DOI: 10.5061/dryad.mcvdnck9d
title: Data from: Spatial and temporal dynamics of a bark beetle outbreak in the Eastern European Alps

description: The European Alps are currently considered among the ecoregions with the
 highest magnitude of average bark beetle disturbance per year. The dataset
 and code can be used for a disturbance regime characterization
 based on a unique database including more than 50,000 records of
 ground-based bark beetle disturbance observations that were collected in
 the Eastern Alps for the years 2020 to 2023. The proposed methodology,
 which is built on the extraction of disturbance metrics and parameters,
 can help with the correct parameterization of forest disturbance models,
 and thus support our capability of predicting future patterns of beetle
 dispersal and effects on carbon stocks in the alpine region and beyond.
description: # Data from: Spatial and temporal dynamics of a bark beetle outbreak in
 the Eastern European Alps
 [https://doi.org/10.5061/dryad.mcvdnck9d](https://doi.org/10.5061/dryad.mcvdnck9d) ## Description of the data and file structure The dataset comprises all data used for: i) the extraction of yearly forest disturbance metrics (event areas, inter-year distances, intra-year distances, event intensity, and event frequency) from bark beetle disturbance polygons ii) the modelling of spatial point processes at regional and landscape scale (landscape polygon examples 1-2-3) iii) the retrieval of disturbance regime parameters from bark beetle disturbance polygons iv) the calculation of disturbance-induced biomass loss from the ESA CCI biomass product (User guide: [https://climate.esa.int/media/documents/D4.3_CCI_PUG_V4.0_20230605.pdf](https://climate.esa.int/media/documents/D4.3_CCI_PUG_V4.0_20230605.pdf)) and spatially explicit disturbance vectors (bark beetle disturbance polygons) v) the cartographic representations of the study area and bark beetle-induced forest disturbances The forest disturbance dataset is based on *in-situ* observations and was collected by the Forestry Department of the Province of Bozen-Bolzano between 2020 and 2023. Data are stored in data.zip ### Files and variables **file formats**: .shp: spatial polygons .shx, .dbf, .prj: associated attributes of the .shp spatial polygons .tif: rasters **coordinate reference system (CRS)**: EPSG:32632 - WGS 84 / UTM zone 32N **file names**: * bb_2020.shp: bark beetle disturbance polygons year 2020 * bb_2021.shp: bark beetle disturbance polygons year 2021 * bb_2022.shp: bark beetle disturbance polygons year 2022 * 2023_unito.shp: bark beetle disturbance polygons year 2023 * bb_complete2.shp: bark beetle disturbance polygons of all years * Land_polygon_2.shp: regional extent polygon * example.shp: landscape polygon example 1 extracted within the regional boundaries * example_21.shp: landscape polygon example 2 extracted within the regional boundaries * example_3.shp and example3.shp: landscape polygon example 3 extracted within the regional boundaries * forest_types_polygon.shp: regional forest cover polygon * spruce_mask_province.shp: regional Norway spruce cover polygon * masked_province.tif: ESA CCI biomass product masked by regional boundaries * biomass_sd.tif: uncertainty related to ESA CCI biomass product ## Code/software **Language and environment**: * bark_beetle_disturbance_metrics_regional_south_tyrol.R: This script analyzes bark beetle disturbance data (2020–2023) in South Tyrol using spatial point pattern and raster methods. It calculates event frequency, size, intensity, distances (intra- and inter-year), clustering metrics, and fits spatial point process models to assess deviation from random (Poisson) patterns. Finally, it estimates affected forest biomass and carbon loss by combining disturbance polygons with ESA CCI biomass rasters. * R Environment for Statistical Computing **Version**: R 4.2.2 **Dependencies** library(sf) library(onpoint) library(spatstat) library(raster) library(tidyverse) library(purrr) library(dplyr) library(sf) library(stars) library(raster) library(terra) library(exactextractr) library(spatstat.explore) library(ggplot2) library(sf) library(viridis) library(nngeo) library(dispfit) library(plotrix) library(ggpubr) library(rnaturalearth) library(rnaturalearthhires) library(elevatr)