trackdem: Automated particle tracking to obtain population counts and size distributions from videos in r

The possibilities for image analysis in scientific research are substantial: the costs of digital cameras and data storage are sharply decreasing, and automated image analyses greatly increase the scale, reproducibility and robustness of biological studies. However, automated image analysis in ecolo...

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Bibliographic Details
Published in:Methods in ecology and evolution 2018-04, Vol.9 (4), p.965-973
Main Authors: Bruijning, Marjolein, Visser, Marco D., Hallmann, Caspar A., Jongejans, Eelke, Golding, Nick
Format: Article
Language:English
Subjects:
automated population counts
Automation
Biological evolution
Body size
Cameras
Computer simulation
Cost analysis
Counting
Data processing
Data storage
Digital cameras
Digital imaging
Ecological monitoring
Ecology
Estimates
Image analysis
Image processing
individual trajectories
Insects
Learning algorithms
Machine learning
movement behaviour
neural net
noise filtering
Noise reduction
particle identification
Particle tracking
Population
Population statistics
Reproducibility
Robustness
size distribution
Video data
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Summary:The possibilities for image analysis in scientific research are substantial: the costs of digital cameras and data storage are sharply decreasing, and automated image analyses greatly increase the scale, reproducibility and robustness of biological studies. However, automated image analysis in ecological and evolutionary studies is still in its infancy. There is a clear need for easy to use and accessible tools. Here, we provide a general purpose method to obtain estimates of population densities, individual body sizes and behavioural metrics from video material of moving organisms. The methods are supplied as a new r‐package trackdem, which provides a flexible, easy to install and use, generally applicable and accurate way to analyse ecological video data. The package can detect and track moving particles, count individuals and estimate individual sizes using background detection, particle identification and particle tracking algorithms. Machine learning is implemented to reduce the influence of noise in lower quality videos or to distinguish a single species in multi‐species systems. We show that trackdem provides accurate population counts and body size distributions. Using a series of simulations, we show that our estimates are robust against high levels of noise in videos. When applied to live populations of Daphnia magna, our methods obtained accurate and unbiased estimates of population counts, individual sizes and size distributions, as verified by manual counting and measuring. The package trackdem is also directly usable for movement analysis, for instance in behavioural ecology, as illustrated by the tracking of insects, fish, cars and humans. Within 24 hr, we obtained 192 accurate population counts and body sizes of 22,154 individuals. Such results underscore that automated analysis can improve robustness and reproducibility, and greatly increase the scope of studies in ecology and evolution.
ISSN:2041-210X
2041-210X
DOI:10.1111/2041-210X.12975