1. Mixed signals of environmental change and a trend towards ecological homogenization in ground vegetation across different forest typesJanez Kermavnar, Lado Kutnar, 2024, original scientific article Abstract: Forest ground vegetation may serve as an early warning system for monitoring anthropogenic global-change impacts on temperate forests. Climate warming may induce a decline of cool-adapted species to the benefit of more thermophilous plants. Nitrogen deposition has been documented to potentially result in soil eutrophication or acidification, which can increase the proportion of species with higher nutrient requirements and species impoverishment caused by competitive exclusion. Abiotic forest disturbances are changing the light conditions in the forest understorey environment. In this resurvey study, we tested the magnitude and direction of change in alpha (species richness) and beta (within-site dissimilarity) diversity and composition of forest ground vegetation in forests of different types in Slovenia over fifteen years. Using plant-derived characteristics (Ellenberg-type indicator values) and by testing a priori predictions concerning expected effects of environmental drivers, we show that the magnitude and direction of forest ground vegetation diversity and floristic changes varies greatly between forest sites. Divergent responses at different sites resulted in low net change of alpha and beta diversity and a weak overall environmental signal. The largest decrease in species number was observed in lowland oak-hornbeam forests, which were also among the sites with the greatest compositional shifts. Changes in beta diversity did not show any consistent trend, and anticipated floristic convergence was not confirmed when all sites were considered. Thermophilization was mainly detected in montane beech sites and alpine spruce forests whereas eutrophication signal was most significant on nutrient-poor sites. Vegetation responses were strongly dependent on initial site conditions. Shrinkage of ecological gradients (process of ecological homogenization) suggests that sites positioned at the ends of the gradients are losing their original ecological character and are becoming more similar to mid-gradient sites that generally exhibit smaller changes. Our results point to the importance of local stand dynamics and overstorey disturbances in explaining the temporal trends in forest ground vegetation. Ground vegetation in Slovenian forests is changing in directions also dictated by multiple regional and global change drivers.
Keywords: vegetation resurvey, thermophilization, eutrophication, forest disturbances, alpha and beta diversity, initial site conditions, ICP-Forests network
Published in DiRROS: 09.04.2024; Views: 92; Downloads: 43
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3. Mapping dreams in a computational space : a phrase-level model for analyzing Fight/Flight and other typical situations in dream reportsMaja Gutman, Pavan Holur, Kelly Bulkeley, 2022, original scientific article Abstract: This article demonstrates that an automated system of linguistic analysis can be developed – the Oneirograph – to analyze large collections of dreams and computationally map their contents in terms of typical situations involving an interplay of characters, activities, and settings. Focusing the analysis first on the twin situations of fighting and fleeing, the results provide densely detailed empirical evidence of the underlying semantic structures of typical dreams. The results also indicate that the Oneirograph analytic system can be applied to other typical dream situations as well (e.g., flying, falling), each of which can be computationally mapped in terms of a distinctive constellation of characters, activities, and settings.
Keywords: dreams, patterns, natural language processing, Oneirograph, network analysis, semantic structure
Published in DiRROS: 22.03.2023; Views: 365; Downloads: 207
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4. Prediction of the peak shear strength of the rock joints with artificial neural networksKarmen Fifer Bizjak, Rok Vezočnik, 2022, original scientific article Abstract: With the development of computer technology, artificial neural networks are becoming increasingly useful in the field of engineering geology and geotechnics. With artificial neural networks, the geomechanical properties of rocks or their behaviour could be predicted under different stress conditions. Slope failures or underground excavations in rocks mostly occurred through joints, which are essential for the stability of geotechnical structures. This is why the peak shear strength of a rock joint is the most important parameter for a rock mass stability. Testing of the shear characteristics of joints is often time consuming and suitable specimens for testing are difficult to obtain during the research phase. The roughness of the joint surface, tensile strength and vertical load have a great influence on the peak shear strength of the rock joint. In the presented paper, the surface roughness of joints was measured with a photogrammetric scanner, and the peak shear strength was determined by the Robertson direct shear test. Based on six input characteristics of the rock joints, the artificial neural network, using a backpropagation learning algorithm, successfully learned to predict the peak shear strength of the rock joint. The trained artificial neural network predicted the peak shear strength for similar lithological and geological conditions with average estimation error of 6%. The results of the calculation with artificial neural networks were compared with the Grasselli experimental model, which showed a higher error in comparison with the artificial neural network model.
Keywords: artificial neural network, camera-type 3D scanner, rock mechanics, rock joint, joint roughness
Published in DiRROS: 18.01.2023; Views: 354; Downloads: 183
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5. Definition, aims, and implementation of GA [sup] 2 LEN/HAEi Angioedema Centers of Reference and ExcellenceMarcus Maurer, Werner Aberer, Rosana Agondi, Mona Al-Ahmad, Maryam Ali Al-Nesf, Ignacio J. Ansotegui, Rand Arnaout, Luisa Karla Arruda, Riccardo Asero, Emel Aygören-Pürsü, Mojca Bizjak, Mitja Košnik, 2020, short scientific article Keywords: centres of reference and excelence, Global Allergy and Asthma European Network, GA2LEN
Published in DiRROS: 08.07.2020; Views: 1743; Downloads: 979
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6. Daily climate data reveal stronger climate-growth relationships for an extended European tree-ring networkJernej Jevšenak, 2019, original scientific article Abstract: An extended European tree-ring network was compiled from various sources of tree-ring data from Europe, northern Africa and western Asia. A total of 1860 tree-ring chronologies were used to compare correlation coefficients calculated with aggregated day-wise and month-wise mean temperature, sums of precipitation and standardised precipitation-evapotranspiration index (SPEI). For the daily approach, climate data were aggregated over periods ranging from 21 to 365 days. Absolute correlations calculated with day-wise aggregated climate data were on average higher by 0.060 (temperature data), 0.076 (precipitation data) and 0.075 (SPEI data). Bootstrapped correlations are computationally expensive and were therefore calculated on a 69.4% subset of the data. Bootstrapped correlations indicated statistically significant differences between the daily and monthly approach in approximately 1% of examples. A comparison of time windows used for calculations of correlations revealed slightly later onset and earlier ending day of the year for the daily approach, while the largest differences between the two approaches arise from window lengths: Correlations calculated with day-wise aggregated climate data were calculated using fewer days than the monthly approach. Differences in the onset and ending dates of periods for the daily and monthly approaches were greater for precipitation and SPEI data than for temperature data.
Keywords: tree rings, dendroclimatology, tree-ring network, daily climate data, climate-growth relationships, dendroTools
Published in DiRROS: 21.08.2019; Views: 2281; Downloads: 1050
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