Author(s): Natalie Cooper
This repository contains all the code and some data used in the paper.
To cite the paper:
Natalie Cooper, Alexander L. Bond, Joshua L. Davis, Roberto Portela Miguez, Louise Tomsett, and Kristofer M. Helgen. 2019. Sex biases in bird and mammal natural history collections. Proceedings of the Royal Society Series B: Biological Sciences. 286, 20192025. DOI:10.1098/rspb.2019.2025.
To cite this repo:
Natalie Cooper. 2019. GitHub: nhcooper123/sex-bias-museums: code for the paper. Zenodo. DOI: 10.5281/zenodo.3459138.
While working with this code for another project I discovered a small issue with the code. A shortcut in some of the scripts means that species with no female specimens were excluded from the analyses/figures. Overall this is 2945 species (2427 birds and 518 mammals). However, all bar one of these species (Emberiza schoeniclus, 324 specimens all males) has fewer than 100 specimens so these were removed from the analyses at a later step anyway. In terms of the paper, this means there are teeny tiny quantitative changes in the results of the models, but these are within the rounding errors so none of the numbers or figures in the paper change. The only difference is in our supplemental figures (Fig A1 and Fig A2) showing the overall distribution of the proportion of females for all the data prior to the removal of species with fewer than 100 specimens. These have an additional bar at 0 percent (Fig A1) and one hexagon has changed colour (Fig A2). I've fixed the code and added these two new figures here. The changes were not substantial enough to merit a correction as they only apply to these two supplemental figures. However, in the interests of transparency and reproducibility I thought I should comment on this here.
All cleaned data are available from the NHM Data Portal.
For reproducibility purposes download this and place it into a rawdata/ or data/ folder as appropriate to rerun our analyses.
We were unable to upload this to GitHub because the files are too large.
data/should includeall-specimen-data.csvandall-extra-data.csv. These are the datasets required to run the analyses.- For the sensitivity analyses you will also need
genera-specimen-data.csvandszekely2014.csv. rawdata/should contain all other data. Note that we did not upload raw data that can be accessed from the original data sources due to copyright issues, but these can be downloaded from GBIF (references and links below). The raw data are only necessary if you want to repeat the data wrangling steps.
If you use the cleaned data please cite as follows:
Natalie Cooper, Alexander L Bond, Joshua L. Davis, Roberto Portela Miguez, Louise Tomsett and Kristofer M Helgen (2019). Dataset: Sex biases in natural history collections of birds and mammals. Natural History Museum Data Portal (data.nhm.ac.uk). https://doi.org/10.5519/0065209.
Please also cite the original sources of the data as follows:
Specimen data
GBIF: The Global Biodiversity Information Facility. https://www.gbif.org/ (2018).
AMNH birds: Trombone, T., AMNH Bird Collection. American Museum of Natural History. Occurrence dataset https://doi.org/10.15468/xvzdcm accessed via GBIF.org on 2018-08-03 (2013). Trombone, T., AMNH mammals: AMNH Mammal Collections. American Museum of Natural History. Occurrence Dataset https://doi.org/10.15468/wu3poe accessed via GBIF.org on 2018-08-09 (2016).
FMNH birds: Grant, S. & Marks, B., Field Museum of Natural History (Zoology) Bird Collection. Version 14.4. Field Museum. Occurrence dataset https://doi.org/10.15468/exkxdx accessed via GBIF.org on 2018-08-03. (2018). FMNH mammals: Grant, S. & Ferguson, A., Field Museum of Natural History (Zoology) Mammal Collection. Version 9.3. Field Museum. Occurrence Dataset https://doi.org/10.15468/n4zgxw accessed via GBIF.org on 2018-08-09 (2018).
MNHN birds: Museum National d’Histoire Naturelle, The birds collection (ZO) of the Muse´um national d’Histoire Naturelle (MNHN - Paris). Version 42.70. Occurrence dataset https://doi.org/10.15468/h0xtwv accessed via GBIF.org on 2018-08-03 (2018). MNHN mammals: Museum National d’Histoire Naturelle, The mammals collection (ZM) of the Muse´um national d’Histoire naturelle (MNHN - Paris). Version 43.55. Occurrence Dataset https://doi.org/10.15468/j0xw9i accessed via GBIF.org on 2018-08-09 (2018).
NMNH: Orrell, T. & Hollowell, T., NMNH Extant Specimen Records. Version 1.16. National Museum of Natural History, Smithsonian Institution. Occurrence Dataset https://doi.org/10.15468/hnhrg3 accessed via GBIF.org on 2018-08-09 (2018).
NHMUK: Natural History Museum, Natural History Museum (London) Collection Specimens. Collection Specimens. Occurrence dataset https://doi.org/10.5519/0002965 accessed via GBIF.org on 2018-08-09. (2018).
Bird mass data
Lislevand, T., Figuerola, J. & Sze´kely, T., Avian body sizes in relation to fecundity, mating system, display behavior, and resource sharing. Ecology 88, 1605–1605 (2007).
Bird plumage data
Dale, J., Dey, C. J., Delhey, K., Kempenaers, B. & Valcu, M., Data from: The effects of life history and sexual selection on male and female plumage colouration. Dryad Digital Repository. https://doi.org/10.5061/dryad.1rp0s (2015). Dale, J., Dey, C. J., Delhey, K., Kempenaers, B. & Valcu, M., The effects of life history and sexual selection on male and female plumage colouration. Nature 527, 367 (2015).
Mammal mass data
Jones, K. E., Bielby, J., Cardillo, M., Fritz, S. A., O’Dell, J., Orme, C. D. L., Safi, K., Sechrest, W., Boakes, E. H., Carbone, C., Connolly, C., Cutts, M. J., Foster, J. K., Grenyer, R., Habib, M., Plaster, C. A., Price, S. A., Rigby, E. A., Rist, J., Teacher, A., Bininda-Emonds, O. R. P., Gittleman, J. L., Mace, G. M. & Purvis, A., PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648–2648 (2009).
Bird adult sex ratio data
Szekely T, Liker A, Freckleton RP, Fichtel C, Kappeler PM. Sex-biased survival predicts adult sex ratio variation in wild birds. Proceedings of the Royal Society of London B: Biological Sciences. 2014;281(1788):20140342.
Prior to analyses some intense data wrangling occurred.
It was the best of times, it was the worst of times.
To get from raw specimen data to specimen-data-all.csv you need:
- 01A-wrangling-museum-data.R
- 01B-taxonomy-check.R
- 01C-taxonomy-corrections.R
In reality, I ran script 01A to the taxonomy check stage, then ran script 01B to identify specimens with taxonomy that needed to be updated. I then created script 01C which corrects the taxonomy. To repeat the analyses you only need script 01A because the taxonomy corrections of 01C are sourced from that script.
To combine body size, plumage and ornamentation datasets into extra-data-all.csv you need:
- 02A-extract-extra-data.R
- 02B-taxonomy-corrections-mass-birds.R
- 02C-taxonomy-corrections-plumage-birds.R
Similarly to the specimen data, scripts 02B and 02C are called from script 02A
There is also a 03-revision-extract-genus-level-data.R script which extracts the data at the genus-level, rather than species-level, for sensitivity analyses.
The analysis code is divided into .Rmd files that run the analyses and plot the figures for each section of the paper/supplementary materials, and more detailed scripts for the figures found in the paper and called by the .Rmd files.
Note that throughout I've commented out ggsave commands so you don't clog your machine up with excess plots you don't need.
- 01-summary-stats.Rmd calculates summary statistics and tables.
- 02-specimens-per-species.Rmd investigates % female specimens for each species.
- 03-orders.Rmd looks at % females across orders.
- 04-time.Rmd looks at how % females changes through time.
- 05-ssd.Rmd investigates how sexual size dimorphism is related to % female specimens.
- 06-ornaments.Rmd investigates how plumage colouration and ornamentation (horns, antlers, tusks, manes etc.) are related to % female specimens.
- 07-revision-unsexed.Rmd investigates the unsexed specimens.
- 08-revision-apodiformes.Rmd looks at % females across families of Apodiformes.
- 09-revision-gaudy-females.Rmd looks at % females across orders where the females are larger or more showy.
- 10-revision-wild-sex-ratios.Rmd compares sex ratios in wild birds to those in the museum collections where we have data on both.
- 11-revision-genus-level-summary-stats.Rmd looks at % females using the genus-level data.
- figure-bodymass.R
- figure-orders.R
- figure-plumage-ornaments.R
- figure-specimens-all.R
- figure-ssd-all.R
- figure-types-all.R
- figure-years-all.R
- make-tables.R
/figurescontains the figures/imgcontains the silhouettes from fromPhyloPic.orgneeded for plotting. Contributed by: Ferran Sayol (parrot, hummingbird, tit), Steven Traver (woodpecker), Alexandre Vong (shorebird), Daniel Jaron (mouse), Yan Wong (bat), Becky Barnes (shrew), Lukasiniho (tiger), Sarah Werning (monkey), and Oscar Sanisidro (deer)./manuscriptcontains the manuscript materials in LaTeX format
For reproducibility purposes, here is the output of devtools::session_info() used to perform the analyses in the publication.
─ Session info ────────────────────────────────────────────────────────────────────────────────
setting value
version R version 3.6.1 (2019-07-05)
os OS X El Capitan 10.11.6
system x86_64, darwin15.6.0
ui RStudio
language (EN)
collate en_IE.UTF-8
ctype en_IE.UTF-8
tz Europe/Dublin
date 2019-08-12
Packages ────────────────────────────────────────────────────────────────────────────────────
package * version date lib source
assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.0)
backports 1.1.4 2019-04-10 [1] CRAN (R 3.6.0)
broom * 0.5.2 2019-04-07 [1] CRAN (R 3.6.0)
callr 3.3.1 2019-07-18 [1] CRAN (R 3.6.0)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 3.6.0)
cli 1.1.0 2019-03-19 [1] CRAN (R 3.6.0)
colorspace 1.4-1 2019-03-18 [1] CRAN (R 3.6.0)
crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.0)
crul 0.8.4 2019-08-02 [1] CRAN (R 3.6.0)
curl 4.0 2019-07-22 [1] CRAN (R 3.6.0)
desc 1.2.0 2018-05-01 [1] CRAN (R 3.6.0)
devtools 2.1.0 2019-07-06 [1] CRAN (R 3.6.0)
digest 0.6.20 2019-07-04 [1] CRAN (R 3.6.0)
dplyr * 0.8.3 2019-07-04 [1] CRAN (R 3.6.0)
evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.0)
forcats * 0.4.0 2019-02-17 [1] CRAN (R 3.6.0)
fs 1.3.1 2019-05-06 [1] CRAN (R 3.6.0)
generics 0.0.2 2018-11-29 [1] CRAN (R 3.6.0)
ggfortify * 0.4.7 2019-05-26 [1] CRAN (R 3.6.0)
ggplot2 * 3.2.1 2019-08-10 [1] CRAN (R 3.6.0)
glue 1.3.1 2019-03-12 [1] CRAN (R 3.6.0)
gridBase 0.4-7 2014-02-24 [1] CRAN (R 3.6.0)
gridExtra 2.3 2017-09-09 [1] CRAN (R 3.6.0)
gtable 0.3.0 2019-03-25 [1] CRAN (R 3.6.0)
haven 2.1.1 2019-07-04 [1] CRAN (R 3.6.0)
here * 0.1 2017-05-28 [1] CRAN (R 3.6.0)
hexbin 1.27.3 2019-05-14 [1] CRAN (R 3.6.0)
hms 0.5.0 2019-07-09 [1] CRAN (R 3.6.0)
htmltools 0.3.6 2017-04-28 [1] CRAN (R 3.6.0)
httpcode 0.2.0 2016-11-14 [1] CRAN (R 3.6.0)
httr 1.4.1 2019-08-05 [1] CRAN (R 3.6.0)
jsonlite 1.6 2018-12-07 [1] CRAN (R 3.6.0)
knitr * 1.24 2019-08-08 [1] CRAN (R 3.6.0)
labeling 0.3 2014-08-23 [1] CRAN (R 3.6.0)
lattice 0.20-38 2018-11-04 [1] CRAN (R 3.6.1)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 3.6.0)
lubridate 1.7.4 2018-04-11 [1] CRAN (R 3.6.0)
magrittr 1.5 2014-11-22 [1] CRAN (R 3.6.0)
memoise 1.1.0 2017-04-21 [1] CRAN (R 3.6.0)
modelr 0.1.5 2019-08-08 [1] CRAN (R 3.6.0)
munsell 0.5.0 2018-06-12 [1] CRAN (R 3.6.0)
nlme 3.1-140 2019-05-12 [1] CRAN (R 3.6.1)
patchwork * 0.0.1 2019-08-12 [1] Github (thomasp85/patchwork@fd7958b)
pillar 1.4.2 2019-06-29 [1] CRAN (R 3.6.0)
pkgbuild 1.0.4 2019-08-05 [1] CRAN (R 3.6.0)
pkgconfig 2.0.2 2018-08-16 [1] CRAN (R 3.6.0)
pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.0)
png * 0.1-7 2013-12-03 [1] CRAN (R 3.6.0)
prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.6.0)
processx 3.4.1 2019-07-18 [1] CRAN (R 3.6.0)
ps 1.3.0 2018-12-21 [1] CRAN (R 3.6.0)
purrr * 0.3.2 2019-03-15 [1] CRAN (R 3.6.0)
R6 2.4.0 2019-02-14 [1] CRAN (R 3.6.0)
Rcpp 1.0.2 2019-07-25 [1] CRAN (R 3.6.0)
readr * 1.3.1 2018-12-21 [1] CRAN (R 3.6.0)
readxl 1.3.1 2019-03-13 [1] CRAN (R 3.6.0)
remotes 2.1.0 2019-06-24 [1] CRAN (R 3.6.0)
rlang 0.4.0 2019-06-25 [1] CRAN (R 3.6.0)
rmarkdown 1.14 2019-07-12 [1] CRAN (R 3.6.0)
rphylopic * 0.2.0.9100 2019-08-12 [1] Github (sckott/rphylopic@35165d6)
rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.6.0)
rstudioapi 0.10 2019-03-19 [1] CRAN (R 3.6.0)
rvest 0.3.4 2019-05-15 [1] CRAN (R 3.6.0)
scales 1.0.0 2018-08-09 [1] CRAN (R 3.6.0)
sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.0)
stringi 1.4.3 2019-03-12 [1] CRAN (R 3.6.0)
stringr * 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
testthat 2.2.1 2019-07-25 [1] CRAN (R 3.6.0)
tibble * 2.1.3 2019-06-06 [1] CRAN (R 3.6.0)
tidyr * 0.8.3 2019-03-01 [1] CRAN (R 3.6.0)
tidyselect 0.2.5 2018-10-11 [1] CRAN (R 3.6.0)
tidyverse * 1.2.1 2017-11-14 [1] CRAN (R 3.6.0)
usethis 1.5.1 2019-07-04 [1] CRAN (R 3.6.0)
vctrs 0.2.0 2019-07-05 [1] CRAN (R 3.6.0)
viridisLite 0.3.0 2018-02-01 [1] CRAN (R 3.6.0)
withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.0)
xfun 0.8 2019-06-25 [1] CRAN (R 3.6.0)
xml2 1.2.2 2019-08-09 [1] CRAN (R 3.6.0)
yaml 2.2.0 2018-07-25 [1] CRAN (R 3.6.0)
zeallot 0.1.0 2018-01-28 [1] CRAN (R 3.6.0)
To rerun all the code with packages as they existed on CRAN at time of our analyses we recommend using the checkpoint package, and running this code prior to the analysis:
checkpoint("2019-08-12")