Multistage time-to-event models improve survival inference by partitioning mortality processes of tracked organisms

Suresh A. Sethi; Alex L. Koeberle; Anna J. Poulton; Daniel W. Linden; Duane R. Diefenbach; Frances E. Buderman; Mary Jo Casalena; Kenneth Duren

doi:10.1038/s41598-024-64653-w

Multistage time-to-event models improve survival inference by partitioning mortality processes of tracked organisms

Scientific Reports

By: Suresh A. Sethi, Alex L. Koeberle, Anna J. Poulton, Daniel W. Linden, Duane R. Diefenbach, Frances E. Buderman, Mary Jo Casalena, and Kenneth Duren

https://doi.org/10.1038/s41598-024-64653-w

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Abstract

Advances in tagging technologies are expanding opportunities to estimate survival of fish and wildlife populations. Yet, capture and handling effects could impact survival outcomes and bias inference about natural mortality processes. We developed a multistage time-to-event model that can partition the survival process into sequential phases that reflect the tagged animal experience, including handling and release mortality, post-release recovery mortality, and subsequently, natural mortality. We demonstrate performance of multistage survival models through simulation testing and through fish and bird telemetry case studies. Models are implemented in a Bayesian framework and can accommodate left, right, and interval censorship events. Our results indicate that accurate survival estimates can be achieved with reasonable sample sizes ( $n \approx 100 +)$ and that multimodel inference can inform hypotheses about the configuration and length of survival stages needed to adequately describe mortality processes for tracked specimens. While we focus on survival estimation for tagged fish and wildlife populations, multistage time-to-event models could be used to understand other phenomena of interest such as migration, reproduction, or disease events across a range of taxa including plants and insects.

Additional publication details
Publication type	Article
Publication Subtype	Journal Article
Title	Multistage time-to-event models improve survival inference by partitioning mortality processes of tracked organisms
Series title	Scientific Reports
DOI	10.1038/s41598-024-64653-w
Volume	14
Year Published	2024
Language	English
Publisher	Nature Publications
Contributing office(s)	Coop Res Unit Leetown
Description	14628, 11 p.
Google Analytic Metrics	Metrics page