Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)
April 8th, 2019
Here we present a protocol to individually track animals over a long period of time. It uses computer vision methods to identify a set of manually constructed tags by using a group of lobsters as case study, simultaneously providing information on how to house, manipulate, and mark the lobsters.
This protocol allows automation of the tracking of multiple animals. Using a simple label to identify each animal an ID analysis tool to detect the tags. This protocol significantly reduces the number of hours needed to analyze the video recordings using leskey data.
to estimate more accurate completions from the experimental studies. Animals in other environments that can be tagged in similar fashion can be tracked with similar protocols providing application in chronobiology, ecology, viral research or neuroscience. We have provided the hardware design and open source programming language and provide a model we recommend users adopt in the protocols to learn Python programming language.
To construct a tag for the target animal, first cut four 40 millimeter diameter circles from the black plastic sheet, and two 26 millimeter sided equilateral triangles and two 26 millimeter diameter circles from a white plastic PVC sheet. Mark the center of one white triangle and one white circle. And make a 10 millimeter hole at each mark.
Then glue one white shape into the center of each of the four black circles. To set up the experimental area, place infrared dark time LEDs into the tank. Keep the infrared lights on at all times.
Next, place blue light time LED lights into a modified fiberglass tank containing sand and four flexible PVC pipe burrows, and connect the lights to an apparatus for managing the photo period. When all of the lights have been placed, position the chilled sea water inlet at one corner of the tank, with the corresponding outlet at the opposite corner. Check that the sea water input is set up at a flow rate of about 4 liters per minute.
Surround the chamber with a black curtain to provide full isolation from external light. Place the tripod equipped with the web camera to the side of the experimental arena, with the camera 130 centimeters above and at the center of the experimental arena. Connect the camera to a computer outside of the curtain, and adjust the parameters of the video recording according to the characteristics of the species, making sure to create a timestamp, including the date in the time-lapse video, for eventual behavioral scoring.
To tag the animals, add 7 degree Celsius water to an icebox with submerged compartments, and place 4 lobster into 4 separated compartments. After allowing the lobsters to acclimate for about 30 minutes, transfer one lobster onto a tray of crushed ice for five minutes, and use absorptive paper to dry the upper part of the immobilized lobster's cephalothorax. Place a drop of fast drying glue on the dried carapace and press the tag onto the glue for about 20 seconds, until the glue hardens.
Then return the lobster to its compartment in the icebox, and label the other three animals in the same manner. When all of the lobsters have been labeled, place the lobsters back into their cell for 24 hours. The next day, use the same icebox to transfer the lobsters from the acclimation facility to the experimental chamber, and initiate the video recording.
Execute the tagging and transfer steps under the red light conditions to avoid radial damage to the lobster for the receptors. Additional water species or terrestrial species may not need this care. Then obtain an averaged background image from five minutes of video or from the initial 100 frames before introducing the animals one by one into the experimentation tank inside their respective compartments of water, and wait for the lobsters to come out of their compartments.
Of the total number of animals counted in this representative experiment, 79 percent of the animal detections were correctly matched by the program, and 89.5 percent of the tags were correctly identified. Only 3.8 percent of tags were incorrectly classified. But the remaining 6.6 percent corresponding to false positives.
After the completion of a specific video analysis, the obtained positions data can be used to evaluate the different behavioral patterns of the lobsters. For example, space occupancy map plotting, using a two-dimensional kernal density estimation with an axis aligned by variate normal kernal evaluated on a square grid, allows the areas in which the lobsters spend a higher percentage of their time to be represented with high color intensity plots. The daily activity rhythms of the lobsters can also be plotted as millimeters covered at ten minute binned intervals over time.
It's important to maintain exact specifications in terms of the size and shape and to always execute the binned analysis step in the background with no user interface. The image analysis model can be expanded to look at specific components of the crustaceans to help to improve the behavioral study in terms of animal interactions. These complex animal social interactions occur in a similar fashion in an actual environment allowing the style and important aspects of biological research with some application for ecology and neuroscience.
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Overview
This protocol allows for the automation of tracking multiple animals over extended periods. Using simple labels for identification, it employs an ID analysis tool to detect tags, significantly reducing analysis time.
Key Study Components
Area of Science
- Chronobiology
- Ecology
- Neuroscience
Background
- Tracking animals individually is crucial for various biological studies.
- Manual tracking can be time-consuming and labor-intensive.
- Computer vision methods can enhance tracking efficiency.
- Using lobsters as a case study provides insights into practical applications.
Purpose of Study
- To develop a protocol for long-term tracking of animals.
- To provide a method for tagging and identifying animals easily.
- To facilitate the study of animal behavior in different environments.
Methods Used
- Computer vision techniques for tag detection.
- Open source programming for analysis.
- Hardware design for tracking setup.
- Python programming for users to learn and implement.
Main Results
- Significant reduction in analysis time for video recordings.
- Improved accuracy in estimating experimental outcomes.
- Protocol applicable to various animal species and environments.
- Accessible resources for researchers to replicate the study.
Conclusions
- The protocol enhances the efficiency of animal tracking studies.
- It provides a scalable method applicable across different research fields.
- Open source tools empower researchers to adapt and innovate.
Frequently Asked Questions
Executive Industry Relevance
Automated long-term tracking of cohoused aquatic animals enables high-throughput behavioral phenotyping for target validation in neuropharmacology and chronobiology. The protocol supports mechanistic de-risking by quantifying locomotor activity patterns under controlled light-dark cycles, providing predictive confidence in phenotypic screening assays. Its open-source, scalable design facilitates enterprise reuse across discovery biology workflows.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses through quantification of individual locomotor rhythms in cohoused models.
- Operational Value: Reduces manual analysis burden via automated tag detection and background subtraction, increasing throughput for behavioral endpoint measurement.
Screening & Assay Development
- Scientific Value: Generates quantitative dependent variable outputs (e.g., space occupancy, binned movement) suitable for dose-response modeling in assay development.
- Operational Value: Standardizes behavioral readouts through reproducible video tracking, supporting assay reproducibility and cross-lab comparability.
Translational & Preclinical Research
- Scientific Value: Supports disease-relevant system modeling by capturing naturalistic behavioral patterns in socially housed crustaceans, enhancing translational continuity.
- Operational Value: Facilitates preclinical model validation through longitudinal monitoring of activity rhythms, aiding risk-adjusted advancement decisions.
Pipeline & Workflow Integration
The method integrates into discovery biology by enabling hypothesis-driven behavioral screening, transitions to lead identification via quantifiable phenotypic outputs, and supports preclinical work through longitudinal activity profiling.
- Discovery Biology: Supports mechanistic de-risking by isolating independent variables (e.g., light-dark cycles) to clarify pathway-level effects on locomotor behavior.
- Screening: Delivers assay-ready, standardized quantitative outputs (e.g., millimeters covered per ten-minute bin) for reliable compound evaluation.
- Analytics: Provides kernel density estimation and binned time-series data enabling statistical comparison of behavioral conditions.
- Translational Research: Connects to preclinical continuity through ethologically relevant activity patterns in group-housed models.
- Enterprise Reuse: Leverages open-source tools and customizable tagging for scalable deployment across species and laboratories.
Operational & Enterprise Impact
- Scientific Value: Predictive confidence in target validation through objective, high-resolution behavioral phenotyping.
- Operational Value: Standardization and scalability via automated tracking reducing inter-operator variability.
- Strategic Value: Improved go/no-go decisions by reducing false negatives in behavioral screening through reliable individual identification.
- Portfolio Impact: Risk-adjusted prioritization via reproducible locomotor activity metrics enabling data-driven progression criteria.
Implementation Considerations
- Requires expertise in computer vision, behavioral neuroscience, and aquatic husbandry for proper tagging and environmental control.
- Depends on infrared lighting, webcam hardware, and open-source software infrastructure for background subtraction and tag detection.
- Necessitates cross-team standardization of tag design, video parameters, and analysis pipelines to ensure reproducibility across sites.
- Involves adaptation considerations for species-specific tag size, locomotion rates, and burrowing behaviors affecting detection accuracy.
- Limited by occlusion and tag misidentification rates (6.6% false positives, 3.8% misclassification) necessitating validation against manual scoring for critical endpoints.
