ABSTRACT. Although the invention and widespread use of artificial light is clearly one of the most important human technological advances, the transformation of nightscapes is increasingly recognized as having adverse effects. Night lighting may have serious physiological consequences for humans, ecological and evolutionary implications for animal and plant populations, and may reshape entire ecosystems. However, knowledge on the adverse effects of light pollution is vague. In response to climate change and energy shortages, many countries, regions, and communities are developing new lighting programs and concepts with a strong focus on energy efficiency and greenhouse gas emissions. Given the dramatic increase in artificial light at night (0 -20% per year, depending on geographic region), we see an urgent need for light pollution policies that go beyond energy efficiency to include human well-being, the structure and functioning of ecosystems, and inter-related socioeconomic consequences. Such a policy shift will require a sound transdisciplinary understanding of the significance of the night, and its loss, for humans and the natural systems upon which we depend. Knowledge is also urgently needed on suitable lighting technologies and concepts which are ecologically, socially, and economically sustainable. Unless managing darkness becomes an integral part of future conservation and lighting policies, modern society may run into a global self-experiment with unpredictable outcomes.

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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.

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The main goal of non‐invasive genetic capture‐mark‐recapture (CMR) analysis is to gain an unbiased and reliable population size estimate of species that cannot be sampled directly. The method has become an important and widely used tool to research and manage wildlife populations. However, researchers have to struggle with low amplification success rates and genotyping errors, which substantially bias subsequent analysis. To receive reliable results and to minimize the time and costs required for non‐invasive microsatellite genotyping, one must carefully choose a species‐specific sampling design, methods that maximize the amount of template DNA, and methods that could overcome genotyping errors, especially when using low‐quality samples. This article reviews the literature and the pros and cons of the main methods used along the process described above. The review is strengthened by a case study on Eurasian otters (Lutra lutra) using feces; we tested several methods for their appropriateness to accommodate for genotyping errors. Based on this method testing, we demonstrated that high genotyping error rates are the key problem in this process leading to a severely flawed dataset if no consensus genotype is formed. However, even if generating consensus genotypes minimizes errors dramatically, we show that it may not achieve a definite eradication of all errors, which results in overestimated population sizes if conventional estimators are used. In conjunction with these findings, we offer a step‐by‐step protocol for non‐invasive genetic CMR studies to achieve a reliable estimate of population sizes in the presence of high genotyping error rates. © 2013 The Wildlife Society.

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.forest ecosystem change through effective global coordination [3] good observations, indicators and scenarios of biodiversity and ecosystem services change [4].There is still a large discrepancy between the information required by forest managers and scientists and the information that is available for understanding and assessing the complexity and multidimensionality of forest health drivers, stressors, disturbances, and effects. Decision makers require information on forest health in high spatial and temporal accuracy, from the local to the global, for short and long-term periods that can be recorded ...

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Quantifying population status is a key objective in many ecological studies, but is often difficult to achieve for cryptic or elusive species. Here, non-invasive genetic capture-mark-recapture (CMR) methods have become a very important tool to estimate population parameters, such as population size and sex ratio. The Eurasian otter (Lutra lutra) is such an elusive species of management concern and is increasingly studied using faecal-based genetic sampling. For unbiased sex ratios or population size estimates, the marking behaviour of otters has to be taken into account. Using 2132 otter faeces of a wild otter population in Upper Lusatia (Saxony, Germany) collected over six years (2006–2012), we studied the marking behaviour and applied closed population CMR models accounting for genetic misidentification to estimate population sizes and sex ratios. We detected a sex difference in the marking behaviour of otters with jelly samples being more often defecated by males and placed actively exposed on frequently used marking sites. Since jelly samples are of higher DNA quality, it is important to not only concentrate on this kind of samples or marking sites and to invest in sufficiently high numbers of repetitions of non-jelly samples to ensure an unbiased sex ratio. Furthermore, otters seemed to increase marking intensity due to the handling of their spraints, hence accounting for this behavioural response could be important. We provided the first precise population size estimate with confidence intervals for Upper Lusatia (for 2012: = 20 ± 2.1, 95% CI = 16–25) and showed that spraint densities are not a reliable index for abundances. We further demonstrated that when minks live in sympatry with otters and have comparably high densities, a non-negligible number of supposed otter samples are actually of mink origin. This could severely bias results of otter monitoring if samples are not genetically identified.