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  1. Abstract

    Do related populations that are separated by barriers predictably evolve differences from one another over time, or is such divergence idiosyncratic and unpredictable? We test these alternatives by investigating patterns of trait evolution for 54 sister pairs of Andean forest birds that live in similar environments on either side of the arid Marañón Gap, a strong dispersal barrier for humid montane species. We measured divergence in both sexual (song and plumage) and ecological (beak size and beak shape) traits. Sexual traits evolve in a clock-like fashion, with trait divergence positively correlated with genetic distance (r = 0.6–0.7). In contrast, divergence in ecological traits is uncorrelated or only loosely correlated with genetic distance (r = 0.0–0.3). Thus, for geographically isolated Andean montane forest birds that live in similar environments, divergence is predictable in sexual traits, but not for ecological traits. This means that sexual trait divergence occurs independently of adaptive ecological divergence within the mega-diverse tropical Andean avifauna. Last, we show that variation in genetic divergence across a biogeographic barrier is associated with traits that are proxies for species’ opportunities for dispersal (low elevation limit and elevational niche breadth), but not with traits that are proxies for species’ dispersal abilities (hand-wing index and foraging strata).

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  2. Abstract

    Understanding the factors that govern variation in genetic structure across species is key to the study of speciation and population genetics. Genetic structure has been linked to several aspects of life history, such as foraging strategy, habitat association, migration distance, and dispersal ability, all of which might influence dispersal and gene flow. Comparative studies of population genetic data from species with differing life histories provide opportunities to tease apart the role of dispersal in shaping gene flow and population genetic structure. Here, we examine population genetic data from sets of bird species specialized on a series of Amazonian habitat types hypothesized to filter for species with dramatically different dispersal abilities: stable upland forest, dynamic floodplain forest, and highly dynamic riverine islands. Using genome‐wide markers, we show that habitat type has a significant effect on population genetic structure, with species in upland forest, floodplain forest, and riverine islands exhibiting progressively lower levels of structure. Although morphological traits used as proxies for individual‐level dispersal ability did not explain this pattern, population genetic measures of gene flow are elevated in species from more dynamic riverine habitats. Our results suggest that the habitat in which a species occurs drives the degree of population genetic structuring via its impact on long‐term fluctuations in levels of gene flow, with species in highly dynamic habitats having particularly elevated gene flow. These differences in genetic variation across taxa specialized in distinct habitats may lead to disparate responses to environmental change or habitat‐specific diversification dynamics over evolutionary time scales.

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  3. Abstract

    How the microbiome interacts with hosts across evolutionary time is poorly understood. Data sets including many host species are required to conduct comparative analyses. Here, we analyzed 142 intestinal microbiome samples from 92 birds belonging to 74 species from Equatorial Guinea, using the 16S rRNA gene. Using four definitions for microbial taxonomic units (97%OTU, 99%OTU, 99%OTU with singletons removed, ASV), we conducted alpha and beta diversity analyses. We found that raw abundances and diversity varied between the data sets but relative patterns were largely consistent across data sets. Host taxonomy, diet and locality were significantly associated with microbiomes, at generally similar levels using three distance metrics. Phylogenetic comparative methods assessed the evolutionary relationship between the microbiome as a trait of a host species and the underlying bird phylogeny. Using multiple ways of defining “microbiome traits”, we found that a neutral Brownian motion model did not explain variation in microbiomes. Instead, we found a White Noise model (indicating little phylogenetic signal), was most likely. There was some support for the Ornstein‐Uhlenbeck model (that invokes selection), but the level of support was similar to that of a White Noise simulation, further supporting the White Noise model as the best explanation for the evolution of the microbiome as a trait of avian hosts. Our study demonstrated that both environment and evolution play a role in the gut microbiome and the relationship does not follow a neutral model; these biological results are qualitatively robust to analytical choices.

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  4. A widespread adaptive change in antiherbivore response is seen in a common plant species in urban environments across 160 cities. 
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