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Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

 

The extraordinary number of species in the tropics when compared to the extra-tropics is probably the most prominent and consistent pattern in biogeography, suggesting that overarching processes regulate this diversity gradient. A major challenge to characterizing which processes are at play relies on quantifying how the frequency and determinants of tropical and extra-tropical speciation, extinction, and dispersal events shaped evolutionary radiations. We address this question by developing and applying spatiotemporal phylogenetic and paleontological models of diversification for tetrapod species incorporating paleoenvironmental variation. Our phylogenetic model results show that area, energy, or species richness did not uniformly affect speciation rates across tetrapods and dispute expectations of a latitudinal gradient in speciation rates. Instead, both neontological and fossil evidence coincide in underscoring the role of extra-tropical extinctions and the outflow of tropical species in shaping biodiversity. These diversification dynamics accurately predict present-day levels of species richness across latitudes and uncover temporal idiosyncrasies but spatial generality across the major tetrapod radiations.

 

Myriad branches in the tree of life are intertwined through ecological relationships. Biologists have long hypothesized that intimate symbioses between lineages can influence diversification patterns to the extent that it leaves a topological imprint on the phylogenetic trees of interacting clades. Over the past few decades, cophylogenetic methods development has provided a toolkit for identifying such histories of codiversification, yet it is often difficult to determine which tools best suit the task at hand. In this review, we organize currently available cophylogenetic methods into three categories—pattern-based statistics, event-scoring methods, and more recently developed generative model–based methods—and discuss their assumptions and appropriateness for different types of cophylogenetic questions. We classify cophylogenetic systems based on their biological properties to provide a framework for empiricists investigating the macroevolution of symbioses. In addition, we provide recommendations for the next generation of cophylogenetic models that we hope will facilitate further methods development. 

Regional features of geography, such as size or distance, are expected to shape how lineages disperse, go extinct, and speciate. Yet this fundamental link between geographical context and evolutionary consequence has not been fully incorporated into phylogenetic models of biogeography. We designed a model that allows variation in regional features (size, distance, insularity, and oceanic separation) to inform rates of biogeographic change. Our approach uses a Bayesian hierarchical modeling framework to transform regional values of quantitative and categorical features into evolutionary rates. We also make use of a parametric range split score to quantify range cohesion for widespread species, thereby allowing geographical barriers to initiate “range-splitting” speciation events. Applying our approach to Anolis lizards, a species-rich neotropical radiation, we found that distance between regions, especially over water, decreases dispersal rates and increases between-region speciation rates. For distances less than ∼470 km over land, anoles tended to disperse faster than they speciate between regions. Over oceans, the equivalent maximum range cohesion distance fell to ∼160 km. Our results suggest that the historical biogeography of founder event speciation may be productively studied when the same barriers that inhibit dispersal also promote speciation between regions. 

No abstract available.