Biology subjects

Rabosky, D. L.

Publications and source records attributed to Rabosky, D. L..

5 recordsLinked to original sources

bioRxiv2020

Macroevolutionary analysis of discrete character evolution using parsimony-informed likelihood

AO_SCPLOWBSTRACTC_SCPLOWRates of character evolution in macroevolutionary datasets are typically estimated by maximizing the likelihood function of a continuous-time Markov chain (CTMC) model of character evolution over all possible histories of character state change, a technique known as maximum average likelihood. An alternative approach is to estimate ancestral character states independently of rates using parsimony and to then condition likelihood-based estimates of transition rates on the resulting ancestor-descendant reconstructions. We use maximum parsimony reconstructions of possible pathways of evolution to implement this alternative approach for single-character datasets simulated on empirical phylogenies using a two-state CTMC. We find that transition rates estimated using parsimonious ancestor-descendant reconstructions have lower mean squared error than transition rates estimated by maximum average likelihood. Although we use a binary state character for exposition, the approach remains valid for an arbitrary number of states. Finally, we show how this method can be used to rapidly and easily detect phylogenetic variation in tempo and mode of character evolution with two empirical examples from squamates. These results highlight the mutually informative roles of parsimony and likelihood when testing hypotheses of character evolution in macroevolution.

evolutionary biology↗

bioRxiv2020

Macroevolutionary analysis of discrete traits with rate heterogeneity

AO_SCPLOWBSTRACTC_SCPLOWOrganismal traits show dramatic variation in phylogenetic patterns of origin and loss across the Tree of Life. Understanding the causes and consequences of this variation depends critically on accounting for heterogeneity in rates of trait evolution among lineages. Here, we describe a method for modeling among-lineage evolutionary rate heterogeneity in a trait with two discrete states. The method assumes that the present-day distribution of a binary trait is shaped by a mixture of stochastic processes in which the rate of evolution varies among lineages in a phylogeny. The number and location of rate changes, which we refer to as rate-shift events, are inferred automatically from the data. Simulations reveal that the method accurately reconstructs rates of trait evolution and ancestral character states even when simulated data violate model assumptions. We apply the method to an empirical dataset of mimetic coloration in snakes and find elevated rates of trait evolution in two clades of harmless snakes that are broadly sympatric with dangerously venomous New World coral snakes, recapitulating an earlier analysis of the same dataset. Although the method performed well on many simulated data sets, we caution that overall power for inferring heterogeneous dynamics of single binary traits is low.

evolutionary biology↗

bioRxiv2019

Complex ecological phenotypes on phylogenetic trees: a hidden Markov model for comparative analysis of multivariate count data

The evolutionary dynamics of complex ecological traits - including multistate representations of diet, habitat, and behavior - remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature and intraspecific variability. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. Thus, a necessary first step when using standard CTMC models is to categorize species into a pre-determined number of ecological states. This approach potentially confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations of resource use into the statistical inference model. The neglect of sampling variation, along with univariate representations of true multivariate phenotypes, potentially leads to the distortion and loss of information, with substantial implications for downstream macroevolutionary analyses. In this study, we develop a hidden Markov model using a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Unlike existing CTMC implementations, states are unobserved probability distributions from which observed data are sampled. Our approach is expressly designed to model ecological traits that are intra-specifically variable and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.

evolutionary biology↗

bioRxiv2018

Phylogenies and diversification rates: variance cannot be ignored

The concept of variance is the foundation of modern statistics; it reflects our awareness that independent samples from a single population or stochastic process can produce a range of outcomes. A recent pair of articles in the journal Evolution abandons the notion of the sample variance and advocates for uncorrected comparisons of numerical point estimates between groups. The articles in question (Meyer and Wiens 2017; Meyer et al., 2018) criticize BAMM, a scientific software program that uses a Bayesian mixture model to estimate rates of evolution from phylogenetic trees. The authors use BAMM to estimate rates from large phylogenies (n > 60 tips) and they apply the method separately to subclades within those phylogenies (median size: n = 3 tips); they find that point estimates of rates differ between these levels and conclude that the method is flawed, but they do not test whether the observed differences are statistically meaningful. There is no consideration of sampling variation and its impact at any level of their analysis. Here, I show that numerical differences across groups that they report are fully explained by high variance in their subclade estimates, which is approximately 55 times greater than the corresponding variance for estimates from large phylogenies. Variance in evolutionary rate estimates - from BAMM and all other methods - is an inverse function of clade size; this variance is extreme for clades with 5 or fewer tips (e.g., 70% of clades in the focal study). The articles in question rely on negative results that are easily explained by low statistical power to reject their preferred null hypothesis, and this low power is a trivial consequence of high variance in their point estimates. By ignoring variance, the testing approach outlined in these articles can be misused to demonstrate that all statistical estimators, including the arithmetic mean, are \"flawed\". I describe additional mathematical and statistical mistakes that render the proposed testing framework invalid on first principles. Evolutionary rates are no different than any other population parameters we might wish to estimate, and biologists should use the training and tools already at their disposal to avoid erroneous results that follow from the neglect of variance.

evolutionary biology↗

bioRxiv2016

A positive association between population genetic differentiation and speciation rates in New World birds

Although an implicit assumption of speciation biology is that population differentiation is an important stage of evolutionary diversification, its true significance remains largely untested. If population differentiation within a species is related to its speciation rate over evolutionary time, the causes of differentiation could also be driving dynamics of organismal diversity across time and space. Alternatively, geographic variants might be short-lived entities with rates of formation that are unlinked to speciation rates, in which case the causes of differentiation would have only ephemeral impacts. Combining population genetics datasets including 17,746 individuals from 176 New World bird species with speciation rates estimated from phylogenetic data, we show that the population differentiation rates within species predict their speciation rates over long timescales. Although relatively little variance in speciation rate is explained by population differentiation rate, the relationship between the two is robust to diverse strategies of sampling and analyzing both population-level and species-level datasets. Population differentiation occurs at least three to five times faster than speciation, suggesting that most populations are ephemeral. Population differentiation and speciation rates are more tightly linked in tropical species than temperate species, consistent with a history of more stable diversification dynamics through time in the Tropics. Overall, our results suggest investigations into the processes responsible for population differentiation can reveal factors that contribute to broad-scale patterns of diversity.

evolutionary biology↗