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. 2018 Oct 23;95(1):fiy212. doi: 10.1093/femsec/fiy212

Do soil bacterial communities respond differently to abrupt or gradual additions of copper?

Michael McTee 1,✉, Lorinda Bullington 1, Matthias C Rillig 2,3, Philip W Ramsey 1
PMCID: PMC6579733  EMSID: EMS81221  PMID: 30351419

ABSTRACT

Many experiments that measure the response of microbial communities to heavy metals increase metal concentrations abruptly in the soil. However, it is unclear whether abrupt additions mimic the gradual and often long-term accumulation of these metals in the environment where microbial populations may adapt. In a greenhouse experiment that lasted 26 months, we tested whether bacterial communities and soil respiration differed between soils that received an abrupt or a gradual addition of copper or no copper at all. Bacterial richness and other diversity indices were consistently lower in the abrupt treatment compared to the ambient treatment that received no copper. The abrupt addition of copper yielded different initial bacterial communities than the gradual addition; however, these communities appeared to converge once copper concentrations were approximately equal. Soil respiration in the abrupt treatment was initially suppressed but recovered after four months. Afterwards, respiration in both the gradual and abrupt treatments wavered between being below or equal to the ambient treatment. Overall, our study indicates that gradual and abrupt additions of copper can yield similar bacterial communities and respiration, but these responses may drastically vary until copper concentrations are equal.

Keywords: heavy metals, community, ecosystem, press, pulse, disturbance

Experiments measuring the response of microbial communities to abrupt additions of heavy metals may yield different results than if they had added the metals gradually, which often better mimics environmental processes.

INTRODUCTION

Heavy metals accumulate worldwide from mining and smelting, agriculture and coal-fired power plants (Moore and Luoma 1990; Pavlish et al. 2003; Brandt et al. 2010; Sherman et al. 2012). Consequently, organisms are exposed to both sub-lethal and lethal concentrations of heavy metals that alter the structure and function of ecological communities (Bååth 1989; Wang et al. 2007). Much attention focuses on how this exposure affects soil microbial communities because soil microbes are fundamental to biogeochemical cycles and overall ecosystem function (Singh and Gupta 1977; Falkowski, Fenchel and Delong 2008; Gadd 2010; Gall, Boyd and Rajakaruna 2015). Many experiments that expose microbial communities to heavy metals apply the metals abruptly at a single time (Gremion et al. 2004; Frey et al. 2006; Oorts, Bronckaers and Smolders 2006), which may limit interpretation if the contaminant accumulates gradually in the environment.

An abrupt addition of heavy metals to soil often leads to decreased microbial biomass production, drastically altered compositions of microbial communities, and declines in species diversity and soil respiration (Bååth 1989; Oorts, Bronckaers and Smolders 2006; Gall, Boyd and Rajakaruna 2015). However, weaker responses have been observed when soil has been exposed to heavy metals for long periods of time. For instance, fluvial sediments that had been contaminated with heavy metals for 93 years were shown to have species richness comparable to that in pristine sediments (Ramsey et al. 2005). This suggests that long-term heavy metal exposure allows microbial populations to adapt. Indeed, an acquired tolerance to heavy metals has been observed in bacteria both experimentally and in the field (Diaz-Ravina and Bååth 1996; Pennanen et al. 1996; Silver and Phung 1996). However, many short-term contamination experiments may not provide adequate time to allow bacterial populations to adapt, so these studies may over-estimate the effect of imposed contaminants (Mukherjee et al. 2014). In addition to the duration of heavy metal exposure, dose is also important in determining community responses. For instance, the ability of a microbial community to respond to a heavy metal may depend on whether the metal was applied abruptly, or instead, gradually over time. For example, climate change experiments often expose a community to high concentrations of gaseous CO2 to simulate future atmospheric conditions. These studies do not account for the decades of time needed for CO2 concentrations to reach those levels or the potential for biota to adapt. To address this concern, Klironomos et al. (2005) exposed soil microcosms to projected future concentrations of atmospheric CO2 either abruptly or gradually. They found that previous studies may have grossly overestimated community responses to elevated concentrations of atmospheric CO2. This may be important for microbes facing other perturbations, such as heavy metal contamination, because microbes have rapid doubling times (Cooper and Helmstetter 1968; Brock 1971), and in some cases, the ability to transfer genes that allow the tolerance of heavy metals (Rensing, Newby and Pepper 2002; Hemme et al. 2010).

In a greenhouse experiment, we dosed soil communities with CuSO4, either abruptly at the beginning of the experiment, or gradually over 26 months until the concentrations of CuSO4 were approximately equal in each treatment. We used Cu due to its ubiquity in heavy metal contamination (Moore and Luoma 1990) and agriculture (Brandt et al. 2010). Elevated concentrations of Cu can decrease soil productivity and alter the structure of microbial communities (Bååth 1989). Although plants require Cu as a micronutrient, Cu becomes toxic for many species when concentrations in the soil exceed 20 µg/L (Påhlsson 1989). Soils were kept in a greenhouse where soil chemistry and air temperature could vary seasonally. We also planted and harvested cheatgrass (Bromus tectorum) every two months to add a plant component that would likely have occurred in nature, with the intention of yielding more realistic results (De Boeck et al. 2015). Over the course of the study, we measured soil respiration and collected soil for chemical and bacterial analysis. We addressed two research questions: (i) Do bacterial communities respond differently to gradual and abrupt additions of Cu in terms of composition, richness and diversity? (ii) How does the gradual or abrupt addition of Cu influence soil respiration? We hypothesized that abrupt Cu additions would suppress bacterial richness, diversity and soil respiration, while also altering bacterial communities relative to the ambient treatment. We expected similar but less dramatic responses in the gradual treatment.

METHODS

Soil collection and preparation

We collected soil from a site that had a diverse native plant community in the Bitterroot Valley, Montana (45° 40′ 42.70″ N, 113° 59′ 20.45″ W; elevation of 1226 m). Soil was sieved (< 2 mm) and then mixed with previously washed sand at a 1:1 ratio to reduce compaction due to handling. The soil mixture was poured into 24 separate 3.8 L bags so that each bag contained 1.8 kg of soil. We grouped the bags, which were only used to mix soil, in sets of eight, which became the treatments. The ambient treatment (control) received 100 mL of distilled H2O. Each replicate for the gradual treatment received 4.9 mL 400.5 mM CuSO4 (1.96 mmol) mixed with 95.1 mL of distilled H2O. Each replicate for the abrupt treatment received 58.7 mL of 400.5 mM CuSO4 (23.51 mmol) mixed with 41.3 mL distilled H2O. Concentrations of CuSO4 were based on our pilot study that determined the highest tolerable concentration of CuSO4 to cheatgrass seedlings. Soils were mixed in the bags and then poured into 10 cm square × 13 cm tall pots. Soils were incubated in pots for the duration of the study. Four seeds of cheatgrass were planted in each pot. All seeds germinated in all instances. Each of the four seedlings were kept until harvest. We used cheatgrass because it germinates rapidly, grows fast and tolerates moderate concentrations of Cu based on our pilot study. Differences in biomass among treatments could influence soil chemistry and microbial communities differently, but by including a plant, we were aiming to obtain results that would be more transferrable to environmental conditions. Indirect effects on our experimental system that were created by the presence of the plant would also be expected to occur under field conditions, and as such, are part of the expected suite of effects that accompany pollution. Pots were incubated in a greenhouse that included a heater. Air temperatures in the greenhouse commonly ranged from 12–35°C; the temperature fell outside this range during several extreme weather events. The positions of pots in the greenhouse were randomized monthly, soil moisture was monitored by visual inspection and held as near constant as possible at 8.8% ± 0.2% (mean ± se), and measured gravimetrically every two months.

Soil respiration and harvest

We measured respiration, harvested plants, and added more CuSO4 to the gradual treatment in two month cycles. Specifically, two months after we planted cheatgrass, we clipped the aboveground biomass, dried it at 65°C, and weighed it. PVC collars that were 10 cm in diameter and 8 cm tall were driven into the soil (2.5 cm depth). The next day, we measured soil respiration with a Li-6400 (Licor Instruments, Lincoln, NE, USA) infrared gas analyzer that used a 6400–09 soil chamber. We also measured soil temperature. After measuring respiration and temperature, we removed the PVC collars. The third day, we poured and mixed the contents of the pots in 3.8 L plastic bags, keeping replicates and treatments separate, to obtain homogenous soil samples. A subset of soil (∼5 g) was collected, weighed and dried at 105°C to determine soil moisture. Soil was also collected for chemical (∼42 g) analysis at 2, 4, 8, 12, 16, 20, 24 and 26 months and for microbial analysis at 2, 8, 16 and 26 months. We limited the harvest of soils for chemical and microbial analysis to these months to minimize soil loss throughout the experiment. All soils were frozen and stored at 0°C. After harvest, we added a 4.9 mL of 400.5 mM CuSO4 that was mixed with distilled water to the gradual treatment. We added distilled water to the ambient and abrupt treatments to reach the same volume of liquid added to the gradual treatment. After the soil and plant harvest at 2 months, we added 1.25 mL 1M Ca(NO3)2 and 1.25 mL of 1M KNO3 to 1 L of distilled water and added 50 mL of the solution to ambient and gradual treatments. Adding nutrients helped compensate for the nutrients lost due to vigorous plant growth in these treatments relative to the abrupt treatment (Table S1, Supporting Information). Soils were mixed again, potted and planted with four cheatgrass seeds and incubated in a greenhouse each two months. We repeated this process for 13 cycles, or 26 months, until the mean Cu concentrations in the gradual treatment approximately equaled the mean Cu concentration in the abrupt treatment. After eight months, we noticed a decrease in Cu concentrations in abrupt treatment, likely due to leaching (Oorts, Bronckaers and Smolders 2006) (Table 1), so we began supplementing Cu to that treatment to maintain high concentrations. We then began adding a more concentrated CuSO4 solution to the gradual treatment to compensate for leaching to reach the same final Cu concentration as that in the abrupt treatment. Table 1 shows the doses of CuSO4 administered every two months. Ward Laboratories (Kearney, NE) performed all chemical analysis. pH was measured with a 1:1 soil:water solution. Soil organic matter (SOM) was analyzed by loss on ignition. Nitrate was extracted with Ca(H2PO4)2 for 15 min and P was extracted using a Mehlich III solution for 5 minutes. Both N and P were analyzed by flow-injection analysis. Potassium was extracted with C2H7NO2 and analyzed with an ICP. Available Cu was extracted with DTPA and analyzed with an ICAP.

Table 1.

Amount of CuSO4 added to gradual and abrupt treatments every two months and concentrations of available Cu in soil at a subset of months. No CuSO4 was added to the ambient treatment and Cu concentrations were less than 5 mg kg−1. The mass of dry soil that received CuSO4 equaled 1.8 kg. n = 8 for each treatment.

Month CuSO4 added (mmol) Mean (se) Cu in soil (mg kg−1)
Gradual Abrupt Gradual Abrupt
2 1.96 23.51 48.55 (1.41) 736.56 (20.22)
4 1.96 0.00 107.41 (6.76) 668.56 (15.42)
6 1.96 0.00
8 2.26 2.20 219.35 (6.76) 560.63 (23.05)
10 2.26 0.00
12 3.01 5.88 432.35 (20.99) 857.98 (22.00)
14 3.01 2.35
16 1.96 0.00 559.32 (20.99) 927.83 (15.26)
18 1.96 0.00
20 1.96 0.00 714.25 (18.05) 882.53 (19.60)
22 1.96 0.00
24 1.96 0.00 801.13 (22.35) 830.44 (24.08)
26 1.96 0.00 602.46 (5.26) 557.54 (14.90)
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DNA extraction and PCR

After the final harvest, soil samples that had been collected for microbial analyses at 2, 8, 16 and 26 months were kept on ice or stored at −20°C until processing. At the end of the experiment, four samples representing each month and treatment combination were weighed and the weights for individual samples were corrected for percent moisture content. Soil was then freeze-dried using a Labconco Freezone benchtop freeze dry system (Labconco, Kansas City, MO, USA). Genomic DNA was extracted from 275.0 +/− 5.9 mg dried soil per sample using a PowerSoil™ DNA isolation kit (MoBio Laboratories, Inc. Solana Beach, CA), following the manufacturer's instructions. Samples were then prepared for Illumina sequencing using a two-step PCR protocol to first amplify our target region and then attach unique sample identifiers. We targeted the V4 region of the 16S SSU rRNA using primers 515F and 806R (Caporaso et al. 2011). Briefly, forward primer 515F and reverse primer 806R with a Fluidigm CS1 and CS2 fused to their 5' ends were used for the first PCR step. Reactions were carried out in 12.5 μL reaction volumes containing 1 μL of template, 20 pmol of each primer in 1x GoTaq® Green Master Mix [(Green GoTaq® Reaction Buffer, 200 μM dATP, 200 μM dGTP, 200 μM dCTP, 200 μMdTTP and 1.5mMMgCl2) Promega, USA]. Each reaction was performed in a Techne TC-4000 thermocycler (Bibby Scientific, Burlington, USA) under the following conditions: initial denaturation at 94°C for 5 min, followed by 30 cycles of denaturation at 94°C for 45 s, primer annealing at 50°C for 60 s and 72°C for 90 s, with a final elongation for 5 min at 72°C. To confirm the presence of our target amplicon, all reactions were analyzed by 1.5% agarose gel electrophoresis using a 100 bp ladder (O'GeneRuler DNA Ladder, Thermo Scientific, USA). In the secondary PCR reaction, we flanked PCR1 amplicons with unique barcodes and Illumina flowcell adapters. PCR2 primer complexes consisted of the same Fluidigm tags (CS1 or CS2) as PCR1, 8 bp Illumina Nextera barcodes (Illumina Inc., San Diego, CA, USA), and Illumina adapters. Amplicons generated during PCR1 were diluted 1:10 and the PCR2 barcoding step was carried out in accordance with Bullington et al. (2018). Sequencing was done at the Institute for Bioinformatics and Evolutionary Studies (IBEST) genomics resources core at the University of Idaho (http://www.ibest.uidaho.edu/; Moscow, ID, USA). Amplicon libraries were sequenced using 2 × 300 paired-end reads on an Illumina MiSeq sequencing platform (Illumina Inc., San Diego, CA, USA).

Bioinformatics

Initial bioinformatics analyses were conducted using ‘Quantitative insights into microbial ecology 2’ (QIIME2 version 2017.7; https://qiime2.org/) (Caporaso et al. 2010). Sequence reads were demultiplexed using the q2-demux plugin (https://github.com/qiime2/q2-demux). Forward and reverse reads were trimmed at 220 and 180 base pairs, respectively. Paired sequences were quality filtered and de-replicated with the q2-dada2 plugin (Callahan et al. 2016), which simultaneously removes chimeras. The q2-dada2 plugin uses nucleotide quality scores to produce sequence variants (SVs), or sequence clusters with 100% similarity representing the estimated true biological variation within each sample. Although sequences are clustered at 100% similarity as opposed to the traditional 97% similarity, DADA2 produces fewer spurious sequences, fewer clusters and results in a more accurate representation of the true biological variation present (Callahan et al. 2016). Resulting sequence variants (SVs) were assigned a taxonomic classification using the greengenes 16S rRNA gene database (http://greengenes.lbl.gov) and QIIME2 q2-feature-classifier (https://github.com/qiime2/q2-feature-classifier), a naive Bayes machine-learning classifier, which has been shown to meet or exceed classification accuracy of other existing methods (Bokulich et al. 2017). For all bacterial community analyses, we rarefied data to a sequencing depth to 4410 sequences per soil sample and removed SVs represented by fewer than 0.001% of sequences. Two samples were subsequently excluded from further analyses due to low quality sequences. All diversity indices were calculated in QIIME2.

Statistical analysis

To determine the effects of treatment and time on bacterial communities we performed permutational multivariate analyses of variance (perMANOVA) using the adonis2 function in R (version 0.99.484) (R Core Team 2013) with 999 permutations on Hellinger transformed Bray–Curtis distances of relative SV abundance (Oksanen et al. 2007). Prior to these analyses, data were also checked for overdispersion using the betadisper function in the Vegan package (Oksanen et al. 2007). We performed canonical correspondence analysis (CCA) on the same Bray–Curtis distances to visually assess patterns in soil bacterial community composition between treatments and months, with soil nutrients superimposed (Oksanen et al. 2007). We ran the model sequentially until only significant predictors were included.

We used a generalized linear mixed model with a Poisson distribution to model SV richness that used time nested in treatment as random factors to account for repeated measures. While this experiment sampled across time, we did not analyze this as a time-series because we sampled randomly from often different, independent pots, at each harvest. We modeled Shannon diversity, Faith's phylogenetic diversity (Faith 1992), and Pielou's evenness with a gamma distribution because it accommodates non-integers (Tables S2–S5, Supporting Information). We initially included aboveground plant biomass as a factor, but we omitted it from final analyses because it was not a significant factor when we narrowed the analyses to single time intervals or treatments. Treatment and time were significant factors in predicting the diversity indices in all models. For further investigation, we then analyzed differences among treatments at each time interval and differences among time intervals for individual treatments. We used one-way analysis of variance (ANOVA) when the raw or log-transformed data fit the assumptions of that test otherwise we used a Kruskall–Wallis. We ran Tukey's HSD tests for individual contrasts, unless the data were non-parametric, in which case we ran a Nemenyi test. We used Pearson correlations between the diversity indices and pH and Cu.

To assess differences in taxon abundances among treatments, we used ANCOM (Mandal et al. 2015) in QIIME2 (Caporaso et al. 2010). ANCOM performs pairwise comparisons of the log-ratio of the abundance of each taxon to the abundance of all other taxa. W-scores represent the number of times each pairwise comparison results in a rejection of the null hypothesis that abundances between groups are the same. Significance is then determined based on the empirical distribution of W (Mandal et al. 2015). ANCOM accounts for the underlying structure in high-throughput microbial datasets, including constraints on sequence numbers, to reduce false discoveries in detecting differentially abundant taxa; as such, it can be used to investigate differences among microbiomes in multiple treatments or populations (Mandal et al. 2015).

To compare soil respiration between treatments, we grouped data by month. When the data for all treatments were normally distributed and exhibited constant variance, we compared treatments with a one-way ANOVA with a Tukey's post hoc test. For data that were non-normal and/or had non-constant variance, we log-transformed the data and ran an ANOVA if the assumptions of the test were met. When log transformations did not lead to normally distributed data with constant variance, we compared treatments with a Kruskall–Wallis test followed by a Nemenyi test to identify differences between treatments (PMCMR package) (Pohlert 2014). For months 1 and 10, some soil respiration values equaled zero. For these data, we replaced zeros with 0.0001 so that we could run a generalized linear model (GLM) that used a gamma distribution. The gamma distribution accommodates severely right skewed distributions (Faraway 2006). We compared means with a Tukey post hoc test using the multcomp package (α = 0.05) (Hothorn, Bretz and Westfall 2008). We also ran multiple linear regression to determine which factors best predicted respiration. A simple linear regression was used to identify trends in plant biomass and SOM over time. We used Pearson correlations between plant biomass and both pH and nitrate.

RESULTS

Responses of bacterial community to Cu

The initial addition of Cu to the abrupt treatment increased concentrations of available Cu to an average of 737 mg/kg compared to less than 5 mg/kg in the ambient treatment that served as the control (Table 1). Concentrations of Cu increased by approximately 60 mg/kg every two months in the gradual treatment. These additions of Cu caused the compositions of bacterial communities to differ among treatments (F = 13.48, P < 0.001; Fig. 1) and month of sampling (F = 17.89, P < 0.001), based on clustering in the CCA and results from the perMANOVA. Additionally, aboveground plant biomass contributed to differences in bacterial communities (F = 8.60, P < 0.001), although it explained less variation in the model than treatment and month of sampling (Table S6, Supporting Information). Interactions occurred between treatment and both month of sampling and plant biomass (Table S6, Supporting Information). Patterns remained similar when we re-ran the analysis after replacing abundances with presence or absence. The ambient and gradual treatments had similar bacterial compositions initially, but compositions in all treatments diverged by eight months (Fig. 1).

Figure 1.

Figure 1.

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Canonical correspondence analysis of bacterial communities for subsamples taken from each treatment at four different time intervals over the course of the experiment. Vectors that are superimposed represent significant soil chemical variables. n = 3–4 for each treatment at each month.

There were 3067 SVs representing soil bacterial communities in all three treatments and across all time intervals. Briefly, SVs represent sequence clusters that represent 100% similarity of the estimated true biological variation in the sample. SVs in the abrupt and gradual treatments at all months overlapped with those in the control treatments at 12% and 19%, respectively, while gradual and abrupt soils shared 24% of total SVs. At two months, bacteria belonging to the Order Acidiobacteriales (W = 23), the families Intrasporangiaceae, Acidobacteriaceae and Caulobacteraceae (W = 43, 30 and 28, respectively), and the genus Rhodanobacter (W = 55) appeared initially tolerant of Cu additions because they were more abundant in the abrupt treatment than the gradual and ambient treatments (Table S7–S8, Supporting Information). W-scores represent the number of times each pairwise comparison results in a rejection of the null hypothesis that abundances between groups are the same. All W-scores presented represent significant differences in abundance.

Over the course of the experiment, bacterial communities in each treatment became more dissimilar from their initial bacterial communities and all treatments had high turnover in SVs (Fig. 2). The ambient treatment had only 8.1% overlap in SVs between month 2 and month 26. The gradual treatment had 9.0%, and the abrupt treatment retained 14.1% of SVs. When CuSO4 concentrations were approximately equal between the gradual and abrupt treatments at 26 months, the bacterial communities were still distinct, although they appeared to be converging (Fig. 1). Gradual and abrupt treatments shared only 20.4% of SVs at two months but that increased to 26.6% by 26 months. At 26 months, bacteria in the order Acidimicrobiales (W = 56), the family Iamiaceae (W = 70), and the genus Geodermatophilus (W = 143) were more abundant in abrupt and gradual treatments and were recovered at very low abundances from ambient soils (Fig. S1, Supporting Information). Rhodospirillales showed the opposite pattern, with higher abundances in ambient soils and extremely low abundances in Cu-treated soils (W = 55). Gemmatimonadetes (N1423WL) were also recovered at significantly lower abundances in abrupt soils (W = 52) compared to other treatments. The Phylum Fibrobacteres was recovered only from ambient soils at 26 months (W = 13).

Figure 2.

Figure 2.

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Composition of classes of bacterial communities for each treatment across sampling intervals. The size of bands within each bar represents mean relative abundances of each class. The 10 most dominant classes are presented. n = 3–4 for each treatment at each month.

SV richness did not differ among treatments at two months (Fig. 3a) but changed within treatments throughout the duration of the experiment. Richness was higher in the ambient treatment at 16 and 26 months compared to two months (P = 0.015; P = 0.002, respectively). By eight months, SV richness in the gradual treatment exceeded that in both the ambient and abrupt treatments (P = 0.015; P = 0.011, respectively). The abrupt treatment had the fewest total SVs (1140) followed by the gradual treatment (1437) and then ambient treatments (1752). Trends in Shannon diversity for each treatment were similar to richness, although Shannon diversity showed a potential decrease in the ambient treatment at eight months (P = 0.071) before recovering by 16 months (Fig. 3b). Trends for phylogenetic diversity were also similar to richness (Fig. 3c). The main differences occurred at eight months when, unlike richness, phylogenetic diversity in the gradual treatment was not higher than that in the ambient treatment (Fig. 3d). Evenness was lower in the abrupt treatment than the ambient treatment at 16 and 26 months (P = 0.007; P = 0.002, respectively) and remained stable over time. Evenness in the gradual treatment declined between two and eight months (P = 0.010), but remained stable thereafter.

Figure 3.

Figure 3.

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Sequence variant (SV) richness (a), Shannon diversity (b), phylogenetic diversity (Faith's PD) (c) and evenness (d) for each treatment at four time intervals across the experiment. Symbols represent mean and error bars represent standard error. n = 3–4 for each treatment at each month.

Concentrations of Cu in the abrupt and gradual treatments negatively correlated with Shannon diversity (r = −0.50, P = 0.004) and evenness (r = −0.61, P < 0.001). Concentrations of Cu positively correlated with Acidobacteria, Bacteroidetes and Proteobacteria while negatively correlating with Verrucomicrobia. SV richness and phylogenetic diversity correlated with soil pH (r = 0.39, P = 0.007; r = 0.46, P = 0.001, respectively). Soil pH was negatively correlated with Cu, K, SO4 and Mn and positively correlated with Ca. Additionally, pH positively correlated with the relative abundance of Acidobacteria but negatively with Actinobacteria, Bacteroides and Proteobacteria (Table S9, Supporting Information ).

Soil chemistry, respiration and plant biomass

Qualitative comparisons showed that pH initially decreased in the abrupt treatment (5.89; Table S1, Supporting Information) compared to the ambient treatment (7.68), but pH values increased in all treatments over time. However, the gradual treatment often had a pH slightly lower than the pH in the ambient treatment. By the end of the experiment, pH in the abrupt treatment equaled the pH in the ambient treatment. Qualitatively, the percent of SOM fluctuated across the experiment for all treatments and decreased in the ambient treatment over time (r = 0.35, P = 0.003) (Table S1, Supporting Information). Compared to the gradual and abrupt treatments, concentrations of NO3− were low for the ambient treatment at two months, increased after a nutrient amendment at four months, but decreased by eight months and remained low (Table S1, Supporting Information). Nitrate concentrations were always elevated in the gradual and abrupt treatments relative to the ambient treatment after four months. Concentrations of P were consistently higher in the gradual and abrupt treatments than in the ambient treatment after 12 months (Table S1, Supporting Information). Potassium concentrations were often higher in the gradual and abrupt treatments than the ambient treatment after 16 months (Table S1, Supporting Information).

The ambient treatment had soil respiration values that were always greater or equal to values in the gradual and abrupt treatment, and by the end of the experiment, respiration did not differ among treatments (Table 1). The initial pulse of CuSO4 to the abrupt treatment led to almost no microbial respiration at two months (Table 1). Soil respiration recovered by four months but was also suppressed between 6 and 10 months and again between 16 and 18 months. Soil respiration in the gradual treatment was suppressed at 10 and 22 months, but recovered by the end of the experiment. Significant factors in predicting respiration using a linear model included the generation of measurement, aboveground plant biomass, concentrations of Cu and water content (Table S10, Supporting Information).

Aboveground plant biomass was similar between the ambient and gradual treatments at two months, but became dissimilar afterwards for most of the subsequent harvests (Table S11, Supporting Information). Aboveground biomass in the ambient treatment decreased over time (r = −0.52, P < 0.001) and were negatively correlated with pH and nitrate (r = −0.55, P < 0.001; r = −0.31, P = 0.025, respectively). Production of aboveground biomass was below 0.01 g after 14 months for the gradual treatment and for the duration of the study for the abrupt treatment.

DISCUSSION

Community composition depends on method of Cu accumulation

We aimed to determine whether an abrupt addition of CuSO4 yielded a different bacterial community than a gradual accumulation of CuSO4 with the same final concentration. After the first Cu addition, bacterial communities in the gradual and abrupt treatments were distinct (Fig. 1). We expected this because previous work has shown that microbial communities are often dissimilar in composition when they are exposed to a gradient of heavy metal concentrations (Frostegård, Tunlid and Bååth 1993; Frostegård, Tunlid and Bååth 1996). This is likely a result of sensitive species dying at elevated concentrations while tolerant species persist and can access nutrients from the decomposition of the sensitive organisms that died (Frostegård, Tunlid and Bååth 1996). This was additionally supported by the higher abundances of multiple taxa in the gradual and abrupt treatments two months. The communities in the gradual and abrupt treatments remained distinct until 26 months when Cu concentrations were approximately equal between treatments and the communities nearly converged (Fig. 1). This suggests that the difference between treatments may have only a minor effect on community structure once Cu concentrations equalize.

In the ambient treatment, the community structure became increasingly dissimilar from that in the contaminated soils at each sampling month. This divergence may have been driven by two factors. First, bacteria that were sensitive to Cu could survive in the ambient treatment but were inhibited in soils treated with Cu, where only metal tolerant species could persist. Second, cheatgrass in the ambient treatment grew vigorously, but was mostly absent from the abrupt treatment and later months of the gradual treatment (Table S11, Supporting Information). Plants release exudates from their roots that influence and feed microbial communities (Berg and Smalla 2009; Philippot et al. 2013), and the plant used in this study, cheatgrass, may release significant amounts of exudates (Morris et al. 2016). This disparity in plant effects between treatments complicates whether responses for microbial communities were driven solely by the presence of Cu. However, the purpose of growing cheatgrass was to more accurately mimic natural conditions where both direct and indirect effects of Cu additions can alter soil bacteria. We feel that our results are both valid and more transferable to field conditions than an experiment that did not include a plant. Changes in community structure over the duration of the experiment could also have been partly driven by different incubation times in the greenhouse or storage times of soil samples while frozen (Rubin et al. 2013). Overall, bacterial communities were distinct at each time interval for all treatments, suggesting that successional shifts occurred throughout the experiment, which was further supported by examining the composition of taxa (Fig. 2)

Bacterial communities are sensitive to Cu

It is well accepted that the richness and diversity of bacterial communities are sensitive to heavy metals (Ramsey et al. 2005; Feris et al. 2009). Copper additions in the gradual treatment decreased species richness by the end of the experiment, as some species likely could not tolerate the higher Cu concentrations or did not have sufficient time to adapt. This was reflected in taxa abundances where some groups were only present in the ambient treatment at 26 months (Table S7, Supporting Information). In the gradual treatment, SV richness and phylogenetic diversity peaked at eight months when Cu concentrations were 39% (219 mg kg−1) of those in the abrupt treatment (Fig. 2; Table 1). A high phylogenetic diversity suggests that, at these moderate concentrations of Cu, it did not create a strong habitat filter in terms of structuring the community. Species richness has also been shown to be high in river sediments that were exposed to slightly elevated concentrations of metals (Feris et al. 2009). In that study, the authors suggested that if the presence of metals is considered a disturbance, then the findings fit within the scope of the Intermediate Disturbance Hypothesis (IDH). This hypothesis posits that moderate (i.e. intermediate) levels of disturbance create a disequilibrium that allows both early and late successional species to coexist (Connell 1978; Townsend, Scarsbrook and Dolédec 1997). Here, the shifting structure of the bacterial communities (Fig. 1) suggests they were not in equilibrium, and possibly, moderate concentrations of Cu suppressed the abundances of organisms that would otherwise have been competitive and dominant. Overall, recent scrutiny has challenged the utility of the IDH (Fox 2013), but regardless, conditions at eight months in the gradual treatment were favorable for SV richness to be maximized. We observed opposite trends for the ambient treatment where richness, Shannon diversity, and evenness trended downward between two and eight months. The semi-controlled conditions in the greenhouse and the absence of Cu may have also led to these dynamic bacterial communities where some species were eliminated, or were in such low abundances they were undetected, while others thrived. Compared to the ambient treatment, richness in the abrupt treatment appeared to vary less over time. The stress of elevated concentrations of Cu likely killed sensitive species that never recovered, depleting the pool of species that could later be detected.

Many species of bacteria tolerate heavy metals by using efflux mechanisms that transport toxic ions out of the cell (Silver and Phung 1996). These mechanisms exist in most bacterial phyla (Bruins, Kapil and Oehme 2000) and can be transferred among bacterial species through lateral gene transfer (Rensing, Newby and Pepper 2002; Hemme et al. 2010). Although we did not quantify the abundance of heavy metal tolerance genes, if their prevalence increased over time, it did not help communities maintain SV richness when Cu concentrations exceeded a value that ranged between 219 and 432 mg kg−1 (Fig. 2; Table 1); within this range, SV richness and phylogenetic diversity in the Gradual treatment declined after 8 months. This range of concentrations has previously been found to be the threshold where nitrification, glucose respiration, and maize mineralization were all inhibited by at least 50% (Oorts, Bronckaers and Smolders 2006). The consistently elevated concentrations of nitrate in the gradual and abrupt treatments may result from the lack of vigorous plant growth that would deplete available nitrogen. By the end of the experiment, all diversity indices besides phylogenetic diversity were equal between the gradual and abrupt treatments, the latter of which had low phylogenetic diversity for the duration of the study, supporting that higher concentrations of Cu may create a strong habitat filter (Fig. 2). This disagrees with our overall hypothesis because we expected the gradual accumulation of Cu to allow the bacterial community to maintain a higher richness than the abrupt treatment at higher concentrations of Cu. This draws into question how much time is required for a community's richness and diversity to recover after abruptly being exposed to heavy metals, as seen in-situ in sediments that had been contaminated for nearly a century (Ramsey et al. 2005). Also at the end of the experiment, all diversity indices for the ambient treatment were at the highest levels observed in the experiment. This could have occurred due to two reasons. First, species could have been introduced from outside the pots, although we avoided splashing soils among pots while watering. Second, rare species that had low abundances may have been undetected at the first two sampling intervals but were recovered at 16 and 26 months when their abundances became high enough for evenness to remain high. In the abrupt treatment, for example, the relative abundance of Synechococcophycideae, a cyanobacteria found in habitats as disparate as arctic ice and desert soils (Patzelt et al. 2014; Lutz et al. 2017), greatly increased in the abrupt treatment at the end of the experiment. By 26 months, conditions were likely favorable for this class of bacteria to thrive.

Inconsistent soil respiration over time

Soil respiration captures the activity of plant roots and decomposition of organic matter by soil biota, giving an impression of carbon cycling (Hanson et al. 2000). We observed an immediate suppression in soil respiration after the first addition of CuSO4 in the abrupt treatment (Table 2). We expected this because respiration is often low at high concentrations of heavy metals (Ramsey et al. 2005; Åkerblom et al. 2007). But for the remainder of the study, soil respiration in the abrupt treatment wavered from being suppressed to being no different from ambient conditions. The same was true for the gradual treatment, but without an initial suppression in respiration at two months. This surprised us because we had expected higher concentrations of Cu to consistently suppress respiration. Multiple reasons may explain these varying respiration rates in the contaminated treatments. First, at low concentrations of heavy metals, organisms sometimes increase their metabolic rate to tolerate the metals (Odum 1985; Fließbach, Martens and Reber 1994). Although concentrations of Cu were only low in the first months in the gradual treatment, perhaps the metabolic rates for organisms exposed to Cu were continually high throughout the experiment. Similarly, communities may have adapted by developing a tolerance to Cu and were functionally similar to those in the ambient treatment. Second, the addition of CuSO4 to the gradual and abrupt treatments likely killed many organisms, providing a glut of organic matter that could be metabolized. Third, concentrations of soil nutrients (e.g. N and P) varied during the experiment (Table S1, Supporting Information), which could have further influenced decomposition rates (Silver and Miya 2001; Zhang et al. 2008). Lastly, respiration in the ambient treatment may have been limited by the availability of SOM, which declined over time. Throughout the experiment in the ambient treatment, carbon may have respired at a faster rate than it could be replenished through primary production or the addition of water that contained carbonates.

Table 2.

CO2 efflux for each treatment every two months. Respiration values represent mean with standard errors in parenthesis. Temperatures represent the average soil temperature for all treatments. n = 7–8 for each treatment at each month.

Respiration (µmol CO2 m2 s−1)
Month Month of year Avg. temp (°C) Ambient Gradual Abrupt Test Test statistic P
2 October 16.87 (0.33) 1.23 (0.30)a 0.81 (0.08)a 0.17 (0.03)b Gamma AICc = 27.38 <0.001*
4 December 14.23 (0.15) 1.50 (0.10)a 1.69 (0.13)a 1.67 (0.09)a ANOVA F = 0.86 0.624
6 February 16.43 (0.22) 2.59 (0.17)a 1.96 (0.21)ab 1.51 (0.11)b ANOVA* F = 9.87 <0.001
8 April 19.44 (0.05) 2.74 (0.21)a 2.22 (0.26)ab 1.81 (0.16)b ANOVA* F = 5.26 0.014
10 June 19.08 (0.21) 5.48 (0.46)a 4.09 (0.33)b 3.67 (0.24)b ANOVA F = 7.17 0.004
12 August 16.24 (0.35) 1.46 (0.15)a 1.23 (0.23)a 0.93 (0.07)a K.W. H = 4.21 0.122
14 October 18.27 (0.14) 3.27 (0.30)a 3.40 (0.45)a 2.60 (0.33)a ANOVA F = 1.37 0.276
16 December 20.18 (0.42) 3.06 (0.17)a 1.96 (0.20)ab 1.50 (0.14)b ANOVA F = 21.69 <0.001
18 February 19.52 (0.38) 2.11 (0.75)a 1.37 (0.72)ab 0.49 (0.06)b K.W. H = 10.81 <0.004
20 April 25.92 (0.26) 1.29 (0.21)a 0.88 (0.37)a 0.38 (0.14)a Gamma AICc = 20.70 0.007*
22 June 19.33 (0.15) 1.58 (0.15)a 1.04 (0.05)b 1.42 (0.30)ab K.W. H = 7.14 0.028
24 August 25.47 (0.26) 3.55 (1.47)a 1.85 (0.26)a 1.92 (0.13)a K.W. H = 1.50 0.473
26 October 25.43 (0.42) 1.35 (0.13)a 2.49 (0.48)a 2.29 (0.49)a K.W. H = 5.14 0.077
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Different letters next to numbers represent differences between treatments (P < 0.05) as determined with a Tukey's post hoc test for comparisons that used ANOVA or a Nemenyi test for comparisons that used a Kruskall−Wallis ANOVA (K.W.). Asterisks next to a test statistic indicate that the data were log transformed. Gamma represents a GLM that used a gamma distribution with an identity link function. *Denotes the P value for the intercept of the model.

Limitations

Controlled experiments are critical to predict the outcome of ecological processes; however, their results can be limited if they do not incorporate natural variability (De Boeck et al. 2015). In this study, we included natural variability in a semi-controlled setting by allowing air temperature and soil chemistry to seasonally fluctuate while planting and harvesting cheatgrass every two months. A downside of allowing these processes to vary was that it was difficult to attribute responses to a single factor (De Boeck et al. 2015). For example, plant biomass generally declined over time in the ambient treatment and was negatively correlated with both pH and concentrations of nitrate. Although soil chemistry may have been a main driver of plant biomass, we cannot rule out the possibility that pathogens became enriched over time, as seen in field sites (Gibbons et al. 2017). These pathogens could have also hindered the production of biomass. But it can be difficult to identify single factors that influence responses when studying the effects of heavy metals, particularly on microorganisms. Heavy metals often decrease soil pH (Ginocchio et al. 2009), which is a main driver of bacterial communities and nutrient availability (Fierer and Jackson 2006; Lauber et al. 2009; Rousk et al. 2010). Consequently, it can be difficult to determine whether the heavy metal or pH caused the response. Here, pH increased over time to over 7, likely because the water used to irrigate the pots was slightly alkaline (pH = 7.7). At this neutral value, pH likely did not stress organisms as much as the elevated concentrations of Cu. For instance, Acidobacteria are generally associated with soils that have low pH, but we found the opposite because Acidobacteria were positively correlated with pH (Table S9, Supporting Information), suggesting that Cu concentrations could have driven the relative abundances Acidobacteria.

CONCLUSIONS

While many experiments show that bacterial communities are sensitive to heavy metals (Bååth 1989; Oorts, Bronckaers and Smolders 2006; Gall, Boyd and Rajakaruna 2015), experimental designs that abruptly add heavy metals may not accurately mimic the natural accumulation of those metals. By comparing the responses of bacterial communities to abrupt and gradual additions of Cu, we show that application dose changes the initial trajectory of bacterial communities, but that with long-term exposure, gradual application of Cu may result in communities similar to those exposed to abrupt additions. In this experiment, we did not observe strong differences in richness and diversity between the gradual and abrupt treatments at the end of the experiment. Surprisingly, soil respiration was not always suppressed in the abrupt treatment compared to the ambient treatment, indicating that Cu concentrations alone cannot explain trends in respiration and that the communities may have been functionally redundant; however, Cu additions likely create a cascade of effects in relation to nutrient availability and plant growth, both of which would likely influence respiration. Using different heavy metals or collecting soil from different settings may yield different results, but overall, we show that gradual and abrupt additions of Cu yield only slight differences in the characteristics of bacterial communities.

Supplementary Material

Supplementary Data
Click here for additional data file. (2.3MB, zip)

ACKNOWLEDGEMENTS

We thank Chris Henderson, Alex Dalgleish, Katie Bouma, Chris Harris, Dan Stone and Jeff Clarke for assisting with soil harvests over the course of the experiment. We appreciate the assistance in the lab by Emily Martin and Ben Mason and the input provided by Dan Mummey. We thank Ylva Lekberg and the anonymous reviewers who provided comments that greatly improved the manuscript.

FUNDING

This work was supported by MPG Ranch and MR acknowledges funding from an ERC Advanced Grant (Gradual_Change). Data collection and analyses performed by the IBEST Genomics Resources Core at the University of Idaho were supported in part by NIH COBRE grant P30GM103324.

Conflicts of interest. None declared.

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