Biased richness and evenness relationships within Shannon–Wiener index values

Strong, Wayne L

doi:10.1016/j.ecolind.2016.03.043

Cited by 122 publications

(43 citation statements)

References 47 publications

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“…Shannon diversity index (H′) is greatly influenced by species richness of communities ( Sun et al, 2012 ). A higher diversity index indicated that the stored rice microbial community metabolic functional diversity was larger ( Strong, 2016 ). The higher the Shannon evenness index (E) was, the more evenly the individuals distributed ( Zhanga et al, 2013 ).…”

Section: Resultsmentioning

confidence: 99%

“… Zak et al (1994) proposed that the calculation method based on functional diversity indices of BIOLOG ECO microplates could investigate the diversity of communities. Moreover, Keylock (2005) and Strong (2016) extended the concept of evenness to characterize the utilization levels and utilization patterns of microorganisms by carbon source.…”

Section: Methodsmentioning

confidence: 99%

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Analysis on Metabolic Functions of Stored Rice Microbial Communities by BIOLOG ECO Microplates

Gao

et al. 2018

Front. Microbiol.

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Microbial contamination has been a pervasive issue during the rice storage and triggers extensive researches. The metabolism of microorganisms was proved as an indicator to mirror the degree of microbial contamination. It is necessary to develop a scientific method to analyze the metabolism of rice microbial communities, thereby monitoring the microbial contamination. In this study, the metabolism of rice microbial communities in different storing-year were investigated by BIOLOG ECO microplates. The three rice samples were respectively stored for 1–3 years. The related indicators of BIOLOG ECO microplates were determined, including average well-color development (AWCD) of carbon sources and three metabolic functional diversity indices. The results showed that there were significant differences in the AWCD of all carbon sources among the three rice microbial communities (p < 0.05), and the functional diversity indices except Simpson index showed significant differences (p < 0.05). Additionally, the three rice microbial communities differed significantly in the metabolic utilization of carboxylic acids and miscellaneous (p < 0.05), and there were, however, no significant differences in the other four types of carbon sources. Furthermore, principal component analysis revealed that the microbial communities of stored rice had obviously different metabolic functions in different storage period. Therefore, the study indicated that the BIOLOG ECO microplate was applicable to evaluate the metabolic functions of rice microbial communities, and carboxylic acids and miscellaneous were two crucial parameters of carbon sources to identify the metabolic differences of microbial communities, a case in which it reflected the conditions of rice microbial contamination.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Analysis on Metabolic Functions of Stored Rice Microbial Communities by BIOLOG ECO Microplates

Gao

et al. 2018

Front. Microbiol.

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“…Alpha diversity indices, including the Chao1 and Shannon-Weaver diversity indices, are generally used to compare the differences between microbial communities [32][33][34] . Both saliva and tonsils showed similar richness in microbial communities, but the saliva microbiome presented higher evenness of distribution than that of tonsillar tissue.…”

Section: Discussionmentioning

confidence: 99%

Association between the microbiomes of tonsil and saliva samples isolated from pediatric patients subjected to tonsillectomy for the treatment of tonsillar hyperplasia

et al. 2020

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Oral microbes have the capacity to spread throughout the gastrointestinal system and are strongly associated with multiple diseases. Given that tonsils are located between the oral cavity and the laryngopharynx at the gateway of the alimentary and respiratory tracts, tonsillar tissue may also be affected by microbiota from both the oral cavity (saliva) and the alimentary tract. Here, we analyzed the distribution and association of the microbial communities in the saliva and tonsils of Korean children subjected to tonsillectomy because of tonsil hyperplasia (n = 29). The microbiome profiles of saliva and tonsils were established via 16S rRNA gene sequencing. Based on the alpha diversity indices, the microbial communities of the two groups showed high similarities. According to Spearman’s ranking correlation analysis, the distribution of Treponema, the causative bacterium of periodontitis, in saliva and tonsils was found to have a significant positive correlation. Two representative microbes, Prevotella in saliva and Alloprevotella in tonsils, were negatively correlated, while Treponema 2 showed a strong positive correlation between saliva and tonsils. Taken together, strong similarities in the microbial communities of the tonsils and saliva are evident in terms of diversity and composition. The saliva microbiome is expected to significantly affect the tonsil microbiome. Furthermore, we suggest that our study creates an opportunity for tonsillar microbiome research to facilitate the development of novel microbiome-based therapeutic strategies.

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“…Alpha-diversity indices, including Chao1 and Shannon-Weaver diversity indicies, are generally used to compare the differences between microbial communities [21][22][23]. Both saliva and tonsils showed similar richness in microbial communities but the saliva microbiome presented higher evenness than tonsil tissue.…”

Section: Discussionmentioning

confidence: 99%

Characterization of the microbiomic association between tonsil and saliva isolated from Korean children subjected to tonsillectomy using 16S rRNA sequencing: a pilot study

Choi¹,

Park²,

Choi³

et al. 2020

Preprint

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BackgroundOral microbes have the capacity to spread throughout the gastrointestinal system and are significantly associated with multiple diseases. Given that tonsils are located between the oral cavity and laryngoesophagus at the gateway of both alimentary and respiratory tracts, tonsillar tissue may also be affected by both oral (saliva) and alimentary tract microbiota. While several independent reports on oral microbiomes from saliva and tonsils are documented in the literature, limited studies have compared tonsil and saliva microbiota compositions. Here, we analyzed the distribution of the microbial communities in saliva and tonsils of healthy Korean children subjected to tonsillectomy. ResultsIn total, 29 study subjects were enrolled from an initial 45 participants subjected to tonsillectomy owing to hyperplasia over two years. The microbiome profiles of saliva and tonsils were established via 16S rRNA gene sequencing. Based on the Chao1 diversity index, the tonsil group showed higher richness in species, compared to the saliva group. On the other hand, the Shannon index revealed a significantly higher biodiversity value for saliva relative to tonsils. The microbial communities of the two groups were highly similar. The top 10 ranked taxa common to both groups determined based on average relative abundance were as follows: Haemophilus,

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Biased richness and evenness relationships within Shannon–Wiener index values

Cited by 122 publications

References 47 publications

Analysis on Metabolic Functions of Stored Rice Microbial Communities by BIOLOG ECO Microplates

Analysis on Metabolic Functions of Stored Rice Microbial Communities by BIOLOG ECO Microplates

Association between the microbiomes of tonsil and saliva samples isolated from pediatric patients subjected to tonsillectomy for the treatment of tonsillar hyperplasia

Characterization of the microbiomic association between tonsil and saliva isolated from Korean children subjected to tonsillectomy using 16S rRNA sequencing: a pilot study

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