To record and assess wildlife behaviour and environmental conditions in remote locations, the GAIA Initiative developed an artificial intelligence (AI) algorithm that reliably and automatically classifies the behaviours of white-backed vultures using animal tag data. 

As scavengers, vultures always look for the next carcass. With the help of tagged animals and a second AI algorithm, scientists can automatically locate carcasses across vast landscapes. The algorithms described in a recently published article in the “Journal of Applied Ecology” are, therefore, key components of an early warning system that can be used to quickly and reliably recognise critical changes or incidents in the environment, such as droughts, disease outbreaks or the illegal killing of wildlife. 

Harnessing Animal, Human, and Artificial Intelligence for Conservation 

The GAIA Initiative is an alliance of research institutes, conservation organisations, and enterprises that aims to create a high-tech early warning system for environmental changes and critical ecological incidents. The new AI algorithms were developed by the Leibniz Institute for Zoo and Wildlife Research (Leibniz-IZW) in cooperation with the Fraunhofer Institute for Integrated Circuits IIS and the Tierpark Berlin. 

The GAIA Initiative uses the natural abilities of white-backed vultures (Gyps africanus) in combination with highly developed biologging technologies and artificial intelligence. “This combination of three forms of intelligence ­– animal, human and artificial – is the core of our new I³ approach with which we aim to make use of the impressive knowledge that wildlife has about ecosystems”, says Dr Jörg Melzheimer, GAIA project head and scientist at the Leibniz-IZW. 

“For us as wildlife conservation scientists, the knowledge and skills of vultures as sentinels are very helpful to be able to quickly recognise problematic exceptional cases of mortality and initiate appropriate responses”, says Dr Ortwin Aschenborn, GAIA project head alongside Melzheimer at the Leibniz-IZW. “To use vulture knowledge, we need an interface – and at GAIA, this interface is created by combining animal tags with artificial intelligence.” 

Tracking Vulture Behavior 

The animal tags with which GAIA-equipped white-backed vultures in Namibia record two data groups. The GPS sensor provides the tagged individual’s exact location at a specific time. The so-called ACC sensor (ACC is short for acceleration) stores detailed movement profiles of the tag – and thus of the animal – along the three spatial axes simultaneously. Both groups of data are used by the artificial intelligence algorithms developed at the Leibniz-IZW. “Specific acceleration patterns represent every behaviour and thus create specific signatures in the ACC data of the sensors”, explains wildlife biologist and AI specialist Wanja Rast from the Leibniz-IZW. 

“In order to recognise these signatures and reliably assign them to specific behaviours, we trained an AI using reference data. These reference data come from two white-backed vultures that we fitted with tags at Tierpark Berlin and from 27 wild vultures fitted with tags in Namibia.” In addition to the ACC data from the tags, the scientists recorded data on the behaviour of the animals – in the zoo through video recordings and in the field by observing the animals after they had been tagged. “In this way, we obtained around 15,000 data points of ACC signatures ascribed to a verified, specific vulture behaviour. These included active flight, gliding, lying, feeding and standing. This data set enabled us to train a so-called support vector machine. This AI algorithm assigns ACC data to specific behaviours with high reliability,” explains Rast. 

Predicting Carcass Locations with 92% Accuracy 

In the second step, the scientists combined the behaviour and classified it with the GPS data from the tags. Using algorithms for spatial clustering, they identified locations where certain behaviours occurred more frequently. In this way, they obtained spatially and temporally finely resolved locations where vultures fed. “The GAIA field scientists and their partners in the field were able to verify more than 500 suspected carcass locations derived from the sensor data, as well as more than 1300 clusters of other non-carcass behaviours”, says Aschenborn. 

The field-verified carcass locations ultimately established vulture-feeding site signatures in the scientists’ final AI training dataset. This algorithm indicates high-precision locations where an animal most likely dies and a carcass is on the ground. “We could predict carcass locations with an impressive 92 per cent probability, and so demonstrated that a system which combines vulture behaviour, animal tags and AI is beneficial for large-scale monitoring of animal mortality”, says Aschenborn. 

Transforming Wildlife Monitoring 

This AI-based behaviour classification, carcass detection and carcass localisation are key components of the GAIA early warning system for critical environmental changes or incidents. “Until now, this methodological step has been carried out in the GAIA I³ data lab at the Leibniz-IZW in Berlin”, says Melzheimer. “But with the new generation of animal tags developed by our consortium, AI analyses are implemented directly on the tag. This will provide reliable information on whether and where an animal carcass is located without prior data transfer in real-time without losing time.” 

Transferring all GPS and ACC raw data is no longer necessary, allowing data communication with a significantly lower bandwidth to transmit the relevant information. This makes it possible to use a satellite connection instead of terrestrial GSM networks, which guarantees coverage even in remote wilderness regions completely independent of local infrastructure. Even at the most remote locations, critical changes or incidents in the environment – such as disease outbreaks, droughts or illegal killing of wildlife – could then be recognised without delay. 

Conclusion 

In recent decades, the populations of many vulture species have declined sharply and are now acutely threatened with extinction. The leading causes are the loss of habitat and food in landscapes shaped by humans and a high number of direct or indirect incidences of poisoning. The population of the white-backed vulture, for example, declined by around 90 per cent in just three generations – equivalent to an average decline of 4 per cent per year. “Owing to their ecological importance and rapid decline, it is essential to significantly improve our knowledge and understanding of vultures to protect them”, says Aschenborn. “Our research using AI-based analysis methods will not only provide us with insights into ecosystems. It will also increase our knowledge of how vultures communicate, interact and cooperate, forage for food, breed, rear their young and pass on knowledge from one generation to the next.”  

Leibniz Institute for Zoo and Wildlife Research (IZW)