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Neotropical ecosystems are renowned for numerous examples of adaptive radiation in both plants and animals resulting in high levels of biodiversity and endemism. However, we still lack a comprehensive review of the abiotic and biotic factors that contribute to these adaptive radiations. To fill this gap, we delve into the geological history of the region, including the role of tectonic events such as the Andean uplift, the formation of the Isthmus of Panama, and the emergence of the Guiana and Brazilian Shields. We also explore the role of ecological opportunities created by the emergence of new habitats, as well as the role of key innovations, such as novel feeding strategies or reproductive mechanisms. We discuss different examples of adaptive radiation, including classic ones like Darwin's finches and Anolis lizards, and more recent ones like bromeliads and lupines. Finally, we propose new examples of adaptive radiations mediated by ecological interactions in their geological context. By doing so, we provide insights into the complex interplay of factors that contributed to the remarkable diversity of life in the Neotropics and highlight the importance of this region in understanding the origins of biodiversity. 

Abstract Understanding phenotypic disparity across the tree of life requires identifying where and when evolutionary rates change on phylogeny. A primary methodological challenge in macroevolution is therefore to develop methods for accurate inference of among-lineage variation in rates of phenotypic evolution. Here, we describe a method for inferring among-lineage evolutionary rate heterogeneity in both continuous and discrete traits. The method assumes that the present-day distribution of a trait is shaped by a variable-rate process arising from a mixture of constant-rate processes and uses a single-pass tree traversal algorithm to estimate branch-specific evolutionary rates. By employing dynamic programming optimization techniques and approximate maximum likelihood estimators where appropriate, our method permits rapid exploration of the tempo and mode of phenotypic evolution. Simulations indicate that the method reconstructs rates of trait evolution with high accuracy. Application of the method to data sets on squamate reptile reproduction and turtle body size recovers patterns of rate heterogeneity identified by previous studies but with computational costs reduced by many orders of magnitude. Our results expand the set of tools available for detecting macroevolutionary rate heterogeneity and point to the utility of fast, approximate methods for studying large-scale biodiversity dynamics. [Brownian motion; continuous characters; discrete characters; macroevolution; Markov process; rate heterogeneity.] 

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. 

Abstract What we mean by species and whether they have any biological reality has been debated since the early days of evolutionary biology. Some biologists even suggest that plant species are created by taxonomists as a subjective, artificial division of nature. However, the nature of plant species has been rarely tested critically with data while ignoring taxonomy. We integrate phenomic and genomic data collected across hundreds of individuals at a continental scale to investigate this question inEscallonia(Escalloniaceae), a group of plants which includes 40 taxonomic species (the species proposed by taxonomists). We first show that taxonomic species may be questionable as they match poorly to patterns of phenotypic and genetic variation displayed by individuals collected in nature. We then use explicit statistical methods for species delimitation designed for phenotypic and genomic data, and show that plant species do exist inEscalloniaas an objective, discrete property of nature independent of taxonomy. We show that such species correspond poorly to current taxonomic species ($$< 20\%$$ $<20\%$) and that phenomic and genomic data seldom delimit congruent entities ($$< 20\%$$ $<20\%$). These discrepancies suggest that evolutionary forces additional to gene flow can maintain the cohesion of species. We propose that phenomic and genomic data analyzed on an equal footing build a broader perspective on the nature of plant species by helping delineate different ‘types of species’. Our results caution studies which take the accuracy of taxonomic species for granted and challenge the notion of plant species without empirical evidence. Note: A version of the complete manuscript in Spanish is available in the Supplemental Materials. 

This work focuses on the problem of forecasting the energy availability of renewable sources such as solar and wind in smart grids. To solve this problem, we propose to use weather radar information to make a short-time prediction of the weather conditions in the area where the renewable sources are located. For this purpose, an object tracking method will be used to make the predictions by using as cues the reflectivity and the velocity. This paper centers in an object modeling approach based on clustering, which will be used for the tracking algorithm.