DOI: 10.5061/dryad.905qfttwv
title: Do wolves protect forests? Investigating the link between wolf density, deer browse, and plant recovery

description: Large ungulate populations can threaten forest regeneration and many rare
 or declining understory plants, birds, and small mammals. Reintroduction
 of large predators is often proposed as a remedy to reduce negative
 ecosystem effects associated with high ungulate populations, but we know
 little about the effectiveness of this approach. We assessed whether
 wolves (Canis lupus) can protect forest understory plants from excessive
 white-tailed deer (Odocoileus virginianus) browse. We planted white oak
 (Quercus alba), red oak (Quercus rubra), and zigzag goldenrod (Solidago
 flexicaulis) seedlings across a gradient of wolf density and occupancy
 time in northern Wisconsin and recorded deer browse and frequency of 11
 common understory plants at each site. We found that wolf density and
 residence time had negative effects on deer browse intensity, but these
 effects were generally weak except when understory vegetation was
 abundant. Additionally, the presence of common understory plants decreased
 as a function of wolf density, opposite to what we would expect for a
 wolf-driven trophic cascade. The weak reduction in browse probability that
 is associated with wolves, particularly when vegetation is scarce, is
 unlikely to improve forest regeneration and recovery of understory plant
 communities currently threatened by high deer populations.
description: # Do wolves protect forests? Investigating the link between wolf density,
 deer browse, and plant recovery Dataset DOI:
 [10.5061/dryad.905qfttwv](10.5061/dryad.905qfttwv) ## Description of the
 data and file structure This dataset contains the data and code required
 to replicate analyses in Brice et al. (in review), testing the hypothesis
 that wolves decrease deer browsing and subsequently increase forest
 regeneration. Data cover 42 sites in northern Wisconsin across a gradient
 of wolf density and wolf residence time, at which we planted white oak
 (*Quercus alba*), red oak (*Quercus rubra*), and zigzag goldenrod
 (*Solidago flexicaulis*) seedlings in the spring of 2022 and 2023. We
 monitored the seedlings for deer herbivory over 3 summers (2022-2024), and
 collected data on height growth and surrounding site conditions (e.g.,
 vegetation height, leaf litter depth, canopy cover). During the 2022 field
 season, we also collected data on 11 common understory plants at each
 site. Files also include data on wolf density and residence time and site
 conditions (e.g., vegetation height, leaf litter depth, precipitation,
 tree size), and a Markdown file with code needed to replicate the
 analysis. Wolf density was estimated for the winter of 2020-2021 across
 100-km2 hexagonal cells by Wisconsin Department of Natural Resources
 (WDNR) staff using a scaled occupancy model built using data derived from
 winter track surveys and GPS-collared wolves. We derived the number of
 years a site was occupied by wolves (i.e., wolf residence time) from
 overlapping annual maps of wolf pack locations from 1980, 1982, 1983,
 1986, 1987, 1991-2010, and 2015-2019. These maps were created by WDNR by
 incorporating ground-based snow tracking surveys, aerial observations, and
 location data from radio-collared wolves to delineate pack areas and
 estimate pack size ### Files and variables #### File: Analysis_Code.Rmd
 **Description:** An R Markdown file of code needed to replicate all
 analyses. #### File: Covariates.csv **Description:** site-level variables
 used in analyses ##### Variables * Site: number used to identify the site
 * Wolf_Den: wolf density at each site (wolves per 100 km2) * Wolf_Time:
 number of years a wolf pack was present at the site * Worms: presence of
 invasive earthworms at the site * Litter_cm: average leaf litter depth at
 each site, measured at 4 points around each seedling and then averaged
 across all seedlings (cm) * Precip_mm: previous summer precipitation as
 average monthly precipitation from May – September at each site (mm) *
 Veg_cover: average vegetation cover (%) at each site, visually estimated
 and averaged at four points within 1-m of the seedling and then averaged
 across all seedlings. * Veg_height: average vegetation height (cm) at each
 site, averaged at four points within 1-m of the seedling and then averaged
 across all seedlings. Height was measured up to 1-m. * QMD_cm: quadratic
 mean diameter (cm) of trees at each site. #### File:
 Browse_Data_Goldenrod.csv **Description:** data used in the analysis of
 goldenrod browsing ##### Variables * Site: number used to identify the
 site * Tag_ID: ID number of individual seedling * Height_1: height at
 planting (cm) * Browse: binary variable for whether the seedling was
 browsed by deer * Wolf_Den: wolf density at each site (wolves per 100 km2)
 * Wolf_Time: number of years a wolf pack was present at the site *
 Worms: presence of nonnative earthworms at the site * Veg_height:
 vegetation height (cm) averaged at four points within 1-m of the seedling.
 Height was measured up to 1-m. * Veg_cover: vegetation cover (%) visually
 estimated and averaged at four points within 1-m of the seedling. *
 Litter: average leaf litter depth measured at 4 points around each
 seedling. * Precip: previous summer precipitation as average monthly
 precipitation from May – September at each site (mm) * QMD_cm: quadratic
 mean diameter (cm) of trees at each site. #### File: Browse_Data_Oaks.csv
 **Description:** data used in the analysis of oak browsing ##### Variables
 * Site: number used to identify the site * Transect: A indicates oaks
 planted in 2022, B indicates oaks planted in 2023. * Tag_ID: ID number of
 individual seedling * Species: oak species of seedling * Height_1: height
 at planting (cm) * Browse: binary variable for whether the seedling was
 browsed by deer * Wolf_Den: wolf density at each site (wolves per 100 km2)
 * Wolf_Time: number of years a wolf pack was present at the site *
 Worms: presence of invasive earthworms at the site *
 Veg_height: vegetation height (cm) averaged at four points within 1-m of
 the seedling. Height was measured up to 1-m. * Veg_cover: vegetation cover
 (%) visually estimated and averaged at four points within 1-m of the
 seedling. * Litter: average leaf litter depth measured at 4 points around
 each seedling. * Precip: previous summer precipitation as average monthly
 precipitation from May – September at each site (mm) * QMD_cm: quadratic
 mean diameter (cm) of trees at each site. #### File:
 Growth_Data_Goldenrod.csv **Description:** data used in the analysis of
 goldenrod growth ##### Variables * Site: number used to identify the site
 * Tag_ID: ID number of individual seedling * Species: species of seedling
 (all goldenrod) * Growth: change in height from May 2023 to August 2023 *
 Height_1: height (cm) at planting (May 2023) * Height_2: height (cm) in
 August 2023 * Browse: binary variable for whether the seedling was browsed
 by deer * Wolf_Den: wolf density at each site (wolves per 100 km2) *
 Wolf_Time: number of years a wolf pack was present at the site *
 Veg_height: vegetation height (cm) averaged at four points within 1-m of
 the seedling. Height was measured up to 1-m. * Veg_cover: vegetation cover
 (%) visually estimated and averaged at four points within 1-m of the
 seedling. * Litter: average leaf litter depth measured at 4 points around
 each seedling. * Precip: previous summer precipitation as average monthly
 precipitation from May – September at each site (mm) * QMD_cm: quadratic
 mean diameter (cm) of trees at each site. #### File: Growth_Data_Oak.csv
 **Description:** data used in the analysis of oak growth ##### Variables *
 Site: number used to identify the site * Tag_ID: ID number of individual
 seedling * Species: oak species of the seedling * Growth: change in height
 from June 2022 to August 2024 * Height_1: height (cm) at planting (June
 2022) * Height_6: height at visit 6 (August 2024) * Browse: binary
 variable for whether the seedling was browsed by deer * Other_damage: a
 binary predictor for other plant damage (e.g., rodent cuts, tip death,
 unknown damage) * Wolf_Den: wolf density at each site (wolves per 100 km2)
 * Wolf_Time: number of years a wolf pack was present at the site *
 Worms: presence of invasive earthworms at the site *
 Veg_height: vegetation height (cm) averaged at four points within 1-m of
 the seedling. Height was measured up to 1-m. * Veg_cover: vegetation cover
 (%) visually estimated and averaged at four points within 1-m of the
 seedling. * Litter:  average leaf litter depth measured at 4 points around
 each seedling. * Precip: previous summer precipitation as average monthly
 precipitation from May – September at each site (mm) * QMD_cm: quadratic
 mean diameter (cm) of trees at each site. #### File:
 PlantCommunity_Data.csv **Description:** data used in the analysis of the
 plant community ##### Variables * Site: number used to identify the site *
 Species: species * Freq: number of times that species was recorded at a
 site * N: number of times we checked for species at a site *
 Wolf_Den: wolf density at each site (wolves per 100 km2) *
 Wolf_Time: number of years a wolf pack was present at the site *
 Worms: presence of invasive earthworms at the site *
 Veg_height: vegetation height (cm) averaged at four points within 1-m of
 the seedling, and averaged across all seedlings at a site. Height was
 measured up to 1-m. * Veg_cover: vegetation cover (%) visually estimated
 and averaged at four points within 1-m of the seedling, and averaged
 across all seedlings at a site. * Litter_site: average leaf litter depth
 at each site, measured at 4 points around each seedling and then averaged
 across all seedlings. * Precip_Avg: previous summer precipitation as
 average monthly precipitation from May – September at each site (mm) *
 Browse: the proportion of sentinel individuals browsed at each site *
 QMD_cm: quadratic mean diameter (cm) of trees at each site. ##
 Code/software We used R v4.3.1 for all analyses, loaded with the following
 packages: tidyverse, lme4, lmertest, AICcmodavg, emmeans, gridExtra,
 DHARMa, car, and lmtest. The attached Markdown file includes the code
 needed to replicate the analysis.