Hill-Chao numbers allow decomposing gamma multifunctionality into alpha and beta components.

Biodiversity-ecosystem functioning (BEF) research has provided strong evidence and mechanistic underpinnings to support positive effects of biodiversity on ecosystem functioning, from single to multiple functions. This research has provided knowledge gained mainly at the local alpha scale (i.e. within ecosystems), but the increasing homogenization of landscapes in the Anthropocene has raised the potential that declining biodiversity at the beta (across ecosystems) and gamma scales is likely to also impact ecosystem functioning. Drawing on biodiversity theory, we propose a new statistical framework based on Hill-Chao numbers. The framework allows decomposition of multifunctionality at gamma scales into alpha and beta components, a critical but hitherto missing tool in BEF research; it also allows weighting of individual ecosystem functions. Through the proposed decomposition, new BEF results for beta and gamma scales are discovered. Our novel approach is applicable across ecosystems and connects local- and landscape-scale BEF assessments from experiments to natural settings.

Meta-analysis of the Microbial Diversity Cultured in Bioreactors Simulating the Gut Microbiome.

Understanding the intricate ecological interactions within the gut microbiome and unravelling its impact on human health is a challenging task. Bioreactors are valuable tools that have contributed to our understanding of gut microbial ecology. However, there is a lack of studies describing and comparing the microbial diversity cultivated in these models. This knowledge is crucial for refining current models to reflect the gastrointestinal microbiome accurately. In this study, we analysed the microbial diversity of 1512 samples from 18 studies available in public repositories that employed cultures performed in batches and various bioreactor models to cultivate faecal microbiota. Community structure comparison between samples using t-distributed stochastic neighbour embedding and the Hellinger distance revealed a high variation between projects. The main driver of these differences was the inter-individual variation between the donor faecal inocula. Moreover, there was no overlap in the structure of the microbial communities between studies using the same bioreactor platform. In addition, α-diversity analysis using Hill numbers showed that highly complex bioreactors did not exhibit higher diversities than simpler designs. However, analyses of five projects in which the samples from the faecal inoculum were also provided revealed an amplicon sequence variants enrichment in bioreactors compared to the inoculum. Finally, a comparative analysis of the taxonomy of the families detected in the projects and the GMRepo database revealed bacterial families exclusively found in the bioreactor models. These findings highlight the potential of bioreactors to enrich low-abundance microorganisms from faecal samples, contributing to uncovering the gut microbial "dark matter".

Metabarcoding data reveal vertical multitaxa variation in topsoil communities during the colonization of deglaciated forelands.

Ice-free areas are expanding worldwide due to dramatic glacier shrinkage and are undergoing rapid colonization by multiple lifeforms, thus representing key environments to study ecosystem development. It has been proposed that the colonization dynamics of deglaciated terrains is different between surface and deep soils but that the heterogeneity between communities inhabiting surface and deep soils decreases through time. Nevertheless, tests of this hypothesis remain scarce, and it is unclear whether patterns are consistent among different taxonomic groups. Here, we used environmental DNA metabarcoding to test whether community diversity and composition of six groups (Eukaryota, Bacteria, Mycota, Collembola, Insecta, and Oligochaeta) differ between the surface (0-5 cm) and deeper (7.5-20 cm) soil at different stages of development and across five Alpine glaciers. Taxonomic diversity increased with time since glacier retreat and with soil evolution. The pattern was consistent across groups and soil depths. For Eukaryota and Mycota, alpha-diversity was highest at the surface. Time since glacier retreat explained more variation of community composition than depth. Beta-diversity between surface and deep layers decreased with time since glacier retreat, supporting the hypothesis that the first 20 cm of soil tends to homogenize through time. Several molecular operational taxonomic units of bacteria and fungi were significant indicators of specific depths and/or soil development stages, confirming the strong functional variation of microbial communities through time and depth. The complexity of community patterns highlights the importance of integrating information from multiple taxonomic groups to unravel community variation in response to ongoing global changes.

Multi-taxon biodiversity responses to the 2019-2020 Australian megafires.

Conditions conducive to fires are becoming increasingly common and widespread under climate change. Recent fire events across the globe have occurred over unprecedented scales, affecting a diverse array of species and habitats. Understanding biodiversity responses to such fires is critical for conservation. Quantifying post-fire recovery is problematic across taxa, from insects to plants to vertebrates, especially at large geographic scales. Novel datasets can address this challenge. We use presence-only citizen science data from iNaturalist, collected before and after the 2019-2020 megafires in burnt and unburnt regions of eastern Australia, to quantify the effect of post-fire diversity responses, up to 18 months post-fire. The geographic, temporal, and taxonomic sampling of this dataset was large, but sampling effort and species discoverability were unevenly spread. We used rarefaction and prediction (iNEXT) with which we controlled sampling completeness among treatments, to estimate diversity indices (Hill numbers: q = 0-2) among nine broad taxon groupings and seven habitats, including 3885 species. We estimated an increase in species diversity up to 18 months after the 2019-2020 Australian megafires in regions which were burnt, compared to before the fires in burnt and unburnt regions. Diversity estimates in dry sclerophyll forest matched and likely drove this overall increase post-fire, while no taxon groupings showed clear increases inconsistent with both control treatments post-fire. Compared to unburnt regions, overall diversity across all taxon groupings and habitats greatly decreased in areas exposed to extreme fire severity. Post-fire life histories are complex and species detectability is an important consideration in all post-fire sampling. We demonstrate how fire characteristics, distinct taxa, and habitat influence biodiversity, as seen in local-scale datasets. Further integration of large-scale datasets with small-scale studies will lead to a more robust understanding of fire recovery.

The precautionary principle and dietary DNA metabarcoding: Commonly used abundance thresholds change ecological interpretation.

Dietary DNA metabarcoding enables researchers to identify and characterize trophic interactions with a high degree of taxonomic precision. It is also sensitive to sources of bias and contamination in the field and laboratory. One of the earliest and most common strategies for dealing with such sensitivities has been to remove all low-abundance sequences and conduct ecological analyses based on the presence or absence of food taxa. Although this step is now often perceived to be necessary, evidence of its sufficiency is lacking and more attention to the risk of introducing other errors is needed. Using computer simulations, we demonstrate that common strategies to remove low-abundance sequences can erroneously eliminate true dietary sequences in ways that impact downstream inferences. Using real data from well-studied wildlife populations in Yellowstone National Park, we further show how these strategies can markedly alter the composition of dietary profiles in ways that scale-up to obscure ecological interpretations about dietary generalism, specialism, and composition. Although the practice of removing low-abundance sequences may continue to be a useful strategy to address research questions that focus on a subset of relatively abundant foods, its continued widespread use risks generating misleading perceptions about the structure of trophic networks. Researchers working with dietary DNA metabarcoding data-or similar data such as environmental DNA, microbiomes, or pathobiomes-should be aware of drawbacks and consider alternative bioinformatic, experimental, and statistical solutions.

Bacterial communities of the Salvia lyrata rhizosphere explained by spatial structure and sampling grain.

Advancements in molecular technology have reduced the constraints that the grain of observation, or the spatial resolution and volume of the sampling unit, has on the characterization of plant-associated microbiomes. With discrete ecological sampling and massive parallel sequencing, we can more precisely portray microbiome community assembly and microbial recruitment to host tissue over space and time. Here, we differentiate rarefied community richness and relative abundance in bacterial microbiomes of Salvia lyrata dependent on three spatial depths, which are discrete physical distances from the soil surface within the rhizosphere microhabitat as a proxy for the root system zones. To assess the impact of sampling grain on rarefied community richness and relative abundance, we evaluated the variation of these metrics between samples pooled prior to DNA extraction and samples pooled after sequencing. A distance-based redundancy analysis with the quantitative Jaccard distance revealed that rhizosphere microbiomes vary in richness between rhizosphere soil depths. At all orders of diversity, rarefied microbial richness was consistently lowest at the deepest samples taken (approximately 4 cm from soil surface) in comparison with other rhizosphere soil depths. We additionally show that finer grain sampling (i.e., three samples of equal volume pooled after sequencing) recovers greater microbial richness when using 16S rRNA gene sequencing to describe microbial communities found within the rhizosphere system. In summary, to further elucidate the extent host-specific microbiomes assemble within the rhizosphere, the grain at which bacterial communities are sampled should reflect and encompass fine-scale heterogeneity of the system.

Representational Rényi Heterogeneity.

A discrete system's heterogeneity is measured by the Rényi heterogeneity family of indices (also known as Hill numbers or Hannah-Kay indices), whose units are the numbers equivalent. Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require a priori (A) categorical partitioning and (B) pairwise distance measurement on the observable data space, thereby precluding application to problems with ill-defined categories or where semantically relevant features must be learned as abstractions from some data. We thus introduce representational Rényi heterogeneity (RRH), which transforms an observable domain onto a latent space upon which the Rényi heterogeneity is both tractable and semantically relevant. This method requires neither a priori binning nor definition of a distance function on the observable space. We show that RRH can generalize existing biodiversity and economic equality indices. Compared with existing indices on a beta-mixture distribution, we show that RRH responds more appropriately to changes in mixture component separation and weighting. Finally, we demonstrate the measurement of RRH in a set of natural images, with respect to abstract representations learned by a deep neural network. The RRH approach will further enable heterogeneity measurement in disciplines whose data do not easily conform to the assumptions of existing indices.

Diversity indices for ecological networks: a unifying framework using Hill numbers.

Describing how ecological interactions change over space and time and how they are shaped by environmental conditions is crucial to understand and predict ecosystem trajectories. However, it requires having an appropriate framework to measure network diversity locally, regionally and between samples (α-, γ- and ß-diversity). Here, we propose a unifying framework that builds on Hill numbers and accounts both for the probabilistic nature of biotic interactions and the abundances of species or groups. We emphasise the importance of analysing network diversity across different species aggregation levels (e.g. from species to trophic groups) to get a better understanding of network structure. We illustrate our framework with a simulation experiment and an empirical analysis using a global food-web database. We discuss further usages of the framework and show how it responds to recent calls on comparing ecological networks and analysing their variation across environmental gradients and time.

Proportional mixture of two rarefaction/extrapolation curves to forecast biodiversity changes under landscape transformation.

Progressive habitat transformation causes global changes in landscape biodiversity patterns, but can be hard to quantify. Rarefaction/extrapolation approaches can quantify within-habitat biodiversity, but may not be useful for cases in which one habitat type is progressively transformed into another habitat type. To quantify biodiversity patterns in such transformed landscapes, we use Hill numbers to analyse individual-based species abundance data or replicated, sample-based incidence data. Given biodiversity data from two distinct habitat types, when a specified proportion of original habitat is transformed, our approach utilises a proportional mixture of two within-habitat rarefaction/extrapolation curves to analytically predict biodiversity changes, with bootstrap confidence intervals to assess sampling uncertainty. We also derive analytic formulas for assessing species composition (i.e. the numbers of shared and unique species) for any mixture of the two habitat types. Our analytical and numerical analyses revealed that species unique to each habitat type are the most important determinants of landscape biodiversity patterns.

A hierarchical Bayesian model to incorporate uncertainty into methods for diversity partitioning.

Recently there have been major theoretical advances in the quantification and partitioning of diversity within and among communities, regions, and ecosystems. However, applying those advances to real data remains a challenge. Ecologists often end up describing their samples rather than estimating the diversity components of an underlying study system, and existing approaches do not easily provide statistical frameworks for testing ecological questions. Here we offer one avenue to do all of the above using a hierarchical Bayesian approach. We estimate posterior distributions of the underlying "true" relative abundances of each species within each unit sampled. These posterior estimates of relative abundance can then be used with existing formulae to estimate and partition diversity. The result is a posterior distribution of diversity metrics describing our knowledge (or beliefs) about the study system. This approach intuitively leads to statistical inferences addressing biologically motivated hypotheses via Bayesian model comparison. Using simulations, we demonstrate that our approach does as well or better at approximating the "true" diversity of a community relative to naïve or ad-hoc bias-corrected estimates. Moreover, model comparison correctly distinguishes between alternative hypotheses about the distribution of diversity within and among samples. Finally, we use an empirical ecological dataset to illustrate how the approach can be used to address questions about the makeup and diversities of assemblages at local and regional scales.

Rarefaction and Extrapolation: Making Fair Comparison of Abundance-Sensitive Phylogenetic Diversity among Multiple Assemblages.

Measures of phylogenetic diversity are basic tools in many studies of systematic biology. Faith's PD (sum of branch lengths of a phylogenetic tree connecting all focal species) is the most widely used phylogenetic measure. Like species richness, Faith's PD based on sampling data is highly dependent on sample size and sample completeness. The sample-size- and sample-coverage-based integration of rarefaction and extrapolation of Faith's PD was recently developed to make fair comparison across multiple assemblages. However, species abundances are not considered in Faith's PD. Based on the framework of Hill numbers, Faith's PD was generalized to a class of phylogenetic diversity measures that incorporates species abundances. In this article, we develop both theoretical formulae and analytic estimators for seamless rarefaction and extrapolation for this class of abundance-sensitive phylogenetic measures, which includes simple transformations of phylogenetic entropy and of quadratic entropy. This work generalizes the previous rarefaction/extrapolation model of Faith's PD to incorporate species abundance, and also extends the previous rarefaction/extrapolation model of Hill numbers to include phylogenetic differences among species. Thus a unified approach to assessing and comparing species/taxonomic diversity and phylogenetic diversity can be established. A bootstrap method is suggested for constructing confidence intervals around the phylogenetic diversity, facilitating the comparison of multiple assemblages. Our formulation and estimators can be extended to incidence data collected from multiple sampling units. We also illustrate the formulae and estimators using bacterial sequence data from the human distal esophagus and phyllostomid bat data from three habitats.

Pairwise beta diversity resolves an underappreciated source of confusion in calculating species turnover.

Beta diversity is an important metric in ecology quantifying differentiation or disparity in composition among communities, ecosystems, or phenotypes. To compare systems with different sizes (N, number of units within a system), beta diversity is often converted to related indices such as turnover or local/regional differentiation. Here we use simulations to demonstrate that these naive measures of dissimilarity depend on sample size and design. We show that when N is the number of sampled units (e.g., quadrats) rather than the "true" number of communities in the system (if such exists), these differentiation measures are biased estimators. We propose using average pairwise dissimilarity as an intuitive solution. That is, instead of attempting to estimate an N-community measure, we advocate estimating the expected dissimilarity between any random pair of communities (or sampling units)-especially when the "true" N is unknown or undefined. Fortunately, measures of pairwise dissimilarity or overlap have been used in ecology for decades, and their properties are well known. Using the same simulations, we show that average pairwise metrics give consistent and unbiased estimates regardless of the number of survey units sampled. We advocate pairwise dissimilarity as a general standardization to ensure commensurability of different study systems.

Taxonomic and functional divergence of tree assemblages in a fragmented tropical forest.

Tropical forests are being exposed to increasing levels of habitat loss and fragmentation, threatening the maintenance of global biodiversity. However, the effect that land-use change may have on the spatial dissimilarities in taxonomic and functional composition of remaining assemblages (i.e., taxonomic/functional ß-diversity) remains poorly understood. We examined a large vegetation database from an old and severely fragmented Atlantic forest landscape to test two alternative hypotheses: (1) tree assemblages experience a taxonomic and functional homogenization (reduced ß-diversity) between forest fragments and along forest edges, or alternatively, (2) these edge-affected forests show increased taxonomic and functional differentiation (increased ß-diversity) when compared to forest interior (reference) stands. Taxonomic and functional ß-diversity were examined via novel abundance-based metrics and considering functional traits related to plant dispersion, recruitment, and growth. Overall, taxonomic ß-diversity among fragments was significantly higher than among edge and reference plots. Edge plots also showed higher ß-diversity than reference plots, but only when considering dominant species. In functional terms, ß-diversity among reference plots was also lower than among forest fragments and among edge plots. These patterns support the landscape-divergence hypothesis, which postulates that variable human disturbances among forest fragments and along forest edges can lead to contrasting trajectories of vegetation changes, thus increasing the compositional and functional differentiation of tree communities in these emerging environments. Our results also show that such differentiation can preserve landscape-wide biodiversity, thus overriding negative effects of habitat fragmentation on local (α) diversity. Therefore, our findings demonstrate that forest fragments and forest edges can be more valuable for maintaining species diversity and ecosystem function in fragmented tropical landscapes than previously thought.

Multifaceted diversity-area relationships reveal global hotspots of mammalian species, trait and lineage diversity.

AIM: To define biome-scale hotspots of phylogenetic and functional mammalian biodiversity (PD and FD, respectively) and compare them to 'classical' hotspots based on species richness (SR) only. LOCATION: Global. METHODS: SR, PD & FD were computed for 782 terrestrial ecoregions using distribution ranges of 4616 mammalian species. We used a set of comprehensive diversity indices unified by a recent framework that incorporates the species relative coverage in each ecoregion. We build large-scale multifaceted diversity-area relationships to rank ecoregions according to their levels of biodiversity while accounting for the effect of area on each diversity facet. Finally we defined hotspots as the top-ranked ecoregions. RESULTS: While ignoring species relative coverage led to a relative good congruence between biome top ranked SR, PD and FD hotspots, ecoregions harboring a rich and abundantly represented evolutionary history and functional diversity did not match with top ranked ecoregions defined by species richness. More importantly PD and FD hotspots showed important spatial mismatches. We also found that FD and PD generally reached their maximum values faster than species richness as a function of area. MAIN CONCLUSIONS: The fact that PD/FD reach faster their maximal value than SR may suggest that the two former facets might be less vulnerable to habitat loss than the latter. While this point is expected, it is the first time that it is quantified at global scale and should have important consequences in conservation. Incorporating species relative coverage into the delineation of multifaceted hotspots of diversity lead to weak congruence between SR, PD and FD hotspots. This means that maximizing species number may fail at preserving those nodes (in the phylogenetic or functional tree) that are relatively abundant in the ecoregion. As a consequence it may be of prime importance to adopt a multifaceted biodiversity perspective to inform conservation strategies at global scale.

Quantifying mitochondrial heteroplasmy diversity: A computational approach.

Biodiversity plays a pivotal role in sustaining ecosystem processes, encompassing diverse biological species, genetic types and the intricacies of ecosystem composition. However, the precise definition of biodiversity at the individual level remains a challenging endeavour. Hill numbers, derived from Rényi's entropy, have emerged as a popular measure of diversity, with a recent unified framework extending their application across various levels, from genetics to ecosystems. In this study, we employ a computational approach to exploring the diversity of mitochondrial heteroplasmy using real-world data. By adopting Hill numbers with q = 2, we demonstrate the feasibility of quantifying mitochondrial heteroplasmy diversity within and between individuals and populations. Furthermore, we investigate the alpha diversity of mitochondrial heteroplasmy among different species, revealing heterogeneity at multiple levels, including mitogenome components and protein-coding genes (PCGs). Our analysis explores large-scale mitochondrial heteroplasmy data in humans, examining the relationship between alpha diversity at the mitogenome components and PCGs level. Notably, we do not find a significant correlation between these two levels. Additionally, we observe significant correlations in alpha diversity between mothers and children in blood samples, exceeding the reported R2 value for allele frequency correlations. Moreover, our investigation of beta diversity and local overlay similarity demonstrates that heteroplasmy variant distributions in different tissues of children more closely resemble those of their mothers. Through systematic quantification and analysis of mitochondrial heteroplasmy diversity, this study enhances our understanding of heterogeneity at multiple levels, from individuals to populations, providing new insights into this fundamental dimension of biodiversity.

Mathematical constraints on a family of biodiversity measures via connections with Rényi entropy.

The Hill numbers are statistics for biodiversity measurement in ecological studies, closely related to the Rényi and Shannon entropies from information theory. Recent developments in the mathematics of diversity in the setting of population genetics have produced mathematical constraints that characterize how standard measures depend on the highest-frequency class in a discrete probability distribution. Here, we apply these constraints to diversity statistics in ecology, focusing on the Hill numbers and the Rényi and Shannon entropies. The mathematical bounds can shift perspectives on the diversities of communities, in that when upper and lower bounds on Hill numbers are evaluated in a classic butterfly example, Hill numbers that are initially larger in one community switch positions-so that associated normalized Hill numbers are instead smaller than those of the other community. The new bounds hence add to the tools available for interpreting a commonly used family of statistics for ecological data.

Drivers of wood-inhabiting fungal diversity in European and Oriental beech forests.

The hyperdiverse wood-inhabiting fungi play a crucial role in the global carbon cycle, but often are threatened by deadwood removal, particularly in temperate forests dominated by European beech (Fagus sylvatica) and Oriental beech (Fagus orientalis). To study the impact of abiotic drivers, deadwood factors, forest management and biogeographical patterns in forests of both beech species on fungal composition and diversity, we collected 215 deadwood-drilling samples in 18 forests from France to Armenia and identified fungi by meta-barcoding. In our analyses, we distinguished the patterns driven by rare, common, and dominant species using Hill numbers. Despite a broad overlap in species, the fungal composition with focus on rare species was determined by Fagus species, deadwood type, deadwood diameter, precipitation, temperature, and management status in decreasing order. Shifting the focus on common and dominant species, only Fagus species, both climate variables and deadwood type remained. The richness of species within the deadwood objects increased significantly only with decay stage. Gamma diversity in European beech forests was higher than in Oriental beech forests. We revealed the highest gamma diversity for old-growth forests of European beech when focusing on dominant species. Our results implicate that deadwood retention efforts, focusing on dominant fungi species, critical for the decay process, should be distributed across precipitation and temperature gradients and both Fagus species. Strategies focusing on rare species should additionally focus on different diameters and on the conservation of old-growth forests.

Exploring depth-related patterns of sponge diversity and abundance in marginal reefs.

Marine sponges play a vital role in the reef's benthic community; however, understanding how their diversity and abundance vary with depth is a major challenge, especially on marginal reefs in areas deeper than 30 m. To help bridge this gap, we used underwater videos at 24 locations between 2- and 62-meter depths on a marginal reef system in the Southwestern Atlantic to investigate the effect of depth on the sponge metacommunity. Specifically, we quantified the abundance, density, and taxonomic composition of sponge communities, and decomposed their gamma (γ) diversity into alpha (α) and beta (ß) components. We also assessed whether beta diversity was driven by species replacement (turnover) or by nesting of local communities (nestedness). We identified 2020 marine sponge individuals, which belong to 36 species and 24 genera. As expected, deep areas (i.e., those greater than 30 m) presented greater sponge abundance and more than eightfold the number of sponges per square meter compared to shallow areas. About 50% of the species that occurred in shallow areas (<30 m) also occurred in deep areas. Contrarily to expectations, alpha diversity of rare (0 D α), typical (1 D α), or dominant (2 D α) species did not vary with depth, but the shallow areas had greater beta diversity than the deep ones, especially for typical (1 D ß) and dominant (2 D ß) species. Between 92.7% and 95.7% of the beta diversity was given by species turnover both inside and between shallow and deep areas. Our results support previous studies that found greater sponge abundance and density in deep areas and reveal that species sorting is stronger at smaller depths, generating more beta diversity across local communities in shallow than deep areas. Because turnover is the major driver at any depth, the entire depth gradient should be considered in management and conservation strategies.

Phlebotomine sand flies (Diptera: Psychodidae, Phlebotominae): Diversity of potential Leishmania vectors in northern Colombia.

Phlebotomine sand flies are critical vectors of Leishmania parasites, impacting public health significantly. This study focused on assessing the diversity of sand flies in a rural area of El Carmen de Bolívar Municipality, northern Colombia, employing rarefaction curves and Hill numbers to understand potential vector communities and inform environmental management. From January 2018 to April 2019 (five samplings), sand flies were collected using CDC light traps with blue LED in domestic/peridomestic/sylvatic ecotopes, identifying species per Young and Duncan (1994) and Galati (2003). Hill numbers provided diversity estimates across samples, while Principal Component Analysis correlated with environmental factors with phlebotomine species presence and abundance. 8,784 phlebotomine individuals were collected; 56.4 % females and 43.6% males (ratio 3:2). These individuals belonged to eight species: Pintomyia evansi, Psychodopygus panamensis, Lutzomyia gomezi, Micropygomyia cayennensis, Evandromyia dubitans, Psathyromyia aclydifera, Pintomyia serrana, and Pintomyia rangeliana; with Pi. evansi being the most abundant species (74.39 %; 6,530 exemplars). The ANOVA showed no significant differences between phlebotomine sand flies abundances across ecotopes (p = 0.018). Species of epidemiological relevance as Pi. evansi and Lu. gomezi not show a positive correlation with environmental variables evaluated, only Ps. panamensis was positively correlated with precipitation. However, the study emphasizes the need for a continuous sand fly monitoring and research to enhance leishmaniasis control strategies, highlighting the necessity to expand knowledge on phlebotomine diversity and environmental interactions to understand vector ecology and disease dynamics better.

A comprehensive diversity analysis on the gut microbiomes of ASD patients: from alpha, beta to gamma diversities.

Autism spectrum disorder (ASD) is estimated to influence as many as 1% children worldwide, but its etiology is still unclear. It has been suggested that gut microbiomes play an important role in regulating abnormal behaviors associated with ASD. A de facto standard analysis on the microbiome-associated diseases has been diversity analysis, and nevertheless, existing studies on ASD-microbiome relationship have not produced a consensus. Here, we perform a comprehensive analysis of the diversity changes associated with ASD involving alpha-, beta-, and gamma-diversity metrics, based on 8 published data sets consisting of 898 ASD samples and 467 healthy controls (HC) from 16S-rRNA sequencing. Our findings include: (i) In terms of alpha-diversity, in approximately 1/3 of the studies cases, ASD patients exhibited significantly higher alpha-diversity than the HC, which seems to be consistent with the "1/3 conjecture" of diversity-disease relationship (DDR). (ii) In terms of beta-diversity, the AKP (Anna Karenina principle) that predict all healthy microbiomes should be similar, and every diseased microbiome should be dissimilar in its own way seems to be true in approximately 1/2 to 3/4 studies cases. (iii) In terms of gamma-diversity, the DAR (diversity-area relationship) modeling suggests that ASD patients seem to have large diversity-area scaling parameter than the HC, which is consistent with the AKP results. However, the MAD (maximum accrual diversity) and RIP (ratio of individual to population diversity) parameters did not suggest significant differences between ASD patients and HC. Throughout the study, we adopted Hill numbers to measure diversity, which stratified the diversity measures in terms of the rarity-commonness-dominance spectrum. It appears that the differences between ASD patients and HC are more propounding on rare-species side than on dominant-species side. Finally, we discuss the apparent inconsistent diversity-ASD relationships among different case studies and postulate that the relationships are not monotonic.