DOI: 10.5061/dryad.x3ffbg81f
title: Data from: The effects of moose- and pine density on browsing damage in Swedish pine forests

description: Moose (Alces alces) is a culturally and economically important game
 species in Sweden, but their browsing on regenerating Scots pine trees
 (Pinus sylvestris) often causes extensive damage to the production and
 quality of timber. Forest- and wildlife managers are faced with the
 dilemma of how to reduce damage to timber trees while also supporting
 moose populations and hunting opportunities. The proportion of damaged
 trees can be reduced by decreasing the number of moose, but also by
 increasing the number of pines. However, the relative effectiveness of
 these two approaches is debated and has not been conclusively determined.
 Here we addressed this question by analyzing the effects of moose- and
 pine density on pine damage based on yearly data from almost all of
 Sweden’s moose management areas (MMAs) over 10 years, 2015-2024 (718
 observations). We developed a mechanistic model to realistically represent
 the browsing process and used regression with mixed models to account for
 variable vulnerability (damage at a common number of moose per pine tree)
 among MMAs in the statistical analysis. The model explained 53% of the
 variation in the proportion of damaged trees and showed that, on average,
 the relative damage reduction effect of a decreased moose population was
 ~1.5x larger (25%) than the effect of increased pine density (17%).
 Vulnerability to browsing varied substantially among MMAs and between
 years within each MMA, especially in areas with low pine density. This
 variability prevents reliable predictions of management effects at the
 individual MMA level for most MMAs. Such local predictions may be improved
 in the future by incorporating longer time series of observations and
 additional variables, such as alternative forage sources, browsing by
 other deer species, and snow cover and duration.
description: # Data from: The effects of moose- and pine density on browsing damage in
 Swedish pine forests Dataset DOI:
 [10.5061/dryad.x3ffbg81f](https://doi.org/10.5061/dryad.x3ffbg81f) ##
 Description of the data and file structure The data include reported
 browsing damages from the Swedish browsing damage inventory and estimated
 moose densities. Further information in the paper. ### Files and variables
 #### File: R_code_and_data.zip ##### File: Estimates_moosedensity_MMA.txt
 * The dataset contains a time series for each moose management area (MMA)
 in Sweden with estimates of the number of moose per 1,000 hectares in the
 winter population. The estimates were produced by the Swedish University
 of Agricultural Sciences on behalf of the Swedish Environmental Protection
 Agency. The estimates are part of official statistics. The unit is the
 number per 1,000 hectares. 1 moose per 1,000 ha = 0.1 moose per km².\
 Variables:\ Year: year of observation\ ID: identification number for the
 MMA-County combination\ MMA: moose management area\ Countynr: County
 number\ MMAnr: MMA number\ MooseDensity: Estimated number of moose per
 1000 hectares in the winter population ##### File:
 Timedata-moosedamageMMA.txt * The dataset contains a time series for each
 MMA in Sweden with results from the national moose browsing inventory
 (Älgbetesinventeringen in Swedish). Approximately 40,000 randomly selected
 sample plots are inventoried annually in Sweden within MMA areas. The
 results are presented for each moose management area. The surveyed
 population consists of young Scots pine trees in stands with an average
 height between 1 and meters.\ Variables:\ Region: not used\ Countynr:
 County number\ MMAnr: MMA number\ MMA: moose management area\ Year: year
 of observation\ PropDamagedPines: Proportion of pines damaged in the year
 of observation in the MMA\ PropUndamagedPines: Proportion of pines with no
 damage ever in the MMa\ NumberOfDamagePines: Number of pines damaged thin
 e year of observation in the MMA\ NumberOfUndamaged: Number of pines with
 no damage ever in the MMA\ NumberOfPines: Number of pines in the MMA #####
 File: MMAData.txt * The dataset contains land-use data for each MMA as
 well as the amount of land registered as hunting land in 2024. The unit is
 hectares.\ The variables are:\ ID: identification number for the
 MMA-County combination\ MMA: moose management area\ Countynr: County
 number\ MMAnr: MMA number\ HuntingArea: The size of the area where moose
 and pine numbers were measured or estimated. ##### File:
 Mooseobs_MMA_Tidsserie.txt * The dataset contains time-series data on
 observed moose during the first week of the autumn hunting season each
 year. The unit is the number of observations per hunter-hour. A
 hunter-hour is the total number of hours spent observing. The data are
 part of a citizen science program and constitute official statistics.
 Moose observations per hunter-hour are used to monitor trends in the
 development of the moose population.\ variables:\ ID: identification
 number for the MMA-County combination\ MMA: moose management area\
 Countynr: County number\ MMAnr: MMA number\ Columns 2015-2034: Observed
 number of moose each year ##### File: algskador 2025 for submission
 English.R The R code used for the analysis. This script analyzes how moose
 density and pine density affect the proportion and number of damaged pines
 across management areas and years. It first performs area-specific linear
 models, then fits and compares spatiotemporal mixed-effects models to
 identify the best overall model. Finally, it estimates average ecological
 effects, evaluates model performance, and generates predictions and
 visualizations of damage patterns.