Expert-driven development of conservation technologies to close knowledge gaps in small animal research

To decelerate the human-induced extinction of species, humanity needs to mitigate its impact and implement effective conservation measures. This requires detailed knowledge about the behaviour and ecology of species generated by suitable observation methods. Hence, both efficient data-collection methods, and analysis tools are indispensable. Especially small species ([100g) are a challenge for classical field methods but also commercially available technical solutions reach their limits with decreasing body size. This work addresses the challenge of making technologies, successfully used in current research practice, available for small animals ([100g) at low cost, and open source. The potential for successful transfer of technological developments into conservation practice and ways to ensure their continued availability are explored. To achieve these goals, this thesis addresses four research dimensions: 1. Sensor development Tracking the movements of animals has provided ground-breaking insights into their behaviour, ecology, and interaction with their environment. Tracking involves equipping animals with different kinds of transmitters (GPS, accelerometers). However, the transmitters used are generally too heavy for small species. One goal of this work was to develop a low-cost automated radio-tracking system, the tRackIT-system, that allows live tracking of movements, behaviour, and physiological states of small animals equipped with Very High Frequency (VHF) transmitters (weight [0.5g). Camera traps are becoming increasingly popular in ecological research. However, their use for small animal species is limited due to the lack of sensitivity of the sensors used to trigger a recording. With the development of a multi-sensor tool (the BatRack) which combines acoustic and visual sensors with the tRackIT-system, the behaviour of small nocturnal animals (bats) can be reliably recorded. The combination of sensors also allows individuals to be recognised in video and audio recordings. Both sensors were used to investigate the movements and behaviours of bats and songbirds and optimized for stable and permanent deployment over four years in the research forest of the Phillips-University Marburg (Marburg Open Forest (MOF)). 2. Data Analysis Automation of environmental observation involves an increasing amount of data to be analysed. Appropriate tools for processing them are as important as the sensors themselves. In this work, various functionalities and tools, ranging from providing an exchangeable data structure to trained machine learning models for classifying behaviours in radio-tracking data and bat calls in sound recordings, were developed and made available open-source. Case studies on behaviours of different bat and songbird species demonstrate that relevant insights for conservation and ecological research can be generated using these sensors and analysis tools. 3. Practice transfer Academically driven developments often remain at the status of prototypes, whose applicability in nature conservation practice by users with different degrees of technical expertise is not guaranteed. For the successful transfer to conservation practice, certain requirements must be met. The hardware and software must function reliably even under adverse field conditions, and access barriers such as the requirement of technical knowledge for implementation or costs must be kept low. In addition, the application range should be as diverse as possible. The applicability of the tRackIT-system for various conservation-related issues was verified by two field tests. Firstly, wader chicks were tracked to be able to determine time and cause of their death with high temporal resolution. Secondly, it was tested whether the system can substitute labour-intensive and error prone manual methods in the context of environmental assessments in advance of the construction of wind turbines. Both tests were a success in terms of saving labour and improving the data basis for conservation research and practice. 4. Long-term availability of technologies One reason for the poor uptake of academically developed promising technologies in conservation research and practice results from the fact that technical support for users often ceases after the corresponding projects have ended. One way to ensure continued support and improvement of the technologies is to establish a company that provides this support as a service and drives further development. The foundation of tRackIT-Systems company is supported by the EXIST program of the Federal Ministry of Economics and Climate Protection. The long-term access to the existing and future developments will thus be secured. From the definition of demands to a user-oriented and feedback-driven development of permanently available products, this work realises all criteria for successful conservation technologies

Exploring the Dotterel Mountains : Improving the understanding of breeding habitat characteristics of an Arctic-breeding specialist bird

Arctic-breeding birds are of particular conservation concern since their habitats are subject to severe changes and shifts upwards in both altitude and latitude due to global warming. Detailed knowledge on habitat characteristics of those species is required to understand how specialized Arctic-breeding species deal with changing habitat conditions. Therefore, sufficient data and methods to assess habitat suitability on large spatial scales in a time- and cost-efficient way are needed. The Eurasian Dotterel Charadrius morinellus is a specialist highaltitude and Arctic-breeding wader and can serve as an ideal model species for addressing habitat requirements of Arctic-breeding birds and consequences for conservation. We combined field surveys with remote sensing data to develop a distribution model for the breeding habitat of the Eurasian Dotterel in the Vindelfjällen Nature Reserve in northern Sweden. The remote sensing data comprised 211 spectral, structural and topographic indices derived from freely available satellite images and digital elevation models. For species distribution modeling we used MaxEnt with an advanced variable and parameter selection method for model training. The trained model produced excellent results (AUC = 0.99) with seven resulting predictor variables reflecting the habitat requirements of the Dotterel: Sparsely vegetated mountain tops with dry ground which are very open. This study further highlights the potential of combining survey data with freely available remote sensing data for detailed area-wide population predictions and the monitoring of habitat change as a tool in species conservation

BatRack: An open‐source multi‐sensor device for wildlife research

Abstract Bats represent a highly diverse group of mammals and are essential for ecosystem functioning. However, knowledge about their behaviour, ecology and conservation status is limited. Direct observation of marked individuals (commonly applied to birds) is not possible for bats due to their small size, rapid movement and nocturnal lifestyle, while neither popular observation methods such as camera traps nor conventional tracking technologies sufficiently capture the behaviour of individuals. The combination and networking of different sensors in a single system can overcome these limitations, but this potential has been explored only to a limited extent. We present BatRack, a multi‐sensor device that combines ultrasonic audio recordings, automatic radio telemetry and video camera recordings in a single modular unit. BatRack facilitates the individual or combined scheduling of sensors and includes a mutual triggering mode. It consists of off‐the‐shelf hardware and both its hardware blueprints and the required software have been published under an open license to allow scientists and practitioners to replicate the system. We tested the suitability of radio telemetry and audio sensors as camera triggers and evaluated the detection of individuals in video recordings compared to radio telemetry signals. Specifically, BatRack was used to monitor the individual swarming behaviour of six members of a maternity colony of Bechstein's bat. Preliminary anecdotal results indicate that swarming intensity is related to reproductive state and roost switching. BatRack allows researchers to recognize individual bats and monitor their behavioural patterns using an easily deployed and scalable system. BatRack is thus a promising approach to obtaining detailed insights into the behavioural ecology of bats

Classifying the activity states of small vertebrates using automated VHF telemetry

Abstract The most basic behavioural states of animals can be described as active or passive. While high‐resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which a combination of automatic radiotracking and machine learning is used to distinguish between active and passive behaviour in small vertebrates fitted with lightweight transmitters (3 million signals from very‐high‐frequency (VHF) telemetry from two forest‐dwelling bat species (Myotis bechsteinii [n = 52] and Nyctalus leisleri [n = 20]) to train and test a random forest model in assigning either active or passive behaviour to VHF‐tagged individuals. The generalisability of the model was demonstrated by recording and classifying the behaviour of tagged birds and by simulating the effect of different activity levels with the help of humans carrying transmitters. The model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F1 0.96–0.98). We provide an ecological case‐study demonstrating the potential of this automated monitoring tool. We used the trained models to compare differences in the daily activity patterns of two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night‐time activity of M. bechsteinii was relatively constant. These results show that subtle differences in the timing of species' activity can be distinguished using our method. Our approach can classify VHF‐signal patterns into fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radiotracking method, we provide the trained random forest models together with an R package that includes all necessary data processing functionalities. In combination with state‐of‐the‐art open‐source automated radiotracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation

Classifying the activity states of small vertebrates using automated VHF telemetry

The most basic behavioural states of animals can be described as active or passive. However, while high-resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which the automated VHF radio-tracking of small vertebrates fitted with lightweight transmitters (< 0.2 g) is used to distinguish between active and passive behavioural states. A dataset containing > 3 million VHF signals was used to train and test a random forest model in the assignment of either active or passive behaviour to individuals from two forest-dwelling bat species (Myotis bechsteinii (n = 50) and Nyctalus leisleri (n = 20)). The applicability of the model to other taxonomic groups was demonstrated by recording and classifying the behaviour of a tagged bird and by simulating the effect of different types of vertebrate activity with the help of humans carrying transmitters. The random forest model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F-score 0.96–0.98). The utility of the model in tackling ecologically relevant questions was demonstrated in a study of the differences in the daily activity patterns of the two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night-time activity of M. bechsteinii was relatively constant. These results show that significant differences in the timing of species activity according to ecological preferences or seasonality can be distinguished using our method. Our approach enables the assignment of VHF signal patterns to fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radio-tracking method, we provide the trained random forest models together with an R-package that includes all necessary data-processing functionalities. In combination with state-of-the-art open-source automated radio-tracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation

Nature 4.0: A networked sensor system for integrated biodiversity monitoring

Zeuss D, Bald L, Gottwald J, et al. Nature 4.0: A networked sensor system for integrated biodiversity monitoring. Global Change Biology. 2024;30(1): e17056.**Abstract** Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services

Nature 4.0: A networked sensor system for integrated biodiversity monitoring

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services