Hidden Markov models (HMMs) have emerged as an important tool for understanding the evolution of characters that take on discrete states. Their flexibility and biological sensibility make them appealing for many phylogenetic comparative applications. Previously available packages placed unnecessary limits on the number of observed and hidden states that can be considered when estimating transition rates and inferring ancestral states on a phylogeny. To address these issues, we expanded the capabilities of the R package corHMM to handle n‐state and n‐character problems and provide users with a streamlined set of functions to create custom HMMs for any biological question of arbitrary complexity. We show that increasing the number of observed states increases the accuracy of ancestral state reconstruction. We also explore the conditions for when an HMM is most effective, finding that an HMM is an appropriate model when the degree of rate heterogeneity is moderate to high. Finally, we demonstrate the importance of these generalizations by reconstructing the phyllotaxy of the ancestral angiosperm flower. Partially contradicting previous results, we find the most likely state to be a whorled perianth, whorled androecium, whorled gynoecium. The difference between our analysis and previous studies was that our modelling explicitly allowed for the correlated evolution of several flower characters.
1. Hidden Markov models (HMM) have emerged as an important tool for understanding the evolution of characters that take on discrete states. Their flexibility and biological sensibility make them appealing for many phylogenetic comparative applications. 2. Previously available packages placed unnecessary limits on the number of observed and hidden states that can be considered when estimating transition rates and inferring ancestral states on a phylogeny. 3. To address these issues, we expanded the capabilities of the R package corHMM to handle n-state and n-character problems and provide users with a streamlined set of functions to create custom HMMs for any biological question of arbitrary complexity. 4. We show that increasing the number of observed states increases the accuracy of ancestral state reconstruction. We also explore the conditions for when an HMM is most effective, finding that an HMM outperforms a Markov model when the degree of rate heterogeneity is moderate to high. 5. Finally, we demonstrate the importance of these generalizations by reconstructing the morphology of the ancestral angiosperm flower. Exactly opposite to previous results, we find the most likely state to be a spiral perianth, spiral androecium, whorled gynoecium. The difference between our analysis and previous studies was that our modeling allowed for the correlated evolution of several flower characters.
Aim: Due to the sessile nature of flowering plants, movements to new geographical areas occur mainly during seed dispersal. Frugivores tend to be efficient dispersers because animals move within the boundaries of their preferable niches, so seeds are more likely to be transported to environments that are similar to where the parent plant occurs. However, this efficiency can result in less opportunity for niche shifts over macroevolutionary time, 'trapping' plant lineages in particular climatic conditions.Here we test this hypothesis by analysing the role that the interaction with frugivores play in changing dynamics of climatic niche evolution in five clades of flowering plants. Location: Global.Taxon: The flowering plant families Apocynaceae, Ericaceae, Melastomataceae, Rosaceae and Solanaceae. Methods:We model climatic niche evolution as a variable parameter Ornstein-Uhlenbeck process. However, rather than assuming regimes a priori, we use a hidden Markov model (HMM) to infer the complex evolutionary history associated with different modes of seed dispersal. In addition to allowing for a more accurate picture of the regimes, the use of HMMs allows partitioning the variance of climatic niche evolution to include dynamics independent of our focal character.Results: Lineages dispersed by frugivores tend to have warmer and wetter climatic optima and are generally associated with areas where potential for vegetation growth is higher. However, lineages distributed in more mesic habitats, such as rainforests, are generally associated with slower rates of climatic niche evolution regardless of their mode of seed dispersal.Main Conclusions: Characteristics of the abiotic environment may facilitate the evolution of some types of plant-animal interactions. Association with frugivores is an important modulator of how plants move in space, but its impact on their climatic niche evolution appears to be indirect. Seed dispersal by frugivores may facilitate the establishment of lineages in closed canopy biomes, but the general slower rates of climatic niche evolution in these habitats are possibly related to other general aspects of the 'mesic syndrome' rather than the behaviour of the animals that disperse their seeds.
The correlation between two characters is often interpreted as evidence that there exists a significant and biologically important relationship between them. However, Maddison and FitzJohn (2015) recently pointed out that evidence of correlated evolution between two categorical characters is often spurious, particularly, when the dependent relationship stems from a single replicate deep in time. Here we will show that there may, in fact, be a statistical solution to the problem posed by Maddison and FitzJohn (2015) naturally embedded within the expanded model space afforded by the hidden Markov model (HMM) framework. We demonstrate that the problem of single unreplicated evolutionary events manifests itself as rate heterogeneity within our models and that this is the source of the false correlation. Therefore, we argue that this problem is better understood as model misspecification rather than a failure of comparative methods to account for phylogenetic pseudoreplication. We utilize HMMs to develop a multi-rate independent model which, when implemented, drastically reduces support for correlation. The problem itself extends beyond categorical character evolution, but we believe that the practical solution presented here may lend itself to future extensions in other areas of comparative biology.
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