Misidentification of marked individuals is unavoidable in most studies of wild animal populations. Models commonly used for the estimation of survival from such capture–recapture data ignore misidentification errors potentially resulting in biased parameter estimates. With a simulation study, we show that ignoring misidentification in Cormack–Jolly–Seber (CJS) models results in systematic positive biases in the estimates of survival and in spurious declines of survival over time. We developed an extended robust design capture mark–resight (RDM) model that includes correct identification parameters to get unbiased survival estimates when resighting histories are prone to misidentification. The model assumes that resightings occur repeatedly within a season, which in practice is often the case when resightings of colour-marked individuals are collected. We implemented the RDM model in a state-space formulation and also an approximate, but computationally faster, model (RDMa) in JAGS and evaluated their performances using simulated and empirical capture–resight data on black-tailed godwits Limosa limosa. The CJS models applied to simulated data under an imperfect identification scenario data produced strongly positively biased estimates of survival. For a range of degrees of correct identification probabilities, the RDM model provided unbiased and accurate estimates of survival, reencounter and correct-identification probabilities. The RDMa model performed well for large datasets (>25 individuals), with high resighting (>0.3) and high correct identification (>0.7) probabilities. For the empirical data, the CJS model estimated average juvenile survival at 0.997% and adult survival at 0.939% and also detected a strong decline in adult survival over time at a rate of −0.14 ± 0.029. In contrast, the RDMa model estimated a probability of correct identification of 0.94, annual juvenile survival at 0.234%, adult at 0.834% and less strong decline over time (−0.046 ± 0.016). We conclude that estimates of survival probabilities obtained from data that include misidentification errors and analysed with standard CJS model are unlikely to be correct. The bias in survival increases with the magnitude of misidentification errors, which is inevitable as datasets become longer. Since misidentification due to tag misreads is common in empirical data, we recommend the use of the here presented RDM model to provide unbiased parameter estimates.
- Bayesian analysis
- black-tailed godwit