Adjusting for capture, recapture, and identity uncertainty when estimating detection probability from capture-recapture surveys

Abstract

When applying capture-recapture analysis methods, estimates of detection probability, and hence abundance estimates, can be biased if individuals of a population are not correctly identified (Creel et. al., 2003). My research, motivated by the 2010 and 2011 surveys of Western Arctic bowhead whales conducted off the shores of Barrow, Alaska, offers two methods for addressing the complex scenario where an individual may be mistaken as another individual from that population, thus creating erroneous recaptures. The first method uses a likelihood weighted capture recapture method to account for three sources of uncertainty in the matching process. I illustrate this approach with a detailed application to the whale data. The second method develops an explicit model for match errors and uses MCMC methods to estimate model parameters. Implementation of this approach must overcome significant hurdles dealing with the enormous number and complexity of potential catch history configurations when matches are uncertain. The performance of this approach is evaluated using a large set of Monte Carlo simulation tests. Results of these test vary from good performance to weak performance, depending on factors including detection probability, number of sightings, and error rates. Finally, this model is applied to a portion of the bowhead survey data and found to produce plausible and scientifically informative results as long as the MCMC algorithm is started at a reasonable point in the space of possible catch history configurations.

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