As the modern digital era continues to evolve, groundbreaking advancements in Big Data management and Machine Learning are not only revolutionising our societal perspectives, but also catalysing a paradigm shift in the study of animals.
Around the world, technologists are developing ambitious digital environmental monitoring systems. Some of these Digital Earth systems are as small as a pond, while others encompass entire continents. (…) These digital systems rely on an astounding range of hardware, from sensors to drones to satellites.
The implementation of these sophisticated technologies enables enhanced or automated data collection (ex. object detection, segmentation, tracking), processing, analysis and interpretation. That data might be handled in unsupervised and self-supervised learning techniques, that are able uncover complex patterns, undetectable by conventional, human-biased methods, thus deepening our understanding of a specific research area, that is animal behaviour, migratory patterns, and overall welfare. For example, researchers are already able to effectively utilise a combination of radar technology, atmospheric data, and advanced artificial intelligence algorithms to accurately predict bird migration patterns across North America up to a week in advance, contributing significantly to bird conservation efforts.
The integration and analysis of diverse data streams, as stated by Jennifer E. Smith and Noa Pinter‐Wollman (2020), are crucial in animal behaviour research. To comprehensively decode the dynamics of animal communication, it is imperative to analyse not only the communicators and the mechanisms employed, but also the contexts in which the communication occurs, thereby enabling the identification of the messages that are being transmitted. That’s why a holistic approach that encompasses diverse data types, beyond just sound, is necessary for efficient decoding. This approach is deeply intertwined with the concept and practical applications of the Internet of Animals (IoA - analogy to the Internet of Things), initially coined to describe live animal-tracking systems and later conceptually developed, to include other types of data.
“What animals currently exist? How do they move around the planet? What do they do underground or at night? Whom do they eat, and who eats them?
The data gathered from the Animal Internet answer these questions. They produce a new image of nature. This image truly takes on form when considered within the context of a parallel development that began with the Information Age and continues to revolutionize our perspective on society. (…) What happens when wild animals start pinging us, and we are able to identify them as unique individuals with their own backstory? The discussion on the ways in which digital technology can reshape our relationship with other living creatures and with nature is still new; in fact, it hasn’t even really begun.“
Martin Wikelski, director of the Max Planck Institute for Ornithology, who developed the vision of IoA, in 2002 launched the ICARUS (International Cooperation for Animal Research Using Space), a project designed to track wildlife from space. The data were meant to be transmitted to the International Space Station (ISS), using a specially designed three-metre antenna on the station. For many years, this idea was met with ridicule and scepticism from various space agencies; however, as the project and technology developed, NASA recognized the potential of ICARUS and the IoA, investing its time and resources into Wikelski’s idea. Wikelski’s team has eventually developed lightweight (around 5g) GPS, temperature, heart rate, and acceleration sensors. This innovative approach allows for the monitoring of species previously inaccessible due to size limitations, including fish, small birds and mammals, which comprise 75% of all such species. NASA has initiated a five-year study of potential space-based solutions, named ‘Internet of Animals’, that aims to assess the needs of animal movement and ecology researchers, design a next-generation space-based animal tracking system, science and technology, that is needed to link animal tracking data to the many other dimensions of biodiversity that can now be mapped from space using remote sensing‘. This initiative builds on first experiments with space-based tracking from the 1970s. Researchers from NASA tracked an elk, named ‘Monique’, and a black bear, using ground-based radiolocation collars integrated with the Nimbus satellite system, which allowed for monitoring their movements and physiological parameters.
In June 2023 ICARUS project entered a phase, involving a CubeSat—a miniaturised satellite measuring 10 cm on each side and weighing approximately 2 kg. CubeSat will house an advanced ICARUS receiver designed to track wildlife globally, including previously inaccessible regions such as polar areas. The CubeSat is part of an 8U payload for the SeRANIS mission, and it will orbit in low Earth orbit (LEO). The system’s operational deployment is scheduled for October 2024, with subsequent CubeSats planned for 2025 and 2026, aiming to establish a network capable of near real-time data transmission.
To properly investigate the context of communication, precise geolocation of the individual is necessary, since it provides essential spatial data that enhances the accuracy of behavioural analysis. In tracking animal locations, Movebank, established in 2008 by Wikelski and other researchers at the Max Planck Institute, has revolutionised aggregating diverse data types from methodologies like GPS, acoustic telemetry, light-level loggers, time of arrival systems, as well as advanced sensor outputs like accelerometry, barometry, and thermometry. This system is supported by a PostgreSQL database, stored on servers at the Max Planck Computing & Data Facility, which ensures data integrity and allows for secure, user-defined sharing settings. With over 21 manufacturers and 7 different communication networks, Movebank can provide near-live data feeds. It is not only a database; it is an integrated platform with tools for data visualisation, quality control, analysis, and archival. It can be accessed via a website, a mobile app, and application programming interfaces (APIs), integrated with the EnvDATA System and the MoveApps platform to allow users to perform analyses without extensive coding skills.