Photographic mark-recapture is a cost-effective, non-invasive way to study populations. However, to efficiently apply photographic mark-recapture to large populations, computer software is needed for image manipulation and pattern matching. We created an open-source application for the storage, pattern extraction and pattern matching of digital images for the purposes of mark-recapture analysis. The resulting software package is a stand-alone, multiplatform application implemented in Java. Our program employs the Scale Invariant Feature Transform (SIFT) operator that extracts distinctive features invariant to image scale and rotation. We applied this system to a population of Masai giraffe (Giraffa camelopardalis tippelskirchi) in the Tarangire Ecosystem in northern Tanzania. Over 1200 images were acquired in the field during three primary sampling periods between September 2008 and December 2009. The pattern information in these images was extracted and matched resulting in capture histories for over 600 unique individuals. Estimated error rates of the matching system were low based on a subset of test images that were independently matched by eye. Encounter histories were subsequently analysed with open population models to estimate apparent survival rates and population size. This new open-access tool allowed photographic mark-recapture to be applied successfully to this relatively large population.
All Science Journal Classification (ASJC) codes
- Ecology, Evolution, Behavior and Systematics
- Ecological Modeling