William T. Sloan

Estimating prokaryotic diversity and its limits

The absolute diversity of prokaryotes is widely held to be unknown and unknowable at any scale in any environment. However, it is not necessary to count every species in a community to estimate the number of different taxa therein. It is sufficient to estimate the area under the species abundance curve for that environment. Log-normal species abundance curves are thought to characterize communities, such as bacteria, which exhibit highly dynamic and random growth. Thus, we are able to show that the diversity of prokaryotic communities may be related to the ratio of two measurable variables: the total number of individuals in the community and the abundance of the most abundant members of that community. We assume that either the least abundant species has an abundance of 1 or Prestons canonical hypothesis is valid. Consequently, we can estimate the bacterial diversity on a small scale (oceans 160 per ml; soil 6,400–38,000 per g; sewage works 70 per ml). We are also able to speculate about diversity at a larger scale, thus the entire bacterial diversity of the sea may be unlikely to exceed 2 × 106, while a ton of soil could contain 4 × 106 different taxa. These are preliminary estimates that may change as we gain a greater understanding of the nature of prokaryotic species abundance curves. Nevertheless, it is evident that local and global prokaryotic diversity can be understood through species abundance curves and purely experimental approaches to solving this conundrum will be fruitless.

Accurate determination of microbial diversity from 454 pyrosequencing data.

We present an algorithm, PyroNoise, that clusters the flowgrams of 454 pyrosequencing reads using a distance measure that models sequencing noise. This infers the true sequences in a collection of amplicons. We pyrosequenced a known mixture of microbial 16S rDNA sequences extracted from a lake and found that without noise reduction the number of operational taxonomic units is overestimated but using PyroNoise it can be accurately calculated.

Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform

With read lengths of currently up to 2 × 300 bp, high throughput and low sequencing costs Illuminas MiSeq is becoming one of the most utilized sequencing platforms worldwide. The platform is manageable and affordable even for smaller labs. This enables quick turnaround on a broad range of applications such as targeted gene sequencing, metagenomics, small genome sequencing and clinical molecular diagnostics. However, Illumina error profiles are still poorly understood and programs are therefore not designed for the idiosyncrasies of Illumina data. A better knowledge of the error patterns is essential for sequence analysis and vital if we are to draw valid conclusions. Studying true genetic variation in a population sample is fundamental for understanding diseases, evolution and origin. We conducted a large study on the error patterns for the MiSeq based on 16S rRNA amplicon sequencing data. We tested state-of-the-art library preparation methods for amplicon sequencing and showed that the library preparation method and the choice of primers are the most significant sources of bias and cause distinct error patterns. Furthermore we tested the efficiency of various error correction strategies and identified quality trimming (Sickle) combined with error correction (BayesHammer) followed by read overlapping (PANDAseq) as the most successful approach, reducing substitution error rates on average by 93%.

Combined niche and neutral effects in a microbial wastewater treatment community

It has long been assumed that differences in the relative abundance of taxa in microbial communities reflect differences in environmental conditions. Here we show that in the economically and environmentally important microbial communities in a wastewater treatment plant, the population dynamics are consistent with neutral community assembly, where chance and random immigration play an important and predictable role in shaping the communities. Using dynamic observations, we demonstrate a straightforward calibration of a purely neutral model and a parsimonious method to incorporate environmental influence on the reproduction (or birth) rate of individual taxa. The calibrated model parameters are biologically plausible, with the population turnover and diversity in the heterotrophic community being higher than for the ammonia oxidizing bacteria (AOB) and immigration into AOB community being relatively higher. When environmental factors were incorporated more of the variance in the observations could be explained but immigration and random reproduction and deaths remained the dominant driver in determining the relative abundance of the common taxa. Consequently we suggest that neutral community models should be the foundation of any description of an open biological system.

Microbial landscapes: new paths to biofilm research.

It is the best of times for biofilm research. Systems biology approaches are providing new insights into the genetic regulation of microbial functions, and sophisticated modelling techniques are enabling the prediction of microbial community structures. Yet it is also clear that there is a need for ecological theory to contribute to our understanding of biofilms. Here, we suggest a concept for biofilm research that is spatially explicit and solidly rooted in ecological theory, which might serve as a universal approach to the study of the numerous facets of biofilms.

Sustainable wastewater treatment: How might microbial fuel cells contribute

The need for cost-effective low-energy wastewater treatment has never been greater. Clean water for our expanding and predominantly urban global population will be expensive to deliver, eats into our diminishing carbon-based energy reserves and consequently contributes to green house gases in the atmosphere and climate change. Thus every potential cost and energy cutting measure for wastewater treatment should be explored. Microbial fuel cells (MFCs) could potentially yield such savings but, to achieve this, requires significant advances in our understanding in a few critical areas and in our designs of the overall systems. Here we review the research which might accelerate our progress towards sustainable wastewater treatment using MFCs: system control and modelling and the understanding of the ecology of the microbial communities that catalyse the generation of electricity.

The rational exploration of microbial diversity

The exploration of the microbial world has been an exciting series of unanticipated discoveries despite being largely uninformed by rational estimates of the magnitude of task confronting us. However, in the long term, more structured surveys can be achieved by estimating the diversity of microbial communities and the effort required to describe them. The rates of recovery of new microbial taxa in very large samples suggest that many more taxa remain to be discovered in soils and the oceans. We apply a robust statistical method to large gene sequence libraries from these environments to estimate both diversity and the sequencing effort required to obtain a given fraction of that diversity. In the upper ocean, we predict some 1400 phylotypes, and a mere fivefold increase in shotgun reads could yield 90% of the metagenome, that is, all genes from all taxa. However, at deep ocean, hydrothermal vents and diversities in soils can be up to two orders of magnitude larger, and hundreds of times the current number of samples will be required just to obtain 90% of the taxonomic diversity based on 3% difference in 16S rDNA. Obtaining 90% of the metagenome will require tens of thousands of times the current sequencing effort. Although the definitive sequencing of hyperdiverse environments is not yet possible, we can, using taxa-abundance distributions, begin to plan and develop the required methods and strategies. This would initiate a new phase in the exploration of the microbial world.

Challenges in microbial ecology: building predictive understanding of community function and dynamics

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

What is the extent of prokaryotic diversity

The extent of microbial diversity is an intrinsically fascinating subject of profound practical importance. The term ‘diversity’ may allude to the number of taxa or species richness as well as their relative abundance. There is uncertainty about both, primarily because sample sizes are too small. Non-parametric diversity estimators make gross underestimates if used with small sample sizes on unevenly distributed communities. One can make richness estimates over many scales using small samples by assuming a species/taxa-abundance distribution. However, no one knows what the underlying taxa-abundance distributions are for bacterial communities. Latterly, diversity has been estimated by fitting data from gene clone libraries and extrapolating from this to taxa-abundance curves to estimate richness. However, since sample sizes are small, we cannot be sure that such samples are representative of the community from which they were drawn. It is however possible to formulate, and calibrate, models that predict the diversity of local communities and of samples drawn from that local community. The calibration of such models suggests that migration rates are small and decrease as the community gets larger. The preliminary predictions of the model are qualitatively consistent with the patterns seen in clone libraries in ‘real life’. The validation of this model is also confounded by small sample sizes. However, if such models were properly validated, they could form invaluable tools for the prediction of microbial diversity and a basis for the systematic exploration of microbial diversity on the planet.

Headwaters are critical reservoirs of microbial diversity for fluvial networks

Streams and rivers form conspicuous networks on the Earth and are among natures most effective integrators. Their dendritic structure reaches into the terrestrial landscape and accumulates water and sediment en route from abundant headwater streams to a single river mouth. The prevailing view over the last decades has been that biological diversity also accumulates downstream. Here, we show that this pattern does not hold for fluvial biofilms, which are the dominant mode of microbial life in streams and rivers and which fulfil critical ecosystem functions therein. Using 454 pyrosequencing on benthic biofilms from 114 streams, we found that microbial diversity decreased from headwaters downstream and especially at confluences. We suggest that the local environment and biotic interactions may modify the influence of metacommunity connectivity on local biofilm biodiversity throughout the network. In addition, there was a high degree of variability in species composition among headwater streams that could not be explained by geographical distance between catchments. This suggests that the dendritic nature of fluvial networks constrains the distributional patterns of microbial diversity similar to that of animals. Our observations highlight the contributions that headwaters make in the maintenance of microbial biodiversity in fluvial networks.