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      Niche filling slows the diversification of Himalayan songbirds

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          Abstract

          Speciation generally involves a three-step process--range expansion, range fragmentation and the development of reproductive isolation between spatially separated populations. Speciation relies on cycling through these three steps and each may limit the rate at which new species form. We estimate phylogenetic relationships among all Himalayan songbirds to ask whether the development of reproductive isolation and ecological competition, both factors that limit range expansions, set an ultimate limit on speciation. Based on a phylogeny for all 358 species distributed along the eastern elevational gradient, here we show that body size and shape differences evolved early in the radiation, with the elevational band occupied by a species evolving later. These results are consistent with competition for niche space limiting species accumulation. Even the elevation dimension seems to be approaching ecological saturation, because the closest relatives both inside the assemblage and elsewhere in the Himalayas are on average separated by more than five million years, which is longer than it generally takes for reproductive isolation to be completed; also, elevational distributions are well explained by resource availability, notably the abundance of arthropods, and not by differences in diversification rates in different elevational zones. Our results imply that speciation rate is ultimately set by niche filling (that is, ecological competition for resources), rather than by the rate of acquisition of reproductive isolation.

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          The species richness (diversity) of local plant and animal assemblages-biological communities-balances regional processes of species formation and geographic dispersal, which add species to communities, against processes of predation, competitive exclusion, adaptation, and stochastic variation, which may promote local extinction. During the past three decades, ecologists have sought to explain differences in local diversity by the influence of the physical environment on local interactions among species, interactions that are generally believed to limit the number of coexisting species. But diversity of the biological community often fails to converge under similar physical conditions, and local diversity bears a demonstrable dependence upon regional diversity. These observations suggest that regional and historical processes, as well as unique events and circumstances, profoundly influence local community structure. Ecologists must broaden their concepts of community processes and incorporate data from systematics, biogeography, and paleontology into analyses of ecological patterns and tests of community theory.
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            Bayesian phylogenetic analysis of combined data.

            The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.
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              A distance-based framework for measuring functional diversity from multiple traits

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                Author and article information

                Journal
                Nature
                Nature
                Springer Science and Business Media LLC
                0028-0836
                1476-4687
                May 2014
                April 30 2014
                May 2014
                : 509
                : 7499
                : 222-225
                Article
                10.1038/nature13272
                24776798
                © 2014

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