Gut microbial composition depends on different dietary habits just as health depends on microbial metabolism, but the association of microbiota with different diets in human populations has not yet been shown. In this work, we compared the fecal microbiota of European children (EU) and that of children from a rural African village of Burkina Faso (BF), where the diet, high in fiber content, is similar to that of early human settlements at the time of the birth of agriculture. By using high-throughput 16S rDNA sequencing and biochemical analyses, we found significant differences in gut microbiota between the two groups. BF children showed a significant enrichment in Bacteroidetes and depletion in Firmicutes (P < 0.001), with a unique abundance of bacteria from the genus Prevotella and Xylanibacter, known to contain a set of bacterial genes for cellulose and xylan hydrolysis, completely lacking in the EU children. In addition, we found significantly more short-chain fatty acids (P < 0.001) in BF than in EU children. Also, Enterobacteriaceae (Shigella and Escherichia) were significantly underrepresented in BF than in EU children (P < 0.05). We hypothesize that gut microbiota coevolved with the polysaccharide-rich diet of BF individuals, allowing them to maximize energy intake from fibers while also protecting them from inflammations and noninfectious colonic diseases. This study investigates and compares human intestinal microbiota from children characterized by a modern western diet and a rural diet, indicating the importance of preserving this treasure of microbial diversity from ancient rural communities worldwide.

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Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa

/pdf/the-impact-of-diet-in-shaping-gut-microbiota-revealed-by-a-3gi1n03d19.pdf

The impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and Rural Africa

We review the biogeography of microorganisms in light of the biogeography of macroorganisms A large body of research supports the idea that free-living microbial taxa exhibit biogeographic patterns Current evidence confirms that, as proposed by the Baas-Becking hypothesis, 'the environment selects' and is, in part, responsible for spatial variation in microbial diversity However, recent studies also dispute the idea that 'everything is everywhere' We also consider how the processes that generate and maintain biogeographic patterns in macroorganisms could operate in the microbial world

/pdf/microbial-biogeography-putting-microorganisms-on-the-map-1k7e5do5pn.pdf

Microbial biogeography : putting microorganisms on the map

Ecologists often face the task of studying the association between single species and one or several groups of sites representing habitat types, community types, or other categories. Besides characterizing the ecological preference of the species, the strength of the association usually presents a lot of interest for conservation biology, landscape mapping and management, and natural reserve design, among other applications. The indices most frequently employed to assess these relationships are the phi coefficient of association and the indicator value index (IndVal). We compare these two approaches by putting them into a broader framework of related measures, which includes several new indices. We present permutation tests to assess the statistical significance of species-site group associations and bootstrap methods for obtaining confidence intervals. Correlation measures, such as the phi coefficient, are more context-dependent than indicator values but allow focusing on the preference of the species. In contrast, the two components of an indicator value index directly assess the value of the species as a bioindicator because they can be interpreted as its positive predictive value and sensitivity. Ecologists should select the most appropriate index of association strength according to their objective and then compute confidence intervals to determine the precision of the estimate.

/pdf/associations-between-species-and-groups-of-sites-indices-and-8v4wcr1gz0.pdf

Associations between species and groups of sites: indices and statistical inference

https://www.cell.com/cell-host-microbe/pdf/S1931-3128(14)00063-8.pdf

The Treatment-Naive Microbiome in New-Onset Crohn’s Disease

The classic Jaccard and Sorensen indices of compositional similarity (and other indices that depend upon the same variables) are notoriously sensitive to sample size, especially for assemblages with numerous rare species. Further, because these indices are based solely on presence–absence data, accurate estimators for them are unattainable. We provide a probabilistic derivation for the classic, incidence-based forms of these indices and extend this approach to formulate new Jaccard-type or Sorensen-type indices based on species abundance data. We then propose estimators for these indices that include the effect of unseen shared species, based on either (replicated) incidence- or abundancebased sample data. In sampling simulations, these new estimators prove to be considerably less biased than classic indices when a substantial proportion of species are missing from samples. Based on species-rich empirical datasets, we show how incorporating the effect of unseen shared species not only increases accuracy but also can change the interpretation of results.

http://viceroy.eeb.uconn.edu/estimates/EstimateSPages/EstSUsersGuide/References/ChaoEtAl2005.pdf

A new statistical approach for assessing similarity of species composition with incidence and abundance data

A biological community usually has a large number of species with relatively small abundances. When a random sample of individuals is selected and each individual is classified according to species identity, some rare species may not be discovered. This paper is concerned with the estimation of Shannon’s index of diversity when the number of species and the species abundances are unknown. The traditional estimator that ignores the missing species underestimates when there is a non-negligible number of unseen species. We provide a different approach based on unequal probability sampling theory because species have different probabilities of being discovered in the sample. No parametric forms are assumed for the species abundances. The proposed estimation procedure combines the Horvitz–Thompson (1952) adjustment for missing species and the concept of sample coverage, which is used to properly estimate the relative abundances of species discovered in the sample. Simulation results show that the proposed estimator works well under various abundance models even when a relatively large fraction of the species is missing. Three real data sets, two from biology and the other one from numismatics, are given for illustration.

Nonparametric estimation of Shannon’s index of diversity when there are unseen species in sample

A wide variety of similarity indices for comparing two assemblages based on species incidence (i.e., presence/absence) data have been proposed in the literature. These indices are generally based on three simple incidence counts: the number of species shared by two assemblages and the number of species unique to each of them. We provide a new probabilistic derivation for any incidence-based index that is symmetric (i.e., the index is not affected by the identity ordering of the two assemblages) and homogeneous (i.e., the index is unchanged if all counts are multiplied by a constant). The probabilistic approach is further extended to formulate abundance-based indices. Thus any symmetric and homogeneous incidence index can be easily modified to an abundance-type version. Applying the Laplace approximation formulas, we propose estimators that adjust for the effect of unseen shared species on our abundance-based indices. Simulation results show that the adjusted estimators significantly reduce the biases of the corresponding unadjusted ones when a substantial fraction of species is missing from samples. Data on successional vegetation in six tropical forests are used for illustration. Advantages and disadvantages of some commonly applied indices are briefly discussed.

/pdf/abundance-based-similarity-indices-and-their-estimation-when-3nesgsbzer.pdf

Abundance-based similarity indices and their estimation when there are unseen species in samples.

In evaluating the effectiveness of further sampling in species taxonomic surveys, a practical and important problem is predicting the number of new species that would be observed in a second survey, based on data from an initial survey. This problem can also be approached by estimating the corresponding expected number of new species. A. R. Solow and S. Polasky recently proposed a predictor (or estimator), with the form of a sum of many terms, that was derived under the assumption that all unobserved species in the initial sample have equal relative abundances. We show in this paper that the summation can be expressed as only one term. We provide a direct justification for the simplified estimator and connect it to an extrapolation formula based on a special type of species accumulation curve. Using the proposed justification, we show that, for large sample sizes, the estimator is also valid under an alternative condition, i.e., species that are represented the same number of times in the initial sample have equal relative abundances in the community. This condition is statistically justified from a Bayesian approach, although the estimator exhibits moderate negative bias for predicting larger samples in highly heterogeneous communities. In such situations, we recommend the use of a modified estimator that incorporates a measure of heterogeneity among species abundances. An example using field data from the extant rare vascular plant species patterns in the southern Appalachians is presented to compare the various methods.

Corresponding Editor: A. R. Solow

Predicting the number of new species in further taxonomic sampling

Consider a continuous-time stochastic model in which species arrive in the sample according to independent Poisson processes and where the species discovery rates are heterogeneous. Based on an initial survey, we are concerned with the problem of predicting the number of new species that would be discovered by additional sampling. When the sampling time or sample size of the additional sample tends to infinity, this problem reduces to the prediction of the number of undetected species in the original sample, or equivalently, the estimation of species richness. The topic has a wide range of applications in various disciplines. We propose a simple prediction method and apply it to two datasets. One set of data deals with the capture counts of the Malayan butterfly and the other set deals with identification records of organic pollutants in a water environment. Simulation results are shown to investigate the performance of the proposed method and to compare it with the existing estimators.

https://www.jstor.org/stable/info/1400481

Tsung-Jen Shen

Papers

A new statistical approach for assessing similarity of species composition with incidence and abundance data

Nonparametric estimation of Shannon’s index of diversity when there are unseen species in sample

Abundance-based similarity indices and their estimation when there are unseen species in samples.

Predicting the number of new species in further taxonomic sampling

Nonparametric prediction in species sampling