Abstract

Many systems involve progression through a series of distinct stages, such as disease or developmental stages. In ecological studies, often individuals such as small arthropods cannot be marked, so data are collected on cohort development. Multistage models for unmarked cohort data use a distribution for each stage duration and possibly stage-specific mortality rates. We generalize previous models and present computational methods for smoothed maximum likelihood estimation. The general model allows arbitrary distribution assumptions, stage-specific mortality, unobserved stages, and correlations between stage durations using Gaussian copulas. Monte Carlo integration of the stage distributions is used to approximate the probabilities needed for the likelihood. We establish a heuristic smoothing step for the simulated probabilities that yields a smooth approximate likelihood surface. For the case of classic grasshopper cohort data, we demonstrate AIC model selection to determine which among past arbitrary constraints are actually justified by the data. Finally, we demonstrate how estimates of stage distribution parameters depend on the unknown stage correlations.

Keywords

Disease progression; Insect development; Life table; Simulated likelihood; Stage-frequency data; Stage-structured population models

Published in

Journal of Agricultural, Biological, and Environmental Statistics
2015, Volume: 20, number: 1, pages: 140-155
Publisher: SPRINGER

SLU Authors

• Knape, Jonas

  • Department of Ecology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Ecology
Probability Theory and Statistics


Publication identifier

DOI: https://doi.org/10.1007/s13253-014-0189-7

Permanent link to this page (URI)

https://res.slu.se/id/publ/68545