Long-term monitoring of a winter bat assemblage revealed large fluctuations and trends in species abundance

Monitoring studies in Strzaliny, one of the greatest hibernacula in Poland, comprised 31 annual bat censuses (1989–2019). The abundance peaked in 2002 for Myotis myotis, 2009 for Myotis nattereri and 2008 for the whole assemblage. Comparison of the maximum abundance in the monitoring period with that from 1980 to 1982 showed an almost fourfold increase for the whole assemblage, tenfold increase for M. nattereri and fourfold increase for M. myotis. In 1989–2019, the numbers of M. myotis, M. nattereri, Myotis daubentonii and Plecotus auritus were fluctuating, but most of the recorded changes could not be explained by methodological problems or a direct human impact. Therefore, the cumulative results largely reflected the real trends in the species abundance. A long-term upward trend in the whole bat assemblage was recognisable, but with a stable or slightly decreasing phase in the last decade. An upward trend in M. nattereri was even stronger and has only slightly flattened recently. In M. myotis, the trend was clearly upwards up to the early 2000s, but weakly downwards in the following years. In M. daubentonii and P. auritus, no significant trend was determined. In strongly fluctuating M. daubentonii, the numbers were mostly moderate or high, and even increasing, up to 2008 and only moderate or low in the following years. In P. auritus, an increase occurred in the 1980s and early 1990s, and then, after the stochastic human-induced drop in 1994, its abundance remained relatively stable. The population trends in Strzaliny largely reflected the general trends assessed for a large part of Europe. This suggests that the general population trends may be recognisable even in one large winter assemblage if it is reliably and consistently monitored through a long period. In this context, the hibernaculum in Strzaliny appeared to be a model object for such studies

Legal Fiction

While numerous ancient human DNA datasets from across Europe have been published till date, modern-day Poland in particular, remains uninvestigated. Besides application in the reconstruction of continent-wide human history, data from this region would also contribute towards our understanding of the history of the Slavs, whose origin is hypothesized to be in East or Central Europe. Here, we present the first population-scale ancient human DNA study from the region of modern-day Poland by establishing mitochondrial DNA profiles for 23 samples dated to 200 BC - 500 AD (Roman Iron Age) and for 20 samples dated to 1000-1400 AD (Medieval Age). Our results show that mitochondrial DNA sequences from both periods belong to haplogroups that are characteristic of contemporary West Eurasia. Haplotype sharing analysis indicates that majority of the ancient haplotypes are widespread in some modern Europeans, including Poles. Notably, the Roman Iron Age samples share more rare haplotypes with Central and Northeast Europeans, whereas the Medieval Age samples share more rare haplotypes with East-Central and South-East Europeans, primarily Slavic populations. Our data demonstrates genetic continuity of certain matrilineages (H5a1 and N1a1a2) in the area of present-day Poland from at least the Roman Iron Age until present. As such, the maternal gene pool of present-day Poles, Czechs and Slovaks, categorized as Western Slavs, is likely to have descended from inhabitants of East-Central Europe during the Roman Iron Age

Deep learning surrogate models for spatial and visual connectivity

Spatial and visual connectivity are important metrics when developing workplace layouts. Calculating those metrics in real time can be difficult, depending on the size of the floor plan being analysed and the resolution of the analyses. This article investigates the possibility of considerably speeding up the outcomes of such computationally intensive simulations by using machine learning to create models capable of identifying the spatial and visual connectivity potential of a space. To that end, we present the entire process of investigating different machine learning models and a pipeline for training them on such task, from the incorporation of a bespoke spatial and visual connectivity analysis engine through a distributed computation pipeline, to the process of synthesizing training data and evaluating the performance of different neural networks

Spatial Solutions and Solution Spaces: The use of Virtual and Augmented Reality in Design Exploration

The recent wave of Virtual and Augmented Reality (VAR) technologies has coincided with new technologies for processing, analyzing and evaluating large amounts of data. In general, the purpose of Data Visualization is to enable the user to discover and understand patterns in data. Good visualizations present large amounts of data in a way that is easily understood, and good interactive visualizations promote intuitive means of exploring relationships. Over the past few years many researchers have looked into the development of immersive Virtual Environment platforms for Big Data visualization, such as, iViz (Donalek et al, 2014) and the work carried out by Masters of Pie and Lumacode for the Big Data VR Challenge in 2016 (Lumapie, 2016). Filtering, combination and scaling have all been identified elsewhere as important interactive techniques used in contemporary data visualization (Olshannikova et al, 2015). Of these, scaling may be the most familiar to architects: for centuries, designers have attempted to experience architectural space in different scales simultaneously, by using models at different scales (Yaneva, 2005), and by employing various drawing techniques to achieve an embodied perception of the designed space. With the use of VAR technologies this becomes easier than ever. At the same time, designers increasingly must understand not just the experience of a design proposal but also the data associated with it