The ancestral population size conditioned on the reconstructed phylogenetic tree with occurrence data

Manceau, Marc; Gupta, Ankit; Vaughan, Timothy G.; Stadler, Tanja

doi:10.1101/755561

Cited by 3 publications

(5 citation statements)

References 22 publications

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“…We hope that our analysis here will suggest directions for a further substantiating of the framework. The approach taken by Manceau et al (2019) may be promising, as it also provides numerical evidence for the correctness of the framework in the diversitydependent case. Most existing macroevolutionary models rely on the hypothesis that the subcomponents of trees do not interact (and one can thus apply a breaking-the-tree approach, as in Nee et al 1994, p. 308), therefore letting the likelihood be a factorization of terms that comes independently from the tree's edges and nodes.…”

Section: Discussionmentioning

confidence: 99%

Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models

2020

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Molecular phylogenies have been increasingly recognized as an important source of information on species diversification. For many models of macroevolution, analytical likelihood formulas have been derived to infer macroevolutionary parameters from phylogenies. A few years ago, a general framework to numerically compute such likelihood formulas was proposed, which accommodates models that allow speciation and/or extinction rates to depend on diversity. This framework calculates the likelihood as the probability of the diversification process being consistent with the phylogeny from the root to the tips. However, while some readers found the framework presented in Etienne et al. (Proc R Soc Lond B Biol Sci 279(1732):1300-1309, 2012) convincing, others still questioned it (personal communication), despite numerical evidence that for special cases the framework yields the same (i.e., within double precision) numerical value for the likelihood as analytical formulas do that were independently derived for these special cases. Here we prove analytically that the likelihoods calculated in the new framework are correct for all special cases with known analytical likelihood formula. Our results thus add substantial mathematical support for the overall coherence of the general framework.

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Section: Discussionmentioning

confidence: 99%

Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models

2020

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“…Almost all these methods also require estimating diversification rates (e.g. by maximum likelihood; but see Manceau et al, 2019) prior to computing the curves, while ours estimate diversification rates and diversity jointly along the MCMC. Our implementation thus allows estimating diversity-through-time curves while accounting for heterogeneous speciation rates among lineages and uncertainty in model parameters.…”

Section: Discussionmentioning

confidence: 99%

“…This can be useful to reconstruct past dynamics, as we have illustrated here with diversity-through-time and rate-through-time curves; we show that these curves are well estimated for most of our simulated trees. Other methods that allow the estimation of diversity-through-time curves from reconstructed phylogenies assume that diversification rates are homogeneous across lineages (Etienne et al, 2012; Manceau et al, 2019), or require an a priori specification of nodes in the phylogeny where rate shifts occurred (Morlon et al, 2011; Billaud et al, 2020). Almost all these methods also require estimating diversification rates (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…This dramatically reduces the computational cost of ClaDS, because simulation is fast and the likelihood of the enhanced tree can be quickly computed analytically. In addition to making ClaDS much more computationally efficient, the DA implementation outputs distributions of the enhanced data; this provides estimates than can otherwise be hard to obtain, such as estimates of diversity through time (Morlon et al, 2011; Etienne et al, 2012; Manceau et al, 2019; Billaud et al, 2020), or of speciation rates through time. We test our new implementation of ClaDS using simulations and illustrate its application by fitting ClaDS to the entire bird radiation.…”

Section: Introductionmentioning

confidence: 99%

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Fast and accurate estimation of species-specific diversification rates using data augmentation

Maliet

Morlon

2020

Preprint

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Diversification rates vary across species as a response to various factors, including environmental conditions and species-specific features. Phylogenetic models that allow accounting for and quantifying this heterogeneity in diversification rates have proven particularly useful for understanding clades diversification. Recently, we introduced the cladogenetic diversification rate shift model (ClaDS), which allows inferring subtle rate variations across lineages. Here we present a new inference technique for this model that considerably reduces computation time through the use of data augmentation and provide an implementation of this method in Julia. In addition to drastically reducing computation time, this new inference approach provides a posterior distribution of the augmented data, that is the tree with extinct and unsampled lineages as well as associated diversification rates. In particular, this allows extracting the distribution through time of both the mean rate and the number of lineages. We assess the statistical performances of our approach using simulations and illustrate its application on the entire bird radiation.

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“…An analogous approach can be used to coherently specify fossil data for molecular clock calibration Heath & Moore, 2014). This approach and others have been modelled, but not applied in phylodynamics hitherto (Gupta et al, 2020;Manceau et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Infectious disease phylodynamics with occurrence data

Featherstone

Giallonardo²,

Holmes

et al. 2019

Preprint

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Genomic surveillance is increasingly common for infectious pathogens. Phylodynamic models can take advantage of pathogen genome sequence data to infer epidemiological dynamics, such as those based on the exponential growth coalescent and the birth-death process. Here we investigate the potential of including case notification data without associated genome sequences in such phylodynamic analyses. Using simulations, we demonstrate that birth-death phylodynamic models can capitalise on notification data to eliminate bias in estimates of the basic reproductive number, R0, particularly when the sampling rate varies over time. In addition, an analysis of data collected from the 2009 pandemic H1N1 influenza virus demonstrates that using only samples from the prevalence peak results in biased estimates of the reproductive number over time, whereas using case notification data has a comparable accuracy to that achieved when using genome samples throughout the duration of the pandemic.

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The ancestral population size conditioned on the reconstructed phylogenetic tree with occurrence data

Cited by 3 publications

References 22 publications

Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models

Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models

Fast and accurate estimation of species-specific diversification rates using data augmentation

Infectious disease phylodynamics with occurrence data

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