The state-dependent speciation and extinction (SSE) models have recently been criticized due to their high rates of "false positive" results. Many researchers have advocated avoiding SSE models in favor of other "nonparametric" or "semiparametric" approaches. The hidden Markov modeling (HMM) approach provides a partial solution to the issues of model adequacy detected with SSE models. The inclusion of "hidden states" can account for rate heterogeneity observed in empirical phylogenies and allows for reliable detection of state-dependent diversification or diversification shifts independent of the trait of interest. However, the adoption of HMM has been hampered by the interpretational challenges of what exactly a "hidden state" represents, which we clarify herein. We show that HMMs in combination with a model-averaging approach naturally account for hidden traits when examining the meaningful impact of a suspected "driver" of diversification. We also extend the HMM to the geographic state-dependent speciation and extinction (GeoSSE) model. We test the efficacy of our "GeoHiSSE" extension with both simulations and an empirical dataset. On the whole, we show that hidden states are a general framework that can distinguish heterogeneous effects of diversification attributed to a focal character.
Most existing methods for modeling trait evolution are univariate, although researchers are often interested in investigating evolutionary patterns and processes across multiple traits. Principal components analysis (PCA) is commonly used to reduce the dimensionality of multivariate data so that univariate trait models can be fit to individual principal components. The problem with using standard PCA on phylogenetically structured data has been previously pointed out yet it continues to be widely used in the literature. Here we demonstrate precisely how using standard PCA can mislead inferences: The first few principal components of traits evolved under constant-rate multivariate Brownian motion will appear to have evolved via an "early burst" process. A phylogenetic PCA (pPCA) has been proprosed to alleviate these issues. However, when the true model of trait evolution deviates from the model assumed in the calculation of the pPCA axes, we find that the use of pPCA suffers from similar artifacts as standard PCA. We show that data sets with high effective dimensionality are particularly likely to lead to erroneous inferences. Ultimately, all of the problems we report stem from the same underlying issue--by considering only the first few principal components as univariate traits, we are effectively examining a biased sample of a multivariate pattern. These results highlight the need for truly multivariate phylogenetic comparative methods. As these methods are still being developed, we discuss potential alternative strategies for using and interpreting models fit to univariate axes of multivariate data.
The large Neotropical family Gonyleptidae comprises nearly 820 species divided into 16 subfamilies. The majority of publications on harvestman ecology, behaviour and scent gland secretion chemistry have focused on this family. We used the information available in the literature and combined it with an intensive search for ecological, behavioural and chemical data to infer the phylogeny of the Gonyleptidae. We included 28 species belonging to 14 of the 16 gonyleptid subfamilies in the ingroup and four species belonging to the families Cosmetidae, Stygnidae and Manaosbiidae in the outgroup. We performed the analyses using equally weighted characters and coded 63 characters comprising 153 states, which makes this the largest non‐morphological, non‐molecular phylogenetic data matrix published to date. We obtained five most parsimonious trees, and the strict consensus resulted in six collapsed nodes. The results show that the monophyly of Gonyleptidae is equivocal because Metasarcinae is placed at a basal polytomy with the outgroups Cosmetidae and Stygnidae. Gonyleptinae, Pachylinae and Progonyleptoidellinae are polyphyletic groups, but the remaining subfamilies are monophyletic and have several synapomorphies. Based on the resulting topology, we discuss the performance of ecological, behavioural and chemical characters, and map a selected set of characters to discuss their evolutionary patterns in the family.
Abstract1. Evolutionary integration occurs when two or more phenotypes evolve in a correlated fashion. Correlated evolution among traits can happen due to genetic constraints, ontogeny, and selection and have an important impact on the trajectory of phenotypic evolution. Phylogenetic trees can be used to study such pattern on macroevolutionary time scales by estimating the strength of evolutionary covariance among traits through time and across clades. However, only few applications implement models to conduct comparative analyses of evolutionary integration.2. We introduce a Bayesian Markov chain Monte Carlo approach to estimate the evolutionary correlation among two or more traits using the evolutionary rate matrix (R). R is a covariance matrix that represents both the rates of evolution of each trait and the structure of evolutionary correlation among traits.3. Here, we present the R package ratematrix, a resource to test hypotheses of evolutionary integration using multivariate data and phylogenetic trees.ratematrix provides a flexible framework allowing for any number of evolutionary rate matrix regimes fitted to the same phylogenetic tree and it incorporates the uncertainty associated with parameter estimates, ancestral state reconstruction and phylogenetic estimation in the analyses.4. The ratematrix package uses a novel pruning algorithm that significantly improve computational time. We also provide specific functions that facilitate users to conduct long MCMC analysis when computational resources are limited. K E Y W O R D SAnolis, comparative methods, evolutionary integration, evolutionary rates, modularity
Despite claims that genitalia are among the fastest evolving phenotypes, few studies have tested this trend in a quantitative and phylogenetic framework. In systems where male and female genitalia coevolve, there is a growing effort to explore qualitative patterns of evolution and their underlying mechanisms, but the temporal aspect remains overlooked. An intriguing question is how fast male and female genitalia may change in a coevolutionary scenario. Here, we apply a series of comparative phylogenetic analyses to reveal a scenario of correlated evolution and to investigate how fast male and female external, nonhomologous and functionally integrated genitalia change in a group of stink bugs. We report three findings: the female gonocoxite 8 and the male pygophore showed a clear pattern of correlated evolution, both genitalia were estimated to evolve much faster than nongenital traits, and rates of evolution of the male genitalia were twice as fast as the female genitalia. Our results corroborate the widely held view that male genitalia evolve fast and add to the scarce evidence for rapidly evolving female genitalia. Different rates of evolution exhibited by males and females suggest either distinct forms or strengths of selection, despite their tight functional integration and coevolution. The morphological characteristics of this coevolutionary trend are more consistent with a cooperative adjustment of the genitalia, suggesting a scenario of female choice, morphological accommodation, lock-and-key or some combination of the three.
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