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R. Graham Reynolds

Researcher at University of North Carolina at Asheville

Publications -  42
Citations -  1115

R. Graham Reynolds is an academic researcher from University of North Carolina at Asheville. The author has contributed to research in topics: Population & Endangered species. The author has an hindex of 16, co-authored 41 publications receiving 952 citations. Previous affiliations of R. Graham Reynolds include University of Tennessee & University of Massachusetts Boston.

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Phenotypic shifts in urban areas in the tropical lizard Anolis cristatellus

TL;DR: The data suggest that anoles in urban areas are under significant differential natural selection and may be evolutionarily adapting to their human‐modified environments.
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What can DNA tell us about biological invasions

TL;DR: It is concluded that genetic analysis of biological invasions is justified only under exceptional circumstances and recommended that biologists ask whether the questions to be addressed will materially affect management practice or policy.
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Assortative mating in poison-dart frogs based on an ecologically important trait.

TL;DR: This study provides a rare example of one phenotypic trait affecting both ecological viability and nonrandom mating, indicating that mating/ecology pleiotropy is plausible in wild populations, particularly for organisms that are aposematically colored and visually orienting.
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Toward a Tree-of-Life for the boas and pythons: multilocus species-level phylogeny with unprecedented taxon sampling.

TL;DR: A revised taxonomy for the boas and pythons is suggested, significant evidence of discordance between taxonomy and evolutionary relationships in the genera Tropidophis, Morelia, Liasis, and Leiopython is found, and support is found for elevating two previously suggested boid species.
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A new Bayesian method for fitting evolutionary models to comparative data with intraspecific variation.

TL;DR: A new Bayesian method for fitting evolutionary models to comparative data that incorporates intraspecific variability is described, which differs from an existing likelihood‐based approach in that it requires no a priori inference about species means and variances.