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4 results for “Ancestral state prediction”
Modeling pulsed evolution and time-independent variation improves the confidence level of ancestral and hidden state predictions
<p><span><span><span><span><span><span><span><span><span><span>Ancestral state reconstruction is not only a fundamental tool for studying trait evolution, but also very useful for predicting the unknown trait values (hidden states) of extant species. A well-known problem in ancestral and hidden state predictions is that the uncertainty associated with predictions can be so large that predictions themselves are of little use. Therefore, for meaningful interpretation of predicted traits and hypothesis testing, it is prudent to accurately assess the uncertainty of the predictions. Commonly used constant-rate Brownian motion (BM) model fails to capture the complexity of tempo and mode of trait evolution in nature, making predictions under the BM model vulnerable to lack-of-fit errors from model misspecification. Using empirical data (mammalian body size and bacterial genome size), we show that the distribution of residual Z-scores under the BM model is neither homoscedastic nor normal as expected. Consequently, the 95% confidence intervals (CIs) of predicted traits are so unreliable that the actual coverage probability ranges from 33% (strongly permissive) to 100% (strongly conservative). Alternative methods such as BayesTraits and StableTraits that allow variable rates in evolution improve the predictions but are computationally expensive. Here we develop RasperGade, a method of ancestral and hidden state prediction that uses the Levy process to explicitly model gradual evolution, pulsed evolution and time-independent variation. Using the same empirical data, we show that RasperGade outperforms both BayesTraits and StableTraits and is orders-of-magnitude faster. Our results suggest that, when predicting the ancestral and hidden states of continuous traits, the tempo and mode of evolution should always be assessed and the quality of confidence estimates should always be examined.</span></span></span></span></span></span></span></span></span></span></p>
Modeling pulsed evolution and time-independent variation improves the confidence level of ancestral and hidden state predictions
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Data from: Predicting the ancestral character changes in a tree is typically easier than predicting the root state
Predicting the ancestral sequences of a group of homologous sequences related by a phylogenetic tree has been the subject of many studies, and numerous methods have been proposed for this purpose. Theoretical results are available that show that when the substitution rate become too large, reconstructing the ancestral state at the tree root is no longer feasible. Here, we also study the reconstruction of the ancestral changes that occurred along the tree edges. We show that, depending on the tree and branch length distribution, reconstructing these changes (i.e. reconstructing the ancestral state of all internal nodes in the tree) may be easier or harder than reconstructing the ancestral root state. However, results from information theory indicate that for the standard Yule tree, the task of reconstructing internal node states remains feasible, even for very high substitution rates. Moreover, computer simulations demonstrate that for more complex trees and scenarios, this result still holds. For a large variety of counting, parsimony-based and likelihood-based methods, the predictive accuracy of a randomly selected internal node in the tree is indeed much higher than the accuracy of the same method when applied to the tree root. Moreover, parsimony- and likelihood-based methods appear to be remarkably robust to sampling bias and model mis-specification.
Data from: Predicting the ancestral character changes in a tree is typically easier than predicting the root state
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