Wanja Rast

Amidst the sixth mass extinction, some animal groups, such as vultures, the only obligate scavengers among vertebrates, are disappearing at an unprecedented rate. Vulture populations worldwide are declining, primarily due to poisoning. As many vulture species are social foragers, they can congregate in large numbers to scavenge at a carcass, potentially increasing their exposure to poisoning risk. Current anti‐poisoning prevention and mitigation measures are insufficient to tackle this threat. There is an urgent need for new effective strategies to prevent mass vulture mortality. In this study, we applied agent‐based modelling using white‐backed vulture (Gyps africanus) data from Namibia to: (1) quantify the impact of different foraging strategies on vulture poisoning risk and (2) evaluate the cost‐effectiveness of using vultures as sentinels for poisoning detection. This approach involves GPS tracking of various numbers of vultures and using the data to quickly detect poisoning incidents and decontaminate carcasses. These actions help mitigate further vulture mortality and prevent mass poisoning. Our findings demonstrate that social foraging significantly increases the risk of poisoning among white‐backed vultures. However, GPS tracking of individual vultures enables earlier detection of poisoning events, thereby reducing associated mortalities. Poisoning mitigation effectiveness improves with both the number of tracked individuals and the speed of decontamination response. According to our agent‐based model tailored to our study system and species, tracking approximately 5% of the population (25 individuals) offers a good balance between cost and effectiveness, requiring an estimated budget of USD 60,000. Using this strategy and approach, and assuming a response time within 2 h, up to 45% of poisoning‐related deaths could be prevented. Synthesis and applications: Our results suggest that, in order to reduce mortality incidences from poisoning in our study system and species, it is sufficient to track a small proportion of the vulture population, which would act as sentinels for the rest. By evaluating the costs and ecological benefits of alternative strategies, varying in number of birds tagged or response time, we provide evidence‐based solutions that practitioners can use to design conservation plans. These findings are therefore instrumental in supporting vulture and scavenger conservation policy and practice.

Bio‐logging technologies allow scientists to remotely monitor animal behaviour and the environment. In this study, we used the combination of natural abilities of African white‐backed vultures Gyps africanus and state‐of‐the‐art bio‐logging technology for detecting and locating carcasses in a vast landscape. We used data from two captive and 27 wild vultures to create a reference data set for the training of a support vector machine to distinguish between six behaviour classes based on acceleration data. Next, we combined the classified behaviour of the initial 27 and 7 additional vultures with GPS data and used the ‘Density‐Based Spatial Clustering of Applications with Noise’ algorithm to cluster all GPS data to get a position of potential feeding locations. Finally, we used the clustered data set to train a Random Forest algorithm to distinguish between clusters with and without a carcass. The behaviour classifier was trained on 14,682 samples for all behaviour classes, which were classified with a high performance (overall precision: 0.95, recall: 0.89). This enabled a ground team to examine 1900 clusters between May 2022 and March 2023 in the field, 580 linked to a carcass and 1320 without a carcass. The cluster classifier trained on this data set was able to correctly distinguish between carcass and no carcass clusters with high performance (overall precision: 0.92, recall: 0.89). Synthesis and applications. We showed that a carcass detection system using vultures, loggers and artificial intelligence (AI) can be used to monitor the mortality of numerous species in a vast landscape. This method has broad applications, such as studying the feeding ecology of vultures, detecting and monitoring of disease outbreaks, environmental poisoning or illegal killing of wildlife. Similar to vultures and carcasses, our methodological framework can be applied to other species to locate their respective food resources. It could also be applied to other types of resources like temporary water sources, roosting sites and to other behaviours such as marking to locate marking sites.

Accelerometers capture rapid changes in animal motion, and the analysis of large quantities of such data using machine learning algorithms enables the inference of broad animal behaviour categories such as foraging, flying, and resting over long periods of time. We deployed GPS-GSM/GPRS trackers with tri-axial acceleration sensors on common woodpigeons (Columba palumbus) from Hesse, Germany (forest and urban birds) and from Lisbon, Portugal (urban park). We used three machine learning algorithms, Random Forest, Support Vector Machine, and Extreme Gradient Boosting, to classify the main behaviours of the birds, namely foraging, flying, and resting and calculated time budgets over the breeding and winter season. Woodpigeon time budgets varied between seasons, with more foraging time during the breeding season than in winter. Also, woodpigeons from different sites showed differences in the time invested in foraging. The proportion of time woodpigeons spent foraging was lowest in the forest habitat from Hesse, higher in the urban habitat of Hesse, and highest in the urban park in Lisbon. The time budgets we recorded contrast to previous findings in woodpigeons and reaffirm the importance of considering different populations to fully understand the behaviour and adaptation of a particular species to a particular environment. Furthermore, the differences in the time budgets of Woodpigeons from this study and previous ones might be related to environmental change and merit further attention and the future investigation of energy budgets. Significance statement: In this study we took advantage of accelerometer technology and machine learning methods to investigate year-round behavioural time budgets of wild common woodpigeons (Columba palumbus). Our analysis focuses on identifying coarse-scale behaviours (foraging, flying, resting) using various machine learning algorithms. Woodpigeon time budgets varied between seasons and among sites. Particularly interesting is the result showing that urban woodpigeons spend more time foraging than forest conspecifics. Our study opens an opportunity to further investigate and understand how a successful bird species such as the woodpigeon copes with increasing environmental change and urbanisation. The increase in the proportion of time devoted to foraging might be one of the behavioural mechanisms involved but opens questions about the costs associated to such increase in terms of other important behaviours.

Behavioural studies of elusive wildlife species are challenging but important when they are threatened and involved in human-wildlife conflicts. Accelerometers (ACCs) and supervised machine learning algorithms (MLAs) are valuable tools to remotely determine behaviours. Here we used five captive cheetahs in Namibia to test the applicability of ACC data in identifying six behaviours by using six MLAs on data we ground-truthed by direct observations. We included two ensemble learning approaches and a probability threshold to improve prediction accuracy. We used the model to then identify the behaviours in four free-ranging cheetah males. Feeding behaviours identified by the model and matched with corresponding GPS clusters were verified with previously identified kill sites in the field. The MLAs and the two ensemble learning approaches in the captive cheetahs achieved precision (recall) ranging from 80.1% to 100.0% (87.3% to 99.2%) for resting, walking and trotting/running behaviour, from 74.4% to 81.6% (54.8% and 82.4%) for feeding behaviour and from 0.0% to 97.1% (0.0% and 56.2%) for drinking and grooming behaviour. The model application to the ACC data of the free-ranging cheetahs successfully identified all nine kill sites and 17 of the 18 feeding events of the two brother groups. We demonstrated that our behavioural model reliably detects feeding events of free-ranging cheetahs. This has useful applications for the determination of cheetah kill sites and helping to mitigate human-cheetah conflicts.

Anthropogenic activities can result in both transient and permanent changes in the environment. We studied spatial and temporal behavioural responses of European hedgehogs (Erinaceus europaeus) to a transient (open-air music festival) and a permanent (highly fragmented area) disturbance in the city of Berlin, Germany. Activity, foraging and movement patterns were observed in two distinct areas in 2016 and 2017 using a “Before & After“ and “Control & Impact“ study design. Confronted with a music festival, hedgehogs substantially changed their movement behaviour and nesting patterns and decreased the rhythmic synchronization (DFC) of their activity patterns with the environment. These findings suggest that a music festival is a substantial stressor influencing the trade-off between foraging and risk avoidance. Hedgehogs in a highly fragmented area used larger home ranges and moved faster than in low-fragmented and low-disturbed areas. They also showed behaviours and high DFCs similar to individuals in low-fragmented, low disturbed environment, suggesting that fragmentation posed a moderate challenge which they could accommodate. The acute but transient disturbance of a music festival, therefore, had more substantial and severe behavioural effects than the permanent disturbance through fragmentation. Our results are relevant for the welfare and conservation measure of urban wildlife and highlight the importance of allowing wildlife to avoid urban music festivals by facilitating avoidance behaviours.

1. Remotely tracking distinct behaviours of animals using acceleration data and machine learning has been carried out successfully in several species in captive settings. In order to study the ecology of animals in natural habitats, such behaviour classification models need to be transferred to wild individuals. However, at present, the development of those models usually requires direct observation of the target animals. 2. The goal of this study was to infer the behaviour of wild, free-roaming animals from acceleration data by training behaviour classification models on captive individuals, without the necessity to observe their wild conspecifics. We further sought to develop methods to validate the credibility of the resulting behaviour extrapolations. 3. We trained two machine learning algorithms proposed by the literature, Random Forest (RF) and Support Vector Machine (SVM), on data from captive red foxes (Vulpes vulpes) and later applied them to data from wild foxes. We also tested a new advance for behaviour classification, by applying a moving window to an Artificial Neural Network (ANN). Finally, we investigated four strategies to validate our classification output. 4. While all three machine learning algorithms performed well under training conditions (Kappa values: RF (0.82), SVM (0.78), ANN (0.85)), the established methods, RF and SVM, failed in classifying distinct behaviours when transferred from captive to wild foxes. Behaviour classification with the ANN and a moving window, in contrast, inferred distinct behaviours and showed consistent results for most individuals. 5. Our approach is a substantial improvement over the methods previously proposed in the literature as it generated plausible results for wild fox behaviour. We were able to infer the behaviour of wild animals that have never been observed in the wild and to further illustrate the credibility of the output. This framework is not restricted to foxes but can be applied to infer the behaviour of many other species and thus empowers new advances in behavioural ecology.

Remotely tracking distinct behaviours of animals using acceleration data and machine learning has been carried out successfully in several species in captive settings. In order to study the ecology of animals in natural habitats, such behaviour classification models need to be transferred to wild individuals. However, at present the development of those models usually requires direct observation of the target animals. The goal of this study was to infer behaviour of wild, free roaming animals from acceleration data by training behaviour classification models on captive individuals, without the necessity to observe their wild conspecifics. We further sought to develop methods to validate the credibility of the resulting behaviour extrapolations. We trained two machine learning algorithms proposed by the literature, Random Forest (RF) and Support Vector Machine (SVM), on data from captive red foxes (Vulpes vulpes) and later applied them to data from wild foxes. We also tested a new advance for behaviour classification, by applying a moving window to an Artificial Neural Network (ANN). Finally, we investigated four strategies to validate our classification output. While all three machine learning algorithms performed well under training conditions, the established methods, RF and SVM, failed in classifying distinct behaviours when transferred from captive to wild foxes. Behaviour classification with the ANN and a moving window, in contrast, inferred distinct behaviours and showed consistent results for most individuals. Our approach is a substantial improvement over the methods previously proposed in the literature as it generated plausible results for wild fox behaviour. We were able to infer the behaviour of wild animals that have never been observed in the wild and to further illustrate the outputs credibility. This framework is not restricted to foxes but can be applied to infer the behaviour of many other species and thus empowers new advances in behavioural ecology.

Understanding the impact of human activities on wildlife behavior and fitness can improve their sustainability. In a pilot study, we wanted to identify behavioral responses to anthropogenic stress in an urban species during a semi-experimental field study. We equipped eight urban hedgehogs (Erinaceus europaeus; four per sex) with bio-loggers to record their behavior before and during a mega music festival (2 × 19 days) in Treptower Park, Berlin. We used GPS (Global Positioning System) to monitor spatial behavior, VHF (Very High Frequency)-loggers to quantify daily nest utilization, and accelerometers to distinguish between different behaviors at a high resolution and to calculate daily disturbance (using Degrees of Functional Coupling). The hedgehogs showed clear behavioral differences between the pre-festival and festival phases. We found evidence supporting highly individual strategies, varying between spatial and temporal evasion of the disturbance. Averaging the responses of the individual animals or only examining one behavioral parameter masked these potentially different individual coping strategies. Using a meaningful combination of different minimal-invasive bio-logger types, we were able to show high inter-individual behavioral variance of urban hedgehogs in response to an anthropogenic disturbance, which might be a precondition to persist successfully in urban environments.