Re-identification of individuals from images using spot constellations: a case study in Arctic charr (Salvelinus alpinus)

Abstract

The ability to re-identify individuals is fundamental to the individual-based studies that are required to estimate many important ecological and evolutionary parameters in wild populations. Traditional methods of marking individuals and tracking them through time can be invasive and imperfect, which can affect these estimates and create uncertainties for population management. Here we present a photographic re-identification method that uses spot constellations in images to match specimens through time. Photographs of Arctic charr ( Salvelinus alpinus ) were used as a case study. Classical computer vision techniques were compared with new deep-learning techniques for masks and spot extraction. We found that a U-Net approach trained on a small set of human-annotated photographs performed substantially better than a baseline feature engineering approach. For matching the spot constellations, two algorithms were adapted, and, depending on whether a fully or semi-automated set-up is preferred, we show how either one or a combination of these algorithms can be implemented. Within our case study, our pipeline both successfully identified unmarked individuals from photographs alone and re-identified individuals that had lost tags, resulting in an approximately 4% increase in our estimate of survival rate. Overall, our multi-step pipeline involves little human supervision and could be applied to many organisms.

Life Sciences New Talent collection

“…Thomas Richards, a Royal Society URF at the University of Oxford, presents research from his group focused on characterization of the RNA-interference pathways of Paramecium bursaria, opening the door to the investigation of its nascent endosymbiotic relationship with a green algae partner [ 8 ]. Michael Morrissey, a Royal Society URF at the University of St Andrews, and colleagues report on their research to apply new deep-learning techniques for re-identification of individuals in wild populations, (in their case the Arctic charr) an area that is fundamental to ecological and evolutionary studies [ 9 ]. Benjamin Hall, a Royal Society URF at University College London, and colleagues identify an approximate Bayesian computation-based approach as the most accurate for estimating uncertainties in lineage tracing datasets, thus informing experimental design in transgenic systems [ 10 ].…”

“…their research to apply new deep-learning techniques for re-identification of individuals in wild populations, (in their case the Arctic charr) an area that is fundamental to ecological and evolutionary studies [9]. Benjamin Hall, a Royal Society URF at University College London, and colleagues identify an approximate Bayesian computation-based approach as the most accurate for estimating uncertainties in lineage tracing datasets, thus informing experimental design in transgenic systems [10].…”