An inordinate fondness for eukaryotic diversity

Why do some groups of organisms, like beetles, have so many species, and others, like the tuataras, so few? This classic question in evolutionary biology has a deep history and has been studied using both fossils and phylogenetic trees. Phylogeny-based studies have focused on tree balance, which compares the number of species across clades of the same age in the tree. These studies have suggested that rates of speciation and extinction vary tremendously across the tree of life. In this issue, Rabosky et al. report the most ambitious study to date on the differences in species diversity across clades in the tree of life. The authors bring together a tremendously large dataset of multicellular eukaryotes, including all living species of plants, animals, and fungi; they divide these organisms into 1,397 clades, accounting for more than 1.2 million species in total. Rabosky et al. find tremendous variation in diversity across the tree of life. There are old clades with few species, young clades with many species, and everything in between. They also note a peculiar aspect of their data: it is difficult or impossible to predict how many species will be found in a particular clade knowing how long a clade has been diversifying from a common ancestor. This pattern suggests complex dynamics of speciation and extinction in the history of eukaryotes. Rabosky et al.''s paper represents the latest development in our efforts to understand the Earth''s biodiversity at the broadest scales.J.B.S. Haldane—one of the most quotable of all evolutionary biologists—had a favorite saying about what patterns of species richness tell us about the nature of the Universe: “God has an inordinate fondness for beetles” (see [1] for more details). With this quip, Haldane is referring to the overwhelming number of beetle species on Earth. We still don''t know exactly how many species of beetles there are on the Earth—perhaps around 400,000—but certainly, there are a lot.One of the primary mysteries in macroevolution is the tremendous difference in numbers of species among different taxonomic groups. Modern systematists classify species into clades (groups of species that represent all of the descendents of a common ancestor, like turtles or arthropods). Different clades in the tree of life have dramatically different diversities. This might not be surprising—after all, species in one clade can be distinct from other species in size, energy use, and a thousand other ways. Also, some clades are much older than others. However, even when we control for differences in age by comparing sister clades—that is, pairs of clades that are each others'' closest relative—we still see profound differences in number of species. For example, there are currently two living species of tuatara, a clade of lizard-like reptiles that currently inhabit small islands around New Zealand (Figure 1, left). These tuatara are the sister clade to the squamates, a clade of 7,000 species that includes all living snakes and lizards (Figure 1, right) [2]. Since these two groups are sister clades, they diverged from a common ancestor at exactly the same time (∼250 million years ago [3]). Tuataras used to be far more diverse in the past (though almost certainly not as diverse as squamates [4]), but their current diversity is dwarfed by the tremendous number and variety of snakes and lizards around the globe. Similar patterns occur across the whole tree of life. In fact, old, low diversity clades contain some of the most enigmatic species on Earth: ginkgo trees, coelacanths, tailed frogs, horseshoe crabs, and monotremes, among others. These species are sometimes called “living fossils,” although only some of them are actually thought to resemble their ancient ancestors [5].Open in a separate windowFigure 1The tuatara (Sphenodon punctatus, left) is one of only two surviving species in the clade Sphenodontia.The sister clade to the tuatara is Squamata, which includes the ∼7,000 living species of snakes and lizards, including the ornate day gecko (Phelsuma ornata, right). (Left) from Wikimedia commons, taken by user KeresH, http://commons.wikimedia.org/wiki/File:Henry_at_Invercargill.jpg; (Right) by the author.We can learn a lot about the differences in diversity across clades from phylogenetic trees. In particular, phylogenetic tree balance summarizes the pattern of differences in the number of species between sister clades across a whole phylogeny [6]. A phylogenetic tree can be completely balanced, such that each pair of sister taxa in the tree have exactly the same number of species (Figure 2A; this is only possible if the number of species in the tree is a power of 2: 2, 4, 8, 16, 32, etc.). A phylogenetic tree can also be completely imbalanced, so that every comparison of sister clades has a single species in one clade and the remainder in the other (such a tree is also called pectinate; Figure 2B). There are a few different ways to quantify tree balance, but they all work in basically the same way: compare the number of species between sister clades in the tree, and summarize those differences across a whole phylogeny [7].Open in a separate windowFigure 2Balanced (A), imbalanced (B), and random birth-death (C) phylogenetic trees of eight species (a–h).Imagine for a moment that you have the tree of life (a phylogenetic tree of all species on Earth). We don''t have such a tree yet, but scientists are moving in that direction and trees are getting bigger and bigger (e.g., [8]). The tree of life is a huge and complicated structure, and—as one might imagine at a scale that encompasses all living things, bacteria to beetles to beagles—resists generalizations. But even as the tree of life takes shape, we already know that it is highly imbalanced. This statement applies broadly across living things, and applies equally well to plants as it does to animals, and everything else (as far as we know). Dramatic differences in diversity among clades is a characteristic feature of life on Earth.To better understand patterns of balance in the tree of life, we can start with the birth-death model, a simple model of how phylogenetic trees might grow through time (reviewed in [9]). Under a birth-death model, phylogenetic trees “grow” through time following two processes: speciation, where one species splits into two, and extinction, when one species dies out. For simple birth-death models we assume that both of these processes happen at a constant rate through time for each species alive at that moment, and use the parameters λ (speciation rate) and μ (extinction rate). If we simulate this birth-death model using a computer, we will obtain a phylogenetic tree (Figure 2C). If the extinction rate is greater than zero, such a tree will include surviving species as well as species that have gone extinct. Since we will be comparing this tree to phylogenies of living species, we can assume that any species that went extinct before the present has been pruned. We can then measure the balance of the resulting birth-death tree (our simulated tree will also have branch lengths, but we will ignore those for now). If we repeat this process a large number of times, we obtain a statistical distribution of our tree balance measure, which represents the expectation of that distribution under the birth-death model. It turns out, perhaps surprisingly, that this distribution of balance depends only on the fact that the trees are simulated under a birth-death model; in terms of tree balance, the actual rates of speciation and extinction do not matter, as long as they are constant across clades [10].There is another counterintuitive feature of the balance of birth-death trees: these trees are surprisingly imbalanced compared to what, perhaps, your intuition might suggest. For example, imagine that a certain phylogenetic tree contains 100 species. If you look at the deepest split in that tree and compare the diversity of the two sister clades, what do you expect to find? Are you more likely to find an even number of species in each of these two clades—say, 50/50 or 49/51—or a very uneven number, like 2/98 or 1/99? The surprising answer is that all four of these listed possibilities are equally likely. In fact, all possible combinations of diversity for each of the two clades are equally probable [11].This property of birth-death models means that birth-death trees are quite imbalanced: it is not uncommon, for example, to find sister clades that differ in diversity by a factor of 20 just by random chance. However, the tree of life is imbalanced even compared to birth-death trees! This general observation was first discovered in an influential paper by Arne Mooers and colleagues [7]. In that paper, the authors measured the balance of phylogenetic trees that had been reconstructed using trait data, DNA sequences, or both. They then compared their balance to what we might expect under a birth-death model. They found that phylogenetic trees from a wide range of taxa are extraordinarily imbalanced. This paper showed that the general “shape” of the tree of life is highly imbalanced.The classical interpretation of the imbalanced tree of life is that clades vary in their rates of speciation and/or extinction. There are many reasons to suspect that species in some clades might speciate more frequently, or go extinct less frequently, than their relatives. For example, perhaps a species'' range affects its probability of speciating or going extinct—as suggested by a recent paper in PLOS Biology by Pigot et al. [12]—so that clades of species with different distributions of range sizes will experience different rates of diversification. Many studies have attempted to measure speciation and extinction rates in groups with a good fossil record, and compare these rates across different types of organisms and time periods (e.g., [13],[14]). These studies have generally found wide variation in both rates and in their difference (speciation−extinction = net diversification rate).Recent studies go beyond measures of tree balance by using the tree''s branch lengths to gain information about speciation rates. One simple way to do this is to compare the diversity of a clade to its age; one can then estimate the speciation rate as λ = ln(n)/t, where n is the number of living species in the clade and t is the clade''s stem age (the time since divergence from the clade''s sister group) [15]. There are also modifications of this equation that incorporate extinction and that can use the clade''s crown age (the time since all living species in the clade shared a common ancestor; see [15]).Perhaps surprisingly, one can even estimate extinction from a phylogenetic tree based only on living species [16]. This is because old and young lineages are hit by extinction with different probabilities: since young lineages have not been around very long, they are less likely to have gone extinct than older lineages. The phenomenon is called the “pull of the present” [16] and means that extinction leads to an overabundance of very young lineages in a tree. We can look for this pull in patterns of lineage accumulation through time, which can thus be used to estimate both speciation and extinction rates and to compare these rates across clades (e.g., [17]; but see [18], which points out that this method does not work well when its assumptions of rate constancy are strongly violated). More recently, new methods have been developed to search for variation in speciation and/or extinction rates across large phylogenetic trees, and to try to correlate these rates with the traits of lineages [19],[20].In this issue, Rabosky et al. [21] attempt the most ambitious study to date investigating the differences in species diversity across clades in the tree of life. The authors bring together a tremendously large dataset that spans the multicellular eukaryotes, including all living species of plants, animals, and fungi. For each of 1,397 eukaryotic clades, the authors gathered estimates of age and diversity from the literature – accounting for more than 1.2 million species in total. The authors also summarize the evolutionary relationships among these clades using a “backbone” phylogenetic tree with branch lengths in millions of years. This provides a remarkably complete view of what we currently know about the species diversity of clades across a huge section of the tree of life.The diversity data in Rabosky et al. [21] are broadly consistent with the historical background above: there are major differences in diversification rates across the tree of life. There are old clades with few species, young clades with many species, and everything in between. But Rabosky et al. also note a peculiar aspect of their data: there is typically either a very weak or no relationship between the number of species in a clade and its age. That is, in the data they analyze, it is difficult to guess how many species are in a clade on the basis of how long it has been diversifying from a common ancestor. The traditional explanation for this pattern would be differences in diversification rates across clades—although the authors use simulations to show that, at least under one scenario about how rates might vary across trees, one rarely finds such weak or absent relationships between age and diversity. The authors speculate about other possible explanations for this peculiar (lack of) pattern, from bias in the way clades are named to ecological processes that limit the number of coexisting, competing species.Rabosky et al.''s [21] analysis is not the final chapter; the tree of life is still under construction, and the total number of species in some clades is best viewed as an educated guess. Specifically, I suspect that we have very poor estimates of the extant diversity of many eukaryotic groups, particularly small, understudied organisms. Indeed, new techniques that use genomic sequencing to identify undiscovered species from DNA sequences from environmental samples often reveal that the species we know are only a small component of natural ecosystems [22]. One might also note that the total diversity of multicellular eukaryotes counted in this study might be a vast underestimate compared to recent estimates that use statistical analyses to correct for incomplete sampling [23]. Still, the results in Rabosky et al. [21] are intriguing and will certainly inspire further study, which I expect will be focused on testing more sophisticated mathematical models, beyond the constant-rate birth-death models prevalent today, that might be able to explain patterns in the data.I first learned of the Huxley quote that opens this article in a classic paper, “Homage to Santa Rosalia, or why are there so many kinds of animals?” [24]. This paper, written by the great ecologist G.E. Hutchinson, speculates about the mechanisms that allow so many different species to coexist in natural communities. Hutchinson describes collecting Italian water boatmen from a small pond in the shadow of Santa Rosalia''s shrine, and wondering why the pond contained two species of water beetle—no more, no less. Hutchinson says “Nothing in her history being known to the contrary, perhaps for the moment we may take Santa Rosalia as the patroness of evolutionary studies…” [24]. Rabosky et al.''s [21] paper represents the latest development in our efforts to understand why the Earth has the particular number of species that it has – no more, no less. Santa Rosalia would be proud.     

Evaluating eukaryotic secreted protein prediction

Background  

Improvements in protein sequence annotation and an increase in the number of annotated protein databases has fueled development of an increasing number of software tools to predict secreted proteins. Six software programs capable of high throughput and employing a wide range of prediction methods, SignalP 3.0, SignalP 2.0, TargetP 1.01, PrediSi, Phobius, and ProtComp 6.0, are evaluated.     

A unified classification system for eukaryotic transposable elements

Our knowledge of the structure and composition of genomes is rapidly progressing in pace with their sequencing. The emerging data show that a significant portion of eukaryotic genomes is composed of transposable elements (TEs). Given the abundance and diversity of TEs and the speed at which large quantities of sequence data are emerging, identification and annotation of TEs presents a significant challenge. Here we propose the first unified hierarchical classification system, designed on the basis of the transposition mechanism, sequence similarities and structural relationships, that can be easily applied by non-experts. The system and nomenclature is kept up to date at the WikiPoson web site.     

Evaluating classification strategies

Abstract. This contribution is a comment on the paper by Belbin & McDonald (1993) on the comparison of three classification strategies for use in ecology. There are two problems in evaluating clustering methods: does the sample adequately reflect the population structure, and what is the nature of the clusters sought. First, one has to decide on the number of clusters to be obtained. Possibly the best approach of all is the Bayesian coding theory for inductive inference. This may depend on the objectives of the clustering, which can be manifold. Phytosociologists do not agree on the nature of the clusters they seek, and are reticent in providing a formal definition of their clusters. As a method for identifying gradients Correspondence Analysis has had some success, so that a classification method largely based on it, notably TWINSPAN, may better reflect what phytosociologists are intuitively seeking than alternative variance minimisation methods. Additionally, TWINSPAN incorporates the characterisation through indicator species. Maybe we are more interested in these differentiating species than in the existence of clusters per se.     

TEM evidence for eukaryotic diversity in mid-Proterozoic oceans

Biomarker molecular fossils in 2770 Ma shales suggest that the Eucarya diverged from other principal domains early in Earth history. Nonetheless, at present, the oldest fossils that can be assigned to an extant eukaryotic clade are filamentous red algae preserved in ca. 1200 Ma cherts from Arctic Canada. Between these records lies a rich assortment of potentially protistan microfossils. Combined light microscopy, scanning electron microscopy, and transmission electron microscopy on 1500‐1400 Ma fossils from the Roper Group, Australia, and broadly coeval rocks from China show that these intermediate assemblages do indeed include a moderate diversity of eukaryotic remains. In particular, preserved cell wall ultrastructure, observed using transmission electron microscopy (TEM), can help to bridge the current stratigraphic gap between the unambiguous eukaryotic morphologies of later Proterozoic assemblages and molecular biomarkers in much older rocks.     

Protein-based phylogenies support a chimeric origin for the eukaryotic genome

The phylogenetic position of the archaebacteria and the place of eukaryotes in the history of life remain a question of debate. Recent studies based on some protein-sequence data have obtained unusual phylogenies for these organisms. We therefore collected the protein sequences that were available with representatives from each of the major forms of life: the gram-negative bacteria, gram-positive bacteria, archaebacteria, and eukaryotes. Monophyletic, unrooted phylogenies based on these sequence data show that seven of 24 proteins yield a significant gram-positive-archaebacteria clade/gram-negative- eukaryotic clade. The phylogenies for these seven proteins cannot be explained by the traditional three-way split of the eukaryotes, archaebacteria, and eubacteria. Nine of the 24 proteins yield the traditional gram-positive-gram-negative clade/archaebacteria-eukaryotic clade. The remaining eight proteins give phylogenies that cannot be statistically distinguished. These results support the hypothesis of a chimeric origin for the eukaryotic cell nucleus formed from the fusion of an archaebacteria and a gram-negative bacteria.      

Convex hull principle for classification and phylogeny of eukaryotic proteins

This study quantitatively validates the principle that the biological properties associated with a given genotype are determined by the distribution of amino acids. In order to visualize this central law of molecular biology, each protein was represented by a point in 250-dimensional space based on its amino acid distribution. Proteins from the same family are found to cluster together, leading to the principle that the convex hull surrounding protein points from the same family do not intersect with the convex hulls of other protein families. This principle was verified computationally for all available and reliable protein kinases and human proteins. In addition, we generated 2,328,761 figures to show that the convex hulls of different families were disjoint from each other. The classification performs well with high and robust accuracy (95.75% and 97.5%) together with reasonable phylogenetic trees validate our methods further.     

Evaluating the stability of the classification of community data

We propose a method for a posteriori evaluation of classification stability which compares the classification of sites in the original data set (a matrix of species by sites) with classifications of subsets of its sites created by without‐replacement bootstrap resampling. Site assignments to clusters of the original classification and to clusters of the classification of each subset are compared using Goodman‐Kruskal's lambda index. Many resampled subsets are classified and the mean of lambda values calculated for the classifications of these subsets is used as an estimation of classification stability. Furthermore, the mean of the lambda values based on different resampled subsets, calculated for each site of the data set separately, can be used as a measure of the influence of particular sites on classification stability. This method was tested on several artificial data sets classified by commonly used clustering methods and on a real data set of forest vegetation plots. Its strength lies in the ability to distinguish classifications which reflect robust patterns of community differentiation from unstable classifications of more continuous patterns. In addition, it can identify sites within each cluster which have a transitional species composition with respect to other clusters.     

Inventory of the benthic eukaryotic diversity in the oldest European lake

We have profound knowledge on biodiversity on Earth including plants and animals. In the recent decade, we have also increased our understanding on microorganisms in different hosts and the environment. However, biodiversity is not equally well studied among different biodiversity groups and Earth''s systems with eukaryotes in freshwater sediments being among the least known. In this study, we used high‐throughput sequencing of the 18S rRNA gene to investigate the entire diversity of benthic eukaryotes in three distinct habitats (littoral sediment and hard substrate, profundal sediment) of Lake Ohrid, the oldest European lake. Eukaryotic sequences were dominated by annelid and arthropod animals (54% of all eukaryotic reads) and protists (Ochrophyta and Ciliophora; together 40% of all reads). Eukaryotic diversity was 15% higher in the deep profundal than on either near‐surface hard substrates or littoral sediments. The three habitats differed in their taxonomic and functional community composition. Specifically, heterotrophic organisms accounted for 92% of the reads in the profundal, whereas phototrophs accounted for 43% on the littoral hard substrate. The profundal community was the most homogeneous, and its network was the most complex, suggesting its highest stability among the sampled habitats.     

真核生物转座子鉴定和分类计算方法

重复序列是真核生物基因组的重要组成成分,根据其序列特征及在基因组中的存在形式,可以进一步分为串联重复、片段重复和散在重复。其中,散在重复大多起源于转座子。根据转座介质的不同,转座子又可分为DNA和逆转录转座子。转座子的转座和扩增对基因的进化和基因组的稳定具有显著的影响;同时与其他类型的重复序列相比,转座子的结构和分类更为复杂多样,使得对转座子的鉴定和分类更为复杂和困难。鉴于此,文章简要概括了转座子的功能及分类,总结了真核生物转座子鉴定、分类和注释的3个步骤:(1)重复序列库的构建;(2)重复序列的校正和分类;(3)基因组注释。着重介绍了每一步骤所采用的不同计算方法,比较了不同方法的优缺点。只有把多种方法结合起来使用才能实现全基因组转座子的精确鉴定、分类和注释,这将为转座子的全基因组鉴定和分类提供借鉴意义。     

Genome comparisons highlight similarity and diversity within the eukaryotic kingdoms

In 2000, the number of completely sequenced eukaryotic genomes increased to four. The addition of Drosophila and Arabidopsis into this cohort permits additional insights into the processes that have shaped evolution. Analysis and comparisons of both completed genomes and partially sequenced genomes have already shed light on mechanisms such as gene duplication and gene loss that have long been hypothesized to be major forces in speciation. Indeed, duplicate gene pairs in Saccharomyces, Arabidopsis, Caenorhabditis and Drosophila are high: 30%, 60%, 48% and 40%, respectively. Evidence of horizontal gene-transfer, thought to be a major evolutionary force in bacteria, has been found in Arabidopsis. The release of the 'first draft' of the human genome sequence in 2000 heralds a new stage of biological study. Understanding the as-yet-unannotated human genome will be largely based on conclusions, techniques and tools developed during the analysis and comparison of the genome of these four model organisms.     

A metagenomic assessment of microbial eukaryotic diversity in the global ocean

Surveying microbial diversity and function is accomplished by combining complementary molecular tools. Among them, metagenomics is a PCR free approach that contains all genetic information from microbial assemblages and is today performed at a relatively large scale and reasonable cost, mostly based on very short reads. Here, we investigated the potential of metagenomics to provide taxonomic reports of marine microbial eukaryotes. We prepared a curated database with reference sequences of the V4 region of 18S rDNA clustered at 97% similarity and used this database to extract and classify metagenomic reads. More than half of them were unambiguously affiliated to a unique reference whilst the rest could be assigned to a given taxonomic group. The overall diversity reported by metagenomics was similar to that obtained by amplicon sequencing of the V4 and V9 regions of the 18S rRNA gene, although either one or both of these amplicon surveys performed poorly for groups like Excavata, Amoebozoa, Fungi and Haptophyta. We then studied the diversity of picoeukaryotes and nanoeukaryotes using 91 metagenomes from surface down to bathypelagic layers in different oceans, unveiling a clear taxonomic separation between size fractions and depth layers. Finally, we retrieved long rDNA sequences from assembled metagenomes that improved phylogenetic reconstructions of particular groups. Overall, this study shows metagenomics as an excellent resource for taxonomic exploration of marine microbial eukaryotes.     

Glucanase gene diversity in prokaryotic and eukaryotic organisms

A number of bacteria and eukaryotes produce extracellular enzymes that degrade various types of polysaccharides including the glucans starch, cellulose and hemicellulose (xylan). The similarities in the modes of expression and specificity of enzyme classes, such as amylase, cellulose and xylanase, suggest common genetic origins for particular activities. Our determination of the extent of similarity between these glucanases suggests that such data may be of very limited use in describing the early evolution of these proteins. The great diversity of these proteins does allow identification of their most highly conserved (and presumably functionally important) regions.     

Structural similarities and functional diversity of eukaryotic discoidin-like domains

The discoidin domain is a approximately 150 amino acid motif common in both eukaryotic and prokaryotic proteins. It is found in a variety of extracellular, intracellular and transmembrane multidomain proteins characterized by a considerable functional diversity, mostly involved in developmental processes. The biological role of the domain depends on its interactions with different molecules, including growth factors, phospholipids and lipids, galactose or its derivatives, and collagen. The conservation of the motif, as well as the serious physiological consequences of discoidin domain disorders underscore the importance of the fold, while the ability to accommodate such an extraordinarily broad range of ligand molecules makes it a fascinating research target. In present review we characterize the distinctive features of discoidin domains and briefly outline the biological role of this module in various eukaryotic proteins.     

Evaluating the performance of artificial neural networks for the classification of freshwater benthic macroinvertebrates

Macroinvertebrates form an important functional component of aquatic ecosystems. Their ability to indicate various types of anthropogenic stressors is widely recognized which has made them an integral component of freshwater biomonitoring. The use of macroinvertebrates in biomonitoring is dependent on manual taxa identification which is currently a time-consuming and cost-intensive process conducted by highly trained taxonomical experts. Automated taxa identification of macroinvertebrates is a relatively recent research development. Previous studies have displayed great potential for solutions to this demanding data mining application. In this research we have a collection of 1350 images from eight different macroinvertebrate taxa and the aim is to examine the suitability of artificial neural networks (ANNs) for automated taxa identification of macroinvertebrates. More specifically, the focus is drawn on different training algorithms of Multi-Layer Perceptron (MLP), probabilistic neural network (PNN) and Radial Basis Function network (RBFN). We performed thorough experimental tests and we tested altogether 13 training algorithms for MLPs. The best classification accuracy of MLPs, 95.3%, was obtained by two conjugate gradient backpropagation variations and scaled conjugate gradient backpropagation. For PNN 92.8% and for RBFN 95.7% accuracies were achieved. The results show how important a proper choice of ANN is in order to obtain high accuracy in the automated taxa identification of macroinvertebrates and the obtained model can outperform the level of identification which is made by a taxonomist.     

Predator diversity and abundance provide little support for the enemies hypothesis in forests of high tree diversity

Predatory arthropods can exert strong top-down control on ecosystem functions. However, despite extensive theory and experimental manipulations of predator diversity, our knowledge about relationships between plant and predator diversity--and thus information on the relevance of experimental findings--for species-rich, natural ecosystems is limited. We studied activity abundance and species richness of epigeic spiders in a highly diverse forest ecosystem in subtropical China across 27 forest stands which formed a gradient in tree diversity of 25-69 species per plot. The enemies hypothesis predicts higher predator abundance and diversity, and concomitantly more effective top-down control of food webs, with increasing plant diversity. However, in our study, activity abundance and observed species richness of spiders decreased with increasing tree species richness. There was only a weak, non-significant relationship with tree richness when spider richness was rarefied, i.e. corrected for different total abundances of spiders. Only foraging guild richness (i.e. the diversity of hunting modes) of spiders was positively related to tree species richness. Plant species richness in the herb layer had no significant effects on spiders. Our results thus provide little support for the enemies hypothesis--derived from studies in less diverse ecosystems--of a positive relationship between predator and plant diversity. Our findings for an important group of generalist predators question whether stronger top-down control of food webs can be expected in the more plant diverse stands of our forest ecosystem. Biotic interactions could play important roles in mediating the observed relationships between spider and plant diversity, but further testing is required for a more detailed mechanistic understanding. Our findings have implications for evaluating the way in which theoretical predictions and experimental findings of functional predator effects apply to species-rich forest ecosystems, in which trophic interactions are often considered to be of crucial importance for the maintenance of high plant diversity.     

Evaluating multilocus Bayesian species delimitation for discovery of cryptic mycorrhizal diversity

The increasing availability of DNA sequence data enables exciting new opportunities for fungal ecology. However, it amplifies the challenge of how to objectively classify the diversity of fungal sequences into meaningful units, often in the absence of morphological characters. Here, we test the utility of modern multilocus Bayesian coalescent-based methods for delimiting cryptic fungal diversity in the orchid mycorrhiza morphospecies Serendipita vermifera. We obtained 147 fungal isolates from Caladenia, a speciose clade of Australian orchids known to associate with Serendipita fungi. DNA sequence data for 7 nuclear and mtDNA loci were used to erect competing species hypotheses by clustering isolates based on: (a) ITS sequence divergence, (b) Bayesian admixture analysis, and (c) mtDNA variation. We implemented two coalescent-based Bayesian methods to determine which species hypothesis best fitted our data. Both methods found strong support for eight species of Serendipita among our isolates, supporting species boundaries reflected in ITS divergence. Patterns of host plant association showed evidence for both generalist and specialist associations within the host genus Caladenia. Our findings demonstrate the utility of Bayesian species delimitation methods and suggest that wider application of these techniques will readily uncover new species in other cryptic fungal lineages.