Once plants have been collected, they are transferred to our conservation nursery for propagation, or to our seed lab for viability testing and storage. We are seeing increased effectiveness of these methods with freshly collected seeds and cuttings making it quickly to our staff. As many of these individual plants were not previously known, these actions boost the genetic diversity of ex-situ collections, providing a safe place in the face of environmental degradation.

The Mamba tool allows us to collect plant material via seeds or cuttings from endangered species that we have identified and mapped in the previous building block. This tool has an effective range well over 1000m, making even the most inaccessible areas available for management actions. 

Drone tools have been instrumental as a first step in the assessment of cliff floras. Using drones to get unique viewpoints of these environments, we can now map the distribution and abundance of critically endangered endemic cliff species and expedite their conservation. Field surveys have been conducted in Hawaii, the Republic of Palau, and Madeira (Portugal) with extremely positive results.

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. It is the first application to include information about over 800 amphibian species, in four languages, including call type, photo, CITES information (whether species are trafficked or used for commercial purposes, addressing GBF targets 5 and 9), IUCN status (whether species are endangered, addressing GBF target 4) and general information on animal behavior and reproduction. 

Conserving ecosystems is key to curbing climate change, and maintaining ecosystem services (GBF target 11), which are closely linked to over 50% of the world’s GDP. Over 1 million species face the threat of extinction this century; however, selecting which areas to conserve is challenging with the existing data gap, which is biased towards observations in the global north. Increasing the amount of biodiversity data in the Global South is critical in the conservation of endangered species, found at high density in biodiversity hotspots in the Global South. Amphibians are ideal for acoustic identification due to their diverse vocalizations and are crucial ecosystem indicators (Estes-Zumpf et al., 2022), with over 40% of species at risk of extinction (Cañas et al., 2023). Increasing labeled data for the more than 7,000 amphibian species worldwide would enhance conservation efforts and reduce knowledge gaps in vulnerable ecosystems. By using a citizen science platform to aid in the mitigation of biodiversity loss, we help establish local environmental stewardship of these critical habitats (GBF Target 20).

Other citizen apps have shown the potential that citizen science has on mitigating biodiversity loss. eBird, the largest citizen science project related to biodiversity, has 100 million bird observations from users around the world. These observations help to "document the distribution, abundance, habitat use and bird trends through collected species list, within a simple scientific framework." (Sánchez-Clavijo et. al., 2024).  

iNaturalist, another citizen science app, that uses computer vision algorithms for species identification, has also proven successful in mitigating biodiversity loss. To date, the app has over 200,000,000 observations, with 6 million observations per month, globally. On iNaturalist, research-grade observations are shared with GBIF, which in turn uses that knowledge for policy decisions, research, and community building (GBIF, 2023). 

Currently, our app identifies 71 species of frogs and toads, worldwide. Though many of them are identified as least concern (LC) under the IUCN, we do have one IUCN endangered species, the Southern Bell Frog (Ranoidea raniformis). This lack of threatened species included, underscores the need for diverse practitioners to participate in bioacoustic ecological monitoring. Increasing data points on vulnerable species can serve to inform policy decisions using data-driven insights. Local communities and Indigenous Peoples will be a key asset in increasing the number of species included in the app, as their local knowledge allows us to track species in remote regions. 

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 (GBF target 11). 

Ribbit's approach to data democratization represents a carefully curated process of citizen-driven scientific contribution. By leveraging existing public datasets from iNaturalist sounds and Anuraset, the application establishes a robust foundation for acoustic biodiversity monitoring. These initial datasets provide a comprehensive baseline for machine learning training, ensuring high-quality initial models for anuran identification.

The application's innovative data collection strategy goes beyond gathering information, implementing a rigorous quality control process for user-contributed data. Each citizen-submitted recording will undergo careful verification before potential contribution to the Global Biodiversity Information Facility (GBIF). This approach transforms passive data collection into an active, collaborative scientific process where citizens can meaningfully contribute to conservation research, addressing GBF Target 14, to "Integrate Biodiversity in Decision-Making at Every Level".

Critically, Ribbit maintains stringent data privacy and protection protocols. Recognizing the sensitive nature of ecological data, particularly regarding rare species and precise location information, the application implements strict user consent mechanisms. No user data will be shared or distributed without explicit, informed approval from the contributor, protecting both ecological subjects and citizen scientists' privacy.

Relevant ecological processes and incidents that are of interest in environmental change research typically occur in remote areas beyond the reach of terrestrial communication infrastructures. Data generated in the field using animal tags in these regions can often only be transmitted with a delay of days or even weeks. To overcome this delay and ensure no delay in the early-warning system, GAIA develops a satellite communication module for the tags as well as a nanosatellite operating in low earth orbit (LEO): In order to be able to transmit collected data and information directly from the transmitting node to the LEO satellite (Low Earth Orbit), a high-performance satellite IoT radio module will be integrated into the new tags. This guarantees immediate, secure and energy-efficient transmission of the extracted data. The communication system is based on the terrestrial mioty® technology and will be adapted to satellite-typical frequency bands such as L- and S-band for the project. Typical communication protocols, which are sometimes used in the IoT sector, are usually designed for small packet sizes. Further development of the mioty® system will therefore also aim to increase the data rate and message size to enable application scenarios such as image transmissions.

The satellite IoT system will be key for a no-delay communication and thus for an early-warning system. It greatly contributes to the GAIA system in achieving GBF target 4 "Halt Extinction, Protect Genetic Diversity and Manage Human-Wildlife Conflicts". 

To meet the goal of the GAIA Initiative to develop and put into practice a high-tech early-warning system for environmental changes, a new generation of animal tags is a key component. GAIA teams are working on the hardware and software development of miniaturized animal tags with lowest-power sensor technology with camera and image processing. The tags will be energy-autonomous, optimally adapted to the anatomy of vultures and are the basis for further technological features under development such as on-board artificial intelligences for behaviour detection and image recognition as well as a satellite-based IoT communication system.

Additionally, GAIA is developing concepts of distributed artificial intelligence and networks of micro-processors – animal tags that act just like a swarm. Analogous to natural swarm intelligence, the GAIA initiative is mapping digital swarm intelligence in an ad hoc network of microprocessors. These spontaneously forming networks are the foundation for distributed and sensor-based analysis of large amounts of data. Following this path will make it possible for vulture tags, for example, that are present at the same location during feeding events, to link and share tasks such as artificial intelligence analyses and data transmission.

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.

This novel carcass detection pipeline is a key asset in halting species extinction and managing human-wildlife conflicts and therefore aligns with GBF target 4. The pipeline allows for the swift detection of either vultures' death or the death of the animal that the vultures are feeding on. Both scenarious are relevant to halting species extinction: Poisoning at carcasses contributes significantly to the decline in populations of many vulture species. As vultures use social strategies in their search for food, one poisoned carcass can kill hundreds of birds. Scientists from the GAIA Initiative have shown that tagging vultures allows for an early detection of deaths and the carcass to be removed. Tagging vultures and using the AI pipelines described here can substantially reduce further mortalities. Secondly, early detection of poaching incidents of threatened species can put a local full stop to poaching and contribute significantly to combating extinction.