The purpose of community-based permanent nursery beds is to ensure the production of high-quality, resilient seedlings for reforestation efforts while building local capacity. Each of the four project districts (Luwero, Mbale, Busia, and Kapchorwa) established one centralized nursery bed per location, equipped with essential tools, irrigation facilities, and trained nursery operators. Seeds were delivered early (December 2023–January 2024) to allow for the full growth and hardening process, ensuring seedlings met survival standards. The nurseries produced 96,423 seedlings of multi-purpose tree species, including Grevillea and Agrocarpus, which were selected for their adaptability to local climatic conditions, drought resistance, and soil stabilization properties. Nurseries also served as training hubs, where farmers learned good agroforestry techniques, seed propagation, pest control, and seedling management techniques.

The system combines data from drones, satellites, camera traps, and geospatial tools to create a comprehensive monitoring framework. This approach can be adapted for other environmental challenges, such as flood monitoring, by integrating relevant data sources specific to those contexts.

Amphibians are more threatened and are declining more rapidly than either birds or mammals. Amphibian populations are decreasing due to multiple factors, such as climate change, the chytrid fungus, and other anthropogenic factors such as species trafficking. However, the level of threat to amphibians is undoubtedly underestimated because 1294 species (22.5%) are too poorly known to assess, as compared with only 78 birds (0.8%) (Stuart et al., 2004). 

This knowledge deficit underscores the vital importance of educational tools like Ribbit in democratizing scientific research. By lowering barriers to ecological monitoring, apps like Ribbit transform passive observers into active conservation participants. Educational technologies enable citizen scientists to directly contribute to understanding and protecting vulnerable ecosystems, addressing critical research limitations through expanded data collection in under-researched regions.

These innovative platforms increase public awareness about biodiversity challenges while providing accessible pathways for scientific engagement. Unlike bird-focused apps with well-established research infrastructures, anuran conservation has lacked comprehensive citizen science platforms. Ribbit fills this critical gap by empowering individuals to become crucial contributors to amphibian research, turning the tide on data deficiency and supporting global conservation efforts through collaborative, technology-enabled environmental stewardship.

Citizen science apps have been shown to aid in biodiversity monitoring while engaging nature enthusiasts (Callaghan et al., 2019). For instance,  FrogID, an app by the Australian Museum, allows users to record frog calls whose identity is verified by human validators. To date, FrogID has published papers related to monitoring invasive species (Rowley and Callaghan, 2023), informing IUCN red list assessments (Gallagher et al., 2024), assessing fire impacts (Mitchell et. al., 2023), understanding urbanization impacts (Callaghan et al., 2020) and studying frog call behavior (Liu et al., 2022). Our goal is to achieve similar results with Ribbit, with anuran species around the world, and in a shorter time frame. To date, the FrogID team has a backlog of over 18,000 calls, which could be greatly reduced with our app, since the processing time is greatly reduced with the implementation of machine learning algorithms. 

During the first round of beta testing of our app, 50 users submitted recordings for identification. Their feedback has been positive: subject matter experts have pointed out that the species they recorded matched the one predicted by Ribbit, and nature enthusiasts have enjoyed the "Frog of the Day" feature introducing them to a new anuran species or allowing them to re-acquaint with familiar anurans through name and most common vocalization of the species. 

It is the analysis that produces a map with the gradient of vulnerability to the potential impacts of mining tailings dam collapses for environmental risk management. It is the product of cross-referencing information on the impact of potential environmental degradation resulting from the collapse of mining dams and the sensitivity of biodiversity.

Process that defines the most suitable areas for offsetting environmental impacts based on analyses of the similarity of the composition of biodiversity and geodiversity sensitive to mining. This map assumes that the best place to invest efforts to offset the impacts of a mining activity will be those that share the largest number of conservation targets affected by the project. To this end, a spatially explicit hierarchical cluster analysis was performed, with the aid of the vegan and sf packages of the R statistical program, which indicates a gradient of similarity between impacted and protected areas, grouped into groups and clusters for offsetting.

This is a stage in the integration of information to generate a map that indicates the different levels of compatibility between the conservation of biodiversity and speleological heritage and mining, in search of solutions that help to avoid, mitigate and compensate for environmental impacts. This product is obtained by overlaying the Exposure to Impacts with the map of Sensitive Areas of Biodiversity and Speleological Heritage, which makes it possible to understand the gradient of compatibility between biodiversity conservation and mining, by pointing out: (i) which areas should be avoided and which should be prioritized for investments in mineral exploration; (ii) the environmental cost associated with each locational choice; and (iii) the type of mitigating measures to be adopted with greater intensity in each location. The method used to develop the tool can be easily replicated for other locations and even other threat vectors. 

Modern Zoological gardens and aquariums worldwide provide unique opportunities by contributing expertise in animal care, species conservation, and public education, forming a strong foundation for modern conservation and scientific research. By working closely with these institutions and utilizing the data and insights they generate, the GAIA Initiative aims at bridging the gap between in-situ and ex-situ conservation efforts. Animals under human care can serve as valuable models for understanding species’ biology, behaviour, and responses to environmental changes. Furthermore, the controlled conditions of zoological gardens allow for the development and testing of advanced technologies, such as animal-borne sensors and AI systems, under more predictable and accessible settings before deployment in the wild.

Key focus areas of this building block include:

• Generating reference and training data for the development of the AI pipeline for the sensor data. By deploying the tags on vultures in captivity in a large aviary and recoding their behaviour simultaneously, we were able to create a paired dataset for the training of the AI.  With the trained AI there is no more need to observe the animals to detect relevant behaviour, e.g. feeding; the AI can very reliably predict behaviour from the sensor data giving us insights in the behaviour of the target animals throughout their life.
• Education and public engagement: Zoo Berlin integrates GAIA’s findings into its educational programs and collaborates in media relations and public outreach, fostering public awareness and participation in biodiversity conservation and technological innovations. Visitors are introduced to cutting-edge tools and their impact on wildlife conservation.

For ecological research as well as for GAIA use cases, it is necessary to reliably and accurately recognise the behaviour of different animal species over a long period of time in remote wilderness regions. To do this, GAIA scientists have developed and trained an artificial intelligence (AI) that can perform behavioural classification from GPS and acceleration data and tell us exactly what, for example, white-backed vultures fitted with animal tags are doing at any given time and place. This AI will eventually run directly on the GAIA animal tags and generate behavioural information from sensor data. In a second step, the scientists combined the behaviour thus classified 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. Last but not least, GAIA is developing an AI for image recognition that will analyse photos taken by the integrated camera of the new tag system. All those algorithms will run directly on the tag and can perform efficient embedded data processing. This also places very special demands on image recognition AI, which must operate particularly sparingly and with small amounts of data. To this end, GAIA teams are developing appropriate strategies and models for sparse AI.

Satellites and aircrafts play a crucial role in gathering environmental data from the distance, helping us to better understand our climate and ecosystems. Remote sensing, often conducted from aircraft, balloons, or satellites, allows us to monitor large areas and remote regions over extended periods. These “eyes in the sky” are invaluable complements to land-based observations, helping us understand ocean and air currents, land cover changes, and climate change. However, animals also possess extraordinary senses and a unique ability to detect changes in their habitats. By combining animal capabilities with remote sensing technologies, GAIA aims at enhancing our ability to monitor and understand our planet. Animals have superior sensory abilities and behavioural strategies that enable them to sense subtle and dramatic changes in their ecosystems, as well as to detect critical incidents. Vultures, for example, act as “sentinel species” and can elevate the concept of remote sensing to new heights. They regularly patrol vast areas in search of food, operating without emissions, additional resources, or repairs. Furthermore, their patrols are guided by their exceptional vision and the mission to find carcasses. The way they patrol, what they search for, and the incidents they lead us to may be linked to specific environmental changes and ecological events.

To fully exploit the potential of vulture-borne remote sensing, GAIA focuses on two essential aspects. Firstly, powerful tracking devices are attached to vultures to monitor their movements and behaviour on detailed temporal and spatial scales. Secondly, new technological solutions are being developed to better understand what the animals observe and do. This includes a newly developed camera tag featuring an integrated camera, artificial intelligence algorithms for behaviour detection and image recognition, and satellite uplink for real-time coverage in remote regions. With these tools, animals can capture imagery and provide data of their surroundings faster, with higher resolution and specificity than satellite imagery. This innovative approach allows us to see nature through the eyes of animals.