Evolutionary criteria outperform operational approaches in producing ecologically relevant fungal species inventories

Abstract

Analyses of the structure and function of microbial communities are highly constrained by the diversity of organisms present within most environmental samples. A common approach is to rely almost entirely on DNA sequence data for estimates of microbial diversity, but to date there is no objective method of clustering sequences into groups that is grounded in evolutionary theory of what constitutes a biological lineage. The general mixed Yule-coalescent (GMYC) model uses a likelihood-based approach to distinguish population-level processes within lineages from processes associated with speciation and extinction, thus identifying a distinct point where extant lineages became independent. Using two independent surveys of DNA sequences associated with a group of ubiquitous plant-symbiotic fungi, we compared estimates of species richness derived using the GMYC model to those based on operational taxonomic units (OTUs) defined by fixed levels of sequence similarity. The model predicted lower species richness in these surveys than did traditional methods of sequence similarity. Here, we show for the first time that groups delineated by the GMYC model better explained variation in the distribution of fungi in relation to putative niche-based variables associated with host species identity, edaphic factors, and aspects of how the sampled ecosystems were managed. Our results suggest the coalescent-based GMYC model successfully groups environmental sequences of fungi into clusters that are ecologically more meaningful than more arbitrary approaches for estimating species richness.

Pseudamnicola Paulucci, 1878 (Caenogastropoda: Truncatelloidea) from the Aegean Islands: a long or short story?

“…According to the phylogenetic analysis and p-distance values for COI, the studied Pseudamnicola populations can be assigned to 16 clades, most of which are probably distinct species, according to the GMYC and ABGD analysis. The GMYC procedure has been applied in several previous studies Papadopoulou et al 2009a, b;Adolfsson et al 2010;Pagès et al 2010;Powell et al 2011;Vuataz et al 2011), including some on molluscs (Nekola et al 2009;Lorion et al 2010;Jörger et al 2012;Jörger and Schrödl 2013). Reliability of species delimitated by GMYC has also been discussed in the literature (e.g.…”

Accounting for uncertainty in species delineation during the analysis of environmental DNA sequence data

“…This issue, with regard to bacteria, was partly addressed by Barraclough et al (2009); they referred to the genetic clusters identified by the GMYC model as 'evolutionarily significant units' owing to the fact that they represent independently evolving lineages (at least at the level of the individual gene locus) as well as biological phenomena that need to be explained even if they are not representative of species, per se. The fact that these clusters exist and that they correspond with biological/ecological characteristics of the taxa in which they are found (Jousselin et al 2009;Powell et al 2011) suggests that they are representative of some fundamental evolutionary process.…”

“…The Firmicutes phylogeny was then split into two subtrees containing 1112 and 962 tips, respectively. I divided the sequences into these three groups to facilitate more rapid GMYC model calculations; each of these trees was large enough to provide sufficient statistical power (Powell et al 2011).…”

“…In addition, previous studies have shown that this approach can identify genetic clusters that generally correspond with existing classification schemes (Monaghan et al 2009) and highlight patterns of cospeciation between an endosymbiont and its host (Jousselin, Desdevises & Coeur d'acier 2009) while also performing better than operational clustering approaches at accounting for the environmental associations of taxa (Powell et al 2011). The procedure estimates lineage birth rates associated with both Yule diversification (speciation events; Yule 1924) and neutral coalescent (merging of lineages within a population; Kingman 1982) processes from a multispecies coalescent tree, calculating the likelihood assuming a threshold age between these processes at each node in the phylogeny.…”

“…The ML single-threshold model usually provides a significantly better fit to the data than the null model (e.g. Pons et al 2006;Fontaneto et al 2007;Ahrens, Monaghan & Vogler 2007;Barraclough et al 2009;Jousselin et al 2009;Leliaert et al 2009;Monaghan et al 2009;Bode et al 2010;Powell et al 2011), but the ML multiple-threshold model usually does not provide a better fit to the data than the ML single-threshold model (personal observation).…”