scholarly journals Estimating Diversity Through Time Using Molecular Phylogenies: Old and Species-Poor Frog Families are the Remnants of a Diverse Past

2019 ◽  
Author(s):  
O Billaud ◽  
D S Moen ◽  
T L Parsons ◽  
H Morlon
Keyword(s):  
Species Richness ◽  
Probabilistic Approach ◽  
R Package ◽  
Extinction Rate ◽  
Competitive Effects ◽  
Phenotypic Divergence ◽  
Poor Group ◽  
Diversity Dynamics ◽  
Diversity Estimates ◽  
Better Than

Abstract Estimating how the number of species in a given group varied in the deep past is of key interest to evolutionary biologists. However, current phylogenetic approaches for obtaining such estimates have limitations, such as providing unrealistic diversity estimates at the origin of the group. Here, we develop a robust probabilistic approach for estimating diversity through time curves and uncertainty around these estimates from phylogenetic data. We show with simulations that under various realistic scenarios of diversification, this approach performs better than previously proposed approaches. We also characterize the effect of tree size and undersampling on the performance of the approach. We apply our method to understand patterns of species diversity in anurans (frogs and toads). We find that Archaeobatrachia—a species-poor group of old frog clades often found in temperate regions—formerly had much higher diversity and net diversification rate, but the group declined in diversity as younger, nested clades diversified. This diversity decline seems to be linked to a decline in speciation rate rather than an increase in extinction rate. Our approach, implemented in the R package RPANDA, should be useful for evolutionary biologists interested in understanding how past diversity dynamics have shaped present-day diversity. It could also be useful in other contexts, such as for analyzing clade–clade competitive effects or the effect of species richness on phenotypic divergence.

scholarly journals Estimating Diversity Through Time using Molecular Phylogenies: Old and Species-Poor Frog Families are the Remnants of a Diverse Past

2019 ◽  
Author(s):  
O. Billaud ◽  
D. S. Moen ◽  
T. L. Parsons ◽  
H. Morion
Keyword(s):  
Probabilistic Approach ◽  
R Package ◽  
Phylogenetic Comparative Methods ◽  
Extinction Rate ◽  
Competitive Effects ◽  
Phenotypic Divergence ◽  
Poor Group ◽  
Diversity Dynamics ◽  
Diversity Estimates ◽  
Better Than

Estimating how the number of species in a given group varied in the deep past is of key interest to evolutionary biologists. However, current phylogenetic approaches for obtaining such estimates have limitations, such as providing unrealistic diversity estimates at the origin of the group. Here we develop a robust probabilistic approach for estimating Diversity-Through-Time (DTT) curves and uncertainty around these estimates from phylogenetic data. We show with simulations that under various realistic scenarios of diversification, this approach performs better than previously proposed approaches. We also characterize the effect of tree size and undersampling on the performance of the approach. We apply our method to understand patterns of species diversity in anurans (frogs and toads). We find that Archaeobatrachia – a species-poor group of old frog clades often found in temperate regions – formerly had much higher diversity and net diversification rate, but the group declined in diversity as younger, nested clades diversified. This diversity decline seems to be linked to a decline in speciation rate rather than an increase in extinction rate. Our approach, implemented in the R package RPANDA, should be useful for evolutionary biologists interested in understanding how past diversity dynamics have shaped present-day diversity. It could also be useful in other contexts, such as for analyzing clade-clade competitive effects or the effect of species richness on phenotypic divergence. [phylogenetic comparative methods; birth-death models; diversity curves; diversification; extinction; anurans]

Macrofungal diversity of a temperate oak forest: a test of species richness estimators

1999 ◽  
Vol 77 (7) ◽  
pp. 1014-1027 ◽  
Author(s):  
John Paul Schmit ◽  
John F Murphy ◽  
Gregory M Mueller
Keyword(s):  
Species Richness ◽  
Species Diversity ◽  
Deciduous Forest ◽  
Hardwood Forests ◽  
Oak Forest ◽  
Species Richness Estimation ◽  
Indiana Dunes ◽  
Richness Estimators ◽  
Diversity Estimates ◽  
Better Than

Two 0.1-ha plots, each divided into 10 contiguous subplots, were established in a Quercus-dominated deciduous forest in the Indiana Dunes National Lakeshore. Macrofungi were surveyed on these plots at weekly intervals during the fruiting season over 3 years. During this survey 177 species were recorded, including 30 species inhabiting leaf litter, 36 ectomycorrhizal species, 29 non-mycorrhizal soil-inhabiting species, and 79 wood-inhabiting species. This species richness is comparable to, but slightly higher than, that reported by other plot-based studies undertaken in hardwood forests. We compared the ability of seven species-richness estimation techniques to determine the true species richness on these plots. While some estimators performed better than others, in general the estimations were too low based on the following year's data and were not consistent from year to year. We found some evidence of spatial autocorrelation of communities of fungi found in adjacent subplots. This indicates that the benefit of using contiguous subplots to increase the homogeneity of the area sampled needs to be balanced against the possibility of underestimating the species richness of an area because of spatial autocorrelation.Key words: macrofungi, species diversity, diversity estimates, Indiana Dunes.

scholarly journals ACDC: Analysis of Congruent Diversification Classes

2022 ◽  
Author(s):  
Sebastian Hoehna ◽  
Bjoern Tore Kopperud ◽  
Andrew F Magee
Keyword(s):  
Congruence Class ◽  
R Package ◽  
Rate Function ◽  
Diversification Rate ◽  
Extinction Rate ◽  
Diversification Rates ◽  
Common Features ◽  
Alternative Hypotheses ◽  
Rate Functions ◽  
Congruence Classes

Diversification rates inferred from phylogenies are not identifiable. There are infinitely many combinations of speciation and extinction rate functions that have the exact same likelihood score for a given phylogeny, building a congruence class. The specific shape and characteristics of such congruence classes have not yet been studied. Whether speciation and extinction rate functions within a congruence class share common features is also not known. Instead of striving to make the diversification rates identifiable, we can embrace their inherent non-identifiable nature. We use two different approaches to explore a congruence class: (i) testing of specific alternative hypotheses, and (ii) randomly sampling alternative rate function within the congruence class. Our methods are implemented in the open-source R package ACDC (https://github.com/afmagee/ACDC). ACDC provides a flexible approach to explore the congruence class and provides summaries of rate functions within a congruence class. The summaries can highlight common trends, i.e. increasing, flat or decreasing rates. Although there are infinitely many equally likely diversification rate functions, these can share common features. ACDC can be used to assess if diversification rate patterns are robust despite non-identifiability. In our example, we clearly identify three phases of diversification rate changes that are common among all models in the congruence class. Thus, congruence classes are not necessarily a problem for studying historical patterns of biodiversity from phylogenies.

scholarly journals optimalFlow: optimal transport approach to flow cytometry gating and population matching

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Eustasio del Barrio ◽  
Hristo Inouzhe ◽  
Jean-Michel Loubes ◽  
Carlos Matrán ◽  
Agustín Mayo-Íscar
Keyword(s):  
Flow Cytometry ◽  
Supervised Learning ◽  
Optimal Transport ◽  
Cell Types ◽  
R Package ◽  
Supervised Machine Learning ◽  
Intrinsic Variability ◽  
Flow Cytometry Data ◽  
Different Characteristics ◽  
Better Than

Abstract Background Data obtained from flow cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. The use of different settings for measurement, the variation of the conditions during experiments and the different types of flow cytometers are some of the technical causes of variability. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. The present work is conceived as a combination of strategies to facilitate the task of supervised gating. Results We propose optimalFlowTemplates, based on a similarity distance and Wasserstein barycenters, which clusters cytometries and produces prototype cytometries for the different groups. We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We also present optimalFlowClassification, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that this procedure can outperform state of the art techniques in the proposed datasets. Our code is freely available as optimalFlow, a Bioconductor R package at https://bioconductor.org/packages/optimalFlow. Conclusions optimalFlowTemplates + optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. Our main innovation is the methodology itself and the optimal transport techniques that we apply to flow cytometry analysis.

On the Optimal Hyperparameter Behavior in Bayesian Clustering

2015 ◽  
Vol 19 (6) ◽  
pp. 818-824
Author(s):  
Keisuke Yamazaki ◽  
Keyword(s):  
Prior Distribution ◽  
Evaluation Method ◽  
Probabilistic Approach ◽  
Expectation Maximization Algorithm ◽  
Parametric Models ◽  
Likelihood Method ◽  
Bayesian Clustering ◽  
Mixture Of Gaussian ◽  
Theoretical Accuracy ◽  
Better Than

In a probabilistic approach to cluster analysis, parametric models, such as a mixture of Gaussian distributions, are often used. Since the parameter is unknown, it is necessary to estimate both the parameter and the labels of the clusters. Recently, the statistical properties of Bayesian clustering have been studied. The theoretical accuracy of the label estimation has been analyzed, and it has been found to be better than the maximum-likelihood method, which is based on the expectation-maximization algorithm. However, the effect of a prior distribution on the clustering result remains unknown. The prior distribution has the parameter, which is the hyperparameter. In the present paper, we theoretically and experimentally investigate the behavior of the optimal hyperparameter, and we propose an evaluation method for the clustering result, based on the prior optimization.

scholarly journals Individual-level trait diversity predicts phytoplankton community properties better than species richness or evenness

2017 ◽  
Vol 12 (2) ◽  
pp. 356-366 ◽  
Author(s):  
Simone Fontana ◽  
Mridul Kanianthara Thomas ◽  
Mirela Moldoveanu ◽  
Piet Spaak ◽  
Francesco Pomati
Keyword(s):  
Species Richness ◽  
Phytoplankton Community ◽  
Individual Level ◽  
Trait Diversity ◽  
Better Than

scholarly journals Variance Adaptive Shrinkage (vash): Flexible Empirical Bayes estimation of variances

2016 ◽  
Author(s):  
Mengyin Lu ◽  
Matthew Stephens
Keyword(s):  
Gamma Distribution ◽  
Empirical Bayes ◽  
Variance Estimation ◽  
R Package ◽  
Bayes Estimation ◽  
Inverse Gamma Distribution ◽  
Inverse Gamma ◽  
Tissues Expression ◽  
Flexible Model ◽  
Better Than

AbstractMotivationWe consider the problem of estimating variances on a large number of “similar” units, when there are relatively few observations on each unit. This problem is important in genomics, for example, where it is often desired to estimate variances for thousands of genes (or some other genomic unit) from just a few measurements on each. A common approach to this problem is to use an Empirical Bayes (EB) method that assumes the variances among genes follow an inverse-gamma distribution. Here we describe a more flexible EB method, whose main assumption is that the distribution of the variances (or, as an alternative, the precisions) is unimodal.ResultsWe show that this more flexible assumption provides competitive performance with existing methods when the variances truly come from an inverse-gamma distribution, and can outperform them when the distribution of the variances is more complex. In analyses of several human gene expression datasets from the Genotype Tissues Expression (GTEx) consortium, we find that our more flexible model often fits the data appreciably better than the single inverse gamma distribution. At the same time we find that, for variance estimation, the differences between methods is often small, suggesting that the simpler methods will often suffice in practice.AvailabilityOur methods are implemented in an R package vashr available from http://github.com/mengyin/vashr.

scholarly journals Statistical Reporting Inconsistencies in Experimental Philosophy

2017 ◽  
Author(s):  
Matteo Colombo ◽  
Georgi Duev ◽  
Michele B. Nuijten ◽  
Jan Sprenger
Keyword(s):  
Null Hypothesis ◽  
Experimental Philosophy ◽  
R Package ◽  
Experimental Methods ◽  
Point Of View ◽  
Behavioral Sciences ◽  
Null Hypothesis Significance Testing ◽  
Psychological Science ◽  
Philosophical Questions ◽  
Better Than

Experimental philosophy (x-phi) is a young field of research in the intersection of philosophy and psychology. It aims to make progress on philosophical questions by using experimental methods traditionally associated with the psychological and behavioral sciences, such as null hypothesis significance testing (NHST). Motivated by recent discussions about a methodological crisis in the behavioral sciences, questions have been raised about the methodological standards of x-phi. Here, we focus on one aspect of this question, namely the rate of inconsistencies in statistical reporting. Previous research has examined the extent to which published articles in psychology and other behavioral sciences present statistical inconsistencies in reporting the results of NHST. In this study, we used the R package statcheck to detect statistical inconsistencies in x-phi, and compared rates of inconsistencies in psychology and philosophy. We found that rates of inconsistencies in x-phi are lower than in the psychological and behavioral sciences. From the point of view of statistical reporting consistency, x-phi seems to do no worse, and perhaps even better, than psychological science.

scholarly journals normGAM: an R package to remove systematic biases in genome architecture mapping data

BMC Genomics ◽  
2019 ◽  
Vol 20 (S12) ◽  
Author(s):  
Tong Liu ◽  
Zheng Wang
Keyword(s):  
Fragment Length ◽  
R Package ◽  
Genome Architecture ◽  
Systematic Bias ◽  
Length Bias ◽  
Detection Frequency ◽  
Normalization Methods ◽  
New Type ◽  
Systematic Biases ◽  
Better Than

Abstract Background The genome architecture mapping (GAM) technique can capture genome-wide chromatin interactions. However, besides the known systematic biases in the raw GAM data, we have found a new type of systematic bias. It is necessary to develop and evaluate effective normalization methods to remove all systematic biases in the raw GAM data. Results We have detected a new type of systematic bias, the fragment length bias, in the genome architecture mapping (GAM) data, which is significantly different from the bias of window detection frequency previously mentioned in the paper introducing the GAM method but is similar to the bias of distances between restriction sites existing in raw Hi-C data. We have found that the normalization method (a normalized variant of the linkage disequilibrium) used in the GAM paper is not able to effectively eliminate the new fragment length bias at 1 Mb resolution (slightly better at 30 kb resolution). We have developed an R package named normGAM for eliminating the new fragment length bias together with the other three biases existing in raw GAM data, which are the biases related to window detection frequency, mappability, and GC content. Five normalization methods have been implemented and included in the R package including Knight-Ruiz 2-norm (KR2, newly designed by us), normalized linkage disequilibrium (NLD), vanilla coverage (VC), sequential component normalization (SCN), and iterative correction and eigenvector decomposition (ICE). Conclusions Based on our evaluations, the five normalization methods can eliminate the four biases existing in raw GAM data, with VC and KR2 performing better than the others. We have observed that the KR2-normalized GAM data have a higher correlation with the KR-normalized Hi-C data on the same cell samples indicating that the KR-related methods are better than the others for keeping the consistency between the GAM and Hi-C experiments. Compared with the raw GAM data, the normalized GAM data are more consistent with the normalized distances from the fluorescence in situ hybridization (FISH) experiments. The source code of normGAM can be freely downloaded from http://dna.cs.miami.edu/normGAM/.

Taxonomic evolution in North American Neogene horses (subfamily Equinae): the rise and fall of an adaptive radiation

Paleobiology ◽  
1993 ◽  
Vol 19 (2) ◽  
pp. 216-234 ◽  
Author(s):  
Richard C. Hulbert
Keyword(s):  
Species Richness ◽  
Steady State ◽  
Adaptive Radiation ◽  
Middle Miocene ◽  
Extinction Rate ◽  
Extinction Rates ◽  
Speciation Rates ◽  
History Of ◽  
Very High ◽  
Radiation Phase

The 18 m.y. history of the subfamily Equinae (exclusive of Archaeohippus and “Parahippus”) in North America consisted of a 3-m.y. radiation phase, a 9-m.y. steady-state diversity phase, and a 6-m.y. reduction phase. During the steady-state phase, species richness varied between 14 and 20, with two maxima at about 13.5 and 6.5 Ma. Species richness of the tribes Hipparionini and Equini was about equal through the middle Miocene, but hipparionines consistently had more species in the late Miocene and early Pliocene. Overall mean species duration was 3.2 m.y. (n = 50), or an average extinction rate of 0.31 m.y.-1 During the radiation phase, speciation rates were very high (0.5 to 1.4 m.y.-1), while extinction rates were low (<0.10 m.y.-1). Speciation and extinction rates both averaged about 0.15 m.y.-1 during the steady-state phase, with extinction rates having more variation. Extinction rates increased fourfold during the reduction phase, while speciation rates declined slightly. Late Hemphillian extinctions affected both tribes severely, not just the three-toed hipparionines, and were correlated with global climatic change.

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