Carinthia Nature Tech (Carinthia II - Part 3) is a scientific journal dedicated to the application of disruptive technologies in the field of nature conservation. The journal aims to feature advanced testing, research, and development of novel technologies for recording and analyzing biodiversity, including species, populations, habitats, and ecosystems. It also covers monitoring geological, hydrological, and climatic systems, and innovative methods to monitor environmental pressures.
Natural Science Association for Carinthia, Carinthia University of Applied Sciences
Copyright (c) 2026 Carinthia II - Part 3 https://creativecommons.org/licenses/by-nc/4.0
Integrating remote sensing and in situ data to assess wetland ecosystem health within the Pressure-State-Response (PSR) framework in Lake Tana UNESCO Biosphere Reserve, Ethiopia
The Lake Tana Biosphere Reserve (LTBR) consists of wetlands that provide critical ecosystem services but face degradation from anthropogenic pressures and climate change. This study integrates the Pressure-State-Response framework with multi-temporal remote sensing (Landsat and Sentinel2, 2011–2025) and participatory citizen science. Landscape metrics (patch density, Shannon diversity index, net primary productivity) were derived alongside demographic and infrastructure layers to assess pressures. Quantitative data were triangulated with focus group discussions and participatory mapping. Results show that high population density (up to 13,000 individuals/km²) and cultivated land expansion (78% to 85% from 2011 to 2025) are primary degradation drivers, manifesting as landscape fragmentation and declining vegetation productivity especially in the eastern floodplain areas of LTBR. Integrating spatial analysis with local knowledge reveals spatially differentiated wetland vulnerability shaped by demographic, agricultural, and climatic factors. This approach demonstrates the feasibility of fusing objective spatial data with participatory evidence, offering a scalable model for evidencebased conservation policy in the LTBR and other sensitive wetland systems.
Advancing biodiversity research in the agroecosystem
The EU Biodiversity Strategy 2030 recognizes farmers’ dual role in safeguarding and impacting biodiversity. Within this policy context, the Horizon Europe project BioMonitor4CAP addresses monitoring of agroecosystem biodiversity and stakeholder-informed policy design to contribute perspectives and guidance in advance of the next Common Agricultural Policy period. The project integrates above- and below-ground indicators—pollinator insects, farmland birds, soil microbiota, and landscape structure—by pairing traditional methods with state-of-the-art tools across a network of European research sites. In Carinthia, six sites spanning low-elevation and high-elevation areas are used for testing traditional and state-of-the-art workflows targeting farmland birds, pollinator insects, soil communities, vegetation plots, and landscape structure. Sampling timing is based on a growing degree-day (GDD) approach. Carinthia University of Applied Sciences leads regional implementation, device feasibility testing, GDD scheduling, and remote sensing case studies, contributing to systematic reviews, indicator frameworks, and data infrastructure. A socio-economic component of the project engages farmers, advisors, and policymakers through focus groups, co-creation workshops, and a discrete choice experiment on subsidy trade-offs, complemented by mapping a Europe-wide network of ~600 agrobiodiversity observatories. Early findings highlight operational feasibility, ethical considerations, and context dependence of methods; regulatory and site constraints necessitated adaptive designs. BioMonitor4CAP is synthesizing evidence to deliver results-based monitoring guidance and policy options that reconcile practicality of monitoring approaches with conservation effectiveness. The project underscores that no single measure fits all farms, but co-designed, evidence-based approaches can align agricultural production, biodiversity outcomes, and circular economy goals for Europe.
Implementing GPS logger data for visitor monitoring in the Karawanken–Karavanke UNESCO Global Geopark
Human-Nature coexistence is an increasingly important component in the sustainable management of conservation areas. Pioneering conservation approaches are focused on integrating these two aspects rather than creating a strict separation. Disruptions to the balance between humans and nature can affect both natural resource conservation and the tourist experience. To avoid this, resource, infrastructure, and tourism flow management plans are needed. The HUMANITA project aims to develop innovative solutions to monitor tourism and its impact on the environment within selected conservation areas in Central Europe. It includes several methods to estimate tourism numbers, hotspots, and activities. We distributed 69 GPS loggers in the Karawanken–Karavanke UNESCO Global Geopark and created visitor density and intensity maps. This study demonstrates that GPS logger tools can support science-based management in conservation areas by providing quantitative insights for managing tourism flows. However, part of our objective is also to highlight key limitations, including low tourist participation, technical challenges, and concerns related to the high spatial resolution of the data in the UNESCO Global Geopark.
From wildlife management to biodiversity assessment
Camera traps are widely used in wildlife management to monitor focal species such as ungulates. However, the large amount of data on non-target species is rarely analyzed systematically. In this study, we examine whether management-oriented camera trap surveys can be used to infer broader biodiversity patterns. Using camera trap data from four study sites, we recorded 57 species, including 25 mammals and 32 birds. Species richness varied between sites but this was not primarily driven by total sampling effort. Despite substantially greater effort invested, a long-term opportunistic survey detected only slightly more species than a short-term survey using systematic random placement, consistent with spatial coverage, camera density and area size jointly influencing species detection rather than sampling duration alone. We analyzed species accumulation using Michaelis–Menten models fitted to taxa that could be reliably detected by camera traps. Systematic random surveys reached species saturation more rapidly and exhibited faster accumulation than opportunistic designs. The proportion of mammal species listed under the EU Habitats Directive remained consistent across sites, suggesting that camera traps can effectively detect species of conservation significance, even when deployed for management purposes. Late detections of rare or hard-to-detect species disrupted saturation at one site, illustrating the sensitivity of accumulation models to detectability. Overall, our results suggest that camera trap surveys targeting specific species can provide valuable additional information on biodiversity, and that by-catch data should be systematically integrated into wildlife monitoring programs. Systematic random designs may offer efficiency advantages for species inventories, although this comparison is observational and confounded by differences in area size and camera density.
Interdisciplinary approaches exploring the connection between biology and technology through slime mold simulation
Slime molds are fascinating organisms that, despite their name, are not fungi but amoeboid protists. Among them, the plasmodial slime mold Physarum polycephalum forms a multinucleate syncytium, known as the plasmodium, through repeated nuclear divisions without cytokinesis. In this phase, the organism appears as a viscous network-like mass containing millions of nuclei and is capable of solving complex tasks, such as navigating mazes, through self-organized behavior. Additionally, P. polycephalum is easy to culture and handle, making it an ideal model organism for studying self-organization, adaptive behavior, and biological network formation. The Smart Grids research group in the Department of Networked and Embedded Systems (NES) at the University of Klagenfurt conducts research involving slime molds. Specifically, the group investigates the potential of self-organizing applications in energy networks. In cooperation with the Austrian Research Promotion Agency (FFG), the Faculty of Technical Sciences at the University of Klagenfurt offers Austrian students aged 15 and older the opportunity to gain insight into university-level research through a four-week summer internship. The project “Experiments with Slime Molds” was offered for the second time in 2024 by the Smart Grids research group. This interdisciplinary project allows participants to explore the intersection of biology and computer science. The internship offers participants the chance to explore the interfaces between biology, computer science, and mathematics. It is especially aimed at students interested in biological systems, the application of mathematical models, and the development and use of software tools for simulating natural phenomena. The interns begin by cultivating slime molds in Petri dishes and then simulate their behavior using SISMO (Simulation of Slime Molds), a tool developed by the Smart Grids group. This article explores how biology and technology were combined in a four-week internship program and provides insights into the individual experiments conducted. A central element is the report by intern Viviane Elmenreich, who shares her experiences from the 2024 IT internship at the University of Klagenfurt. The focus of the internship was on biological experiments with the slime mold P. polycephalum, including the cultivation and reactivation of sclerotia, analysis of information processing within the plasmodium, and observations of behavioral and color response to stimuli. These experiments were complemented by technical components, such as programming a Raspberry Pi to automate time lapse photography and using the simulation tool SISMO to model slime mold behavior. The results demonstrated that slime molds are capable of absorbing information from their environment, processing dyes from food sources, and finding efficient paths to nutrients, both in physical experiments and in simulated environments. Furthermore, the combination of hands-on and digital approaches enabled deeper insights into the principles of biological self-organization. The internship not only contributed to the development and refinement of the SISMO simulation tool but also played a key role in enhancing the scientific education of the participants. The students gained valuable skills in experimental biology, programming, documentation, media production, and interdisciplinary research. These experiences fostered scientific thinking and also served as a strategic investment in talent development and in strengthening the University of Klagenfurt’s position as an innovative research institution.
How fast must one run to stay in the same place? On the second issue of Carinthia Nature Tech
Copyright (c) 2025 Carinthia II / Part 3 - Carinthia Nature Tech https://creativecommons.org/licenses/by-nc/4.0
Carinthia Nature Tech: a new forum for integrating natural sciences and emerging technologies
Copyright (c) 2024 Carinthia II / Part 3 - Carinthia Nature Tech https://creativecommons.org/licenses/by-nc/4.0
“Tage der Biodiversität 2025” – Collection of abstracts
Making science visible: On the third issue of Carinthia Nature Tech
We are pleased to present the third publication of the journal Carinthia Nature Tech with this issue. While the first release marked the achievement of launching this journal after a long process, the second release provided an opportunity to consolidate certain workflows, results, and presentations. Now, the current issue demonstrates that Carinthia Nature Tech has already evolved into a series. Many more issues are planned to showcase and communicate the potential of new technologies for capturing, monitoring, and conveying nature in general, and biodiversity in particular. In doing so, we know we are in distinguished company worldwide.
Handbuch Naturschutzfachkraft: a guide to getting things done in conservation
Monitoring biodiversity: Hitting the nail on the head with Conservation Technology
Restoring ecosystems, advancing science: The Metschacher Moos outdoor lab
On the occasion of the realignment of research activities at the Metschacher Moos, this article compiles key information about the natural environment, land use history, previous scientific studies, and planned initiatives. The State of Carinthia maintained a long-term lease on the site, located in a side branch of the Glan Valley (Carinthia), to restore wet meadows that were drained in the 20th century. With the acquisition of the land by the Kärntner Sparkasse Foundation, these restoration efforts and scientific activities can now be continued. This presents an opportunity to develop a research site dedicated to restoration and the application of biodiversity technologies (BiDiTechs). The project "S.O.S. Metschacher Moos," funded by the Austrian Biodiversity Fund, enabled initial measures to resume management activities, including control of invasive species, restoration of monitoring plots, and installation of a sensor network. Further research projects are underway, aiming to contribute to the assessment of biodiversity and ecosystem services in restoration initiatives.
Development and field testing of a portable eDNA water sampler
One of the methods currently revolutionizing biodiversity assessment is the analysis of environmental DNA (eDNA). Here we present a prototype of a semi-automatic sampler for water eDNA collection. The sampler is a portable, affordable, battery-powered device that supports hands-free filtration using peristaltic pumps and allows for the processing of multiple water samples under field conditions. The prototype system consists of commonly available components including an ESP32 microcontroller, a 3D-printed battery adapter, voltage converters, and two peristaltic pumps. The system is controlled by a simple push-button starting a 20-minute sampling cycle. The filtration setup ensures that contamination is avoided by placing the filter before the pump in the water flow path. Initial field testing was conducted at Lake Silbersee in Carinthia, Austria, where successful filtrations were carried out using different filters. Concentration measurements of extracted eDNA samples ranged from 9.8 to 24.0 ng/µL, confirming that the device can collect eDNA for water biodiversity assessment. Tests also revealed that filters became clogged after 1 L of filtration in the given water source, supporting the decision to limit operation time. Future improvements to the prototype may be possible by adding further pump capacity, a display, data logging, and a web-based interface to further increase usability and automation.
Establishment of the Miyawaki forest at Carinthia University of Applied Sciences in Villach
The Miyawaki method offers a fast and effective nature-based solution for the restoration of ecosystems, especially in urban areas. Developed by Akira Miyawaki, the approach relies on dense planting of native species to create diverse, multi-layered forest ecosystems, resulting in a so-called Miyawaki Forest (MF). In April 2025, an MF was created on the campus of Carinthia University of Applied Sciences (CUAS) in Villach as part of the Interreg IT-AT project BioBox to promote urban biodiversity, especially for pollinators and birds. Following soil analysis, an area of 91 m² was prepared by plowing and enrichment with straw and compost. Inspired by the species composition of local riparian forests along the Drava River, nearly 300 saplings of native trees and shrubs were planted in three vertical layers by 16 volunteers. The MF at CUAS will serve as an outdoor laboratory for the students to monitor soil, growth and biodiversity. Although they are still rare in Austria, MF — like the CUAS pilot project — demonstrate the potential of small-scale urban forests to increase biodiversity, improve ecosystem services and engage communities. The Miyawaki method offers significant long-term environmental benefits, including better air and soil quality, urban cooling and carbon sequestration.
Applying a slime mold-inspired algorithm to network planning challenges in the Carinthian region
This article presents the application of the slime mold-based algorithm Simulation of Slime Molds (SISMO) using case studies from the Carinthian region, demonstrating its applicability to various network structures. SISMO is inspired by the ability of the myxomycete Physarum polycephalum to process information and solve optimization tasks. The effectiveness of SISMO in network formation and finding shortest paths is first evaluated using parts of Carinthia’s bus and railway infrastructure. Our results show that SISMO can create networks similar to the existing transportation network, consider obstacles, and replicate existing connections. These findings allow conclusions for the future optimization of further networks using bio-inspired approaches. Based on these insights, this paper presents an approach that uses SISMO to create a repair plan for the Carinthian power transmission network after a simulated electromagnetic pulse attack. The algorithm is applied to a corresponding graph model to identify the most critical areas to be repaired, analogous to the nutrient supply of the slime mold between food sources. SISMO was adapted for this grid planning task and fed with the positions of relevant power plants and substations of the Carinthian electricity transmission grid. This approach has significant potential for diverse applications, especially those where the algorithms are not limited by the physical or infrastructural constraints that shaped the original network topology. Further exploration of this approach could yield significant insights into various fields. Overall, the paper provides insights into the potential applications of bio-inspired algorithms, such as slime mold simulation, for solving network planning tasks and presents concrete case studies from the Carinthian region.
Assessing forest structure with LiDAR: A method benchmark in the Rohrach Natural Forest Reserve
The Rohrach Natural Forest Reserve, located in the northern foothills of the Alps, serves as a long-term reference area for preserving and studying natural forest development. In this study, three methods for assessing forest structure—classical field-based inventory, terrestrial laser scanning (TLS), and drone-based LiDAR (UAV-LS)—were systematically compared. Building on an initial survey conducted in 1996, 44 sample plots were revisited and supplemented with high-resolution 3D measurements. The results show that TLS provides highly accurate volume estimates that closely match those obtained through the classical inventory. The UAV-based approach enabled a comprehensive, area-wide survey of the 48-ha study site and yielded average growing stock values of 547 m³/ha—almost identical to those from the classical inventory (549 m³/ha). However, automated detection of lying deadwood using UAV data underestimated the volume by up to 50% compared to the line intersect method. Whether this discrepancy is due to the line intersect method’s assumption of randomly distributed logs being unsuitable for this site, or whether UAV-LS underestimates deadwood due to canopy shadowing or algorithmic omission of fine structures, requires further investigation. The study also highlights persistent challenges in tree segmentation within steep or densely vegetated areas: overlapping crowns often result in misclassifications or undetected stems. While TLS continues to offer the highest geometric accuracy, UAV-LS provides the advantage of rapid, large-scale data acquisition with minimal disturbance to sensitive environments. These findings underscore the importance of integrated methodological approaches for effective long-term monitoring in natural forest ecosystems.
Predictive maintenance in infrastructure: Utilizing 3D point clouds for efficient damage detection
Growing urbanization is driving the demand for infrastructure such as parking lots, roads, and bicycle lanes. While green spaces and trees are often integrated into these development projects to mitigate negative climate impacts, they can cause root-related damage that poses safety risks and requires costly monitoring. Public road networks are typically inspected with advanced but expensive surveillance vehicles that are too costly for private applications, leaving private infrastructure such as parking lots, private roads, and storage areas without comparable solutions. Thus, this paper presents a methodology for detecting and classifying damage areas in 3D point clouds of parking lots, distinguishing root-related damage from construction joints using a combination of deep learning and classical statistics. The approach is evaluated on data from Vienna International Airport and validated against manually labeled ground truth data. Results show that accurate localization and classification of damage is feasible using only a single laser scanner, providing a cost-effective alternative to conventional monitoring. Moreover, the method facilitates predictive maintenance by automatically detecting damage and enabling integration into Building Information Modeling software.
Assessment of biodiversity patterns based on iNaturalist observation data from Carinthia
This study examines the spatial and temporal patterns of citizen science contributions to biodiversity monitoring in Carinthia, Austria, utilizing iNaturalist research-grade observations collected from 2015 to 2022. It investigates potential data collection biases, such as time of day and season, as well as species phenology, including seasonal life cycles, which manifest in temporal patterns of data contributions. Additionally, the study explores how land cover and other variables influence observation counts across 5 × 5 km² grid cells, employing a negative binomial regression model with Eigenvector Spatial Filtering. The temporal analysis also analyzes seasonal shifts in the internationality of iNaturalist contributors in Carinthia. The results reveal significant effects of time of day, season, and land cover on observed species and biodiversity. Most taxonomic families were primarily recorded in forested and semi-natural areas during the summer months. Although artificial surfaces, such as urban fabric, contribute fewer observations in total, they exhibit a bias due to ease of access and longer observation hours during winter, aided by artificial lighting. The study also highlights that iNaturalist contributions in Carinthia during the summer months are predominantly from users who tend to contribute more frequently outside of Austria, suggesting that the summer period attracts more internationally active contributors, such as foreign tourists. This research expands on prior studies of biodiversity monitoring by integrating both local and global scales of contributor behavior.
“Die Pflanzenwelt der Insel Krk”: a richly illustrated field guide for vascular plants, algae, mosses, lichens and macrofungi on the island of Krk
Comprehensive overview of Germany’s biodiversity: Faktencheck Artenvielfalt
BioDivTech Lab – conceptualizing a new research infrastructure for biodiversity monitoring
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.
TREEgital – digital knowledge transfer about forests for schools
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.
Comparative effectiveness of acoustic devices for monitoring bat species: a case study of Plecotus macrobullaris in Thomatal
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.
Deep-learning based population monitoring of the endangered plant species Gladiolus illyricus: lessons learned for implementation of a technology-based biodiversity monitoring approach
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.
Review of the framework for designing biodiversity monitoring programs: the latest IUCN WCPA technical report series contribution
A comprehensive overview of Carinthia’s endangered fauna: review of the Red List of endangered animals of Carinthia
Critical update on Carinthia’s flora: review of the Red List of endangered ferns and flowering plants of Carinthia