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Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity
This dataset comprises a large array of ecological data for the European Alps: (1) Current soil and climate predictors at various resolutions. (2) GBIF observations of the European Alps Flora (~4,000 species). (3) Species habitat suitability maps (1,109 species; based on species observations filtered at 40x40-km) at various resolutions used in the study to generate (4); except 'expert'... (4) Expert, Taxonomic, phylogenetic and functional diversity of the study region at various resolutions (from 100-m to 40-km --> 100-m aggregated & mean to km + non-aggregated/predicted) for CLIM, SOIL and CLIM-SOIL models. (5) Ecological and altitudinal preferences of the European Alps Flora. (6) Data outputs of the related published article. (7) All scripts used for analyses. (8) Additional files used for analyses. (9) Improved set of species habitat suitability maps (~2,600 species; based on species observations filtered at 1x1-km) and related taxonomic diversity at 100-m resolution (aggregated to km + non-aggregated/predicted) for CLIM, SOIL and CLIM-SOIL models ---> not incorporated in the study.
Comprehensive dataset of pollinator diversity and visitation rates with individual-based traits and pollination success across four urban garden plant species
This dataset contains detailed records of pollinator communities and plant reproductive outcomes from an urban garden experiment in Zurich, Switzerland. It includes flower visitation frequency and pollinator species richness for four insect-pollinated plant species observed across 24 home gardens. The dataset spans 167 pollinator taxa, with over 5,700 individuals identified, mostly to species or genus level. It features individual-level trait measurements for pollinators, such as body size and tongue lengths. Measures of pollination success, including seed and fruit set, are provided for each plant species.
Compressive stick slip and snow-micro-quakes
When snow is compressed with a certain speed, micro-snowquakes are triggered in the porous structure of bonded crystals. The present dataset covers uniaxial compression experiments of snow at different strain rates and concurrent X-ray tomography imaging documenting this feature. The experiments were conducted in a micro-compression stage operated in the X-ray tomography scanner in the SLF cold laboratory. The dataset comprises the compression force data of 17 compression experiments, the 3D image data from 4 X-ray tomography scans and the results of numerical simulations.
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.
Carabid beetles in forests
Carabidae data from all historic up to the recent projects (21.10.2019) of WSL, collected with various methods in forests of different types. Version 2 ('FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae') contains additional data field PROJ_FALLENBEZEICHNUNG. Data are provided on request to contact person against bilateral agreement.
Global Cryosphere Watch data survey
Two surveys on the topic of data usage where conducted for the Global Cryosphere Watch data portal. The first one focused on the data provider point of view while the second one focused on the data user point of view. 37 data providers (ie institutions) worldwide provided their answers for the first survey (from fall 2017 until summer 2018) while 54 users (contacted through various mailing list such as the Cryolist) answered the questions on their third party data usage (fall 2019 until January 2020).
Data set of: Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources
The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal 'Methods in Ecology and Evolution'.
Soil measurements of Seewer Berg and Davos
The dataset contains measured values of soil liquid water content, matric potential, and soil texture of 40 soil samples in Davos, Switzerland. The measurements were used for determining van Genuchten parameter values through fitting of water retention curves, pedotransfer functions, and inverse fitting with Hydrus-1D.
Escalating effects of multiple perturbations on soil functionality
Soil chemical and biological properties of soils affected by 10 different perturbations related with global change, applied individually or in combination. Greenhouse experiment. Perturbations applied to intact soil cores (15 cm diameter, 15 cm deep) collected in an extensively managed grassland on the WSL grounds in June 2023. This dataset contains all data on which the publication below was based: "Fioratti Junod M, Gombeer S, Holmes J, Zimmerman S, Rillig M, Risch AC and Cordero I. Soil functionality declines under multiple superimposed global change perturbations. XXXX " Please, cite this publication together with the citation of the datafile. Database includes: Pot_ID: pot or soil core identifier. Batch: numeric, 1 or 2. Enzyme_plate: numeric, from 1 to 6. Number_of_perturbations: numeric, from 0 to 10. N_addition: binary. Yes = 1, No = 0. P_addition: binary. Yes = 1, No = 0. Defoliation: binary. Yes = 1, No = 0. Trampling: binary. Yes = 1, No = 0. Insecticide: binary. Yes = 1, No = 0. Fungicide: binary. Yes = 1, No = 0. Herbicide: binary. Yes = 1, No = 0. Antibiotic: binary. Yes = 1, No = 0. Drought: binary. Yes = 1, No = 0. Heat_wave: binary. Yes = 1, No = 0. Treatment: character. Description of the treatment, 15 levels. SWC: soil water content. Numeric, unitless. Green_biomass: green plant biomass. Numeric, g. Brown_biomass: brown or dead plant biomass. Numeric, g. Biomass_cut: plant biomass cut during defoliation treatments. Numeric, g. CO2_flux_light: net CO2 flux under ambient light. Numeric, mg CO2 m-2 h-1. CO2_flux_dark: ecosystem respiration. Numeric, mg CO2 m-2 h-1. S: organic matter stabilisation factor (tea bag index). Numeric, unitless. k: organic matter decomposition rate (tea bag index). Numeric, unitless. DOC: dissolved organic carbon, mg Kg-1 dry soil. IC: inorganic carbon, mg Kg-1 dry soil. Ammonium_KCl: plant available ammonium, mg Kg-1 dry soil. Nitrate_KCl: plant available nitrate, mg Kg-1 dry soil. Phosphate: phosphate, mg Kg-1 dry soil. MBC: microbial biomass carbon, mg Kg-1 dry soil. MBN: microbial biomass nitrogen, mg Kg-1 dry soil. PHO: phosphatase activity, nmol h-1 g-1 dry soil. BG: β-glucosidase, nmol h-1 g-1 dry soil. XYL: xylosidase, nmol h-1 g-1 dry soil. CBH: cellobiohydrolase, nmol h-1 g-1 dry soil. NAG: N-acetylglucosaminidase, nmol h-1 g-1 dry soil. LAP: leucine aminopeptidase, nmol h-1 g-1 dry soil. POX: phenoloxidase, nmol h-1 g-1 dry soil. PER: peroxidase, nmol h-1 g-1 dry soil. pH: soil pH, unitless Water_stable_aggregates: water stable aggregates, % Surface_infiltration_time: water surface infiltration time, s.
Individual Detected Trees
This dataset provides individual detected trees for selected Swiss Cantons (Appenzell Outer-Rhodes - AR, Grisons - GR, Nidwald - NW, Obwald - OW and St. Gall - SG), including the derived diameter at breast height and the derived NFI development stage. Data show the Individual detected trees (ITD) with the x- and y-positions of each detected tree based on a spike-free vegetation height model based on nationwide LiDAR data. The shapefile contains the following attributes: tree height [m] (Height), estimated diameter at breast height [cm] (BHD), and the NFI development stage (Ent; 1: young growth/thicket (< 12 cm); 2: pole timber (12-30 cm); 3: young timber (31-40 cm); 4: medium timber (41-50 cm); 5: old timber (> 50 cm).