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Towards the development of multifunctional molecular indicators combining soil biogeochemical and microbiological variables to predict the ecological integrity of silvicultural practices.

Peck V, Quiza L, Buffet JP, Khdhiri M, Durand AA, Paquette A, Thiffault N, Messier C, Beaulieu N, Guertin C, Constant P - Microb Biotechnol (2016)

Bottom Line: Analysis of soil nutrients, abundance of bacteria and gas exchanges unveiled no significant difference among the plots.However, inverting site preparation resulted in higher variations of gas exchanges when compared with trenching, mounding and unlogged natural forest.According to this classification model, simple trenching was the approach that represented the lowest ecological risk potential at the microsite level.

View Article: PubMed Central - PubMed

Affiliation: INRS-Institut Armand-Frappier, 531 boulevard des Prairies, Laval, Québec, Canada, H7V 1B7.

No MeSH data available.


Related in: MedlinePlus

Comparison of soil samples according to their 16S rRNA gene profile. (A) UPGMA agglomerative clustering of soil samples derived from a matrix of Euclidean distance calculated after Hellinger transformation of OTU (97% identity threshold) absolute abundance. The grey circles denote the nodes delineating the four groups of samples significantly discriminated by SIMPROF permutation procedure (P < 0.05). The scale bar represents the Euclidean distance in the dendrogram. Colour labels show the assignation of the soil samples to their multifunctional class (red; class I, green; class II, blue; class II and black; class IV). (B) Parsimonious RDA triplot of Hellinger‐transformed OTU absolute frequency matrix explained by soil pH and C:N ratio. Only the 14 OTUs displaying extreme distribution in the reduced space are depicted for clarity. These OTUs are identified in the legend with colour bars discriminating α‐Proteobacteria (black), β‐Proteobacteria (blue), δ‐Proteobacteria (red) and other phyla (orange), as determined using the Greengene reference database V13_8_99 (McDonald et al., 2012). The colour labels used to present soil samples in the RDA triplot correspond to the clusters identified in the UPGMA (Fig. 4A). The sample M‐A is absent due the low yield of the DNA extraction procedure for this soil (see the Material and Methods section for more details).
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mbt212348-fig-0003: Comparison of soil samples according to their 16S rRNA gene profile. (A) UPGMA agglomerative clustering of soil samples derived from a matrix of Euclidean distance calculated after Hellinger transformation of OTU (97% identity threshold) absolute abundance. The grey circles denote the nodes delineating the four groups of samples significantly discriminated by SIMPROF permutation procedure (P < 0.05). The scale bar represents the Euclidean distance in the dendrogram. Colour labels show the assignation of the soil samples to their multifunctional class (red; class I, green; class II, blue; class II and black; class IV). (B) Parsimonious RDA triplot of Hellinger‐transformed OTU absolute frequency matrix explained by soil pH and C:N ratio. Only the 14 OTUs displaying extreme distribution in the reduced space are depicted for clarity. These OTUs are identified in the legend with colour bars discriminating α‐Proteobacteria (black), β‐Proteobacteria (blue), δ‐Proteobacteria (red) and other phyla (orange), as determined using the Greengene reference database V13_8_99 (McDonald et al., 2012). The colour labels used to present soil samples in the RDA triplot correspond to the clusters identified in the UPGMA (Fig. 4A). The sample M‐A is absent due the low yield of the DNA extraction procedure for this soil (see the Material and Methods section for more details).

Mentions: Quality control, classification and equalization of the 16S rRNA gene sequence libraries yielded 5451 bacterial OTU (97% identity threshold). Overall, 50% of the sequences belonged to Proteobacteria mostly represented by alpha‐ (74%) and delta‐Proteobacteria (11%). The Acidobacteria and Actinobacteria were the two other phyla dominating the bacterial communities with 19% and 9% relative abundance respectively (Fig. S1). Neither the conversion of unlogged natural mixed forest to a hybrid larch monoculture nor MSP treatments caused significant alteration at the microsite level in bacterial OTU richness as evaluated by the Shannon diversity index and ACE estimator (Table 1). In general, beta diversity defined as the variability in OTU composition among replicated plots measured by multivariate dispersion showed more variations in the larch plantation than in unlogged natural mixed forest conservation areas (Fig. S2). Agglomerative clustering of the samples according to their microbial community profile showed that soil samples collected in different treatments could not be discriminated on the basis of OTU composition (Fig. 3A). Unlogged natural forest clustered together with M‐B, while all other clusters were composed either of unique plots or plots originating from different MSP procedures. A redundancy analysis (RDA) was performed to infer the relationship between 16S rRNA gene profiles and environmental variables (Fig. 3B). The most parsimonious model to explain variation in the distribution of 16S rRNA gene sequences included soil C:N stoichiometry and pH. The other variables being redundant to soil C:N and pH, their addition to the analysis increased the variance inflation factor unduly, and they were therefore ignored in the analysis. The first two canonical axes explained 49% of the total variance of bacterial OTU distribution. Significance of the RDA was confirmed with 1000 permutations of OTU data matrix (P = 0.001). Soil pH played an important role in the dispersion of the samples along the first axis, while C:N discriminated the samples along the second.


Towards the development of multifunctional molecular indicators combining soil biogeochemical and microbiological variables to predict the ecological integrity of silvicultural practices.

Peck V, Quiza L, Buffet JP, Khdhiri M, Durand AA, Paquette A, Thiffault N, Messier C, Beaulieu N, Guertin C, Constant P - Microb Biotechnol (2016)

Comparison of soil samples according to their 16S rRNA gene profile. (A) UPGMA agglomerative clustering of soil samples derived from a matrix of Euclidean distance calculated after Hellinger transformation of OTU (97% identity threshold) absolute abundance. The grey circles denote the nodes delineating the four groups of samples significantly discriminated by SIMPROF permutation procedure (P < 0.05). The scale bar represents the Euclidean distance in the dendrogram. Colour labels show the assignation of the soil samples to their multifunctional class (red; class I, green; class II, blue; class II and black; class IV). (B) Parsimonious RDA triplot of Hellinger‐transformed OTU absolute frequency matrix explained by soil pH and C:N ratio. Only the 14 OTUs displaying extreme distribution in the reduced space are depicted for clarity. These OTUs are identified in the legend with colour bars discriminating α‐Proteobacteria (black), β‐Proteobacteria (blue), δ‐Proteobacteria (red) and other phyla (orange), as determined using the Greengene reference database V13_8_99 (McDonald et al., 2012). The colour labels used to present soil samples in the RDA triplot correspond to the clusters identified in the UPGMA (Fig. 4A). The sample M‐A is absent due the low yield of the DNA extraction procedure for this soil (see the Material and Methods section for more details).
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Related In: Results  -  Collection

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Show All Figures
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mbt212348-fig-0003: Comparison of soil samples according to their 16S rRNA gene profile. (A) UPGMA agglomerative clustering of soil samples derived from a matrix of Euclidean distance calculated after Hellinger transformation of OTU (97% identity threshold) absolute abundance. The grey circles denote the nodes delineating the four groups of samples significantly discriminated by SIMPROF permutation procedure (P < 0.05). The scale bar represents the Euclidean distance in the dendrogram. Colour labels show the assignation of the soil samples to their multifunctional class (red; class I, green; class II, blue; class II and black; class IV). (B) Parsimonious RDA triplot of Hellinger‐transformed OTU absolute frequency matrix explained by soil pH and C:N ratio. Only the 14 OTUs displaying extreme distribution in the reduced space are depicted for clarity. These OTUs are identified in the legend with colour bars discriminating α‐Proteobacteria (black), β‐Proteobacteria (blue), δ‐Proteobacteria (red) and other phyla (orange), as determined using the Greengene reference database V13_8_99 (McDonald et al., 2012). The colour labels used to present soil samples in the RDA triplot correspond to the clusters identified in the UPGMA (Fig. 4A). The sample M‐A is absent due the low yield of the DNA extraction procedure for this soil (see the Material and Methods section for more details).
Mentions: Quality control, classification and equalization of the 16S rRNA gene sequence libraries yielded 5451 bacterial OTU (97% identity threshold). Overall, 50% of the sequences belonged to Proteobacteria mostly represented by alpha‐ (74%) and delta‐Proteobacteria (11%). The Acidobacteria and Actinobacteria were the two other phyla dominating the bacterial communities with 19% and 9% relative abundance respectively (Fig. S1). Neither the conversion of unlogged natural mixed forest to a hybrid larch monoculture nor MSP treatments caused significant alteration at the microsite level in bacterial OTU richness as evaluated by the Shannon diversity index and ACE estimator (Table 1). In general, beta diversity defined as the variability in OTU composition among replicated plots measured by multivariate dispersion showed more variations in the larch plantation than in unlogged natural mixed forest conservation areas (Fig. S2). Agglomerative clustering of the samples according to their microbial community profile showed that soil samples collected in different treatments could not be discriminated on the basis of OTU composition (Fig. 3A). Unlogged natural forest clustered together with M‐B, while all other clusters were composed either of unique plots or plots originating from different MSP procedures. A redundancy analysis (RDA) was performed to infer the relationship between 16S rRNA gene profiles and environmental variables (Fig. 3B). The most parsimonious model to explain variation in the distribution of 16S rRNA gene sequences included soil C:N stoichiometry and pH. The other variables being redundant to soil C:N and pH, their addition to the analysis increased the variance inflation factor unduly, and they were therefore ignored in the analysis. The first two canonical axes explained 49% of the total variance of bacterial OTU distribution. Significance of the RDA was confirmed with 1000 permutations of OTU data matrix (P = 0.001). Soil pH played an important role in the dispersion of the samples along the first axis, while C:N discriminated the samples along the second.

Bottom Line: Analysis of soil nutrients, abundance of bacteria and gas exchanges unveiled no significant difference among the plots.However, inverting site preparation resulted in higher variations of gas exchanges when compared with trenching, mounding and unlogged natural forest.According to this classification model, simple trenching was the approach that represented the lowest ecological risk potential at the microsite level.

View Article: PubMed Central - PubMed

Affiliation: INRS-Institut Armand-Frappier, 531 boulevard des Prairies, Laval, Québec, Canada, H7V 1B7.

No MeSH data available.


Related in: MedlinePlus