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Hendrickx, F.

Publications and source records attributed to Hendrickx, F..

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Evolution at two time frames: polymorphisms from an ancient singular divergence event fuel contemporary parallel evolution

When species occur in repeated ecologically distinct habitats across their range, adaptation may proceed surprisingly fast and result in parallel evolution. There is increasing evidence that such cases of rapid parallel evolution are fueled by standing genetic variation, but the origin of this genetic variation remains poorly understood. In Pogonus chalceus beetles, short- and long-winged ecotypes have diverged in response to contrasting hydrological regimes and can be repeatedly found along the Atlantic European coast. By analyzing genomic variation across the beetles distribution, we reveal that genomically widespread short-wing selected alleles evolved during a singular divergence event, estimated at ~0.19 Mya. The ancient and differentially selected alleles are currently polymorphic in all populations across the range, allowing for the fast evolution of one ecotype from a small number of random individuals, as low as 5 to 15, of the populations of the other ecotype. Our results suggest that cases of fast parallel ecological divergence might be the result of evolution at two different time frames: divergence in the past, followed by repeated selection on the divergently evolved alleles after admixture. We suggest that this mechanism may be common and potentially further driven by periods of geographic isolation imposed by large-scale environmental changes such as glacial cycles.

evolutionary biology

Patternize: An R Package For Quantifying Color Pattern Variation

O_LIThe use of image data to quantify, study and compare variation in the colors and patterns of organisms requires the alignment of images to establish homology, followed by color-based segmentation of images. Here we describe an R package for image alignment and segmentation that has applications to quantify color patterns in a wide range of organisms.\nC_LIO_LIpatternize is an R package that quantifies variation in color patterns obtained from image data. patternize first defines homology between pattern positions across specimens either through manually placed homologous landmarks or automated image registration. Pattern identification is performed by categorizing the distribution of colors using an RGB threshold, k-means clustering or watershed transformation.\nC_LIO_LIWe demonstrate that patternize can be used for quantification of the color patterns in a variety of organisms by analyzing image data for butterflies, guppies, spiders and salamanders. Image data can be compared between sets of specimens, visualized as heatmaps and analyzed using principal component analysis (PCA).\nC_LIO_LIpatternize has potential applications for fine scale quantification of color pattern phenotypes in population comparisons, genetic association studies and investigating the basis of color pattern variation across a wide range of organisms.\nC_LI

bioinformatics