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Phylogenetic analysis suggests that habitat filtering is structuring marine bacterial communities across the globe.

Pontarp M, Canbäck B, Tunlid A, Lundberg P - Microb. Ecol. (2012)

Bottom Line: Different bacterial types seem to have different ecological niches that dictate their survival in different habitats.Other eco-evolutionary processes that may contribute to the observed phylogenetic patterns are discussed.The results also imply a mapping between phenotype and phylogenetic relatedness which facilitates the use of community phylogenetic structure analysis to infer ecological and evolutionary assembly processes.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Population Ecology and Evolution Group, Lund University, Lund, Sweden. mikael.pontarp@biol.lu.se

ABSTRACT
The phylogenetic structure and community composition were analysed in an existing data set of marine bacterioplankton communities to elucidate the evolutionary and ecological processes dictating the assembly. The communities were sampled from coastal waters at nine locations distributed worldwide and were examined through the use of comprehensive clone libraries of 16S ribosomal RNA genes. The analyses show that the local communities are phylogenetically different from each other and that a majority of them are phylogenetically clustered, i.e. the species (operational taxonomic units) were more related to each other than expected by chance. Accordingly, the local communities were assembled non-randomly from the global pool of available bacterioplankton. Further, the phylogenetic structures of the communities were related to the water temperature at the locations. In agreement with similar studies, including both macroorganisms and bacteria, these results suggest that marine bacterial communities are structured by “habitat filtering”, i.e. through non-random colonization and invasion determined by environmental characteristics. Different bacterial types seem to have different ecological niches that dictate their survival in different habitats. Other eco-evolutionary processes that may contribute to the observed phylogenetic patterns are discussed. The results also imply a mapping between phenotype and phylogenetic relatedness which facilitates the use of community phylogenetic structure analysis to infer ecological and evolutionary assembly processes.

Show MeSH
Pairwise comparison of community structure. The Morisita’s index of similarity between communities (x-axis). Converted Unifrac metrics (1 − Unifrac metric) (y-axis). Overlapping points from different communities represents the Morisita’s and Unifrac values for the pairwise comparisons. Similarity is stated in percent where 0 denotes no similarity and 1 denotes identical communities. Correlation coefficient between Morisita’s and Unifrac values, 0.70
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Fig1: Pairwise comparison of community structure. The Morisita’s index of similarity between communities (x-axis). Converted Unifrac metrics (1 − Unifrac metric) (y-axis). Overlapping points from different communities represents the Morisita’s and Unifrac values for the pairwise comparisons. Similarity is stated in percent where 0 denotes no similarity and 1 denotes identical communities. Correlation coefficient between Morisita’s and Unifrac values, 0.70

Mentions: The Morisita’s index, based on OTU pairwise similarities and abundances between samples, showed both high and low values (Fig. 1). The highest similarity index was found between Cape Town and Sydney with a joint Morisita’s index of 0.81. This value tells us, given some internal diversity in the compared communities, the probability that two sequences randomly drawn from the two communities, respectively, belong to the same OTU. Consequently, the Cape Town and Sidney samples are 81% similar in terms of OTU composition and abundance. Baffin Bay is relatively dissimilar to all other localities (in some cases 0% similarity), except the Arctic Ocean. Consequently, this sample is to a large extent unique in terms of OTU composition. Note that a majority (26 out of 36) of the pairwise similarity values were found in the two bottom quartiles of the distribution of similarity values. The Unifrac analysis of phylogenetic distance gave somewhat different results; the pairwise Unifrac metric values were on average lower and had less variation (ranging from 0.1 to 0.37). The distribution of Unifrac values was also more evenly distributed; ten out of the 36 pairwise Unifrac values were found in the lower two quartiles. The qualitative pattern among samples was, however, similar between the Unifrac and Morisita’s results. Baffin Bay again stands out, sharing less than 20% of the nodes and branch lengths of any of the other communities (Fig. 1). The significance test and Unifrac P test that were used for analysing the significance of the pairwise distance between communities all showed high P values. A possible explanation for this is the effect of the Bonferroni correction for multiple comparisons; these tests are primarily suited for analysis of two or a few samples [20].Figure 1


Phylogenetic analysis suggests that habitat filtering is structuring marine bacterial communities across the globe.

Pontarp M, Canbäck B, Tunlid A, Lundberg P - Microb. Ecol. (2012)

Pairwise comparison of community structure. The Morisita’s index of similarity between communities (x-axis). Converted Unifrac metrics (1 − Unifrac metric) (y-axis). Overlapping points from different communities represents the Morisita’s and Unifrac values for the pairwise comparisons. Similarity is stated in percent where 0 denotes no similarity and 1 denotes identical communities. Correlation coefficient between Morisita’s and Unifrac values, 0.70
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3375428&req=5

Fig1: Pairwise comparison of community structure. The Morisita’s index of similarity between communities (x-axis). Converted Unifrac metrics (1 − Unifrac metric) (y-axis). Overlapping points from different communities represents the Morisita’s and Unifrac values for the pairwise comparisons. Similarity is stated in percent where 0 denotes no similarity and 1 denotes identical communities. Correlation coefficient between Morisita’s and Unifrac values, 0.70
Mentions: The Morisita’s index, based on OTU pairwise similarities and abundances between samples, showed both high and low values (Fig. 1). The highest similarity index was found between Cape Town and Sydney with a joint Morisita’s index of 0.81. This value tells us, given some internal diversity in the compared communities, the probability that two sequences randomly drawn from the two communities, respectively, belong to the same OTU. Consequently, the Cape Town and Sidney samples are 81% similar in terms of OTU composition and abundance. Baffin Bay is relatively dissimilar to all other localities (in some cases 0% similarity), except the Arctic Ocean. Consequently, this sample is to a large extent unique in terms of OTU composition. Note that a majority (26 out of 36) of the pairwise similarity values were found in the two bottom quartiles of the distribution of similarity values. The Unifrac analysis of phylogenetic distance gave somewhat different results; the pairwise Unifrac metric values were on average lower and had less variation (ranging from 0.1 to 0.37). The distribution of Unifrac values was also more evenly distributed; ten out of the 36 pairwise Unifrac values were found in the lower two quartiles. The qualitative pattern among samples was, however, similar between the Unifrac and Morisita’s results. Baffin Bay again stands out, sharing less than 20% of the nodes and branch lengths of any of the other communities (Fig. 1). The significance test and Unifrac P test that were used for analysing the significance of the pairwise distance between communities all showed high P values. A possible explanation for this is the effect of the Bonferroni correction for multiple comparisons; these tests are primarily suited for analysis of two or a few samples [20].Figure 1

Bottom Line: Different bacterial types seem to have different ecological niches that dictate their survival in different habitats.Other eco-evolutionary processes that may contribute to the observed phylogenetic patterns are discussed.The results also imply a mapping between phenotype and phylogenetic relatedness which facilitates the use of community phylogenetic structure analysis to infer ecological and evolutionary assembly processes.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Population Ecology and Evolution Group, Lund University, Lund, Sweden. mikael.pontarp@biol.lu.se

ABSTRACT
The phylogenetic structure and community composition were analysed in an existing data set of marine bacterioplankton communities to elucidate the evolutionary and ecological processes dictating the assembly. The communities were sampled from coastal waters at nine locations distributed worldwide and were examined through the use of comprehensive clone libraries of 16S ribosomal RNA genes. The analyses show that the local communities are phylogenetically different from each other and that a majority of them are phylogenetically clustered, i.e. the species (operational taxonomic units) were more related to each other than expected by chance. Accordingly, the local communities were assembled non-randomly from the global pool of available bacterioplankton. Further, the phylogenetic structures of the communities were related to the water temperature at the locations. In agreement with similar studies, including both macroorganisms and bacteria, these results suggest that marine bacterial communities are structured by “habitat filtering”, i.e. through non-random colonization and invasion determined by environmental characteristics. Different bacterial types seem to have different ecological niches that dictate their survival in different habitats. Other eco-evolutionary processes that may contribute to the observed phylogenetic patterns are discussed. The results also imply a mapping between phenotype and phylogenetic relatedness which facilitates the use of community phylogenetic structure analysis to infer ecological and evolutionary assembly processes.

Show MeSH