https://researchdata.se/en/catalogue Search results en Data and R code used in piecewise structural equation modelling for a study that compared the direct and indirect impacts of temperature and precipitation on moose calf mass in northern Sweden. The study was initiated in 1988 in an effort to examine the impacts of climate change on common forage species of the economically and culturally important moose in Sweden. It ran until 1997 and was re-started in 2017. Temperature and precipitation variables are derived from SMHI weather station data. Average moose calf mass for study sites is derived from data from the Swedish Hunter's Association and individual hunting teams. Both weather and moose calf mass represent mean values within a 50km radius of each study site. Nitrogen and neutral detergent fibre measures are the result of near-infrared spectroscopy modelling, using 50 samples to calibrate the model. Samples were collected from 1-ha sites and included material from 30 individuals of either downy birch or fireweed. The dataset contains the following files. DataWeatherVegMoose.tsv is the data itself (TSV format, 236 rows × 10 columns). This includes the following variables: Total precipitation (mm) from the start of the growing season, defined as the first day of the first four consecutive days each calendar year that each have a mean daily temperature greater than or equal to 5 degrees C, to July 17 of that year. This is an average value for all SMHI weather stations within a 50 km radius of a site. Mean daily average temperature from the start of the growing season, defined as the first day of the first four consecutive days each calendar year that each have a mean daily temperature greater than or equal to 5 degrees C, to July 17 of that year. This is an average value for all SMHI weather stations within a 50 km radius of a site. Proportion of days from the start of the growing season, defined as the first day of the first four consecutive days each calendar year that each have a mean daily temperature greater than or equal to 5 degrees C, to July 17 of that year, when the maximum daily temperature was greater than or equal to 20 degrees C. This is an average value for all SMHI weather stations within a 50 km radius of a site. Neutral detergent fibre content of downy birch leaves at the site, based on a representative sample and calculated using Near Infrared Spectroscopy Neutral detergent fibre content of fireweed stems, leaves, and flowers at the site, based on a representative sample and calculated using Near Infrared Spectroscopy Nitrogen content of downy birch leaves at the site, based on a representative sample and calculated using Near Infrared Spectroscopy Nitrogen content of fireweed stems, leaves, and flowers at the site, based on a representative sample and calculated using Near Infrared Spectroscopy Mean date-adjusted moose calf slaughter weight for calves reportedly shot within 50km of the site. Values must represent the mean weight of at least 10 calves to be included. The documentation file Key_DataWeatherVegMoose.tsv contains detailed information about the variables in the dataset. The documentation file sites_no.tsv contains codes for the different sites where data was collected. It corresponds with the variable Site in the dataset DataWeatherVegMoose.tsv. R_code_piecewise_SEM.r is the R script used to calculate the piecewise structural equation models linking weather to moose calf mass directly and via forage chemistry. R_code_piecewise_SEM_log.txt is output of the script with session information. If R , with the packages nlme and piecewiseSEM, is installed, it can be generated by running this from a shell: Rscript R_code_piecewise_SEM.r > R_code_piecewise_SEM_log.txt Wed, 18 Jan 2023 14:57:29 GMT https://researchdata.se/en/catalogue/dataset/2022-245-1 https://researchdata.se/en/catalogue/dataset/2022-245-1 Swedish University of Agricultural Sciences Sheila Holmes Kjell Danell John Ball Göran Ericsson This dataset contains the SLU Forage Availability Map. This dataset contains 12 maps, distributed as .tif files. They show the predicted abundance of the pine (Pinus sylvestris), rowan (Sorbus aucuparia), oak (Quercus spp.), willow (Salix caprea), aspen (Populus tremula) and birch (Betula spp.) trees from 10 cm height to 4 cm diameter at breast height across the country using data from the Swedish National Forest Inventory and remote sensing datasets. Map products are aggregated to a 1ha and a 1km2 scale and show the average amount of trees estimated per spatial unit. For more information on the development of this dataset, please see the article: https://doi.org/10.1016/j.jag.2025.104850 For access to the 10x10m maps, contact the authors. Tue, 04 Nov 2025 15:22:50 GMT https://researchdata.se/en/catalogue/dataset/2025-259 https://researchdata.se/en/catalogue/dataset/2025-259 Swedish University of Agricultural Sciences Lukas Graf Inka Bohlin Per-Ola Hedwall Jonas Dahlgren Annika M. Felton Inventories of forest damage are carried out within the Swedish University of Agricultural Sciences (SLU) Forests programme. An annual monitoring of the most important sources of forest damage is carried out by the Swedish National Forest inventory (NFI). Although the Swedish NFI is an objective and uniform inventory of forest damage in Swedish forests at national and regional scales, less common or less widespread occurrences of forests pests and pathogens are difficult to survey solely through large-scale monitoring programmes. There is a need for complementary inventories to facilitate timely delivery of relevant information. Thus Target-tailored forest damage inventories (TFDI) aiming at providing data for operational decisions making at local level, and linked to specific damage events were introduced. TFDI’s are developed to give rapid response to requested information of specific damage outbreaks. The TFDIs are carried out in limited and concentrated samples, with flexible but robust methods and design. The data collected in the TFDI shall also be of such quality that it can be useful in research. Young forests suffering from multiple types of damage—primarily due to wildlife browsing (especially by moose) and infections by resin top disease (Cronartium flaccidum) and other pathogens—are a current concern in Swedish forests. There is growing apprehension regarding the density and damage status of these young stands, and a significant need for information on the extent and nature of this damage. Consequently, in 2022, the Swedish National Forest Inventory (NRS) conducted a sample-based survey of damage in young forests across the four northernmost counties (Västernorrland, Jämtland, Västerbotten, and Norrbotten). This survey aims to provide insights into the condition of young forests. It is hoped that the practical application of this methodology will prove successful, potentially leading the National Forest Inventory to incorporate parts of it into its regular operations, thereby improving the ability to characterize young forest stands. This year’s survey also serves as a follow-up to previous assessments of resin top disease infections in northern Norrland, conducted in 2007, 2008, and 2012. The findings are intended to serve as a basis for decision-making regarding potential forest management interventions. The survey is based on the National Forest Inventory’s permanent sample plots located in the counties of Norrbotten, Västerbotten, Västernorrland, and Jämtland. A selection was made from all permanent sampling clusters containing plots situated in stands that had undergone final felling—specifically, stands in Lapland (Region 1) harvested between 1993 and 2013, and stands in other areas (Regions 21 and 22) harvested between 1998 and 2013. To select the sample plots, the Local Pivotal Method (LPM; Grafström et al.) was used. This method distributes the selection based on the plots' geographic location, degree of damage, canopy closure, and the time elapsed since the final felling. This selection procedure ensures a representative sample across these dimensions, yielding more reliable data for any given outcome compared to a purely random selection. At each selected sample plot, measurements were taken within three subplots, each with a radius of 3.99 meters. One subplot was placed at the plot’s primary center point, while the other two were located 15 meters to the north and south of this center. Collectively, this approach provides data that effectively represents the 20-meter-radius area—the scale typically used for many of the ordinary assessments within the Swedish NFI. In cases where the 20-meter-radius area was truncated by the boundary of another stand or land-use type, only those subplots (or portions thereof) falling within the stand in question were surveyed. The proportion of each subplot located within the target stand was recorded. Within each subplot, all stems within a defined height range were counted. From each sample plot, 16 "main stems" (hpl)—selected independently of the site index (SI) or the stand's mean height—were identified. For truncated subplots, the maximum number of main stems was reduced in proportion to the subplot's size relative to a full-sized subplot. All other counted stems are classified as subsidiary stems. The height of each main stem was measured, and its damage status was assessed and recorded individually. For subsidiary stems, only those with observed damage were assessed and recorded individually. Damage to overstory trees (overstanders) was not assessed. In addition, every individual dead conifer stem estimated to have died within the last 5 years was recorded. Certain data were recorded for the 20-meter plot: the number of overstory pines, as well as the number of overstory pines and recently dead (within 5 years) pines exhibiting damage from resin top disease. Furthermore, the abundance of Melampyrum spp. was estimated—expressed in whole square meters—within an area of 50 m². The dataset consist at plot level of 594 rows with 44 columns. The dataset contains information at tree level of 19012 rows with 46 columns and at dead tree level of 548 rows med 20 columns. The content and scope of the inventory has been developed in consultation with Swedish Forest Agency. References: Grafström, A., Lundström, N., & Schelin, L. Spatially Balanced Sampling through the Pivotal Method. Biometrics, 68(2), 514-520. Retrieved December 1, 2020, from https://doi.org/10.1111/j.1541-0420.2011.01699.x Some assessed and used variables: At sample plot level: Plot area measured Number of trees by species At tree level: Type of plant Different type of damage At dead tree level: Deduction cause Deduction season See the document "Data_description" for more detailed information. As additional documentation, field instructions for the inventories are also provided, both for the specific inventory and for the National Forest Inventory (NFI). An English version of the National Forest Inventory field instruction is available for year 2021 only, which is why this is included here. Wed, 11 Nov 2026 13:40:49 GMT https://researchdata.se/en/catalogue/dataset/2026-291 https://researchdata.se/en/catalogue/dataset/2026-291 Swedish University of Agricultural Sciences Sören Wulff Cornelia Roberge Mats Walheim