- Spot the match – wildlife photo-identification using information theory
At least three reference points are required by I3S to construct a fingerprint ; we chose the most easily identifiable and consistent reference points visible in flank photographs: 1) the top of the 5th gill slit, 2) the point on the flank corresponding to the posterior point of the pectoral fin and 3) the bottom of the 5th gill slit (Fig. 1a). The requirement of all three reference points to be visible in the photograph for a fingerprint to be created meant that not all 797 photos could be used. As such, we could compare 433 (54%) of the original photographs, of which 212 were of the left side (LS) and 221 were of the right side (RS) of the shark.
In this updated database, images were matched by an operator highlighting spots within the reference area on a computer screen. Three initial reference points for each image were entered (Fig. 1a), followed by the manual adding of a digital point to the centre of the most obvious spots within the reference frame. Using a search function, the software compares the new fingerprint file against all other fingerprint files in the database by using a two-dimensional linear algorithm, which is simply the sum of the distances between spot pairs divided by the square of the number of spot pairs . The matched spot pairs with the minimum overall score (ranging from 0 [perfect match] to a value 3S text output into the R Package  for further analysis [see Additional file 1].
where k = an assumed number of parameters under a simple linear model (set to 1 for all models) and n' = 100/n that accounts for the fact that an increasing number of spots automatically leads to a higher SS (the 100 multiplier scales the term to be >1); (3) finally, we calculated the IC weight (w) as:
where ΔIC = IC - ICmin for the ith image (ith 'model') from 1 through m (where m = 49). We also calculated the information-theoretic evidence ratio (ER)  for each matched image relative to the top-ranked image based on the w to provide a likelihood ratio of match performance. Here, ER1 is the w of the top-ranked matched photograph divided by the next most highly ranked photograph's w, ER2 is the w of the top-ranked match divided by the w of the third-best match, and so on. Therefore, ER1 provides a likelihood ratio for the match of the top-ranked photograph relative to the next most highly ranked photograph.
R code to calculate Information Criterion (IC) weights for match parsimony. Full instructions for use of R code are contained within the text file.
We acknowledge the support of the whale shark ecotourism industry based in Exmouth and Coral Bay (Western Australia), the Natural Heritage Trust (NHT) Marine Species Recovery Protection fund administered by the Department of Environment and Heritage (Australia), Hubbs-SeaWorld Research Institute, BHP Billiton Petroleum, Woodside Energy, the U.S. NOAA Ocean Exploration Program, the Whale Shark Research Fund administered by the Western Australia Department of Environment and Conservation (DEC), the Australian Institute of Marine Science, NOAA Fisheries and CSIRO Marine and Atmospheric Research. We particularly thank E. Wilson, C. Simpson, J. Cary, R. Mau and B. Fitzpatrick of DEC, and the logistical support and advice of C. McLean, M. Press, A. Richards, I. Field, S. Quasnichka, J. Polovina, B. Stewart, K. Wertz, T. Maxwell, J. Stevens, S. Wilson and J. Taylor, as well as assistance with I3S by Jurgen den Hartog and Renate Reijns (I3S developers). This research was reviewed and approved by the Charles Darwin University Animal Ethics Committee, the Institutional Animal Care and Use Committee of Hubbs-SeaWorld Research Institute and the animal ethics committee of DEC. We thank D. Lohman, G. Taylor, D. Bickford and J. Kirwan for supplying images.
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