1 | 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 (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, Microstegium vimineum (Trin.)
A. Camus (Japanese stiltgrass), and the other variables in the model.
2 | MATERIALS AND METHODS
2.1 | Study sites and species
Experimental plots (16 m2) 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 (Acer rubrum , A.
saccharum ), oaks (Quercus rubrum , Q. velutina , Q.
alba , Q. prinus ), hickories (Carya spp.), tulip poplar
(Liriodendron tulipifera ), American beech (Fagus
grandifolia ), green ash (Fraxinus pennsylvanica ), sour gum
(Nyssa sylvatica ), and sweet gum (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/km2 , 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 Fagus grandifolia Ehrh. (American beech) andFraxinus 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 .
2.2 | Experimental design
In each forest, 32-40 16 m2 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 (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.
2.3 | 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< 20 cm, compound leaves with stem height
> 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° C for three days, in preparation for chemical analysis.
2.4 | 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 µL of FRAP reagent was added to 30 µL
of sample and 90 µL 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 µmole 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 µL of the sample was mixed with 60 µL of Na2CO3, 900 µL of
ultrapure water and 20 µL 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 µL of sample was added to 400 µL
of ultrapure water. 30 µL of NaNO2 was added and allowed to sit for 5
minutes, followed by addition of 30 µL of AlCl3. After 1 minute, 400 µL
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.
2.5 | 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 <1%, 1-10%, 11-20%, 21-30%, etc. (in 10%
intervals up to 100%) in 0.25 m2 quadrat frames,
which were dropped without looking into each 1 m2section of the 16 m2 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: Carya spp.,F. grandifolia, Fraxinus pennsylvanica, Acer
rubrum , and 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.
2.6 | 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 log10 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’) andMicrostegium 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 <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< 0.10), we still
reported it to avoid ignoring a potentially causal relationship and
conducted the planned multiple comparisons, following .
2.7 | 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
log10 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.
3 | RESULTS
3.1 | Mixed models
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).
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
>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 >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).
3.2 | Structural equation models
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.
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 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.
4 | DISCUSSION
4.1 | 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 < 0.09) in the univariate models
between the highest stiltgrass cover level and the zero or
>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.
4.2 | 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 Fagus and 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.
4.3 | 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 .
4.4 | 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 M. vimineum , which invaded
some plots much more readily than others.
4.5 | 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.
4.6 | 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 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 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
(R2 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, .
5 | 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.