Multi-scale features for identifying individuals in large biological databases: an application of pattern recognition technology to the marbled salamander Ambystoma opacum.
Capture-mark-recapture (CMR) studies provide essential information on demography, movement and other ecological characteristics of rare and endangered species. This information is required by managers to focus conservation strategies on the most relevant threats and life stages, identify critical habitat areas, and develop benchmarks for measuring success in recovery plans. However, CMR studies have been limited by individual identification methods that are not effective or practical for many types of organisms. We develop a pattern recognition algorithm and photo-identification method that uses photographs taken in the field to identify individual marbled salamanders (Ambystoma opacum), using their dorsal patterns as 'fingerprints.' The algorithm ranks all images in a database against each other in order of visual similarity. We couple this technology with a graphic user interface to visually confirm or reject top-ranked algorithm results. Using this process, we analyse all adult salamander captures from one year of a long-term study. In a database of 1008 images, the algorithm identified 95% of 101 known matches in the top 10 ranks (i.e. top 1% of all images). Time spent on manual elements of the matching process was estimated at one minute per image, permitting full indexing of all capture records. Capture histories constructed from matched images identified 366 individuals that were captured between 2 and 5 times. Of these, less than 2% were captured at more than one of the 14 pond basins included in the study, suggesting that migrations were strongly directional to and from basins and that 'pond-shopping' among first-time breeders was infrequent. Females arrived at basins later, remained longer, and experienced more weight-loss than males during the breeding period. Synthesis and applications. We develop, test, and apply a pattern recognition algorithm that enables efficient identification of individual marbled salamanders in a database exceeding 1000 images. We expect that this algorithm can be modified to facilitate individual identification in many other organisms because it does not rely on manual coding or discrete geometric pattern features. High performance results suggest that it can be scaled to larger databases, allowing biologists to address critical conservation-based questions regarding demography, reproduction and dispersal of rare and endangered species.