data and script for: Afrotropical passerines grow wing feathers faster than their European counterparts

This dataset contains the data and code required to replicate analyses in Horák et al. (2025; doi: 10.1093/zoolinnean/zlaf123). The study examined interspecific variation in wing and tail feather growth rates and quality in passerine birds from temperate and tropical regions using data from 679 adults representing 132 species. Feather samples were collected between 2014 and 2019 from various locations in two different countries across distinct climatic zones: the Czech Republic (latitude 48.78°-50.10° N, longitude 13.94°-16.91° E, 34 sites) and Cameroon (latitude 4.10°-6.09° N, longitude 9.06°-10.29° E, 4 sites). To determine feather growth rate, we utilized ptilochronological measurements, following the method described by Grubb (2006). A segment from the distal part of each feather, typically consisting of 10 growth bars, was marked and measured using a calliper. The FGR (in mm per day) was calculated by dividing the segment length by the number of growth bars within that segment. Each feather was examined against light to identify the presence of fault bars, coded as 1 if present and 0 if absent. Fault bars are defined as narrow bars of missing barbules oriented perpendicularly to the rachis. The mass of each feather was measured to the nearest 0.1 mg.
Description:This file contains two sheets:
the main data used in the associated manuscriptscript used for data analysis in the statistical software RVariablesID - identification code of individualSpecies - species scientific nameBreedingMoultCategory - three categories describing breeding and moulting site (breeding site followed by moulting site)BodyMassAv - species specific body mass (g)FeatherMassInd - individual feather mass (mg)FeatherLengthInd - individual feather length (mm)FGRInd - individual feather growth rate (mm/day)FB - fault bar occurrence in a given feather (1- yes, 0 - no)Table S8 - use of the sample in Table S8 (1- yes, 0 - no)Code/softwareWe used the statistical software R (ver. 3.6.1 , R Core Team, 2019), packages: MCMCglmm, caper.