Hölker, F., T. Moss, B. Griefahn, W. Kloas, C. C. Voigt, D. Henckel, A. Hänel, P. M. Kappeler, S. Völker, A. Schwope, S. Franke, D. Uhrlandt, J. Fischer, R. Klenke, C. Wolter, and K. Tockner. 2010. The dark side of light: a transdisciplinary research agenda for light pollution policy. Ecology and Society 15(4): 13. https://doi.org/10.5751/ES-03685-150413

Organisms living in urban environments are exposed to different environmental conditions compared to their rural conspecifics. Especially anthropogenic noise and artificial night light are closely linked to urbanization and pose new challenges to urban species. Songbirds are particularly affected by these factors, because they rely on the spread of acoustic information and adjust their behaviour to the rhythm of night and day, e.g. time their dawn song according to changing light intensities. Our aim was to clarify the specific contributions of artificial night light and traffic noise on the timing of dawn song of urban European Blackbirds (Turdus merula). We investigated the onset of blackbird dawn song along a steep urban gradient ranging from an urban forest to the city centre of Leipzig, Germany. This gradient of anthropogenic noise and artificial night light was reflected in the timing of dawn song. In the city centre, blackbirds started their dawn song up to 5 hours earlier compared to those in semi-natural habitats. We found traffic noise to be the driving factor of the shift of dawn song into true night, although it was not completely separable from the effects of ambient night light. We additionally included meteorological conditions into the analysis and found an effect on the song onset. Cloudy and cold weather delayed the onset, but cloud cover was assumed to reflect night light emissions, thus, amplified sky luminance and increased the effect of artificial night light. Beside these temporal effects, we also found differences in the spatial autocorrelation of dawn song onset showing a much higher variability in noisy city areas than in rural parks and forests. These findings indicate that urban hazards such as ambient noise and light pollution show a manifold interference with naturally evolved cycles and have significant effects on the activity patterns of urban blackbirds.

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.