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Diversity and heterogeneity often are conflated but are fundamentally different. An aphorism proposed by Shavit and Ellison (2021; J. Phil. 118: 525–548) for distinguishing them is that ‘a zoo is diverse whereas an ecosystem is heterogeneous.' That is, a zookeeper measuring diversity simply enumerates the different types of animals; interactions are not expected to occur between animals separated by fences or other barriers. In contrast, measures of heterogeneity ought to include both interspecific interactions and relationships between species and their heterogeneous habitats. Here, we use cross‐scale, dual scaling‐law analyses of heterogeneity and diversity of animal gut microbiomes (AGMs) to address three objectives: 1) estimate the spatial heterogeneity and diversity of animal‐gut microbiomes; 2) analyze influences of phylogeny and diets on scaling of diversity and heterogeneity; 3) explore mechanistic differences between diversity and heterogeneity in AGMs. From 4903 AGM samples collected from 318 animal species covering all six classes of vertebrates and four major classes of invertebrates, we estimated that ≈640 000 operational taxonomic units (OTUs or ‘species') make up the pool of microbial species that could inhabit animal guts, among which ≈8000 are relatively common and ≈800 are dominant. The gut of any single animal, however, includes only 0.01–0.5% of the total species pool. We applied Ma's diversity‐area relationship for scaling diversity, and extend Taylor's power law and Luna et al.'s (2020; Diversity 12: 86) interaction diversity for scaling heterogeneity. At the community scale, phylogeny significantly influenced heterogeneity, but diets did not. Phylogeny and diets had limited influence on diversity at both community and landscape scales. We demonstrated that diversity and heterogeneity measure two different properties. Further, differences in scaling of diversity and heterogeneity are a result of heterogeneity being primarily the result of evolved dispersal behavior and interspecific interactions, whereas diversity results from abundances controlled on ecological time scales. [ABSTRACT FROM AUTHOR]

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]

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]

The 3rd generation of sequencing (3GS) technologies generate ultra-long reads (up to 1 Mb), which makes it possible to eliminate gaps and effectively resolve repeats in genome assembly. However, the 3GS technologies suffer from the high base-level error rates (15%–40%) and high sequencing costs. To address these issues, the hybrid assembly strategy, which utilizes both 3GS reads and inexpensive NGS (next generation sequencing) short reads, was invented. Here, we use 10×-Genomics® technology, which integrates a novel bar-coding strategy with Illumina® NGS with an advantage of revealing long-range sequence information, to replace common NGS short reads for hybrid assembly of long erroneous 3GS reads. We demonstrate the feasibility of integrating the 3GS with 10×-Genomics technologies for a new strategy of hybrid de novo genome assembly by utilizing DBG2OLC and Sparc software packages, previously developed by the authors for regular hybrid assembly. Using a human genome as an example, we show that with only 7× coverage of ultra-long Nanopore® reads, augmented with 10× reads, our approach achieved nearly the same level of quality, compared with non-hybrid assembly with 35× coverage of Nanopore reads. Compared with the assembly with 10×-Genomics reads alone, our assembly is gapless with slightly high cost. These results suggest that our new hybrid assembly with ultra-long 3GS reads augmented with 10×-Genomics reads offers a low-cost (less than ¼ the cost of the non-hybrid assembly) and computationally light-weighted (only took 109 calendar hours with peak memory-usage = 61GB on a dual-CPU office workstation) solution for extending the wide applications of the 3GS technologies. Unlabelled Image [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]

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]

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]

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]