https://researchdata.se/en/catalogue Search results en Occurrences in this dataset on Swedish Insects have been identified from specimens collected from for the Swedish Malaise Project (SMTP), an inventory funded by the Swedish Species Information Centre (ArtDatabanken). These records comprise the foundation for recent estimates on the size and composition of the Swedish insect fauna, published herein as sample-based datasets. Wed, 13 May 2020 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/gbif-sweden-10-15468-cvcqeb https://researchdata.se/en/catalogue/dataset/gbif-sweden-10-15468-cvcqeb Station Linné Dave Karlsson Fredrik Ronquist Here is presented all data and code used in the article "Strong diel variation in the activity of insect taxa sampled by Malaise traps" by Viktor Gårdman, Emme McDonald & Tomas Roslin. The sampling of insects through Malaise traps was conducted by the authors. 24 malaise traps were erected in a boreal forest in central Sweden (Lat. 60.024855, long. 17.751336) and emptied every second hour, with the exception of night (samplng events during night = 22:00, 02:00, 06:00) for five consecutive days between 14-19th of July 2022. The sampling design is described in further detail in the article (Fig. 1B). Insects were identified to taxonomic Family for Diptera, Coleoptera, and Hymenoptera, except for the superfamilies of Chalcidoidea and Cynipoidea (Hymenoptera). Chalcidoids and Cynipoids were only identified to the superfamily level, due to difficulties in assigning lower taxonomic levels without risking misidentification. Hemiptera was divided into taxonomic families for Heteroptera, and into suborders for Auchenorrhyncha and Sternorrhyncha. To simplify identification of a large group with similar morphology, all microlepidopteras were grouped as such with no further identification. Furthermore, to speed up the identification task, all insects not belonging to Diptera, Hymenoptera, Coleoptera, Lepidoptera or Hemiptera were identified to Order alone. The HRS_SpeciesData file contains information about each captured individual across all taxa for each 2 hour sampling interval during the five days of sampling. Dates are given as DD/M. TrapID refers to which of the 24 traps used the individual was found in. Time is given in hh:mm and refers to the time of sampling, Time_con refers to time in only hh, and time_Num shows time of day as a fraction between 0 (00:00) and 1 (23:59). The superfamily belonging for each taxon used is given. Note that for taxa were only taxonomic Order or Suborders are given, the superfamily column refers to this Order or Suborder. The HRS_EnviData file contains information about how many individual were captured at each timestep for the 17 most common taxa (appearing as >49 individuals or in >19 timesteps), along with weather covariates for each timestep. The weather covariates are average values from the five half hour measurements per sampling period (expect for 22:00-02:00 and 02:00-06:00 where n=9). The taxonomic columns follow the same principle as in HRS_SpeciesData, with an additional column of taxonomic Order. Times and date also follow the same principle as in HRS_SpeciesData. ID is a unique mix of Date and time, given as DDHH (Date, Hour). The emptying of trap at 20:00 on the 15h would have ID 1520. Temperature is given in degrees Celsius (°C), wind speed in m/s, cloud cover as a fraction between 0 (no cloud) and 1 (complete cloud cover), rain in mm, wind direction in cardinal directions, and relative humidity in %. Data on weather covariates was provided by the Swedish Transport Administration (https://www.trafikverket.se/) from weather station 327 Björklinge (Lat. 60.05042, long. 17.62149). All code was created using R version 4.4.0 and is presented through Rmarkdown Fri, 05 Dec 2025 12:45:22 GMT https://researchdata.se/en/catalogue/dataset/2025-211 https://researchdata.se/en/catalogue/dataset/2025-211 Swedish University of Agricultural Sciences Viktor Gårdman Emme McDonald Tomas Roslin Physicochemical and biological data 110 national monitoring lakes for the years 1992 to 2022 were downloaded from the data host https://miljodata.slu.se/MVM/. Climate data were extracted from Climate Research Unit gridded Time Series dataset (version 4.06; Harris et al., 2020; https://crudata.uea.ac.uk/cru/data/hrg/) Eight data sets were compiled: 1. Physicochemical variables from surface water samples (10 variables): EU id Year Colour_absorbance_420nm Ca mg/l Conductivity_mS/m pH TOC mg/l C DIN_ µg/l_N TP_ µg/l_P Water_Temperature_°C. Lake physicochemical and climate data were log10(x + 1) transformed to approximate normal distribution prior to the analyses. 2. Climate data: annual mean air temperature (°C) and precipitation (mm per month for each lake in each year) extracted from the interpolated Climate Research Unit gridded Time Series dataset (version 4.06; Harris et al., 2020; https://crudata.uea.ac.uk/cru/data/hrg/) 3. Phytoplankton taxon abundances (biovolumes) for individual taxa collected in August (n = 780 taxa). Biovolumes were Hellinger transformed. 4. Phytoplankton metrics (n=5): number of taxa, taxon biovolumes, diversity (Hills N2), % cyanobacteria and between-year Euclidean distance*. 5. Littoral taxon abundances (numbers per unit effort) for samples collected in October/November (n=681 taxa). Abundances were Hellinger transformed. 6. Littoral metrics (n=4): number of taxa, total abundance, diversity (N2) and between-year Euclidean distance 7. Profundal taxa abundances (individual per m2) for samples collected in October/November (n=321 taxa). Abundances were Hellinger transformed. 8. Profundal metrics (n=4): number of taxa, total abundance, diversity (N2) and between-year Euclidean distance. * Between-year Euclidean distances were calculated using detrended correspondence analysis axes 1-3 as as d=√((x2 – x1)² + (y2 – y1)²) + (z2 – z1)²), where x1, y1, z1 represent DC axis scores one, two and three, respectively, at any year and x2, y2, z2 represent DC axis scores one, two and three, respectively, for the previous year. Tue, 10 Jun 2025 11:03:00 GMT https://researchdata.se/en/catalogue/dataset/2025-136 https://researchdata.se/en/catalogue/dataset/2025-136 Swedish University of Agricultural Sciences Richard Johnson