Case Study - Enhancing Conservation Efforts With Tuna Track
Project Overview
Over the last five decades, human actions – from burning fossil fuels to overfishing – have dramatically altered the Earth’s atmosphere, oceans, and land. These disruptions pose significant threats to both the environment and human health. This is a harsh reality we must confront, as a well as a responsibility we all share. Over time, bluefin tuna has come to symbolise the issue of overfishing. Despite extensive research, our understanding of the migration patterns and behaviours of these elusive fish remains limited. In this era of environmental challenges, the development of innovative tools is not merely a choice; it is a necessity. Our planet’s delicate balance is increasingly threatened by human activities. Among the myriad of consequences of these disruptions, overfishing stands as a prominent issue.
Bluefin tuna, a species emblematic of our oceans, has faced relentless exploitation due to its high market demand. As the pressures of overfishing continue to mount, the need for a comprehensive conservation effort becomes paramount. To safeguard the future of this species, we must not only study the specie’s elusive migration patterns and behaviours but also equip ourselves with tools capable of mitigating the threats it faces.
Tuna Track is one of such innovations. It is a tool that will aid researchers in monitoring and analyzing Bluefin Tuna populations which will ultimately inform government policies that can positively influence conservation efforts. Tuna Track will aid the preservation of not only this specie, but also the intricate web of life in our oceans.
Project Goals
- Design an intuitive and user-friendly interface for researchers to access and analyze data effortlessly.
- Ensure that users can easily import, export, and manage data, promoting efficient research workflows.
- Present complex data and visualizations in a clear and comprehensible manner to facilitate data interpretation.
Tuna Track’s main purpose is to aid researchers in the analysis/visualization of data gathered from tagged Tuna.
Data Visualization
Tuna Track serves two main purposes – Data Visualization and Data Analysis. Data visualization involves displaying data collected from tagged Tuna in a more user-friendly manner. Data researchers can view on Tuna Track include:
- Visualization of Bluefin Tuna Movement: Researchers can generate clear visualizations of bluefin tuna movement. By mapping the movement of tagged individuals over time, users gain a comprehensive understanding of migration routes and destinations.
- Visualize Environmental Conditions: Environmental Data obtained from satellites and buoy networks allow researchers to correlate tuna behaviour with environmental conditions, such as sea surface temperature, salinity, chlorophyll levels, and more. Researchers can track trends and anomalies in these factors, shedding light on how they influence tuna behaviour.
- Visualization of Behaviour Changes: Tuna Track enables visualization of bluefin tuna behaviour, including swimming speed, depth changes, and responses to various stimuli. Researchers can study how factors like temperature, time of day, and oceanographic fronts influence these behaviours.
Using The Control Panel
The general idea is a map viewport that displays data directly controlled by the ‘Control Panel’. The Control Panel allows researchers to edit different types of data, ranging from ecological factors to animal behaviour and human activity. Once a data item is selected on the control panel, it shows up on the ‘Data Tray’. The data tray provides a bird’s eye view of all data in view, as well as allowing quick edits to the data.
Additionally, some datasets may be viewed as real time animated graphs as well as visually on the map viewport. This is where the timeline comes into play. It enables researchers visualize environmental conditions over time, and view them in context to movement of Tuna across the oceans.
The File Action panel makes managing tracking data effortless. Researchers can seamlessly load, save and share customized tracking data, which opens up the door to the next stage of research – Analysis.
My Insights / Considerations
My first design exploration included just a map viewport and a control panel. I soon realized that this made editing data in view a hassle and took an unnecessary number of clicks and time just searching for the data to edit. I modified this exploration and added the Data Tray, which gives researchers a bird’s eye view, allowing quick edits and customization.
Additionally, while designing the File Action Panel, I realized that using just icons to represent the actions users can take would not be make actions clear to users. I explored several options, like using a collapsible menu. I ultimately decided that it would be best for users to see the actions without having to take any extra steps. To remedy this, I added a tooltip on hover that describes the action. I also made all the Panels collapsible so users can view data unobstructed if they wish to.
Data Analysis
The Analysis section equips researchers with in built statistical tools to gain deeper insights into the behaviour, movements, and environmental interactions of bluefin tuna. This section allows researchers to interpret and make meaningful use of the tracking data gathered from the tagged tuna, Researchers can use a wide range of statistical methods to help validate hypotheses, detect trends, and determine the significance of various factors in relation to bluefin tuna behaviour and movement.
Conducting an Analysis
This basically involves importing saved data from the visualization section, and conducting a suitable statistical analysis with the added data.
Once the data is added, researchers can conduct the test. The results provide data in two sections. The first is an APA style summary report generated by an AI tool. The second section displays the calculations done.
Areas For Improvement
- Real-Time Data Integration: Tuna Track was built to take data from retrieved tags. I believe incorporating real-time data from tracking tags and oceanographic sensors would be a significant improvement. This would provide researchers with up-to-the-minute information on tuna movements and environmental conditions.
- Machine Learning: The integration of machine learning algorithms could help predict tuna migration patterns and behaviours based on historical data. It would be a valuable addition to the project’s analytical capabilities.
- Data Archiving: Going forward, I believe researchers should be encouraged to archive and share their data, contributing to a more extensive and open dataset for future research.