Trees of Unusual Size: Biased Inference of Early Bursts from Large Molecular Phylogenies

An early burst of speciation followed by a subsequent slowdown in the rate of diversification is commonly inferred from molecular phylogenies. This pattern is consistent with some verbal theory of ecological opportunity and adaptive radiations. One often-overlooked source of bias in these studies is that of sampling at the level of whole clades, as researchers tend to choose large, speciose clades to study. In this paper, we investigate the performance of common methods across the distribution of clade sizes that can be generated by a constant-rate birth-death process. Clades which are larger than expected for a given constant-rate branching process tend to show a pattern of an early burst even when both speciation and extinction rates are constant through time. All methods evaluated were susceptible to detecting this false signature when extinction was low. Under moderate extinction, both the gamma-statistic and diversity-dependent models did not detect such a slowdown but only because the signature of a slowdown was masked by subsequent extinction. Some models which estimate time-varying speciation rates are able to detect early bursts under higher extinction rates, but are extremely prone to sampling bias. We suggest that examining clades in isolation may result in spurious inferences that rates of diversification have changed through time.Comment: 17 pages, 5 figure

Type-1 Error Rate for the

<p><b>-Statistic.</b> Results from simulations showing the Type-1 error rate for the statisitc, signifying a false inference of a slowdown. All trees were generated under a constant-rate birth-death (or pure-birth) process. We recognize a Type-1 error if the value of (significant at ; one-tailed test). Extinction rate, , varies across the plots (); speciation rate, , and total tree-depth, are held constant ( and ). All are plotted against the expected number of taxa across the cumulative distribution of probability densities (from 0.99 to 0.01). The dashed vertical line represents the expected value for under the simulating conditions. Each point represents 1000 simulations. (Results for and not shown.)</p

Proportion of Trees Showing Support for Temporally-Varying Speciation Model Using Coalescent Approximation.

<p>Results from simulations showing the proportion of phylogenies for which a temporally-varying speciation (TVS) model (Model 4a of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043348#pone.0043348-Morlon1" target="_blank">[23]</a>) is preferred over a constant-rate birth-death model (BD; Model 3 of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043348#pone.0043348-Morlon1" target="_blank">[23]</a>) using AIC to select amongst the models. Both the models were formulated according to a coalescent-based approximation of the likelihood <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043348#pone.0043348-Morlon1" target="_blank">[23]</a>. We used a AIC cutoff of 4 to favor the TVS model when the generating model was a constant-rate process. Extinction rate, , varies across the plots (); speciation rate, , and total tree-depth, are held constant ( and ). All are plotted against the expected number of taxa across the cumulative distribution of probability densities (from 0.99 to 0.01). The dashed vertical line represents the expected value for under the simulating conditions. Each point represents 1000 simulations. (Results for and not shown.)</p

Exemplar Lineages-Through-Time Plot.

<p>An example of a lineages-through-time (LTT) plot for a tree (shown on left) drawn from the far right tail of the distribution of tree sizes (5 percent of surviving trees are expected to be this large or larger) for , and . The dotted line is the expected number of lineages under a constant diversification rate. This LTT plot shows the typical signature of an early burst of speciation yet this signature is not captured by the -statistic (; not significant) as the burst is masked by later extinction events.</p

Proportion of Trees Showing Support for Diversity-Dependent Model.

<p>Results from simulations showing the proportion of phylogenies for which a density-dependent (DD) model is preferred over a constant-rate birth-death (BD) model in using AIC to select amongst the models. Only the DD model and a BD model were compared. We used a AIC cutoff of 4 to favor a DD model when the generating model was a constant-rate process. Extinction rate, , varies across the plots (); speciation rate, , and total tree-depth, are held constant ( and ). All are plotted against the expected number of taxa across the cumulative distribution of probability densities (from 0.99 to 0.01). The dashed vertical line represents the expected value for under the simulating conditions. Each point represents 1000 simulations. (Results for and not shown.)</p

Endocrine rhythms in the brown bear (Ursus arctos): Evidence supporting selection for decreased pineal gland size

Many temperate zone animals adapt to seasonal changes by altering their physiology. This is mediated in large part by endocrine signals that encode day length and regulate energy balance and metabolism. The objectives of this study were to determine if the daily patterns of two important hormones, melatonin and cortisol, varied with day length in captive brown bears (Ursus arctos) under anesthetized and nonanesthetized conditions during the active (March–October) and hibernation periods. Melatonin concentrations varied with time of day and season in nonanesthetized female bears despite exceedingly low nocturnal concentrations (1–4 pg/mL) in the active season. In contrast, melatonin concentrations during hibernation were 7.5-fold greater than those during the summer in anesthetized male bears. Functional assessment of the pineal gland revealed a slight but significant reduction in melatonin following nocturnal light application during hibernation, but no response to beta-adrenergic stimulation was detected in either season. Examination of pineal size in two bear species bears combined with a phylogenetically corrected analysis of pineal glands in 47 other species revealed a strong relationship to brain size. However, pineal gland size of both bear species deviated significantly from the expected pattern. Robust daily plasma cortisol rhythms were observed during the active season but not during hibernation. Cortisol was potently suppressed following injection with a synthetic glucocorticoid. The results suggest that melatonin and cortisol both retain their ability to reflect seasonal changes in day length in brown bears. The exceptionally small pineal gland in bears may be the result of direct or indirect selection

Phylogenetic Diversity and Microsphere Array-Based Genotyping of Human Pathogenic Fusaria, Including Isolates from the Multistate Contact Lens-Associated U.S. Keratitis Outbreaks of 2005 and 2006▿ ‡

In 2005 and 2006, outbreaks of Fusarium keratitis associated with soft contact lens use occurred in multiple U.S. states and Puerto Rico. A case-control study conducted by the Centers for Disease Control and Prevention (CDC) showed a significant association between infections and the use of one particular brand of lens solution. To characterize the full spectrum of the causal agents involved and their potential sources, partial DNA sequences from three loci (RPB2, EF-1α, and nuclear ribosomal rRNA) totaling 3.48 kb were obtained from 91 corneal and 100 isolates from the patient's environment (e.g., contact lens and lens cases). We also sequenced a 1.8-kb region encoding the RNA polymerase II second largest subunit (RPB2) from 126 additional pathogenic isolates to better understand how the keratitis outbreak isolates fit within the full phylogenetic spectrum of clinically important fusaria. These analyses resulted in the most robust phylogenetic framework for Fusarium to date. In addition, RPB2 nucleotide variation within a 72-isolate panel was used to design 34 allele-specific probes to identify representatives of all medically important species complexes and 10 of the most important human pathogenic Fusarium in a single-well diagnostic assay, using flow cytometry and fluorescent microsphere technology. The multilocus data revealed that one haplotype from each of the three most common species comprised 55% of CDC's corneal and environmental isolates and that the corneal isolates comprised 29 haplotypes distributed among 16 species. The high degree of phylogenetic diversity represented among the corneal isolates is consistent with multiple sources of contamination

A Bayesian framework for efficient and accurate variant prediction.

There is a growing need to develop variant prediction tools capable of assessing a wide spectrum of evidence. We present a Bayesian framework that involves aggregating pathogenicity data across multiple in silico scores on a gene-by-gene basis and multiple evidence statistics in both quantitative and qualitative forms, and performs 5-tiered variant classification based on the resulting probability credible interval. When evaluated in 1,161 missense variants, our gene-specific in silico model-based meta-predictor yielded an area under the curve (AUC) of 96.0% and outperformed all other in silico predictors. Multifactorial model analysis incorporating all available evidence yielded 99.7% AUC, with 22.8% predicted as variants of uncertain significance (VUS). Use of only 3 auto-computed evidence statistics yielded 98.6% AUC with 56.0% predicted as VUS, which represented sufficient accuracy to rapidly assign a significant portion of VUS to clinically meaningful classifications. Collectively, our findings support the use of this framework to conduct large-scale variant prioritization using in silico predictors followed by variant prediction and classification with a high degree of predictive accuracy

Soybean sudden death syndrome species diversity within North and South America revealed by multilocus genotyping

Sudden death syndrome (SDS) of soybean has become a serious constraint to the production of this crop in North and South America. Phenotypic and multilocus molecular phylogenetic analyses, as well as pathogenicity experiments, have demonstrated that four morphologically and phylogenetically distinct fusaria can induce soybean SDS. Published molecular diagnostic assays for the detection and identification of these pathogens have reported these pathogens as F. solani, F. solani f. sp. glycines, or F. solani f. sp. phaseoli, primarily because the species limits of these four pathogens were only recently resolved. In light of the recent discovery that soybean SDS and Phaseolus and mung bean root rot (BRR) are caused by four and two distinct species, respectively, multilocus DNA sequence analyses were conducted to assess whether any of the published molecular diagnostic assays were species-specific. Comparative DNA sequence analyses of the soybean SDS and BRR pathogens revealed that highly conserved regions of three loci were used in the design of these assays, and therefore none were species-specific based on our current understanding of species limits within the SDS-BRR clade. Prompted by this finding, we developed a high-throughput multilocus genotyping (MLGT) assay which accurately differentiated the soybean SDS and two closely related Phaseolus and mung BRR pathogens based on nucleotide polymorphism within the nuclear ribosomal intergenic spacer region rDNA and two anonymous intergenic regions designated locus 51 and 96. The single-well diagnostic assay, employing flow cytometry and a novel fluorescent microsphere array, was validated by independent multilocus molecular phylogenetic analysis of a 65 isolate design panel. The MLGT assay was used to reproducibly type a total of 262 soybean SDS and 9 BRR pathogens. The validated MLGT array provides a unique molecular diagnostic for the accurate identification and molecular surveillance of these economically important plant pathogens.Fil: O'Donnell, Kerry. United States Department of Agriculture. Agriculture Research Service; Estados UnidosFil: Sink, Stacy. United States Department of Agriculture. Agriculture Research Service; Estados UnidosFil: Scandiani, María Mercedes. Laboratorio Agrícola Río Paraná; ArgentinaFil: Luque, Alicia. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas; ArgentinaFil: Colletto, Analía. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas; ArgentinaFil: Biasoli, Marisa. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas; ArgentinaFil: Lenzi, Lisandro. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Córdoba. Estación Experimental Agropecuaria Marcos Juárez; ArgentinaFil: Salas, Graciela. Nidera S.A.; ArgentinaFil: González, Victoria. Gobierno de Tucumán. Ministerio de Desarrollo Productivo. Estación Experimental Agroindustrial Obispo Colombres; ArgentinaFil: Ploper, Leonardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Gobierno de Tucumán. Ministerio de Desarrollo Productivo. Estación Experimental Agroindustrial Obispo Colombres; ArgentinaFil: Formento, Norma. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; ArgentinaFil: Pioli, Rosanna Nora. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; ArgentinaFil: Aoki, Takayuki. National Institute of Agrobiological Sciences. Genetic Diversity Department; JapónFil: Yang, X. B.. Iowa State University. Department of Plant Pathology; Estados UnidosFil: Sarver, Brice A. J.. University of Idaho. Department of Biological Sciences; Rusi