Code associated with publication of analysis of the timing of migration for Divergent trends in migration timing of shorebirds along the Pacific Flyway.
The scripts included are mostly for reference. The simulation scripts can be run without further data, but the analysis scripts require the original data, which is available from the authors or in the data repositories described in the manuscript.
Publicly available data are found here:
Other data available by contacting data owners described in manuscript.
Python 3.4 and R 3.4
Some modules will need to also be install for each software.
R: tidyverse cowplot boot lme4 lattice corrgram broom purrr gamlss gamlss.tr lazyeval
To install these packages from the R command line type:
install.packages(c("tidyverse", "cowplot","boot","lme4","lattice","corrgram","broom","purrr","gamlss","gamlss.tr","lazyeval"))
Python: numpy pandas time itertools math
If you have the prerequisites you can run the python scripts from the command line by typing:
python simPower.py
Modify the siteibm.ctl control file to modify the paramaters for the model. The output of the model can be analyzed using Simulation2.r. See the example code at the bottom of this script.
The power analysis can be run using in R using source("Simulation1.r"). To recreate the data from scratch, you will need to uncomment lines 51-57 and run the simulations again.
The final analysis presented in Hope et al. Migratory Progression of Shorebirds can be run using Post-hoc_analysis.R. The data is imported from rds files. The estimated peak passaged dates are described in detail in Supplementary Table S1 of the paper. The function used to calculate the dates of peak passage is shown in muEstimation.R. Data cleaning and modifications from raw data are shown in data.clean.r, except for Copper River and Hartney data, which are shown in CRD_HART_data.r.
- David Hope - Coding and bug testing
- Mark Drever - Interpolation of species composition -
HART_species_composition.RandCRD_species_composition.R
Co-authors on the manuscript provided input into the methodology. Their information is described in the text.
This project is licensed under the MIT License - see the LICENSE.md file for details