New technologies offer promising possibilities in biodiversity monitoring to increase standardization of sampling methods and improve cost efficiency. Among the former, uncrewed aerial systems (UAS) are widely used today to produce orthomosaics of a particular area. At the same time, computer-intensive methods for automated object detection within images have increased accordingly. While they are widely used in science, applied nature conservation makes little use of these methods. The current study aimed to test the applicability of UAS in combination with a deep-learning based object detection workflow in Schütt-Graschelitzen, a small-scale Natura 2000 protected area near Villach, Austria. For this purpose, we trained a YOLO_v8 algorithm with flowers of Gladiolus illyricus from an orthomosaic. The orthomosaic was split into about 1000 equally sized tiles with 80 tiles used for training and 20 tiles used for validation. For ground truthing, the individual inflorescences were counted manually. Our main findings indicated moderate model performance with the training and validation dataset and also with new data. Moderate – rather than strong – performance is likely a result of too little training data. While object detection worked considerably well, background revealed too high variability, making reliable classifications challenging. Comparing the different work steps (without UAS mission) suggests that creating a representative training dataset is the most time-intensive part of the workflow. For small areas and a single survey, this is likely not efficient compared to traditional field sampling methods. However, its efficiency increases with each resurvey event, as pretrained deep-learning models developed during prior monitoring cycles can be reused. This can reduce the amount of training data required in a subsequent survey. Additionally, UAS- and deep-learning based monitoring can help at sites with high sensitivity to trampling and favors large study areas, as its efficiency increases with the sample size area.

The current study reports on the presence and activity of bats at the St. Georg Parish Church, Thomatal, Salzburg, Austria. This site was known in previous years to host a maternity roost of the alpine long-eared bat (Plecotus macrobullaris). Passive acoustic monitoring (PAM) and traditional roost emergence counting techniques were used to document bat activity. The study compared the effectiveness of three ultrasonic detection devices: Song Meter Mini Bat, Echo Meter Touch 2 Pro, and batcorder. Following expert verification, the study confirmed the presence and seasonal emergence phenology of P. macrobullaris and documented additional bat species including Eptesicus nilssonii and Myotis daubentonii. The results highlighted variations in the accuracy and species detection capabilities of the devices. While differences in device performance were clear, the findings emphasize the applicability of PAM for monitoring bat populations. Findings underscore the importance of proper device handling and appropriate data analytical techniques to ensure reliable species identification.

With the advent of new technological capabilities for recording and monitoring biodiversity, the requirements for research infrastructure are changing. Currently, Carinthia University of Applied Sciences (CUAS) is developing testing facilities designed to enable synchronized biodiversity assessment at various scales in the medium term. This major investment is supported by the Austrian Research Promotion Agency, FFG, and is scheduled to become fully operational in the coming years. The facilities comprise several components, including the SKS research site at Metschacher Moos, stationary measurement and observation networks, a mobile biodiversity laboratory, and laboratory and computational resources at CUAS. These infrastructure components are utilized by researchers and students at CUAS and are also intended to be made available to collaborating scientific institutions and companies.

The COVID-19 pandemic has accelerated digitalization in education. The TREEgital project is taking advantage of this trend to use digital tools to impart forest knowledge in schools. It explores the forest as a living, recreational and economic space and emphasizes its role in protecting the climate and biodiversity. Key topics are biodiversity, climate change, and career guidance in green industries, all within the framework of Education for Sustainable Development (ESD). In addition, TREEgital teaches pupils digital skills.
Digital educational products for schools have been developed, such as the augmented reality app “Öswald - App in den Wald!”, which improves learning by overlaying virtual elements with real environments, and YouTube career guidance videos focusing on STEM topics. In the “TreeCast” video podcast, forest experts discuss topics ranging from climate change to soil health. The social media series “Ask the Expert” provides insights into the professional world of forest research.