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Contrasting microbial community assembly hypotheses: a reconciling tale from the Río Tinto.

Palacios C, Zettler E, Amils R, Amaral-Zettler L - PLoS ONE (2008)

Bottom Line: We found that environmental factors dictate the distribution of the most abundant taxa in this system, but stochastic niche differentiation processes, such as mutation and dispersal, also contribute to observed diversity patterns.We predict that studies providing clues to the evolutionary and ecological processes underlying microbial distributions will reconcile the ongoing debate between the Baas Becking vs.Hubbell community assembly hypotheses.

View Article: PubMed Central - PubMed

Affiliation: The Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA, USA.

ABSTRACT

Background: The Río Tinto (RT) is distinguished from other acid mine drainage systems by its natural and ancient origins. Microbial life from all three domains flourishes in this ecosystem, but bacteria dominate metabolic processes that perpetuate environmental extremes. While the patchy geochemistry of the RT likely influences the dynamics of bacterial populations, demonstrating which environmental variables shape microbial diversity and unveiling the mechanisms underlying observed patterns, remain major challenges in microbial ecology whose answers rely upon detailed assessments of community structures coupled with fine-scale measurements of physico-chemical parameters.

Methodology/principal findings: By using high-throughput environmental tag sequencing we achieved saturation of richness estimators for the first time in the RT. We found that environmental factors dictate the distribution of the most abundant taxa in this system, but stochastic niche differentiation processes, such as mutation and dispersal, also contribute to observed diversity patterns.

Conclusions/significance: We predict that studies providing clues to the evolutionary and ecological processes underlying microbial distributions will reconcile the ongoing debate between the Baas Becking vs. Hubbell community assembly hypotheses.

Show MeSH

Related in: MedlinePlus

CCA biplot of the SARST-V6 dataset with relevant environmental variables at Río Tinto samples and sites.Superimposed canonical correspondence analysis (CCA) biplots of RT samples and SARST-V6 OTUs at the 99% similarity cut-off value displaying 68% of the variance of the OTUs with respect to the environmental variables. The inset represents the CCA biplot when pooling samples by site. The canonical eigenvalues for axes 1–4 of the sample analysis are 0.367, 0.272, 0.112, and 0.062 respectively. Environmental variables are indicated by arrows that point in the direction of increasing values of each variable. The coordinates of the arrowheads indicate the degree of correlation with the axes. Samples and sites are represented by black circles. For sample names see Materials and Methods. OTUs with total abundances higher than 10 RSTs are represented by grey triangles. To avoid overcrowding of points only one OTU per strain is plotted. The relative frequency of OTUs in samples can be determined using the biplot rule. To do this, drop a perpendicular from each sample onto a line through the OTU and the origin. Samples projecting on the line in the direction towards the OTU and beyond it are predicted to have a higher relative frequency of that OTU than samples projecting onto the line in the opposite direction. Interpretation of environmental arrows with respect to sites, OTUs and other environmental variables follows the same rule. OTU numbers correspond to: (1, 12, 14, 36) = Acidithiobacillus sp. SS5; (2, 11) = Uncultured bacterial clone MPKCSC9; (3) = Acidithiobacillus sp. SK5; (4) = Leptospirillum ferrooxidans P3a; (5, 26) = L. ferrooxidans Parys; (6) = Acidithiobacillus sp. B9; (7) = L. ferrooxidans Sy; (8) = Thermicanus aegyptius; (9) = Acidiphilium sp. Pk46; (10) = Eubacterium clones TRA5-3 and MeBr10; (13) = Uncultured bacterium BA18; (15) = F. acidiphilium; (16) = Bacterium clone 015C-C11; (17) = Actinomycetales clone TM167; (18) = Leptospirillum sp. strain DSM 2391; (19) = Thermicanus aegyptius; (20) = Bacterium Ellin5017; (21) = Pseudomonas sp. B35; (22) = Nostoc sp. PCC 9231; (23) = Acidiphilium sp. CCP3; (24) = Uncultured bacterium clone RCP2-12; (25) = Uncultured actinobacterium clone BPM2_A01; (27) = Acidithiobacillus sp. SK5; (28) = Acidobacteria clone BPC3_E10; (29) = Uncultured bacterium clone 300A-B12; (30) = Bacterium Ellin5114; (31) = Corynebacterium sp. S18-03; (32) = Uncultured bacterium clone RCP1-34; (33) = Uncultured bacterium clone RH1-L2; (34) = Uncultured bacterium clone RH1-i3; (35) = Uncultured bacterium clone RCP2-16; (37) = Uncultured actinobacterium clone BPM3_G08.
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pone-0003853-g006: CCA biplot of the SARST-V6 dataset with relevant environmental variables at Río Tinto samples and sites.Superimposed canonical correspondence analysis (CCA) biplots of RT samples and SARST-V6 OTUs at the 99% similarity cut-off value displaying 68% of the variance of the OTUs with respect to the environmental variables. The inset represents the CCA biplot when pooling samples by site. The canonical eigenvalues for axes 1–4 of the sample analysis are 0.367, 0.272, 0.112, and 0.062 respectively. Environmental variables are indicated by arrows that point in the direction of increasing values of each variable. The coordinates of the arrowheads indicate the degree of correlation with the axes. Samples and sites are represented by black circles. For sample names see Materials and Methods. OTUs with total abundances higher than 10 RSTs are represented by grey triangles. To avoid overcrowding of points only one OTU per strain is plotted. The relative frequency of OTUs in samples can be determined using the biplot rule. To do this, drop a perpendicular from each sample onto a line through the OTU and the origin. Samples projecting on the line in the direction towards the OTU and beyond it are predicted to have a higher relative frequency of that OTU than samples projecting onto the line in the opposite direction. Interpretation of environmental arrows with respect to sites, OTUs and other environmental variables follows the same rule. OTU numbers correspond to: (1, 12, 14, 36) = Acidithiobacillus sp. SS5; (2, 11) = Uncultured bacterial clone MPKCSC9; (3) = Acidithiobacillus sp. SK5; (4) = Leptospirillum ferrooxidans P3a; (5, 26) = L. ferrooxidans Parys; (6) = Acidithiobacillus sp. B9; (7) = L. ferrooxidans Sy; (8) = Thermicanus aegyptius; (9) = Acidiphilium sp. Pk46; (10) = Eubacterium clones TRA5-3 and MeBr10; (13) = Uncultured bacterium BA18; (15) = F. acidiphilium; (16) = Bacterium clone 015C-C11; (17) = Actinomycetales clone TM167; (18) = Leptospirillum sp. strain DSM 2391; (19) = Thermicanus aegyptius; (20) = Bacterium Ellin5017; (21) = Pseudomonas sp. B35; (22) = Nostoc sp. PCC 9231; (23) = Acidiphilium sp. CCP3; (24) = Uncultured bacterium clone RCP2-12; (25) = Uncultured actinobacterium clone BPM2_A01; (27) = Acidithiobacillus sp. SK5; (28) = Acidobacteria clone BPC3_E10; (29) = Uncultured bacterium clone 300A-B12; (30) = Bacterium Ellin5114; (31) = Corynebacterium sp. S18-03; (32) = Uncultured bacterium clone RCP1-34; (33) = Uncultured bacterium clone RH1-L2; (34) = Uncultured bacterium clone RH1-i3; (35) = Uncultured bacterium clone RCP2-16; (37) = Uncultured actinobacterium clone BPM3_G08.

Mentions: Amils et al. [24] proposed a geomicrobiological model for the RT controlled by iron and based on the geochemistry and the metabolism of the most abundant bacteria and archaea. The major non-photosynthetic primary producers A. ferrooxidans and L. ferrooxidans obtain their energy from pyrite (FeS2) and the oxidized metabolites can in turn be mineralized by heterotrophic microbes like Acidiphilium spp., Ferromicrobium or sulfate reducers. Ferric iron buffers the pH at or below pH 3. In our study, of the 22 environmental parameters measured, seven best explained the variation in the data (As, Fe, Mn, Sr, Zn, pH, and redox) (see Material & Methods). We used these variables to perform Canonical Correspondence Analysis (CCA) using OTUs at a 99% similarity cut-off with both samples and sites. The CCA plot for samples, sites or OTUs with respect to environmental variables showed a strong correlation of the canonical axes with the variables chosen (Fig. 6). Monte Carlo permutation tests for the first and all axes for samples and sites were highly significant (P = 0.002) indicating that these environmental parameters are important in explaining community diversity. For instance, AG2 is more similar in geochemistry and relative abundance of OTUs to BE (∼30 km away) than to AG1 and AG3, only meters away (Fig. 1). OTUs that plotted near BE and AG2 sites may therefore be better adapted to relatively higher concentrations of Zn and lower concentrations of As than OTUs with a higher relative abundance at other sites (Fig. 1 and 5). Furthermore, we observed that several OTUs had exactly the same match in GenBank and occupied the same position in the ordination plot (Fig. 6). We infer they are members of the same subspecific unit or ecotype that is better adapted to particular environmental characteristics.


Contrasting microbial community assembly hypotheses: a reconciling tale from the Río Tinto.

Palacios C, Zettler E, Amils R, Amaral-Zettler L - PLoS ONE (2008)

CCA biplot of the SARST-V6 dataset with relevant environmental variables at Río Tinto samples and sites.Superimposed canonical correspondence analysis (CCA) biplots of RT samples and SARST-V6 OTUs at the 99% similarity cut-off value displaying 68% of the variance of the OTUs with respect to the environmental variables. The inset represents the CCA biplot when pooling samples by site. The canonical eigenvalues for axes 1–4 of the sample analysis are 0.367, 0.272, 0.112, and 0.062 respectively. Environmental variables are indicated by arrows that point in the direction of increasing values of each variable. The coordinates of the arrowheads indicate the degree of correlation with the axes. Samples and sites are represented by black circles. For sample names see Materials and Methods. OTUs with total abundances higher than 10 RSTs are represented by grey triangles. To avoid overcrowding of points only one OTU per strain is plotted. The relative frequency of OTUs in samples can be determined using the biplot rule. To do this, drop a perpendicular from each sample onto a line through the OTU and the origin. Samples projecting on the line in the direction towards the OTU and beyond it are predicted to have a higher relative frequency of that OTU than samples projecting onto the line in the opposite direction. Interpretation of environmental arrows with respect to sites, OTUs and other environmental variables follows the same rule. OTU numbers correspond to: (1, 12, 14, 36) = Acidithiobacillus sp. SS5; (2, 11) = Uncultured bacterial clone MPKCSC9; (3) = Acidithiobacillus sp. SK5; (4) = Leptospirillum ferrooxidans P3a; (5, 26) = L. ferrooxidans Parys; (6) = Acidithiobacillus sp. B9; (7) = L. ferrooxidans Sy; (8) = Thermicanus aegyptius; (9) = Acidiphilium sp. Pk46; (10) = Eubacterium clones TRA5-3 and MeBr10; (13) = Uncultured bacterium BA18; (15) = F. acidiphilium; (16) = Bacterium clone 015C-C11; (17) = Actinomycetales clone TM167; (18) = Leptospirillum sp. strain DSM 2391; (19) = Thermicanus aegyptius; (20) = Bacterium Ellin5017; (21) = Pseudomonas sp. B35; (22) = Nostoc sp. PCC 9231; (23) = Acidiphilium sp. CCP3; (24) = Uncultured bacterium clone RCP2-12; (25) = Uncultured actinobacterium clone BPM2_A01; (27) = Acidithiobacillus sp. SK5; (28) = Acidobacteria clone BPC3_E10; (29) = Uncultured bacterium clone 300A-B12; (30) = Bacterium Ellin5114; (31) = Corynebacterium sp. S18-03; (32) = Uncultured bacterium clone RCP1-34; (33) = Uncultured bacterium clone RH1-L2; (34) = Uncultured bacterium clone RH1-i3; (35) = Uncultured bacterium clone RCP2-16; (37) = Uncultured actinobacterium clone BPM3_G08.
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pone-0003853-g006: CCA biplot of the SARST-V6 dataset with relevant environmental variables at Río Tinto samples and sites.Superimposed canonical correspondence analysis (CCA) biplots of RT samples and SARST-V6 OTUs at the 99% similarity cut-off value displaying 68% of the variance of the OTUs with respect to the environmental variables. The inset represents the CCA biplot when pooling samples by site. The canonical eigenvalues for axes 1–4 of the sample analysis are 0.367, 0.272, 0.112, and 0.062 respectively. Environmental variables are indicated by arrows that point in the direction of increasing values of each variable. The coordinates of the arrowheads indicate the degree of correlation with the axes. Samples and sites are represented by black circles. For sample names see Materials and Methods. OTUs with total abundances higher than 10 RSTs are represented by grey triangles. To avoid overcrowding of points only one OTU per strain is plotted. The relative frequency of OTUs in samples can be determined using the biplot rule. To do this, drop a perpendicular from each sample onto a line through the OTU and the origin. Samples projecting on the line in the direction towards the OTU and beyond it are predicted to have a higher relative frequency of that OTU than samples projecting onto the line in the opposite direction. Interpretation of environmental arrows with respect to sites, OTUs and other environmental variables follows the same rule. OTU numbers correspond to: (1, 12, 14, 36) = Acidithiobacillus sp. SS5; (2, 11) = Uncultured bacterial clone MPKCSC9; (3) = Acidithiobacillus sp. SK5; (4) = Leptospirillum ferrooxidans P3a; (5, 26) = L. ferrooxidans Parys; (6) = Acidithiobacillus sp. B9; (7) = L. ferrooxidans Sy; (8) = Thermicanus aegyptius; (9) = Acidiphilium sp. Pk46; (10) = Eubacterium clones TRA5-3 and MeBr10; (13) = Uncultured bacterium BA18; (15) = F. acidiphilium; (16) = Bacterium clone 015C-C11; (17) = Actinomycetales clone TM167; (18) = Leptospirillum sp. strain DSM 2391; (19) = Thermicanus aegyptius; (20) = Bacterium Ellin5017; (21) = Pseudomonas sp. B35; (22) = Nostoc sp. PCC 9231; (23) = Acidiphilium sp. CCP3; (24) = Uncultured bacterium clone RCP2-12; (25) = Uncultured actinobacterium clone BPM2_A01; (27) = Acidithiobacillus sp. SK5; (28) = Acidobacteria clone BPC3_E10; (29) = Uncultured bacterium clone 300A-B12; (30) = Bacterium Ellin5114; (31) = Corynebacterium sp. S18-03; (32) = Uncultured bacterium clone RCP1-34; (33) = Uncultured bacterium clone RH1-L2; (34) = Uncultured bacterium clone RH1-i3; (35) = Uncultured bacterium clone RCP2-16; (37) = Uncultured actinobacterium clone BPM3_G08.
Mentions: Amils et al. [24] proposed a geomicrobiological model for the RT controlled by iron and based on the geochemistry and the metabolism of the most abundant bacteria and archaea. The major non-photosynthetic primary producers A. ferrooxidans and L. ferrooxidans obtain their energy from pyrite (FeS2) and the oxidized metabolites can in turn be mineralized by heterotrophic microbes like Acidiphilium spp., Ferromicrobium or sulfate reducers. Ferric iron buffers the pH at or below pH 3. In our study, of the 22 environmental parameters measured, seven best explained the variation in the data (As, Fe, Mn, Sr, Zn, pH, and redox) (see Material & Methods). We used these variables to perform Canonical Correspondence Analysis (CCA) using OTUs at a 99% similarity cut-off with both samples and sites. The CCA plot for samples, sites or OTUs with respect to environmental variables showed a strong correlation of the canonical axes with the variables chosen (Fig. 6). Monte Carlo permutation tests for the first and all axes for samples and sites were highly significant (P = 0.002) indicating that these environmental parameters are important in explaining community diversity. For instance, AG2 is more similar in geochemistry and relative abundance of OTUs to BE (∼30 km away) than to AG1 and AG3, only meters away (Fig. 1). OTUs that plotted near BE and AG2 sites may therefore be better adapted to relatively higher concentrations of Zn and lower concentrations of As than OTUs with a higher relative abundance at other sites (Fig. 1 and 5). Furthermore, we observed that several OTUs had exactly the same match in GenBank and occupied the same position in the ordination plot (Fig. 6). We infer they are members of the same subspecific unit or ecotype that is better adapted to particular environmental characteristics.

Bottom Line: We found that environmental factors dictate the distribution of the most abundant taxa in this system, but stochastic niche differentiation processes, such as mutation and dispersal, also contribute to observed diversity patterns.We predict that studies providing clues to the evolutionary and ecological processes underlying microbial distributions will reconcile the ongoing debate between the Baas Becking vs.Hubbell community assembly hypotheses.

View Article: PubMed Central - PubMed

Affiliation: The Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA, USA.

ABSTRACT

Background: The Río Tinto (RT) is distinguished from other acid mine drainage systems by its natural and ancient origins. Microbial life from all three domains flourishes in this ecosystem, but bacteria dominate metabolic processes that perpetuate environmental extremes. While the patchy geochemistry of the RT likely influences the dynamics of bacterial populations, demonstrating which environmental variables shape microbial diversity and unveiling the mechanisms underlying observed patterns, remain major challenges in microbial ecology whose answers rely upon detailed assessments of community structures coupled with fine-scale measurements of physico-chemical parameters.

Methodology/principal findings: By using high-throughput environmental tag sequencing we achieved saturation of richness estimators for the first time in the RT. We found that environmental factors dictate the distribution of the most abundant taxa in this system, but stochastic niche differentiation processes, such as mutation and dispersal, also contribute to observed diversity patterns.

Conclusions/significance: We predict that studies providing clues to the evolutionary and ecological processes underlying microbial distributions will reconcile the ongoing debate between the Baas Becking vs. Hubbell community assembly hypotheses.

Show MeSH
Related in: MedlinePlus