For everything there is a season: Analysing periodic mortality patterns with the cyclomortr package

• Published in: Methods in Ecology and Evolution
• Main Authors: Gurarie, Eliezer, Thompson, Peter R., Kelly, Allicia P., Larter, Nicholas C., Fagan, William F., Joly, Kyle
• Other Authors: Graham, Laura, National Science Foundation, National Park Service
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2019
• Subjects: Rangifer tarandus
• Online Access: http://dx.doi.org/10.1111/2041-210x.13305; https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13305; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/2041-210X.13305; https://besjournals.onlinelibrary.wiley.com/doi/am-pdf/10.1111/2041-210X.13305; https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13305

Abstract Many important demographic processes are seasonal, including survival. For many species, mortality risk is significantly higher at certain times of the year than at others, whether because resources are scarce, susceptibility to predators or disease is high, or both. Despite the importance of survival modelling in wildlife sciences, no tools are available to estimate the peak, duration and relative importance of these ‘seasons of mortality’. We present cyclomort , an r package that estimates the timing, duration and intensity of any number of mortality seasons with reliable confidence intervals. The package includes a model selection approach to determine the number of mortality seasons and to test whether seasons of mortality vary across discrete grouping factors. We illustrate the periodic hazard function model and workflow of cyclomort with simulated data. We then estimate mortality seasons of two caribou Rangifer tarandus populations that have strikingly different mortality patterns, including different numbers and timing of mortality peaks, and a marked change in one population over time. The cyclomort package was developed to estimate mortality seasons for wildlife, but the package can model any time‐to‐event processes with a periodic component.