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\begin{document}

\title{Woody plant secondary chemicals increase in response to abundant deer
and arrival of invasive plants in suburban forests~~~}



\author[1]{Janet A. Morrison}%
\author[2]{Bernadettte Roche}%
\author[3]{Maren Veatch-Blohm}%
\affil[1]{Department of Biology, The College of New Jersey, Ewing, NJ 08628 <morrisja@tcnj.edu>}%
\affil[2]{Department of Biology, Loyola University Maryland, Baltimore, MD 21210}%
\affil[3]{Department of Biology, Loyola University Maryland, Baltimore MD 21210}%


\vspace{-1em}



  
  \date{April 29, 2024}


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\selectlanguage{english}
\begin{abstract}
Plants in suburban forests of eastern North America face the dual
stressors of high white-tailed deer density and invasion by
nonindigenous plants. The combination of chronic deer herbivory and
strong competition from invasive plants could alter a plant's stress-
and defense-related secondary chemistry, especially for long-lived
juvenile trees in the understory, but this has not been studied. We
measured foliar total antioxidants, phenolics, and flavonoids in
juveniles of two native trees, \emph{Fraxinus pennsylvanica} (green ash)
and \emph{Fagus grandifolia} (American beech), growing in six forests in
the suburban landscape of central New Jersey, USA. The trees grew in
experimental plots that had been subject for 2.5 years to factorial
treatments of deer access/exclosure X addition/no addition of the
nonindigenous invasive grass \emph{Microstegium vimineum} (Japanese
stiltgrass). As other hypothesized drivers of plant secondary chemistry,
we also measured non-stiltgrass herb layer cover, light levels, and
water availability. Univariate mixed model analysis of the deer and
stiltgrass effects and multivariate structural equation modeling (SEM)
of all variables showed that both greater stiltgrass cover and greater
deer pressure induced antioxidants, phenolics, and flavonoids, with some
variation between species. Deer were generally the stronger factor, and
stiltgrass effects were most apparent at high stiltgrass density. SEM
also revealed that soil dryness directly increased the chemicals; deer
had additional positive, but indirect, effects via influence on the
soil; in beech PAR positively affected flavonoids; and herb layer cover
had no effect. Juvenile trees' chemical defense/stress responses to deer
and invasive plants can be protective, but also could have a
physiological cost, with negative consequences for recruitment to the
canopy. Ecological implications for species and their communities will
depend on costs and benefits of stress/defense chemistry in the specific
environmental context, particularly with respect to invasive plant
competitiveness, extent of invasion, local deer density, and deer browse
preferences.%
\end{abstract}%



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\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/image1/image1}
\caption{{Recovery of suburban forest vegetation in a deer exclosure%
}}
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\end{figure}

\par\null

\textbf{1} \textbar{} \textbf{INTRODUCTION}

Woodland plants within a suburban landscape live in circumstances that
differ in many ways from rural environments with fewer anthropogenic
influences , including the presence of many nonindigenous, invasive
plant species and very high white-tailed deer (\emph{Odocoileus
virginianus} Zimmerman) densities . Fragmentation of suburban natural
areas creates a high edge to interior ratio, creating many entry points
for nonindigenous species and rapid spread via trails and roads . In
suburban forests, the combination of forest patches with open areas is
excellent deer habitat , while hunting is very limited and most natural
predators of deer are uncommon. These features of suburban forests cause
plants to face the dual stressors of competition from spreading
nonindigenous species and deer herbivory, but no studies have
investigated plants' chemical responses to these combined stressors.
Here, we report on the foliar antioxidant, phenolic, and flavonoid
responses in juveniles of two native tree species in forests of suburban
New Jersey, USA.

The ability of plants to respond to biotic and abiotic stressors depends
on regulatory networks that help balance resource allocation to growth
or defense . Reactive oxygen species (ROS) increase during stress ,
causing oxidative destruction of cells, but this can be countered by
antioxidants, which play a scavenging role and minimize plant cell
damage . Overall antioxidant production, or more specific categories of
antioxidants such as phenolics or flavonoids (a type of phenolic), can
act as proxies for the degree of stress experienced by plants .
Phenolics and flavonoids have dual roles as antioxidants and inducible
defenses; they defend plant tissues against future herbivory, scavenge
ROS involved in signaling bursts as a result of wounding , and play a
role in a generalized stress response . Thus, we may expect antioxidants
in general, and phenolics and flavonoids in particular, to increase in
suburban woody plants subjected to the dual stressors of invasive plants
and chronic deer pressure.

Nonindigenous, invasive plants can broadly influence plant communities
through direct effects, e.g. strong competition for resources and
allelopathy from plant chemicals , and indirectly via modifications of
biotic factors such as microbial communities and natural enemies, or of
abiotic factors such as light and moisture availability . How such
impacts from invasive plants in particular may influence secondary
chemistry of resident plants has not been studied. However, plant
competition in general causes various stress responses, with increased
antioxidants , phenolics , and flavonoids , or alteration of the overall
metabolomic profile . Exposure to competitors' allelopathic chemicals
also can alter a plant's secondary chemistry . Therefore, competition
from nonindigenous, invasive plants, especially those with allelopathic
effects, could elicit strong chemical responses in the native community.
Negative trade-offs between defense and competitive ability also are
possible , so a resident plant faced with a new plant invader may be
particularly vulnerable due to both strong competition and the cost of
chemical response to that competition.

Browsing by ungulates also can broadly influence plant communities.
White-tailed deer are selective generalists , but exhibit an array of
preferences for woody species, which can influence recruitment , shift
canopy composition , and extirpate rare species . Browsing on woody
plants can lead to induction of defense chemicals; phenolics and
flavonoids have been shown to increase after damage. Defense chemicals
can reduce palatability to deer , but they also can be correlated with
slower growth rates due to trade-offs between growth and defense , which
can leave plants vulnerable as they remain within the reach of deer .

Recent work compares the ecological effects of nonindigenous plant
invasion and deer pressure on native communities , but has not compared
the chemical responses of native plants to both stressors. Given the
protective role of plant secondary chemistry, but also its possible
physiological cost , such a comparison will aid our understanding of the
relative importance of invasive plants and abundant deer in suburban
plant communities. We hypothesized that both would prompt increased
production of antioxidants, phenolics, and flavonoids in woody plants in
our experiment, with the greatest responses under both stressors
together, but we posed no a priori hypothesis about their relative
importance.

The analysis of ecological experiments benefits from combining
univariate methods with multivariate structural equation modeling (SEM)
pp. 233-258) that focuses on system-wide responses . We therefore also
proposed a system-wide hypothesis (Fig 1), represented as a structural
equation meta-model (SEMM). This hypothesis predicted that plant
chemical responses would be increased by deer pressure and a new
invader, as presented above, but additionally would increase due to
direct effects from competition with the rest of the herb layer and from
abiotic stressors known to influence secondary chemistry, specifically
light and soil moisture . We also hypothesized that deer pressure and
abiotic stressors would indirectly decrease the chemical responses via
negative direct effects on the herb layer. For example, if herb layer
plants declined due to an abiotic stress like drought, then there would
be less stress from competition and a decreased chemical stress response
in the target plants experiencing that competition. We limited the new
invader's hypothesized effect to just that on plant chemistry because we
had not observed any strong relationships between the manipulated
invasive species in the experiment, \emph{Microstegium vimineum} (Trin.)
A. Camus (Japanese stiltgrass), and the other variables in the model.

\textbf{2} \textbar{} \textbf{MATERIALS AND METHODS}

\textbf{2.1} \textbar{} \textbf{Study sites and species}

Experimental plots (16 m\textsuperscript{2}) were located in six forest
stands within a suburban region of central New Jersey, USA, in Hopewell
and Princeton Townships, Mercer County. The 131-174 year old stands
consist of closed canopies of mixed deciduous trees. The dominant canopy
species in the forests are maples (\emph{Acer rubrum} , \emph{A.
saccharum} ), oaks (\emph{Quercus rubrum} , \emph{Q. velutina} ,
\emph{Q. alba} , \emph{Q. prinus} ), hickories (\emph{Carya} spp.),
tulip poplar (\emph{Liriodendron tulipifera} ), American beech
(\emph{Fagus grandifolia} ), green ash (\emph{Fraxinus pennsylvanica} ),
sour gum (\emph{Nyssa sylvatica} ), and sweet gum (\emph{Liquidambar
styraciflua} ) . Their soils are silt loam or loam with 0-12\% slopes
(Natural Resources Conservation Service Web Soil Survey). Deer density
in the area was estimated at 32 deer/km\textsuperscript{2} , exceeding
or similar to densities in studies that have shown significant
influences on the vegetation of other eastern deciduous forests similar
in species composition to the forests we studied . They represent a
sample of the fragmented forest parcels in the region, and display a
range of ambient deer pressure (Table 1).

The two native, woody species that were the subject of this foliar
chemistry study were \emph{Fagus grandifolia} Ehrh. (American beech)
and\emph{Fraxinus pennsylvanica} Marsh. (green ash). Both were common
enough in the herb layers of the forests for our investigation, with the
exceptions that Curlis Lake Woods had insufficient ash and Nayfield
Preserve had insufficient beech to be included. A 2015 deer browse
survey in the forests (unpublished data) showed that both species were
browsed by deer, with 16.8\% of beeches (total N=143) and 1.4\% of ashes
(total N=559) exhibiting the tell-tale shredded twig tips indicative of
deer browse . Study of both beech and ash allowed for consideration of
the relative impact of deer preference on foliar chemistry.

It is worth noting that deer preferences and browse rates can vary
widely among regions. Therefore, the browse rates measured in our
central New Jersey forests should be seen as specific to our study and
not applicable to other forests, which likely have lower or higher
browse rates on beech and ash. For example, one review of beech ecology
reflected the view that deer rarely feed on beech . Other studies have
shown 40\% deer browse rates on beech and ash , 18\% on beech , a range
from 0\% to 11\% on beech depending on the site characteristics , and
widely variable per-plant browse intensity for both species .

\textbf{2.2} \textbar{} \textbf{Experimental design}

In each forest, 32-40 16 m\textsuperscript{2} plots were arranged on a
grid with 4 m between plots. Each plot was randomly assigned a fencing
or no-fencing treatment and a stiltgrass seed addition or no-addition
treatment. The fences were installed in spring 2013. They were 2.3 m
tall, consisting of plastic material with 4 x 4.5 cm mesh, made for deer
exclosures (Deerbusters.com). The fencing was staked to the ground but
had three cut-outs at ground level on each side. This allowed entry by
rabbits and voles and ensured that the only excluded herbivore would be
deer. This fencing has no effect on light or wind speed . Any leaf
litter that accumulated against the fences in the border was removed
twice per year, and vines that began to grow up the fences were clipped
away as needed.

The stiltgrass seed addition treatment was applied in fall 2012. Each
addition plot received 2.95 g of locally collected, pooled seeds
(approximately 2,420), mixed with 75 ml sand for easier distribution,
after which the leaf litter and the soil surface were disturbed with a
stout stick, allowing the seeds to settle down onto the soil surface
(the no-addition plots were disturbed in the same manner). We used this
randomly assigned stiltgrass addition treatment to avoid any confounding
site effects that could be associated with naturally occurring
stiltgrass abundances. The seed additions were done after gaining
permission from the forest preserve owners. Stiltgrass was not present
in the specific study sites prior to the experiment, but was common
elsewhere in the forests, as in nearly all forested areas of central New
Jersey (personal observations). It is important to note that stiltgrass
was removed where it appeared in the study sites outside of addition
plots, and when ongoing research in the sites is concluded, it will be
removed from addition plots until the seed bank is depleted. Subsequent
recruitment and persistence of the introduced stiltgrass was highly
variable among forests and plots, providing a range of densities that
aligned with those found in naturally occurring stands in these forests:
from nearly zero to nearly 100\% cover

We manipulated stiltgrass, specifically, because it is one of the most
common and abundant invasive herb layer species in the region, and it
has many documented negative effects on invaded plant communities .
However, no research exists on its possible effects on indigenous
plants' foliar chemistry. There were other, naturally-occurring,
nonindigneous, invasive plant species present in all of the forests and
many of the plots, but they varied among the forests and most were
shrubs with low percent cover. The only herbaceous invasive plant with
substantial cover was Japanese honeysuckle (\emph{Lonicera
japonica),}but the most cover it had in any plot was only 9\%, and its
average cover was 0.8\% and median cover was zero.

\textbf{2.3} \textbar{} \textbf{Leaf collection}

All leaves from beech and ash used in the study were collected on 2 Sept
2015. The number of plots sampled from each forest varied, based on the
presence of beech and ash. In order to avoid biasing the results by tree
age/size, in each sampled plot leaves were collected from one juvenile
plant in each of three distinct size classes, as possible based on
availability. If multiple plants in a size class were present, they were
numbered and a random number generator dictated the choice. For ash, the
size classes were: 0-10 cm, 20-40 cm, 50-140 cm. For beech they were:
only one set of simple leaves present, compound leaves with stem
height\emph{\textless{}} 20 cm, compound leaves with stem height
\textgreater{} 25 cm. The two most distal (youngest) leaves were removed
from all ashes and from unbranched beeches; for branched beeches, the
most distal leaf on the lowest branch and the terminal branch were used.
Leaves were collected from beech in five forests (not Nayfield), from 18
to 35 plots per forest and 19 to 53 plants per forest, with 101 plants
from fenced plots and 83 from unfenced plots. Ash leaves were also taken
from five forests (not Curlis), including 18 to 39 plots per forest and
30 to 95 plants per forest, with 163 in fenced plots and 156 unfenced.
The two leaves from one plant were put into one envelope and then dried
at 50\selectlanguage{ngerman}° C for three days, in preparation for chemical analysis.

\textbf{2.4} \textbar{} \textbf{Foliar chemical analysis}

We measured three categories of non-enzymatic antioxidants, from most to
least inclusive: total antioxidants, total phenolics, and total
flavonoids. Leaf samples (30 mg +/- 0.1 mg dry weight) were taken from
multiple parts of the leaf for both leaves within a sampled plant. The
leaf samples were mixed with clean sea sand in a 1.5 ml microcentrifuge
tube and ground into a fine powder before extraction with 1.52 ml of
methanol. The tube was vortexed for 10 seconds; then the samples were
put in a shaker at 150 rpm at 25 °C for 60 minutes. The samples were
then centrifuged for 5 minutes at 5,000 RPM, followed by removal of the
supernatant. Assays for antioxidant capacity, phenolic concentration,
and flavonoid concentration were conducted on the supernatant.

Antioxidant capacity was analyzed in a 48 well plate using the FRAP
assay according to . In brief, 900 \selectlanguage{greek}µ\selectlanguage{english}L of FRAP reagent was added to 30 \selectlanguage{greek}µ\selectlanguage{english}L
of sample and 90 \selectlanguage{greek}µ\selectlanguage{english}L of ultrapure water, incubated for 4 minutes, and
absorbance read at 593 nm on UV-Vis spectrometer. The standard curve was
generated using Trolox from 0-1500 \selectlanguage{greek}µ\selectlanguage{english}mole per liter. Antioxidant capacity
of the samples is expressed as Trolox Equivalents (TE) per gram dry
weight.

Phenolic concentration was tested using the Folin-Ciocalteu method . In
brief, 20 \selectlanguage{greek}µ\selectlanguage{english}L of the sample was mixed with 60 \selectlanguage{greek}µ\selectlanguage{english}L of Na2CO3, 900 \selectlanguage{greek}µ\selectlanguage{english}L of
ultrapure water and 20 \selectlanguage{greek}µ\selectlanguage{english}L of three-fold diluted Folin-Ciocaltue reagent.
The samples were then vortexed and left to sit at room temperature for 2
hours. Absorbance was read at 760 nm. Gallic acid (0 to 0.4 mg per ml)
was used to generate the standard curve. Phenolic concentration of the
samples is expressed as Gallic Acid Equivalents (GAE) per gram dry
weight.

Flavonoid concentration was analyzed in a 48 well plate using the
aluminum chloride precipitation . 100 \selectlanguage{greek}µ\selectlanguage{english}L of sample was added to 400 \selectlanguage{greek}µ\selectlanguage{english}L
of ultrapure water. 30 \selectlanguage{greek}µ\selectlanguage{english}L of NaNO2 was added and allowed to sit for 5
minutes, followed by addition of 30 \selectlanguage{greek}µ\selectlanguage{english}L of AlCl3. After 1 minute, 400 \selectlanguage{greek}µ\selectlanguage{english}L
of NaOH was added. The absorbance was immediately measured at 510 nm.
(+)-Catechin (0-1,000 ppm) was used to generate the standard curve.
Flavonoid concentration of the samples is expressed as Catechin
Equivalents (CE) per gram dry weight.

\textbf{2.5} \textbar{} \textbf{Field data collection}

The proportion cover of all herb layer plants was quantified in each
plot before leaf drop in the fall of 2015. Each species' cover was
scored as \textless{}1\%, 1-10\%, 11-20\%, 21-30\%, etc. (in 10\%
intervals up to 100\%) in 0.25 m\textsuperscript{2} quadrat frames,
which were dropped without looking into each 1
m\textsuperscript{2}section of the 16 m\textsuperscript{2} plot. The
score was converted to the interval's midpoint, and the mean of the 16
values provided one cover value per plot for each species, including
stiltgrass. The values for all other species were summed to calculate
the cover for all non-stiltgrass plants in the plot.

Photosynthetically active radiation at ground level was measured in each
plot with a 1 meter long ceptometer (AccuPAR model PAR-80 by Decagon
Devices, Pullman, Washington, USA). Measurements for a plot were made
under cloudless conditions between 10 am and 2 pm of one day, at the
four corners and center of each plot, and in nearby fields for full-sun
measures. Percent of full-sun PAR was calculated for each plot by
dividing the average of the five in-plot readings by the full-sun values
from the same time point, and multiplying by 100. The measurements were
done from 16 July to 20 October, as weather and schedules allowed,
before leaf drop except for canopy ash trees (they were uncommon near
the plots measured in October).

Soil water potential was measured as mPa with a bench-top WP4 soil water
potential meter (also Decagon Devices) on two soil samples taken from
the top 3 cm of each plot on 14 September 2014. To capture conditions
when variation in soil moisture could be detected, we ensured each
collection was made when there had been a light rain the previous day (6
mm) and no rain for the six previous days.

We calculated an ambient deer browse index (DBI) for each forest to use
in the SEMs. It consisted of the proportion of deer-browsed individuals
in unfenced plots of five native plant taxa: \emph{Carya} spp.\emph{,F.
grandifolia,} \emph{Fraxinus pennsylvanica,} \emph{Acer rubrum} , and
\emph{Rubus allegheniensis} . These were included because they were
sufficiently common in the forests' understories to allow for one index
applicable to all of the forests and because they were, in our sites,
neither the most browsed species nor completely avoided by deer. Other
studies have used one sentinel species for a browse index . However, in
our suburban forests with varying deer pressure and some very
depauperate herb layers, no one species was suitable as a consistent
indicator among forests. An index with multiple species offers a robust
measure when species' frequencies are highly variable among sites, as in
our forests. Deer browse is readily identifiable. Deer have no upper
incisors so they bite up on the stem, causing distinctive shredded tips,
whereas a rodent clips the stem and leaves a clean, angled tip . Deer
browse data were collected in 16 to 20 unfenced plots per forests;
within each plot all woody and semi-woody individuals in a 0.5 x 7.5
belt transect were examined for the presence of deer browse.

\textbf{2.6} \textbar{} \textbf{Statistical analysis -- Mixed models}

We analyzed separate mixed models for antioxidant capacity, phenolic
concentration, and flavonoid concentration, using PROC MIXED in SAS v
9.4 . Where enough plants were available, we collected leaves from three
plants per species per plot for chemical analysis (in the plots where
the species was present), but there were plots with just one or two
plants of ash or beech. Therefore, the analyzed response variable was
the mean value for all sampled individuals in a plot, thereby providing
one value per plot. To normalize model residuals, all response variables
were log\textsubscript{10} transformed, except for ash flavonoids, which
were square-root transformed. All models were randomized complete
blocks, with ``forest'' the random blocking factor (five forests). Fixed
effects were ``fencing'' (either `fence' or `no fence')
and\emph{Microstegium vimineum} (stiltgrass) percent cover (``mivi''),
with four categorical levels based on the ranges of cover resulting from
the experimental seed treatment: 0\%, 0.03\%-1.3\%, 1.6\%-5.6\%,
12.2\%-65\%. Using these categories allowed us to test the idea that
stiltgrass cover may have a threshold effect on foliar chemistry. The
models also included the ``fencing x mivi'' interaction term. The
omnibus tests were considered significant when P \emph{\textless{}}0.05.
Because we hypothesized that greater competition for the invasive
species would increase the foliar chemicals, we did planned comparisons
among all stiltgrass cover levels, using the Tukey-Kramer method to
adjust for mutliple contrasts and unequal sample sizes . If the omnibus
test was only close to significant (P\emph{\textless{}} 0.10), we still
reported it to avoid ignoring a potentially causal relationship and
conducted the planned multiple comparisons, following .

\textbf{2.7} \textbar{} \textbf{Statistical analysis -- Structural
equation modeling}

We conducted structural equation modeling with the `piecewiseSEM'
package v. 2.0 in R v. 4.0.3 using R Studio v. 1.2.5001. In this method,
the psem() function was applied to the set of multiple linear
regressions, built with lm(), that were specified in initial structural
equation measurement models (Fig. 2A and 2B) based on the proposed
concepts and pathways in the conceptual SEMM (Fig. 1) and informed by
the results of the univariate mixed models. Specifically, the initial
measurement models did not include paths from the deer browse pressure
variable or stiltgrass cover to a chemical group if the fencing effect
or stiltgrass cover effect was not signficant in the mixed model. Using
such prior knowledge of a system when developing an iniital model is a
key practice in structural equation modeling . Antioxidants, phenolics,
and flavonoids as described above measured the `plant chemical response'
concept from the SEMM; the ambient DBI measured `deer browse pressure'
in unfenced plots and was set to zero for fenced plots; a stiltgrass
cover category, with four levels, measured `competition from new
invasion'; the total non-stiltgrass proportion cover measured `other
herb layer competition'; and soil dryness (-1 x soil water potential)
and percent of full-sun PAR measured `abiotic stressors'.

Note that stiltgrass cover and DBI were exogenous variables in the SEM,
with no paths to them from other variables. This was because they were
experimentally manipulated; by design, half of the plots had zero
stiltgrass and the half that were fenced had zero deer browse pressure.
Additionally, none of the mixed models indicated an interactive effect
of deer exclosure fencing and stiltgrass cover on any of the foliar
chemical groups, which supported not having any indirect paths from
stiltgrass cover to the chemicals through the deer browse index, or vice
versa.

All endogenous variables in the model were first transformed to better
normalize the residuals from their regressions, which were checked by
the Shapiro-Wilk statistic and with visualizations produced by the
`fitdistrplus' package v. 1.1-1 . Good transformations were indicated by
the `bestNormalize' package v. 1.6.1 , and included either
log\textsubscript{10} or square root transformations. In addition, prior
to modeling, we removed several outliers and checked for any
nonlinearities between variables by plotting the data and fitting
various nonlinear functions in Excel. In no case was it necessary to
include nonlinear relationships in the SEM regressions. The Fisher's C
statistic indicated model fit . The modeling process was iterative. We
began with the hypothesized measurment models in Figures 2A and 2B, then
removed nonsignificant paths and added any significant and ecologically
sensible paths that were indicated by psem() to be necessary for model
fit.

\textbf{3} \textbar{} \textbf{RESULTS}

\textbf{3.1} \textbar{} \textbf{Mixed models}

\emph{Ash} -- Foliar antioxidant concentration of ash plants was
significantly greater in unfenced plots compared to fenced plots
(P=0.02, Figure 3A). Stiltgrass cover level had a marginal overall
effect on ash antioxidant capacity (P=0.10), with somewhat greater mean
values in plots with 12-65\% stiltgrass cover versus zero cover (P=0.09,
Figure 3B). There was no significant interaction between fencing and
stiltgrass cover for ash antioxidants. Only the fencing treatment had a
significant effect on phenolics (P=0.01) and flavonoids (P=0.05) in ash,
with greater mean values for plants in the unfenced plots (Figures 3C,
3D).

\emph{Beech} -- Stiltgrass cover level significantly affected
antioxidants in beech (P=0.05), with significantly greater mean
concentrations in plots with the 12-65\% cover level compared to the
\textgreater{}0-1.5\% cover level (Figure 4A). Flavonoids showed the
same overall trend (P=0.08; Figure 4B). Neither beech antioxidants nor
flavonoids were affected by the fencing treatment or its interaction
with stiltgrass cover. Beech phenolics were not affected by deer,
stiltgrass cover, or their interactions.

One goal of the initial experimental design was to test the hypothesis
that the dual stressors of competition from high stiltgrass cover and
chronic deer browsing would cause the greatest increases in foliar
secondary chemicals. This could have been indicated from significant
fencing x stiltgrass cover level interactions, but none were detected in
the full models. We had expected the stiltgrass seed addition treatments
to result in uniformly high cover of stiltgrass, but this occurred only
in a small number of plots scattered across the forests. Therefore, as
another test of this hypothesis, for each species-chemical combination
we did a set of simple planned comparisons between four groups (pooled
across the forests): fenced/zero stiltgrass cover, fenced/high
stiltgrass cover, unfenced/zero stiltgrass cover, unfenced/high
stiltgrass cover. High cover was defined as \textgreater{}12\%-65\%. For
four of the six species-chemical combinations there were no significant
contrasts between any groups. However, ash antioxidant values were
greater in the unfenced/high cover group vs. the fenced/zero cover group
and the unfenced/zero cover group (Figure 5A), and beech phenolics were
greater in the unfenced/high cover group vs. the fenced/zero cover group
(Figure 5B).

\textbf{3.2} \textbar{} \textbf{Structural equation models}

\emph{Ash} -- We arrived at a final, fitted SE model (Fig. 6A) that both
reinforced many of the findings above for ash, and also provided
additional insights. First, as in the univariate mixed models, the SEM
revealed a strong, direct, positive effect of stiltgrass cover on
antioxidants and no effect on phenolics or flavonoids. Second, as in the
univariate models, deer browse pressure (measured as DBI) positively
affected antioxidants and phenolics, but did not affect flavonoids,
mirroring the somewhat weaker effect of fencing on flavonoids (P=0.05 vs
0.01 and 0.02 for the other chemicals). DBI had a strong negative effect
on herb layer cover, but there was no signficant effect of the herb
layer on any foliar chemicals. Third, the two abiotic variables in the
ash SEM were very influential, with various strong direct and indirect
effects on foliar chemistry, e.g. greater concentrations of all three
chemical types with increasing soil dryness and a direct positive effect
on flavonoids from increased PAR.

\emph{Beech --} The final SEM for beech also provided many similar
findings as the univariate models, along with some new and different
results (Fig. 6B). First, as in the univariate models, stiltgrass cover
positively influenced antioxidants and flavonoids, but not phenolics.
Second, DBI had direct, positive influences on all three chemical types,
in addition to a net positive effect via the indirect pathway through
soil dryness, which differed from the univariate analysis in which there
were no significant effects of deer exclosure fencing. As in the ash
SEM, deer had a very strong negative effect on the other herb layer
vegetation, but that in turn had no paths to the beech chemical
variables. Third, all chemical concentrations increased with greater
soil dryness, but PAR did not have any effects and was dropped from the
model.

Overall, the SEMs suggested that 1) deer and abiotic factors had greater
influences on leaf chemistry than did the invasive species \emph{M.
vimineum} or competition from other plants; 2) although the three
chemicals' values were positively correlated, as expected, they did not
respond identically to the variables and were more similar in the beech
SEM; 3) a substantial amount of variation in the models remains to be
explained by unmeasured factors.

\textbf{4} \textbar{} \textbf{DISCUSSION}

\textbf{4.1} \textbar{} \textbf{Effects of stiltgrass cover}

This study provided partial support for the hypothesis that a newly
introduced, invasive, nonindigenous species can increase foliar
antioxidants, phenolics, and flavonoids of plants in the invaded
community. Support was shown in both types of analysis, by the positive
SEM paths from stiltgrass cover to ash and beech antioxoidants and beech
flavoinoids and by the significant effect of stiltgrass cover level on
beech antioxidants in the univariate model. In addition, there were
several contrasts (P \emph{\textless{}} 0.09) in the univariate models
between the highest stiltgrass cover level and the zero or
\textgreater{}0-1.5\%, suggesting that high stiltgrass cover may have
caused greater antioxidants in ash and flavonoids in beech. However,
stiltgrass had no effects on phenolics in either species or flavonoids
in ash, except indirectly in the SEM via correlations between the
chemical groups. Ash and beech are mid- to late-successional tree
species, respectively . They may remain as juveniles for years, and so
must contend with long-term competition from plants in the herb layer,
which can be intense from a rapidly increasing invader , potentially
limiting resources, depressing growth rates, and reducing a tree's
chance of reaching the canopy. If the competition also induces increased
production of secondary chemicals, as observed here in some cases, the
plants may incur an added cost in even lower growth rates, given the
possibility of growth-defense tradeoffs. Indeed, such tradeoffs have
been documented for induced defenses in woody species .

To our knowledge, no other studies have demonstrated induction of
secondary chemistry by a nonindigenous, invasive species, including the
well-studied Japanese stiltgrass. Previous research indicates that it
has allelopathic potential , and since secondary chemical responses to
allelopathy have been shown in other systems, this is a possible
mechanism worth further study. Stiltgrass's influence likely relies on
it reaching a certain threshold of density during invasion; in our study
its influence was generally due to its highest cover level.

The differences among the species-chemical combinations for the effects
of stiltgrass have several possible explanations. Plant secondary
chemistry is influenced by a wide array of factors (e.g. resource
availability, ontogeny), and variation in their production is common
within populations and communities . The data sets for each species came
from a somewhat different set of forests and plots, so they may have
experienced different resource conditions that mediated the competitive
impact of stiltgrass. Competition intensity can alter chemical responses
as shown, for example, in a study where specific flavonoids increased
under low competition but decreased under high competition . The
stronger influence of stiltgrass on flavonoids in beech vs. ash could be
due to beech's greater shade tolerance . Its slower growth rate in the
herb layer may allow it to invest more in secondary chemicals than the
faster-growing ash, as has been predicted by the Resource Availability
Hypothesis and shown , particularly for forest tree seedlings . Total
antioxidants in ash were directly affected by stiltgrass, but phenolics
and flavonoids were not; it is likely the case that antioxidants other
than phenolics were induced by stiltgrass competition. These differences
could be resolved with a metabolomics approach in future research.

\textbf{4.2} \textbar{} \textbf{Effects of deer pressure}

The hypothesis that deer pressure increases foliar concentrations of
antioxidants, phenolics, and flavonoids was also partially supported for
both species in this study. For ash, all three chemical groups were
significantly greater in unfenced plots versus fenced plots, as shown by
the univariate analyses. The SEM also indicated a positive effect of the
deer browse index on ash antioxidants and phenolics, but not on
flavonoids, except indirectly through the other chemical groups. For
beech, a positive effect of deer on all three foliar chemical groups was
apparent in the SEM, but the mixed models showed no signficant effects
from the fencing treatment. These induced chemical responses to deer
pressure could have been recent or even months old, as long-lasting
effects on induced defenses have been shown previously for woody species
, including in \emph{Fagus} and \emph{Fraxinus} . The responses to deer
can have two functions with ecological implications for browsed plants.
On the one hand, they can become more protected against future browse,
which should be very beneficial for growth and survival and could create
an advantage in the plant community of suburban forests with high deer
densities. On the other hand, if there is a substantial cost to induced
defenses, a browsed woody plant could experience double jeopardy: loss
of tissue coupled with lower growth potential that prevents it from
escaping above the browse line. However, we cannot always assume a cost
of induced defense . Which scenario applies will depend on the relative
costs and benefits, which rely on a complex suite of intersecting
factors, e.g. the level of herbivory pressure, competition, and
tolerance traits.

Beech was browsed much more frequently than ash in the forests of this
study, so we would expect it to have stronger secondary chemical
responses to deer. This was indicated by the SEMs, but the mixed models
suggested that ash was more affected. We have no specific explanation
for this difference, except to note that SEM is a multivariate approach
that is more representative of real ecological communities. Even so,
given the low browse rate on ash in these forests, it seems to have
mounted a strikingly strong chemical response to deer pressure.

\textbf{4.3} \textbar{} \textbf{Effects of plant invasion + deer
pressure}

The hypothesis that deer pressure and the invasive species together
would cause the greatest foliar chemical responses was partially
supported in this study. Although the availability of data for plots
with high stiltgrass was limited, we still detected significantly
greater ash antioxidants and beech phenolics in the plots with the dual
stressors of deer access (unfenced) and high stiltgrass cover compared
to the plots with neither stressor (fenced, zero stiltgrass), whereas
high stiltgrass cover or unfenced treatment alone did not cause a
significant increase in beech phenolics. However, there were no
differences among the treatment groups for any of the other
species-chemical combinations, and no significant fencing x stiltgrass
cover level interaction terms in the full mixed models. Still, these
results illustrate that some woody plants experience an enhanced
secondary chemical response when faced with multiple stressors. The
roles of multiple stressors in biological systems is increasingly
recognized across disciplines , and has specifically been documented for
deer pressure combined with earthworm invasions, non-native plant
invasion, and herbivory by rodents .

\textbf{4.4} \textbar{} \textbf{Relative strengths of plant invasion and
deer pressure effects}

We sought to determine which factor -- plant invasion or deer pressure
-- had greater influence on plant secondary chemistry. The SEMs suggest
that, in this study, deer pressure was the more important factor. It had
direct positive influences on nearly all of the chemical groups.
Additionally, greater deer pressure in the SEM increased soil dryness in
the beech SEM, which in turn increased antioxidants, phenolics, and
flavonoids. In contrast, no strong indirect paths from stiltgrass to the
chemicals were apparent, and while the strengths of the signficiant
direct paths from stiltgrass cover to the chemical variables (0.18,
0.28, 0.21) were similar to those from the deer browse index variable
(0.26, 0.24, 0.21, 0.21, 0.24), there were fewer of these direct paths.
The univariate analyses were mixed on this point, showing stronger
effects of deer on ash foliar chemicals, but stronger effects of
stiltgrass cover in beech.

A recent review of published deer-invasive plants experiments concluded
that deer are generally a more influential factor in deciduous forest
communities of eastern North America than are invasive plants. Our
research provides a new dimension to this comparison: for at least some
woody species, deer pressure likely placed greater demands on plant
secondary chemistry than competition from an invading plant. Even so, it
is perhaps more important to recognize that both stressors induced
responses in both species, and with a more widespread invasion, stress
from stiltgrass competition likely would increase. In our study, effects
from free-ranging deer were likely much more spatially homogenous and
widespread than that of the patchy \emph{M. vimineum} , which invaded
some plots much more readily than others.

\textbf{4.5} \textbar{} \textbf{Dual analysis: univariate models and
structural equation modeling}

One reviewer objected to presenting two different statistical
philosophies, one rooted in experimental design (univariate mixed
models) and the other in observational data analysis (SEM). While this
reviewer felt that we should choose one or the other to present, like
other authors we think that the two different approaches complement each
other and provide a more holistic picture of what is driving the
induction of foliar chemistry in young saplings of two woody species.
The experiment was designed to test for main effects of and interactions
between deer exclosure fencing and stiltgrass cover, as in any standard
factorial design. The univariate analyses revealed these effects, but
they also helped guide development of the intitial SE measurement model.
In turn, the fitted SEMs provided additional insight into the univariate
results. Specifically, they confirmed the positive influences of
stiltgrass cover only on antioxidants for ash and, for beech, on just
antioxidants and flavonoids. However, it turned out that significant
paths did exist between the deer browse index and all of the beech
chemical groups. The fencing effect in the mixed models only compared
fenced and unfenced plots, without taking into account any important
variation in ambient deer pressure among the forests, which could affect
the unfenced plots. The SEMs' deer browse index, however, was modeled in
a regression context, with deer browse pressure estimates for the
unfenced plots that were distinct for each forest, and this variation in
ambient deer browse pressure was important for most of the foliar
chemicals. The larger context of the SEM also allowed for consideration
of the relative importance of the experimental treatments when modeled
alongside other drivers in the system. For example, stiltgrass cover
significantly increased antioxidants in beech in the mixed model, but in
the SEM its positive path was weaker than the effect of droughty soil.

\textbf{4.6} \textbar{} \textbf{Other variables as drivers in the SEM:
herb layer cover, soil dryness, and PAR}

Not surprisingly, in both ash and beech SE models, there was a strong
negative effect of deer on the non-stiltgrass herb layer cover. We had
hypothesized in the structural equation meta-model that, in turn, this
reduced cover would cause less competitive stress on beech and ash
juveniles, thereby decreasing their antioxodants, phenolics, and
flavonoids. This would therefore have revealed a positive, indirect
effect of deer on the foliar chemistry. However, the SEM did not show
any effects on beech and ash foliar chemistry due to the non-stiltgrass
cover. This contrasts to the positive effect that stiltgrass cover had,
suggesting that greater stress was caused by the invasive species.

Soil dryness had direct, positive effects on each of the three chemicals
in the SEMs, for both species. Levels of antioxidants are generally
increased under drought stress . For example, studies of \emph{Quercus
ilex} , which shares beech's family (Fagaceae), found increased phenolic
production under drought conditions , and flavonoids have been proposed
as a secondary antioxidant system activated in severe stress . Beech and
ash exhibited somewhat different strengths of their chemical responses
to drought stress, which is not surprising. Within \emph{Quercus} , for
example, three species had different foliar concentrations of
antioxidants in response to drought , and even within a species local
adaptation can result in different strategies for drought stress
tolerance .

Photosynthetically active radiation (PAR) signficantly, positively
affected flavonoids in ash, but not in beech. High PAR can lead to
excess excitation energy, resulting in reactive oxygen species (ROS)
production that may cause damage to photosystems I and II. ROS produce
signaling cascades that adjust metabolism using a variety of
stress-protective mechanisms, including non-enzymatic antioxidants . For
example, excess light is known to upregulate the production of
flavonoids, which act as ROS scavengers and are involved in
photoprotection . In the ash SEM, PAR also had indirect, positive
effects on all three chemical groups through its positive effect on soil
dryness, which in turn had positive effects on the chemical levels.

The SEMs were designed to include major factors that we hypothesized to
be important drivers of plant secondary chemistry in the forests. They
revealed a number of significant paths, but the explained variation
(R\textsuperscript{2} values) for the three chemical groups ranged only
from 0.13 -- 0.33. Intraspecific variation in specific types of
secondary metabolites and the overall metabolome is common, with many
possible causes . Our research has uncovered several important drivers
in suburban forests, but other factors that were not considered in our
models also must be influential and are important for future study (e.g.
plant-soil feedbacks, .

\textbf{5} \textbar{} \textbf{CONCLUSIONS}

Suburban forests are important sites for biodiversity, but many have
experienced steep declines in step with deer overabundance and
nonindigenous plant invasions. Here, we showed that these two common
stressors can increase juvenile trees' secondary chemicals involved in
defense and stress responses. Deer generally had stronger and/or more
consistent effects than stiltgrass and in some cases their combination
increased the chemical responses. The SEM analysis revealed additional,
important influences on the trees' secondary chemistry. The ecological
implications for each species -- and the overall suburban forest
community -- will depend on the relative costs and benefits to each
species in their particular environmental contexts, which is a goal for
future research in this area.

\textbf{References}

Adams, S.N. \& Engelhardt, K.A.M. (2009). Diversity declines
in\emph{Microstegium vimineum} (Japanese stiltgrass)
patches.\emph{Biological Conservation} , 142, 1003-1010.

Afifi, M. \& Swanton, C. (2012). Early physiological mechanisms of weed
competition. \emph{Weed Science} , 60(4), 542-551.
https://doi.org/10.1614/WS-D-12-00013.1.

Agati, G., Azzarello, E., Pollastri, S. \& Tattini, M. (2012).
Flavonoids as antioxidants in plants: Location and functional
significance. \emph{Plant Science} , 196, 67-76.
https://doi.org/10.1016/j.plantsci.2012.07.014.

Alverson, W.S., Waller, D.M. \& Solheim, S.L. (1988). Forests too deer:
Edge effects in northern Wisconsin. \emph{Conservation Biology} , 2(4),
348-358. https://doi.org/10.1111/j.1523-1739.1988.tb00199.x.

Aronson, M.F.J. \& Handel, S.N. (2011). Deer and invasive plant species
suppress forest herbaceous communities and canopy tree
regeneration.\emph{Natural Areas Journal} , 31(4), 400-407.
https://doi.org/10.3375/043.031.0410.

Aronson, M.F., Handel, S.N., La Puma, I.P. \& Clemants, S.E. (2015).
Urbanization promotes non-native woody species and diverse plant
assemblages in the New York metropolitan region. Urban Ecosystems,
18(1), 31-45. https://doi.org/10.1007/s11252-014-0382-z.

Ashraf, M.A., Iqbal, M., Rasheed, R., Hussain, I., Riaz, M. \& Arif,
M.S. (2018). Chapter 8 - Environmental stress and secondary metabolites
in plants: An overview. \emph{Plant Metabolites and Regulation Under
Environmental Stress} (eds P. Ahmad, M.A. Ahanger, V.P. Singh, D.K.
Tripathi, P. Alam \& M.N. Alyemeni), pp. 153-167. Academic Press.

Augustine, D.J. \& McNaughton, S.J. (1998). Ungulate effects on the
functional species composition of plant communities: Herbivore
selectivity and plant tolerance. \emph{Journal of Wildlife Management} ,
62(4), 1165-1183. https://doi.org/10.2307/3801981.

Augustine, D.J. \& deCalesta, D. (2003). Defining deer overabundance and
threats to forest communities: From individual plants to landscape
structure. \selectlanguage{ngerman}\emph{Écoscience} , 10(4), 472-486.
https://doi.org/10.1080/11956860.2003.11682795.

Ballhorn, D., Godschalx, A., Smart, S., Kautz, S. \& Schädler, M.
(2014). Chemical defense lowers plant competitiveness. \emph{Oecologia}
, 176(3), 811-824. https://doi.org/10.1007/s00442-014-3036-1.

Baxter, A., Mittler, R. \& Suzuki, N. (2014). ROS as key players in
plant stress signalling. \emph{Journal of Experimental Botany} , 65(5),
1229-1240. https://doi.org/10.1093/jxb/ert375.

Bee, J.N., Tanentzap, A.J., Lee, W.G., Lavers, R.B., Mark, A.F., Mills,
J.A. \& Coomes, D.A. (2011). Influence of foliar traits on forage
selection by introduced red deer in new zealand. \emph{Basic and Applied
Ecology} , 12(1), 56-63. https://doi.org/10.1016/j.baae.2010.09.010.

Benzie, I.F.F. \& Strain, J.J. (1996). The ferric reducing ability of
plasma (FRAP) as a measure of ``antioxidant power'': The FRAP
assay.\emph{Analytical Biochemistry} , 239(1), 70-76.
https://doi.org/10.1006/abio.1996.0292.

Bilska, K., Wojciechowska, N., Alipour, S. \& Kalemba, E.M. (2019).
Ascorbic Acid---The little-known antioxidant in woody
plants.\emph{Antioxidants} , 8(12).
https://doi.org/10.3390/antiox8120645.

Blossey, B., Curtis, P., Boulanger, J. \& Dávalos, A. (2019). Red oak
seedlings as indicators of deer browse pressure: Gauging the outcome of
different white\selectlanguage{english}-tailed deer management approaches. \emph{Ecology and
Evolution} , 9(23), 13085-13103. https://doi.org/10.1002/ece3.5729.

Blossey, B., Davalos, A. \& Nuzzo, V. (2017). An indicator approach to
capture impacts of white-tailed deer and other ungulates in the presence
of multiple associated stressors. \emph{AoB PLANTS} , 9(5), plx034.
https://doi.org/10.1093/aobpla/plx034.

Blossey, B. \& Gorchov, D.L. (2017). Introduction to the special issue:
Ungulates and invasive species: Quantifying impacts and understanding
interactions. \emph{AoB PLANTS} , 9(6), plx063.
https://doi.org/10.1093/aobpla/plx063.

Brunetti, C., Guidi, L., Sebastiani, F. \& Tattini, M. (2015).
Isoprenoids and phenylpropanoids are key components of the antioxidant
defense system of plants facing severe excess light
stress.\emph{Environmental and Experimental Botany} , 119, 54-62.
https://doi.org/10.1016/j.envexpbot.2015.04.007

Burns, R.M. \& Honkala, B.H. (1990). \emph{Silvics of North America.
Volume 2. Hardwoods.} United States Department of Agriculture (USDA),
Forest Service, Agriculture Handbook 654.

Cadenasso, M.L. \& Pickett, S.T. (2001). Effect of edge structure on the
flux of species into forest interiors. \emph{Conservation Biology} ,
15(1), 91-97. https://doi.org/10.1111/j.1523-1739.2001.99309.x.

Callaway, R.M. \& Ridenour, W.M. (2004). Novel weapons: Invasive success
and the evolution of increased competitive ability. \emph{Frontiers in
Ecology and the Environment} , 2(8), 436-443.

https://doi.org/10.1890/1540-9295(2004)002{[}0436:NWISAT{]}2.0.CO;2.

Chalker-Scott, L. \& Fuchigami, L.H. (1989). The role of phenolic
compounds in plant stress responses. \emph{Low Temperature Stress
Physiology in Crops} (ed P.H. Li), pp. 67-80. CRC Press.

Champagne, E., Royo, A.A., Tremblay, J. \& Raymond, P. (2020).
Phytochemicals involved in plant resistance to leporids and cervids: A
systematic review. \emph{Journal of Chemical Ecology} , 46(1), 84-98.
https://doi.org/10.1007/s10886-019-01130-z.

Cipollini, K. \& Bohrer, M.G. (2016). Comparison of allelopathic effects
of five invasive species on two native species. \emph{The Journal of the
Torrey Botanical Society} , 143(4), 427-436.
https://doi.org/10.3159/TORREY-D-15-00062.1.

Coley, P.D., Bryant, J.P. \& Chapin, F.S. (1985). Resource availability
and plant antiherbivore defense. \emph{Science} , 230(4728), 895-899.
https://doi.org/10.1126/science.230.4728.895.

Corbett, B.F. \& Morrison, J.A. (2012). The allelopathic potentials of
the non-native invasive plant \emph{Microstegium vimineum} and the
native \emph{Ageratina altissima} : Two dominant species of the eastern
forest herb layer. \emph{Northeastern Naturalist} , 19(2), 297-312.
https://doi.org/10.1656/045.019.0211.

Cote, S.D., Rooney, T.P., Tremblay, J.P., Dussault, C. \& Waller, D.M.
(2004). Ecological impacts of deer overabundance. \emph{Annual Review of
Ecology and Systematics} , 35, 113-147.
https://doi.org/10.1146/annurev.ecolsys.35.021103.105725

Crimmins, S.M., Edwards, J.W., Ford, W.M., Keyser, P.D. \& Crum, J.M.
(2010). Browsing patterns of white-tailed deer following increased
timber harvest and a decline in population density. \emph{International
Journal of Forestry Research} , 2010, 1-7.
https://doi.org/10.1155/2010/592034.

Darmanti, S., Santosa, Nugroho, L.H. \& Dewi, K. (2018). Reactive oxygen
species accumulations, phenylalanine ammonia-lyase activity and phenolic
acid composition of soybean {[}\emph{Glycine max} (L.) merr.{]} cv.
grobogan that exposed to multiple stress of purple nutsedge
(\emph{Cyperus rotundus} L.) interference and drought. \emph{The Journal
of Animal \& Plant Sciences} , 28(1), 244-251.

Das, K. \& Roychoudhury, A. (2014). Reactive oxygen species (ROS) and
response of antioxidants as ROS-scavengers during environmental stress
in plants. \emph{Frontiers in Environmental Science} , 2, 53.
https://doi.org/10.3389/fenvs.2014.00053.

Del Rio, L.A. (2015). ROS and RNS in plant physiology: An
overview.\emph{Journal of Experimental Botany} , 66(10), 2827-2837.
https://doi.org/10.1093/jxb/erv099.

Delignette-Muller, M.L. \& Dutang, C. (2015). Fitdistrplus : An R
package for fitting distributions. \emph{Journal of Statistical
Software} , 64(4), 1-34. https://doi.org/10.18637/jss.v064.i04.

Dobson, A., Richardson, J. \& Blossey, B. (2020). Effects of earthworms
and white\selectlanguage{english}-tailed deer on roots, arbuscular mycorrhizae, and forest
seedling performance. \emph{Ecology} , 101(1), e02903.
https://doi.org/10.1002/ecy.2903.

Dolan, R.W., Moore, M.E. \& Stephens, J.D. (2011). Documenting effects
of urbanization on flora using herbarium records. \emph{Journal of
Ecology} , 99(4), 1055-1062.
https://doi.org/10.1111/j.1365-2745.2011.01820.x.

Donaldson, J.R., Kruger, E.L. \& Lindroth, R.L. (2006). Competition\selectlanguage{english}- and
resource\selectlanguage{english}-mediated tradeoffs between growth and defensive chemistry in
trembling aspen (\emph{Populus tremuloides} ). \emph{New Phytologist} ,
169(3), 561-570. https://doi.org/10.1111/j.1469-8137.2005.01613.x.

Du, B., Jansen, K., Kleiber, A., Eiblmeier, M., Kammerer, B., Ensminger,
I., Gessler, A., Rennenberg, H. \& Kreuzwieser, J. (2016). A coastal and
an interior douglas fir provenance exhibit different metabolic
strategies to deal with drought stress. \emph{Tree Physiology} , 36(2),
148-163. https://doi.org/10.1093/treephys/tpv105.

Endara, M. \& Coley, P.D. (2011). The resource availability hypothesis
revisited: A meta\selectlanguage{english}-analysis. \emph{Functional Ecology} , 25(2), 389-398.

https://doi.org/10.1111/j.1365-2435.2010.01803.x

Estravis-Barcala, M., Mattera, M.G., Soliani, C., Bellora, N.,
Opgenoorth, L., Heer, K. \& Arana, M.V. (2020). Molecular bases of
responses to abiotic stress in trees. \emph{Journal of Experimental
Botany} , 71(13), 3765-3779. https://doi.org/10.1093/jxb/erz532.

Fernandez, C., Monnier, Y., Santonja, M., Gallet, C., Weston, L.A.,
Prevosto, B., Saunier, A., Baldy, V. \& Bousquet-Melou, A. (2016). The
impact of competition and allelopathy on the trade-off between plant
defense and growth in two contrasting tree species. \emph{Frontiers in
Plant Science} , 7, 594. https://doi.org/10.3389/fpls.2016.00594.

Fini, A., Guidi, L., Ferrini, F., Brunetti, C., Di Ferdinando, M.,
Biricolti, S., Pollastri, S., Calamai, L. \& Tattini, M. (2012). Drought
stress has contrasting effects on antioxidant enzymes activity and
phenylpropanoid biosynthesis in \emph{Fraxinus ornus} leaves: An excess
light stress affair? \emph{Journal of Plant Physiology} , 169(10),
929-939. https://doi.org/10.1016/j.jplph.2012.02.014.

Fisichelli, N.A. \& Miller, K.M. (2018). Weeds, worms, and deer:
Positive relationships among common forest understory
stressors.\emph{Biological Invasions} , 20(5), 1337-1348.
https://doi.org/10.1007/s10530-017-1630-y.

Flory, S.L. \& Clay, K. (2010). Non-native grass invasion suppresses
forest succession. \emph{Oecologia} , 164(4), 1029-1038.
https://doi.org/10.1007/s00442-010-1697-y

Frelich, L.E. \& Lorimer, C.G. (1985). Current and predicted long-term
effects of deer browsing in hemlock forests in Michigan,
USA.\emph{Biological Conservation} , 34(2), 99-120.
https://doi.org/10.1016/0006-3207(85)90103-X.

Friedman, M.S., Rigsby, C.M. \& Cipollini, D. (2020). Light limitation
impacts growth but not constitutive or jasmonate induced defenses
relevant to emerald ash borer (\emph{Agrilus planipennis} ) in white
fringetree (\emph{Chionanthus virginicus} ) or black ash (\emph{Fraxinus
nigra} ). \emph{Journal of Chemical Ecology} , 46(11-12), 1117-1130.
https://doi.org/10.1007/s10886-020-01223-0.

Gidman, E., Goodacre, R., Emmett, B., Smith, A.R. \& Gwynn-Jones, D.
(2003). Investigating plant--plant interference by metabolic
fingerprinting. \emph{Phytochemistry} , 63(6), 705.
https://doi.org/10.1016/S0031-9422(03)00288-7.

Gill, S.S. \& Tuteja, N. (2010). Reactive oxygen species and antioxidant
machinery in abiotic stress tolerance in crop plants. \emph{Plant
Physiology and Biochemistry} , 48(12), 909-930.
https://doi.org/10.1016/j.plaphy.2010.08.016.

Gioria, M. \& Osborne, B.A. (2014). Resource competition in plant
invasions: Emerging patterns and research needs. \emph{Frontiers in
Plant Science} , 5, 501. https://doi.org/10.3389/fpls.2014.00501.

Gniazdowska, A. \& Bogatek, R. (2005). Allelopathic interactions between
plants: Multi site action of allelochemicals. \emph{Acta Physiologiae
Plantarum} , 27(3), 395-407. https://doi.org/10.1007/s11738-005-0017-3.

Gorchov, D.L., Blossey, B., Averill, K.M., Davalos, A., Heberling, J.M.,
Jenkins, M.A., Kalisz, S., McShea, W.J., Morrison, J.A., Nuzzo, V.,
Webster, C.R. \& Waller, D.M. (2021). Differential and interacting
impacts of invasive plants and white-tailed deer in eastern U.S.
forests. \emph{Biological Invasions} , 23(9), 2711-2727.
https://doi.org/10.1007/s10530-021-02551-2.

Grace, J.B., Anderson, T.M., Olff, H. \& Scheiner, S.M. (2010). On the
specification of structural equation models for ecological
systems.\emph{Ecological Monographs} , 80(1), 67-87.
https://doi.org/10.1890/09-0464.1.

Grace, J.B. (2006). \emph{Structural Equation Modeling and Natural
Systems} . Cambridge University Press, New York.

Grace, J.B., Youngblood, A. \& Scheiner, S.M. (2009). Structural
equation modeling and ecological experiments. \emph{Real World Ecology:
Large-Scale and Long-Term Case Studies and Methods} (eds S. Miao, S.
Carstenn \& M. Nungesser), pp. 19-45. Springer, New York.

Griggs, J.A., Rock, J.H., Webster, C.R. \& Jenkins, M.A. (2006).
Vegetative legacy of a protected deer herd in Cades Cove, Great Smoky
Mountains National Park. \emph{Natural Areas Journal} , 26(2), 126-136.

https://doi.org/10.3375/0885-8608(2006)26{[}126:VLOAPD{]}2.0.CO;2.

Hahn, P.G. \& Maron, J.L. (2016). A framework for predicting
intraspecific variation in plant defense. \emph{Trends in Ecology and
Evolution} , 31(8), 646-656. https://doi.org/10.1016/j.tree.2016.05.007.

Hazrati, H., Fomsgaard, I.S. \& Kudsk, P. (2021). Targeted metabolomics
unveil alteration in accumulation and root exudation of flavonoids as a
response to interspecific competition. \emph{Journal of Plant
Interactions} , 16(1), 53-63.
https://doi.org/10.1080/17429145.2021.1881176.

Herms, D.A. \& Mattson, W.J. (1992). The dilemma of plants: To grow or
defend. \emph{Quarterly Review of Biology} , 67(3), 283-335.
https://doi.org/10.1086/417659.

Hopewell Valley Deer Management Task Force (2014). Report,
https://www.hopewelltwp.org/ArchiveCenter/ViewFile/Item/237.

Horsley, S.B., Stout, S.L. \& deCalesta, D.S. (2003). White-tailed deer
impact on the vegetation dynamics of a northern hardwood
forest.\emph{Ecological Applications} , 13(1), 98-118.
https://doi.org/10.1890/1051-0761(2003)013{[}0098:WTDIOT{]}2.0.CO;2.

Huberty, M., Choi, Y.H., Heinen, R. \& Bezemer, T.M. (2020).
Above\selectlanguage{english}-ground plant metabolomic responses to plant--soil feedbacks and
herbivory. \emph{Journal of Ecology} , 108(4), 1703-1712.
https://doi.org/10.1111/1365-2745.13394.

Hunter, J.C. \& Mattice, J.A. (2002). The spread of woody exotics into
the forests of a northeastern landscape, 1938-1999. \emph{Journal of the
Torrey Botanical Society} , 129(3), 220-227.
https://doi.org/10.2307/3088772.

Imaji, A. \& Seiwa, K. (2010). Carbon allocation to defense, storage,
and growth in seedlings of two temperate broad-leaved tree
species.\emph{Oecologia} , 162(2), 273-281.
https://doi.org/10.1007/s00442-009-1453-3.

Kalisz, S., Kivlin, S.N. \& Bialic-Murphy Lalasia (2021). Allelopathy is
pervasive in invasive plants. \emph{Biological Invasions} , 23(2),
367-371.

https://doi-org.ezproxy.tcnj.edu/10.1007/s10530-020-02383-6

Koh, S., Bazely, D.R., Tanentzap, A.J., Voigt, D.R. \& Da Silva, E.
(2010). \emph{Trillium grandiflorum} height is an indicator of
white-tailed deer density at local and regional scales. \emph{Forest
Ecology and Management} , 259(8), 1472-1479.
https://doi.org/10.1016/j.foreco.2010.01.021

Krueger, L.M., Peterson, C.J., Royo, A. \& Carson, W.P. (2009).
Evaluating relationships among tree growth rate, shade tolerance, and
browse tolerance following disturbance in an eastern deciduous
forest.\emph{Canadian Journal of Forest Research} , 39(12), 2460-2469.
https://doi.org/10.1139/X09-155.

Lamb, E.G. \& Cahill Jr, J.F. (2008). When competition does not matter:
Grassland diversity and community composition. \emph{The American
Naturalist} , 171(6), 777-787. https://doi.org/10.1086/587528.

Lefcheck, J.S. \& Freckleton, R. (2016). piecewiseSEM: Piecewise
structural equation modelling in R for ecology, evolution, and
systematics. \emph{Methods in Ecology and Evolution} , 7(5), 573-579.
https://doi.org/10.1111/2041-210X.12512.

Levine, J.M., Vila, M., D'Antonio, C.M., Dukes, J.S., Grigulis, K. \&
Lavorel, S. (2003). Mechanisms underlying the impacts of exotic plant
invasions. \emph{Proceedings of the Royal Society of London, Series B:
Biological Sciences} , 270(1517), 775-781.
https://doi.org/10.1098/rspb.2003.2327.

Liang, S.Y. \& Seagle, S.W. (2002). Browsing and microhabitat effects on
riparian forest woody seedling demography. \emph{Ecology} , 83(1),
212-227.
https://doi.org/10.1890/0012-9658(2002)083{[}0212:BAMEOR{]}2.0.CO;2.

Lindroth, R.L., Donaldson, J.R., Stevens, M.T. \& Gusse, A.C. (2007).
Browse quality in quaking aspen (\emph{Populus tremuloides} ): Effects
of genotype, nutrients, defoliation, and coppicing. \emph{Journal of
Chemical Ecology} , 33(5), 1049-1064.
https://doi.org/10.1007/s10886-007-9281-6.

Massad, T.J., Martins de Moraes, M., Philbin, C., Oliveira, C., Cebrian
Torrejon, G., Fumiko Yamaguchi, L., Jeffrey, C.S., Dyer, L.A., Richards,
L.A. \& Kato, M.J. (2017). Similarity in volatile communities leads to
increased herbivory and greater tropical forest diversity.\emph{Ecology}
, 98(7), 1750-1756. https://doi.org/10.1002/ecy.1875.

McGarvey, J.C., Bourg, N.A., Thompson, J.R., McShea, W.J. \& Shen, X.
(2013). Effects of twenty years of deer exclusion on woody vegetation at
three life-history stages in a Mid-Atlantic temperate deciduous
forest.\emph{Northeastern Naturalist} , 20(3), 451-468.
https://doi.org/10.1656/045.020.0301.

Miranda-Apodaca, J., Mena-Petite, A., Lacuesta, M., Munoz-Rueda, A. \&
Perez-Lopez, U. (2020). A physiological approach to study the
competition ability of the grassland species \emph{Trifolium
pratense}and \emph{Agrostis capillaris} . \emph{Journal of Plant
Physiology} , 254, 153284. https://doi.org/10.1016/j.jplph.2020.153284.

Morrison, J.A. \& Brown, L. (2004). Effect of herbivore exclosure caging
on the invasive plant \emph{Alliaria petiolata} in three southeastern
New York forests. \emph{Bartonia} , 62, 25-43.

Morrison, J.A., Fertitta, M., Zymaris, C., diBartolo, A. \& Akparanta,
C. (2021). Deer and invasive plants in suburban forests: Assessing
variation in deer pressure and herbivory. \emph{Ecoscience} ,
https://doi.org/10.1080/11956860.2021.1958535.

Morse, N.B., Pellissier, P.A., Cianciola, E.N., Brereton, R.L.,
Sullivan, M.M., Shonka, N.K., Wheeler, T.B. \& McDowell, W.H. (2014).
Novel ecosystems in the anthropocene: A revision of the novel ecosystem
concept for pragmatic applications. \emph{Ecology and Society} , 19(2),
12. https://doi.org/10.5751/ES-06192-190212.

Nosko, P. \& Embury, K. (2018). Induction and persistence of
allelochemicals in the foliage of balsam fir seedlings following
simulated browsing. \emph{Plant Ecology} , 219(6), 611-619.
https://doi.org/10.1007/s11258-018-0821-7.

Nyland, R.D., Bashant, A.L., Bohn, K.K. \& Verostek, J.M. (2006).
Interference to hardwood regeneration in northeastern North America:
Ecological characteristics of American beech, striped maple, and
hobblebush. \emph{Northern Journal of Applied Forestry} , 23(1), 53-61.
https://doi.org/10.1093/njaf/23.1.53.

Ohse, B., Hammerbacher, A., Seele, C., Meldau, S., Reichelt, M.,
Ortmann, S. \& Wirth, C. (2017). Salivary cues: Simulated roe deer
browsing induces systemic changes in phytohormones and defence chemistry
in wild\selectlanguage{english}-grown maple and beech saplings. \emph{Functional Ecology} ,
31(2), 340-349. https://doi.org/10.1111/1365-2435.12717.

Orr, J.A., Vinebrooke, R.D., Jackson, M.C., Kroeker, K.J., Kordas, R.L.,
Mantyka-Pringle, C., Van den Brink, Paul J, De Laender, F., Stoks, R. \&
Holmstrup, M. (2020). Towards a unified study of multiple stressors:
Divisions and common goals across research disciplines.\emph{Proceedings
of the Royal Society B} , 287(1926), 20200421.
https://doi.org/10.1098/rspb.2020.0421.

Oswalt, C.M., Oswalt, S.N. \& Clatterbuck, W.K. (2007). Effects
of\emph{Microstegium vimineum} (trin.) A. Camus on native woody species
density and diversity in a productive mixed-hardwood forest in
Tennessee. \emph{Forest Ecology and Management} , 242(2-3), 727-732.
https://doi.org/10.1016/j.foreco.2007.02.008.

Ozsoy, N., Can, A., Yanardag, R. \& Akev, N. (2008). Antioxidant
activity of \emph{Smilax excelsa} L. leaf extracts. \emph{Food
Chemistry} , 110(3), 571-583.
https://doi.org/10.1016/j.foodchem.2008.02.037.

Peters, K., Worrich, A., Weinhold, A., Alka, O., Balcke, G., Birkemeyer,
C., Bruelheide, H., Calf, O.W., Dietz, S., Duhrkop, K., Gaquerel, E.,
Heinig, U., Kucklich, M., Macel, M., Muller, C., Poeschl, Y., Pohnert,
G., Ristok, C., Rodriguez, V.M., Ruttkies, C. et al. (2018). Current
challenges in plant eco-metabolomics. \emph{International Journal of
Molecular Sciences} , 19(5), 1385. https://doi.org/10.3390/ijms19051385.

Peterson, R.A. \& Cavanaugh, J.E. (2020). Ordered quantile
normalization: A semiparametric transformation built for the
cross-validation era. \emph{Journal of Applied Statistics} , 47(13-15),
2312-2327. https://doi.org/10.1080/02664763.2019.1630372.

Pickering, C.M., Hill, W., Newsome, D. \& Leung, Y. (2010). Comparing
hiking, mountain biking and horse riding impacts on vegetation and soils
in Australia and the United States of America. \emph{Journal of
Environmental Management} , 91(3), 551-562.
https://doi.org/10.1016/j.jenvman.2009.09.025.

Pierson, T.G. \& deCalesta, D.S. (2015). Methodology for estimating deer
browsing impact. \emph{Human-Wildlife Interactions} , 9(1), 67.
https://doi.org/10.26077/rbw0-bn49.

Potapov, E., Bedford, A., Bryntesson, F., Cooper, S., Nyholm, B. \&
Robertson, D. (2014). White-tailed deer (\emph{Odocoileus virginianus} )
suburban habitat use along disturbance gradients. \emph{American Midland
Naturalist} , 171(1), 128-138.
https://doi.org/10.1674/0003-0031-171.1.128.

R Core Team (2020). R: A language and environment for statistical
computing, https://www.R-project.org/.

Reddy, A.R., Chaitanya, K.V. \& Vivekanandan, M. (2004). Drought-induced
responses of photosynthesis and antioxidant metabolism in higher
plants.\emph{Journal of Plant Physiology} , 161(11), 1189-1202.
https://doi.org/10.1016/j.jplph.2004.01.013.

Rivas\selectlanguage{english}-Ubach, A., Gargallo\selectlanguage{english}-garriga, A., Sardans, J., Oravec, M.,
Mateu\selectlanguage{english}-Castell, L., Perez\selectlanguage{english}-Trujillo, M., Parella, T., Ogaya, R., Urban, O.
\& Penuelas, J. (2014). Drought enhances folivory by shifting foliar
metabolomes in \emph{Quercus ilex} trees. \emph{New Phytologist} ,
202(3), 11-21. https://doi.org/10.1111/nph.12687.

Rockenbach, A.P., Rizzardi, M.A. \& Hause, B. (2020). Competition at the
soybean V6 stage affects root morphology and biochemical
composition.\emph{Plant Biology} , 22(2), 252-258.
https://doi.org/10.1111/plb.13070.

Russell, F.L., Zippin, D.B. \& Fowler, N.L. (2001). Effects of
white-tailed deer (\emph{Odocoileus virginianus} ) on plants, plant
populations and communities: A review. \emph{American Midland
Naturalist} , 146(1), 1-26.

https://doi.org/10.1674/0003-0031(2001)146{[}0001:EOWTDO{]}2.0.CO;2.

Ruxton, G.D. \& Beauchamp, G. (2008). Time for some a priori thinking
about post hoc testing. \emph{Behavioral Ecology} , 19(3), 690-693.
https://doi.org/10.1093/beheco/arn020.

Ryan, K.G., Swinny, E.E., Winefield, C. \& Markham, K.R. (2001).
Flavonoids and UV photoprotection in \emph{Arabidopsis}
mutants.\emph{Zeitschrift Fur Naturforschung} , 56c, 745-754.
https://doi.org/10.1515/znc-2001-9-1013.

Sampedro, L., Moreira, X. \& Zas, R. (2011). Costs of constitutive and
herbivore\selectlanguage{english}-induced chemical defences in pine trees emerge only under low
nutrient availability. \emph{Journal of Ecology} , 99(3), 818-827.

SAS Institute Inc. (2015). \emph{SAS/STAT(r) 14.1 User's Guide} . SAS
Institute, Inc., Cary, NC.

Schramm, J.W. \& Ehrenfeld, J.G. (2012). Patterns of patch colonization
and expansion in the non-native annual grass \emph{Microstegium
vimineum} (Poaceae). \emph{Rhodora} , 114(957), 1-20.
https://doi.org/10.3119/0035-4902-114.957.1.

Sedio, B.E., Rojas Echeverri, J.C., Boya P, C.A. \& Wright, S.J. (2017).
Sources of variation in foliar secondary chemistry in a tropical forest
tree community. \emph{Ecology} , 98(3), 616-623.
http://dx.doi.org/10.1002/ecy.1689.

Sedio, B.E., Devaney, J.L., Pullen, J., Parker, G.G., Wright, S.J. \&
Parker, J.D. (2020). Chemical novelty facilitates herbivore resistance
and biological invasions in some introduced plant species. \emph{Ecology
and Evolution} , 10(16), 8770-8792. https://doi.org/10.1002/ece3.6575.

Shams Ardekani, M.R., Hajimahmoodi, M., Oveisi, M.R., Sadeghi, N.,
Jannat, B., Ranjbar, A.M., Gholam, N. \& Moridi, T. (2011). Comparative
antioxidant activity and total flavonoid content of Persian pomegranate
(\emph{Punica granatum} L.) cultivars. \emph{Iranian Journal of
Pharmaceutical Research} , 10(3), 519-524.
https://doi.org/10.1016/j.arabjc.2013.10.011.

Skurski, T.C., Rew, L.J. \& Maxwell, B.D. (2014). Mechanisms underlying
nonindigenous plant impacts: A review of recent experimental
research.\emph{Invasive Plant Science and Management} , 7(3), 432-444.
https://doi.org/10.1614/IPSM-D-13-00099.1.

Sokal, R.R. \& Rohlf, F.J. (1981). \emph{Biometry} , 2nd edn. W.H.
Freeman and Company, New York.

Steppuhn, A. \& Baldwin, I.T. (2008). Induced defenses and the
cost-benefit paradigm. \emph{Induced Plant Resistance to Herbivory} (ed
A. Schaller), pp. 61-83. Springer.

Sudnick, M., Brodie, B.S. \& Williams, K.A. (2021). Nature versus
nurture: Structural equation modeling indicates that parental care does
not mitigate consequences of poor environmental conditions in eastern
bluebirds (\emph{Sialia sialis} ). \emph{Ecology and Evolution} ,
11(21), 15237-15248. https://doi.org/10.1002/ece3.8207.

Swihart, R.K. \& Bryant, J.P. (2001). Importance of biogeography and
ontogeny of woody plants in winter herbivory by mammals. \emph{Journal
of Mammalogy} , 82(1), 1-21.
https://doi.org/10.1644/1545-1542(2001)0822.0.CO;2.

Urbanek, R.E. \& Nielsen, C.K. (2013). Influence of landscape factors on
density of suburban white-tailed deer. \emph{Landscape and Urban
Planning} , 114(0), 28-36.
https://doi-org.ezproxy.tcnj.edu/10.1016/j.landurbplan.2013.02.006.

Valkama, E., Koricheva, J., Ossipov, V., Ossipova, S., Haukioja, E. \&
Pihlaja, K. (2005). Delayed induced responses of birch glandular
trichomes and leaf surface lipophilic compounds to mechanical
defoliation and simulated winter browsing. \emph{Oecologia} , 146(3),
385-393. https://doi.org/10.1007/s00442-005-0216-z.

Vila, B., Vourc'h, G., Gillon, D., Martin, J. \& Guibal, F. (2002). Is
escaping deer browse just a matter of time in \emph{Picea sitchensis} ?
A chemical and dendroecological approach. \emph{Trees} , 16(7), 488-496.
http://dx.doi.org/10.1007/s00468-002-0191-5.

Vila, M., Espinar, J.L., Hejda, M., Hulme, P.E., Jaro\selectlanguage{polish}š\selectlanguage{ngerman}ík, V., Maron,
J.L., Pergl, J., Schaffner, U., Sun, Y. \& Py\selectlanguage{polish}š\selectlanguage{english}ek, P. (2011). Ecological
impacts of invasive alien plants: A meta-analysis of their effects on
species, communities and ecosystems. \emph{Ecology Letters} , 14(7),
702-708. https://doi.org/10.1111/j.1461-0248.2011.01628.x.

Viola, D.V., Mordecai, E.A., Jaramillo, A.G., Sistla, S.A., Albertson,
L.K., Gosnell, J.S., Cardinale, B.J. \& Levine, J.M. (2010).
Competition-defense tradeoffs and the maintenance of plant
diversity.\emph{Proceedings of the National Academy of Sciences of the
United States of America} , 107(40), 17217.
https://doi.org/10.1073/pnas.1007745107.

Vuleta, A., Manita\selectlanguage{polish}š\selectlanguage{english}evi\selectlanguage{polish}ć \selectlanguage{english}Jovanovi\selectlanguage{polish}ć, \selectlanguage{english}S. \& Tuci\selectlanguage{polish}ć, \selectlanguage{english}B. (2015). How do plants
cope with oxidative stress in nature? A study on the dwarf bearded iris
(\emph{Iris pumila} ). \emph{Acta Physiologiae Plantarum} , 37(1), 1-12.
https://doi.org/10.1007/s11738-014-1711-9.

Waite, T.A. \& Campbell, L.G. (2006). Controlling the false discovery
rate and increasing statistical power in ecological
studies.\selectlanguage{ngerman}\emph{Écoscience} , 13(4), 439-442.
https://doi.org/10.2980/1195-6860(2006)13{[}439:CTFDRA{]}2.0.CO;2.

Walters, M.B., Farinosi, E.J. \& Willis, J.L. (2020). Deer browsing and
shrub competition set sapling recruitment height and interact with light
to shape recruitment niches for temperate forest tree
species.\emph{Forest Ecology and Management} , 467, 118134.
https://doi.org/10.1016/j.foreco.2020.118134.

Williams, S.C., Denicola, A.J., Almendinger, T. \& Maddock, J. (2013).
Evaluation of organized hunting as a management technique for
overabundant white-tailed deer in suburban landscapes. \emph{Wildlife
Society Bulletin} , 37(1), 137-145. https://doi.org/10.1002/wsb.236.

Wu, J. \& Baldwin, I.T. (2009). Herbivory\selectlanguage{english}-induced signalling in plants:
Perception and action. \emph{Plant, Cell \& Environment} , 32(9),
1161-1174. https://doi.org/10.1111/j.1365-3040.2009.01943.x.

\textbf{Table 1.} Deer pressure-related forest characteristics. All
variables except hunting were measured in 32-40 16
m\textsuperscript{2}plots per forest. Values for shrub cover and species
richness are the mean and SE. All data were from 2012, except percent
browse was for species that were browsed in 2015 (with total sampled
plants in parentheses). The canopy importance values for red + black oak
are shown in parentheses, followed by the ranking of their importance
value (IV) in that forest.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Forest\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Years of hunting\textsuperscript{1}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Percent native shrub cover\textsuperscript{2}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Herb layer native species richness\textsuperscript{2}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\# Plots with red/black oak juveniles in spring,
fall\textsuperscript{3}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Percent browse index\textsuperscript{4}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Baldpate (BAL)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
56 (4)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
22 (0.9)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
18, 17 (IV=33.1, \#3)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0.39\% (out of 520)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Nayfield (NAY)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
27 (4)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
13 (0.5)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
18, 22 (IV=84.7, \#2)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
2.3\% (out of 442)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Herronton (HER)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
17\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
15 (3)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
21 (0.8)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
9, 11 (IV=32.4, \#5)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
2.1\% (out of 280)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Eames (EAM)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
6.2 (3)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
7.9 (0.3)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
6, 0 (IV=16.0, \#5)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
6.3\% (out of 160)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Curlis (CUR)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
2.5 (0.9)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
6.8 (0.4)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
4, 5 (IV=94.0, \#2)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
10\% (out of 228)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Rosedale (ROS)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0.55 (0.4)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
8.7 (0.4)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
2, 1 (IV=29.8, \#4)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
11\% (out of 186)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{1} Hunting history was provided by Hopewell Valley
Friends of Open Space and the Mercer County Parks Department, the owners
and managers of these natural areas.

\textsuperscript{2} Native shrub cover and herb layer native species
richness decrease with deer overabundance (Rawinski 2008). Shrub cover
was measured with a `forest secchi' method (from Michael Van Clef,
Hopewell Valley Friends of Open Space). It quantifies the percent
vertical foliage cover of native woody plants in the deer browse zone,
0.4 m -- 1.4 m from the ground (Pierson \& deCalesta 2015), by a
researcher observing from across the plot a 1 m\textsuperscript{2} board
that was divided into a 4 x 4 grid, and counting the percentage of grid
squares intercepted by native woody plants. This was done in two
perpindicular directions and the values were averaged. Native species
richness was from a spring herb layer census, using the census method
described in the paper; values shown are for the number of species in
the 16 m\textsuperscript{2} plots.

\textsuperscript{3} \emph{Quercus rubra} and/or \emph{Q. velutina} (red
and black oak) were the only preferred deer food species (Wakeland \&
Swihart 2009) that also are common seed-source canopy trees in each of
this study's forests. \emph{Quercus} presence was from spring and fall
censuses. Canopy tree importance values for \emph{Q. rubra} plus\emph{Q.
velutina} were obtained with standard procedures (Brower et al. 1990).

\textsuperscript{4} The presence of tell-tale shredded twig tips
indicated deer browse (Pierson \& deCalesta 2015). The browse index for
each forest consisted of the proportion of browsed individuals in
unfenced plots of five native species that were browsed by deer and
sufficiently common in the forests' understories to use for comparison
between forests: \emph{Carya} spp.\emph{,} \emph{Fagus
grandifolia,Fraxinus pennsylvanica,} \emph{Acer rubrum} , and
\emph{Rubus allegheniensis} .

Table 2. Mixed model results for the effects of fencing
treatment,\emph{Microstegium vimineum} (MIVI) cover, and interactions on
foliar antioxidant capacity (A), phenolics concentration (B), and
flavonoid concentration (C) in juveniles of the tree species
\emph{Fraxinus pennsylvanica} and \emph{Fagus grandifolia} growing in
forests of central New Jersey, USA.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Source of variation}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
F. pennsylvanica\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
F. pennsylvanica\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
F. pennsylvanica\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
F. grandifolia\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
F. grandifolia\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
F. grandifolia\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{DF} \textbf{(num, den)}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{F}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{P}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{DF} \textbf{(num, den)}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{F}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{P}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{A) ANTIOXIDANTS}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
fencing\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1, 140\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.02\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1, 105\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.04\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
MIVI cover category\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 140\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 105\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.05\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
fencing x MIVI cover\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 140\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.40\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 105\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{B) PHENOLICS}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
fencing\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1, 144\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.01\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1, 105\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.46\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
MIVI cover category\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 144\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 105\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.14\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
fencing x MIVI cover\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 144\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 105\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{C) FLAVONOIDS}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
fencing\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1, 144\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1, 97\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.11\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.7\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
MIVI cover category\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 144\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 97\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.08\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
fencing x MIVI cover\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 144\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3, 97\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.2\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 1. Structural equation meta-model (SEMM), a system-wide
hypothesis of theoretical, interconnected drivers of woody plant
chemistry in suburban forests.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 2. Initial structural equation measurement model, based on the
SEMM of Fig. 1 and guided by results from the univariate analyses.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 3. Total antioxidants (A), phenolics (B, C) and flavonoids (D) in
leaves of \emph{Fraxinus pennsylvanica} juveniles in central New Jersey,
USA forests. Plants grew in fenced or unfenced plots (A, C, D) and with
four levels of \emph{Microstegium vimineum} cover (B). Graphs show
least-squares means \emph{+} 95\% CL, backtransformed from
log\textsubscript{10} for antioxidants and phenolics and from square
roots for flavonoids. N for each mean, from right to left: A) 77, 75; B)
84, 35, 24, 9; C and D) 79, 77. Means labeled with different letters in
B were different only at the P=0.09 level, based on adjustment for six
multiple comparisons with the Tukey-Kramer method.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 4. Total antioxidants (A) and flavonoids (B) in leaves
of\emph{Fagus grandifolia} juveniles in central New Jersey, USA forests
growing with four levels of \emph{Microstegium vimineum} cover. Graphs
show least-squares means \emph{+} 95\% CL, backtransformed from
log\textsubscript{10}. N for each mean, from right to left: A) 61, 25,
22, 9; B) 60, 13, 19, 7. Means labeled with different letters in A were
different at P = 0.03 and in B they were different only at P=0.06 (none
vs. 16-65\%) and P=0.07 (\textgreater{}0-1,5\% vs. 16-65\%) level, based
on adjustment for six multiple comparisons with the Tukey-Kramer method.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 5. Total \emph{Fraxinus pennsylvanica} antioxidants (A)
and\emph{Fagus grandifolia} phenolics (B) in leaves of juveniles in
central New Jersey, USA forests growing in fenced or unfenced plots and
with no\emph{Microstegium vimineum} cover or high cover, defined as
12-65\%. Graphs show least-squares means \emph{+} 95\% CL,
backtransformed from log\textsubscript{10}. N for each mean, from right
to left: A) 39, 4, 49, 4; B)33, 6, 28, 3. Based on adjustment for six
multiple comparisons with the Tukey-Kramer method, means labeled in A
with different letters were different at P = 0.005 (fenced/no cover vs.
unfenced/high cover) and P=.03 (unfenced/no cover vs. unfenced/high
cover). In B, they were different at P=0.04 (fenced/no cover vs.
unfenced/high cover).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Figure 6. Fitted structural equation models of drivers of foliar plant
secondary chemistry in juveniles of the trees \emph{Fraxinus
pennsylvanica} and \emph{Fagus grandifolia} , growing in suburban
forests of central New Jersey, USA. Path thickness is proportional to
the values of the standardized path coefficient labels. All paths are
significant at P \textless{} 0.05.

\textbf{DATA ACCESSIBILITY STATEMENT}

Data used for this paper will be available from the Dryad Digital
Repository.

\textbf{COMPETING INTERESTS STATEMENT}

There are no competing interests.

\textbf{AUTHORS' CONTRIBUTIONS}

Morrison and Roche formulated and designed the study, planned the
statistical analyses, interpreted the results, outlined the introduction
and discussion, edited all drafts, and responded to review comments.
Morrison was responsible for plot installation and treatments,
collection of leaf samples and field data, leading the statistical
analysis, and writing most sections of the paper. Roche participated in
statistical analysis and interpretation and wrote sections of the paper.
Veatch-Blohm conducted the chemical analyses and wrote the methods for
that part. All authors gave final approval for publication.

\textbf{ACKNOWLEDGEMENTS}

This research was supported by the National Science Foundation (USA;
NSF-DEB 1257833; PI, JA Morrison) and Academic Affairs at The College of
New Jersey, for reassigned time to Morrison through the Support for
Scholarly Activity committee and the Barbara Meyers Pelson `59 Chair in
Faculty-Student Engagement. Thanks to many TCNJ undergraduate students
who contributed to this study, especially the summer/fall 2015 crew:
Scott Eckert Ryan Goolic, Marisa Grillo, Elizabeth Matthews, Cindy
Timko, Gio Tomat-Kelly Mitchell Vaughn, Jen Wells, Ryan Goolic. Thanks
also to an associate editor and two anonymous reviewers for valuable
critiques, which greatly improved the manuscript.

\selectlanguage{english}
\FloatBarrier
\end{document}

