Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer‐assisted pattern‐identification algorithm

• Published in: Ecology and evolution Vol. 14; no. 2; pp. e10922 - n/a
• Main Authors: Dytkowicz, Margarete, Tania, Marcello, Hinds, Rachel, Megill, William M., Buttschardt, Tillmann K., Rosell, Frank
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.02.2024; John Wiley and Sons Inc; Wiley
• Subjects: Accuracy; Algorithms; Animals; Applied Ecology; Aquatic mammals; Beavers; Castor fiber; Conservation Ecology; Demography; Digital cameras; Eurasian beaver; Gorillas; Identification; Image processing; individual pattern recognition; Invariants; Neural networks; non‐invasive mark–recapture; Population Ecology; scale‐invariant feature transform; Single lens reflex; Tails; wildlife ecology; Zoology; Norway; Eurasian beaver; scale‐invariant feature transform; wildlife ecology; Castor fiber; individual pattern recognition; non‐invasive mark–recapture
• ISSN: 2045-7758; 2045-7758
• DOI: 10.1002/ece3.10922

Individual recognition of animals is an important aspect of ecological sciences. Photograph‐based individual recognition options are of particular importance since these represent a non‐invasive method to distinguish and identify individual animals. Recent developments and improvements in computer‐based approaches make possible a faster semi‐automated evaluation of large image databases than was previously possible. We tested the Scale Invariant Feature Transform (SIFT) algorithm, which extracts distinctive invariant features of images robust to illumination, rotation or scaling of images. We applied this algorithm to a dataset of 800 tail pattern images from 100 individual Eurasian beavers (Castor fiber) collected as part of the Norwegian Beaver Project (NBP). Images were taken using a single‐lens reflex camera and the pattern of scales on the tail, similar to a human fingerprint, was extracted using freely accessible image processing programs. The focus for individual recognition was not on the shape or the scarring of the tail, but purely on the individual scale pattern on the upper (dorsal) surface of the tail. The images were taken from two different heights above ground, and the largest possible area of the tail was extracted. The available data set was split in a ratio of 80% for training and 20% for testing. Overall, our study achieved an accuracy of 95.7%. We show that it is possible to distinguish individual beavers from their tail scale pattern images using the SIFT algorithm. The individual differentiation of beavers based on images and their scale pattern is a non‐invasive method. The evaluation of the images using a SIFT algorithm enables an exact differentiation of the animals. Thus, this approach represents a new non‐invasive method for beaver monitoring.