Effects of Photo and Genotype-Based Misidentification Error on Estimates of Survival, Detection and State Transition using Multistate Survival Models

TitleEffects of Photo and Genotype-Based Misidentification Error on Estimates of Survival, Detection and State Transition using Multistate Survival Models
Publication TypeJournal Article
Year of Publication2016
AuthorsWiniarski, Kristopher J., and McGarigal Kevin M.
Secondary AuthorsBanks, Sam C.
JournalPLOS ONE
Volume11
Issue1
Paginatione0145640
Date Published1/2016
KeywordsCerebral metabolic rate assay, image processing, invasive species, population dynamics, population modeling, population size, simulation and modeling, variant genotypes
Abstract

We simulated multistate capture histories (CHs) by varying state survival (φ), detection (p) and transition (ψ), number of total capture occasions and releases per capture occasion and then modified these scenarios to mimic false rejection error (FRE), a common misiden- tification error, resulting from the failure to match samples of the same individual. We then fit a multistate model and estimated accuracy, bias and precision of state-specific φ, p and ψ to better understand the effects of FRE on different simulation scenarios. As expected, φ, and p, decreased in accuracy with FRE, with lower accuracy when CHs were simulated under a shorter-term study and a lower number of releases per capture occasion (lower sample size). Accuracy of ψ estimates were robust to FRE except in those CH scenarios simulated using low sample size. The effect of FRE on bias was not consistent among parameters and differed by CH scenario. As expected, φ was negatively biased with increased FRE (except for the low φ low p CH scenario simulated with a low sample size), but we found that the magnitude of bias differed by scenario (high p CH scenarios were more negatively biased). State transition was relatively unbiased, except for the low p CH scenarios simulated with a low sample size, which were positively biased with FRE, and high p CH scenarios simulated with a low sample size. The effect of FRE on precision was not consistent among parameters and differed by scenario and sample size. Precision of φ decreased with FRE and was lowest with the low φ low p CH scenarios. Precision of p esti- mates also decreased with FRE under all scenarios, except the low φ high p CH scenarios. However, precision of ψ increased with FRE, except for those CH scenarios simulated with a low sample size. Our results demonstrate how FRE leads to loss of accuracy in parameter estimates in a multistate model with the exception of ψ when estimated using an adequate sample size.

DOI10.1371/journal.pone.0145640
Short TitlePLoS ONE