The Effects of Sampling Location and Predictor Point Estimate Certainty on Posterior Support in Bayesian Phylogeographic Generalized Linear Models

Abstract The use of generalized linear models in Bayesian phylogeography has enabled researchers to simultaneously reconstruct the spatiotemporal history of a virus and quantify the contribution of predictor variables to that process. However, little is known about the sensitivity of this method to...

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Autores principales: Daniel Magee, Jesse E. Taylor, Matthew Scotch
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Lenguaje:EN
Publicado: Nature Portfolio 2018
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Acceso en línea:https://doaj.org/article/79ea25c0e10c4e058f1208e66440e22e
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spelling oai:doaj.org-article:79ea25c0e10c4e058f1208e66440e22e2021-12-02T15:09:01ZThe Effects of Sampling Location and Predictor Point Estimate Certainty on Posterior Support in Bayesian Phylogeographic Generalized Linear Models10.1038/s41598-018-24264-82045-2322https://doaj.org/article/79ea25c0e10c4e058f1208e66440e22e2018-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-24264-8https://doaj.org/toc/2045-2322Abstract The use of generalized linear models in Bayesian phylogeography has enabled researchers to simultaneously reconstruct the spatiotemporal history of a virus and quantify the contribution of predictor variables to that process. However, little is known about the sensitivity of this method to the choice of the discrete state partition. Here we investigate this question by analyzing a data set containing 299 sequences of the West Nile virus envelope gene sampled in the United States and fifteen predictors aggregated at four spatial levels. We demonstrate that although the topology of the viral phylogenies was consistent across analyses, support for the predictors depended on the level of aggregation. In particular, we found that the variance of the predictor support metrics was minimized at the most precise level for several predictors and maximized at more sparse levels of aggregation. These results suggest that caution should be taken when partitioning a region into discrete locations to ensure that interpretable, reproducible posterior estimates are obtained. These results also demonstrate why researchers should use the most precise discrete states possible to minimize the posterior variance in such estimates and reveal what truly drives the diffusion of viruses.Daniel MageeJesse E. TaylorMatthew ScotchNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-12 (2018)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Daniel Magee
Jesse E. Taylor
Matthew Scotch
The Effects of Sampling Location and Predictor Point Estimate Certainty on Posterior Support in Bayesian Phylogeographic Generalized Linear Models
description Abstract The use of generalized linear models in Bayesian phylogeography has enabled researchers to simultaneously reconstruct the spatiotemporal history of a virus and quantify the contribution of predictor variables to that process. However, little is known about the sensitivity of this method to the choice of the discrete state partition. Here we investigate this question by analyzing a data set containing 299 sequences of the West Nile virus envelope gene sampled in the United States and fifteen predictors aggregated at four spatial levels. We demonstrate that although the topology of the viral phylogenies was consistent across analyses, support for the predictors depended on the level of aggregation. In particular, we found that the variance of the predictor support metrics was minimized at the most precise level for several predictors and maximized at more sparse levels of aggregation. These results suggest that caution should be taken when partitioning a region into discrete locations to ensure that interpretable, reproducible posterior estimates are obtained. These results also demonstrate why researchers should use the most precise discrete states possible to minimize the posterior variance in such estimates and reveal what truly drives the diffusion of viruses.
format article
author Daniel Magee
Jesse E. Taylor
Matthew Scotch
author_facet Daniel Magee
Jesse E. Taylor
Matthew Scotch
author_sort Daniel Magee
title The Effects of Sampling Location and Predictor Point Estimate Certainty on Posterior Support in Bayesian Phylogeographic Generalized Linear Models
title_short The Effects of Sampling Location and Predictor Point Estimate Certainty on Posterior Support in Bayesian Phylogeographic Generalized Linear Models
title_full The Effects of Sampling Location and Predictor Point Estimate Certainty on Posterior Support in Bayesian Phylogeographic Generalized Linear Models
title_fullStr The Effects of Sampling Location and Predictor Point Estimate Certainty on Posterior Support in Bayesian Phylogeographic Generalized Linear Models
title_full_unstemmed The Effects of Sampling Location and Predictor Point Estimate Certainty on Posterior Support in Bayesian Phylogeographic Generalized Linear Models
title_sort effects of sampling location and predictor point estimate certainty on posterior support in bayesian phylogeographic generalized linear models
publisher Nature Portfolio
publishDate 2018
url https://doaj.org/article/79ea25c0e10c4e058f1208e66440e22e
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