scholarly journals DETECTING PHYLODIVERSITY-DEPENDENT DIVERSIFICATION WITH A GENERAL PHYLOGENETIC INFERENCE FRAMEWORK

2021 ◽  
Author(s):  
Francisco Richter ◽  
Ernst C. Wit ◽  
Rampal S. Etienne ◽  
Thijs Janzen ◽  
Hanno Hildenbrandt
Keyword(s):  
Species Richness ◽  
Expectation Maximization ◽  
Phylogenetic Diversity ◽  
R Package ◽  
Inference Method ◽  
Likelihood Methods ◽  
Species Diversification ◽  
As Species ◽  
Monte Carlo Expectation Maximization ◽  
Phylogenetic Divergence

Diversity-dependent diversification models have been extensively used to study the effect of ecological limits and feedback of community structure on species diversification processes, such as speciation and extinction. Current diversity-dependent diversification models characterise ecological limits by carrying capacities for species richness. Such ecological limits have been justified by niche filling arguments: as species diversity increases, the number of available niches for diversification decreases. However, as species diversify they may diverge from one another phenotypically, which may open new niches for new species. Alternatively, this phenotypic divergence may not affect the species diversification process or even inhibit further diversification. Hence, it seems natural to explore the consequences of phylogenetic diversity-dependent (or phylodiversity-dependent) diversification. Current likelihood methods for estimating diversity-dependent diversification parameters cannot be used for this, as phylodiversity is continuously changing as time progresses and species form and become extinct. Here, we present a new method based on Monte Carlo Expectation-Maximization (MCEM), designed to perform statistical inference on a general class of species diversification models and implemented in the R package emphasis. We use the method to fit phylodiversity-dependent diversification models to 14 phylogenies, and compare the results to the fit of a richness-dependent diversification model. We find that in a number of phylogenies, phylogenetic divergence indeed spurs speciation even though species richness reduces it. Not only do we thus shine a new light on diversity-dependent diversification, we also argue that our inference framework can handle a large class of diversification models for which currently no inference method exists.

scholarly journals Avoiding potential biases in ses.PD estimations with the Picante software package

2019 ◽  
Author(s):  
Rafael Molina-Venegas
Keyword(s):  
Species Richness ◽  
Software Package ◽  
Phylogenetic Diversity ◽  
Phylogenetic Analyses ◽  
R Package ◽  
List Type ◽  
Routine Procedure ◽  
Phylogenetic Structure ◽  
Simulation Exercise ◽  
Simple Simulation

AbstractFaith’s phylogenetic diversity (PD) is one of the most widespread used indices of phylogenetic structure in the eco-phylogenetic literature. The metric became notably popular with the publication of the function pd as part of the Picante R package, which is nowadays a reference software for phylogenetic analyses.Because PD is not statistically independent of species richness, the routine procedure is to standardize the observed PD values for unequal richness across samples. The function ses.pd, which is also implemented in the Picante R package, is the reference function to conduct such standardization.Unfortunately, I have detected an anomaly in the function that may result in biased estimations of standardized PD values, particularly in communities with low species richness (i.e. less than four species) and unbalanced phylogenies.I conduct a simple simulation exercise to illustrate the issue and propose two alternative and easy to implement solutions to go around the problem.

scholarly journals Traits, trees and taxa: global dimensions of biodiversity in mammals

2012 ◽  
Vol 279 (1749) ◽  
pp. 4997-5003 ◽  
Author(s):  
Shan Huang ◽  
Patrick R. Stephens ◽  
John L. Gittleman
Keyword(s):  
Species Richness ◽  
Phylogenetic Diversity ◽  
Functional Trait ◽  
Global Scale ◽  
Conservation Value ◽  
Strongly Correlated ◽  
As Species ◽  
Trait Variance ◽  
Potential Losses ◽  
Functional Trait Diversity

Measures of biodiversity encompass variation along several dimensions such as species richness (SR), phylogenetic diversity (PD) and functional/trait diversity (TD). At the global scale, it is widely recognized that SR and PD are strongly correlated, but the extent to which either tends to capture variation in TD is unclear. Here, we assess relationships among PD, SR and TD for a number of traits both across clades and regional assemblages of mammals. We also contrast results using two different measures of TD, trait variance and a new measure we refer to as trait bin filling (the number of orders of magnitude of variation that contain at least one species). When TD is defined as trait variance, PD is a much stronger correlate of TD than SR across clades, consistent with hypotheses about the conservation value of PD. However, when TD is defined as bin filling, PD and SR show similar correlations with TD across clades and space. We also investigate potential losses of SR, PD and TD if species that are currently threatened were to go extinct, and find that threatened PD is often a similar predictor of threatened TD as SR.

scholarly journals Flower‐visitor communities of an arcto‐alpine plant—Global patterns in species richness, phylogenetic diversity and ecological functioning

2018 ◽  
Vol 28 (2) ◽  
pp. 318-335 ◽  
Author(s):  
Mikko Tiusanen ◽  
Tea Huotari ◽  
Paul D. N. Hebert ◽  
Tommi Andersson ◽  
Ashley Asmus ◽  
...  
Keyword(s):  
Species Richness ◽  
Phylogenetic Diversity ◽  
Alpine Plant ◽  
Flower Visitor ◽  
Global Patterns ◽  
Ecological Functioning

scholarly journals Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 373
Author(s):  
Branislav Panić ◽  
Jernej Klemenc ◽  
Marko Nagode
Keyword(s):  
Em Algorithm ◽  
Density Estimation ◽  
Mixture Model ◽  
Expectation Maximization ◽  
State Of The Art ◽  
R Package ◽  
Likelihood Estimator ◽  
Local Optima ◽  
The Em Algorithm ◽  
Initialization Algorithm

A commonly used tool for estimating the parameters of a mixture model is the Expectation–Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density–estimation datasets and image–segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.

Metabolic asymmetry and the global diversity of marine predators

Science ◽  
2019 ◽  
Vol 363 (6425) ◽  
pp. eaat4220 ◽  
Author(s):  
John M. Grady ◽  
Brian S. Maitner ◽  
Ara S. Winter ◽  
Kristin Kaschner ◽  
Derek P. Tittensor ◽  
...  
Keyword(s):  
Species Richness ◽  
Spatial Patterns ◽  
Marine Mammals ◽  
Phylogenetic Diversity ◽  
Latitudinal Gradient ◽  
Latitudinal Gradients ◽  
Competitive Interactions ◽  
Marine Predators ◽  
Theoretical Predictions ◽  
The Tropics

Species richness of marine mammals and birds is highest in cold, temperate seas—a conspicuous exception to the general latitudinal gradient of decreasing diversity from the tropics to the poles. We compiled a comprehensive dataset for 998 species of sharks, fish, reptiles, mammals, and birds to identify and quantify inverse latitudinal gradients in diversity, and derived a theory to explain these patterns. We found that richness, phylogenetic diversity, and abundance of marine predators diverge systematically with thermoregulatory strategy and water temperature, reflecting metabolic differences between endotherms and ectotherms that drive trophic and competitive interactions. Spatial patterns of foraging support theoretical predictions, with total prey consumption by mammals increasing by a factor of 80 from the equator to the poles after controlling for productivity.

scholarly journals A quantile variant of the expectation–maximization algorithm and its application to parameter estimation with interval data

2018 ◽  
Vol 12 (3) ◽  
pp. 253-272 ◽  
Author(s):  
Chanseok Park
Keyword(s):  
Monte Carlo ◽  
Expectation Maximization ◽  
Likelihood Function ◽  
Expectation Maximization Algorithm ◽  
Parametric Models ◽  
Maximum Likelihood Estimates ◽  
Computational Technique ◽  
Stable Convergence ◽  
Explicit Integration ◽  
Monte Carlo Expectation Maximization

The expectation–maximization algorithm is a powerful computational technique for finding the maximum likelihood estimates for parametric models when the data are not fully observed. The expectation–maximization is best suited for situations where the expectation in each E-step and the maximization in each M-step are straightforward. A difficulty with the implementation of the expectation–maximization algorithm is that each E-step requires the integration of the log-likelihood function in closed form. The explicit integration can be avoided by using what is known as the Monte Carlo expectation–maximization algorithm. The Monte Carlo expectation–maximization uses a random sample to estimate the integral at each E-step. But the problem with the Monte Carlo expectation–maximization is that it often converges to the integral quite slowly and the convergence behavior can also be unstable, which causes computational burden. In this paper, we propose what we refer to as the quantile variant of the expectation–maximization algorithm. We prove that the proposed method has an accuracy of [Formula: see text], while the Monte Carlo expectation–maximization method has an accuracy of [Formula: see text]. Thus, the proposed method possesses faster and more stable convergence properties when compared with the Monte Carlo expectation–maximization algorithm. The improved performance is illustrated through the numerical studies. Several practical examples illustrating its use in interval-censored data problems are also provided.

Elasmobranch diversity, conservation and management along Wadge Bank, South India

2019 ◽  
Author(s):  
K. Karuppasamy ◽  
P. Jawahar ◽  
S. David Kingston ◽  
V. K. Venkataramani ◽  
V. Vidhya
Keyword(s):  
Species Richness ◽  
Phylogenetic Diversity ◽  
South India ◽  
Fish Communities ◽  
Taxonomic Diversity ◽  
Management Measures ◽  
Conservation And Management ◽  
D Value

The study was undertaken to document the elasmobranch diversity and their abundance along Wadge Bank. Species were collected fortnightly during June 2015 to May 2016 from three landing centres viz., Chinnamuttom,Colachel and Vizhinjam of Wadge Bank. A total of 1,575 specimens were collected during the period and 44 species were identified belonging to 8 orders, 13 families and 25 genera. Among the recorded \families,Carcharhinidae is the most dominant family with 12 species. The Colachel landing centre was rich in diversity with 43 species followed by Chinnamuttom 39 species and Vizhinjam26 species. The highest Shannon Weiner diversity (H’ value) was observed at Colachel (4.17) followed by Chinnamuttom (4.11) and Vizhinjam (3.76). The Margalef’s species richness (‘d’) value was assessed at Colachel (4.55) followed by Chinnamuttom (4.01) and Vizhinjam (2.91). The Pielou’s evenness (J’) estimated was 0.7786, 0.7700 and 0.8005respectively, for Chinnamuttom, Colachal and Vizhinjam. The highest taxonomic diversity value was observed at Colachel (60.33) and the lowest during at Vizhinjam (54.08). Among the three landing centres studied, the total phylogenetic diversity (sPhi+) was found to be the lowest at Vizhinjam (940) and highest at Colachel landing centre (1720). Bray Curtis similarities measure was also calculated, fish communities were separated into several clusters based on seasons. The conservation of elasmobranchs and the management measures to be followed along the Wadge Bank is also discussed.

On taxonomic status of two species of orchids (Orchidaceae) from Turkmenistan

Turczaninowia ◽  
2020 ◽  
Vol 23 (4) ◽  
pp. 65-71
Author(s):  
Alexander V. Fateryga ◽  
Alexander V. Pavlenko ◽  
Valentina V. Fateryga
Keyword(s):  
Species Richness ◽  
Taxonomic Status ◽  
As Species

The orchid genera Epipactis Zinn and Ophrys L. are well-known by their complicated taxonomy and extensive debates over species richness within them. These genera are represented in Turkmenistan by two species each. Two of them, namely E. turcomanica K. P. Popov et Neshat. and O. kopetdagensis K. P. Popov et Neshat., were hitherto accepted as species endemic to Turkmenistan. In the present paper, these taxa are synonymized with broadly distributed E. persica (Soó) Hausskn. ex Nannf. and O. oestrifera M. Bieb., respectively. Thus, the genus Epipactis is represented in Turkmenistan by E. persica and E. veratrifolia Boiss. et Hohen., and the genus Ophrys is represented by O. mammosa Desf. and O. oestrifera. There are no species of orchids endemic to Turkmenistan.

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