Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer‐assisted pattern‐identification algorithm
Abstract 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 c...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-02-01
|
| Series: | Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ece3.10922 |
| _version_ | 1797290902296723456 |
|---|---|
| author | Margarete Dytkowicz Marcello Tania Rachel Hinds William M. Megill Tillmann K. Buttschardt Frank Rosell |
| author_facet | Margarete Dytkowicz Marcello Tania Rachel Hinds William M. Megill Tillmann K. Buttschardt Frank Rosell |
| author_sort | Margarete Dytkowicz |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-07T19:28:58Z |
| format | Article |
| id | doaj.art-6dbc171f553343da932782998dffbb72 |
| institution | Directory Open Access Journal |
| issn | 2045-7758 |
| language | English |
| last_indexed | 2024-03-07T19:28:58Z |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj.art-6dbc171f553343da932782998dffbb722024-02-29T08:56:40ZengWileyEcology and Evolution2045-77582024-02-01142n/an/a10.1002/ece3.10922Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer‐assisted pattern‐identification algorithmMargarete Dytkowicz0Marcello Tania1Rachel Hinds2William M. Megill3Tillmann K. Buttschardt4Frank Rosell5FabLab Blue, Faculty of Technology and Bionics University of Applied Sciences Kleve GermanyFabLab Blue, Faculty of Technology and Bionics University of Applied Sciences Kleve GermanyDepartment of Natural Sciences and Environmental Health, Faculty of Technology, Natural Sciences and Maritime Sciences University of South‐Eastern Norway Bø i Telemark NorwayFabLab Blue, Faculty of Technology and Bionics University of Applied Sciences Kleve GermanyResearch Group Applied Landscape Ecology and Ecological Planning, Institute of Landscape Ecology WWU Münster Münster GermanyDepartment of Natural Sciences and Environmental Health, Faculty of Technology, Natural Sciences and Maritime Sciences University of South‐Eastern Norway Bø i Telemark NorwayAbstract 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.https://doi.org/10.1002/ece3.10922Castor fiberEurasian beaverindividual pattern recognitionnon‐invasive mark–recapturescale‐invariant feature transformwildlife ecology |
| spellingShingle | Margarete Dytkowicz Marcello Tania Rachel Hinds William M. Megill Tillmann K. Buttschardt Frank Rosell Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer‐assisted pattern‐identification algorithm Ecology and Evolution Castor fiber Eurasian beaver individual pattern recognition non‐invasive mark–recapture scale‐invariant feature transform wildlife ecology |
| title | Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer‐assisted pattern‐identification algorithm |
| title_full | Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer‐assisted pattern‐identification algorithm |
| title_fullStr | Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer‐assisted pattern‐identification algorithm |
| title_full_unstemmed | Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer‐assisted pattern‐identification algorithm |
| title_short | Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer‐assisted pattern‐identification algorithm |
| title_sort | individual recognition of eurasian beavers castor fiber by their tail patterns using a computer assisted pattern identification algorithm |
| topic | Castor fiber Eurasian beaver individual pattern recognition non‐invasive mark–recapture scale‐invariant feature transform wildlife ecology |
| url | https://doi.org/10.1002/ece3.10922 |
| work_keys_str_mv | AT margaretedytkowicz individualrecognitionofeurasianbeaverscastorfiberbytheirtailpatternsusingacomputerassistedpatternidentificationalgorithm AT marcellotania individualrecognitionofeurasianbeaverscastorfiberbytheirtailpatternsusingacomputerassistedpatternidentificationalgorithm AT rachelhinds individualrecognitionofeurasianbeaverscastorfiberbytheirtailpatternsusingacomputerassistedpatternidentificationalgorithm AT williammmegill individualrecognitionofeurasianbeaverscastorfiberbytheirtailpatternsusingacomputerassistedpatternidentificationalgorithm AT tillmannkbuttschardt individualrecognitionofeurasianbeaverscastorfiberbytheirtailpatternsusingacomputerassistedpatternidentificationalgorithm AT frankrosell individualrecognitionofeurasianbeaverscastorfiberbytheirtailpatternsusingacomputerassistedpatternidentificationalgorithm |