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Utilizing novel diversity estimators to quantify multiple dimensions of microbial biodiversity across domains.

ABSTRACT: BACKGROUND: Microbial ecologists often employ methods from classical community ecology to analyze microbial community diversity. However, these methods have limitations because microbial communities differ from macro-organismal communities in key ways. This study sought to quantify microbial diversity using methods that are better suited for data spanning multiple domains of life and dimensions of diversity. Diversity profiles are one novel, promising way to analyze microbial datasets. Diversity profiles encompass many other indices, provide effective numbers of diversity (mathematical generalizations of previous indices that better convey the magnitude of differences in diversity), and can incorporate taxa similarity information. To explore whether these profiles change interpretations of microbial datasets, diversity profiles were calculated for four microbial datasets from different environments spanning all domains of life as well as viruses. Both similarity-based profiles that incorporated phylogenetic relatedness and naïve (not similarity-based) profiles were calculated. Simulated datasets were used to examine the robustness of diversity profiles to varying phylogenetic topology and community composition. RESULTS: Diversity profiles provided insights into microbial datasets that were not detectable with classical univariate diversity metrics. For all datasets analyzed, there were key distinctions between calculations that incorporated phylogenetic diversity as a measure of taxa similarity and naïve calculations. The profiles also provided information about the effects of rare species on diversity calculations. Additionally, diversity profiles were used to examine thousands of simulated microbial communities, showing that similarity-based and naïve diversity profiles only agreed approximately 50% of the time in their classification of which sample was most diverse. This is a strong argument for incorporating similarity information and calculating diversity with a range of emphases on rare and abundant species when quantifying microbial community diversity. CONCLUSIONS: For many datasets, diversity profiles provided a different view of microbial community diversity compared to analyses that did not take into account taxa similarity information, effective diversity, or multiple diversity metrics. These findings are a valuable contribution to data analysis methodology in microbial ecology.

SUBMITTER: Doll HM

PROVIDER: S-EPMC3840555 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Utilizing novel diversity estimators to quantify multiple dimensions of microbial biodiversity across domains.

Doll Hannah M HM Armitage David W DW Daly Rebecca A RA Emerson Joanne B JB Goltsman Daniela S Aliaga DS Yelton Alexis P AP Kerekes Jennifer J Firestone Mary K MK Potts Matthew D MD

BMC microbiology 20131115

<h4>Background</h4>Microbial ecologists often employ methods from classical community ecology to analyze microbial community diversity. However, these methods have limitations because microbial communities differ from macro-organismal communities in key ways. This study sought to quantify microbial diversity using methods that are better suited for data spanning multiple domains of life and dimensions of diversity. Diversity profiles are one novel, promising way to analyze microbial datasets. Di ...[more]

PMID: 24238386

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Project description:Studies on ecological communities often address patterns of species distribution and abundance, but few consider uncertainty in counts of both species and individuals when computing diversity measures.We evaluated the extent to which imperfect detection may influence patterns of taxonomic, functional, and phylogenetic diversity in ecological communities.We estimated the true abundance of fruit-feeding butterflies sampled in canopy and understory strata in a subtropical forest. We compared the diversity values calculated by observed and estimated abundance data through the hidden diversity framework. This framework evaluates the deviation of observed diversity when compared with diversities derived from estimated true abundances and whether such deviation represents a bias or a noise in the observed diversity pattern.The hidden diversity values differed between strata for all diversity measures, except for functional richness. The taxonomic measure was the only one where we observed an inversion of the most diverse stratum when imperfect detection was included. Regarding phylogenetic and functional measures, the strata showed distinct responses to imperfect detection, despite the tendency to overestimate observed diversity. While the understory showed noise for the phylogenetic measure, since the observed pattern was maintained, the canopy had biased diversity for the functional metric. This bias occurred since no significant differences were found between strata for observed diversity, but rather for estimated diversity, with the canopy being more clustered.We demonstrate that ignore imperfect detection may lead to unrealistic estimates of diversity and hence to erroneous interpretations of patterns and processes that structure biological communities. For fruit-feeding butterflies, according to their phylogenetic position or functional traits, the undetected individuals triggered different responses in the relationship of the diversity measures to the environmental factor. This highlights the importance to evaluate and include the uncertainty in species detectability before calculating biodiversity measures to describe communities.

Project description:IntroductionHuman concerns about fossil fuel depletion, energy security and environmental degradation have driven the rapid development of solar photovoltaic (PV) power generation. Most of the photovoltaic power generation plants are concentrated in desert, grassland and arable land, which means the change of land use type. However, there is still a gap in the research of the PV panel layout on grassland plant species diversity and ecological function.MethodsIn this study, Illumina high-throughput sequencing technology was used to investigate the effects of PV panel arrangement on grassland plant species diversity and soil microbial diversity. In view of the differences in the microclimate at different sites of the PV panels, quadrates were arranged in front edge (FE), beneath the center of each panel (BP), back edge (BE), the uncovered interspace adjacent to each panel (IS) and the undisturbed grassland around the PV panels (Control), respectively.ResultsPV panels (especially FE) significantly increased the total aboveground productivity (total AGB) and plant species diversity in grasslands. FE increased precipitation accumulation and plant species diversity directly and indirectly changed the diversity of soil bacterial and fungal communities. PV panels decreased the relative abundance of Actinobacteriota, while increased the relative abundance of Proteobacteria, Acidobacteriota, and Methylomirabilota. EC, Margalef' s richness and total AGB were the main factors affecting the composition of bacterial communities, while alkaline hydrolysis nitrogen (AN) and available phosphorus (AP) were the main factors affecting the composition of fungal communities.DiscussionIn conclusion, the arrangement of PV panels increased the plant species diversity and soil microorganisms in grassland. This study provides important information for further understanding the impact of PV panels on grassland ecosystem function and is of great significance for maintaining grassland ecosystem function.

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Utilizing novel diversity estimators to quantify multiple dimensions of microbial biodiversity across domains.

Publications

Utilizing novel diversity estimators to quantify multiple dimensions of microbial biodiversity across domains.

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