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 We illustrate the periodic hazard function model and workflow of cyclomort with simulated data. We then estimate mortality seasons of two caribou The
In clades that contain large numbers of undescribed species, DNA barcoding methods can serve as a first pass at identifying species limits. The General Mixed Yule Coalescent (GMYC) approach to species delimitation is accurate under certain conditions that are difficult to verify in the very clades for which the GMYC holds the greatest appeal. To circumvent this challenge, we have developed an R package for assessing how well the statistical model implemented in the GMYC fits empirical data. Our approach uses either a parametric bootstrap or a posterior predictive simulation to evaluate model fit. Computational requirements are modest for our package, and most analyses can be completed within minutes to an hour on a typical laptop, depending on whether a user selects a maximum likelihood or Bayesian framework. Results of simulation testing indicate that our approach is effective for assessing the utility of the GMYC model and suggest that it should be included in the analytical pipeline whenever researchers apply the GMYC to clades with unknown species boundaries.
- NSF-PAR ID:
- 10453760
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 12
- Issue:
- 3
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 487-493
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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