DOI: 10.5061/dryad.cjsxksn9r
title: Feather mite abundance constraints

description: Data and code for analyses for a study on the allometry of symbiont
 abundance by investigating feather mites (Acariformes: Analgoidea,
 Pterolichoidea) on 26,604 individual birds from 106 European passerine
 species. We parameterized metabolic theory equations to predict how bird
 species’ body size and the mean body size of their feather mite species
 relate to feather mite abundance according to potential energy (uropygial
 gland size, microbial abundance) and space constraints (wing
 area, total length of barbs, and number of feather barbs). We compared
 these theoretical predictions with the empirical allometry of feather mite
 abundance across bird species (using phylogenetic modelling and quantile
 regression).
description: Methods are described in related papers.
description: Only R software is required to run the script using the
 datasets.
description: # Feather mite abundance constraints Largest-to-date study on the
 allometry of symbiont abundance by investigating feather mites
 (Acariformes: Analgoidea, Pterolichoidea) on 26,604 individual birds from
 106 European passerine species. We parameterized metabolic theory
 equations to predict how bird species body size and the mean body size of
 their feather mite species relate to feather mite abundance according to
 potential energy (microbial abundance, uropygial gland size) and space
 constraints (wing area, number of feather barbs). We compared these
 theoretical predictions with the empirical allometry of feather mite
 abundance across bird species (using phylogenetic modeling and quantile
 regression). ## Description of the data and file structure * ATTENTION:
 the dataset "global_fmbird.csv" on bird-feather mite species
 associations (Doña et al. 2016) needs to be downloaded from
 * consensus.tre contains the phylogenetic consensus tree as retrieved from BirdTree (Jetz et al. 2012, ) for the studied bird species (see manuscript for details). * mite size data.csv contains the length and estimated body size (in micrograms) of females of each feather mite species, and whether female length was estimated or directly retrieved from literature. It contains the following variables: * mite_species * female_length_micrometre * female_body_size_micrograms: This is calculated according to Edwards (1967) from female_length_micrometre. * estimated: 1 for feather mite species for which the gnathosoma was not illustrated and then it was estimated from illustration of a congeneric mite species. 0 when female length was directly retrieved from literature. NA indicates mite species for which we were unable to retrieve mite size from literature. * data.csv contains the abundance of feather mites for each bird species, and the four bird species traits studied. * species: bird species as used to match phylogeny species names. * species_ioc: bird species names following IOC World Bird List Version 11.2 (). * bird_body_size: bird species body size in grams. * fungi_bacteria_DNA: mean sum of fungi and bacteria DNA retrieved by qPCR from Labrador et al. (2021). * wing_area: wing area in m2. * uropygial_gland: uropygial gland size in mm3. * P9 to P1: mean feather length (in cm) from primary feather 9 to 1 of each species trapped in Manecorro (Doana, SW Spain). * N_P9 to N_P1: sample size (number of birds) used to calculate the mean feather length from primary feather 9 to 1 of each species trapped in Manecorro (Doana, SW Spain). * p1_barb_density: barb density (number of barbs/cm2) for the P1 (innermost) primary feather retrieved from Pap et al. (2015). * barb_amount: estimated mean number of barbs in each bird species by multiplying P1_barb_density by the sum of P1 to P9. * Then, two blocks of columns with quantile data on feather mite abundance (retrieved from Daz-Real et al. 2014): * First, quantiles calculated from the entire dataset of feather mite abundance: all_N show the sample size, and all_QX show each Xth quantile. * Second, quantiles calculated only for bird individuals sampled from October to March (both months included): winter_N and winter_QX. NA indicates that the value was not available for the given bird species. #### Literature Cited * Daz-Real, J., D. Serrano, J. Prez-Tris, S. Fernndez-Gonzlez, A. Bermejo, J. A. Calleja, J. De la Puente, D. De Palacio, J. L. Martnez, R. Moreno-Opo, C. Ponce, . Fras, J. L. Tella, A. P. Mller, J. Figuerola, P. L. Pap, I. Kovcs, C. I. Vgsi, L. Melndez, G. Blanco, E. Aguilera, J. C. Senar, I. Galvn, F. Atinzar, E. Barba, J. L. Cant, V. Corts, J. S. Monrs, R. Piculo, M. Vgeli, A. Borrs, C. Navarro, A. Mestre, and R. Jovani. 2014. Repeatability of feather mite prevalence and intensity in passerine birds. PLoS One 9:e107341. * Doa, J., H. C. Proctor, S. V. Mironov, D. Serrano, and R. Jovani. 2016. Global associations between birds and vane-dwelling feather mites. Ecology 97:3242. * Edwards, C. A. 1967. Relationships between weights, volumes and numbers of soil animals. In: O. Graff, J. E. Satchell editors. Progress in soil biology. Colloquium on dynamics of soil communities; 1966; Braunschweig-Vlkenrode. Amsterdam: North-Holland Publishing Company. * Jetz, W., G. H. Thomas, J. B. Joy, K. Hartmann, and A. O. Mooers. 2012. The global diversity of birds in space and time. Nature 491:444-448. * Pap, P. L., G. Osvth, K. Sndor, O. Vincze, L. Brbos, A. Marton, R. L. Nudds, and C. I. Vgsi. 2015. Interspecific variation in the structural properties of flight feathers in birds indicates adaptation to flight requirements and habitat. Functional Ecology 29:746-757. ## Sharing/Access information Data was derived from the following sources: * global_fmbird.csv contains the dataset from Doa et al. (2016)and can be also retrieved from * consensus.tre was retrieved from BirdTree (Jetz et al. 2012, ) ## Code/Software * analyses.R contains the R code to perform all the statistical analyses and figures and tables in Labrador et al. American Naturalist. Please note that the code is prepared to run the main analyses in the main manuscript. In some cases (as explained in the comments of the code), to run the analyses for the supplementary material it is needed to comment/uncomment some code lines. * Information about versions of packages and software used: R version 4.0.2 (2020-06-22) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows &gt;= 8 x64 (build 9200) Matrix products: default locale: LC_COLLATE=Spanish_Spain.1252 LC_CTYPE=Spanish_Spain.1252 LC_MONETARY=Spanish_Spain.1252 LC_NUMERIC=C LC_TIME=Spanish_Spain.1252\ Attached base packages: stats graphics grDevices utils datasets methods base\ Other attached packages: sjPlot_2.8.9 quantreg_5.67 SparseM_1.78 geiger_2.0.7 patchwork_1.1.1 nlme_3.1-149 ggplot2_3.3.2 dplyr_1.0.2 ape_5.4-1