Attitudes Toward Engagement in Citizen Science Increase Self-Related, Ecology-Related, and Motivation-Related Outcomes in an Urban Wildlife Project.

Citizen science projects are crucial for engaging citizens in conservation efforts. Although attitudes toward engagement in citizen science were mostly considered an outcome of citizen science participation, citizens may also have a certain attitude toward engagement in citizen science when starting with a citizen science project. Moreover, there is a lack of citizen science studies that consider changes over longer periods of time. Therefore, in this research, we present two-wave data from four field studies of a citizen science project about urban wildlife ecology using cross-lagged panel analyses. We investigated the influence of attitudes toward engagement in citizen science on self-related, ecology-related, and motivation-related outcomes. We found that positive attitudes toward engagement in citizen science at the beginning of the project had positive influences on the participants' psychological ownership and pride in their participation, their attitudes toward and enthusiasm about wildlife, and their internal and external motivation 2 months later. We discuss the implications for citizen science research and practice.

Spatiotemporal interactions of a novel mesocarnivore community in an urban environment before and during SARS-CoV-2 lockdown.

Studying species interactions and niche segregation under human pressure provides important insights into species adaptation, community functioning and ecosystem stability. Due to their high plasticity in behaviour and diet, urban mesocarnivores are ideal species for studying community assembly in novel communities. We analysed the spatial and temporal species interactions of an urban mesocarnivore community composed of the red fox Vulpes vulpes and the marten Martes sp. as native species, the raccoon Procyon lotor as invasive species, and the cat Felis catus as a domestic species in combination with human disturbance modulated by the SARS-CoV-2 lockdown effect that happened while the study was conducted. We analysed camera trap data and applied a joint species distribution model to understand not only the environmental variables influencing the detection of mesocarnivores and their use intensity of environmental features but also the species' co-occurrences while accounting for environmental variables. We then assessed whether they displayed temporal niche partitioning based on activity analyses, and finally analysed at a smaller temporal scale the time of delay after the detection of another focal species. We found that species were more often detected and displayed a higher use intensity in gardens during the SARS-CoV-2 lockdown period, while showing a shorter temporal delay during the same period, meaning a high human-induced spatiotemporal overlap. All three wild species spatially co-occurred within the urban area, with a positive response of raccoons to cats in detection and use intensity, whereas foxes showed a negative trend towards cats. When assessing the temporal partitioning, we found that all wild species showed overlapping nocturnal activities. All species displayed temporal segregation based on temporal delay. According to the temporal delay analyses, cats were the species avoided the most by all wild species. To conclude, we found that although the wild species were positively associated in space, the avoidance occurred at a smaller temporal scale, and human pressure in addition led to high spatiotemporal overlap. Our study sheds light to the complex patterns underlying the interactions in a mesocarnivore community both spatially and temporally, and the exacerbated effect of human pressure on community dynamics.

Beyond the landscape: Resistance modelling infers physical and behavioural gene flow barriers to a mobile carnivore across a metropolitan area.

Urbanization affects key aspects of wildlife ecology. Dispersal in urban wildlife species may be impacted by geographical barriers but also by a species' inherent behavioural variability. There are no functional connectivity analyses using continuous individual-based sampling across an urban-rural continuum that would allow a thorough assessment of the relative importance of physical and behavioural dispersal barriers. We used 16 microsatellite loci to genotype 374 red foxes (Vulpes vulpes) from the city of Berlin and surrounding rural regions in Brandenburg in order to study genetic structure and dispersal behaviour of a mobile carnivore across the urban-rural landscape. We assessed functional connectivity by applying an individual-based landscape genetic optimization procedure. Three commonly used genetic distance measures yielded different model selection results, with only the results of an eigenvector-based multivariate analysis reasonably explaining genetic differentiation patterns. Genetic clustering methods and landscape resistance modelling supported the presence of an urban population with reduced dispersal across the city border. Artificial structures (railways, motorways) served as main dispersal corridors within the cityscape, yet urban foxes avoided densely built-up areas. We show that despite their ubiquitous presence in urban areas, their mobility and behavioural plasticity, foxes were affected in their dispersal by anthropogenic presence. Distinguishing between man-made structures and sites of human activity, rather than between natural and artificial structures, is thus essential for better understanding urban fox dispersal. This differentiation may also help to understand dispersal of other urban wildlife and to predict how behaviour can shape population genetic structure beyond physical barriers.

Machine learning goes wild: Using data from captive individuals to infer wildlife 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.

Individual dietary specialization in a generalist predator: A stable isotope analysis of urban and rural red foxes.

Some carnivores are known to survive well in urban habitats, yet the underlying behavioral tactics are poorly understood. One likely explanation for the success in urban habitats might be that carnivores are generalist consumers. However, urban populations of carnivores could as well consist of specialist feeders. Here, we compared the isotopic specialization of red foxes in urban and rural environments, using both a population and an individual level perspective. We measured stable isotope ratios in increments of red fox whiskers and potential food sources. Our results reveal that red foxes have a broad isotopic dietary niche and a large variation in resource use. Despite this large variation, we found significant differences between the variance of the urban and rural population for δ13C as well as δ15N values, suggesting a habitat-specific foraging behavior. Although urban regions are more heterogeneous regarding land cover (based on the Shannon index) than rural regions, the dietary range of urban foxes was smaller compared with that of rural conspecifics. Moreover, the higher δ13C values and lower δ15N values of urban foxes suggest a relatively high input of anthropogenic food sources. The diet of most individuals remained largely constant over a longer period. The low intraindividual variability of urban and rural red foxes suggests a relatively constant proportion of food items consumed by individuals. Urban and rural foxes utilized a small proportion of the potentially available isotopic dietary niche as indicated by the low within-individual variation compared to the between-individual variation. We conclude that generalist fox populations consist of individual food specialists in urban and rural populations at least over those periods covered by our study.