Microbial diversity: considerations and analysis

Several factors have hampered the study of microbial diversity. In the past, methods for cataloguing microbial communities were time-consuming, expensive and inadequate to describe for large-scale screening efforts. Recent developments in high-throughput technologies, such as pyrosequencing, almost certainly address these issues, if the

considerable confusion on how to define a microbial species is resolved (Tamames et al., 2010). Current approaches are based upon genotypic similarity but these approaches are known to group strains together inappropriately (Hall et al., 2010). OTU (operational taxonomic unit) grouping, based upon 16S rRNA sequence identity of 97 to 98% for bacteria is the most common designation, as this corresponds most closely with previously established species divisions (Griffin et al., 2011), but there has been increasing sway in favour of a more polyphasic approach where functional role is considered alongside genotype (Tamames, et

al., 2010; Cai et al., 2009; Gillis et al., 2005).

In addition, the resolution of modern community profiling techniques highlights inadequacies in sampling strategy, the issue of microorganisms being abundantly distributed throughout the soil environment (Schloss and Handlesman, 2006) needs to be considered. Due to this, experimental strategies which attempt to compare microbial communities between transects or across a gradient would need to analyze an extremely large sample in order to capture the true nature of community composition. Furthermore, more cost effective molecular community profiling techniques currently utilized such as DGGE, are not sensitive enough to detect rare species (Woodcock et al., 2006) so many studies do not capture true diversity in a population, only give an indication of the abundance of the most common organisms.

Microbial community profile data is commonly represented by presence/absence or by quantitative matrices of species abundance which are often zero heavy. The majority of biomass tends to belong to several dominant phylotypes, though many species occur at only a few sites but contribute little to overall abundance. Thus, the interpretation of microbial diversity into a meaningful vector of quantity is a continuing challenge for ecologists. Indices classically used for assessing plant and animal communities, though increasing in their popularity in microbial ecology as a ‘quick fix’, offer a distorted or erroneous solution and lack meaningful biological interpretation (Jost, 2007). The ubiquitous nature and vast abundance exhibited by microorganisms means common indices such as Shannon (Shannon, 1948), which do not account for sample size, are likely to represent abundance and richness disproportionally amongst samples. Furthermore, after the financial and time costs of generating high complexity community information, the use of diversity indices has been viewed as ‘sacrificial pseudoreplication’ (Hurlbert, 1984), where all information about species identity and relative functions is lost only to produce a vector of limited biological

value. The pitfalls highlighted previously indicate a less binary more dynamic approach is needed to extract the maximum value from the data we have.

An all-encompassing ecosystem approach has been suggested to best describe patterns and functions in microbial diversity (Ramette et al., 2007). This involves identifying spatial and temporal scales at which populations vary, used in combination with environmental parameters as a means of explaining patterns or functions of the community. The commitment of collecting such a dataset may however be outside of the logistical or financial capabilities of many studies. Multivariate analysis has been applied to microbial data sets although has been largely dominated by exploratory techniques such as Cluster analysis and Principal Component Analysis (PCA) (Ramette, 2007). This is reflective of several factors. First, the complexity of the microbe-environment relationship instills reticence towards conclusions drawn from confirmatory techniques, such as multiple regression analysis, as often these models are driven by the analyst’s personal hypotheses, and do not allow ‘free’ expression of the data. Second, traditionally microbiologists have felt that the dispersal of microorganisms throughout the environment is random and unrelated to the mechanisms driving macro-organism distribution (Finlay, 2002), thus the application of more intensive ecological analyses are not appropriate. O’Donnell et al. (2007) highlighted the limited ability of modeling approaches to address spatial heterogeneity in microbial ecology. With the ability to analyse multiple scales within the data and across sampling regimes, recent developments in spatial eigenanalysis techniques may come some of the way to remedy this problem.

The analysis of diversity almost always involves two properties: species richness and species evenness or abundance. Species richness is a relative term that refers to the number of species in a community, and is directly associated with measuring the diversity of species in a given area. Evenness is another dimension of diversity which defines the number of individuals from each species in an area, so that areas can be compared. Together, these terms have been used to describe species diversity patterns on Earth, though potentially they represent separate aspects or mechanisms of community composition. Richness is often attributed to historical events or climatic isolation whilst abundance is much more likely to represent contemporary biotic and/or abiotic conditions (Barrantes and Sandoval, 2008). Diversity for microbial communities can be seen as a species list as a first approximation of site-specific species diversity (Wilson, 1992), but to provide a more detailed picture species

richness is normally used which encompasses the number of each species type into the diversity measurement (Wilson, 1992). However a drawback of using species richness is that this can hide the absolute abundance of each species, and pays no consideration to specialised species or specific functional characteristics (Wilson, 1992). To ensure that these concerns are captured, a measure of species evenness is often used in conjunction with species richness, and provides the balance or ‘spread’ of species by type and numbers (Whittaker, 1972).

Diversity as a property is commonly examined in three forms, Alpha, Beta and Gamma (Whittaker, 1960). Alpha diversity is considered as the local species diversity at a subunit level (Whittaker, 1972), however it has also been classed as the mean species diversity across a number of subunits (Tuomisto, 2011), and represents the diversity within a site specific community (Vane-Wright, 1991). Beta diversity considers the species diversity among communities (Wilson, 1992), and provides an estimate of the regional or environmental diversity gradient. There are a number of proposed definitions for beta diversity, but largely there are major types: non-directional variation and directional variation (Anderson et al., 2010). Non-directional variation considers variation between a group of samples within a sampling area. Directional beta diversity looks at the changes in samples over a temporal or spatial gradient, this is also known as ‘species’turnover. Gamma diversity is considered as the total diversity and is derived from the Alpha and Beta component independently (Dα + Dß = Dγ ) (Jost, 2007).

Often diversity is measured by common diversity indices (Shannon, 1948; Simpson, 1949), and each in their own way provide an estimation that is representative of one or more aspects of species diversity, whether primarily concerned with species richness (e.g. Shannon indices), species evenness (e.g. Brillouin E indices) or with species concentration of dominance (e.g. Simpson indices) (Jost, 2007). Considering distinct communities within species diversity is furthered by the proposed use of similarity matrices (e.g. Sorensen, 1948) with an effective species alpha diversity metric, considering the similarity between sites where abundance weight is unequal (Magurran, 2004).