How mountains shape biodiversity: The role of the Andes in biogeography, diversification, and reproductive biology in South America's most species‐rich lizard radiation (Squamata: Liolaemidae)

Adaptive radiation refers to diversification from an ancestral species that produces descendants adapted to use a great variety of distinct ecological niches. In this review, I examine two aspects of adaptive radiation: first, that it results from ecological opportunity and, second, that it is deterministic in terms of its outcome and evolutionary trajectory. Ecological opportunity is usually a prerequisite for adaptive radiation, although in some cases, radiation can occur in the absence of preexisting opportunity. Nonetheless, many clades fail to radiate although seemingly in the presence of ecological opportunity; until methods are developed to identify and quantify ecological opportunity, the concept will have little predictive utility in understanding a priori when a clade might be expected to radiate. Although predicted by theory, replicated adaptive radiations occur only rarely, usually in closely related and poorly dispersing taxa found in the same region on islands or in lakes. Contingencies of a variety of types may usually preclude close similarity in the outcome of evolutionary diversification in other situations. Whether radiations usually unfold in the same general sequence is unclear because of the unreliability of methods requiring phylogenetic reconstruction of ancestral events. The synthesis of ecological, phylogenetic, experimental, and genomic advances promises to make the coming years a golden age for the study of adaptive radiation; natural history data, however, will always be crucial to understanding the forces shaping adaptation and evolutionary diversification.

Historical biogeography is increasingly studied from an explicitly statistical perspective, using stochastic models to describe the evolution of species range as a continuous-time Markov process of dispersal between and extinction within a set of discrete geographic areas. The main constraint of these methods is the computational limit on the number of areas that can be specified. We propose a Bayesian approach for inferring biogeographic history that extends the application of biogeographic models to the analysis of more realistic problems that involve a large number of areas. Our solution is based on a "data-augmentation" approach, in which we first populate the tree with a history of biogeographic events that is consistent with the observed species ranges at the tips of the tree. We then calculate the likelihood of a given history by adopting a mechanistic interpretation of the instantaneous-rate matrix, which specifies both the exponential waiting times between biogeographic events and the relative probabilities of each biogeographic change. We develop this approach in a Bayesian framework, marginalizing over all possible biogeographic histories using Markov chain Monte Carlo (MCMC). Besides dramatically increasing the number of areas that can be accommodated in a biogeographic analysis, our method allows the parameters of a given biogeographic model to be estimated and different biogeographic models to be objectively compared. Our approach is implemented in the program, BayArea.