Deer-mediated changes in environment compound the
direct impacts of herbivory on understorey plant
communities
Autumn E. Sabo*,1
, Katie L. Frerker2
, Donald M. Waller3
and Eric L. Kruger1
1
Department of Forest & Wildlife Ecology, UW-Madison, 1630 Linden Dr., Madison, WI 53706, USA; 2
US Forest
Service, Superior National Forest, 8901 Grand Ave. Place, Duluth, MN 55808, USA; and 3
Botany Department,
UW-Madison, 430 Lincoln Dr., Madison, WI 53706, USA
Summary
1. In forests of eastern North America, white-tailed deer (Odocoileus virginianus) can directly affect,
via herbivory, the presence, abundance and reproductive success of many plant species. In addition,
deer indirectly inﬂuence understorey communities by altering environmental conditions.
2. To examine how deer indirectly inﬂuence understorey plants via environmental modiﬁcation, we
sampled vegetation and environmental variables in- and outside deer exclosures (10–20 years old)
located in temperate forests in northern Wisconsin and the Upper Peninsula of Michigan, USA. We
assessed how excluding deer affected understorey community composition and structure, the soil
and light environment, and relationships between direct and indirect effects, using non-metric multidimensional
scaling (NMDS), mixed linear models and nonparametric multiplicative regression
(NPMR).
3. Excluding deer altered sapling communities and several aspects of the understorey environment.
Excluding deer from plots with lower overstory basal area increased sapling abundance, decreasing
the amount of light available to groundlayer plants. Exclusion also reduced soil compaction and the
thickness of the soil E horizon.
4. The composition of understorey communities covaried in apparent response to the environmental
factors affected by exclusion. In several common species and groups, E horizon thickness, compaction,
openness, and/or total (sapling and overstory) basal area were signiﬁcant predictors of plant
frequency.
5. Complementary analyses revealed that deer exclusion also altered the frequency distributions of
several species and groups across environmental space.
6. Synthesis. Deer alter many facets of the understorey environment, such as light availability, soil
compaction and thickness of the soil E horizon, which, in turn, appear to mediate variation in plant
communities. Those environmental modiﬁcations likely compound direct impacts of herbivory as
drivers of understorey community change. Our results provide evidence that deer effects on the environment
have important implications for forest composition. Thus, we suggest a re-examination of
the common assumption that understorey community shifts stem primarily from tissue removal.
Key-words: community composition, non-metric multidimensional scaling, nonparametric multiplicative
regression, openness, soil compaction, soil E horizon, soil fertility, tree regeneration, understorey
vegetation, white-tailed deer (Odocoileus virgnianus)
Introduction
Ungulates are widely regarded as keystone herbivores and
ecosystem engineers in wetland, grassland and forest systems
around the world (e.g. reviewed by Hobbs 1996;
Waller & Alverson 1997; Knapp et al. 1999; C^ote et al.
2004; Barrios-Garcia & Ballari 2012). Native and introduced
ungulates shape plant community composition and
structure directly via frugivory, grazing of grasses, and
browsing of woody plants and forbs (Bodmer 1990). Selective
browsing has profound inﬂuences on vegetation communities
(reviewed by Hanley 1997), with deer (members
*Correspondence author. E-mail: aesabo@wisc.edu
© 2017 The Authors. Journal of Ecology © 2017 British Ecological Society
Journal of Ecology 2017, 105, 1386–1398 doi: 10.1111/1365-2745.12748
of the Cervidae family) being common browsers in temperate
forests (Putman 1988).
In temperate deciduous forests of eastern North America,
white-tailed deer (Odocoileus virginianus) overabundance can
markedly alter forest composition, structure and function
(Rooney 2001; Russell, Zippin & Fowler 2001; Rooney &
Waller 2003; C^ote et al. 2004). In particular, large increases
in deer abundance since the mid-1900s have led to tree regeneration
failures (e.g. Rooney & Waller 2003; C^ote et al.
2004; White 2012), pronounced declines in the relative abundance
of palatable vs. less palatable plants (e.g. Frelich &
Lorimer 1985; Tilghman 1989; Miller, Bratton & Hadidian
1992; Anderson 1994; Balgooyen & Waller 1995; Fletcher
et al. 2001; Horsley, Stout & DeCalesta 2003; Frerker, Sabo
& Waller 2014; Nuttle, Ristau & Royo 2014; Bradshaw &
Waller 2016), losses of plant species richness and community
diversity (e.g. Rooney & Dress 1997; Horsley, Stout & DeCalesta
2003; Bressette, Beck & Beauchamp 2012; Shelton
et al. 2014; Habeck & Schultz 2015), changes in community
developmental trajectories (Frelich & Lorimer 1985; Augustine,
Frelich & Jordan 1998; Nuttle, Ristau & Royo 2014)
and enhanced ecosystem vulnerability to biotic invasions (e.g.
Baiser et al. 2008; Knight et al. 2009; Kalisz, Spigler &
Horvitz 2014; Davalos, Nuzzo & Blossey 2015; Dobson &
Blossey 2015).
A number of these changes result directly from deer herbivory,
which leads to declines in plant vigour, competitive
ability and fecundity (e.g. Balgooyen & Waller 1995; Shelton
& Inouye 1995; Augustine & Frelich 1998; Fletcher et al.
2001; Webster, Jenkins & Rock 2005), increased susceptibility
to other biotic or abiotic stressors (e.g. Long, Pendergast
& Carson 2007; Davalos, Nuzzo & Blossey 2014), and in
many instances, decreased survival (e.g. Tripler et al. 2005;
Krueger et al. 2009). Moreover, the direct effects of herbivory,
along with the impacts of associated deer activities
(e.g. defecation, bedding and trampling), may perturb the forest
understorey environment, e.g. by altering microclimate
(Yamada & Takatsuki 2015), soil biotic and physical properties
(e.g. Heckel et al. 2010; Bressette, Beck & Beauchamp
2012; Chips et al. 2014; Shelton et al. 2014), and the overall
availability as well as spatial patterning of understorey light
(Murray, Webster & Bump 2013) and soil resources (e.g.
Seagle 2003; Jensen et al. 2011; Murray, Webster & Bump
2013; Tahtinen et al. 2014).
Results of several studies have indicated that deer-mediated
environmental modiﬁcations may drive pervasive changes in
the demography of woody and herbaceous plant species
(Dufresne et al. 2009; Knight et al. 2009; Heckel et al. 2010;
Holmes & Webster 2011; Nuttle, Ristau & Royo 2014; Tahtinen
et al. 2014; Dobson & Blossey 2015). Here, we adopt a
comprehensive approach, asking how such environmental perturbations
interact with direct consequences of deer herbivory
to reshape woody and herbaceous communities. We take
advantage of the comparisons possible between conditions inand
outside deer exclosures located in hardwood-dominated
forests in northern Wisconsin and the Upper Peninsula of
Michigan, USA (Fig. 1). Previous work with these exclosures
(Frerker, Sabo & Waller 2014) revealed that deer exclusion
affects populations of common understorey species, increasing
tree regeneration and shrub and forb cover, and decreasing
the abundance of graminoids. To extend this study, we have
coupled vegetation and environmental data from the same
exclosures to test these hypotheses:
H1: Deer exclusion alters the structure and composition
of understorey plant communities;
H2: Changes resulting from deer exclusion alter key
facets of the understorey environment, such as light
availability and soil properties;
H3a: Changes in understorey communities following
deer exclusion stem, in part, from changes in the
understorey environment identiﬁed in H2;
H3b: Alternatively, or additionally, deer exclusion modiﬁes
the distribution of understorey plant species across
environmental space by allowing palatable taxa to
occupy a larger portion of their fundamental niche.
Materials and methods
STUDY LOCATIONS
In Summer 2011, we visited 17 exclosures in upland forests of northern
Wisconsin and Michigan’s Upper Peninsula (UP) in stands dominated
by Acer saccharum with variable, but often substantial,
Fig. 1. Map showing exclosure locations in Wisconsin and the Upper
Peninsula of Michigan, USA. Exclosures and paired deer-access plots
were located in the Kemp Natural Resources Station (‘Kemp’), three
State Parks in Door County, Plum Creek industrial timberland (‘Plum
Creek’) and Ottawa National Forest (‘Ottawa’).
© 2017 The Authors. Journal of Ecology © 2017 British Ecological Society, Journal of Ecology, 105, 1386–1398
Deer effects on forest understoreys 1387
amounts of Tsuga canadensis in the overstory (Fig. 1, Table 1,
Table S1, Supporting Information). In each stand, we sampled vegetation
and measured key environmental variables inside the fences and
in paired ‘deer-access’ plots outside the fences. The latter were in
close proximity to the exclosures (always within 100 m), and were
placed in locations with similar management histories and overstory
structure and composition. Additional details about the study locations
are provided in Frerker, Sabo & Waller (2014).
VEGETATION SAMPLING
We employed three sampling designs to accommodate the 100 m2
,
182 m2
and 2–8 ha exclosures (Fig. 2). Depending on the size of
each exclosure, we sampled herbaceous and woody understorey vegetation
in 16 to 84 1-m2
quadrats arrayed along transects that varied
in arrangement, length and number (Table 1, Fig. 2). We measured
two subplots (each 2500 m2
) within the 2 ha and larger exclosures
and paired deer-access plots. Within every quadrat, we recorded all
vascular plant species present. Across larger areas within each plot,
we identiﬁed the species and measured the diameter at breast height
(DBH, 1Á4 m) of saplings and trees. In 2 ha and larger plots
(Ottawa and Plum Creek locations), we tallied small saplings
(0Á6 cm ≤ DBH <3Á2 cm) within 1Á5 m of the entire length of the
transects. We also measured DBH of all large saplings (3Á2 cm ≤
DBH <10 cm) and trees (DBH ≥10 cm) within 3 m of the transects
(Fig. 2). In the smaller plots (100–182 m2
, in Door Co. and Kemp),
we tallied all small saplings and recorded DBHs of all larger saplings
within each plot. Additionally, we measured the diameter of
trees ≥10 cm DBH that were tallied in variable-radius plots sampled
with a prism (basal area factor = 2Á3 m2
haÀ1
) held over the centre
of each smaller plot. For plant species authorities, see USDA,
NRCS (2016).
CHARACTERIZING THE UNDERSTOREY ENVIRONMENT
In all exclosure and paired deer-access plots, we measured biotic and
abiotic variables to characterize the understorey environment. We estimated
openness (a proxy for light availability) at the groundlayer,
using ﬁsheye lens photographs obtained at 0Á5 m height (Promis
et al. 2011), once in the centre of the 100-m2
exclosure/deer-access
plots, once in the centre of the ﬁrst, third and ﬁfth transects of the
182-m2
paired plots, and every 15 m along each of the three transects
within the large exclosure/deer-access subplots (Fig. 2). We recorded
the diameter of coarse woody debris (CWD) that intersected each
transect, and estimated the cross-sectional area per km of transect
Table 1. Site locations (latitude and longitude in decimal degrees), along with soil taxonomic class, number of exclosures (#) per location, installation
date, size of exclosures and population density estimated by state natural resources agencies based on harvest data collected in deer management
units during 2010–2011 (Michigan Department of Natural Resources and Environment 2010; Wisconsin Department of Natural
Resources 2010). Also included are the overstory condition or manipulation just prior to exclosure installation and the total number of quadrats
(Quad) sampled in each exclosure or paired deer-access plot. The three state parks (Peninsula SP, Whiteﬁsh Dunes SP, and Potawatomi SP) are
located in Door County
Location Lat, Long Soil Taxonomic Class # Date Size
Deer
density
kmÀ2
Overstory
condition Quad
Kemp NRS 45Á84, À89Á67 Sandy, mixed, frigid Entic Haplorthods 10 2001 100 m2
2–11 Undisturbed,
blowdown
16
Peninsula SP 45Á15, À87Á22 Loamy, mixed, active, frigid Lithic
Hapludolls; Coarse-loamy, mixed,
superactive, frigid Typic Haplorthods;
Rock outcrop
2 1991,
1992
182 m2
4–8 Undisturbed 21
Whiteﬁsh
Dunes SP
44Á93, À87Á18 Loamy, mixed, active, frigid Lithic
Hapludolls
1 1992 182 m2
4–8 Undisturbed 21
Potawatomi SP 44Á87, À87Á43 Coarse-loamy, mixed, superactive, frigid
Typic Haplorthods
1 1992 182 m2
4–8 Undisturbed 21
Ottawa NF 46Á40, À88Á90 Sandy, isotic, frigid Typic Haplorthods;
Sandy, mixed, frigid Entic Haplorthods –
Sandy, mixed, frigid Alﬁc Haplorthods –
Coarse-loamy, mixed, superactive, frigid
Alﬁc Haplorthods
2 1998,
2002
2 ha 4–8 Gaps created 84
Plum Creek
timberland
45Á83, À87Á51 Fine-loamy, mixed, active, frigid Inceptic
Hapludalfs – Coarse-loamy, mixed,
superactive, frigid Aquic Argiudolls
1 1996 8 ha 12 Thinned 84
Fig. 2. Vegetation sampling design for the 100 m2
(Kemp), 182 m2
(Door Co.) and one of two subplots in the 2+ ha (Ottawa and Plum
Creek) exclosures and paired deer-access plots.
© 2017 The Authors. Journal of Ecology © 2017 British Ecological Society, Journal of Ecology, 105, 1386–1398
1388 A. E. Sabo et al.
(assuming a circular cross-section). Additionally, we used a rapid
assessment protocol to rank exotic earthworm invasion (Loss et al.
2013) in one 1-m2
quadrat near the centre of each 100-m2
plot, once
at a random location close to each transect for 182-m2
plots, and
twice at random locations between transects in each of the two subplots
of 2 ha and larger plots. At each earthworm assessment site, we
measured the thickness of leaf litter and surface soil horizons in a
20-cm core. We measured soil compaction once per quadrat with a
soil compaction meter (Lang penetrometer, Inc., Gulf Shores, AL,
USA). We also collected three soil cores (10-cm depth) per plot in
areas with representative vegetation, and submitted mixed composite
samples (one per plot) to the Soil Testing Laboratory at the University
of Wisconsin-Madison. Samples were analysed for total N using
the Kjeldahl method, P and K using the Bray and Kurtz P-1 procedure,
Ca and Mg via ammonium acetate extraction, organic matter
(OM) by loss-on-ignition and pH in a 1:1 soil:water slurry. Finally,
for every plot, we extracted coarse-scale soil type and texture data
from soil survey maps (USDA, NRCS 2013).
STATISTICAL ANALYSES
For every plant species encountered in one or more quadrats across
our 17 exclosure/deer-access pairs, we estimated plot-level frequencies
as the proportion of plot quadrats containing that species. We
also summed the proportional frequencies of species to form groups
by plant family, growth form (e.g. vascular cryptogams, graminoids,
forbs, etc.), ecological status (e.g. exotic species and late-successional
shrubs – Dirca palustris, Taxus canadensis, Acer spicatum, Cornus
rugosa, Diervilla lonicera, Symphoricarpos albus, Viburnum acerifolium
and Mitchella repens), and for species found in at least 20%
of the plots, by dispersal mode. We determined the dispersal mode of
our 26 most common quadrat species by referring to a plant traits
database compiled from literature and ﬁeld specimens (Amatangelo
et al. 2014). The sapling (0Á6 cm ≤ DBH <10 cm) and tree tallies
were converted to plot-level averages for hardwood and conifer sapling
and overstory basal area (BA, m2
haÀ1
). We also generated plot
averages for all the environmental data sampled at the quadrat, transect
or subplot levels.
To test H1, that deer exclusion alters community composition, we
ﬁrst employed non-metric multidimensional scaling (NMDS) to characterize
variation in sapling, shrub plus tree seedling and herbaceous
communities. Prior to ordinating the vegetation communities, we
deleted species that occurred in fewer than 5% of our plots, leaving
131 herb, shrub and tree species for the frequency data and 20 species
for the sapling BA dataset. Sapling abundance data were square-root
transformed and Wisconsin double standardized (McCune, Grace &
Urban 2002). We used Bray–Curtis distances to calculate all the species
distance matrices. Using the same dataset, we then assessed the
signiﬁcance of differences in treatment centroids, after accounting for
variation among location and plot pairs, for sapling, woody understorey
and herbaceous communities using permutational multivariate
analysis of variance (PERMANOVA; Anderson 2001). In addition,
we tested for treatment differences in variance using permutational
analysis of multivariate dispersions (PERMDISP; Anderson 2004). In
an extension of the herb and shrub frequency analyses conducted in
Frerker, Sabo & Waller (2014), we used mixed models, with plot pair
and location as random effects, to determine the signiﬁcance of deer
exclusion on the sapling BA of tree species or genera that occurred in
at least 20% of the plots. We include location in these models primarily
to account for the potentially confounding inﬂuence of variation
among locations in the number of plot pairs.
To test H2, that deer exclusion altered key biotic and/or abiotic
aspects of the understorey environment, we again used NMDS. This
time, rather than ordinating species data, we characterized variation in
the collective suite of environmental variables across all exclosure
and deer-access plots. Separate NMDS ordinations were generated for
the environments of the groundlayer communities (shrubs plus tree
seedlings, and herbs) and sapling layer. All 20 environmental variables
(Table S1) were included in the groundlayer environment
NMDS, while 17 of the 20 variables were included in the sapling
environment NMDS (excluding openness, the BA of conifer saplings,
and the BA of hardwood saplings because they were confounded with
the corresponding vegetation data). Prior to NMDS, we normalized
environmental variables by the maximum so that each ranged between
0 and 1, and then calculated Euclidean dissimilarities. We assessed
the inﬂuence of individual environmental variables on the NMDS
axes using Kendall’s Tau. Finally, we tested the signiﬁcance of treatment
differences in (i) environmental NMDS scores, after accounting
for variation among location and plot pairs, using PERMANOVA and
PERMDISP; and (ii) individual environmental variables, using mixed
models with plot pair and location as random effects.
We adopted a multifaceted approach to test our two competing
hypotheses (H3a and H3b) that contrasts in species composition
between exclosures and deer-access plots correspond with differences
in the environment (H3a), or that relationships between species composition
and environmental gradients differ between exclosure and
deer-access plots (H3b). First, we ﬁt environmental vectors to the
NMDS ordinations of sapling, shrub plus tree seedling and herbaceous
communities. Next, we employed several tactics to examine
relationships between the environment and species abundances. We
used Kendall’s Tau to determine which environmental NMDS axes
were correlated with plot-level vegetation NMDS axis scores. We
employed mixed models, with location as a random effect, to assess
the inﬂuences of deer exclusion or environmental variables (as ﬁxed
effects) on abundances (frequencies or BA) of individual species that
occurred in at least 20% of the plots and species groups. We also
calculated divergence in groundlayer species abundance, and divergence
in environment, between paired plots by subtracting exclosure
values from corresponding deer-access values. Finally, we again
employed mixed models (with location as the random effect) to
examine relationships between the species and environmental divergences.
As detailed in tables in the results, we usually transformed
abundances (e.g. with square-root) to reduce heteroscedasticity and/
or improve model ﬁt. Sapling abundances were not tested against
deer-mediated environmental variables because they were frequently
confounded.
We used nonparametric multiplicative regression (NPMR) to
detect whether deer exclusion altered the manner in which individual
species or species groups were distributed in environmental space.
Unlike linear mixed models, NPMR does not require assumptions
about model form and it simultaneously tests a set of predictors and
their interactions (McCune 2006). We conducted NPMR using the
stepwise free search function in Hyperniche 2.0 (McCune & Mefford
2011), with the model type set to local mean quantitative,
Gaussian weighting and medium overﬁtting. The dependent variables
again were vegetation NMDS axis scores and plot-level abundances
(frequencies or BA) of individual species or groups, while the predictor
variables were axis 1 and 2 scores from the corresponding
environmental NMDS and a categorical variable indicating deer
treatment. Among the resulting models, we deleted all but those
with the highest cross-calibration R2
(xR2
), which is calculated based
on agreement between observed values and corresponding
© 2017 The Authors. Journal of Ecology © 2017 British Ecological Society, Journal of Ecology, 105, 1386–1398
Deer effects on forest understoreys 1389
predictions using a leave-one-out procedure during each of n model
runs, where n is sample size. For species with best-ﬁt models that
included the deer treatment term and had a positive xR2
value, we
compared the best-ﬁt model against one that included only our environmental
NMDS axes 1 and/or 2 as predictors. When the increase
in xR2
resulting from addition of the deer treatment to the model
was greater than the 3% convention used in Arkle, Pilliod & Welty
(2012), we report the difference, recognizing that a small improvement
in the cross-validated R2
is more meaningful than comparable
changes in traditional R2
(Bruce McCune, personal communication).
Tolerance values, model smoothing parameters that increase as species’
distributions broaden across the range in predictor variables,
and sensitivities, which increase with predictor importance (e.g. sensitivity
equals 1Á0 when a 10% change in the predictor produces a
10% change in the response and a sensitivity of zero means no
change in the response), were provided for NMDS axis 1 and/or 2
when either or both appeared in the best-ﬁt model. Tolerance and
sensitivity were not calculated for treatment, as it was a categorical
variable.
Parametric statistics and Kendall’s Tau correlations were conducted
in JMP Pro 11, while NMDS ordination, PERMANOVA and PERMDISP
were performed using the metaMDS, adonis and betadisper
functions, respectively, in R v. 3.2.2 and Vegan package 2.0–10
(Oksanen et al. 2013). The sample sizes for analyses were 34 when
examining data across all plots, and 17 for tests of divergence
between deer-access and exclosure treatments in plot pairs. Regarding
results of mixed models, the signiﬁcance of treatment effects, relationships
between community composition and environment, and their
interactions, was evaluated using the sequential Bonferroni procedure
(Rice 1989) to account for an increased risk of type I error that
accompanies repeated tests of the same hypothesis on many species.
Results
EXCLOSURE EFFECTS ON THE UNDERSTOREY PLANT
COMMUNITY
Composition varied considerably (Sabo et al. 2017), both
within and across study locations, in communities of tree saplings
(Fig. 3a), shrubs plus tree seedlings (Fig. 3b), and herbs
(Fig. 3c). Scores of common species along vegetation NMDS
axes are presented in Table S2. In support of H1, results of
PERMANOVA indicated that exclosures differed from deeraccess
plots in the composition of saplings (P = 0Á003) but
not groundlayer woody plants (shrubs plus tree seedlings,
P = 0Á11) or herbs (P = 0Á34). According to results of
PERMDISP, dispersion of ordination data about their respective
centroids did not differ between treatments for any understorey
community (P ≥ 0Á31, data not shown).
For saplings, the treatment difference in the average score
along NMDS axis 2 coincided with increases in the BA of
Betula papyrifera, Populus spp. and Tsuga canadensis inside
exclosures (P < 0Á05 after square-root transformation of BA,
although none of these increases were signiﬁcant according to
the sequential Bonferroni test). Correspondingly, under less
dense overstories, sapling BA was higher inside than outside
of exclosures (Fig. 4a). This divergence was driven largely by
treatment differences in the abundance of hardwood rather
than conifer saplings (Table S1).
EXCLOSURE EFFECTS ON THE UNDERSTOREY
ENVIRONMENT
NMDS ordinations of the sapling and groundlayer environments
(Fig. 5) revealed substantial overlap between exclosure
and deer-access plots in environmental space. In a test of H2,
results of PERMANOVA revealed that, after accounting for
variation among location and plot pairs, deer exclusion
modiﬁed the groundlayer environment (P = 0Á02) but not the
sapling environment (P = 0Á11). Relationships between environmental
NMDS axes and individual environmental variables
are provided in Table S3.
Openness was higher outside than inside exclosures, on
average (P < 0Á05, 12Á4% v. 7Á5%, Table S1), and the contrast
became larger as overstory BA (BO) decreased (Fig. 4b).
This trend was closely coupled with the aforementioned effect
of deer exclusion on sapling BA (BS), which was ampliﬁed
under sparse overstories (Fig. 4a). Consequently, while openness
declined with increasing BO (P = 0Á009, n = 34), it was
more strongly and negatively related to variation in total BA
(BT), calculated as the sum of BO and BS (P < 0Á0001,
Fig. 3. Plot positions in ordinations of sapling basal area (a,
stress = 0Á24), tree seedling plus shrub frequency (b, stress = 0Á13)
and herb frequency (c, stress = 0Á19). Based on results of PERMANOVA,
sapling non-metric multidimensional scaling (NMDS) scores
differed between exclosure and deer-access plots (P ≤ 0Á01). [Colour
ﬁgure can be viewed at wileyonlinelibrary.com]
© 2017 The Authors. Journal of Ecology © 2017 British Ecological Society, Journal of Ecology, 105, 1386–1398
1390 A. E. Sabo et al.
n = 34, data not shown). Correspondingly, after accounting
for variation due to BO, BT increased signiﬁcantly in exclosures.
Speciﬁcally, in a mixed model with location as a random
effect and BO and exclosure treatment (D) as ﬁxed
effects: BT
0Á5
= 2Á61 + 0Á57BO
0Á5
À 1Á91D + 0Á33BO
0Á5
D,
where D = 0 and 1 for exclosures and deer-access areas,
respectively (P ≤ 0Á05 for both ﬁxed effects and their interaction,
n = 34, data not shown).
Additionally, based on mixed models with location and plot
pair as random effects and treatment as the ﬁxed effect, soil
compaction was 9% lower (P = 0Á003, Table S1) and soil E
horizon was 27% thinner (P = 0Á03, Table S1) in exclosures
compared with deer-access plots. Soil organic matter and
nutrient concentrations, pH, A and O horizon thickness, volume
of CWD and earthworm invasion scores did not differ
between treatments (all P > 0Á10 in mixed models, Table S1).
We did not test for treatment differences in soil texture (i.e.
% sand, silt or clay) because, for these data, we relied on
coarse-level mapping rather than direct ﬁeld sampling in our
plots.
RELATIONSHIPS BETWEEN UNDERSTOREY
COMMUNITIES AND ENVIRONMENT
Patterns of community composition correlated with several
aspects of the understorey environment (Table 2). Environmental
variables were more closely related to woody and
herbaceous groundlayer communities than to the sapling community.
Of the environmental variables that differed by treatment,
thickness of the soil E horizon was correlated with
NMDS axis scores for all three communities and openness
was correlated with scores for the two groundlayer communities
(Table 2). Additionally, axis 1 of the environmental
NMDS was positively correlated with axis 2 of the sapling
vegetation NMDS (P = 0Á03) as well as axis 1 of both the
woody and herbaceous groundlayer NMDS (P ≤ 0Á0001),
(a)
(b)
Fig. 4. Plots of (a) sapling basal area (BS, m2
haÀ1
) and (b) openness
(O) against overstory BA (BO). BS was negatively related with BO in
the exclosures (solid line), but not in the deer-access plots (dashed
line). Conversely, O was negatively related with BO in the deer-access
plots (solid line), but not in the exclosures (dashed line). Mixed models,
with location as a random effect, and BO, exclosure treatment
(D), and their interaction as ﬁxed effects, were based on data from all
34 plots, and response variables were square-root transformed: (a)
BS
0Á5
= 2Á87 À 0Á048BO À 1Á59D + 0Á039BOD, (b) O0Á5
= 2Á61
À 0Á003BO + 1Á88D À 0Á054BoD. All ﬁxed effects and their interactions
were signiﬁcant (P ≤ 0Á05) except for BO (P = 0Á81) in the
openness model. [Colour ﬁgure can be viewed at wileyonlinelibrary.-
com]
Fig. 5. Ordinations of environmental conditions across all deer-access and exclosure plots. To depict the understorey environment for tree saplings
(a), we conducted non-metric multidimensional scaling (NMDS) of 17 plot-level abiotic (soil surface horizons, chemistry, texture and compaction)
and biotic (earthworm invasion rank, CWD and basal area of trees) environmental variables. Stress = 0Á14. To depict the groundlayer
environment for herbs, shrubs and tree seedlings, we generated an NMDS of 20 plot-level environmental variables (b), which included all variables
from (a) with the addition of openness, the basal area of hardwood saplings and the basal area of conifer saplings. Stress = 0Á12. Based on
results of PERMANOVA, NMDS scores of the groundlayer environment differed between exclosure and deer-access plots (P ≤ 0Á05). [Colour
ﬁgure can be viewed at wileyonlinelibrary.com]
© 2017 The Authors. Journal of Ecology © 2017 British Ecological Society, Journal of Ecology, 105, 1386–1398
Deer effects on forest understoreys 1391
while axis 2 of the environmental NMDS was positively correlated
with axis 2 of both the woody and herbaceous groundlayer
NMDS (P ≤ 0Á04) (data not shown).
In support of H3a, results of mixed models reveal that the
abundances of numerous species and species groups (46 of
55 tested) covaried with one or more environmental variables
that were modiﬁed by the exclosure treatment (Tables S4–
S7). For example, graminoid frequency increased in more
compacted soils (P ≤ 0Á0001, Fig. 6a), while Liliaceae frequency
declined as the soil E horizon thickened (P = 0Á003,
Fig. 6b). Moreover, the frequencies of 15 species and groups
(n = 34, Table 3) were explained by mixed models that
included additive combinations of compaction, E horizon,
openness and/or total BA. When included as a ﬁxed effect in
these additive models, deer exclusion was never signiﬁcant
(P ≥ 0Á16). Divergence between the paired plots (deer-access
minus exclosure) in the frequency of several common species
and groups correlated with the corresponding paired plots’
divergence in environmental conditions (Fig. 7, Table S8). In
several cases (e.g. Carex arctata and common herbs with
unassisted dispersal), these correlations matched the
signiﬁcant overall relationships we observed between species/
group frequency and environmental conditions (Tables 3 and
S4–S7).
Despite indications that shifts in composition are linked to
changes in key environmental conditions, we also found evidence,
albeit limited, that excluding deer altered relationships
between understorey composition and environment, supporting
our ﬁnal hypothesis (H3b). Across environmental space,
the abundance of four species or groups differed across the
fence, as was evidenced in the NPMR results for Betula
papyrifera saplings, several shrubs and one native forb
(Trientalis borealis) (Table S9). The genus Lonicera, with
both native and exotic species, was generally more common
in deer-access plots (Fig. 8a). Higher abundances of
B. papyrifera, late-successional shrubs and T. borealis, were
observed across a broader environmental space in exclosures
compared to deer-access plots (Fig. 8b–d, Table S9). For
most species, however, distributions across environmental
space were best explained with models containing only the
two environmental NMDS axes (Table S10).
Discussion
Although our study focused on temperate forests dominated
mostly by northern hardwoods, there was considerable variation,
both within and across exclosure sites, in key ecosystem
properties, including edaphic conditions, forest structure and
Table 2. Environmental loadings that were correlated to non-metric multidimensional scaling (NMDS) ordinations of sapling, seedling and shrub
and herb communities (Fig. 3) by signiﬁcance category
Community
Signiﬁcance of environmental correlates
P ≤ 0Á001 P ≤ 0Á01 P ≤ 0Á05 P ≤ 0Á10
Saplings Hardwood overstory basal area N, % organic matter,
Ca, Mg, % sand, % silt,
thickness of E and A
horizons
O horizon thickness
Seedlings and
shrubs
N, pH, Ca, Mg, K, % organic
matter, % sand, thickness of E and
A horizons, hardwood overstory
basal area
Openness, % silt Conifer sapling basal
area, O horizon thickness
Herbs N, pH, Mg, % organic matter, %
sand, thickness of E horizon
Ca, openness, hardwood
overstory basal area,
thickness of A horizon
% silt K
(a) (b)
Fig. 6. Plots of (a) graminoid frequency
against soil compaction, and (b) Liliaceae
frequency against thickness of the soil E
horizon, across all 17 exclosures (orange
symbols) and deer-access plots (black
symbols). Mixed models, in which frequency
was square-root transformed, yielded
signiﬁcant trends (dashed line) for graminoids
(P ≤ 0Á001) and lilies (P = 0Á003). In neither
case was the effect of deer treatment, or its
interaction with the continuous independent
variable, signiﬁcant (P > 0Á1). Thus, a single
trend was ﬁt to all 34 data points. [Colour
ﬁgure can be viewed at wileyonlinelibrary.com]
© 2017 The Authors. Journal of Ecology © 2017 British Ecological Society, Journal of Ecology, 105, 1386–1398
1392 A. E. Sabo et al.
disturbance histories. Thus, detection of a consistent exclusion-mediated
shift towards a more abundant and diverse sapling
community, supporting H1, is notable. Regarding the
absence of a discernible shift in the groundlayer, studies
demonstrating an unambiguous effect of deer on the overall
composition of herbaceous communities are particularly
uncommon (Mudrak, Johnson & Waller 2009; Jenkins et al.
2014; Pendergast et al. 2016). Signatures are often conﬁned
to variation in individual or population-level attributes (e.g.
Anderson 1994; Kirschbaum & Anacker 2005; McGraw &
Furedi 2005; Frerker, Sabo & Waller 2014; Dobson & Blossey
2015). Habeck & Schultz (2015) identiﬁed several potential
causes for the paucity of reported impacts on communitylevel
metrics of herbaceous species composition, including
legacy effects of chronic deer overabundance leading to slow
groundlayer responses. A growing body of evidence provides
compelling support for this tenet (Royo et al. 2010; Tanentzap,
Kirby & Goldberg 2012; Nuttle, Ristau & Royo 2014;
Pendergast et al. 2016). We propose an additional possibility:
deer effects on community composition are often partially
obscured by complex species responses to the array of inﬂuences
deer exert on the understorey environment.
Thus, we encourage further exploration of deer impacts on
individual environmental variables, and their subsequent indirect
effects on community composition. In our study, exclosures
clearly affected both biotic and abiotic environmental
factors (H2), including sapling abundance and light availability.
Because saplings compete for soil resources as well as
cast shade, increases in their abundance within exclosures
(e.g. Kuijper et al. 2010; Kain et al. 2011; Tanentzap et al.
2011; Bressette, Beck & Beauchamp 2012) could have strong
cascading effects on groundlayer vegetation. Notably, while it
was not a focus of our hypotheses, we also observed, as have
others (e.g. Kuijper et al. 2010), that variation in local environment
can exert an important inﬂuence on understorey
responses to deer. In particular, deer exclusion impacts on the
sapling community varied substantially across the locations
we studied, and were especially pronounced in stands with
Table 3. In additive models, environmental factors affected by deer exclusion, including thickness of the soil E horizon (E, cm), soil compaction
(C, MPa), openness (O, %) and total BA (BT = overstory BA + sapling BA, m2
haÀ1
) each explained a signiﬁcant amount of variation, across
exclosure and deer-access plots, in the frequencies of several groundlayer species and species groups. Mixed models, which included location as
a random effect, and soil compaction and one of the other three environmental variables as ﬁxed effects, were based on data from all 34 plots.
Collinearity precluded models with more than two environmental variables and certain variable combinations, E + BT, E + O, or BT + O.
Frequencies were square-root transformed to decrease heteroscedasticity. Below, we provide intercepts and ﬁxed effect coefﬁcients for models
with signiﬁcant effects and, for species with more than one signiﬁcant model, corrected Akaike’s information criterion (AICc)
Species/group
Model parameter coefﬁcients
AICca E C O BT
Tree seedlings
Abies balsamea À0Á21 À0Á018** 1Á53* – – 0Á7
À0Á59** – 2Á03** – 0Á006** 4Á0
Acer rubrum À0Á57** – 2Á37*** – 0Á008**** À4Á1
À0Á04 À0Á013* 1Á45* – – 3Á4
Pinus strobus À0Á22 À0Á013** 1Á41** – – –
Tilia americana 0Á22 – À0Á98** – 0Á003** –
Herbs
Carex arctata À0Á59*** – 3Á32**** À0Á009*** – À12Á6
0Á90**** – 3Á63**** – 0Á006*** À12Á0
À0Á54*** À0Á011* 3Á05**** – – À9Á2
Dryopteris carthusiana 0Á48**** 0Á010* À1Á90*** – – –
Maianthemum canadense À0Á74* – 3Á65*** – 0Á017**** 20Á0
0Á19 À0Á032** 2Á38* – – 30Á4
0Á05 – 2Á81* À0Á017** – 34Á1
Oryzopsis asperifolia À0Á98** – 3Á81**** – 0Á01*** 15Á3
À0Á38 À0Á02** 2Á84* – – 17Á7
Taraxacum ofﬁcinale À0Á41** À0Á015** 2Á99**** – – –
Trientalis borealis À0Á27 À0Á030*** 2Á73** – – 21Á3
À0Á36 – 2Á99** À0Á016** – 24Á0
À0Á84** – 3Á39** – 0Á009** 25Á2
Veronica ofﬁcinalis À0Á44** À0Á011* 2Á59*** – – –
Abiotic dispersal À0Á36 – 3Á67** – 0Á010* –
Ingestion dispersal À0Á39 – 2Á97** – 0Á017**** –
Exotics À0Á27 À0Á035** 4Á36** – – –
Graminoids À0Á85* À0Á023* 7Á47**** – – 36Á9
À1Á48*** – 8Á44**** – 0Á010** 37Á3
Asterisks denote signiﬁcance of individual model coefﬁcients: *P ≤ 0Á10; **P ≤ 0Á05; ***P ≤ 0Á01; ****P ≤ 0Á001.
© 2017 The Authors. Journal of Ecology © 2017 British Ecological Society, Journal of Ecology, 105, 1386–1398
Deer effects on forest understoreys 1393
lower canopy coverage (less overstory BA), as was also
reported by Wright et al. (2012) in New Zealand forests with
introduced ungulates. These results highlight the context
dependency of deer impacts on forest systems (Horsley, Stout
& DeCalesta 2003; Tripler et al. 2005; Tremblay, Huot &
Potvin 2007; Dufresne et al. 2009; Collard et al. 2010).
Several other studies have also assessed the effects of deer
exclusion on understorey light environments. Similar to our
study, light availability was greater in deer-access plots than
20-year-old exclosures corresponding with exclusion-mediated
increases in understorey tree density (Tanentzap et al. 2011).
In another case (Nuttle et al. 2011), leaf area index was still
lower in controls due to species composition differences,
despite tree basal areas converging after fences were removed.
In contrast, two studies concluded that light availability was
not signiﬁcantly different between exclosures and deer-access
areas (Holmes & Webster 2011; Murray, Webster & Bump
2013). Such results could reﬂect the fact that these studies
differed from ours in measurement protocols, exclosure ages,
overstory composition and structure, and stand disturbance
histories, any of which could affect light levels or their relation
to deer exclusion.
Our study corroborates ﬁndings by Heckel et al. (2010)
and Shelton et al. (2014) that soil compaction is lower in
exclosures than in adjacent deer-access areas. This may reﬂect
the direct effect of deer trampling, as we found no differences
in leaf litter thickness or ﬁne root abundance (data not
shown). Heckel et al. (2010) surmised that recovery from
trampling could account for the similar effect they saw in 15year-old
exclosures but Shelton et al. (2014) questioned the
importance of trampling in driving differences observed just
2 years after exclusion, suggesting instead that deer exert
indirect effects like reducing mycorrhizal activity (or ﬁne root
abundance).
Fig. 7. Treatment divergences (D) in species or group frequency plotted against corresponding divergences in environment: (a) D Viola spp. frequency
against D E horizon thickness, (b) D Carex arctata frequency against D openness, and (c) D ingestion-dispersed herbs against D BT (total
basal area). To calculate divergence (D), exclosure values were subtracted from deer-access values for each of the 17 plot pairs. Trends were signiﬁcant
in all cases (P ≤ 0Á04 in mixed models with location as a random effect, Table S8). [Colour ﬁgure can be viewed at wileyonlinelibrary.-
com]
Fig. 8. Variation in species abundances
(basal area [m2
haÀ1
] or frequencies) across
our ordination-based characterization of
environmental space (NMDS axes 1 and 2
from Fig. 5). Data are values from each of
the 34 exclosure and deer-access plots. Circle
size denotes basal area or frequency and ‘+’
marks the location of abundance-weighted
centroids. The genus Lonicera (native and
invasive shrubs) was more frequent in deeraccess
plots (a), while Betula papyrifera
saplings (b), late-successional shrubs (c) and
Trientalis borealis (d) were more abundant
with broader distributions across environmental
space in exclosures compared to deer-access
areas. [Colour ﬁgure can be viewed at
wileyonlinelibrary.com]
© 2017 The Authors. Journal of Ecology © 2017 British Ecological Society, Journal of Ecology, 105, 1386–1398
1394 A. E. Sabo et al.
We also observed that excluding deer reduced the thickness
of the E horizon in forest soils. This appears to be the ﬁrst
report of this soil effect. Characteristics of the E horizon and
soil podsolization are shaped by a complex suite of climatological,
geological and biological factors (Sanborn, Lamontagne
& Hendershot 2011). Thus, exclusion impacts could
result from the effects deer have on vegetation composition
and nutrient inputs. Thinner E horizons may be associated
with increases in hardwood sapling BA, as podsolization
tends to be negatively related to the abundance of hardwood
species relative to conifers (Nørnberg, Sloth & Nielsen 1993;
Sanborn, Lamontagne & Hendershot 2011). Without deer
depositing urea and faeces, soils within exclosures may also
experience less leaching of soil dissolved organic carbon
(Chantigny 2003). However, total N concentrations were similar
in- vs. outside exclosures, as has been noted by others
(e.g. Hobbs 1996; Pastor et al. 1998; Persson, Danell &
Bergstr€om 2000; Seagle 2003).
Several lines of evidence support our basic premise that
responses of understorey species to deer exclusion often
reﬂect the effects deer have on environmental factors such as
light availability, soil compaction and E horizon thickness
(H3a). The frequencies of several species covaried with these
key environmental variables. This covariation is also consistent
with the ecology of these species, such as the shade tolerance
of forest herbs, Maianthemum canadense and Trientalis
borealis, the intolerance of Rubus idaeus, and known demographic
responses to these environmental factors. Godefroid
& Koedam (2004) also reported that the abundance of several
congeners of the graminoid taxa present in our study
increased with more soil compaction at their sites. Furthermore,
increased soil compaction is implicated as an important
non-consumptive consequence of deer contributing to declines
in growth and fecundity of several forest groundlayer species
(Heckel et al. 2010).
While few studies have explored how the E horizon affects
understorey communities, Wilde & Leaf (1955) found strong
relationships between the abundances of many species (including
several studied here – Table S4) and the extent of
podsolization in temperate hardwood-conifer forests. The
thickness of the E horizon may reﬂect the extent to which
organic matter, base cations and other nutrients in upper soil
horizons are depleted via leaching of organic acids (Lundstr€om,
van Breemen & Bain 2000). Indeed, across our sites,
E horizon thickness was inversely correlated with nearly
every measure associated with soil fertility (e.g. OM, N, P,
Ca, Mg, pH, R2
for linear relationships ranging from 0Á13 to
0Á28, results not shown). Therefore, E horizon thickness may
provide an integrative index of fertility in the rhizosphere
that, in turn, appears to drive understorey community composition
(Fig. 3; Hutchinson et al. 1999; Burton et al. 2011;
McEwan & Muller 2011).
Excluding deer had fairly modest effects on the environmental
variables, we measured (9–18% of the observed range
for each variable, Table S1), which could reﬂect the relatively
young age of these exclosures (i.e. that deer effects on soils
persist long after exclusion). Nevertheless, there was a
discernible treatment difference in the environmental ordination
space for the groundlayer community (Fig. 5) and
excluding deer had important effects on community and environmental
properties known to affect the distribution and
abundance of species (Figs 6–8, Tables 3 and S4–S8). In
combination over several years, subtle environmental changes
appear to have substantial cumulative impacts on understorey
communities.
Lastly, we found some support for the hypothesis (H3b) that
deer can modify differences between the realized and fundamental
niches of plant species (Hutchinson 1959). This perturbation,
manifested by shifts in species’ relative abundances
along environmental gradients, might have resulted from the
removal of key stressors (e.g. herbivory) and/or the ampliﬁcation
of others (e.g. competition for light and other resources)
within exclosures. In one of the few studies addressing niche
shifts due to ungulates, two native herb species expanded
their spatial distributions to exploit a wider range of environmental
conditions when ungulate pressure was removed
(Gomez 2005). The opposite effect has also been found in
exotic herbs, where abundances can drop rapidly following
deer exclusion (Kalisz, Spigler & Horvitz 2014; Davalos,
Nuzzo & Blossey 2015). However, the best-ﬁt models for
few species/groups included both deer and environmental predictors.
Thus, responses of understorey species to deer exclusion
may actually reﬂect the effects that deer have on
environmental factors to a greater degree than the direct
impacts of deer on understorey species via herbivory.
Variation in ecosystem properties among our study locations
precluded certain potentially informative data analyses.
For example, three of the four oldest exclosures lie on fertile
soils derived from dolomitic parent material in Door County
(Table S1) and have also had the highest historic deer population
densities (15 deer kmÀ2
, or potentially much higher,
reﬂecting historical hunting restrictions in state parks – Wisconsin
Department of Natural Resources 2010). Because location-to-location
variation in edaphic traits and deer population
density was conﬂated with exclosure age, we do not present
data on how age inﬂuenced community change, although we
recognize that it may be an important source of variation
(Collard et al. 2010; Royo et al. 2010; Hidding, Tremblay &
C^ote 2013; Habeck & Schultz 2015; Pendergast et al. 2016).
Results from this study and Frerker, Sabo & Waller (2014)
support the contention that regional abundances of many
native forb, shrub and palatable tree species may be threatened
by overabundant deer. Deer impacts are not only due to
the direct effects of herbivory on susceptible plants but also
the alteration of a broad suite of environmental conditions.
When deer increase light availability and soil resources by
reducing sapling abundance, they also alter soil structure and
morphology. Such indirect effects act in concert with browsing
damage to alter understorey communities in complex and
potentially long-lasting ways. However, we acknowledge that
inferences drawn from our data are based primarily on correlation,
and ﬁrmer conclusions regarding causes vs. consequences
of the various deer impacts on forest understoreys
await the results of future studies manipulating speciﬁc
© 2017 The Authors. Journal of Ecology © 2017 British Ecological Society, Journal of Ecology, 105, 1386–1398
Deer effects on forest understoreys 1395
aspects of the environment and/or plant community. Additional
research could examine how species’ distributions
along environmental gradients change across a continuum of
deer population densities, paying particular attention to how
species perform at their environmental extremes. Results from
such studies will help us understand and predict how species
and community assemblages are likely to respond to both the
direct effects of herbivory and the complex effects that deer
can have on environmental conditions.
Authors’ contributions
A.S., D.W., K.F. and E.K. designed the study. A.S. ad K.F. collected and processed
data. A.S. and E.K. conducted data analyses. A.S. generated the ﬁrst
draft of the manuscript, and all authors contributed substantially to revisions.
Acknowledgements
We thank Friends of Peninsula State Park, the UW-Madison Botany Department
and the McIntire-Stennis Program for funding our research. We appreciate
that Kemp Natural Resources Station, Wisconsin Department of Natural
Resources, U.S. Forest Service and Plum Creek Timber Company built, maintained
and allowed us access to their exclosures. We also thank our ﬁeld assistants,
Andy Jandl, Marion Lee and Tyler Schappe, and two anonymous
reviewers.
Data accessibility
Data used in this study deposited in the Dryad Digital Repository https://doi.
org/10.5061/dryad.4nf75 (Sabo et al. 2017).
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Supporting Information
Details of electronic Supporting Information are provided below.
Table S1. Means (with standard deviations) and ranges, by location
and deer treatment, for the 20 biotic and abiotic environmental variables
used in our environmental NMDS ordinations.
Table S2. Scores of common species along vegetation NMDS axes
for saplings, seedlings and shrubs, and herbs.
Table S3. Correlation coefﬁcients of the relationships between
environmental NMDS axes and their individual environmental
components.
Table S4. The abundances of several species and species groups were
signiﬁcantly related to thickness of the soil E horizon.
Table S5. The abundances of several species and species groups were
signiﬁcantly related to soil compaction.
Table S6. The frequencies of several groundlayer species and species
groups were signiﬁcantly related to openness.
Table S7. The frequencies of several groundlayer species and species
groups were signiﬁcantly related to total basal area.
Table S8. Relationships between exclosure effects on the frequency
of a particular groundlayer species or group and those on individual
environmental parameters.
Table S9. Results of nonparametric multiplicative regression (NPMR)
for species or groups in which patterns of abundance across our ordination
of environmental space differed between exclosure and deeraccess
treatments.
Table S10. Results of nonparametric multiplicative regression
(NPMR) for common species in which abundance varied across our
ordination of environmental space but did not differ between exclosure
and deer-access treatments.
© 2017 The Authors. Journal of Ecology © 2017 British Ecological Society, Journal of Ecology, 105, 1386–1398
1398 A. E. Sabo et al.