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Among the factors influencing the animal gastrointestinal tract microbiome (AGM) diversity, diet and phylogeny have been extensively studied. However, what made the studies particularly challenging is that diet characteristics per se are product of evolution, and hence totally disentangling both effects is unrealistic, likely explaining the lack of consensus in existing literatures. To further explore microbial diversity and host-phylogeny–diet relationships, we performed a big-data meta-analysis with 4903 16S rRNA AGM samples from 318 animal species covering all six vertebrate and four major invertebrate classes. We discovered that the relationship between AGM-diversity and phylogenetic timeline (PT) follows a power-law or log-linear model, including diet specific power-law relationships. The log-linear nature predicts a generally rising trend of AGM diversity along the evolutionary tree starting from the root, which explains the observation why Mammalia exhibited the highest AGM-diversity. The power-law property suggests that a handful of taxa carry disproportionally large weights to the evolution of diversity patterns than the majority of taxa, which explains why the species richness of Insecta was significant different than those from the other nine classes. Finally, we hypothesize that the diversity—PT power-law relationship explains why species-abundance distributions generally follow highly skewed probability distributions. [ABSTRACT FROM AUTHOR]

Background: Ulcerative colitis (UC) is one of the primary types of inflammatory bowel disease (IBD), the occurrence of which has been increasing worldwide. Although IBD is an intensively studied human microbiome-associated disease, research on Chinese populations remains relatively limited, particularly on the mucosal microbiome. The present study aimed to analyze the changes in the mucosal microbiome associated with UC from the perspectives of medical ecology and complex network analysis. Results: In total, 56 mucosal microbiome samples were collected from 28 Chinese UC patients and their healthy family partners, followed by amplicon sequencing. Based on sequencing data, we analyzed species diversity, shared species, and inter-species interactions at the whole community, main phyla, and core/periphery species levels. We identified four opportunistic "pathogens" (i.e., Clostridium tertium, Odoribacter splanchnicus, Ruminococcus gnavus, and Flavonifractor plautii) with potential significance for the diagnosis and treatment of UC, which were inhibited in healthy individuals, but unrestricted in the UC patients. In addition, we also discovered in this study: (i) The positive-to-negative links (P/N) ratio, which measures the balance of species interactions or inhibition effects in microbiome networks, was significantly higher in UC patients, indicating loss of inhibition against potentially opportunistic "pathogens" associated with dysbiosis. (ii) Previous studies have reported conflicting evidence regarding species diversity and composition between UC patients and healthy controls. Here, significant differences were found at the major phylum and core/periphery scales, but not at the whole community level. Thus, we argue that the paradoxical results found in existing studies are due to the scale effect. Conclusions: Our results reveal changes in the ecology and network structure of the gut mucosal microbiome that might be associated with UC, and these changes might provide potential therapeutic mechanisms of UC. The four opportunistic pathogens that were identified in the present study deserve further investigation in future studies. [ABSTRACT FROM AUTHOR]

The human virome is a critical component of the human microbiome, and it is believed to hold the richest diversity within human microbiomes. Yet, the inter-individual scaling (changes) of the human virome has not been formally investigated to the best of our knowledge. Here we fill the gap by applying diversity-area relationship (DAR) modeling (a recent extension to the classic species-area law in biodiversity and biogeography research) for analyzing four large datasets of the human virome with three DAR profiles: DAR scaling (z)—measuring the inter-individual heterogeneity in virome diversity, MAD (maximal accrual diversity: D max ) and LGD ratio (ratio of local diversity to global diversity)—measuring the percentage of individual to population level diversity. Our analyses suggest: (i) The diversity scaling parameter (z) is rather resilient against the diseases as indicated by the lack of significant differences between the healthy and diseased treatments. (ii) The potential maximal accrual diversity (D max ) is less resilient and may vary between the healthy and diseased groups or between different body sites. (iii) The LGD ratio of bacterial communities is much smaller than for viral communities, and relates to the comparatively greater heterogeneity between local vs. global diversity levels found for bacterial-biomes. [ABSTRACT FROM AUTHOR]

There are two major categories of observation data in studying time-dependent processes: one is the time-series data, and the other is the perhaps lesser-recognized but similarly prevalent time-to-event data (also known as survival or failure time). Examples in entomology include molting times and death times of insects, waiting times of predators before the next attack or the hiding times of preys. A particular challenge in analyzing time-to-event data is the observation censoring, or the incomplete observation of survival times, dealing which is a unique advantage of survival analysis statistics. Even with a perfectly designed experiment being conducted perfectly, such 'naturally' censoring may still be unavoidable due to the natural processes, including the premature death in the observation of insect development, the variability in instarship, or simply the continuous nature of time process and the discrete nature of sampling intervals. Here we propose to apply the classic Cox proportional hazards model for modeling both insect development and survival rates (probabilities) with a unified survival analysis approach. We demonstrated the advantages of the proposed approach with the development and survival datasets of 1800 Russian wheat aphids from their births to deaths, observed under 25 laboratory treatments of temperatures and plant growth stages. [ABSTRACT FROM AUTHOR]

The human microbiome consists of five major regional biomes distributed in or on our five body sites including skin, oral, lung, gut, and reproductive tract. Its biogeography (the spatial and temporal distribution of its biodiversity) has far‐reaching implications to our health and diseases. Nevertheless, we currently have very limited understanding on the mechanisms shaping the biogeography, since it is often rather difficult to determine the relative importance of drift, dispersal, speciation, and selection, the four processes (mechanisms) determining the patterns of microbial biogeography and community dynamics according to a recent synthesis in community ecology and biogeography. To disentangle these mechanisms, I utilize multisite neutral (MSN) model and niche‐neutral hybrid (NNH) model to analyze large number of truly multisite microbiome samples covering all five major human microbiome habitats, including 699 metacommunities and 5,420 local communities. Approximately 89% of metacommunities and 92% local communities exhibit patterns indistinguishable from neutral, and 20% indistinguishable from niche‐neutral hybrid model, indicating the relative significance of stochastic neutral forces versus deterministic niche selection in shaping the biogeography of human microbiome. These findings cast supporting evidence to van der Gast's revision to classic Bass‐Becking doctrine of microbial biogeography: "Some things are everywhere and some things are not. Sometimes the environment selects and sometimes it doesn't," offering the first educated guess for "some" and "sometimes" in the revised doctrine. Furthermore, the logistic/Cox regression models describing the relationships among community neutrality, niche differentiation, and key community/species characteristics (including community diversity, community/species dominance, speciation, and migration rates) were constructed to quantitatively describe the niche‐neutral continuum and the influences of community/species properties on the continuum. [ABSTRACT FROM AUTHOR]

Predicting the outbreak risks and/or the inflection (turning or tipping) points of COVID‐19 can be rather challenging. Here, it is addressed by modeling and simulation approaches guided by classic ecological theories and by treating the COVID‐19 pandemic as a metapopulation dynamics problem. Three classic ecological theories are harnessed, including TPL (Taylor's power‐law) and Ma's population aggregation critical density (PACD) for spatiotemporal aggregation/stability scaling, approximating virus metapopulation dynamics with Hubbell's neutral theory, and Ma's diversity‐time relationship adapted for the infection−time relationship. Fisher‐Information for detecting critical transitions and tipping points are also attempted. It is discovered that: (i) TPL aggregation/stability scaling parameter (b > 2), being significantly higher than the b‐values of most macrobial and microbial species including SARS, may interpret the chaotic pandemic of COVID‐19. (ii) The infection aggregation critical threshold (M0) adapted from PACD varies with time (outbreak‐stage), space (region) and public‐health interventions. Exceeding M0, local contagions may become aggregated and connected regionally, leading to epidemic/pandemic. (iii) The ratio of fundamental dispersal to contagion numbers can gauge the relative importance between local contagions vs. regional migrations in spreading infections. (iv) The inflection (turning) points, pair of maximal infection number and corresponding time, are successfully predicted in more than 80% of Chinese provinces and 68 countries worldwide, with a precision >80% generally. [ABSTRACT FROM AUTHOR]

Human gut microbiome could translocate to other tissues, and the relocation triggered by HIV/SIV infection has received increasing attention. However, the underlying mode of this translocation, whether it is deterministic or random (passive) process, is not clear, not to mention quantitative estimation of the relocation probability and rates. Using multi-tissue microbiome datasets collected from SIV-infected macaques, originally reported by Klase et al. (2015), we apply Hubbell's unified neutral theory of biodiversity (UNTB) implemented by Harris et al. (2017) in the form of multi-site neutral (MSN) model to explore the translocation mode and rates of the gut microbiome. We found that (i) The translocation from gastrointestinal tract to tissues was driven by stochastic (neutral) forces as revealed by 100% neutrality-passing rates with MSN testing; (ii) The translocation probability from gastrointestinal tract to tissues is significantly larger than the baseline dispersal rates occurring within gastrointestinal tract (0.234 vs. 0.006 at the phylum level, P < 0.001). (iii) Approximately, 23% of phyla and 55% of genera were migrated from gastrointestinal tract to the tissues (liver and mesenteric lymph nodes). Our findings offer the first interpretation of the microbial translocation mode from gastrointestinal tract to tissues, and the first estimates of the translocation probability and level. [ABSTRACT FROM AUTHOR]

Space is a critical and also challenging frontier in human microbiome research. It has been found that lack of consideration of scales beyond individual and ignoring of microbe dispersal are two crucial roadblocks in preventing deep understanding of the spatial heterogeneity of human microbiome. Assessing and interpreting the heterogeneity and dispersal in microbiomes explicitly are particularly challenging, but implicit approaches such as Taylor's power law (TPL) can be rather effective. Based on TPL, which achieved a rare status of ecological laws, we introduce a general methodology for characterizing the spatial heterogeneity of microbiome (i.e. characterization of microbial spatial distribution) and further apply it for investigating the heterogeneity–disease relationship (HDR) via analyzing a big dataset of 26 MAD (microbiome-associated disease) studies covering nearly all high-profile MADs including obesity, diabetes and gout. It was found that in majority of the MAD cases, the microbiome was sufficiently resilient to endure the disease disturbances. Specifically, in ∼10–16% cases, disease effects were significant—the healthy and diseased cohorts exhibited statistically significant differences in the TPL heterogeneity parameters. We further compared HDR with classic diversity–disease relationship (DDR) and explained their mechanistic differences. Both HDR and DDR cross-verified remarkable resilience of the human microbiomes against MADs. [ABSTRACT FROM AUTHOR]

Maintaining sexual reproduction in a highly competitive world is still one of the major mysteries of biology given the apparently high efficiency of asexual reproduction. Co‐evolutionary theories such as the Red Queen hypothesis would suggest that the microbiomes in human reproductive systems, specifically the microbiomes contained in semen and vaginal fluids, should reach some level of homogeneity thanks to arguably the most conspicuous microbiome transmission between two sexes. The long‐term sexual coevolution should favor the dynamic homogeneity or stability, which should also be beneficial for sexual reproduction such as sperm survival or fertilization on physiological/ecological time scale. We present a piece of quantitative evidence in the form of microbial community spatial heterogeneity to support the stability notion by analyzing three big datasets of the human vaginal, semen and gut microbiome. Methodologically, we applied a recent community‐level extension to the classic Taylor's power law, which reached the rare status of ecological law and has found applications beyond biology. Both ecological and evolutionary theories, such as hologenome/holobiont and Red Queen, as well as consideration of first principles, would predict that microbiome transmissions between two sexes should have homogenizing effects on the composition and stability of the microbiomes in human reproductive systems, and therefore have similar variance structures. This is supported by the finding that the power law analysis revealed human vaginal and semen microbiomes exhibited the same scaling parameter size in their community spatial (inter‐individual) heterogeneities, while the both exhibited significantly different heterogeneity scaling parameter size with the human gut microbiome. [ABSTRACT FROM AUTHOR]

There are two major sequencing technologies for investigating the microbiome: the amplicon sequencing that generates the OTU (operational taxonomic unit) tables of marker genes (e.g., bacterial 16S-rRNA), and the metagenomic shotgun sequencing that generates metagenomic gene abundance (MGA) tables. The OTU table is the counterpart of species abundance tables in macrobial ecology of plants and animals, and has been the target of numerous ecological and network analyses in recent gold rush for microbiome research and in great efforts for establishing an inclusive theoretical ecology. Nevertheless, MGA analyses have been largely limited to bioinformatics pipelines and ad hoc statistical methods, and systematic approaches to MGAs guided by classic ecological theories are still few. Here, we argue that, the difference between "gene kinds" and "gene species" are nominal, and the metagenome that a microbiota carries is essentially a 'community' of metagenomic genes (MGs). Each row of a MGA table represents a metagenome of a microbiota, and the whole MGA table represents a 'meta-metagenome' (or an assemblage of metagenomes) of N microbiotas (microbiome samples). Consequently, the same ecological/network analyses used in OTU analyses should be equally applicable to MGA tables. Here we choose to analyze the heterogeneity of metagenome by introducing classic Taylor's power law (TPL) and its recent extensions in community ecology. Heterogeneity is a fundamental property of metagenome, particularly in the context of human microbiomes. Recent studies have shown that the heterogeneity of human metagenomes is far more significant than that of human genomes. Therefore, without deep understanding of the human metagenome heterogeneity, personalized medicine of the human microbiome-associated diseases is hardly feasible. The TPL extensions have been successfully applied to measure the heterogeneity of human microbiome based on amplicon-sequencing reads of marker genes (e.g., 16s-rRNA). In this article, we demonstrate the analysis of the metagenomic heterogeneity of human gut microbiome at whole metagenome scale (with type-I power law extension) and metagenomic gene scale (type-III), as well as the heterogeneity of gene clusters, respectively. We further examine the influences of obesity, IBD and diabetes on the heterogeneity, which is of important ramifications for the diagnosis and treatment of human microbiome-associated diseases. [ABSTRACT FROM AUTHOR]

Classic concepts of genetic (gene) diversity (heterozygosity) such as Nei & Li's nucleotide diversity were defined within a population context. Although variations are often measured in population context, the basic carriers of variation are individuals. Hence, measuring variations such as SNP of an individual against a reference genome, which has been ignored previously, is certainly in its own right. Indeed, similar practice has been a tradition in community ecology, where the basic unit of diversity measure is individual community sample. We propose to use Renyi's-entropy-based Hill numbers to define individual-level genetic diversity and similarity and demonstrate the definitions with the SNP (single nucleotide polymorphism) datasets from the 1000-Genomes Project. Hill numbers, derived from Renyi's entropy (of which Shannon's entropy is a special case), have found widely applications including measuring the quantum information entanglement and ecological diversity. The demonstrated individual-level SNP diversity not only complements the existing population-level genetic diversity concepts, but also offers building blocks for comparative genetic analysis at higher levels. The concept of individual covers, but is not limited to, individual chromosome, region of chromosome, gene cluster(s), or whole genome. Similarly, the SNP can be replaced by other structural variants or mutation types such as indels. [ABSTRACT FROM AUTHOR]

Aim: The spatio‐temporal distribution of biodiversity is a core field of biogeography, and the so‐termed species–time–area relationship (STAR), together with its siblings, that is the SAR (species–area relationship) and STR (species–time relationship), has achieved the rare status of classic laws in ecology and biogeography. Traditionally, the STAR or its recent generalization DTAR (diversity–time–area relationship) has been described with the bivariate power law (BPL) model or more recently with Whittaker, Triantis, and Ladle (2008, Journal of Biography; 35: 18) general dynamic model (GDM). We propose to extend the classic BPL into a more flexible DTAR model, which offers new quantitative methods for estimating maximal global diversity and charactering the relationship between local and regional diversity. Location: Indoor microbiome. Taxon: Microbes. Method: We revise the BPL model by introducing two taper‐off (cut‐off) parameters or BPLEC (bivariate power law with exponential cutoffs) model, which eventually overwhelms the unsaturated increase of diversity over time and/or space and consequently can offer more realistic modelling of the joint spatio‐temporal distribution of biodiversity. Based on the BPLEC model, we further define three new concepts for DTAR: maximal accrual diversity (MAD) profile, local‐to‐regional diversity (LRD) ratio profile and local‐to‐global diversity (LGD) ratio profile. Results: We introduce and demonstrate the new BPLEC model with the indoor microbiome datasets (Lax et al., 2014, Science; 345: 1048–1052). The new model fitted to the microbiome datasets equally well or slightly better than existing BPL and GDM models, but it possesses two advantages stated below. Main conclusion: First, the new BPLEC model overcomes the unlimited diversity accrual in temporal and/or spatial dimensions and hence offers more realistic modelling to the DTAR. Second, the MAD and LRD/LGD offer useful methods for estimating the "dark" or "potential" diversity, which accounts for the species locally absent but present in a habitat‐specific regional species pool. [ABSTRACT FROM AUTHOR]

Spatial heterogeneity is a fundamental property of any natural ecosystems, including hot spring and human microbiomes. Two important scales that spatial heterogeneity exhibits are population and community scales, and Taylor's power law (PL) and its extensions (PLEs) offer ideal quantitative models to assess population‐ and community‐level heterogeneities. Here we analyse 165 hot spring microbiome samples at the global scale that cover a wide range of temperatures (7.5–99°C) and pH levels (3.3–9). We explore a question of fundamental importance for measuring the spatial heterogeneity of the hot‐spring microbiome and further discuss their ecological implications: How do critical environmental factors such as temperature and pH influence the scaling of community spatial heterogeneity? We are particularly interested in the existence of a universal scaling model that is independent of environmental gradients. By applying PL and PLEs, we were able to obtain such scaling parameters of the hot spring at both community and population levels, which are temperature‐ and pH‐invariant. These findings suggest that while the hot‐spring microbiomes located at different regions may have different environmental conditions, they share a fundamental heterogeneity scaling parameter, analogically similar to the gravitational acceleration on Earth, which may vary slightly depending on altitude and latitude, but is invariant overall. In contrast, similar to the physics of the Moon and Earth, which have different gravitational accelerations, the hot spring and human microbiomes can have different scaling parameters as demonstrated in this study. [ABSTRACT FROM AUTHOR]

SAR (species area relationship) is a classic ecological theory that has been extensively investigated and applied in the studies of global biogeography and biodiversity conservation in macro-ecology. It has also found important applications in microbial ecology in recent years thanks to the breakthroughs in metagenomic sequencing technology. Nevertheless, SAR has a serious limitation for practical applications—ignoring the species abundance and treating all species as equally abundant. This study aims to explore the biogeography discoveries of human microbiome over 18 sites of 5 major microbiome habitats, establish the baseline DAR (diversity-area scaling relationship) parameters, and perform comparisons with the classic SAR. The extension from SAR to DAR by adopting the Hill numbers as diversity measures not only overcomes the previously mentioned flaw of SAR but also allows for obtaining a series of important findings on the human microbiome biodiversity and biogeography. Specifically, two types of DAR models were built, the traditional power law (PL) and power law with exponential cutoff (PLEC), using comprehensive datasets from the HMP (human microbiome project). Furthermore, the biogeography "maps" for 18 human microbiome sites using their DAR profiles for assessing and predicting the diversity scaling across individuals, PDO profiles (pair-wise diversity overlap) for measuring diversity overlap (similarity), and MAD profile (for predicting the maximal accrual diversity in a population) were sketched out. The baseline biogeography maps for the healthy human microbiome diversity can offer guidelines for conserving human microbiome diversity and investigating the health implications of the human microbiome diversity and heterogeneity. [ABSTRACT FROM AUTHOR]

Dominance and evenness can be two sides of the same coin or opposite ends of a spectrum. Although evenness and diversity are community-level concepts, dominance can be applied at both community and species scales. Nevertheless, there is not a metric applicable at both these scales that are unified with a single mathematical framework in the existing literature. Here, by extending Lloyd's meaning crowding concept from the population to community scale, we propose a dominance concept and associated metrics that are applicable at both scales. Such metrics can act as proxies for diversity in diversity-stability and diversity-ecosystem service analyses or for population abundance in species interaction network analysis and population stability analysis, with advantages in cross-scale and unified analyses. Our concept of dominance includes three measures (metrics) that link communities and species. The metrics have the same mathematical form, but different interpretations at the community and species scales. Our community-level metric is a function of Simpson's (1949) diversity index, for which we present a rigorous mathematical proof. Our species-level metrics quantify the difference between community dominance and the dominance of a virtual community whose mean population size, per species, equals the population size of the focal species. We demonstrate the use of these metrics using data from a longitudinal study of the human vaginal microbiome and provide new insights relevant for microbiome stability and disease etiology at the community scale. The new metrics also can be used for species dominance network (SDN) analysis at the species scale. Since the species dominance is a function of both species (population) abundance and community dominance, it has an advantage of capturing both community-scale global and species-scale local information, which becomes even more evident when it is used to construct SDNs. These results demonstrate the significance and applications of our unified dominance concept and metrics. [ABSTRACT FROM AUTHOR]

The first step of any metagenome sequencing project is to get the inventory of OTU abundances (operational taxonomic units) and/or metagenomic gene abundances. The former is generated with 16S‐rRNA‐tagged amplicon sequencing technology, and the latter can be generated from either gene‐targeted or whole‐sample shotgun metagenomics technologies. With 16S‐rRNA data sets, measuring community diversity with diversity indexes such as species richness and Shannon's index has been a de facto standard analysis; nevertheless, similarly comprehensive approaches to metagenomic gene abundances are still largely missing, despite that both OTU and gene abundances are DNA reads. Here, we adapt the Hill numbers, which were reintroduced to macrocommunity ecology recently and are now widely regarded as a most appropriate measure system for ecological diversity, for measuring metagenome alpha‐, beta‐ and gamma‐diversities, and similarity. Our proposal includes the following: (a) Metagenomic gene (MG) diversity measures the single‐gene‐level metagenome diversity; (b) Type‐I metagenome functional gene cluster (MFGC) diversity measures the diversity of functional gene clusters but ignoring within‐cluster gene abundance information; (c) Type‐II MFGC diversity considers within‐cluster gene abundances information and integrates gene‐cluster‐level metagenome diversity and functional gene redundancy information; and (d) Four classes of Hill‐numbers‐based similarity metrics, including local gene overlap, regional gene overlap, gene homogeneity measure and gene turnover complement, were introduced in terms of MG and MFGC, respectively. We demonstrate the proposal with the gut metagenomes from healthy and IBD (inflammatory bowel disease) cohorts. The Hill numbers offer a unified approach to cohesively and comprehensively measuring the ecological and metagenome diversities of microbiomes. [ABSTRACT FROM AUTHOR]

I extend the classic SAR, which has achieved status of ecological law and plays a critical role in global biodiversity and biogeography analyses, to general DAR (diversity–area relationship). The extension was aimed to remedy a serious application limitation of the traditional SAR that only addressed one aspect of biodiversity scaling—species richness scaling over space, but ignoring species abundance information. The extension was further inspired by a recent consensus that Hill numbers offer the most appropriate measures for alpha‐diversity and multiplicative beta‐diversity. In particular, Hill numbers are essentially a series of Renyi's entropy values weighted differently along the rare‐common‐dominant spectrum of species abundance distribution and are in the units of effective number of species (or species equivalents such as OTUs). I therefore postulate that Hill numbers should follow the same or similar law of the traditional SAR. I test the postulation with the American gut microbiome project (AGP) dataset of 1,473 healthy North American individuals. I further propose three new concepts and develop their statistical estimation formulae based on the new DAR extension, including: (i) DAR profile—z–q relationship (DAR scaling parameter z at different diversity order q), (ii) PDO (pair‐wise diversity overlap) profile—g–q relationship (PDO parameter g at order q, and (iii) MAD (maximal accrual diversity: Dmax) profile—Dmax‐q. While the classic SAR is a special case of our new DAR profile, the PDO and MAD profiles offer novel tools for analyzing biodiversity (including alpha‐diversity and beta‐diversity) and biogeography over space. We extend the classic SAR, which has achieved status of ecological law and plays a critical role in global biodiversity and biogeography analyses, to general DAR (diversity–area relationship). The extension was motivated to remedy a serious application limitation of the traditional SAR that only addressed one aspect of biodiversity scaling—species richness scaling over space, but ignoring species abundance information. The extension was further inspired by a recent consensus that Hill numbers offer the most appropriate measures for alpha‐diversity and multiplicative beta‐diversity. We further propose three new concepts and develop their statistical estimation procedures based on the new DAR extension, including: (i) DAR profile, which was inspired by Chao, Chiu, & Jost, Annual Reviews of Ecology, Evolution, and Systematics, 45, 2014 and 297 diversity profile, (ii) PDO (pair‐wise diversity overlap) profile, and (iii) MAD (maximal accrual diversity) profile, which can be harnessed to predict the maximal diversity at a region or global scale. [ABSTRACT FROM AUTHOR]

Relatively little progress in the methodology for differentiating between the healthy and diseased microbiomes, beyond comparing microbial community diversities with traditional species richness or Shannon index, has been made. Network analysis has increasingly been called for the task, but most currently available microbiome datasets only allows for the construction of simple species correlation networks (SCNs). The main results from SCN analysis are a series of network properties such as network degree and modularity, but the metrics for these network properties often produce inconsistent evidence. We propose a simple new network property, the P/N ratio, defined as the ratio of positive links to the number of negative links in the microbial SCN. We postulate that the P/N ratio should reflect the balance between facilitative and inhibitive interactions among microbial species, possibly one of the most important changes occurring in diseased microbiome. We tested our hypothesis with five datasets representing five major human microbiome sites and discovered that the P/N ratio exhibits contrasting differences between healthy and diseased microbiomes and may be harnessed as an in silico biomarker for detecting disease-associated changes in the human microbiome, and may play an important role in personalized diagnosis of the human microbiome-associated diseases. [ABSTRACT FROM AUTHOR]

Several ecological hypotheses (e.g., specific plaque, non-specific plaque and keystone pathogen) regarding the etiology of periodontitis have been proposed since the 1990s, most of which have been centered on the concept of dysbiosis associated with periodontitis. Nevertheless, none of the existing hypotheses have presented mechanistic interpretations on how and why dysbiosis actually occurs. Hubbell’s neutral theory of biodiversity offers a powerful null model to test hypothesis regarding the mechanism of community assembly and diversity maintenance from the metagenomic sequencing data, which can help to understand the forces that shape the community dynamics such as dysbiosis. Here we reanalyze the dataset from Abusleme et al.’s comparative study of the oral microbial communities from periodontitis patients and healthy individuals. Our study demonstrates that 14 out of 61 communities (23%) passed the neutrality test, a percentage significantly higher than the previous reported neutrality rate of 1% in human microbiome (Li & Ma 2016, Scientific Reports). This suggests that, while the niche selection may play a predominant role in the assembly and diversity maintenance in oral microbiome, the effect of neutral dynamics may not be ignored. However, no statistically significant differences in the neutrality passing rates were detected between the periodontitis and healthy treatments with Fisher’s exact probability test and multiple testing corrections, suggesting that the mechanism of community assembly is robust against disturbances such as periodontitis. In addition, our study confirmed previous finding that periodontitis patients exhibited higher biodiversity. These findings suggest that while periodontitis may significantly change the community composition measured by diversity (i.e., the exhibition or ‘phenotype’ of community assembly), it does not seem to cause the ‘mutation’ of the ‘genotype” (mechanism) of community assembly. We argue that the ‘phenotypic’ changes explain the observed link (not necessarily causal) between periodontitis and community dysbiosis, which is certainly worthy of further investigation. [ABSTRACT FROM AUTHOR]

Taylor's (1961, Nature, 189:732) power law, a power function ( V = am b) describing the scaling relationship between the mean and variance of population abundances of organisms, has been found to govern the population abundance distributions of single species in both space and time in macroecology. It is regarded as one of few generalities in ecology, and its parameter b has been widely applied to characterize spatial aggregation (i.e. heterogeneity) and temporal stability of single-species populations. Here, we test its applicability to bacterial populations in the human microbiome using extensive data sets generated by the US- NIH Human Microbiome Project ( HMP). We further propose extending Taylor's power law from the population to the community level, and accordingly introduce four types of power-law extensions ( PLEs): type I PLE for community spatial aggregation ( heterogeneity), type II PLE for community temporal aggregation ( stability), type III PLE for mixed-species population spatial aggregation ( heterogeneity) and type IV PLE for mixed-species population temporal aggregation ( stability). Our results show that fittings to the four PLEs with HMP data were statistically extremely significant and their parameters are ecologically sound, hence confirming the validity of the power law at both the population and community levels. These findings not only provide a powerful tool to characterize the aggregations of population and community in both time and space, offering important insights into community heterogeneity in space and/or stability in time, but also underscore the three general properties of power laws ( scale invariance, no average and universality) and their specific manifestations in our four PLEs. [ABSTRACT FROM AUTHOR]

Background and aims: The types of natural forests have long been suggested to shape below-ground microbial communities in forest ecosystem. However, detailed information on the impressionable bacterial groups and the potential mechanisms of these influences are still missing. The present study aims to deepen the current understanding on the soil microbial communities under four typical forest types in Northeast Asia, and to reveal the environmental factors driving the abundance, diversity and composition of soil bacterial communities. Methods: Four forest types from Changbai Nature Reserve, representing mixed conifer-broadleaf forest and its natural secondary forest, evergreen coniferous forest, and deciduous coniferous forest were selected for this study. Namely, Broadleaf-Korean pine mixed forest (BLKP), secondary Poplar-Birch forest (PB), Spruce-Fir forest (SF), and Larch forest (LA), respectively. Soil bacterial community was analyzed using bar-coded pyrosequencing. Nonmetric multidimensional scaling (NMDS) was used to illustrate the clustering of different samples based on both Bray-Curtis distances and UniFrac distances. The relationship between environmental variables and the overall community structure was analyzed using the Mantel test. Results: The two mixed conifer-broadleaf forests (BLKP and PB) displayed higher total soil nutrients (organic carbon, nitrogen, and phosphorus) and soil pH, but a lower C/N ratio as compared to the two coniferous forests (SF and LA). The mixed conifer-broadleaf forests had higher alpha-diversity and had distinct bacterial communities from the coniferous forests. Soil texture and pH were found as the principle factors for shaping soil bacterial diversity and community composition. The two mixed conifer-broadleaf forests were associated with higher proportion of Acidobacteria, Verrucomicrobia, Bacteroidetes, and Chloroflexi. While the SF and LA forests were dominated by Proteobacteria and Gemmatimonadetes. Conclusions: Different natural forest type each selects for distinct microbial communities beneath them, with mixed conifer-broadleaf forests being associated with the low-activity bacterial groups, and the coniferous forests being dominated by the so-called high-activity members. The differentiation of soil bacterial communities in natural forests are presumably mediated by the differentiation in terms of soil properties, and could be partially explained by the copiotroph/oligotroph ecological classification model and non-random co-occurrence patterns. [ABSTRACT FROM AUTHOR]

Human gut microbiota shows high inter-subject variations, but the actual spatial distribution and co-occurrence patterns of gut mucosa microbiota that occur within a healthy human instestinal tract remain poorly understood. In this study, we illustrated a model of this mucosa bacterial communities' biogeography, based on the largest data set so far, obtained via 454-pyrosequencing of bacterial 16S rDNAs associated with 77 matched biopsy tissue samples taken from terminal ileum, ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum of 11 healthy adult subjects. Borrowing from macro-ecology, we used both Taylor's power law analysis and phylogeny-based beta-diversity metrics to uncover a highly heterogeneous distribution pattern of mucosa microbial inhabitants along the length of the intestinal tract. We then developed a spatial dispersion model with an R-squared value greater than 0.950 to map out the gut mucosa-associated flora's non-linear spatial distribution pattern for 51.60% of the 188 most abundant gut bacterial species. Furthermore, spatial co-occurring network analysis of mucosa microbial inhabitants together with occupancy (that is habitat generalists, specialists and opportunist) analyses implies that ecological relationships (both oppositional and symbiotic) between mucosa microbial inhabitants may be important contributors to the observed spatial heterogeneity of mucosa microbiota along the human intestine and may even potentially be associated with mutual cooperation within and functional stability of the gut ecosystem. [ABSTRACT FROM AUTHOR]

Natural populations are dynamic in both time and space. In biological populations such as insects, spatial distribution patterns are often studied as the first step to characterize population dynamics. In nature, the spatial distribution patterns of insect populations are considered as the emergent expression (property) of individual behaviors at population levels and are fine-tuned or optimized by natural selection. This inspiration prompts us to investigate the possibly similar mechanisms in Genetic Algorithms (GA) populations. In this study, we introduce the mathematical models for the spatial distribution patterns of insect populations to GA with the conjecture that the emulation of biological populations in nature may lead to computational improvement. In particular, we introduce three modeling approaches from the research of spatial distribution patterns of insect populations: (i) probability distribution modeling approach, (ii) aggregation index approach, and (iii) Taylor's (1961, 1977) Power Law, Iwao's (1968, 1976) Mean Crowding Model and Ma's (1991c) population aggregation critical density (PACD), to characterize populations in GA. With these three approaches, we investigate four mappings from the research field of insect spatial distribution patterns to GA populations in order to search for possible counterpart mechanisms or features in GA. They are: (i) mapping insect spatial distribution patterns to GA populations or allowing GA populations to be controlled by stochastic distribution models that describe insect spatial distributions; (ii) mapping insect population distribution to GA population fitness distribution via Power Law and PACD modeling; (iii) mapping population aggregation dynamics to GA fitness progression across generations (or fitness aggregation dynamics in GA) via insect population aggregation index; (iv) mapping insect population sampling model to optimal GA population sizing. With regard to the mapping (i), the experiment results show the significant improvements in GA computational efficiency in terms of the reduced fitness evaluations and associated costs. This prompts us to suggest using probability distribution models, or what we call stochastic GA populations, to replace the fixed-size population settings. We also found the counterpart for the second mapping, the wide applicability of Power Law and Mean Crowding model to the fitness distribution in GA populations. The testing of the third and fourth mappings is very preliminary; we use example cases to suggest two further research problems: the potential to use fitness aggregation dynamics for controlling the number of generations iterated in GA searches, and the possibility to use fitness aggregation distribution parameters [(obtained in mapping (ii)] in determining the optimum population size in GA. A third interesting research problem is to investigate the relationship between mapping (i) and (iii), i.e., the controlling of both population sizes and population generations. [ABSTRACT FROM AUTHOR]

Background: The very large memory requirements for the construction of assembly graphs for de novo genome assembly limit current algorithms to super-computing environments. Methods: In this paper, we demonstrate that constructing a sparse assembly graph which stores only a small fraction of the observed k-mers as nodes and the links between these nodes allows the de novo assembly of even moderately-sized genomes (~500 M) on a typical laptop computer. Results: We implement this sparse graph concept in a proof-of-principle software package, SparseAssembler, utilizing a new sparse k-mer graph structure evolved from the de Bruijn graph. We test our SparseAssembler with both simulated and real data, achieving ~90% memory savings and retaining high assembly accuracy, without sacrificing speed in comparison to existing de novo assemblers. [ABSTRACT FROM AUTHOR]

The collection and documentation of the experimental evidence of chaos in biological populations have always been elusive. We were puzzled by the observation that the most frequently computed demographic parameter for laboratory insect populations, the population intrinsic rate of increase ( r), seems too small to induce chaotic dynamics. For example, when it is directly utilized as an approximation to the parameter ( a) of the one-parameter discrete logistic model, the parameter seems well out of the chaotic range. In a recent reanalysis of our early laboratory demographic data of 1800 Russian wheat aphids (RWA), we discovered that a proper measure unit conversion should be performed to make the link between r and the discrete logistic model. We think that this conversion issue may have been ignored historically in biological literature since we are not aware of any uses of the r in the discussion of chaos. It should be noted that r is different from the r in discrete logistic model (e.g., May in Nature 261:459-467, ). Since extensive demographic data purported to estimate r have been accumulated in literature on population growth, the finding revealed with our RWA experiment data can easily be verified with the published data in literature. We near-arbitrarily surveyed 10 studies (containing 37 datasets of r) published in the literature and archived in JSTOR or BioOne databases to test the conversion, and the results confirmed our finding. The finding should significantly expand the evidence base of chaos in laboratory populations of insects and possibly microorganisms, too. [ABSTRACT FROM AUTHOR]

Microgenderome or sexual dimorphism in microbiome refers to the bidirectional interactions between microbiotas, sex hormones, and immune systems, and it is highly relevant to disease susceptibility. A critical step in exploring microgenderome is to dissect the sex differences in key community ecology properties, which has not been systematically analyzed. This study aims at filling the gap by reanalyzing the Human Microbiome Project datasets with two objectives: (i) dissecting the sex differences in community diversity and their intersubject scaling, species composition, core/periphery species, and high‐salience skeletons (species interactions); (ii) offering mechanistic interpretations for (i). Conceptually, the Vellend–Hanson synthesis of community ecology that stipulates selection, drift, speciation, and dispersal as the four processes driving community dynamics is followed. Methodologically, seven approaches reflecting the state‐of‐the‐art research in medical ecology of human microbiomes are harnessed to achieve the objectives. It is postulated that the revealed microgenderome characteristics (categorized as seven aspects of differences/similarities) exert far reaching influences on disease susceptibility, and are primarily due to the sex difference in selection effects (deterministic fitness differences in microbial species and/or species interactions with each other or with their hosts), which are, in turn, shaped/modulated by host physiology (immunity, hormones, gut–brain communications, etc.). [ABSTRACT FROM AUTHOR]

The diversity–stability relationship is a long‐standing, central focus of community ecology. Two major challenges have impeded studies of the diversity–stability relationship (DSR): the difficulty in obtaining high‐quality longitudinal data sets; and the lack of a general theoretical framework that can encompass the enormous complexity inherent in "diversity," "stability," and their many interactions. Metagenomic "Big Data" now provide high quality longitudinal data sets, and the human microbiome project (HMP) offers an unprecedented opportunity to reinvigorate investigations of DSRs. We introduce a new framework for exploring DSRs that has three parts: (1) a cross‐scale measure of dominance with a simple mathematical form that can be applied simultaneously to individual species and entire communities and can be used to construct species dominance networks (SDNs); (2) analysis of SDNs based on special trio motifs, core‐periphery, rich‐club, and nested structures, and high salience skeletons; and (3) a synthesis of coarse‐scale core/periphery/community‐level stability modeling with fine‐scale analysis of SDNs that further reveals the stability properties of the community structures. We apply this new approach to data from the human vaginal microbiome of the HMP, simultaneously illustrating its utility in developing and testing theories of diversity and stability while providing new insights into the underlying ecology and etiology of a human microbiome‐associated disease. [ABSTRACT FROM AUTHOR]

The original version of this article unfortunately contained a missing image. The flowchart was not captured in PDF version. The original article was corrected. [ABSTRACT FROM AUTHOR]