Loss of microbial diversity in soils is coincident with reductions in some specialized functions
Loss of microbial diversity is considered a major threat because of its importance for ecosystem functions, but there is a lack of conclusive evidence that diversity itself is reduced under anthropogenic stress, and about the consequences of diversity loss. Heavy metals are one of the largest, wides...
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| Published in | Environmental microbiology Vol. 16; no. 8; pp. 2408 - 2420 |
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| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Science
01.08.2014
Blackwell Publishing Ltd Blackwell Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
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| Summary: | Loss of microbial diversity is considered a major threat because of its importance for ecosystem functions, but there is a lack of conclusive evidence that diversity itself is reduced under anthropogenic stress, and about the consequences of diversity loss. Heavy metals are one of the largest, widespread pollutant types globally, and these represent a significant environmental stressor for terrestrial microbial communities. Using combined metagenomics and functional assays, we show that the compositional and functional response of microbial communities to long‐term heavy metal stress results in a significant loss of diversity. Our results indicate that even at a moderate loss of diversity, some key specialized functions (carried out by specific groups) may be compromised. Together with previous work, our data suggest disproportionate impact of contamination on microbes that carry out specialized, but essential, ecosystem functions. Based on these findings, we propose a conceptual framework to explicitly consider diversity of functions and microbial functional groups to test the relationship between biodiversity and soil functions. |
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| Bibliography: | http://dx.doi.org/10.1111/1462-2920.12353 Fig. S1. Observed Shannon entropy number equivalents (S(H)), based on 454 sequencing of 16S rRNA genes, as a function of fractional sequence difference as a percentage of the control H(S) (black line) for the contaminated soil data from (A) Hartwood (n = 21) and (B) Auchincruive (n = 21). Cu50 is 50 mg kg−1 Cu (red); Cu200 is 200 mg kg−1 Cu (light green); Zn150 is 150 mg kg−1 Zn (dark blue); Zn450 is 450 mg kg−1 Zn (dark green); RB is raw blank uncontaminated undigested sludge as a control for Cu (light blue); DB is digested uncontaminated sludge as a control of Zn (magenta). A consistent reduction in S(H) is seen across OUT cut-offs with metal concentration for all except Zn150. Fig. S2. Rarefaction curves for the contaminated soil data (A) Hartwood, 3% OTUs; (B) Hartwood, 5% OTUs; (C) Auchincruive, 3% OTUs; (D) Auchincruive, 5% OTUs demonstrating 15 000 reads do not capture total OTU number in these communities. Control is no sludge (black line); Cu50 is 50 mg kg−1 Cu (red); Cu200 is 200 mg kg−1 Cu (light green); Zn150 is 150 mg kg−1 Zn (dark blue); Zn450 is 450 mg kg−1 Zn (dark green); RB is raw blank uncontaminated undigested sludge as a control for Cu (light blue); DB is digested uncontaminated sludge as a control of Zn (magenta). Fig. S3. Impact of heavy metal (Zn and Cu) on soil bacterial community structure based on TRFLP analysis. Data are presented as ordination plots of canonical variates (CV) of bacterial TRFLP community structure in (A) Hartwood (n = 21) and (B) Auchincruive (n = 21) soils exposed to Cu and Zn sludge additions. Control is no sludge control (black); DB is digested blank sludge (green); RB is raw blank sludge (purple); Cu50 is 50 mg kg−1 Cu (orange); Cu200 is 200 mg kg−1 Cu (red); Zn150 is 150 mg kg−1 Zn (grey); Zn450 is 450 mg kg−1 Zn (blue). Percentages in parenthesis are percentage variance explained by each CV. X represents treatment means, plus symbols represent each replicate and circle represents 95% confidence intervals. For (A) Hartwood, there is clear separation in microbial community structure on the first CV for Zn-contaminated soils and for those with the highest level of Cu contamination. For (B) Auchincruive, there is significant differences between treatments. Fig. S4. Extractable (A) Zn (mg kg−1) and (B) Cu (mg kg−1) in Hartwood (n = 9) and Auchincruive (n = 9) treated with metal-contaminated sludge. Values with different letters are not significantly different (P < 0.05). Control is no sludge; DB is digested uncontaminated sludge as a control of Zn; Zn450 is 450 mg kg−1 Zn; RB is raw blank uncontaminated undigested sludge as a control for Cu, and Cu200 is 200 mg kg−1 Cu. Bars are ± one SE (n = 3). Extractable Zn is significantly higher in Hartwood soils than Auchincruive soils in soils receiving the highest level of Zn-contaminated sludge. Fig. S5. Impact of heavy metal (Zn and Cu)-contaminated sludge on microbial community functional gene structure based GeoChip analysis in Hartwood soils. Ordination plot of canonical variates (CV) generated by CV analysis of functional genes (GeoChip) profiles grouped into gene category (a-i) in Hartwood soil exposed to zinc and copper stress (n = 15). Control is no sludge control (black); DB is digested uncontaminated sludge as a control of Zn (green); RB is raw blank uncontaminated undigested sludge as a control for Cu [light blue (purple)]; Cu200 is 200 mg kg−1 Cu (red); Zn450 is 450 mg kg−1 Zn (blue). Percentages in parenthesis are percentage variance explained by each CV. X represents treatment means, plus symbols represent each replicate and circle represents 95% confidence intervals. Most functional groups (involved in C, N, P, S, antibiotic resistance, metal reduction/resistance) are dramatically impacted by metal. The magnitude and direction of response are different for Zn and Cu. Fig. S6. Impact of heavy metal (Zn and Cu)-contaminated sludge on microbial community functional gene structure based GeoChip analysis in Auchincruive soils. Ordination plot of canonical variates (CV) generated by CV analysis of functional genes (GeoChip) profiles grouped into gene category (a-i) in Auchincruive soil exposed to zinc and copper stress (n = 15). Control is no sludge control (black); DB is digested uncontaminated sludge as a control of Zn (green); RB is raw blank uncontaminated undigested sludge as a control for Cu [light blue (purple)]; Cu200 is 200 mg kg−1 Cu (red); Zn450 is 450 mg kg−1 Zn (blue). Percentages in parenthesis are percentage variance explained by each CV. X represents treatment means, plus symbols represent each replicate and circle represents 95% confidence intervals. Most functional groups (involved in C, N, P, S, antibiotic resistance, metal reduction/resistance) are dramatically impacted by metal. The magnitude and direction of response are different for Zn and Cu. Fig. S7. The impact of Zn- and Cu-contaminated sludge additions on soil (A) Basal respiration and (B) glucose mineralization in Auchincruive and Hartwood soils. Control is no sludge control; Cu200 is 200 mg kg−1 Cu; Zn450 is 450 mg kg−1 Zn. Bars are ± one SE (n = 3). Respiration is a little affected by metal treatment, whereas there is a consistent, but statistically not significant, trend of reduced substrate mineralization in metal-contaminated soils at Hartwood, and to a lesser extent, Auchincruive. Fig. S8. The impact of Zn- and Cu-contaminated sludge addition on the community structure of genes involved in atrazine and triazine mineralization as assessed by GeoChip analysis of soils from two sites: Hartwood (A and B) and Auchincruive (C and D). Ordination plot of canonical variates (CV) generated by CV analysis of atrazine (A and C) and triazine (B and D) degrading genes. Control is no sludge control (black); DB is digested uncontaminated sludge as a control of Zn (green); RB is raw blank uncontaminated undigested sludge as a control for Cu [light blue (purple)]; Cu200 is 200 mg kg−1 Cu (red); Zn450 is 450 mg kg−1 Zn (blue). Percentages in parenthesis are percentage variance explained by each CV. X represents treatment means, plus symbols represent each replicate and circle represents 95% confidence intervals. For Hartwood soils, there is a clear separation of Cu-contaminated soils, and to a lesser extent, Zn-contaminated soils. For Auchincruive, there is a clear separation of Zn, and to a lesser extent, Cu-contaminated soils.Table S1. Summary of the soil OTU numbers across the 14 treatments (n = 42) based on 454 sequencing of 16S rRNA genes for two soils, Hartwood (HW) and Auchincruive (AC), exposed to increasing levels of Cu and Zn contamination. Filtered number gives number of reads after initial pre-filtering. Clean number following removal of PCR chimeras. Unique is the number of different reads present after de-noising. Clean unique after chimera removal. The percentage of clean reads as a fraction of clean number, and percentage of clean unique as a fraction of total unique is given in parentheses. OTUs were then constructed using a complete linkage algorithm and distances based on exact pair-wise alignments and the number of OTUs at 1.5%, 3%, 5% and 10% nucleotide sequence difference given. Control is no sludge control; Cu50 is 50 mg Cu kg−1; Cu200 is 200 mg kg−1 Cu; Zn150 is 150 mg kg−1 Zn; Zn450 is 450 mg kg−1 Zn; RB is uncontaminated undigested sludge as a control for Cu; DB is digested uncontaminated sludge as a control of Zn. Table S2. Summary of the mean soil OTU numbers across the 14 treatments subsampled to 15 000 reads based on 454 sequencing of 16S rRNA genes, for two soils (Hartwood [HW] and Auchincruive [AC]) exposed to increasing levels of Cu and Zn contamination (n = 42). We also give diversity as a percentage of the control in parentheses. Control is no sludge control; Cu50 is 50 mg Cu kg−1; Cu200 is 200 mg kg−1 Cu; Zn150 is 150 mg kg−1 Zn; Zn450 is 450 mg kg−1 Zn; RB is uncontaminated undigested sludge as a control for Cu; DB is digested uncontaminated sludge as a control of Zn. Table S3. Summary of the Shannon entropies H in numbers equivalents [S(H) = exp(H)], based on 454 sequencing of 16S rRNA genes, for two soils (Hartwood [HW] and Auchincruive [AC]) exposed to increasing levels of Cu and Zn contamination. We also give S(H) as a percentage of the control in parentheses. Control is no sludge control; Cu50 is 50 mg Cu kg−1; Cu200 is 200 mg kg−1 Cu; Zn150 is 150 mg kg−1 Zn; Zn450 is 450 mg kg−1 Zn; RB is uncontaminated undigested sludge as a control for Cu; DB is digested uncontaminated sludge as a control of Zn. Table S4. Deviance Information Criterion (DIC) values of abundance distribution fits to the (A) 3% OTU distributions and (B) 5% OUT distributions based on 454 sequencing of 16S rRNA genes for two soils (Hartwood and Auchincruive) exposed to increasing levels of Cu and Zn contamination. Smaller values indicate a better fit. We have indicated the best fit with a *1 or **1 if it is significantly better than the next best denoted *2. Significant was interpreted as a difference in DIC values greater than 7. Control is no sludge control; Cu50 is 50 mg Cu kg−1; Cu200 is 200 mg kg−1 Cu; Zn150 is 150 mg kg−1 Zn; Zn450 is 450 mg kg−1 Zn; RB is uncontaminated undigested sludge as a control for Cu; DB is digested uncontaminated sludge as a control of Zn. Table S5. Total species number estimates from abundance distribution fits to the (A) 3% OTU distributions and (B) 5% OUT distributions in soils with increasing Cu and Zn contamination at two sites, Hartwood (HW) and Auchincruive (AC). Results are quoted as medians with 95% confidence intervals. These were calculated by sampling from the posterior distribution. Control is no sludge control; Cu50 is 50 mg Cu kg−1; Cu200 is 200 mg kg−1 Cu; Zn150 is 150 mg kg−1 Zn; Zn450 is 450 mg kg−1 Zn; RB is uncontaminated undigested sludge as a control for Cu; DB is digested uncontaminated sludge as a control of Zn. Table S6. Estimates of the sample size necessary to obtain 90% of the ( ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ISSN: | 1462-2912 1462-2920 1462-2920 |
| DOI: | 10.1111/1462-2920.12353 |