted filename: Response to Review.docx

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18 Oct 2021

PONE-D-20-33816R3Flooding and ecological restoration promote wetland microbial communities and soil functions on former cranberry farmlandPLOS ONE

Dear Dr. Rachel Rubin,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. I gently ask you to consider the comments regarding the statistics approach the review#I arose since the objection from her/his original review still stands (see below).

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: All comments have been addressed

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Reviewer #3: Yes

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Reviewer #1: No

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #3: Yes

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Reviewer #1: I am not satisfied that the authors have handled the statistical analyses correctly. The objection from my original review two iterations ago still stands.

Reviewer #3: The authors addressed all issues raised in the former revision and I am happy with how the manuscript improved. Before publication, fig. S2 needs to be re-formatted as in my version I can see the graphs with x- and y-axes, but those graphs are not displaying any data.

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15 Nov 2021

Thank you for your prompt reply regarding our manuscript, PONE-D-20-33816R3, "Flooding and ecological restoration promote wetland microbial communities and soil functions on former cranberry farmland". We are hoping you can provide some additional guidance in your role as editor to help us move our manuscript forward to publication.

As you have noted, the sole remaining point of contention regarding our manuscript is the objection of Reviewer 1 to aspects of our statistical analysis. We have now made substantive changes in multiple rounds of revision that directly address Reviewer 1's concerns, and we have provided detailed commentary detailing how our revisions resolve those concerns. Specifically, Reviewer 1 asserted that our analysis suffered from pseudoreplication, since it combined samples across sites into treatment groups. Though we were not in complete agreement with this assessment, we acquiesced and in our most recent revision ran a new mixed model that incorporates "site" as a random effect in the statistical model. Furthermore, we also re-graphed Fig 2 and Fig 6 to display mean values for each site, with technical replicates shown as error bars. These revisions directly and definitively address Reviewer 1's concern. Moreover, beyond our own assertion of the validity of our analysis, we now also have the clear approval of Reviewer #3 who states that, "The authors addressed all issues raised in the former revision and I am happy with how the manuscript improved." In contrast, Reviewer 1 provides only a brief and vague objection, with no commentary at all regarding our prior revisions or our explanation of those revisions and no specific request for changes or corrections. Such a review is not actionable and therefore of no practical use.

We respectfully note that, in the course of peer review, it may sometimes be the case that opinions will differ, and not every Reviewer will be completely satisfied. In such cases, it seems reasonable to call upon the discretion of the Editor to make a judgement or a specific suggestion for resolution, and we expect that you will do this now. Given our substantive revisions, our detailed explication of those revisions, and the specific and definitive endorsement of the other independent Reviewer, can you please either accept our manuscript or tell us what specific change(s) you would like to see to render it suitable for publication?

Signed, Jason Andras (corresponding author)

Submitted filename: Three rounds of revision combined.docx

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22 Nov 2021

Flooding and ecological restoration promote wetland microbial communities and soil functions on former cranberry farmland

PONE-D-20-33816R4

Dear Jason,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Luigimaria Borruso

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

9 Dec 2021

PONE-D-20-33816R4

Flooding and ecological restoration promote wetland microbial communities and soil functions on former cranberry farmland

Dear Dr. Rubin:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Luigimaria Borruso

Academic Editor

PLOS ONE

Table 1

Top twelve soil variables that were identified as important predictors of communities of either prokaryotes or microeukaryotes, as determined by canonical analysis of principal coordinates, along with soil moisture.

Variable	Farm	New Retired	Old Retired	Flooded	Restored	Natural	p-value2
Cation exchange capacity (meq 100 g-1 soil)	6.44 (1.75)b	10.57 (3.09)b	8.75 (2.53)b	12.48 (1.65)b	9.02 (3.46)b	24.84 (12.60)a	<0.001
Copper content (ppm)	0.11 (0.02)	0.77 (1.16)	0.70 (0.95)	0.78 (0.44)	0.19 (0.15)	1.00 (0.63)	ns
Denitrification potential (ng N g dry soil-1 hour-1)	6.2 (12.3)	51.1 (131.3)	124.9 (253.6)	456.5 (727.3)	256.4 (423.8)	883.2 (1,519.8)	ns
Iron content (ppm)	42.75 (22.75)	67.45 (41.87)	68.43 (56.34)	138.01 (49.20)	107.5 (68.32)	561.55 (855.79)	ns
Magnesium base saturation (%)	3.61 (0.65)	3.36 (1.29)	2.32 (1.02)	3.63 (1.14)	4.34 (1.76)	9.61 (8.18)	ns
Methane (ug CH4 g dry soil-1 hour-1)	0.01 (0.00)b	0.02 (0.01)b	0.01 (0.00)b	0.06 (0.14)ab	0.03 (0.04)b	0.29 (0.84)a	<0.001
pH	4.57 (0.19)b	4.32 (0.25)b	4.53 (0.38)b	4.67 (0.52)ab	4.95 (0.27)a	4.17 (0.48)bc	<0.001
Phosphorus content (ppm)	3.84 (1.62)	5.59 (2.03)	3.68 (1.55)	4.56 (2.10)	3.43 (2.72)	4.03 (2.00)	ns
Potassium base saturation (%)	1.94 (0.39)a	1.47 (0.59)a	0.86 (0.31)b	0.85 (0.33)b	0.76 (0.32)b	1.16 (0.46)ab	<0.001
Sodium content (ppm)	10.80 (3.78)b	11.58 (5.19)b	8.35 (5.89)b	51.33 (62.77)b	19.69 (11.27)b	90.47 (41.16)a	<0.001
Soil moisture (%)	19.1 (6.6)b	24.4 (9.7)b	14.0 (10.2)b	34.7 (16.9)b	36.6 (12.7)b	74.4 (21.4)a	<0.001
Soil organic matter (%)	2.22 (0.89)b	3.87 (2.20)b	2.71 (1.42)b	6.02 (2.80)b	3.96 (2.81)b	50.76 (33.45)a	<0.001
Zinc content (ppm)	0.87 (0.38)b	2.01 (1.23)b	0.90 (0.68)b	5.24 (3.02)b	2.18 (1.86)b	9.64 (5.26)a	<0.001

Variables are listed in alphabetical order, values are means and standard deviations, and N is the total number of technical replicates within each treatment category. P-values are derived from linear mixed effects models conducted on each response variable, which were approximated using Type II Wald chisquare tests as implemented in the lme4 and car packages [28]. When omnibus tests were significant, pairwise comparisons were estimated using Tukey contrasts in the multcomp package [29].