Tadpole fingerprinting: Using tail venation patterns to photo‐identify tadpole individuals of a threatened frog

Abstract

AbstractTraditional methods for identifying individual amphibians in capture–mark–recapture (CMR) studies have been primarily confined to post‐metamorphic stages, using artificial markers that come with a variety of limitations. An alternative that may open CMR studies to earlier life stages involves the use of a species' natural external markers in photo‐based identification. In this study, we investigated whether it was possible to distinguish tadpoles of the threatened green and golden bell frog (Litoria aurea) at the individual level based on tail venation patterns. We collected photographs of the tails of captive‐raised tadpoles using a smartphone over a 4‐week period. This photo‐library was used to create an electronic survey where participants were asked to detect matches for query tadpoles from small image pools. We found that most participants agreed on a match for each query, with perfect consensus achieved for most queries (83%). We detected a 14% decline in perfect consensus when participants were asked to match images of tadpoles separated by longer time intervals, suggesting that it is more difficult to visually identify recapture events of L. aurea tadpoles over extended periods due to changes to tail appearance. However, consensus was obtained by participants for all queries, with all matches verified as being correct by the primary researcher. The strength of agreement among participants with no prior experience in matching tadpole tails suggests that there is sufficient inter‐individual variation in this feature for individuals to be manually identified. We thus propose that photo‐identification is likely to be a valid, non‐invasive technique that can be used for short‐term studies on tadpole populations that display tail venation. This offers an alternative to artificial markers that may not allow for individual identification, while also opening up tadpole monitoring programmes to citizen scientists who can be recruited online to process image data from home.