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Drivers shaping the diversity and biogeography of total and active bacterial communities in the South China Sea.

Zhang Y, Zhao Z, Dai M, Jiao N, Herndl GJ - Mol. Ecol. (2014)

Bottom Line: Although the composition of both the total and active bacterial community was strongly correlated with environmental factors and weakly correlated with geographic distance, the active bacterial community displayed higher environmental sensitivity than the total community and particularly a greater distance effect largely caused by the active assemblage from deep waters.This might be due to a high competition between active bacterial taxa as indicated by our community network models.Based on these analyses, we speculate that high competition could cause some dispersal limitation of the active bacterial community resulting in a distinct distance-decay relationship.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Marine Environmental Sciences, Xiamen University, Xiang'an, Xiamen, 361101, China; Institute of Marine Microbes and Ecospheres, Xiamen University, Xiang'an, Xiamen, 361101, China.

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CCA analyses of bacterial communities. (a) Heterotrophic bacterial (noncyanobacterial, the same below) DNA-based libraries; (b) Heterotrophic bacterial RNA-based libraries; (c) Cyanobacterial DNA-based libraries; (d) Cyanobacterial RNA-based libraries. Each square represents an individual sample. Vectors represent statistically significant environmental variables explaining the observed patterns (P < 0.05). Temp: temperature; Sali: salinity; Si: silicate [some of the data are reported in (Du et al. 2013)]; Chl: chlorophyll a; O2: oxygen; DOC: dissolved organic carbon (Dai et al., unpublished).
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fig06: CCA analyses of bacterial communities. (a) Heterotrophic bacterial (noncyanobacterial, the same below) DNA-based libraries; (b) Heterotrophic bacterial RNA-based libraries; (c) Cyanobacterial DNA-based libraries; (d) Cyanobacterial RNA-based libraries. Each square represents an individual sample. Vectors represent statistically significant environmental variables explaining the observed patterns (P < 0.05). Temp: temperature; Sali: salinity; Si: silicate [some of the data are reported in (Du et al. 2013)]; Chl: chlorophyll a; O2: oxygen; DOC: dissolved organic carbon (Dai et al., unpublished).

Mentions: CCA analysis revealed that temperature, salinity, depth, silicate and DOC concentrations were the statistically most significant variables, explaining the pattern of the heterotrophic bacterial community composition based on DNA-based libraries (P < 0.05). Likewise, temperature, salinity, depth, oxygen and chl a concentrations were significant factors determining the composition of the active bacterial community (based on RNA-based libraries; P < 0.05) (Fig.6a and b). However, the CCA models for cyanobacterial assemblages indicated that salinity and chl a concentration, as the statistically significant environmental factors (P < 0.05), were only partly responsible for the community variability over space, yielding low similarity patterns with the NMDS analysis (Fig.6c and d). It appeared that other factors beyond the presently investigated environmental variables might also contribute to the community cluster patterns. In the four CCA models of the heterotrophic bacterial DNA- and RNA-based libraries and the cyanobacterial DNA- and RNA-based libraries, the environmental variables explained approximately 54, 44, 20 and 21% of the total variance in the community composition, respectively. Based on the distance matrix of the significant environmental parameters (without missing values) revealed by the CCA models, Mantel and partial Mantel tests further indicated significant correlations (P < 0.01) between environmental parameters and heterotrophic bacterial community composition (Table1). Moreover, active heterotrophic assemblages displayed tighter correlations to environmental parameters (r = 0.79–0.91) than the total community (r = 0.65–0.82). For cyanobacterial assemblages, environmental factors moderately correlated only with the DNA-based libraries (P < 0.01) but not with the RNA-based libraries (Table1).


Drivers shaping the diversity and biogeography of total and active bacterial communities in the South China Sea.

Zhang Y, Zhao Z, Dai M, Jiao N, Herndl GJ - Mol. Ecol. (2014)

CCA analyses of bacterial communities. (a) Heterotrophic bacterial (noncyanobacterial, the same below) DNA-based libraries; (b) Heterotrophic bacterial RNA-based libraries; (c) Cyanobacterial DNA-based libraries; (d) Cyanobacterial RNA-based libraries. Each square represents an individual sample. Vectors represent statistically significant environmental variables explaining the observed patterns (P < 0.05). Temp: temperature; Sali: salinity; Si: silicate [some of the data are reported in (Du et al. 2013)]; Chl: chlorophyll a; O2: oxygen; DOC: dissolved organic carbon (Dai et al., unpublished).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230472&req=5

fig06: CCA analyses of bacterial communities. (a) Heterotrophic bacterial (noncyanobacterial, the same below) DNA-based libraries; (b) Heterotrophic bacterial RNA-based libraries; (c) Cyanobacterial DNA-based libraries; (d) Cyanobacterial RNA-based libraries. Each square represents an individual sample. Vectors represent statistically significant environmental variables explaining the observed patterns (P < 0.05). Temp: temperature; Sali: salinity; Si: silicate [some of the data are reported in (Du et al. 2013)]; Chl: chlorophyll a; O2: oxygen; DOC: dissolved organic carbon (Dai et al., unpublished).
Mentions: CCA analysis revealed that temperature, salinity, depth, silicate and DOC concentrations were the statistically most significant variables, explaining the pattern of the heterotrophic bacterial community composition based on DNA-based libraries (P < 0.05). Likewise, temperature, salinity, depth, oxygen and chl a concentrations were significant factors determining the composition of the active bacterial community (based on RNA-based libraries; P < 0.05) (Fig.6a and b). However, the CCA models for cyanobacterial assemblages indicated that salinity and chl a concentration, as the statistically significant environmental factors (P < 0.05), were only partly responsible for the community variability over space, yielding low similarity patterns with the NMDS analysis (Fig.6c and d). It appeared that other factors beyond the presently investigated environmental variables might also contribute to the community cluster patterns. In the four CCA models of the heterotrophic bacterial DNA- and RNA-based libraries and the cyanobacterial DNA- and RNA-based libraries, the environmental variables explained approximately 54, 44, 20 and 21% of the total variance in the community composition, respectively. Based on the distance matrix of the significant environmental parameters (without missing values) revealed by the CCA models, Mantel and partial Mantel tests further indicated significant correlations (P < 0.01) between environmental parameters and heterotrophic bacterial community composition (Table1). Moreover, active heterotrophic assemblages displayed tighter correlations to environmental parameters (r = 0.79–0.91) than the total community (r = 0.65–0.82). For cyanobacterial assemblages, environmental factors moderately correlated only with the DNA-based libraries (P < 0.01) but not with the RNA-based libraries (Table1).

Bottom Line: Although the composition of both the total and active bacterial community was strongly correlated with environmental factors and weakly correlated with geographic distance, the active bacterial community displayed higher environmental sensitivity than the total community and particularly a greater distance effect largely caused by the active assemblage from deep waters.This might be due to a high competition between active bacterial taxa as indicated by our community network models.Based on these analyses, we speculate that high competition could cause some dispersal limitation of the active bacterial community resulting in a distinct distance-decay relationship.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Marine Environmental Sciences, Xiamen University, Xiang'an, Xiamen, 361101, China; Institute of Marine Microbes and Ecospheres, Xiamen University, Xiang'an, Xiamen, 361101, China.

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Related in: MedlinePlus