- Phoebe Teoh
- Jan 7, 2024
- 4 min read
Picture of codes being ran on computer program. Credit: Pupaza, David. Everyday Grind. 2021. January 12, 2021.
When I was taking a break from my studies and was waiting for university to start, I chanced upon an article pertaining to how data science is relevant in the world of biodiversity. I was at first hesitant as my knowledge of data science was only limited to the field of biotechnology and the digital world and I was fascinated by how these two different fields of knowledge (that I was both very intrigued by) could work together to facilitate the growth of human knowledge and expedite the goal of environmentalists wanting to make the world a better place. I have summarised a few of the points that I have learnt along the way on how data science empowers animal migration tracking and I hope that this blog can give you a brief introduction to the wonders of data science.
GPS Radio Telemetry
Black winged kites being attached with GPS tags. Credit: The Hebrew University of Jerusalem
In order for data to be collected via GPS radio telemetry, biologists and researchers first capture and attach a GPS device onto the animal and the movements of that particular individual are then meticulously tracked over an extended duration (Perras and Nebel 2012). Data scientists are responsible for collating and analysing the dataset of various animal movements. Their expertise lies in discovering patterns within this data, enabling scientists and biologists to gain deeper insights. This newfound knowledge empowers researchers to make more accurate predictions regarding the migratory routes of specific species, and foster a better understanding of their behaviour. This facilitates the generation of interventions to assist animals when required.
Satellite Imagery
GHGSat Vanguard satellite orbiting around Earth. Credit: GHGSat. 2023. November 11, 2023.
Satellite telemetry has been a widely employed technique for tracking animals since the 1980s (Hatch et al. 2000).Despite common misconceptions that animals are not harmed during the satellite-based data extraction process, the reality is that animals are indeed captured for the purpose of externally attaching or even surgically implanting Platform Transmitter Terminals (PTTs) into their bodies (Perras and Nebel 2012). These PTTs subsequently establish communication with satellite orbits through radio signals, which will then use algorithms to calculate the frequency of the radio waves, thereby pinpointing the precise location of the animals. Data analysts’ role is to integrate this tracking information with insights garnered from satellite-captured images detailing environmental shifts and weather patterns. These different fields of information work together to create a deep understanding of animal habitats, offering a well-rounded view of their ecosystems.
Remote Sensing
Koala resting on a tree. Credit: Australia Koala Foundation, and BBC Wildlife Magazine. 2022. October 11, 2022.
In recent years, the collection of data concerning free-ranging wildlife has been constrained by the constraints of manual methods and ethical considerations have been increasingly integrated into scientific practices (Rey et al. 2017). The utilisation of sensors have since been an essential alternative to further increase our understanding of the animal kingdom. These sensors are attached onto vehicles such as aircraft and drones, effectively expanding their geographic coverage. (Tuia et al. 2022) Data scientists work behind the scene to design the data collection systems that power these sensors which ensures the efficiency and effectiveness of the data collection. Through their expertise, data scientists bridge the gap between technology and scientific discovery, allowing us to better understand the beauty and mysteries of nature.
Social Media Data
Wombats and kangaroos spotted in Australia. Credit: Australia's official Instagram account, 2023
Viewing pressure refers to the amount of attention that posts on social media garner over different time periods and can surprisingly aid data scientists and researchers in gathering information on animal migration patterns. Data scientists and mathematical scientists develop models that can calculate the actual ratio of viewing pressure on wildlife in different time periods on social media (Papafitsoros, Adam, and Schofield 2023). Data scientists are also responsible for gathering, organizing, and refining social media data pertaining to individual animals. Data analytic tools that data scientists use are capable of processing large amounts of data available on social media, discerning and filtering out miscellaneous or misleading information that may arise from various sources.
While these may cover some of the reasons why data science plays a pivotal part in the natural world, it is only a fraction of what the field of data science has to offer. Data science’s reach extends from healthcare with predictive analytics to even business innovation. I hope that this blog sparked or reinforced your interest in the beauty of data science.
References:
Hatch, Scott A., Paul M. Meyers, Daniel M. Mulcahy, and David C. Douglas. “Performance of Implantable Satellite Transmitters in Diving Seabirds.” Waterbirds: The International Journal of Waterbird Biology 23, no. 1 (2000): 84–94. http://www.jstor.org/stable/4641113.
Papafitsoros, Kostas, Lukáš Adam, and Gail Schofield. 2023. “A Social Media-Based Framework for Quantifying Temporal Changes to Wildlife Viewing Intensity.” Ecological Modelling 476 (February): 110223. https://doi.org/10.1016/j.ecolmodel.2022.110223.
Perras, Michael, and Silke Nebel. 2012. “Satellite Telemetry and Its Impact on the Study of Animal Migration | Learn Science at Scitable.” Www.nature.com. Nature Education Knowledge 3(12):4. 2012.
https://www.nature.com/scitable/knowledge/library/satellite-telemetry-and-its- impact-on-the-94842487/.
Rey, Nicolas, Michele Volpi, Stéphane Joost, and Devis Tuia. 2017. “Detecting Animals in African Savanna with UAVs and the Crowds.” Remote Sensing of Environment 200 (September): 341–51. https://doi.org/10.1016/j.rse.2017.08.026.
Tuia, Devis, Benjamin Kellenberger, Sara Beery, Blair R. Costelloe, Silvia Zuffi, Benjamin Risse, Alexander Mathis, et al. 2022. “Perspectives in Machine Learning for Wildlife Conservation.” Nature Communications 13 (1). https://doi.org/10.1038/s41467-022- 27980-y.