Estimating survival in photographic capture–recapture studies: overcoming misidentification error

Summary 1. For many species, noninvasive photographic identification offers a powerful and cost–effective method for estimating demographic parameters and testing ecological hypotheses in large populations. However, this technique is prone to misidentification errors that can severely bias capture–recapture estimates. 2. We present a simple ad hoc data conditioning technique that minimizes bias in survival estimates across all rates of misidentification. We use simulated data sets to characterize trade‐offs in bias, precision and accuracy of survival estimators for a range of misidentification probabilities, sampling intensities, survival rates and population sizes using this conditional approach. 3. Misidentification errors resulted in mean survival estimates that were negatively biased by as much as −24·9% when errors were ignored. Applying the conditional approach resulted in very low levels of bias across parameter space. However, the main cost of conditioning is a loss of precision, which was particularly severe at low sampling intensities. Overall, the conditional approach was superior to the nonconditional approach [in terms of root mean square error (RMSE) of survival estimates] in 51% of the parameter combinations that we explored. 4. We apply the data conditioning technique to a 3‐sample capture–recapture data set compiled from 2551 images of a migratory wildebeest, Connochaetes taurinus, population in northern Tanzania. We estimate the false rejection rate (i.e., the probability of failing to match two photographs of the same individual) using a test set of ‘known‐identity’ individuals. With this information, we compare survival estimates derived from conditioned data (inline image = 0·698 ± 0·176), unconditioned data (inline image = 0·706 ± 0·121) and simulated data to illustrate some of the key considerations for deciding whether to apply a conditional approach to a photographic data set. 5. These analyses demonstrate that ignoring misidentification error can lead to substantial bias in survival estimates. When sampling intensity and misclassification error rates are both relatively high, use of our conditioned data approach is preferred and yields survival estimates with lower RMSE. However, when sampling intensity and misclassification error are both small, the standard approach using unconditioned data yields smaller RMSE.