Three-body structure of low-lying 18Ne states

We investigate to what extent 18Ne can be descibed as a three-body system made of an inert 16O-core and two protons. We compare to experimental data and occasionally to shell model results. We obtain three-body wave functions with the hyperspherical adiabatic expansion method. We study the spectrum of 18Ne, the structure of the different states and the predominant transition strengths. Two 0+, two 2+, and one 4+ bound states are found where they are all known experimentally. Also one 3+ close to threshold is found and several negative parity states, 1-, 3-, 0-, 2-, most of them bound with respect to the 16O excited 3- state. The structures are extracted as partial wave components, as spatial sizes of matter and charge, and as probability distributions. Electromagnetic decay rates are calculated for these states. The dominating decay mode for the bound states is E2 and occasionally also M1.Comment: 17 pages, 5 figures (version to appear in EPJA

UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (bare earth, grassland, trees), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672 m3 is estimated for the test site (48 ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland

Bodenmikrobiologie im Hochgebirge - zentrale Einflussfaktoren vor dem Hintergrund des Climate Change

Die Bodenmikrobiologie bekommt vor dem Hintergrund des climate change eine zusätzliche Bedeutung, da Mikroorganismen anders als alle andere Lebewesen nicht nur von gesteigerten Temperaturen beeinflusst werden, sondern auch aktiv – u.z. positiv wie negativ – in das Klimageschehen eingreifen können. Böden im Hochgebirge sind diesbezüglich und per se noch viel zu wenig erforscht und können darüber hinaus vor dem Hintergrund des Klimawandels als sehr gute Modelle für boreale und polare Regionen dienen, da Änderungen, die mit einer steigenden Seehöhe von 100 m im Gebirge einhergehen und somit den Änderungen in einem S-N-Transekt von ca. 400 km entsprechen, für riesige Gebiete relevant sind. Im internationalen GLORIA-Projekte werden weltweit 116 Standorte in 6 Kontinenten hinsichtlich der Auswirkungen des globalen Klimawandels auf vegetationskundliche Parameter untersucht. Im Rahmen des vorliegenden Projektes konnte erstmals einer der untersuchten master-sites des GLORIA-Forschungsprogramms, der Schrankogel mineralogisch, bodenchemisch und mikrobiologisch umfassend untersucht und die erhaltenen Daten mit abiotischen Standortfaktoren (Temperatur etc.) sowie botanischen Daten in Zusammenhang gebracht werden. Der Schrankogel befindet sich in den Ötztaler Alpen (Tirol/Österreich), ist 3.497 m hoch, verfügt über eine hinsichtlich der Steigung und der Geologie sehr konstante, fast über 1000 Höhenmeter reichende SW-Flanke und war somit für die geplanten Untersuchungen bestens geeignet. Anders als viele vergleichbare Studien, konnten sehr deutliche Beeinflussungen von mineralogischen, bodenchemischen und bodenmikrobiologischen Parametern nachgewiesen werden. Einige der erhobenen Parameter zeigten keinen linearen Zusammenhang mit zentralen Einflussfaktoren wie der Temperatur sondern einen sigmoiden Verlauf, wobei die stärksten Änderungen im mittleren, sogenannten alpin-nivalen Ökoton, in einer Höhe von ca. 3.000 m erfolgten. Der alpin-nivale Ökoton zeigte auch (festgestellt über den Nivalitätsindex) eine zentrale Grenze in der Vegetationsgesellschaft an, die mit Veränderungen der mikrobiellen Communities und Aktivitäten einherging. Dies betraf nicht nur Gesamtzahlen von Bacteria sondern auch die Abundanz methanogener Archaea, die für den Methankreislauf und damit den Klimawandel hochrelevant sind und bis zu einer Höhe von 3.497 m nachgewiesen werden konnten

Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain

In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance

Primary succession and its driving variables – a sphere-spanning approach applied in proglacial areas in the upper Martell Valley (Eastern Italian Alps)

Climate change and the associated glacier retreat lead to considerable enlargement and alterations of the proglacial systems. The colonisation of plants in this ecosystem was found to be highly dependent on terrain age, initial site conditions and geomorphic disturbances. Although the explanatory variables are generally well understood, there is little knowledge on their collinearities and resulting influence on proglacial primary succession. To develop a sphere-spanning understanding of vegetation development, a more interdisciplinary approach was adopted. In the proglacial areas of Fürkeleferner, Zufallferner and Langenferner (Martell Valley, Eastern Italian Alps), in total 65 plots of 5×2 m were installed to perform the vegetation analysis on vegetation cover, species number and species composition. For each of those, 39 potential explanatory variables were collected, selected through an extensive literature review. To analyse and further avoid multicollinearities, 33 of the explanatory variables were clustered via principal component analysis (PCA) to five components. Subsequently, generalised additive models (GAMs) were used to analyse the potential explanatory factors of primary succession. The results showed that primary succession patterns were highly related to the first component (elevation and time), the second component (solar radiation), the third component (soil chemistry), the fifth component (soil physics) and landforms. In summary, the analysis of all explanatory variables together provides an overview of the most important influencing variables and their interactions; thus it provides a basis for the debate on future vegetation development in a changing climate.</p

Application of habitat suitability modelling to tracking data of marine animals as a means of analyszing their feeding habitats

This paper investigates the potential for using quantitative applications of statistical models of habitat suitability based on marine animal tracking data to identify key feeding areas. Presence-only models like Ecological Niche Factor Analysis (ENFA) may be applicable to resolve habitat gradients and potentially project habitat characteristics of tracked animals over large areas of ocean. We tested ENFA on tracking data of the northern gannet (Morus bassanus) obtained from the colony at Bass Rock, western North Sea in 2003. A total of 217 diving events were selected for model development. The ecological variables of the model were calibrated by using oceanographic structures with documented influences on seabird distribution, derived from satellite and bathymetric data. The model parameters were estimates of habitat marginality and specialisation computed by comparing the distribution of the gannet in the multivariate oceanographic space encompassed by the recorded logger data with the whole set of cells in the study area. Marginality was identified by differences to the global mean and specialization was identified by the ratio of species variance to global variance. A habitat suitability index was computed on the basis of the marginality factors and the first four specialisation factors by allocating values to all grid cells in the study area, which were proportional to the distance between their position and the position of the species optimum in the factorial space. Although gannets were using a large sector of the North Sea for feeding, ENFA estimated high habitat suitability scores within a relatively small coherent zone corresponding to a hydrographic frontal area, located east of the colony. The model was evaluated by using Jack-knife cross-validation and by comparison of the predicted core feeding area with results from historic field surveys. We discuss the limitations and potentials for applying habitat suitability models to tracking data in the marine environment, and conclude that the inclusion of hydrodynamic variables seems to be the biggest constraint. Overcoming this constraint, ENFA provides a promising method for achieving improved models of the distribution of marine species with high research and conservation priority. Due to the better coverage of entire feeding ranges, the limited influence of historic factors and the lack of bias from sampling design, marine animal tracking may provide better data than at-sea surveys for habitat suitability modelling