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Data set on snow instability
These data on snow instability include three data subsets that were analyzed and the results published by Reuter and Schweizer (2018) who suggest a novel framework on how to describe snow instability by failure initiation, crack propagation and slab tensile support. Please refer to the Read-me file for further details on the data. These data are the basis of the following publication: Reuter, B. and Schweizer, J., 2018. Describing snow instability by failure initiation, crack propagation and slab tensile support. Geophys. Res. Lett., 45, doi: 10.1029/2018GL078069.
Stand inventory data from the 10‐ha forest research plot in Uholka, Ukraine
In 2000, a permanent forest plot of 10 ha has been established in the core zone of the primeval beech forest of Uholka. All living and dead trees with a diameter at breast height (DBH) ≥ 60 mm were identified to species, DBH measured, stems tagged and mapped. Since then, the plot has been remeasured in 2005, 2010, and 2015. In total, 4,820 individual trees were measured with 14,116 individual measurements throughout all four inventories. In spring 2018, an Airborne Laser Scan was carried out, covering the Uholka‐Shyrokyi Luh forest. This data set allows us to derive a high‐resolution digital elevation model (DEM) of the plot area. The data set allows for important insights into the development and the spatial and temporal dynamics of primeval beech forests. The detailed dataset description can be found in Stillhard et. al (2019): https://doi.org/10.1002/ecy.2845
Information on soil based on the soil dependency of the FOEN red list species
This dataset contains all data on which the following publication below is based. Paper Citation: Frey B., Maurer C., Schneider K. (2023) Informationen zum Boden anhand der Gebundenheit der Rote-Liste Arten BAFU. Bundesamt für Umwelt BAFU. 26 p. Please cite this paper together with the citation for the datafile. Soil is the habitat and basis of life for countless creatures. The great importance of soil organisms for the fertility of our soils is widely recognized, but information is sparse on their distribution and possible endangerment in Switzerland. In the present project, target species, i.e. species that are closely linked to the soil, were identified, and for the the first time, information was gathered on the degree of endangerment of the soil ecosystem in Switzerland on the basis of Red List species. For eleven Red List groups, the species' soil dependency was determined, i.e. whether their life cycle includes stages in or on the soil. The assessment of soil dependency was made using four classes, which enabled the classification not only of insects but also of vertebrates, land snails, lichens and fungi.
Regional bark beetle windthrow and snow breakage disturbance predisposition maps
Regional disturbance predisposition maps for windthrow, bark beetle and snow breakage. The maps are created using spatially explicit data of classified forest structure and site factor parameters. The classified input parameters were weighted according to their effect on windthrow, bark beetle and snow breakage predisposition using an expert-based model. The predisposition raster maps (10 x 10 m) represent the sum of expert-weighted effects on the predisposition and are provided as raw values (sum of the effects on predisposition) and as classified layers. Classified predisposition maps are encoded as follows: 1: low predisposition (<50% quantile based on raw predisposition values) 2: increased predisposition (50–75% quantile) 3: high predisposition (>75–95% quantile) 4: extreme predisposition (>95% quantile) Also included are the derived forest structure maps for development stage, dominant stand height, number of stems and canopy cover used for the predisposition mapping, as well as the R scripts and example data to re-create these parameters. Forest structure parameters are based on a data set of individual trees detected using a high-resolution vegetation height model. Further included are ArcPy scripts and example data to re-create the predisposition maps. Running the ArcPy scripts requires a valid ArcGis license including the extensions "SpatialAnalyst", "ImageAnalyst" and "3D". The example data provided here is assembled from different sources; please check the related publication Bührle et al. (in review) for data sources and further information about the processing, the expert-based model, the forest structure derivation and the predisposition mapping. Also consider reviewing the related datasets and publications Bast et al. (2025) for more information about canopy layering derivation and individual tree detection (ITD).
Data and code for Community structure and range shifts in Arctic marine fish under climate change
Data and code for the paper published in Ecography: Community structure and range shifts in Arctic marine fish under climate change Abstract: Arctic marine ecosystems are rapidly transforming due to climate change. Warming temperatures and shrinking sea ice are enabling boreal fish to expand northward, possibly disturbing cold-adapted Arctic species assemblages. Species range shifts have been documented in the Bering and Barents Seas, raising concerns about ecosystem restructuring. Range shifts are especially difficult to detect in the Arctic due to sparse and inconsistent data. Here, we studied fish composition from eDNA water samples taken in East Greenland, Svalbard, the Barents Sea, and the Kara Sea during the TOPtoTOP and Arctic Century expeditions. We examined the environmental drivers of fish community structure using global dissimilarity models. We calculated the decadal rate of temperature change to identify the fastest-changing areas. We compared fish detections from eDNA with published historical records for the Kara Sea to assess possible range expansions. We found that temperature was the main factor influencing the taxa turnover of fish communities, with Gadidae and Liparis sp. driving the greatest compositional differences. Over the past 30 years, temperatures increased by 0.2 to 0.6°C per decade at our study sites, with the highest increases in western Svalbard and the lowest in the eastern Kara Sea. Despite the apparent dependence on temperature, we identified only one species detected outside its known latitudinal range, and five species in the Kara Sea with recent occurrences or representing an extended distribution. Our study suggests that temperature, the main driver of fish community assembly, is increasing rapidly in the Arctic, and a few species have likely already shifted recently, or at least their detections are new in some areas. While these detections cannot be definitively linked to range shifts, our results highlight the need to improve monitoring of high-latitude fish communities to detect and predict future ecosystem changes. Article: Marques, V., Fopp, F., Jaquier, M., Ellingsen, K. E., Yoccoz, N., Jucker, M., ...Pellissier, L. (2025). Community structure and range shifts in Arctic marine fish under climate change. Ecography, e8014. doi: 10.1002/ecog.08014 Data: The resource contains a zip file with the entire project structure. Data README: Intro This repo presents data and code associated with the paper "Community structure and range shifts in Arctic marine fish under climate change" published in Ecography Usage Launch the `main.R` script to reproduce the entire analysis. It executes code blocks to create the data necessary for the analysis and then creates the figures. 💣 attention, total size is expected ~20 Go and the script will query the CMEMS database to fetch environmental data Tools To make the scripts run, you need R, R packages (see session info below), python3, and the copernicusmarine python tool (https://pypi.org/project/copernicusmarine/). SessionInfo ``` sessionInfo() R version 4.4.0 (2024-04-24) Platform: aarch64-apple-darwin20 Running under: macOS 15.5 Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0 locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 time zone: Europe/Zurich tzcode source: internal attached base packages: [1] stats graphics grDevices [4] utils datasets methods [7] base other attached packages: [1] lwgeom_0.2-14 [2] metR_0.15.0 [3] ggtext_0.1.2 [4] scico_1.5.0 [5] ggnewscale_0.5.0 [6] colorspace_2.1-1 [7] climetrics_1.0-15 [8] rts_1.1-14 [9] xts_0.14.0 [10] zoo_1.8-12 [11] ggpubr_0.6.0 [12] adespatial_0.3-23 [13] ggspatial_1.1.9 [14] colorplaner_0.1.4 [15] ape_5.8 [16] factoextra_1.0.7 [17] corrgram_1.14 [18] FactoMineR_2.11 [19] betapart_1.6 [20] gdm_1.5.0-9.1 [21] terra_1.7-78 [22] rnaturalearth_1.0.1 [23] ggrepel_0.9.5 [24] vegan_2.6-6.1 [25] lattice_0.22-6 [26] permute_0.9-7 [27] cowplot_1.1.3 [28] tidyterra_0.6.1 [29] sf_1.0-16 [30] egg_0.4.5 [31] gridExtra_2.3 [32] patchwork_1.2.0 [33] viridis_0.6.5 [34] viridisLite_0.4.2 [35] mgcv_1.9-1 [36] nlme_3.1-165 [37] MBA_0.1-0 [38] reshape2_1.4.4 [39] conflicted_1.2.0 [40] lubridate_1.9.3 [41] forcats_1.0.0 [42] stringr_1.5.1 [43] dplyr_1.1.4 [44] purrr_1.0.2 [45] readr_2.1.5 [46] tidyr_1.3.1 [47] tibble_3.2.1 [48] ggplot2_3.5.1 [49] tidyverse_2.0.0 [50] devtools_2.4.5 [51] usethis_2.2.3 loaded via a namespace (and not attached): [1] splines_4.4.0 [2] later_1.3.2 [3] bitops_1.0-7 [4] minpack.lm_1.2-4 [5] XML_3.99-0.16.1 [6] lifecycle_1.0.4 [7] rstatix_0.7.2 [8] doParallel_1.0.17 [9] MASS_7.3-60.2 [10] flashClust_1.01-2 [11] backports_1.5.0 [12] magrittr_2.0.3 [13] rmarkdown_2.27 [14] yaml_2.3.8 [15] remotes_2.5.0 [16] httpuv_1.6.15 [17] sp_2.1-4 [18] sessioninfo_1.2.2 [19] pkgbuild_1.4.4 [20] mapproj_1.2.11 [21] pbapply_1.7-2 [22] DBI_1.2.3 [23] RColorBrewer_1.1-3 [24] ade4_1.7-22 [25] maps_3.4.2 [26] abind_1.4-5 [27] pkgload_1.3.4 [28] RCurl_1.98-1.14 [29] itertools_0.1-3 [30] yaImpute_1.0-34 [31] rcdd_1.6 [32] units_0.8-5 [33] adegenet_2.1.10 [34] codetools_0.2-20 [35] xml2_1.3.6 [36] adephylo_1.1-16 [37] DT_0.33 [38] tidyselect_1.2.1 [39] RNeXML_2.4.11 [40] raster_3.6-26 [41] farver_2.1.2 [42] jsonlite_1.8.9 [43] e1071_1.7-14 [44] phylobase_0.8.12 [45] ellipsis_0.3.2 [46] iterators_1.0.14 [47] emmeans_1.10.4 [48] systemfonts_1.1.0 [49] foreach_1.5.2 [50] progress_1.2.3 [51] tools_4.4.0 [52] ragg_1.3.2 [53] snow_0.4-4 [54] Rcpp_1.0.13 [55] glue_1.8.0 [56] xfun_0.44 [57] withr_3.0.0 [58] fastmap_1.2.0 [59] boot_1.3-30 [60] latticeExtra_0.6-30 [61] fansi_1.0.6 [62] spData_2.3.1 [63] digest_0.6.35 [64] timechange_0.3.0 [65] R6_2.5.1 [66] mime_0.12 [67] estimability_1.5.1 [68] wk_0.9.1 [69] textshaping_0.4.0 [70] jpeg_0.1-10 [71] utf8_1.2.4 [72] generics_0.1.3 [73] data.table_1.15.4 [74] class_7.3-22 [75] prettyunits_1.2.0 [76] httr_1.4.7 [77] htmlwidgets_1.6.4 [78] scatterplot3d_0.3-44 [79] spdep_1.3-5 [80] pkgconfig_2.0.3 [81] gtable_0.3.5 [82] picante_1.8.2 [83] adegraphics_1.0-21 [84] htmltools_0.5.8.1 [85] carData_3.0-5 [86] profvis_0.3.8 [87] multcompView_0.1-10 [88] scales_1.3.0 [89] leaps_3.2 [90] png_0.1-8 [91] doSNOW_1.0.20 [92] geometry_0.4.7 [93] rnaturalearthhires_1.0.0.9000 [94] knitr_1.47 [95] rstudioapi_0.16.0 [96] rncl_0.8.7 [97] uuid_1.2-0 [98] tzdb_0.4.0 [99] checkmate_2.3.1 [100] coda_0.19-4.1 [101] magic_1.6-1 [102] proxy_0.4-27 [103] cachem_1.1.0 [104] KernSmooth_2.23-24 [105] parallel_4.4.0 [106] miniUI_0.1.1.1 [107] s2_1.1.6 [108] pillar_1.9.0 [109] grid_4.4.0 [110] vctrs_0.6.5 [111] urlchecker_1.0.1 [112] promises_1.3.0 [113] car_3.1-2 [114] xtable_1.8-4 [115] cluster_2.1.6 [116] evaluate_0.24.0 [117] mvtnorm_1.2-5 [118] cli_3.6.3 [119] compiler_4.4.0 [120] rlang_1.1.4 [121] crayon_1.5.2 [122] ggsignif_0.6.4 [123] labeling_0.4.3 [124] interp_1.1-6 [125] classInt_0.4-10 [126] plyr_1.8.9 [127] fs_1.6.4 [128] stringi_1.8.4 [129] deldir_2.0-4 [130] munsell_0.5.1 [131] Matrix_1.7-0 [132] hms_1.1.3 [133] seqinr_4.2-36 [134] shiny_1.8.1.1 [135] gridtext_0.1.5 [136] igraph_2.0.3 [137] broom_1.0.6 [138] memoise_2.0.1 [139] fastmatch_1.1-4 ``` Data content and reproducibility Uncleaned raw table out of the bioinformatics pipeline can be found concatenated in `outputs/table_raw_before_cleaning.csv`, yet we caution readers to properly read the cleaning scripts should they wish to reproduce our analysis and this file is presently uncleaned.
Simulated avalanche problem types and seismic avalanche activity around Weissfluhjoch
Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016. Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016.
Rockfall gallery testing Parde 2016
Five full-scale field tests were conducted with concrete blocks weighting between 800 and 3200 kg being dropped onto the roof of a gallery structure made from reinforced concrete. The impacts were recorded using high-speed video and acceleration measurements at the falling blocks. The dataset contains the raw data as well as the analyses of the block trajectories, i.e. kinetics and dynamics. Setup of the measurements and the analyses conducted are published in Volkwein, A. "Durchführung und Auswertung von Steinschlagversuchen auf eine Stahlbetongalerie", WSL-Berichte, Heft 68, 2018.
Wind LIDAR Davos Wolfgang
Scanning wind Lidar from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 200 m above ground to 8100 m. The time resolution is up to 5 seconds. The Lidar was measuring wind profiles but also performed plan position indicator (PPI) and range height indicator (RHI) scans.
Simulation parameters and outputs for a rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism
In the associated study [1], two time-lapse temperature gradient metamorphism series of three-dimensional micro-computed tomography images of snow (obtained by [2]) have been used to model the decrease of specific surface area (SSA) over time based on the pore-scale physics. We conducted finite element simulations of one-way coupled heat and mass diffusion in order to estimation the spatial pattern of water vapor deposition and sublimation, which controls the evolution of the SSA over time. We notably studied the influence of the condensation coefficient, a key but poorly constrained physical parameter. This dataset provides the parameters used for the mesh generation and the finite element simulations. It also includes the ice fraction, specific surface area per unit volume and surface average of mean curvature and vapor field obtained as outputs from the mesh process and the simulations. [1]: Braun, A., Fourteau, K., and Löwe, H.: A rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1947, 2023. [2]: Pinzer, B. R., Schneebeli, M., and Kaempfer, T. U.: Vapor flux and recrystallization during dry snow metamorphism under a steady temperature gradient as observed by time-lapse micro-tomography, The Cryosphere, 6, 1141–1155, https://doi.org/10.5194/tc-6-1141-2012, 2012.
Photogrammetric Drone Data Schürlialp
The data was collected on 16.04.2021 and on 28.05.2021 with a Wingtra Gen II and a Sony RX1 II RGB sensor to obtain snow depth and distribution data. Following the data collection, the data was processed with Agisoft Metashape. A 10cm DSM, a 10cm snow depth raster, a 3mm orthophoto and the original drone images are available for download.