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Steel, M.

Publications and source records attributed to Steel, M..

20 records · Page 2Linked to original sources

Swapping Birth and Death: Symmetries and Transformations in Phylodynamic Models

Stochastic birth-death models provide the foundation for studying and simulating evolutionary trees in phylodynamics. A curious feature of such models is that they exhibit fundamental symmetries when the birth and death rates are interchanged. In this paper, we explain and formally derive these transformational symmetries. We also show that these transformational symmetries (encoded in algebraic identities) are preserved even when taxa at the present are sampled with some probability. However, these extended symmetries require the death rate parameter to sometimes take a negative value. In the last part of this paper, we describe the relevance of these transformations and their application to computational phylodynamics, particularly to maximum likelihood and Bayesian inference methods, as well as to model selection. Phylodynamics, phylogenetics, speciation-extinction models, birth-death models, algebraic symmetries, maximum likelihood, Bayesian inference

evolutionary biology↗

Evolutionary Isolation And Phylogenetic Diversity Loss Under Random Extinction Events

The extinction of species at the present leads to the loss of phylogenetic diversity (PD) from the evolutionary tree in which these species lie. Prior to extinction, the total PD present can be divided up among the species in various ways using measures of evolutionary isolation (such as fair proportion and equal splits). However, the loss of PD when certain combinations of species become extinct can be either larger or smaller than the cumulative loss of the isolation values associated with the extinct species. In this paper, we show that for trees generated under neutral evolutionary models, the loss of PD under a null model of random extinction at the present can be predicted from the loss of the cumulative isolation values, by applying a non-linear transformation that is independent of the tree. Moreover, the error in the prediction provably converges to zero as the size of the tree grows, with simulations showing good agreement even for moderate sized trees (n = 64).

evolutionary biology↗