Spotting the difference: towards fully-automated population monitoring of African penguins Spheniscus demersus

Abstract

Placing external monitoring devices onto seabirds can have deleterious effects on welfare and performance, and even the most benign marking and identification methods return sparse population data at a huge time and effort cost. Consequently, there is growing interest in methods that minimise disturbance but still allow robust population monitoring. We have developed a computer vision system that automatically creates a unique biometric identifier for individual adult African penguins Spheniscus demersus using natural markings in the chest plumage and matches this against a population database. We tested this non-invasive system in the field at Robben Island, South Africa. False individual identifications of detected penguins occurred in less than 1 in 10 000 comparisons (n = 73 600, genuine acceptance rate = 96.7%) to known individuals. The monitoring capacity in the field was estimated to be above 13% of the birds that passed a camera (n = 1453). A significant increase in this lower bound was recorded under favourable conditions. We conclude that the system is suitable for population monitoring of this species: the demonstrated sensitivity is comparable to computer-aided animal biometric monitoring systems in the literature. A full deployment of the system would identify more penguins than is possible with a complete exploitation of the current levels of flipper banding at Robben Island. Our study illustrates the potential of fully-automated, non-invasive, complete population monitoring of wild animals.