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, 10 (9), e0136487

BEEtag: A Low-Cost, Image-Based Tracking System for the Study of Animal Behavior and Locomotion


BEEtag: A Low-Cost, Image-Based Tracking System for the Study of Animal Behavior and Locomotion

James D Crall et al. PLoS One.


A fundamental challenge common to studies of animal movement, behavior, and ecology is the collection of high-quality datasets on spatial positions of animals as they change through space and time. Recent innovations in tracking technology have allowed researchers to collect large and highly accurate datasets on animal spatiotemporal position while vastly decreasing the time and cost of collecting such data. One technique that is of particular relevance to the study of behavioral ecology involves tracking visual tags that can be uniquely identified in separate images or movie frames. These tags can be located within images that are visually complex, making them particularly well suited for longitudinal studies of animal behavior and movement in naturalistic environments. While several software packages have been developed that use computer vision to identify visual tags, these software packages are either (a) not optimized for identification of single tags, which is generally of the most interest for biologists, or (b) suffer from licensing issues, and therefore their use in the study of animal behavior has been limited. Here, we present BEEtag, an open-source, image-based tracking system in Matlab that allows for unique identification of individual animals or anatomical markers. The primary advantages of this system are that it (a) independently identifies animals or marked points in each frame of a video, limiting error propagation, (b) performs well in images with complex backgrounds, and (c) is low-cost. To validate the use of this tracking system in animal behavior, we mark and track individual bumblebees (Bombus impatiens) and recover individual patterns of space use and activity within the nest. Finally, we discuss the advantages and limitations of this software package and its application to the study of animal movement, behavior, and ecology.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Fig 1
Fig 1. BEEtag code tructure and generation.
(A) Basic tag design (see text for details). (B) A bumblebee worker (Bombus impatiens) outfitted with a BEEtag and encountered opportunistically in the natural environment. (C) Cockroaches (Blaberus discoidalis) outfitted with BEEtags. (D). Schematic representation of the process for generating a list of unique, usable BEEtags.
Fig 2
Fig 2. Schematic representation of the algorithm for identify unique BEEtags from an image.
Green circles show identified corners of the white quadrangle, and red dots show points where separate pixel values were measured. See text for details.
Fig 3
Fig 3. BEEtag tracking performance.
Performance of the BEEtag tracking system in a sample video (A) in response to variation in resolution (B), gaussian noise (C), and binary threshold value (D). See text for details. Transparent blue lines show data from a single video frame (N = 277 in B and N = 100 in C-D), and thickened red lines show the mean across all frames.
Fig 4
Fig 4. Validation of the BEEtag system in bumblebees (Bombus impatiens).
(A-E, G-I) Spatial position over time for 8 individual bees, and (F) for all identified bees at the same time. Colors show the tracks of individual bees, and lines connect points where bees were identified in subsequent frames. (J) A sample raw image and (K-L) inlays demonstrating the complex background in the bumblebee nest. (M) Portion of tags identified vs. threshold value for individual pictures (blue lines) and averaged across all pictures (red line).

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Grant support

This work was funded by an NSF GRFP fellowship to James Crall and an NSF CAREER grant to Stacey Combes (IOS-1253677). Nick Gravish would like to acknowledge funding from the James S. McDonnell foundation.