Impact of diesel and biodiesel contamination on soil microbial community activity and structure

Soil contamination as a result of oil spills is a serious issue due to the global demand for diesel fuel. As an alternative to diesel, biodiesel has been introduced based on its high degradability rates and potential for reducing of greenhouse gases emissions. This study assessed the impacts diesel and biodiesel contamination on soil microbial community activity and structure. Our results suggest higher microbial activity in biodiesel contaminated soils and analysis of PLFA profiles confirmed shifts in microbial community structure in response to contamination. High-throughput 16S rRNA amplicon sequencing also revealed a lower bacterial richness and diversity in contaminated soils when compared to control samples, supporting evidence of the detrimental effects of hydrocarbons on soil microbiota. Control samples comprised mostly of Actinobacteria, whereas Proteobacteria were predominantly observed in diesel and biodiesel contaminated soils. At genus level, diesel and biodiesel amendments highly selected for Rhodococcus and Pseudomonas spp., respectively. Moreover, predicted functional profiles based on hydrocarbon-degrading enzymes revealed significant differences between contaminated soils mostly due to the chemical composition of diesel and biodiesel fuel. Here, we also identified that Burkholderiaceae, Novosphingobium, Anaeromyxobacter, Pseudomonas and Rhodococcus were the main bacterial taxa contributing to these enzymes. Together, this study supports the evidence of diesel/biodiesel adverse effects in soil microbial community structure and highlights microbial taxa that could be further investigated for their biodegradation potential.

PLFA analysis. Analysis of PLFA biomarkers revealed that microbial community structure was primarily affected by treatment (i.e., diesel or biodiesel amendment) followed by soil type (i.e., upper or lower slope) (Table S3). With the exception of fungal PLFAs, significant differences were detected between treatments for all biomarkers (p < 0.05). For example, Gram-positive (G+) bacteria biomass was highest on diesel treatments in lower slope soils in both absolute and relative abundance (mol%). Compared to control treatments, biodiesel addition stimulated Gram-negative (G−) bacteria, but inhibited G+ bacteria in both soils (Table S3). Similarly, biodiesel treatments exhibited the highest values of total PLFAs (p < 0.05), which varied from 49.6 to 44.2 nmol·g −1 on soils in the upper and lower slope, respectively (Fig. S1).
Non-metric multi-dimensional scaling (MDS) ordination from PLFA profiles indicated clusters by treatment within microbial community profiles that were confirmed by multi-response permutation procedure (MRPP) analyses (p < 0.05) (Fig. 2). Here, two clustering groups were identified including: (i) biodiesel amended soils that positively correlated with soil carbon (TC and TOC), total PLFAs and G− bacteria; (ii) diesel and control treatment groups that exhibited positive correlations with G+ bacteria (i.e., absolute and relative abundance).
High-throughput 16S rRNA amplicon sequencing. High-throughput sequencing analysis of the V4 region of the 16S-rRNA gene indicated a recovery of 458,158 high quality sequences and 1716 unique sequences in 30 soil community samples. A total of 20 phyla was detected in the dataset, in which only five distinct phyla comprised approximately 90% of the profile. Proteobacteria and Actinobacteria were the most abundant phyla www.nature.com/scientificreports/ considering all samples analyzed (Fig. 3). Control soils exhibited a dominance of Actinobacteria (> 40%) while diesel and biodiesel contaminated soil had a high abundance of Proteobacteria (> 60%). Other phyla such as Gemmatimonadetes and Firmicutes corresponded less than 20% and 10% of all profiles, respectively. Correlation analysis between soil chemical parameters and relative abundance of bacterial profiles revealed significant associations at phylum, class and family levels (Table S4) Tables S2 and S3, respectively.  S2). In addition, only 130 ASVs were common to all profiles, thus representing 7.6% of the total number of ASVs. Alpha diversity indexes (i.e., chao1 richness, Shannon and Simpson diversity) showed significant differences between treatments and slope (Table 1). Overall, higher richness and diversity were observed in control samples for both soil slopes analyzed. In biodiesel contaminated soils, the lowest alpha diversity indexes were detected in upper slope soils; whereas in diesel treatments, the lowest alpha diversity indexes were observed in lower slope soils. Spearman's rank correlations with soil chemical parameters also revealed overall negative correlations between alpha diversity indexes and soil carbon (TOC and TC). No significant correlations were observed between diversity indexes and soil inorganic carbon or soil nitrogen (Table S5).
Analysis of ß-diversity using principal coordinate analysis (PCoA) revealed a clear separation in16S rRNA profiles by treatment (p = 0.001) (Fig. 4), and significant differences between slope positions (p = 0.001) when considering unweighted unifrac distances (Fig. 4B). This evidence was further analyzed using a ternary plot at  www.nature.com/scientificreports/ genus level, color coded by the most abundant families in the dataset (Fig. 5). Here, genera from the family Gemmatimonadaceae and Rubrobacteriaceae were more closely associated with control samples, whereas members of the family Burkholderiaceae were mostly detected in both diesel and biodiesel contaminated soils.
To assess the main genera driving differences in microbial community structure after diesel and biodiesel amendment, a heatmap based on Bray-Curtis dissimilarity was generated in order to compare bacterial profiles (Fig. 6). Our analysis confirmed that these profiles clustered mainly by treatment where three main clusters (A-C) were observed after a 65% dissimilarity cut off. Cluster A (left to right) corresponded to diesel amended soils, which consisted mainly of Anaeromyxobacter (31.5%), Rhodococcus (8.67%), Pseudomonas (5.2%), Novosphingobium (4.8%) and unclassified genus from the family Burkholderiaceae (3.7%). Anaeromyxobacter was the indicator genus driving these differences in which it could comprise up to 50% of profiles. Cluster B consisted exclusively of biodiesel samples, which were driven by a high abundance of Pseudomonas (comprising up to 76% of in some profiles and on average 43%). Additional genera such as Bacillus (8.2%), Massilia (4.0%), Blastococcus (3.1%) and Pantoea (3.1%) were also included in cluster B (Fig. 6). Moreover, we also identified a third cluster (Cluster C) consisting only by control samples, in which no particular genera corresponded to more than 15% of the profile. In this cluster, the most abundant genera detected were Rubrobacter (9.9%), an unclassified genus from the family Gemmatimonadaceae (4.2%), Bacillus (4.2) Blastococcus (4.2%) and Tumebacillus (3.4%).
Relative abundance of the most abundant taxa between diesel and biodiesel treated soils was also compared using Welch's t-test (p < 0.05) (Fig. S3). A total of 27 bacterial genera was significantly different between these soils. Whereas diesel treatments had a higher abundance of Anaeromyxobacter and Rhodococcus, soil amendment of biodiesel fuel favoured Pseudomonas ssp.

Discussion
In this study, we provide a detailed assessment on the effects of diesel and biodiesel amendments in soil. We first monitored microbial activity upon the first 5 weeks of contamination followed by a characterization of microbial community structure and microbiome functional prediction after a 1-year incubation. Microbial activity, www.nature.com/scientificreports/ monitored by CO 2 production, in biodiesel-and diesel-contaminated soils confirms the ability of microorganisms to degrade and use these compounds as carbon sources 8 . In turn, microbial respiration of control samples is related to the response of the microbial activity to basic soil nutrients in the absence of organic amendments. In our study, contaminated soils had an overall increase in microbial activity after 14 to 21 days followed by a decrease after 21 days, which may indicate a depletion of the substrate. Studies conducted by Silva et al. 8 suggest that biodiesel amendment in soils resulted in the highest respiration rates, which confirms that biodiesel is more easily biodegradable than diesel. Similar results were also observed in our study as diesel-contaminated soils indicated the lowest microbial activity amongst amended soils. Lapinskiene et al. 15 also observed similar results suggesting that diesel is more resistant to microbial decomposition than biodiesel. According to Schiewer et al. 16 , biodiesel degradation is typically faster than diesel, and biodiesel addition has even been used to stimulate hydrocarbon degradation in contaminated sands. After a 1-year incubation, TOC and TC content in our study was higher in treated soils since hydrocarbon contamination is known to increase total carbon content in soil 17 . Unexpectedly, we also detected a change of soil inorganic carbon in these soils, especially in soils amended with diesel fuel. Soil inorganic carbon, carbonates (HCO 3 − and CO 3 2− ) primarily associated with calcium and magnesium, are mostly affected by soil carbon dioxide, pH, Ca 2+ content and water 18 . Previous studies on diesel contaminated soils found higher degradation rates in carbonate-rich soils 19 and suggested that the CO 2 produced by diesel mineralization could result in the formation of soil carbonates 20 .
Microbial community structure analyses were conducted using culture independent phospholipid fatty acid analysis (PLFA) and high-throughput 16S rRNA amplicon sequencing. We used PLFAs in addition to a nucleic acid based method (i.e., 16S rRNA amplicon sequencing), as phospholipids found in cell membranes of all living organisms and are rapidly degraded upon their death 21 . Hence, this analysis provides a measure of viable community biomass and structure. Significant differences of PLFA profiles where observed in both soil slopes and treatments; however, most differences were observed among treatments. In our study, microbial community profiles mainly clustered by treatments in which two distinct groups were identified: (i) soils amended with biodiesel, (ii) diesel and control treatment groups. Margesin et al. 22 analyzed soil PLFA profiles based on total petroleum hydrocarbons (TPH) and observed a significant increase in the Gram-negative populations in high TPH amended-soils. Our results also indicated that soils amended with biodiesel stimulated the abundance of Gram-negative bacteria. However, diesel treatments and control samples exhibited the lowest amounts of total PLFAs, which may suggest that unlike biodiesel, diesel is not being metabolized at the same rate by bacterial communities, and therefore no increase in microbial abundance was observed. According to Margesin et al. 22 , G− bacteria are r-strategists and can rapidly grow under substrate-rich conditions. In our study, MDS analysis also revealed that an increase in TC and TOC content were highly associated with biodiesel treatments (Fig. 2). Similar results were observed by Owsianiak et al. 23 using diesel/biodiesel blends as a carbon source for bacterial consortia. This study reported that higher biodiesel content in fuel blends led to greater microbial biomass, thus supporting evidence that biodiesel is a favored carbon source over diesel. However, degradation of PLFAs upon cell death is significantly faster than other cell components such as DNA, RNA, and proteins 24 . For this reason, PLFA analysis has long been used as a sensitive tool to detect community shifts in response to changing environmental conditions 25 . Yet, many fatty acids are common to different microorganisms 26 and therefore we used high-throughput 16S rRNA amplicon sequencing to overcome these limitations.
High-throughput sequencing revealed that soil contamination with diesel and biodiesel affected bacterial profiles considerably. Actinobacteria, which were the most abundant phylum in control samples, play an important role in nutrient cycling due to their ability to metabolize complex organic matter 27 . In contrast, a high abundance of Proteobacteria was observed in diesel and biodiesel contaminated soils. Proteobacteria are known for their ability to utilize aliphatic and aromatic compounds, hence an increase in their abundance is often noted in hydrocarbon-amended soils 7,28,29 . Additionally, positive correlations between Proteobacteria and soil total carbon was observed in our study, as Proteobacteria are thought to respond positively to carbon and nutrient inputs in soil 30 . Possibly, the increased soil carbon levels due to biodiesel addition may have selected for bacteria that are able to utilize this amendment as a carbon source. Although Actinobacteria and Proteobacteria comprised most of the bacterial profiles in our dataset, we also observed an increased abundance of Firmicutes in contaminated soils. Firmicutes play a major functional role in the decomposition of plant polymers, yet a broad metabolic activity in aromatic and/or aliphatic hydrocarbons is rare among this phylum 31 . Moreover, some thermophiles such as environmental spore-forming Geobacillus and Bacillus strains, both members of the phylum Firmicutes, are known to inhabit hydrocarbon-impacted environments 32,33 .
In addition to bacterial community structure at phylum level, 44% of ASVs in our dataset were unique to control samples (Fig. S2). In fact, we also detected a significant reduction in bacterial richness and diversity in contaminated soils, thus suggesting the selection for specific bacterial consortia. Similar results were reported by Sutton et al. 28 , in which the presence of diesel contributed significantly to explaining shifts in soil microbial community structure. According to Bundy et al. 34 , hydrocarbon contamination often selects for reduced numbers of generalists and catabolically-versatile bacterial species. Similarly, PCoA analysis of bacterial profiles in our study indicated significant differences between treatments. Here, we observed clustering regions with a low variability between samples such as in biodiesel amended soils, and a high variability in control and diesel treatments.
Supporting the evidence of the selection of a few bacterial taxa in diesel and biodiesel contaminated soils, control soils mostly consisted of members from the family Gemmatimonadaceae and Rubrobacteriaceae, whereas Burkholderiaceae were more associated with contaminated soils. Members of the family Burkholderiaceae have been detected in the crude oil samples 35 , and many species of Burkholderia, such as B. cepacia are known to biodegrade hydrocarbons 36,37 .
Analysis of bacterial profiles at genus level revealed that Anaeromyxobacter, Rhodococcus, Pseudomonas and Bacillus are the main genera driving differences in microbial community structure in contaminated soils. In particular, our data suggests a high abundance of Anaeromyxobacter in diesel amended soils.  38 . In addition, diesel-contaminated soils also indicated the presence of Rhodococcus spp., with an average relative abundance of 10%. Due to their hydrophobic cell surfaces, and their inherent ability to degrade a broad range of organic compounds and to produce biosurfactants, Rhodococcus are potential candidates for hydrocarbon biodegradation in soils 39 . In fact, Lee et al. 40 reported that the inoculation of Rhodococcus sp., combined with mycolic acid as synthetic surfactant, significantly enhanced soil diesel biodegradation. While Anaeromyxobacter and Rhodococcus were the most abundant organisms in diesel contaminated soils, both biodiesel-and diesel-amendments favored the presence of Pseudomonas spp. Numerous studies reported that Pseudomonas are able to degrade naphthalene 41 , phenanthrene 42 , diesel 43 and biodiesel 44 . According to Taccari et al. 45 , Pseudomonas spp. produce biosurfactants that may contribute to the desorption and degradation efficiency of petroleum derived hydrocarbons. In addition to Pseudomonas spp., biodiesel amended soils also exhibited a dominance of Bacillus spp. As Gram-positive, endospore-forming bacteria, Bacillus spp. exhibit a wide range of physiological abilities which includes adaptation to biodiesel-diesel contamination 46 and active biodiesel degradation 47 . Differently from biodiesel-and diesel-contaminated soils, Rubrobacter, a known Actinobacteria well adapted for semi-arid soils 48 , was highest in control samples. In studies assessing soil contamination by hydrocarbons, a high abundance of genera from the phylum Actinobacteria have been previously reported in   49 . Bell et al. 30 also found negative correlations between Actinobacteria and soil hydrocarbon concentrations after diesel contamination. Microbial profiling based on 16S rRNA is a key tool to analyzed changes in microbial community structure, but it lacks to provide direct evidence of their functional capabilities. Therefore, PICRUSt2 provides an opportunity to predict functional profiles based on 16S rRNA and it has been previously used to assess hydrocarbondegrading potential 50,51 . Using the PICRUSt2 pipeline, we detected a higher abundance of metabolic pathways in propanoate degradation, octane oxidation and sugar degradation in contaminated soils. In particular, mean proportions of the octane oxidation pathway was much higher in these treatments when compared to control soils. This pathway describes organisms capable of using intermediate chain length n-alkanes (C6 to C12) as an energy source 14 . The alkane hydroxylase system is a key component of this pathway that in introduces molecular oxygen in the terminal carbon atom of hydrocarbon compounds to form primary alcohols 52 . Hence, PICRUSt2 analysis suggest that bacterial communities in soils contaminated with diesel and biodiesel developed specific mechanisms to adapt their metabolic pathways to hydrocarbon degradation. Moreover, profiles in contaminated soils also indicated a higher abundance of proteinogenic amino acid and vitamin biosynthesis. Similar results were observed by Mukherjee et al. 53 in petroleum hydrocarbon contaminated sites, in which these authors attributed to a wide range of functions such as stress tolerance and redox responses. Therefore, based on the evidence of high proportions of predicted propanoate degradation, octane oxidation and sugar degradation pathways in contaminated soils, we focused our next analysis on specific groups of hydrocarbon degrading enzymes within these samples.
PICRUSt2 analysis revealed, with the exception of 3-oxoadipyl-CoA thiolase (EC:2.3.1.174), a higher abundance of enzymes associated with aromatic compound degradation (i.e., benzoate, cyclohexane and PAH degradation) predicted in diesel contaminated soils. For example, enzymes such as protocatechuate 4,5-dioxygenase (EC:1.13.11.8) and haloalkane dehalogenase (EC:3.8.1.5) both act on aromatic compounds. Protocatechuate 4,5-dioxygenase is a well-known oxidoreductase that catalyze the cleavage of the aromatic ring on aromatic compounds with the insertion of two oxygen atoms 54 . Haloalkane dehalogenases; however, catalyse the hydrolysis of halogenated alkanes where the halogen functional group is replaced with a hydroxyl group 55 . Most likely, a higher abundance of aromatic compound degradation enzymes in these soils are due to the chemical composition of diesel fuel. Diesel is a complex mixture of hydrocarbons (8-26 carbon atoms) which includes aromatic hydrocarbons (23.9%) and cycloalkanes (33.4%) 56 . However, diesel consists mostly n-alkanes (42.7%) 57 and therefore it is expected a high abundance in alkane degradation enzymes in diesel contaminated soils. In fact, alkane 1-monooxygenase (EC:1.14.15.3), one of the most studied enzymes in hydrocarbon degrading bacteria, was detected in high abundance in these soils. Alkane monooxygenases are known key enzymes in aerobic degradation of alkanes by bacteria [58][59][60] . These enzymes hydroxylate alkanes to alcohols, which are further oxidized to fatty acids and catabolized via the bacterial β-oxidation pathway 61 .
In addition to alkane degrading enzymes, other enzymes in the fatty acid degradation pathway (ko00071) such as long-chain acyl-CoA dehydrogenase (EC:1.3.8.8) were also more abundant in diesel contaminated soils. Unlike diesel, which contains aromatic hydrocarbons, biodiesel consists of monoalkyl esters of long-chain fatty acids derived from renewable biolipids 62 . These fatty acid (m)ethyl esters are generally produced from natural oils or fats and it is expected a higher abundance of FAME degradation enzymes in biodiesel contaminated soils. This was true for rubredoxin-NAD + reductase (EC:1.18.1.1) and delta3-delta2-enoyl-CoA isomerase (EC:5.3.3.8). Rubredoxin-NAD + reductase is an important enzyme in the hydrocarbon hydroxylating system 63,64 and several species of Pseudomonas such as P. oleovorans 65 , P. oleovorans and P. putida 66 are known to produce this enzyme. Therefore, the dominance of Pseudomonas spp. in biodiesel profiles may be associated with a higher abundance of predicted Rubredoxin-NAD + reductase in these soils.
We also used PICRUSt2 to identify the taxa contribution of hydrocarbon degrading enzymes (Fig. 7B). Our analyses indicate a high contribution of members of the family Burkholderiaceae and the genus Novosphingobium in enzymes associated with benzoate degradation. Lyu et al. 67 reported that Novosphingobium pentaromativorans US6-1 is able to degrade a large spectrum of aromatic hydrocarbons, ranging from monocyclic to polycyclic hydrocarbons. Most recently, Wang et al. 68 conducted a genomic comparison analysis of 22 genomes of Novosphingobium strains and identified that they shared most degradative pathways including degradation of aromatic compounds and benzoate degradation. In our study, diesel contaminated soils had a higher abundance of Novosphingobium spp. (Figs. 6, S3), which suggest that aromatic hydrocarbons in diesel fuel are selecting for competent taxa do degrade these compounds. Moreover, most of predicted cyclohexane degradation (i.e., haloalkane dehalogenase EC:3.8.1.5) was attributed to the genera Anaeromyxobacter and Rhodococcus. As a facultative anaerobic myxobacterium, the presence of Anaeromyxobacter after a 1-year incubation suggests that natural attenuation has occurred under anoxic conditions. Our analysis revealed that sequences of Rhodococcus spp. not only contributed to predicted degradation of cyclohexenes but also in FAME degradation. For example, predicted alkane 1-monooxygenase (EC:1.14.15.3) was highly attributed to Rhodococcus spp., as multiple alkane hydroxylases have been identified as a common feature of this genus 39 . Although the presence of Rhodococcus spp. highly contributed to FAME degradation enzymes (i.e., EC:1.14.15.3 and EC:1.3.8.8), most of predicted contribution in this pathway was due to Pseudomonas spp. In biodiesel contaminated soils, we previously detected a higher abundance of Pseudomonas spp. (Fig. 6), which may suggest that the presence of long-chain fatty acid (m)ethyl esters in biodiesel fuel most likely selected for FAME degrading Pseudomonas spp. in these soils.

Conclusions
This study assessed the impacts of diesel and biodiesel fuel on soil microbial activity within the first five weeks of contamination and shifts in microbial community structure after a 1-year incubation. We combined methods such as PLFA analysis to detect immediate changes in microbial community structure and high throughput 16S www.nature.com/scientificreports/ rRNA amplicon sequencing for a high-resolution taxonomic assessment. We found the highest microbial activity rates in biodiesel contaminated soils and shifts in microbial community structure. Long-term soil contamination led to an overall lower bacterial richness and diversity when compared to control samples while selecting for specific groups of microorganisms. A significant number of bacteria taxa in our dataset were unique to control soils, which supports the evidence of detrimental effects of hydrocarbon contamination to soil microbial diversity. Diesel contamination highly selected for Anaeromyxobacter and Rhodococcus spp., whereas a high abundance of Pseudomonas and Bacillus was found in biodiesel samples. Analysis of predicted hydrocarbon-degrading enzymes also revealed differences in functional profiles based on diesel and biodiesel chemical composition. Here, we identified potential key bacterial taxa in enhancing natural attenuation (i.e., Burkholderiaceae, Novosphingobium Anaeromyxobacter, Pseudomonas and Rhodococcus). Together, our analyses provide a detailed examination of soil microbial community activity and structure following exposure to anthropogenic recalcitrant hydrocarbons (e.g., diesel and biodiesel) thus confirming its potential adverse effects in soil health.

Methods
Soil collection. A Dark Brown Chernozem soil collected near Saskatoon, SK-Canada was used in the study. The upper and lower slopes included an Ardill Association (upper Apk) upper slope (Rego-low organic matter) and a low-slope (Eluviated-high organic matter) on a transect, respectively. Soils were air-dried, sieved to pass a 5 mm mesh and analyzed for nutrient contents including total nitrogen (TN), measured by dry combustion method using a LECO TruMac CNS Analyzer, total carbon (TC) and total organic carbon (TOC), measured according to Dhillon et al. 69 using a LECO C-632 Carbon Analyzer. Soil organic Matter (OM) was analyzed using the dry-ash method 70 . Soil pH was measured in a 2:1 soil: water slurry. Soil available ammonium and nitrate were determined colorimetrically (660 and 520 nm, respectively) according to Laverty and Bollo-Kamara 71 . Available phosphorus and potassium were measured using a modified Kelowna extraction 72 and available sulfate by a calcium chloride extraction 70 .
Microcosm and microbial activity. Air dried soils (n = 2) were subjected to two treatments including (i) biodiesel and (ii) diesel (10 × 10 4 L/ha), and (iii) untreated control, each replicated five times (total of 30). For the treatments amended with diesel or biodiesel, 100 g of soil were weighed and placed into a 200 cc plastic vial and 5.0 mL of each contaminant poured onto the soil. Deionized water was added to control and contaminated soils as required to ensure the moisture content (60% MHC) at field capacity. Treatments were incubated at room temperature in a 1.0 L Mason jars equipped with a septum for gas sampling and assessed weekly for five weeks using a modified CO 2 evolution method by Anderson and Domsch 73 . After a 1-week incubation, a 20-cc headspace gas sample was withdrawn from the Mason jars using a 25-cc plastic syringe. Samples were analyzed on a Shimatzu GC-8A gas chromatograph equipped with a Porapak-Q column and thermal conductivity detector set at 45 and 60 °C, respectively 74 . After sampling, soils were also checked for moisture content deionized water was added if necessary and jars were left open for a few minutes to allow for re-oxygenation, sealed and re-incubated until the next sampling. The rate of CO 2 evolution was expressed as µg of CO 2 ·g of soil -1 ·day -1 calculated from the difference between each sampling week (1-5) and the initial week. After the microbial activity assessments, soils were incubated for 1-year at room temperature according to Ramirez et al. 75 and Craine et al. 76 . Microbial community structure was determined after incubation by phospholipid fatty acid analysis (PLFA) and highthroughput 16S rRNA amplicon sequencing.
PLFA analysis. PLFA analysis of soil samples was based on a modified protocol from Helgason et al. 77 .
Soil samples were sieved, freeze-dried and ground with mortar and pestle to maximize lipid recovery. Fatty acids were extracted from 4.0 g of lyophilized, ground soil in a methanol/chloroform mixture and then dried down under constant N 2 flow. Neutral-, glyco-and phospho-lipids were separated using solid phase extraction columns (0.50 g Si; Varian Inc. Mississauga, ON), sequentially eluted with chloroform (CHCl 3 ), acetone ((CH 3 ) 2 CO) and methanol (MeOH) respectively, and the phospholipid fraction dried under N 2 flow. The phospholipid fraction was methylated using a solution of 1:1 methanol/toluene and methanolic potassium hydroxide (KOH) at 35 °C. After methylation, the resulting fatty acid methyl esters (FAMEs) were analyzed using a Hewlett Packard 5890 Series II gas chromatograph equipped with a 25 m Ultra 2 column (J&W Scientific). Peaks were identified using fatty acid standards and MIDI identification software (MIDI Inc., Newark, DE) and quantified based on the addition of a known concentration of the internal standard methyl nonadecanoate (19:0) 77,78 . Microbial biomass was determined by biomarker abundance calculated based on the peak area detected for each fatty acid, relative to that of a known quantity of the internal standard. Biomarkers used to represent Grampositive bacteria (G+) were i14:0, i15:0, a15:0, i16:0, i17:0, a17:0. For Gram-negative bacteria (G−), biomarkers used were 16:1 ω7t, 16:1ω9c, 16:1ω7c, 18:1ω7c, 18:1ω9c, cy17:0, and cy19:0 79 . Fungal biomass was evaluated using the PLFA biomarker 18ω2:6,9. Total biomass was calculated as the sum of all detected PLFAs 80 (Total of 48), including 14 biomarkers and 34 general fatty acids (non-biomarkers). All biomass values were reported based on dry soil weight in units of nmol·g −1 soil derived from individual molecular weights of each fatty acid 77,81 . PLFA statistical analyses. Analysis of variance for PLFA biomarkers and total PLFAs was conducted using SAS v. 9.4 (https:// www. sas. com). Non-metric multidimensional scaling (MDS) analysis of PLFA community composition (biomarkers and general fatty acids) was carried out using PCOrd v. 6.0 82 (https:// www. wildb luebe rryme dia. net/ pcord). The PLFA data was transformed to log (mol% + 1) and Sørensen distance measure was selected using the autopilot slow and thorough analysis option 83,84 . A random starting point was used for initial analysis and then optimized in previous ordinations to achieve the lowest stress (expressed as Kruskal stress). DNA extraction. Total soil community DNA was extracted using the MoBio PowerSoil extraction kit (MoBio Laboratories Inc., Carlsbad, CA) following the manufacturer's protocols. The DNA yield was quantified using Qubit Fluorometric Quantitation (Invitrogen) and in a SYBR Safe (Invitrogen) 1% agarose gel by comparison with a high DNA mass ladder (Invitrogen) using a Bio-Rad Gel Doc XR System (Bio-Rad Laboratories, Mississauga, ON). 16S rRNA amplicon sequencing bioinformatics and statistical analysis. Sequence reads were analyzed using QIIME 2 v. 2019.1 86 using QIIME 2 pipelines 87 (https:// qiime2. org). The raw forward and reverse sequences were quality-filtered using DADA2 88 . To remove noise from the data, the first 25 nucleotides were removed from the forward and reverse reads according to visual inspection of the quality of the reads. Highquality reads were down-sampled to the smallest sample size and classified (99% similarity) using the Silva 132 database. Alpha and beta diversity analyses were conducted using the QIIME 2 plug-in q2-diversity. Microbial community composition between groups were plotted in a principal coordinate analysis (PCoA) based on weighted and unweighted unifrac distances generated in QIIME 2. Statistical significance among groups (slope and treatment) was determined by the nonparametric statistical method ADONIS 89 with 999 permutations. Heatmap and ternary plots were generated with R v. 3.6.0 (R Foundation for Statistical Computing; available at http:// www.R-proje ct. org) using the VEGAN package (v. 2.5-7) 90