Evaluation of Bias, Precision and Accuracy of Mortality Cause Proportion Estimators from Ring Recovery Data

Knowledge about proportions of specific mortality causes is important for the design of efficient conservation measures or the determination of harvest regulations. Unfortunately, these proportions are difficult to estimate. We (Schaub and Pradel 2004a) have recently introduced a multistate capture-recapture model that allows one to estimate proportions of specific mortality causes from recoveries of dead animals with known cause of death. However, parameter estimation was found to be difficult, because the likelihood surface of the model relative to most parameters has a flat ridge, unless the proportions of mortality causes vary with time and the cause-specific recovery rates are constant. These conditions are likely to be violated in most empirical situations. For the application of this model, it is therefore important to study the sensitivity of parameter estimates to violations of these assumptions. I use a Bayesian implementation of the model to evaluate bias, precision and accuracy of parameter estimates under variable means and temporal variation of mortality cause proportions and recovery rates. Survival rate estimates were unbiased in all scenarios. Bias and precision of the proportion of mortality causes and of the cause-specific recovery probabilities decreased with increasing temporal variance of the proportion of mortality causes while their accuracy increased. The bias of these estimates also decreased with decreasing difference between cause-specific recovery probabilities and with decreasing temporal variation of them. Moreover, informative priors affected the posterior distribution of the parameters when temporal variation in the proportion of mortality causes was low. Temporal variance of the proportion of mortality causes could be estimated reliably regardless of bias. This result is important, since it allows one to assess whether accuracy of the estimates of mortality proportions is acceptable for the objectives of a study. The bias of the naïve estimator (quotient of the number of animals reported dying from a particular cause to the total number reported altogether) was usually much larger than the bias of the corresponding estimator from the multistate model. In conclusion, a careful application of the multistate capture-recapture model can give useful information about the proportion of mortality causes that is otherwise hard to obtain.