Category
Wildlife Monitoring Solutions for Conservation and Research
One of our current projects, BienABest, illustrates this approach. Commissioned by the German Federal Ministry for the Environment (BMUV) and the Federal Agency for Nature Conservation (BfN), and carried out in collaboration with the University of Ulm together with Europe’s leading bee taxonomists, the aim is to make wild bee identification accessible to everyone. The app enables users to identify the 100 most common wild bee species native to Germany.
An extended professional module with an additional 200 species is available for researchers, students, and monitoring experts. This extension supports scientific field surveys and structured biodiversity data collection. The app is now available for iOS and Android.
BienABest – For the Conservation of Wild Bees
BienABest is a scientific project focused on protecting and strengthening the ecological service of pollination provided by wild bees. The project brings together multiple research institutions to develop effective methods that counteract the severe decline in wild bee populations. Its goal is to restore wild bee biodiversity and enhance their pollination potential across a variety of habitats.
To ensure long-term conservation success, BienABest is establishing standardized methods that can serve as the foundation for systematic and repeatable monitoring. A central aspect of the project is the development of a field identification key that allows wild bees to be identified while alive and released immediately without harm.
These standardized procedures will continue to be used beyond the BienABest project itself and form the basis for long-term monitoring programmes for wild bees.
Public outreach is an important part of the BienABest project. The initiative raises awareness of the ecological value of wild bee biodiversity and presents practical measures for their protection and conservation. Particular emphasis is placed on the use of social media to reach younger audiences and encourage broader engagement.
Sunbird App
The Need for a Field-Ready Wildlife Monitoring App
For the practical implementation of field studies and the collection of wild bee data, a dedicated app is required that supports researchers directly in the field and can also be made accessible to the public. To meet these needs, the app must be intuitive to use, provide a reliable and user-friendly identification key, and include a fast server-based API for efficient data handling. It must also function seamlessly on both tablet and mobile devices.
Native App Development for
Wild Bee Identification
Sunbird Images was selected to carry out the development and hosting of the BienABest app. Within the project, we develop native mobile applications for wild bee identification and field data collection for both Android and iOS. To ensure optimal performance and usability, the apps are programmed natively in Kotlin and Swift and are fully adapted for use on both smartphones and tablets. The basic version, which includes identification information for 100 wild bee species, is available as a free download in the app stores and includes detailed descriptions and representative photographs.
The bee photographs are created by taxonomist Hans Schwenninger using a binocular microscope and image stacking techniques to achieve maximum depth of field. The raw images are then processed by our team, carefully cut out and refined. We combine macro images of the head with full-body plates to create high-quality identification graphics that display diagnostic features in exceptional detail.
Sunbird Images is also responsible for the complete UX and UI design, as well as the implementation of all app functionality, ensuring an intuitive workflow for both researchers and the general public.
Image Stacking and Macro Plate Creation
Image Production Workflow for Wild Bee Identification
Real-Time Taxonomy and Content Updates
The app is connected to a server-based content update system that allows taxonomic information and species data to be updated in real time. Changes to species names, identification keys, or descriptions can be made centrally and delivered directly to users without requiring an app update. This ensures that all monitoring data remains accurate, current, and scientifically reliable.
Development of a Multi-Layer Identification Key for Wild Bees
Another key task undertaken by Sunbird Images is the development of a multi-layered identification key for wild bees. This work is carried out in close collaboration with the research team at the Institute of Evolutionary Ecology and Conservation Genomics, led by Prof. Ayasse, as well as independent taxonomists Hans R. Schwenninger and Erwin Scheuchl. Biologists and developers from the Sunbird Images team contribute expertise in taxonomy, data structure, and user-oriented interface design to ensure practical usability in field conditions.
Extended Identification Module for Advanced Monitoring
An extended version of the app includes an in-app module with an additional 200 wild bee species. This module is designed for expert users, students, and researchers who require more detailed taxonomic resolution for field monitoring and scientific work. The development and hosting of this extended module is also carried out by Sunbird Images.
Partners and Supporters
The joint project BienABest is coordinated by the VDI Society for Technologies of Life Sciences and the University of Ulm, and will run until 2023.
The project is funded by the German Federal Agency for Nature Conservation (BfN) as part of the German Federal Programme on Biological Diversity and supported by the Federal Ministry for the Environment (BMUV).
Additional financial support is provided by the Ministry of the Environment, Climate and Energy of Baden-Württemberg,
BASF SE, and the Bee Care Center of Bayer AG.
