| dc.contributor.author | Suresh A. Sethi | |
| dc.contributor.author | Alex L. Koeberle | |
| dc.contributor.author | Anna J. Poulton | |
| dc.contributor.author | Daniel W. Linden | |
| dc.contributor.author | Duane Diefenbach | |
| dc.contributor.author | Frances E. Buderman | |
| dc.contributor.author | Mary Jo Casalena | |
| dc.contributor.author | Kenneth Duren | |
| dc.contributor.other | Aquatic Research and Environmental Assessment Center, Department of Earth and Environmental Sciences, Brooklyn College | |
| dc.contributor.other | Department of Natural Resources and the Environment, Cornell University | |
| dc.contributor.other | Center for Applied Mathematics, Cornell University | |
| dc.contributor.other | Northeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration | |
| dc.contributor.other | U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, Pennsylvania State University | |
| dc.contributor.other | Department of Ecosystem Science and Management, Pennsylvania State University | |
| dc.contributor.other | Pennsylvania Game Commission | |
| dc.contributor.other | Pennsylvania Game Commission | |
| dc.date.accessioned | 2024-06-30T11:16:38Z | |
| dc.date.accessioned | 2025-10-08T08:27:07Z | |
| dc.date.available | 2025-10-08T08:27:07Z | |
| dc.date.issued | 01-06-2024 | |
| dc.identifier.uri | http://digilib.fisipol.ugm.ac.id/repo/handle/15717717/35919 | |
| dc.description.abstract | 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 ( $$n\approx 100+)$$ n ≈ 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. | |
| dc.language.iso | EN | |
| dc.publisher | Nature Portfolio | |
| dc.subject.lcc | Medicine | |
| dc.title | Multistage time-to-event models improve survival inference by partitioning mortality processes of tracked organisms | |
| dc.type | Article | |
| dc.description.pages | 1-11 | |
| dc.description.doi | 10.1038/s41598-024-64653-w | |
| dc.title.journal | Scientific Reports | |
| dc.identifier.e-issn | 2045-2322 | |
| dc.identifier.oai | 3b5aecee39fc4ebca776d708e1e874f3 | |
| dc.journal.info | Volume 14, Issue 1 | |
