Genomic relatedness in Bromus erectus
Climate and land-use change are major drivers of environmental change in semi-natural grasslands, yet their combined effects on phenotypic and evolutionary dynamics remain poorly understood. We tested how future climate conditions and contrasting land-use regimes (extensively managed meadows and pastures) influence phenotypic trait expression, genetic architecture, and evolutionary potential of Bromus erectus, a dominant grass species in Central European semi-natural grasslands. Using a long-term climate change experiment in Central Germany, ddRAD sequencing, and multivariate quantitative genetic models, we analyzed phenotypic and genetic variation in more than 2000 individuals. Using the ddRAD approach, we genotyped 1714 plant individuals from four experimental treatments and calculated additive genetic relationship matrices for the analysis of additive genetic variance–covariance matrices (G).  Samples underwent ddRAD analysis following Peterson et al. (2012, https://doi.org/10.1371/journal.pone.0037135) using EcoRI/MspI as restriction enzymes and a selected fragment length of 350-450 bp. Samples were sequenced on a Illumina HighSeq 2000 (PE150), resulting in appr. 1.5 Mio. sequences per individual sample. Sequences were demultiplexed with process_radtags --paired -i gzfastq -y gzfastq --barcode_dist_1 1 --barcode_dist_2 1 --inline_index --renz_1 'ecoRI' --renz_2 'mspI' --disable_rad_check --retain_header.
Climate and land-use change are major drivers of environmental change in semi-natural grasslands, yet their combined effects on phenotypic and evolutionary dynamics remain poorly understood. We tested how future climate conditions and contrasting land-use regimes (extensively managed meadows and pastures) influence phenotypic trait expression, genetic architecture, and evolutionary potential of Bromus erectus, a dominant grass species in Central European semi-natural grasslands. Using a long-term climate change experiment in Central Germany, ddRAD sequencing, and multivariate quantitative genetic models, we analyzed phenotypic and genetic variation in more than 2000 individuals. Using the ddRAD approach, we genotyped 1714 plant individuals from four experimental treatments and calculated additive genetic relationship matrices for the analysis of additive genetic variance–covariance matrices (G).  Samples underwent ddRAD analysis following Peterson et al. (2012, https://doi.org/10.1371/journal.pone.0037135) using EcoRI/MspI as restriction enzymes and a selected fragment length of 350-450 bp. Samples were sequenced on a Illumina HighSeq 2000 (PE150), resulting in appr. 1.5 Mio. sequences per individual sample. Sequences were demultiplexed with process_radtags --paired -i gzfastq -y gzfastq --barcode_dist_1 1 --barcode_dist_2 1 --inline_index --renz_1 'ecoRI' --renz_2 'mspI' --disable_rad_check --retain_header.
HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH - UFZ.