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Data Konzeptionelles Systemmodell
This folder contains documents relevant to the project step Konzeptionelles Systemmodell. It specifically contains the following documents: 1. 0201_Konzeptionelles Systemmodell_20221027: This document is the final version of the Konzeptionelles Systemmodell. The underlying structure of the Konzeptionelles Systemmodell is explained in chapter 4.4 of the document 0202_Masterarbeit Elena Siegrist_UniBern_Herbstsemester 2022 also deposited in this folder. The document contains 3 sheets. The sheet "Bausteine" shows the different possible options for table columns of the type Bausteine (see chapter 4.4 of Masterarbeit), the sheet "Direkte Effekte" contains identified direct impact pathways and the sheet "Indirekte Effekte" contains identified indirect impact pathways. 2. 0202_Masterarbeit Elena Siegrist_UniBern_Herbstsemester 2022: This document is the Master Thesis of Elena Grace Siegrist submitted to and accepted by the University of Bern in the Autumn Semester 2022. Chapter 4.4 of the document explains the underlying structure of the Konzeptionelles Systemmodell. 3. 0203_Literaturverzeichnis_konzeptionelles Systemmodell_20221026: This document lists all literature and documents cited in the Konzeptionelle Systemmodell.
The Habitat Map of Switzerland v1_2 2025
Lebensraumkarte der Schweiz v1.2 2025/La carte des milieux naturels de Suisse v1.2 2025 The FOEN funded project ‘The Habitat Map of Switzerland:continual improvement’ conducted at the WSL, has improved on versions 1.0 and 1.1 of a map of Swiss habitats according to the TypoCH classification (Delarze et al. 2015). The datasets is now separated into two data sets: The Habitat Map of Switzerland v1.2, ground cover layer, and The Habitat Map of Switzerland v1.2, individual trees and shrubs outside forest v1.0 (above ground) layer. The Habitat Map of Switzerland maps the TypoCH habitat types wall-to-wall across the whole of Switzerland, to at least the classification’s 2nd level of detail (where possible to the 3rd level of detail). Habitats are mapped through a variety of approaches that can be grouped as either: 1: Derived from the existing Swiss-wide datasets a) the high quality landcover mapping from Swisstopo’s Topographical Landscape Model (TLM), b) the harmonised mapping of agricultural fields area from the ‘Landwirtschaftliche Nutzfläche’ (LWN) mapping and, in settlements areas, c) the harmonised cadastral data (‘Amitliche Vermessung’ AV). 2: Modelled within the project using Random Forest or Ensemble Modelling techniques to model the spatial distribution of individual habitat types (further detail below), 3: Combining existing species distribution models to determine habitat types, or 4: Classification with relatively simple rule-sets based on auxiliary spatial datasets, i.e. vegetation height models (based on digital aerial photogrammetry and/or SwissSurface3D aerial laser scanning (ALS) data), the digital terrain model, the normalised difference vegetation index (NDVI) derived from aerial imagery and/or time-series of growing season Sentinel-2 or Planet satellite imagery. Further detail on the methodology can be found within the README document.
Plant species list from 1775 and 2020 for Uetliberg Zürich
The list gives the clearly identifiable plant species from Schinz (1775). Die Reise auf den Uetliberg. Verlag des Waysenhauses, Zürich). Several species given in Schinz (1775) are not identifiable to the species level and are not included in the list. Similarly, *Vicia pisiformis*, Erbsenartige Wicke, given by Schinz (1775) is probably a misidentification and also not included in the list. Latin plant species name Schinz 1775: Latin plant name (if) given by Schinz (1775); na: not available German plant species name Schinz 1775: German plant name given by Schinz (1775) Latin plant species name Info Flora 2024: Latin plant name according to www.infoflora.ch in 2024 German plant species name Info Flora 2024: German plant name according to www.infoflora.ch in 2024 Plant species still occurring in 2020: Plant species given in Schinz (1775) still occurring at the Uetliberg in 2020 (Zürcherische Botanische Gesellschaft. 2020. Flora des Kanton Zürich. Haupt, Bern); occurrence of *Thesium alpinum*, Alpen-Bergflachs, according to floristic knowledge of Rolf Holderegger and Michèle Büttner; 1: still occurring; 0: no longer occurring / extinct.
Snow Depth Mapping
The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData). For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground > 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths > 15m and smaller than < -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth. The work is published in: Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015
Simulated and observed prevalence of dispersal-related traits in tropical reef fish assemblages worldwide
This dataset contains all data and R codes (R Development Core Team, https://www.R-project.org) used in the following publication: Donati GFA, Parravicini V, Leprieur F, Hagen O, Gaboriau T, Heine C, Kulbicki M, Rolland J, Salamin N, Albouy C, Pellissier L. "A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted by Ecography in August 2019. When using this data and R scripts the above publication should be cited. The interaction of habitat dynamics with species dispersal abilities could generate gradients in species diversity and prevalence of life-history and ecological traits, when the latter are associated with dispersal potential. In this dataset, we use a spatial mechanistic model of speciation, extinction and dispersal, constrained by a dispersal parameter. This model allows to simulate the interplay between reef habitat dynamics over the past 140 million years and dispersal, shaping lineage diversification history and global assemblage composition of over 6000 tropical reef fish species. Global trait distribution data of tropical reef fish are used to evaluate the congruence between simulations and observations.
Preferential deposition of snow and dust over hills: governing processes and relevant scales
Preferential deposition of snow and dust over complex terrain is responsible for a wide range of environmental processes, and accounts for a significant source of uncertainty in surface mass balances of cold and arid regions. Despite the growing body of literature on the subject, previous studies reported contradictory results on the location and magnitude of deposition maxima and minima. This study aims at unraveling the governing processes of preferential deposition in neutrally stable atmosphere and to reconcile seemingly inconsistent results of previous works. For this purpose, a comprehensive modeling approach is developed, based on large eddy simulations of the turbulent airflow, Lagrangian stochastic model of particle trajectories, and immersed-boundary method to represent the underlying topography. The model performance is tested against wind tunnel measurements of dust deposition around isolated and sequential hills. A scale analysis is then performed to investigate the dependence of snowfall deposition on the particle Froude and Stokes numbers, which fully account for the governing processes of inertia, flow advection, and gravity. Additional simulations are performed, to test whether the often used assumption of inertialess particles yields accurate deposition patterns. We finally show that our scale analysis provides qualitatively similar results for hills with different aspect ratios. This dataset contains the results of the LES-LSM model. Each Matlab file contains a 2D array of deposition values (in kg/m2) in each surface node (ix, iy) of the Cartesian grid. The file names are consistent with the simulation numbers listed in the original paper. For additional information, please refer to "Preferential deposition of snow and dust over hills: governing processes and relevant scales" by F. Comola, M. G. Giometto, S. T. Salesky, M. B. Parlange, and M. Lehning, Journal of Geophysical Research: Atmospheres, 2019.
Dataset for OGRS 2018 publication
This dataset contains the road and plot data used for the geospatial analysis example showcased in "Fostering Open Science at WSL with the EnviDat Environmental Data Portal", a contribution to the 5th Open Source Geospatial Research and Education Symposium (OGRS), 2018. The example uses Jupyter Notebook to calculate road densities in the neighbourhood of sample plot locations with Python. Road data were extracted from OpenStreetMap, while the point data (sample plots) were generated manually.
Environmental DNA Freshwater Colombia Magdalena 2022
Monitoring of vertebrates of the lower Magdalena river through eDNA metabarcoding The Magdalena River basin harbors a large biodiversity of vertebrates, with numerous endemic species, many of which are threatened. Here, we used environmental DNA (eDNA) metabarcoding, with two primer sets targeting different regions of the mitochondrial DNA 12S ribosomal RNA gene, to detect vertebrate diversity in the Magdalena River. We detected a total of 159 vertebrate taxa, not only aquatic but also terrestrial, arboreal, and aerial. The diversity of these vertebrates increases in relation to the proximity to the river mouth with a change in the composition of the assemblage of aquatic vertebrates detected. We conclude that eDNA metabarcoding allows characterizing vertebrate assemblages in large rivers, assessing conservation status, and elucidating biodiversity patterns with minimal ecosystem disturbance. Samples were taken at the sides of the river or in the center using a boat. At each station, we performed two filtration replicates using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 1 hours, during which a maximum of 30 liters of water were filtered through each capsule. After filtration, the water inside the capsules was removed, and the capsules were filled with 50 ml of conservation buffer for preservation at room temperature. We followed strict contamination control protocols throughout both the fieldwork and laboratory processes, adhering to the guidelines of Valentini et al. (2016). To prevent contamination, each sample was processed using disposable gloves and single-use filtration equipment. The MiSeq Reagent Kit v3 (2x75 bp) (Illumina, San Diego, CA, USA) was used for paired-end sequencing at a theoretical sequencing depth of 200,000 reads per sample. Data content: * rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences. * taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata. * metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)
Data supporting ‘Examining honeybee (Apis mellifera) dominance patterns within urban bee communities worldwide’
The following repository contains the data and code to reproduce the figures analyses of the paper "Examining honeybee (Apis mellifera) dominance patterns within urban bee communities worldwide". It contains three datasets and one .R file --- The data results from a literature review on abundance distribution of urban bee communities worldwide, aiming at obtaining studies reporting total abundances of honeybees and wildbees in a given city, and species abundances of wildbees and honeybees in a given city. --- 1. **Data**: - *20240308_cities_coords.csv*: the data contains the latitude and longitude of the included cities in the study. It is used for making the world map with the cities. - *20241201_proportion_HB_WB.csv*: the data contains the total wild bee and honeybee abundances per city/urban aggregation, and the proportions. - *20241201_rad_bees.csv*: the data contains the species abundances per city/urban aggregation 2. **Script**: - *HoneybeeDominance_Analyses_Figures.R*: the script to run the analyses and make the plots. Analyses were done in R version 4.2.1, using the packages ggplot2, glmmTMB, multcomp, wesanderson, terra.
Expedition to Princess Elisabeth Antarctica Station, 2016/2017
This dataset contains the data acquired during the expedition to Princess Elisabeth Antarctica Station in December 2016 and January 2017. The dataset consits of meterorological data, drifting snow mass flux data, SnowMicroPen data and Terrestrial Laser Scanning data. Please refer to the README for more information about the data. This dataset is the basis of the following publication: Sommer, C. G., Wever, N., Fierz, C., and Lehning, M.: Wind-packing of snow in Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-36, in review, 2018.