Concept: Measurement of biodiversity
Two recent studies have reanalyzed previously published data and found that when data sets were analyzed independently, there was limited support for the widely accepted hypothesis that changes in the microbiome are associated with obesity. This hypothesis was reconsidered by increasing the number of data sets and pooling the results across the individual data sets. The preferred reporting items for systematic reviews and meta-analyses guidelines were used to identify 10 studies for an updated and more synthetic analysis. Alpha diversity metrics and the relative risk of obesity based on those metrics were used to identify a limited number of significant associations with obesity; however, when the results of the studies were pooled by using a random-effect model, significant associations were observed among Shannon diversity, the number of observed operational taxonomic units, Shannon evenness, and obesity status. They were not observed for the ratio of Bacteroidetes and Firmicutes or their individual relative abundances. Although these tests yielded small P values, the difference between the Shannon diversity indices of nonobese and obese individuals was 2.07%. A power analysis demonstrated that only one of the studies had sufficient power to detect a 5% difference in diversity. When random forest machine learning models were trained on one data set and then tested by using the other nine data sets, the median accuracy varied between 33.01 and 64.77% (median, 56.68%). Although there was support for a relationship between the microbial communities found in human feces and obesity status, this association was relatively weak and its detection is confounded by large interpersonal variation and insufficient sample sizes.
The peanut (Arachis hypogaea) is an important oil crop. Breeding for high oil content is becoming increasingly important. Wild Arachis species have been reported to harbor genes for many valuable traits that may enable the improvement of cultivated Arachis hypogaea, such as resistance to pests and disease. However, only limited information is available on variation in oil content. In the present study, a collection of 72 wild Arachis accessions representing 19 species and 3 cultivated peanut accessions were genotyped using 136 genome-wide SSR markers and phenotyped for oil content over three growing seasons. The wild Arachis accessions showed abundant diversity across the 19 species. A. duranensis exhibited the highest diversity, with a Shannon-Weaver diversity index of 0.35. A total of 129 unique alleles were detected in the species studied. A. rigonii exhibited the largest number of unique alleles (75), indicating that this species is highly differentiated. AMOVA and genetic distance analyses confirmed the genetic differentiation between the wild Arachis species. The majority of SSR alleles were detected exclusively in the wild species and not in A. hypogaea, indicating that directional selection or the hitchhiking effect has played an important role in the domestication of the cultivated peanut. The 75 accessions were grouped into three clusters based on population structure and phylogenic analysis, consistent with their taxonomic sections, species and genome types. A. villosa and A. batizocoi were grouped with A. hypogaea, suggesting the close relationship between these two diploid wild species and the cultivated peanut. Considerable phenotypic variation in oil content was observed among different sections and species. Nine alleles were identified as associated with oil content based on association analysis, of these, three alleles were associated with higher oil content but were absent in the cultivated peanut. The results demonstrated that there is great potential to increase the oil content in A. hypogaea by using the wild Arachis germplasm.
Diversity indices might be used to assess the impact of treatments on the relative abundance patterns in species communities. When several treatments are to be compared, simultaneous confidence intervals for the differences of diversity indices between treatments may be used. The simultaneous confidence interval methods described until now are either constructed or validated under the assumption of the multinomial distribution for the abundance counts. Motivated by four example data sets with background in agricultural and marine ecology, we focus on the situation when available replications show that the count data exhibit extra-multinomial variability. Based on simulated overdispersed count data, we compare previously proposed methods assuming multinomial distribution, a method assuming normal distribution for the replicated observations of the diversity indices and three different bootstrap methods to construct simultaneous confidence intervals for multiple differences of Simpson and Shannon diversity indices. The focus of the simulation study is on comparisons to a control group. The severe failure of asymptotic multinomial methods in overdispersed settings is illustrated. Among the bootstrap methods, the widely known Westfall-Young method performs best for the Simpson index, while for the Shannon index, two methods based on stratified bootstrap and summed count data are preferable. The methods application is illustrated for an example.
Glacio-marine fjords occur widely at high latitudes and have been extensively studied in the Arctic, where heavy meltwater inputs and sedimentation yield low benthic faunal abundance and biodiversity in inner-middle fjords. Fjord benthic ecosystems remain poorly studied in the subpolar Antarctic, including those in extensive fjords along the West Antarctic Peninsula (WAP). Here we test ecosystem predictions from Arctic fjords on three subpolar, glacio-marine fjords along the WAP. With seafloor photographic surveys we evaluate benthic megafaunal abundance, community structure, and species diversity, as well as the abundance of demersal nekton and macroalgal detritus, in soft-sediment basins of Andvord, Flandres and Barilari Bays at depths of 436-725 m. We then contrast these fjord sites with three open shelf stations of similar depths. Contrary to Arctic predictions, WAP fjord basins exhibited 3 to 38-fold greater benthic megafaunal abundance than the open shelf, and local species diversity and trophic complexity remained high from outer to inner fjord basins. Furthermore, WAP fjords contained distinct species composition, substantially contributing to beta and gamma diversity at 400-700 m depths along the WAP. The abundance of demersal nekton and macroalgal detritus was also substantially higher in WAP fjords compared to the open shelf. We conclude that WAP fjords are important hotspots of benthic abundance and biodiversity as a consequence of weak meltwater influences, low sedimentation disturbance, and high, varied food inputs. We postulate that WAP fjords differ markedly from their Arctic counterparts because they are in earlier stages of climate warming, and that rapid warming along the WAP will increase meltwater and sediment inputs, deleteriously impacting these biodiversity hotspots. Because WAP fjords also provide important habitat and foraging areas for Antarctic krill and baleen whales, there is an urgent need to develop better understanding of the structure, dynamics and climate-sensitivity of WAP subpolar fjord ecosystems.
Field studies of wild vertebrates are frequently associated with extensive collections of banked fecal samples-unique resources for understanding ecological, behavioral, and phylogenetic effects on the gut microbiome. However, we do not understand whether sample storage methods confound the ability to investigate interindividual variation in gut microbiome profiles. Here, we extend previous work on storage methods for gut microbiome samples by comparing immediate freezing, the gold standard of preservation, to three methods commonly used in vertebrate field studies: lyophilization, storage in ethanol, and storage in RNAlater. We found that the signature of individual identity consistently outweighed storage effects: alpha diversity and beta diversity measures were significantly correlated across methods, and while samples often clustered by donor, they never clustered by storage method. Provided that all analyzed samples are stored the same way, banked fecal samples therefore appear highly suitable for investigating variation in gut microbiota. Our results open the door to a much-expanded perspective on variation in the gut microbiome across species and ecological contexts.
Seamounts are proposed to be hotspots of deep-sea biodiversity, a pattern potentially arising from increased productivity in a heterogeneous landscape leading to either high species co-existence or species turnover (beta diversity). However, studies on individual seamounts remain rare, hindering our understanding of the underlying causes of local changes in beta diversity. Here, we investigated processes behind beta diversity using ROV video, coupled with oceanographic and quantitative terrain parameters, over a depth gradient in Annan Seamount, Equatorial Atlantic. By applying recently developed beta diversity analyses, we identified ecologically unique sites and distinguished between two beta diversity processes: species replacement and changes in species richness. The total beta diversity was high with an index of 0.92 out of 1 and was dominated by species replacement (68%). Species replacement was affected by depth-related variables, including temperature and water mass in addition to the aspect and local elevation of the seabed. In contrast, changes in species richness component were affected only by the water mass. Water mass, along with substrate also affected differences in species abundance. This study identified, for the first time on seamount megabenthos, the different beta diversity components and drivers, which can contribute towards understanding and protecting regional deep-sea biodiversity.
Estimations of microbial community diversity based on metagenomic data sets are affected, often to an unknown degree, by biases derived from insufficient coverage and reference database-dependent estimations of diversity. For instance, the completeness of reference databases cannot be generally estimated since it depends on the extant diversity sampled to date, which, with the exception of a few habitats such as the human gut, remains severely undersampled. Further, estimation of the degree of coverage of a microbial community by a metagenomic data set is prohibitively time-consuming for large data sets, and coverage values may not be directly comparable between data sets obtained with different sequencing technologies. Here, we extend Nonpareil, a database-independent tool for the estimation of coverage in metagenomic data sets, to a high-performance computing implementation that scales up to hundreds of cores and includes, in addition, a k-mer-based estimation as sensitive as the original alignment-based version but about three hundred times as fast. Further, we propose a metric of sequence diversity (N d ) derived directly from Nonpareil curves that correlates well with alpha diversity assessed by traditional metrics. We use this metric in different experiments demonstrating the correlation with the Shannon index estimated on 16S rRNA gene profiles and show that N d additionally reveals seasonal patterns in marine samples that are not captured by the Shannon index and more precise rankings of the magnitude of diversity of microbial communities in different habitats. Therefore, the new version of Nonpareil, called Nonpareil 3, advances the toolbox for metagenomic analyses of microbiomes. IMPORTANCE Estimation of the coverage provided by a metagenomic data set, i.e., what fraction of the microbial community was sampled by DNA sequencing, represents an essential first step of every culture-independent genomic study that aims to robustly assess the sequence diversity present in a sample. However, estimation of coverage remains elusive because of several technical limitations associated with high computational requirements and limiting statistical approaches to quantify diversity. Here we described Nonpareil 3, a new bioinformatics algorithm that circumvents several of these limitations and thus can facilitate culture-independent studies in clinical or environmental settings, independent of the sequencing platform employed. In addition, we present a new metric of sequence diversity based on rarefied coverage and demonstrate its use in communities from diverse ecosystems.
We aimed to identify regional centres of plant biodiversity in South Australia, a sub-continental land area of 983,482 km2, by mapping a suite of metrics. Broad-brush conservation issues associated with the centres were mapped, specifically climate sensitivity, exposure to habitat fragmentation, introduced species and altered fire regimes. We compiled 727,417 plant species records from plot-based field surveys and herbarium records and mapped the following: species richness (all species; South Australian endemics; conservation-dependent species; introduced species); georeferenced weighted endemism, phylogenetic diversity, georeferenced phylogenetic endemism; and measures of beta diversity at local and state-wide scales. Associated conservation issues mapped were: climate sensitivity measured via ordination and non-linear modelling; habitat fragmentation represented by the proportion of remnant vegetation within a moving window; fire prone landscapes assessed using fire history records; invasive species assessed through diversity metrics, species distribution and literature. Compared to plots, herbarium data had higher spatial and taxonomic coverage but records were more biased towards major transport corridors. Beta diversity was influenced by sampling intensity and scale of comparison. We identified six centres of high plant biodiversity for South Australia: Western Kangaroo Island; Southern Mount Lofty Ranges; Anangu Pitjantjatjara Yankunytjatjara lands; Southern Flinders Ranges; Southern Eyre Peninsula; Lower South East. Species composition in the arid-mediterranean ecotone was the most climate sensitive. Fragmentation mapping highlighted the dichotomy between extensive land-use and high remnancy in the north and intensive land-use and low remnancy in the south. Invasive species were most species rich in agricultural areas close to population centres. Fire mapping revealed large variation in frequency across the state. Biodiversity scores were not always congruent between metrics or datasets, notably for categorical endemism to South Australia versus georeferenced weighted endemism, justifying diverse approaches and cautious interpretation. The study could be extended to high resolution assessments of biodiversity centres and cost:benefit analysis for interventions.
Traditional culture techniques have shown that increased bacterial colonization is associated with viral colonization; however, the influence of viral colonization on the whole microbiota composition is less clear. We thus aimed to understand the interaction of viral infections and the nasal microbiota in early life to appraise their roles in disease development. Thirty-two healthy, unselected infants were included in this prospective longitudinal cohort study within the first year of life. Biweekly nasal swabs (n = 559) were taken, and the microbiota was analyzed by 16S rRNA pyrosequencing, and 10 different viruses and 2 atypical bacteria were characterized by real-time PCR (combination of seven duplex samples). In contrast to asymptomatic human rhinovirus (HRV) colonization, symptomatic HRV infections were associated with lower alpha diversity (Shannon diversity index [SDI]), higher bacterial density (PCR concentration), and a difference in beta diversities (Jaccard and Bray-Curtis index) of the microbiota. In addition, infants with more frequent HRV infections had a lower SDI at the end of the study period. Overall, changes in the microbiota associated with symptomatic HRV infections were characterized by a loss of microbial diversity. The interaction between HRV infections and the nasal microbiota in early life might be of importance for later disease development and indicate a potential approach for future interventions. IMPORTANCE Respiratory viral infections are very frequent in infancy and of importance in acute and chronic disease development. Infections with human rhinovirus (HRV) are, e.g., associated with the later development of asthma. We found that only symptomatic HRV infections were associated with acute changes in the nasal microbiota, mainly characterized by a loss of microbial diversity. Infants with more frequent symptomatic HRV infections had a lower bacterial diversity at the end of the first year of life. Whether the interaction between viruses and the microbiota is one pathway contributing to asthma development will be assessed in the follow-ups of these children. Independent of that, measurements of microbial diversity might represent a potential marker for risk of later lung disease or monitoring of early life interventions.
The evolutionary dissimilarity between communities (phylogenetic beta diversity PBD) has been increasingly explored by ecologists and biogeographers to assess the relative roles of ecological and evolutionary processes in structuring natural communities. Among PBD measures, the PhyloSor and UniFrac indices have been widely used to assess the level of turnover of lineages over geographical and environmental gradients. However, these indices can be considered as ‘broad-sense’ measures of phylogenetic turnover as they incorporate different aspects of differences in evolutionary history between communities that may be attributable to phylogenetic diversity gradients. In the present study, we extend an additive partitioning framework proposed for compositional beta diversity to PBD. Specifically, we decomposed the PhyloSor and UniFrac indices into two separate components accounting for ‘true’ phylogenetic turnover and phylogenetic diversity gradients, respectively. We illustrated the relevance of this framework using simple theoretical and archetypal examples, as well as an empirical study based on coral reef fish communities. Overall, our results suggest that using PhyloSor and UniFrac may greatly over-estimate the level of spatial turnover of lineages if the two compared communities show contrasting levels of phylogenetic diversity. We therefore recommend that future studies use the ‘true’ phylogenetic turnover component of these indices when the studied communities encompass a large phylogenetic diversity gradient.