Waste not, want not: revisiting the analysis that called into question the practice of rarefaction

Contaminant Biomagnification in Polar Bears: Interindividual Differences, Dietary Intake Rate, and the Gut Microbiome

Some persistent hydrophobic pollutants biomagnify, i.e., achieve higher contaminant levels in a predator than in its prey (Cpredator/Cprey > 1). This ratio is called the biomagnification factor (BMF) and is traditionally determined using tissues from carcasses or biopsies. Using a noninvasive method that relies on equilibrium sampling in silicone-film-coated vessels and chemical analysis of paired diet and feces, we determined on three occasions the thermodynamic biomagnification limit (BMFlim) and feces-based biomagnification factor (BMFF) for three zoo-housed polar bears who experience seasonal periods of hyperphagia and hypophagia. All bears had high biomagnification capabilities (BMFlim was up to 200) owing to very efficient lipid assimilation (up to 99.5%). The bears differed up to a factor of 3 in their BMFlim. BMFlim and BMFF of a bear increased by up to a factor of 4 during the hypophagic period, when the ingestion rate was greatly reduced. Much of that variability can be explained by differences in the lipid assimilation efficiency, even though this efficiency ranged only from 98.1 to 99.5%. A high BMFlim was associated with a high abundance of Bacteroidales and Lachnospirales in the gut microbiome. Biomagnification varies to a surprisingly large extent between individuals and within the same individual over time. Future work should investigate whether this can be attributed to the influence of the gut microbiome on lipid assimilation by studying more individual bears at different key physiological stages.

Longitudinal multicompartment characterization of host-microbiota interactions in patients with acute respiratory failure

Critical illness can significantly alter the composition and function of the human microbiome, but few studies have examined these changes over time. Here, we conduct a comprehensive analysis of the oral, lung, and gut microbiota in 479 mechanically ventilated patients (223 females, 256 males) with acute respiratory failure. We use advanced DNA sequencing technologies, including Illumina amplicon sequencing (utilizing 16S and ITS rRNA genes for bacteria and fungi, respectively, in all sample types) and Nanopore metagenomics for lung microbiota. Our results reveal a progressive dysbiosis in all three body compartments, characterized by a reduction in microbial diversity, a decrease in beneficial anaerobes, and an increase in pathogens. We find that clinical factors, such as chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, are associated with specific patterns of dysbiosis. Interestingly, unsupervised clustering of lung microbiota diversity and composition by 16S independently predicted survival and performed better than traditional clinical and host-response predictors. These observations are validated in two separate cohorts of COVID-19 patients, highlighting the potential of lung microbiota as valuable prognostic biomarkers in critical care. Understanding these microbiome changes during critical illness points to new opportunities for microbiota-targeted precision medicine interventions.

Linking microbiome temporal dynamics to host ecology in the wild

Ignoring the dynamic nature of microbial communities risks underestimating the power of microbes to impact the health of their hosts. Microbiomes are thought to be important for host fitness, yet the coarse temporal scale and population-level focus of many studies precludes the ability to investigate the importance of among-individual variation in stability and identify the ecological contexts in which this variation matters. Here we briefly summarise current knowledge of temporal dynamics in wild host-associated microbial communities. We then discuss the implications of among-individual variation in microbiota stability and suggest analytical approaches for understanding these patterns. One major requirement is for future studies to conduct individual-level longitudinal analyses, with some systems already well set up for answering these questions.

Microbial Community Response to Granular Peroxide-Based Algaecide Treatment of a Cyanobacterial Harmful Algal Bloom in Lake Okeechobee, Florida (USA)

Cyanobacterial harmful algal blooms (cyanoHABs) occur in fresh water globally. These can degrade water quality and produce toxins, resulting in ecological and economic damages. Thus, short-term management methods (i.e., algaecides) are necessary to rapidly mitigate the negative impacts of cyanoHABs. In this study, we assess the efficacy of a hydrogen peroxide-based algaecide (PAK® 27) on a Microcystis dominated bloom which occurred within the Pahokee Marina on Lake Okeechobee, Florida, USA. We observed a significant reduction in chlorophyll a (96.81%), phycocyanin (93.17%), and Microcystis cell counts (99.92%), and a substantial reduction in microcystins (86.7%) 48 h after treatment (HAT). Additionally, there was a significant shift in bacterial community structure 48 HAT, which coincided with an increase in the relative abundance of photosynthetic protists. These results indicate that hydrogen peroxide-based algaecides are an effective treatment method for cyanoHAB control and highlight their effects on non-target microorganisms (i.e., bacteria and protists).

A global survey of host, aquatic, and soil microbiomes reveals shared abundance and genomic features between bacterial and fungal generalists

Environmental change, coupled with alteration in human lifestyles, is profoundly impacting the microbial communities critical to the health of the Earth and its inhabitants. To identify bacteria and fungi that are resistant and susceptible to habitat change, we analyze thousands of genera detected in 1,580 host, soil, and aquatic samples. This large-scale analysis identifies 48 bacterial and 4 fungal genera that are abundant across the three biomes, demonstrating fitness in diverse environmental conditions. Samples containing these generalists have significantly higher alpha diversity. These generalists play a significant role in shaping cross-kingdom community structure, boasting larger genomes with more secondary metabolism and antimicrobial resistance genes. Conversely, 30 bacterial and 19 fungal genera are only found in a single habitat, suggesting a limited ability to adapt to different and changing environments. These findings contribute to our understanding of microbial niche breadth and its consequences for global biodiversity loss.

Interplay of biotic and abiotic factors shapes tree seedling growth and root-associated microbial communities

Root-associated microbes can alleviate plant abiotic stresses, thus potentially supporting adaptation to a changing climate or to novel environments during range expansion. While climate change is extending plant species fundamental niches northward, the distribution and colonization of mutualists (e.g., arbuscular mycorrhizal fungi) and pathogens may constrain plant growth and regeneration. Yet, the degree to which biotic and abiotic factors impact plant performance and associated microbial communities at the edge of their distribution remains unclear. Here, we use root microscopy, coupled with amplicon sequencing, to study bacterial, fungal, and mycorrhizal root-associated microbial communities from sugar maple seedlings distributed across two temperate-to-boreal elevational gradients in southern Québec, Canada. Our findings demonstrate that soil pH, soil Ca, and distance to sugar maple trees are key drivers of root-associated microbial communities, overshadowing the influence of elevation. Interestingly, changes in root fungal community composition mediate an indirect effect of soil pH on seedling growth, a pattern consistent at both sites. Overall, our findings highlight a complex role of biotic and abiotic factors in shaping tree-microbe interactions, which are in turn correlated with seedling growth. These findings have important ramifications for tree range expansion in response to shifting climatic niches.

Rarefaction is currently the best approach to control for uneven sequencing effort in amplicon sequence analyses

Considering it is common to find as much as 100-fold variation in the number of 16S rRNA gene sequences across samples in a study, researchers need to control for the effect of uneven sequencing effort. How to do this has become a contentious question. Some have argued that rarefying or rarefaction is "inadmissible" because it omits valid data. A number of alternative approaches have been developed to normalize and rescale the data that purport to be invariant to the number of observations. I generated community distributions based on 12 published data sets where I was able to assess the ability of multiple methods to control for uneven sequencing effort. Rarefaction was the only method that could control for variation in uneven sequencing effort when measuring commonly used alpha and beta diversity metrics. Next, I compared the false detection rate and power to detect true differences between simulated communities with a known effect size using various alpha and beta diversity metrics. Although all methods of controlling for uneven sequencing effort had an acceptable false detection rate when samples were randomly assigned to two treatment groups, rarefaction was consistently able to control for differences in sequencing effort when sequencing depth was confounded with treatment group. Finally, the statistical power to detect differences in alpha and beta diversity metrics was consistently the highest when using rarefaction. These simulations underscore the importance of using rarefaction to normalize the number of sequences across samples in amplicon sequencing analyses.

IMPORTANCE

Sequencing 16S rRNA gene fragments has become a fundamental tool for understanding the diversity of microbial communities and the factors that affect their diversity. Due to technical challenges, it is common to observe wide variation in the number of sequences that are collected from different samples within the same study. However, the diversity metrics used by microbial ecologists are sensitive to differences in sequencing effort. Therefore, tools are needed to control for the uneven levels of sequencing. This simulation-based analysis shows that despite a longstanding controversy, rarefaction is the most robust approach to control for uneven sequencing effort. The controversy started because of confusion over the definition of rarefaction and violation of assumptions that are made by methods that have been borrowed from other fields. Microbial ecologists should use rarefaction.