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Concept: Index numbers

232

Two recent studies have reanalyzed previously published data and found that when data sets were analyzed independently, there was limited support for the widely accepted hypothesis that changes in the microbiome are associated with obesity. This hypothesis was reconsidered by increasing the number of data sets and pooling the results across the individual data sets. The preferred reporting items for systematic reviews and meta-analyses guidelines were used to identify 10 studies for an updated and more synthetic analysis. Alpha diversity metrics and the relative risk of obesity based on those metrics were used to identify a limited number of significant associations with obesity; however, when the results of the studies were pooled by using a random-effect model, significant associations were observed among Shannon diversity, the number of observed operational taxonomic units, Shannon evenness, and obesity status. They were not observed for the ratio of Bacteroidetes and Firmicutes or their individual relative abundances. Although these tests yielded small P values, the difference between the Shannon diversity indices of nonobese and obese individuals was 2.07%. A power analysis demonstrated that only one of the studies had sufficient power to detect a 5% difference in diversity. When random forest machine learning models were trained on one data set and then tested by using the other nine data sets, the median accuracy varied between 33.01 and 64.77% (median, 56.68%). Although there was support for a relationship between the microbial communities found in human feces and obesity status, this association was relatively weak and its detection is confounded by large interpersonal variation and insufficient sample sizes.

Concepts: Bacteria, Gut flora, Taxonomy, Ratio, Diversity index, Shannon index, Measurement of biodiversity, Index numbers

4

How floral traits and community composition influence plant specialization is poorly understood and the existing evidence is restricted to regions where plant diversity is low. Here, we assessed whether plant specialization varied among four species-rich subalpine/alpine communities on the Yulong Mountain, SW China (elevation from 2725 to 3910 m). We analyzed two factors (floral traits and pollen vector community composition: richness and density) to determine the degree of plant specialization across 101 plant species in all four communities. Floral visitors were collected and pollen load analyses were conducted to identify and define pollen vectors. Plant specialization of each species was described by using both pollen vector diversity (Shannon’s diversity index) and plant selectiveness (d' index), which reflected how selective a given species was relative to available pollen vectors.

Concepts: Biodiversity, Life, Species, Plant, Flowering plant, Diversity index, Shannon index, Index numbers

2

Intra-tumour genetic heterogeneity (ITH) fosters drug resistance and is a critical hurdle to clinical treatment. ITH can be well-measured using multi-region sampling but this is costly and challenging to implement. There is therefore a need for tools to estimate ITH in individual samples, using standard genomic data such as SNP-arrays, that could be implemented routinely. We designed two novel scores S and R, respectively based on the Shannon diversity index and Ripley’s L statistic of spatial homogeneity, to quantify ITH in single SNP-array samples. We created in-silico and in-vitro mixtures of tumour clones, in which diversity was known for benchmarking purposes. We found significant but highly-variable associations of our scores with diversity in-silico (p < 0.001) and moderate associations in-vitro (p = 0.015 and p = 0.085). Our scores were also correlated to previous ITH estimates from sequencing data but heterogeneity in the fraction of tumour cells present across samples hampered accurate quantification. The prognostic potential of both scores was moderate but significantly predictive of survival in several tumour types (corrected p = 0.03). Our work thus shows how individual SNP-arrays reveal intra-sample clonal diversity with moderate accuracy.

Concepts: Oncology, Statistics, Estimator, Implementation, Diversity index, Shannon index, Measurement of biodiversity, Index numbers

2

Consuming a variety (vs. monotony) of energy-poor, nutrient-dense foods may help individuals adhere to dietary patterns favorably associated with weight control.

Concepts: Nutrition, Food, Cybernetics, Food security, Diversity index, Seafood, Index numbers, Catering

0

Towards bioremediation of recalcitrant materials like synthetic polymer, soil has been recognized as a traditional site for disposal and subsequent degradation as some microorganisms in soil can degrade the polymer in a non-toxic, cost-effective, and environment friendly way. Microbial functional diversity is a constituent of biodiversity that includes wide range of metabolic activities that can influence numerous aspects of ecosystem functioning like ecosystem stability, nutrient availability, ecosystem dynamics, etc. Thus, in the current study, we assumed that microbial functional diversity could play an important role in polymer degradation in soil. To verify this hypothesis, we isolated soil from five different sites of landfill and examined several microbiological parameters wherein we observed a significant variation in heterotrophic microbial count as well as microbial activities among the soil microcosms tested. Multivariate analysis (principle component analysis) based on the carbon sources utilization pattern revealed that soil microcosms showed different metabolic patterns suggesting the variable distribution of microorganisms among the soil microcosms tested. Since microbial functional diversity depends on both microbial richness and evenness, Shannon diversity index was determined to measure microbial richness and Gini coefficient was determined to measure microbial evenness. The tested soil microcosms exhibited variation in both microbial richness and evenness suggesting the considerable difference in microbial functional diversity among the tested microcosms. We then measured polyhydroxybutyrate (PHB) degradation in soil microcosms after desired period of incubation of PHB in soil wherein we found that soil microcosms having higher functional diversity showed enhanced PHB degradation and soil microcosms having lower functional diversity showed reduced PHB degradation. We also noticed that all the tested soil microcosms showed similar pattern in both microbial functional diversity and PHB degradation suggesting a strong positive correlation (r = 0.95) between microbial functional diversity and PHB degradation. Thus, the results demonstrate that microbial functional diversity plays an important role in PHB degradation in soil by exhibiting versatile microbial metabolic potentials that lead to the enhanced degradation of PHB.

Concepts: Bacteria, Biodiversity, Metabolism, Soil, Ecosystem, Diversity index, Shannon index, Index numbers

0

Bacteria are widely distributed and play an important role in soil carbon © cycling. The impact of soil bacterial diversity on soil C storage has been well established, yet little is known about the underlying mechanisms and the interactions among them. Here, we examined the association between soil bacterial diversity and soil C storage in relation to vegetation restoration on the Loess Plateau. The dominant phyla among land use types (artificial forest, Af; natural shrubland, Ns; artificial grassland, Ag; natural grassland, Ng; slope cropland, Sc) were Acidobacteria, Actinobacteria, Alphaproteobacteria, and Betaproteobacteria, which transited from Acidobacteria-dominant to Actinobacteria-dominant community due to vegetation restoration. Soil C storage and the Shannon diversity index of soil bacterial community (HBacteria) showed the order Ns > Ng > Af > Ag > Sc, whereas no significant difference was found in Good’s coverage (p > .05). Further, a strong relationship was observed between the relative abundance of dominant bacterial groups and soil C storage (p < .05). Additionally, soil bacterial diversity was closely related to soil C storage based on the structural equation model (SEM) and generalized additive models (GAMs). Specifically, soil C storage had the largest deterministic effects, explaining >70% of the variation and suggesting a strong association between soil C storage and soil bacterial diversity. Overall, we propose that further studies are necessary with a focus on the soil bacterial groups with specific functions in relation to soil C storage on the Loess Plateau.

Concepts: Bacteria, Soil, Carbon, Erosion, Proteobacteria, Diversity index, Shannon index, Index numbers

0

To understand the effects that the climate change has on the evolution of species as well as the genetic consequences, we analyze an integrodifference equation (IDE) models for a reproducing and dispersing population in a spatio-temporal heterogeneous environment described by a shifting climate envelope. Our analysis on the IDE focuses on the persistence criterion, travelling wave solutions, and the inside dynamics. First, the persistence criterion, characterizing the global dynamics of the IDE, is established in terms of the basic reproduction number. In the case of persistence, a unique travelling wave is found to govern the global dynamics. The effects of the size and the shifting speed of the climate envelope on the basic reproduction number, and hence, on the persistence criterion, are also investigated. In particular, the critical domain size and the critical shifting speed are found in certain cases. Numerical simulations are performed to complement the theoretical results. In the case of persistence, we separate the travelling wave and general solutions into spatially distinct neutral fractions to study the inside dynamics. It is shown that each neutral genetic fraction rearranges itself spatially so as to asymptotically achieve the profile of the travelling wave. To measure the genetic diversity of the population density we calculate the Shannon diversity index and related indices, and use these to illustrate how diversity changes with underlying parameters.

Concepts: Biodiversity, Organism, Life, Number, Diversity index, Shannon index, Fraction, Index numbers

0

Fungal endophytes isolated from mountain-cultivated ginseng (MCG, Panax ginseng Meyer) were explored for their diversity and biocontrol activity against ginseng pathogens (Alternaria panax, Botrytis cinerea, Cylindrocarpon destructans, Pythium sp. and Rhizoctonia solani). A total of 1,300 isolates were isolated from three tissues (root, stem and leaf) from MCGs grown in 24 different geographic locations in Korea. In total, 129 different fungal isolates were authenticated by molecular identification based on internal transcribed spacer (ITS) sequences. The fungal endophytes belonged to Ascomycota (81.7%), Basidiomycota (7.08%), Zygomycota (10%) and Unknown (1.15%), with 59 genera. Analysis of diversity indices across sampling sites suggested species abundance as a function of geographical and environmental factors of the locations. Shannon diversity index and richness in the different tissues revealed that root tissues are colonized more than stem and leaf tissues, and also certain fungal endophytes are tissue specific. Assessment of the ethyl acetate extracts from 129 fungal isolates for their biocontrol activity against 5 ginseng pathogens revealed that Trichoderma polysporum produces the antimcriobial metabolite against all the pathogens. This result indicates the promise of its potential usage as a biocontrol agent.

Concepts: Fungus, Root, Diversity index, Shannon index, Measurement of biodiversity, Ginseng, Plant pathogens and diseases, Index numbers

0

The diversity of forest trees as an indicator of ecosystem health can be assessed using the spectral characteristics of plant communities through remote sensing data. The objectives of this study were to investigate alpha and beta tree diversity using Landsat data for six dates in the Gönen dam watershed of Turkey. We used richness and the Shannon and Simpson diversity indices to calculate tree alpha diversity. We also represented the relationship between beta diversity and remotely sensed data using species composition similarity and spectral distance similarity of sampling plots via quantile regression. A total of 99 sampling units, each 20 m × 20 m, were selected using geographically stratified random sampling method. Within each plot, the tree species were identified, and all of the trees with a diameter at breast height (dbh) larger than 7 cm were measured. Presence/absence and abundance data (tree species number and tree species basal area) of tree species were used to determine the relationship between richness and the Shannon and Simpson diversity indices, which were computed with ground field data, and spectral variables derived (2 × 2 pixels and 3 × 3 pixels) from Landsat 8 OLI data. The Shannon-Weiner index had the highest correlation. For all six dates, NDVI (normalized difference vegetation index) was the spectral variable most strongly correlated with the Shannon index and the tree diversity variables. The Ratio of green to red (VI) was the spectral variable least correlated with the tree diversity variables and the Shannon basal area. In both beta diversity curves, the slope of the OLS regression was low, while in the upper quantile, it was approximately twice the lower quantiles. The Jaccard index is closed to one with little difference in both two beta diversity approaches. This result is due to increasing the similarity between the sampling plots when they are located close to each other. The intercept differences between two investigated beta diversity were strongly related to the development stage of a number of sampling plots in the tree species basal area method. To obtain beta diversity, the tree basal area method indicates better result than the tree species number method at representing similarity of regions which are located close together. In conclusion, NDVI is helpful for estimating the alpha diversity of trees over large areas when the vegetation is at the maximum growing season. Beta diversity could be obtained with the spectral heterogeneity of Landsat data. Future tree diversity studies using remote sensing data should select data sets when vegetation is at the maximum growing season. Also, forest tree diversity investigations can be identified by using higher-resolution remote sensing data such as ESA Sentinel 2 data which is freely available since June 2015.

Concepts: Biodiversity, Diversity index, Shannon index, Measurement of biodiversity, Remote sensing, Stratified sampling, Species richness, Index numbers

0

The past decades have been a golden era when great tasks were accomplished in the field of microbiology including food microbiology. In the past, culture dependent methods have been the primary choice to investigate the bacterial diversity. However, using culture independent high throughput sequencing of 16S rRNA genes has greatly facilitated studies exploring the microbial compositions and dynamics associated with health and diseases. These culture independent DNA based studies generate large-scale data sets that describe the microbial composition of the certain niche. Consequently, understanding microbial diversity becomes of greater importance when investigating the composition, function, and the dynamics of the microbiota associated with health and diseases. Even though there is no general agreement on which diversity index is the best to use, the use of diversity indices have been used to compare diversity among samples and between treatments to controls. Tools such Shannon-Weaver index and Simpson index that can be used to describe population diversity in samples. The purpose of this review is to explain principles of diversity indices, such as Shannon-Weaver and Simpson to aid general microbiologist in better understanding of bacterial communities. In this review, important questions concerning the microbial diversity are being addressed. Information from this review should facilitate the evidence-based strategies to explore microbial communities.

Concepts: Archaea, Bacteria, Microbiology, Ribosomal RNA, 16S ribosomal RNA, Diversity index, Measurement of biodiversity, Index numbers