In this research, microbial diversity has been estimated to assess the structure of the soil microbial communities across different habitats in NSW. The structural diversity of the soil samples has been characterized using quantitative and qualitative measures to inform about species richness (incidence-based, i.e. presence-absence), evenness (distribution of abundances) and phylogenetic diversity among the microbial communities as also how their distribution change across geographical distances and environmental gradients.
There are several variations on how to characterize these diversity patterns on biological communities (e.g. rank-abundance curves, indices) (Lozupone and Knight, 2008; Magurran, 2004; Morris et al., 2014; Nemergut et al., 2013; Tuomisto, 2010). Biodiversity, a multidimensional property of natural systems, is qualified and quantified using diversity estimators (e.g. indices, coefficient, plots, etc.). These biodiversity measures indicate how rich and even a given community is and how similar and/or dissimilar two or more communities are when compared. Indeed, thanks to a collective and multidisciplinary effort to characterise ‘diversity’ we count today with a set of ‘biodiversity indices’ able to enlighten about diversity patterns from different aspects of interest (e.g. abundance, dominance, phylogenetic- relatedness, commonness, rarity, etc.) and different spatial and temporal scales (e.g.
agroecological zones) (Escarguel et al., 2011; Magurran, 2004; Morris et al., 2014; Whittaker, 1972).
By itself there is no single ‘diversity metric’ (parametric or non-parametric) flexible enough to qualify and quantify the entire extent of the diversity concept (Magurran, 2004; Morris et al., 2014). In this regard, many authors advocate differentiating the use of ‘diversity’ in the entire context of its definition and a ‘diversity index’ as the metric use to estimate the first one. Indeed, it is required the use of different indices to cover a deeper characterisation of the entire diversity and only one ‘diversity index or metric’ may not be sufficiently informative for such purpose (Tuomisto, 2010). Reasonably, the origin of each of these ‘diversity indices’ was not motivated from biological and ecological explorations but from other research areas and disciplines – e.g. one of the most common diversity measures, Shannon index, was developed to estimate the uncertainty (entropy) in telecommunication messages.
According to their different nature, each of these diversity indices carries their own strengths, weaknesses, and perspective on what is actually defining a greater or lesser diversity in a given community. Each of these estimators has its own principles and statistics, but all of them accomplish three main assumptions that must be applied for biodiversity measurement: (i) all species (OTUs) are equal (ii) all individuals are equal (DNA sequences) and (iii) comparable unit of measure for abundance data (e.g. only DNA sequences or only biomass data) (Lozupone and Knight, 2008; Magurran, 2004). On the other hand, the main differentiation among these estimators is the extent to which they weigh the ‘richness’ and ‘evenness’ aspects of diversity
(Lozupone and Knight, 2008; Magurran, 2004).
Despite being a multidisciplinary task, finding the most suitable ‘biodiversity metrics’ has become considerably more challenging for microbial ecology during recent years. The insertion of new genetic techniques has opened new dimensions counting the uncountable diversity of microorganisms inhabiting highly heterogeneous habitats such as natural environment and, particularly, soils (Hughes et al., 2001; Lozupone and Knight, 2008). Consequently, the new format evaluating diversity patterns on the genetic information of miscellaneous and tiny organisms has meant to add new considerations to able these analyses. For instance, some of the new matters have been (i) to define the basic unit for diversity measurements (e.g. cluster of sequences instead of species), (ii) to extend diversity analyses to genetic variations (e.g. divergence lineage
between taxa), (iii) to calibrate the sampling effort to properly represent a given community (e.g.
number of sequences required to copiously represent a given community), (iv) to define the criteria of
comparison between communities (e.g. similarities and dissimilarities), among others (Hughes et al.,
2001; Morris et al., 2014).
Transversally, each of these components affects directly or indirectly the existence, applicability, and interpretation of the diversity estimators, e.g. indices/coefficients, plots or curves. By incorporating new formats for diversity analysis and taking advantage of the massive sequencing data derived from large-scale microbial surveys, these different metrics have evolved to cover more complex perspectives in biodiversity descriptions.
As biodiversity metrics we changed over the time, their pros and cons have been critically reviewed by different authors (Magurran, 2004; Morris et al., 2014). A comprehensive and complete analysis was made in 2004 by Magurran. In this book, Magurran reviewed all aspects of measuring biodiversity: origins, principles, models, surrogates, assumptions, concepts and important applications of the most popular biodiversity indices. A great part of such references is used in this manuscript. However, Magurran (2004) emphasized that she did not review measurements applied to microbial diversity analyses based on molecular techniques and phylogenetic variations. Other authors have explored these more contemporaneous indices and estimators (Hill et al., 2003; Hughes et al., 2001; Lozupone et al., 2007; Lozupone and Knight, 2008; McMurdie and Holmes, 2014; Morris et al., 2014).
A complete and well-structured review on diversity measures focused on microbial communities was published by Lozupone and Knight (2008). In fact, since 2007 Lozupone et al. have proposed to organize all the diversity indices variations as shown in Table 1-1, in which diversity measures are framed into three main distinctions whether diversity is: (i) analysed in terms of species-based measures, by considering all taxa as equally related and excluding distance relatedness among
them from the analysis, and/or divergence-based measures, by quantifying into the analysis the distance among all taxa as a diversity component (ii) measured qualitatively, only based on presence-absence data, and/or quantitatively, including frequency-abundance data, and (iii) analysed within a given community as the α-diversity and/or among different communities as the β-diversity. More about features, parallels, contrasts and extend beyond diversity metrics is described below.
Table 1-1. Categories of diversity measurements as described by Lozupone and Knight (2010).
Measurement of diversity within a single community(αdiversity)
Measurement of diversity shared among communities(β diversity) Only
presence/absence of taxa considered
Qualitative α diversity (Richness) Species-based: Chao 1, ACE, Rarefaction Divergence-based: Phylogenetic Diversity (PD) Qualitative β diversity Species-based: Sörensen index Jaccard index Divergence-based: Unweighted UniFrac Taxonomic Similarity (ΔS) Additionally, accounts for the number of times that each taxon was observed
Quantitative α diversity (Richness and/or Evenness) Species-based: Shannon’s index Simpson’s index Divergence-based: Theta
Quantitative β diversity Species- based:
Sörensen quantitative index Morisita-Horn measure Divergence-based: Weighted UniFrac FST DPCoA
Diversity measures: from species‐based to OTU‐
based methods
The first distinction in modern microbial analyses is the fact of being measuring diversity on the basis of ‘genetic sequences’ instead of ‘species’ itself. Historically, microbial diversity has been characterized by species-based methods, i.e. those using species as the basic unit of measure (Lozupone and Knight, 2008) and/or, others indirect ones, e.g. biomarkers methods such as Phospholipid Fatty Acid Analysis (PLFA). The quantification of diversity by species-based methods
has been generally defined in terms of presence/absence (richness) and frequency-abundance (evenness) of the individuals living in a given sample. Universally, the indices used in these analyses are Shannon or Shannon-Wiener (Shannon, 1948), Chao1 (Chao, 1984), Simpson
(Simpson, 1949) and few others surrogates of them (e.g. Simpson’s dominance index) (Morris et al., 2014).
Species-based methods have been the traditional scheme used for diversity estimation in circumstances when microorganisms were mostly identified by culture-dependent methods and the microbial species were differentiated phenotypically and/or by hybridizing their DNA to replicate the same species to truly classify the one it was. However, these analyses increased its complexity when we began working with thousands of microbial DNA sequences at once or microbial molecular fingerprinting patterns for their diversity characterisation. Moreover, this kind of genetic information has opened new edges on which rely diversity assessments such as the overall ‘relatedness among genomes’ (Lozupone and Knight, 2008).
Species-based diversity measurement based on genomes-relatedness on DNA sequences has been particularly advantageous in prokaryotes whose primary reproduction form is generally asexual. This type of reproduction able bacteria and archaea to recombine genes of very distant species using horizontal genes transfer (HGT) which has complicated their phenotypic differentiation and so diversity characterisation when working on the basis of culture-dependent methods - apart from the fact that it can be unviable for most of the species as explained in earlier in this Chapter. In contrast, the analysis of microbial diversity on the relativeness of their genomes greatly solved this particular issue with prokaryote, as well as, provide more precision when classifying microbes through the assemblage of their DNA sequences.
On its own, this new format for searching into microbial genomes has introduced additional concerns for diversity measures. One of the ongoing discussion about counting and classifying microorganisms based on ‘genetic sequences’ is what defines a species (Gevers et al., 2005). In fact, this definition is still being debated since the boundaries for a given DNA sequence of whether an organism belongs to one or another species is not obviously delineated (Konstantinidis et al., 2006; Tuomisto, 2010). This arrangement is typically made by defining a similarity threshold (e.g. 97% as minimum to equal an empirical limit for same species when isolated by culturing methods) by which are clustering similar sequences within a determined species (Gevers et al., 2005; Lozupone, 2007; Martin, 2002; Mendoza et al., 2014). This has led to questioning whether the number of ‘species’ is truly represented and therefore the concept of the operational taxonomic unit (OTU) is preferable as the basic unit for measuring diversity instead of ‘species’ itself at this taxonomic level. OTU can be any of the basic units of diversity measurements depending on the methodology applied to study the microbial diversity. For example, an OTU can either be representing the number of DNA sequence similarity groups or the number of unique terminal restriction fragments (when microbial communities are profiled using fingerprinting techniques) (Hughes et al., 2001).