A young clade repeating an old pattern: diversity in Nothonotus darters (Teleostei: Percidae) endemic to the Cumberland River

Hypotheses of diversification in eastern North American freshwater fishes have focused primarily on allopatric distributions of species between disjunct highland areas and major river systems. However, these hypotheses do not fully explain the rich diversity of species within highland regions and river systems. Relatively old diversification events at small geographic scales have been observed in the Barcheek Darter subclade that occurs in the Cumberland River drainage (CRD) in Kentucky and Tennessee, United States of America, but it is unknown if this pattern is consistent in other darter subclades. We explored phylogeographic diversity in two species of Nothonotus darters, N. microlepidus and N. sanguifluus, endemic to the CRD to compare phylogenetic patterns between Barcheek Darters and species of Nothonotus. We collected sequence data for a mitochondrial gene (cytb) and three nuclear genes (MLL, S7 and RAG1) from 19 N. microlepidus and 35 N. sanguifluus specimens. Gene trees were estimated using maximum likelihood and Bayesian methods, and a 'species tree' was inferred using a Bayesian method. These trees indicate that species diversity in Nothonotus is underestimated. Five distinct lineages were evident, despite retained ancestral polymorphism and unsampled extirpated populations. Comparison of chronograms for Barcheek Darters and Nothonotus revealed that microendemism resulting from species diversification at small geographic scales in the CRD is a consistent pattern in both old and young darter subclades. Our analyses reveal that geographic isolating mechanisms that result in similar phylogeographic patterns in the CRD are persistent through long expanses of evolutionary time.

A maximum pseudo-likelihood approach for estimating species trees under the coalescent model

Background

Several phylogenetic approaches have been developed to estimate species trees from collections of gene trees. However, maximum likelihood approaches for estimating species trees under the coalescent model are limited. Although the likelihood of a species tree under the multispecies coalescent model has already been derived by Rannala and Yang, it can be shown that the maximum likelihood estimate (MLE) of the species tree (topology, branch lengths, and population sizes) from gene trees under this formula does not exist. In this paper, we develop a pseudo-likelihood function of the species tree to obtain maximum pseudo-likelihood estimates (MPE) of species trees, with branch lengths of the species tree in coalescent units.

Results

We show that the MPE of the species tree is statistically consistent as the number M of genes goes to infinity. In addition, the probability that the MPE of the species tree matches the true species tree converges to 1 at rate O(M ^-1). The simulation results confirm that the maximum pseudo-likelihood approach is statistically consistent even when the species tree is in the anomaly zone. We applied our method, Maximum Pseudo-likelihood for Estimating Species Trees (MP-EST) to a mammal dataset. The four major clades found in the MP-EST tree are consistent with those in the Bayesian concatenation tree. The bootstrap supports for the species tree estimated by the MP-EST method are more reasonable than the posterior probability supports given by the Bayesian concatenation method in reflecting the level of uncertainty in gene trees and controversies over the relationship of four major groups of placental mammals.

Conclusions

MP-EST can consistently estimate the topology and branch lengths (in coalescent units) of the species tree. Although the pseudo-likelihood is derived from coalescent theory, and assumes no gene flow or horizontal gene transfer (HGT), the MP-EST method is robust to a small amount of HGT in the dataset. In addition, increasing the number of genes does not increase the computational time substantially. The MP-EST method is fast for analyzing datasets that involve a large number of genes but a moderate number of species.

Recent trends in molecular phylogenetic analysis: where to next?

The acquisition of large multilocus sequence data is providing researchers with an unprecedented amount of information to resolve difficult phylogenetic problems. With these large quantities of data comes the increasing challenge regarding the best methods of analysis. We review the current trends in molecular phylogenetic analysis, focusing specifically on the topics of multiple sequence alignment and methods of tree reconstruction. We suggest that traditional methods are inadequate for these highly heterogeneous data sets and that researchers employ newer more sophisticated search algorithms in their analyses. If we are to best extract the information present in these data sets, a sound understanding of basic phylogenetic principles combined with modern methodological techniques are necessary.

Ecological opportunity and the origin of adaptive radiations

Ecological opportunity--through entry into a new environment, the origin of a key innovation or extinction of antagonists--is widely thought to link ecological population dynamics to evolutionary diversification. The population-level processes arising from ecological opportunity are well documented under the concept of ecological release. However, there is little consensus as to how these processes promote phenotypic diversification, rapid speciation and adaptive radiation. We propose that ecological opportunity could promote adaptive radiation by generating specific changes to the selective regimes acting on natural populations, both by relaxing effective stabilizing selection and by creating conditions that ultimately generate diversifying selection. We assess theoretical and empirical evidence for these effects of ecological opportunity and review emerging phylogenetic approaches that attempt to detect the signature of ecological opportunity across geological time. Finally, we evaluate the evidence for the evolutionary effects of ecological opportunity in the diversification of Caribbean Anolis lizards. Some of the processes that could link ecological opportunity to adaptive radiation are well documented, but others remain unsupported. We suggest that more study is required to characterize the form of natural selection acting on natural populations and to better describe the relationship between ecological opportunity and speciation rates.

Endocasts of Microsyops (Microsyopidae, Primates) and the evolution of the brain in primitive primates

We describe a virtual endocast produced from ultra high resolution X-ray computed tomography (CT) data for the microsyopid, Microsyops annectens (middle Eocene, Wyoming). It is the most complete and least distorted endocast known for a plesiadapiform primate and because of the relatively basal position of Microsyopidae, has particular importance to reconstructing primitive characteristics for Primates. Cranial capacity is estimated at 5.9 cm(3), yielding encephalization quotients (EQ) of 0.26-0.39 (Jerison's equation) and 0.32-0.52 (Eisenberg's equation), depending on the body mass estimate. Even the lowest EQ estimate for M. annectens is higher than that for Plesiadapis cookei, while the range of estimates overlaps with that of Ignacius graybullianus and with the lower end of the range of estimates for fossil euprimates. As in other plesiadapiforms, the olfactory bulbs of M. annectens are large. The cerebrum does not extend onto the cerebellum or form a ventrally protruding temporal lobe with a clear temporal pole, suggesting less development of the visual sense and a greater emphasis on olfaction than in euprimates. Contrasts between the virtual endocast of M. annectens, and both a natural endocast of the same species and a partial endocast from the earlier-occurring Microsyops sp., cf. Microsyops elegans, suggest that the coverage of the caudal colliculi by the cerebrum evolved within the Microsyops lineage. This implies that microsyopids expanded their cerebra and perhaps evolved an improved visual sense independent of euprimates. With a growing body of data on the morphology of the brain in primitive primates, it is becoming clear that many of the characteristics of the brain common to euprimates evolved after the divergence of stem primates from other euarchontans and likely in parallel in different lineages. These new data suggest a different model for the ancestors of euprimates than has been assumed based on the anatomy of the brain in visually specialized diurnal tree shrews.

Bayesian inference of species trees from multilocus data

Until recently, it has been common practice for a phylogenetic analysis to use a single gene sequence from a single individual organism as a proxy for an entire species. With technological advances, it is now becoming more common to collect data sets containing multiple gene loci and multiple individuals per species. These data sets often reveal the need to directly model intraspecies polymorphism and incomplete lineage sorting in phylogenetic estimation procedures.

For a single species, coalescent theory is widely used in contemporary population genetics to model intraspecific gene trees. Here, we present a Bayesian Markov chain Monte Carlo method for the multispecies coalescent. Our method coestimates multiple gene trees embedded in a shared species tree along with the effective population size of both extant and ancestral species. The inference is made possible by multilocus data from multiple individuals per species.

Using a multiindividual data set and a series of simulations of rapid species radiations, we demonstrate the efficacy of our new method. These simulations give some insight into the behavior of the method as a function of sampled individuals, sampled loci, and sequence length. Finally, we compare our new method to both an existing method (BEST 2.2) with similar goals and the supermatrix (concatenation) method. We demonstrate that both BEST and our method have much better estimation accuracy for species tree topology than concatenation, and our method outperforms BEST in divergence time and population size estimation.