Error rates and variation between observers are reduced with the use of photographic matching software for capture-recapture studies

Abstract

Photographic capture-mark-recapture (CMR) permits individual recognition whilst avoiding many of the concerns involved with marking animals. However, the construction of capture histories from photographs is a time-consuming process. Furthermore, matching accuracy is determined based on subjective judgements of the person carrying out the matching, which can lead to errors in the resulting datasets – particularly in long-term projects where multiple observers match images. We asked 63 volunteers to carry out two photographic-matching exercises using a database of known individuals of the yellow-bellied toad (Bombina variegata). From these exercises, we quantified the matching accuracy of volunteers in terms of false-acceptance and false-rejection rates. Not only were error rates greatly reduced with the use of photographic-matching software, but variation in error rates among volunteers was also lowered. Furthermore, the use of matching software led to substantial increases in matching speeds and an 87% reduction in the false-rejection rate. As even small error rates have the potential to bias CMR analyses, these results suggest that computer software could substantially reduce errors in CMR datasets. The time-savings and reduction in variance among observers suggest that such methods could be particularly beneficial in long-term CMR projects where a large number of images may be matched by multiple observers.