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\begin{document}

\title{Deconstructing white-tailed deer forage selection: the role of plant
chemistry and volatile scent signaling}



\author[1]{Matthew Wuensch}%
\author[1]{Andrew Eagar}%
\author[1]{Mahinda Gangoda}%
\author[1]{David Ward}%
\affil[1]{Kent State University}%


\vspace{-1em}



  
  \date{June 16, 2024}


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\selectlanguage{english}
\begin{abstract}
When foraging optimally, mammalian herbivores should select food items
that confer the greatest nutritional benefits (such as crude protein and
non-structural carbohydrates) and impose minimal to no costs. Many
plants defend themselves from herbivores by producing plant secondary
metabolites (PSMs) that defend against herbivores. PSMs, such as
tannins, deter herbivores by providing post-ingestive feedback cues that
the forager experiences after a plant is consumed, and are therefore
difficult for herbivores to avoid altogether. However, some PSMs such as
terpenes are also volatile organic compounds (VOCs) that become gaseous
at ambient temperatures and can be detected by herbivores via scent
prior to ingestion. PSMs that can be detected prior to ingestion prevent
herbivores from consuming a plant to begin with, and enable herbivores
to avoid incurring costs while foraging. Our objective was to discern if
the foraging preferences of white-tailed deer, a near ubiquitous
herbivore in North America, were influenced by the nutritional (crude
protein and non-structural carbohydrates) or anti-nutritional (tannin
and polyphenol) constituents of trees during summer and winter. We also
determined if the VOCs that were emitted by trees influenced the forage
selection of deer by providing pre-ingestive cues. During summer, we
found that deer preferred trees with a high non-structural carbohydrate
content. During winter, deer preferred trees with a low crude protein
content and a high tannin content. After sniffing trees that emitted a
large proportion of monoterpenes and sesquiterpenes, deer were averse to
begin foraging. In the few instances when deer foraged trees with high
terpene contents, they consumed less than they did from low terpene
trees. Our results suggest that VOCs, a long overlooked aspect of
foraging ecology, may play a larger role in determining which plant
species herbivores will avoid than the nutritional and anti-nutritional
constituents of plants.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Forage selection by large mammalian herbivores is dictated by the
necessity to maximize the intake of nutritionally beneficial plant
constituents (e.g., crude protein or carbohydrates) while minimizing the
intake of plant compounds that can be toxic or disadvantageous to
consume (e.g., tannins, polyphenols and/or, terpenoids; (Freeland and
Janzen 1974, Pyke et al. 1977, McArthur et al. 2014). In the diets of
many mammalian herbivores, crude protein is limiting because most
herbaceous food items generally have a low crude protein content
(Owen-Smith and Novellie 1982, Hobbs and Swift 1985). Therefore, it is
typically expected that herbivores select plant species with a high
protein content when foraging to meet their dietary demands. Likewise,
plant non-structural carbohydrates (e.g., sugars and starches) provide
herbivores with energy and are readily digestible (Van Soest 2018).
Thus, plants that have a high quantity of crude protein or carbohydrates
are broadly considered to be beneficial for herbivores to consume (Van
Soest 2018). Plant species that are highly desirable as forage
frequently have developed either physical or chemical defenses to
protect themselves from herbivory (Robbins et al. 1987, Windels and
Hewitt 2011, Gong and Zhang 2014). This often leads to situations where
the plant that possesses the greatest gross benefits of nutritional
compounds do not result in the greatest net benefit for herbivores
(i.e., a plant with high protein that is well defended may be less
advantageous to consume than a plant with moderate protein and no
defenses). Herbivores must therefore be selective in order to maximize
benefits while reducing costs when foraging, but the myriad of defenses
that plants manifest makes it difficult to predict what plant species
will be selected when browsing.

Plant secondary metabolites (PSMs) are chemical compounds that do not
provide utility in plant development or growth, but instead serve the
plant by deterring herbivores (Pe\selectlanguage{ngerman}ñuelas and Llusià 2004, Iason 2005,
DeGabriel et al. 2014). Tannins, for example, are an often-studied
polyphenolic PSM found in a broad array of plant species (Hagerman et
al. 1992, Wallis et al. 2010, Schmitt et al. 2020b). Tannins deter
herbivores by binding to available proteins in plant matter, which
decreases the net benefit of a plant to an herbivore by reducing the
nutrients that are acquired when consuming the plant (Hagerman et al.
1992, Shrader et al. 2012). Moreover, when ingested at high quantities,
tannins can become toxic to herbivores (Lindroth and Batzli 1984,
Barbehenn and Constabel 2011). Tannins may not always deter mammalian
herbivores, however, due to the production of proline-rich
tannin-binding salivary proteins by some mammalian herbivores, which
increases the availability of protein to the forager (Shimada 2006,
Barbehenn and Constabel 2011, Ward et al. 2020). Due to the complex
interactions between mammalian salivary compounds and tannins, it can be
beneficial to also measure the total polyphenol concentration that is
present within a plant to understand the effects of other PSMs on
herbivore preference (Wigley et al. 2015, Singh et al. 2021). In order
for herbivores to determine if a food item should be selected or
avoided, they must rely on physiological mechanisms that can detect the
presence of beneficial nutrients or PSMs in a food item (Provenza 1995,
Foley et al. 1999, Schmitt et al. 2020a).

One mechanism through which disadvantageous plant compounds can be
detected by herbivores is in the form of a post-ingestive feedback such
as malnourishment or malaise (Provenza 1995, Foley et al. 1999). In
fact, many PSMs, specifically tannins, can only be determined by an
herbivore after ingestion has taken place (Provenza 1995, Schmitt et al.
2020). Such post-ingestive feedbacks may not be the most efficient
mechanism through which herbivores may assess a plant because the
associated costs of consuming a plant must be incurred first and, in
some extreme instances, may be irreversible or fatal. Additionally,
future avoidance of plants that provide post-ingestive feedback is
contingent upon the ability of a forager to learn that there is a cost
associated with a particular plant (Iason and Villalba 2006). Cues that
can be detected prior to ingestion (i.e., pre-ingestive cues) provide a
forager with the means to detect costs that are associated with a plant
prior to consumption and may enable herbivores to avoid costly foraging
items altogether (Bedoya-Pérez et al. 2014, Schmitt et al. 2020a).
Likewise, pre-ingestive cues are also more advantageous for plants than
post-ingestive feedbacks, because pre-ingestive cues have the capacity
to prevent herbivory prior to any biomass being removed from the plant
(Schmitt et al. 2018). One common mechanism through which pre-ingestive
cues can be determined by many mammalian foragers is through scent cues
that are provided by plants (Stutz et al. 2016, Schmitt et al. 2018,
Orlando et al. 2020). Scent cues can be used by an herbivore to
associate a plant with previously incurred post-ingestive costs (such as
tannins), or they can provide foragers with cues that directly indicates
the presence of some PSMs that can be disadvantageous to consume
(Bedoya-Pérez et al. 2014, Schmitt et al. 2020a). In environments that
are highly heterogeneous or abundant in foraging opportunities,
pre-ingestive cues may aid foragers as they navigate the landscape and
enable them to discern which food items should be selected. However,
there are currently few studies that explore this in mammalian
herbivores and they are limited to only a few taxa (Stutz et al. 2016,
Finnerty et al. 2017, Schmitt et al. 2018, 2020a).

In recent years, plant-emitted volatile organic compounds (VOCs) that
serve as pre-ingestive cues to foraging herbivores has been a topic of
growing interest (Bedoya-Pérez et al. 2014, Finnerty et al. 2017, Skopec
et al. 2019, Schmitt et al. 2020a). Plant-emitted VOCs are gaseous
compounds that vary broadly in their chemical form and ecological
function (Peñuelas and Llusià 2004, Holopainen and Gershenzon 2010).
Plant-emitted VOCs play a role in plant-plant communication, serve as
deterrents against herbivores, and attract organisms to perform services
for the plant (Baldwin et al. 2006, Holopainen and Gershenzon 2010,
Burkle and Runyon 2019). To perform such a variety of functions, the
VOCs that plants emit are highly chemically diverse (Peñuelas and Llusià
2004, Holopainen and Gershenzon 2010). VOCs can deter herbivores by
providing a pre-ingestive cue to herbivores that a plant possesses
chemical defenses (Elliott and Loudon 1987, Schmitt et al. 2018). In
some instances, compounds that are PSMs are volatile (meaning they
become gaseous at ambient temperatures), and therefore herbivores can
directly detect the presence of specific PSMs prior to ingestion
(Bedoya-Pérez et al. 2014, Schmitt et al. 2020a). For example, one class
of volatile PSMs that deters herbivores is terpenoids (typically
monoterpenes and sesquiterpenes), which can become toxic if consumed in
large quantities (Peñuelas and Llusià 2004, McLean and Duncan 2006).
However, it is worth noting that not all PSMs can become volatile.
Compounds such as tannins cannot be detected via scent because they will
not evaporate at ambient environmental temperatures (Schoonhoven et al.
2005) . Thus, it is possible that herbivores may use VOCs that are
frequently associated with non-volatile PSMs to provide pre-ingestive
cues. However there are currently limited studies exploring this
(Schmitt et al. 2020a).

White-tailed deer (\emph{Odocoileus virginianus} , henceforth referred
to as ``deer'') are one of the most widespread and abundant large
herbivores in North America (Waller and Alverson 1997, Côté et al.
2004). Throughout much of their range, deer populations persist at
densities that greatly exceed historical estimates (McCabe and McCabe
1997). At these high densities, the foraging habits of white-tailed deer
can have deleterious effects on the function and structure of forest
ecosystems. For example, by over-browsing on tree species that are
preferred forage, deer decrease the recruitment and limit the
regeneration of selected tree species (Tilghman 1989, Rooney and Waller
2003, Bradshaw and Waller 2016). The foraging habits of deer also
drastically alter forest understory communities, by decreasing the
diversity of forbs and herbs which can lead to an increase of abundance
in less desired forages such as grasses, sedges and ferns (Rooney 2001,
Goetsch et al. 2011). These changes in plant community structure have
cascading effects on forest dwelling vertebrates and invertebrates that
can be long-lasting or permanent if deer populations are allowed to
persist at high densities for long durations (Bressette et al. 2012,
Rushing et al. 2020).

Reducing deer population densities has proven to be a complex task for
managing agencies, and a loss of forest understories continues to be an
issue of great concern. As a result, many studies regarding the forage
selection and preference of white-tailed deer have been conducted
(Vangilder et al. 1982, Swihart and Picone 1998, Sauvé and Côté 2007,
Dykes et al. 2020). Studies regarding the forage selection of deer have
focused on \emph{in situ} browse measurements to ascertain which species
deer are selecting in the wild (Castleberry et al. 1999, Morrison et al.
2002) as well as experimental studies that measured the nutritional and
anti-nutritional constituents of forage items that deer prefer and avoid
(Dostaler et al. 2011, Dykes et al. 2020). While many of these studies
verify that deer forage preferentially on some plants, the species that
deer prefer are not universal, as deer preference can vary regionally
and may be influenced by association effects of neighboring plants
(Champagne et al. 2018). Potentially confounding is the influence that
tannins and other PSMs may have on cervid forage selection because deer
species have been shown to produce proline-rich salivary compounds that
bind to tannins (Austin et al. 1989, Robbins et al. 1991). Measuring
tannins alone is therefore inadequate to fully assess deer browse
preference, and measuring a broader array of PSMs in relation to
herbivore selection may provide more insight.

Our objective in this study was to discern if white-tailed deer
preferentially foraged upon or avoided trees depending on the chemical
composition and VOC profiles. Specifically, we wanted to ascertain the
influence of plant nutrients (crude protein concentration and
non-structural carbohydrates) and PSMs (tannin concentration and total
polyphenol concentration) on deer preference. Additionally, we sought to
determine how plant VOCs influence deer forage selection. Terpenes are a
class of VOCs that can be detected by herbivores via scent (Stutz et al.
2016, Schmitt et al. 2020a). Moreover, terpenes serve as PSMs which
other cervids have been found to avoid when foraging (Duncan et al.
1994, Vourc'h et al. 2002). Therefore, we hypothesize that white-tailed
deer will use VOCs as pre-ingestive cues to assist in forage selection
and will avoid plants that emit a large quantity of VOCs, especially
terpenoids. Because food quality and availability vary among seasons
(Baldwin et al. 1987, Sauter and van Cleve 1994), as do deer metabolic
requirements (Moen 1978), we measured deer foraging preferences during
summer and winter. We expected that during summer (when food is
abundant) deer would select for plant species that conferred the
greatest benefit:cost ratio (i.e., high crude protein to low
polyphenols), but during winter (when food is scarce and deer metabolic
demand is high), deer would select for plants with the highest overall
benefits regardless of cost (high crude protein and non-structural
carbohydrates).

\textbf{METHODS}

All methods involving the use of white-tailed deer were approved by the
Pennsylvania State University Institutional Animal Care and Use
Committee (protocol number: PROTO202001387).

\emph{Cafeteria-style preference study}

We assessed the foraging preference of white-tailed deer via
cafeteria-style preference studies at the Pennsylvania State Deer
Research Center, State College, Pennsylvania, USA (40.79° N, 77.86° W).
We conducted cafeteria studies during a summer sampling period
(07/27/2020 -- 08/02/2020) and a winter sampling period (01/14/2021 --
01/20/2021) to account for how phenological shifts in tree species may
affect deer preference. During both sampling periods, we performed seven
separate feeding trials that lasted for approximately 21 h. The
experimental trials during both sampling seasons took place in a 0.4 ha
pen that was seeded with mixed grasses and clovers. In the pen, there
were no understory tree species present for white-tailed deer to forage
upon. Mature trees in the pen were a mix of white oak (\emph{Quercus
alba} ) and white pine (\emph{Pinus strobus} ), none of which possessed
any available forage at or below the browse line (approximately 2.5 m).

Prior to beginning each experimental trial, we placed eight juvenile
trees along the perimeter of a 3 x 3 m square in the center of the pen.
We used five species of deciduous trees and three species of conifers.
The eight tree species that we included were selected due to their
relative commonness throughout the Midwest region of the U.S. The
deciduous species that we included were: American elm (\emph{Ulmus
americana} ), black willow (\emph{Salix nigra} ), silver maple
(\emph{Acer saccharinum} ), white ash (\emph{Fraxinus americana} ), and
white oak (\emph{Quercus alba} ). The conifer species were: eastern
hemlock (\emph{Tsuga canadensis} ), eastern redcedar (\emph{Juniperus
virginiana} ), and white pine (\emph{Pinus strobus} ). During summer
sampling, all trees possessed leafy biomass, and during winter our
deciduous trees senesced and only possessed woody biomass for deer to
browse from. The position of each tree along the perimeter of the square
was determined randomly each day, to spatially account for the
undetermined direction that deer may approach the square from. All trees
were reared in a controlled greenhouse at Kent State University, Kent,
Ohio, USA (41.15° N, 81.34° W), and remained potted during the entire
experimental trial. After each foraging trial, we replaced each tree
with another individual of the same species.

During each trial, we released two adult (\selectlanguage{english}[?] 2 years old), female
white-tailed deer into the 0.4 ha pen at the same time. We decided to
use two female deer in each foraging trial because we found that when
females were released into the pen on their own during preliminary
trials at the facility, they showed signs of stress and were reluctant
to forage. Individual females were not used twice during the same
sampling season, and we avoided using pairs that were siblings or a
mother and daughter. Once released into the pen, deer had access to
water and their normal supplemental diet of protein pellets. We
monitored the 3 m x 3 m square of trees via two Lorex 4 K video systems
that were constantly left operating. Whenever a deer encountered a tree
(i.e., entered an area within 1 m), we recorded both the sniffing and
foraging behavior of the individual, along with tree species and
location. We recorded the duration that an individual spent sniffing a
plant so that we could discern whether deer were using scent cues
provided by the trees to inform foraging decisions and recorded the
number of bites that deer removed from each tree to quantify foraging
effort.

\emph{Leaf chemical analyses of nutritional and anti-nutritional
factors}

After we completed experimental trials, we removed leaf and small twig
biomass from each tree. During winter, we gathered leaf and twig biomass
from trees that had not defoliated, and only gathered twig biomass from
defoliated trees. We dried all plant matter for 72 h in a drying oven
set to 60 degC. The dried plant biomass was then ground in a Wiley mill
through a 2 mm sieve. We derived the crude protein content (6.25 x
mineral N) of each sample by measuring the nitrogen content (g N per g
dry matter) via a Rapid N Exceed\selectlanguage{english}\textsuperscript{?} nitrogen analyzer.
To measure non-structural carbohydrates, we used a modified colorimetric
analysis of sugars and starches (Fournier, 2001, modified by Tomlinson
et al. 2013). We measured total polyphenols through a modified version
of the Prussian blue assay (Price and Butler 1977, modified by Graham
1992) and total tannins were measured by radial diffusion assay
(Hagerman 1987, Graca and Barlocher 2020). There is no standardized unit
by which polyphenols or tannins can be measured via these methods. Thus,
we compared the results of each assay to a known value of a standardized
polyphenol (gallic acid equivalents/GAE in Prussian blue assay; Graham
1992) and tannin (tannic acid equivalents/TAE in radial diffusion assay;
(Hagerman 1987, Graca and Barlocher 2020).

\emph{Volatile organic compound collection and measurement}

We collected volatile organic compounds (VOCs) via dynamic
headspace-analysis extraction (Tholl et al. 2006) from all eight of our
species only during summer months. Due to logistic constraints, we
collected VOCs the summer after we conducted the cafeteria study. All
trees that we collected VOCs from were individuals that were used in the
cafeteria experiment, and VOCs were collected as close to the same
Julian date as possible. Thus, the VOCs we collected from the trees
should be accurate representations of the typical VOC profiles that deer
were exposed to during the cafeteria trials. We conducted headspace
analysis by placing a polyacetate oven bag over a 30 cm x 30 cm section
of leaves from a single tree. We passed both positive and negative
airflow through the bag at a flow rate of 215 mL/min for 6 hours. At the
start of the positive pressure system, we placed an air filter packed
with 100 mg of activated charcoal to remove moisture and ambient VOCs
from the air inflow. To capture VOCs, we placed an Orbo 6 mm diameter
tube, that contained two beds (100 mg and 50 mg) that were packed with
Tenax TA 60/80 mesh, at the end of the negative pressure system.

To extract VOCs from the Orbo 6 mm tube, we eluted the filters by
injecting 1 ml of hexane through the filter, and then purged the filter
by passing nitrogen through it. We concentrated the VOCs within the
hexane by reducing hexane volume to approximately 200 \selectlanguage{greek}μ\selectlanguage{english}l via evaporation
(spun in a centrifuge for 1 h at 3,000 rpm at 35 \selectlanguage{ngerman}°C). We collected four
samples from different trees of each species (except for white oak and
white ash which we were only able to collect three samples from), and
four blank samples with no tree included so that we can account for
ambient VOCs and VOCs that were emitted by the equipment.

We analyzed samples with a Thermo Focus gas chromatograph (GC; Thermo
Fisher Scientific Inc.) coupled to a Thermo DSQ mass spectrometer (MS;
Thermo Fisher Scientific Inc.). We used a midpolar (Restek Rxi-624Sil)
column. We injected 2 \selectlanguage{greek}μ\selectlanguage{english}l of aqueous sample into the GC-MS. The flow of
helium carrier gas was 1 ml min\textsuperscript{-1}. The injector inlet
temperature was set to 250 \selectlanguage{ngerman}°C in splitless mode with splitless time of 1
min. The GC oven temperature was initially set to 40° C with a hold time
of 2 min, and then ramped to 275 °C at a rate of 10 °C
min\textsuperscript{-1} and then held for 10 min. We identified VOCs
using the Xcalibur 1.4 SR1 software and the NIST mass spectral search
program (version 2.0). When possible, we verified compounds by comparing
retention times to known standards. Prior to analyses, we omitted
compounds from our samples that were found in similar quantities to
those we found in the control samples, as we considered these to be
contaminants and not VOCs that were produced by the trees. To account
for inconsistencies that may have occurred during VOC collection (e.g.,
differences in leaf area between samples or hexane retained when eluting
filters), we converted the peak areas of each compound to proportions of
total VOC peak area captured in the sample.

\textbf{Statistical analyses}

\emph{Deer foraging preference}

All statistical analyses were done in R version 4.0.2 (R Core Team
2020). To quantify deer preference, we calculated Manly's selection
ratio with a type I design, meaning that we calculated preference for
the entire population, or rather, foraging data for all deer used during
the feeding trials was pooled during each season (Manly et al. 2007).
Manly's selection ratio is \(w_{i}=\ \frac{o_{i}}{p_{i}}\), in
which\(w_{\text{i\ }}\)= forage ratio of species \emph{i} ,
\(o_{i}\) = proportion or percentage of species \emph{i} in the
diet, and \(p_{i}\)= proportion or percentage of species
\emph{i} available in the environment. We defined \(o_{i}\) as
the number of bites the individual deer removed from a tree during a
foraging trial and \(p_{i}\) was defined as the mean height of
tree species \emph{i} . We calculated Bonferroni-corrected 95\%
confidence limits (CL) for each \(w_{i}\)(Manly et al. 2007).
Preferred species were defined as those in which the lower Bonferroni CL
was greater than 1, and avoided species were defined as those in which
the upper Bonferroni CL was less than 1. If either of the Bonferroni CLs
intersected 1 for a species, we defined preference as neutral.

In addition to the above, we used a metric that enabled us to discern if
sniffing behavior informed the decision of whether a deer would forage
or not. To do this, we calculated an acceptability index (Schmitt et al.
2020a). The acceptability index was calculated by simply dividing the
number of encounters in which deer sniffed a tree and then foraged from
it (foraging encounters) by the total number of encounters in which a
deer sniffed a tree (sniffing encounters). Acceptability index scores
above 0.5 were classified as accepted ratios between 0.25 and 0.5 were
considered neutral, and ratios below 0.25 were classified as averse.

\emph{Analysis of tree wet chemistry}

Using the package \emph{vegan} (Oksanen et al. 2013), we used a
distance-based redundancy analysis (dbRDA), a multivariate ordination,
to test for a relationship between nutritional and anti-nutritional
compounds and tree species (Legendre and Anderson 1999, McArdle and
Anderson 2001, Borcard et al. 2018). This allowed us to determine if the
food items that we offered to deer were nutritionally distinct from one
another. We performed the distance-based redundancy analysis (dbRDA) on
a transformed Bray-Curtis dissimilarity matrix (an abundance-based
dissimilarity metric) instead of on raw nutrient data (McArdle and
Anderson 2001, Borcard et al. 2018). Prior to performing the dbRDA, we
did not perform any normalization of the data because dbRDA implements
nonparametric permutation methods that do not rely on assumptions of
multivariate normality. We constructed two separate dbRDA models, one
for summer and one for winter, to account for changes in plant phenology
that will affect tree chemistry.

Upon finding a significant difference (p \textless{} 0.001; figure 2)
among the nutritional constituents of our tree species, we used
generalized mixed-effect models in the package \emph{lme4} (Bates et al.
2015) to discern if deer preference (defined by Manly's selection ratio)
was influenced by nutritional/anti-nutritional constituents. We
converted the preference categories to numeric values for analysis via
generalized mixed-effects models (0 = avoided, 0.5 = neutral, and 1 =
preferred). For both summer and winter, we fit preference as a function
of the additive relationship of crude protein, TNC, tannin, and total
polyphenols. In each model, we included tree species as a random effect
to account for autocorrelation of nutritional/anti-nutritional compounds
that would occur among trees of the same species within a preference
category. We fit both models with a binomial error distribution and a
logit link function to account for our response variable (preference)
being proportional. We checked for collinearity among the covariates in
our models by calculating variance inflation factors (VIF). No VIF
values were greater than 10, therefore, there was no need to remove any
covariates from our models based on collinear associations.

\emph{VOC analysis}

Similar to the analysis of wet chemistry, we used a dbRDA to test for
the relationship between tree species and VOCs that were emitted by the
trees. We performed the dbRDA on Bray-Curtis dissimilarity scores. The
dbRDA enabled us to discern whether VOCs profiles differed among tree
species included in our experiment.

Upon finding a significant difference (p \textless{} 0.001, figure 3)
among VOC profiles in relation to tree species, we performed a
Spearman's rank correlation in which we examined the proportional peak
area of each VOC within a sample in relation to deer acceptability index
scores. We identified the direction of the correlation of each compound
via the correlation coefficients, and calculated significance to
determine which compounds were correlated with deer acceptability or
aversion after sniffing events occurred. The Spearman correlation
enabled us to measure if deer were detecting specific VOCs, or classes
of VOCs via scent and using this information to aid in their decision on
whether or not they should forage.

Because some VOCs are also PSMs that can be disadvantageous for
herbivores to consume, we also performed a Spearman's rank correlation
for VOCs in relation to deer foraging preference (Manly's selection
ratio). We conducted the preference correlations in the same manner as
the scent correlations. The preference correlations allowed us to
determine if specific VOCs, or classes of VOCs, influenced how much deer
were willing to forage from a tree.

\textbf{Results}

\emph{Deer foraging preference}

During summer, we found that white-tailed deer preferred to forage on
American elm and black willow (\(w_{\text{elm}}\ \) =
3.22,\(w_{\text{willow}}=\ 2.06\); figure 1). Deer exhibited neutral preference of
white ash and white oak trees. Eastern hemlock, eastern redcedar, silver
maple and white pine were all species that deer avoided foraging from.
Of the species that were avoided, neither eastern redcedar nor white
pine were foraged on a single time during the summer.

During the winter, deer selection of black willow remained similar to
summer and continued to be preferred by deer (\(w_{\text{willow}}=\ 3.08\))
whereas American elm changed to be neutrally selected, along with
eastern hemlock and silver maple. White oak changed from a neutrally
selected species during the summer to a preferred species during the
winter \((w_{\text{oak}}=\ 1.66\)) and white ash became an avoided species
alongside eastern redcedar and white pine. Unlike summer, each tree
species was foraged on multiple times by deer.

According to the acceptability index (summer observations only because
we did not measure VOCs in winter), deer found black willow, white ash
and American elm to be acceptable species to forage on after a sniffing
event occurred (acceptability index score \textgreater{} 0.50, table 1).
Of these three species, black willow and American elm were both
preferred species for deer to consume during summer cafeteria trials
according to Manly's selection ratio. White oak and silver maple both
were species that deer found to be of neutral acceptability after
sniffing (acceptability index score \textgreater{} 0.25 and \textless{}
0.50). Deer exhibited aversion to eastern hemlock, eastern redcedar, and
white pine after sniffing events (acceptability index score \textless{}
0.25). White-tailed deer avoided consuming these trees during the summer
according to Manly's selection ratio as well.

\emph{Plant wet chemistry}

The dbRDA that we conducted for summer (figure 2, panel A; model
adjusted R\textsuperscript{2} = 0.65) revealed a significant
relationship between tree chemical constituents (crude protein,
non-structural carbohydrates, tannin and total polyphenols) and tree
species (p \textless{} 0.001, F = 15.031, n = 999 permutations). The
constraining factors of the model (tree species) explained 69.12\% of
the variance in Bray-Curtis distances, whereas 30.88\% of the variance
was unconstrained, meaning it cannot be explained by tree species
identity alone. The dbRDA that we conducted for winter forage trials
(figure 2, panel B; model adjusted R\textsuperscript{2} = 0.60) also
revealed a significant relationship between nutritional/anti-nutritional
compounds and tree species (p \textless{} 0.001, F = 12.524, n = 999
permutations). The constraining factors of the model explained 65.59\%
of the variance in Bray-Curtis distances, and 34.41\% of the variance
was unconstrained.

During summer, the dbRDA scores of the preferred species (black willow
and American elm) were closely associated on the first three dbRDA axes.
Additionally, the dbRDA score of non-structural carbohydrates remained
closely associated with the scores of these two species on the first
three dbRDA axes as well. We also found that the nutrient scores of our
three coniferous tree species were clustered on the first three dbRDA
axes during summer. However, none of the nutritional/anti-nutritional
compounds that we measured were consistently correlated with the dbRDA
scores of these species. We also found that the dbRDA scores of white
ash (a neutrally preferred species) were closely located to the dbRDA
scores of crude protein on the first three dbRDA axes.White oak and
silver maple dbRDA scores were closely related on the first two dbRDA
axes, and were closely associated with tannins and polyphenols on these
axes too.

During winter, we found less separation among the dbRDA scores of all
tree species. We also found that crude protein, tannin, and total
polyphenol scores were consistently grouped together on the first three
dbRDA axes. Black willow was still preferred during winter, but the
dbRDA scores were no longer associated with non-structural carbohydrate
scores. White oak became a preferred species of deer during winter, and
was closely associated with tannins on all three dbRDA axes.

The binomial regression to examine deer preference in relation to tree
nutritional/anti-nutritional compounds revealed that non-structural
carbohydrates were preferred during summer foraging trials (table 2). We
did not find that any other chemical constituents significantly
influenced deer selection during summer. During the winter trials, both
crude protein and tannin content influenced white-tailed deer forage
selection (table 2). Notably, we found that deer preferred trees with a
low crude protein content and high tannin content and avoided trees that
had high crude protein content and low tannin content in the winter.

\emph{VOC analysis}

The dbRDA on VOC emissions (model adjusted R\textsuperscript{2} = 0.60;
figure 3) revealed that there was a significant relationship between VOC
emissions and tree species (p \textless{} 0.001, F = 7.329, n = 999
permutations). The constraining factors of the VOC dbRDA explained
69.98\% of the total variance in the Bray-Curtis distances, and 30.02\%
of the variance was unconstrained. The three conifer species (all of
which deer were averse to after sniffing) were separate from the
deciduous trees on the first and third dbRDA axes. There was little
separation among any of the deciduous trees on the first two dbRDA axes.
However, on the third axis, there was some differentiation among the
deciduous trees, especially in white oak (a neutrally accepted species).

We found that 33 VOCs were significantly correlated with white-tailed
deer acceptability index scores (table 3), indicating that these
compounds may provide deer with pre-ingestive cues prior to foraging. Of
the 33 significantly correlated compounds, 7 compounds (cyclopentanone,
3-hexen-1-ol, cis-3-hexenyl acetate, n-caprylaldehyde, methyl
salicylate, {[}bicyclopentyl{]}-2-one and unknown
{[}m/z{]}84.57.56.55.83) were of greater proportion in accepted trees
and only 1 compound (\selectlanguage{greek}α-\selectlanguage{english}farnesene) was of the greater proportion in
neutrally selected trees. The remaining 25 significant VOCs were of the
greatest proportions in trees that deer were averse to after sniffing.
Of these 25 compounds, 12 were monoterpenes (tricyclene, \selectlanguage{greek}α-\selectlanguage{english}pinene,
camphene, \selectlanguage{greek}β-\selectlanguage{english}phellandrene, \selectlanguage{greek}β-\selectlanguage{english}pinene, (-)-\selectlanguage{greek}α-\selectlanguage{english}thujene, 3-carene, D-limonene,
\selectlanguage{greek}γ-\selectlanguage{english}terpinene, terpinolene, (-)-alcanfor, borneol) and 4 were
sesquiterpenes (\selectlanguage{greek}β-\selectlanguage{english}elemene, caryophyllene, humulene, germacrene D-4-ol).
Terpenes such as these have been increasingly shown to be associated
with herbivore avoidance in recent years (Vourc'h et al. 2002,
Bedoya-P\selectlanguage{ngerman}érez et al. 2014, Stutz et al. 2016, Schmitt et al. 2020a).

In addition to identifying which compounds provided deer with
pre-ingestive cues (according to significant correlations with
acceptability index scores), we also were able to discern 22 VOCs that
were significantly correlated with how much deer were willing to forage
from different tree species (Manly's selection ratio; table 4). Five of
the 22 significant compounds were of the greatest proportion in
preferred trees (cyclopentanone, 3-hexen-1-ol, cis-3-hexenyl acetate,
7-methylpentadecane, and unknown {[}m/z{]}84.57.56.55.83) We also found
4 significant compounds that were of the greatest proportion in neutral
trees (1,1-dimethyl-3-methylene-2-vinylcyclohexane,
{[}bicyclopentyl{]}-2-one, \selectlanguage{greek}α-\selectlanguage{english}farnesene, and unknown
{[}m/z{]}93.91.79.80.77). The remaining 13 compounds were of the
greatest proportion in avoided species. We identified 7 significant
monoterpenes (\selectlanguage{greek}α-\selectlanguage{english}pinene, camphene, \selectlanguage{greek}β-\selectlanguage{english}phellandrene, \selectlanguage{greek}β-\selectlanguage{english}pinene, D-limonene,
\selectlanguage{greek}γ-\selectlanguage{english}terpinene, terpinolenenene) and 3 sesquiterpenes (caryophyllene,
humulene, germacrene D-4-ol), all of which were of the greatest
proportion in avoided trees. All of the terpenes that were significantly
correlated with avoidance according to Manly's selection ratio were also
significantly correlated with trees that deer were averse to according
to acceptability index scores.

\textbf{Discussion}

In our study, we not only confirmed that white-tailed deer forage
preferentially when provided with multiple foraging opportunities
(Morrison et al. 2002, Dostaler et al. 2011, Averill et al. 2016), but
also that this preferential foraging behavior was determined by the VOC
profiles in conjunction with plant nutritional/anti-nutritional
constituents. Our results coincide with recent studies from Bedoya-P\selectlanguage{ngerman}érez
et al. (2014) and Schmitt et al. (2020a), in which the foraging activity
of mammalian herbivores (swamp wallabies {[}\emph{Wallabia bicolor} {]}
and African elephants {[}\emph{Loxodonta africana} {]}, respectively)
decreased with food items that possessed an abundance of VOCs. However,
we exhibit the first (to our knowledge) results that indicate that VOCs
can influence the browse selection of white-tailed deer, one of the most
widespread herbivores in North America. Additionally, we found that deer
selected for different tree nutritional constituents between summer and
winter foraging trials. We did not find that the nutritional and
anti-nutritional constituents of trees influenced deer preference in the
manner that we proposed in our hypotheses, though. We expected that deer
would select trees that possessed the greatest nutritional benefit and
minimal associated costs. This was only evident during the summer when
deer preferred trees with the highest non-structural carbohydrate
content. Surprisingly, we found that deer selection changed during
winter, when they preferred trees with a poor benefit ratio by selecting
trees with a low crude protein content and high tannin content. Overall,
the results of our study suggest that VOCs play an equally important, if
not greater, role in determining deer browse preference compared to
nutritional/anti-nutritional compounds alone.

Our finding that deer preferred trees with a high carbohydrate content
during summer is consistent with our expectations that deer will prefer
beneficial food items in this season. Non-structural carbohydrates are
important in the diet selection of herbivores because they provide
energy (Van Soest 2018, Blumstein et al. 2023) and can be converted into
fat reserves (Moen et al. 1997, Accatino et al. 2017). Deer rely on fat
reserves to survive through winter in regions where snowfall and low
temperatures are common, such as Pennsylvania where this experiment took
place (DelGiudice et al. 1992, Lesage et al. 2001). We expect that we
found that deer were selecting trees with high non-structural
carbohydrates because deer could then convert excess energy into
reserves for the upcoming winter. However, we did not find that deer
were avoiding trees that possessed the greatest cost (tannins or
polyphenols). This may have been because trees during summer had a
relatively similar tannin content in both the preferred and avoided
groups (table 2) and therefore deer simply chose the trees that provided
the greatest net benefit.

Deer foraging preference in relation to tree
nutritional/anti-nutritional compounds during winter was much more
confounding than in summer. We predicted that a forager should prefer
food items that confer the greatest benefit (typically protein or
energy) and possess minimal to no associated costs (tannin or other
PSMs) (Freeland and Janzen 1974, Pyke et al. 1977). During winter
trials, deer preferred trees with a low crude-protein content (benefit)
and a high tannin content (cost). Deer, like many other mammalian
herbivores, can mitigate the negative effects of tannins through
anti-tannin salivary compounds (Austin et al. 1989, Robbins et al.
1991). Because these compounds bind with tannin in food sources and make
protein, that would be otherwise inaccessible, available to the forager
(Shimada 2006, Barbehenn and Constabel 2011), it is likely that salivary
compounds enabled deer to overcome the higher tannin content of the
trees that they were selecting. However, previous studies have indicated
that high tannin foods are less selected by deer than low tannin
alternatives when both options are present (Sauvé and Côté 2007,
Monteith et al. 2019). Moreover, in our study there were other tree
species present that were more advantageous (i.e., trees with a high
protein and low tannin content) that were present and readily available
for deer to consume. We suspect that the presence of VOCs was an
alternative cost that was responsible for the aversion that white-tailed
deer exhibited towards plants that should have been highly beneficial
for deer to consume during winter.

During the summer foraging trials, there were minimal anti-nutritional
costs that deer incurred when consuming their preferred trees. Preferred
trees had a higher carbohydrate content than avoided trees and did not
differ from avoided trees in tannin or polyphenol content. The avoided
group of trees (according to Manly's selection ratio) also had a greater
abundance of terpenes in their VOC emissions than the preferred species
had. Therefore, deer could readily consume high carbohydrate plants
without ingesting a large quantity of terpenes. Moreover, eastern
hemlock, eastern redcedar, and white pine were all avoided by
white-tailed deer according to the acceptability index and in addition
to Manly's selection ratio. This means that deer were reluctant to
forage from these trees after they sniffed them, and that deer consumed
less of these trees than neutral and preferred tree species. This, in
conjunction with the result that avoided trees did not possess a greater
amount of tannins or polyphenols than preferred trees, suggests that
white-tailed deer were directly avoiding terpenoids and detecting them
via scent before consuming the plant. This illustrates that white-tailed
deer were using plant-emitted VOCs as pre-ingestive warnings of the
direct cost of terpenes. We did not find evidence that deer were
associating VOCs with other costs that cannot be detected via scent,
such as tannins. However, because there was not a significant difference
in tannins between any of the preference categories, we urge that this
should be examined in future studies.

We also detected several VOCs that were correlated with high
acceptability index scores, meaning white-tailed deer were likely to
forage after a sniffing event occurred. Most of these compounds, such as
cis-3-hexenyl acetate, were green-leaf volatiles that are very common
and can be found in most plants (Frost et al. 2008, Ameye et al. 2018,
Frontini et al. 2022). The compounds that were correlated with
acceptance by deer were also present in many of the trees that deer were
averse to as well, but due to the abundance of terpenes in averse trees
the green leaf volatiles only comprised a small portion of the total VOC
emissions by averse plants. Therefore, it may be possible that deer were
attracted to these compounds, but rather it is more likely that they
were attracted to these trees by the lack of terpenes or other avoided
compounds.

We were unable to measure VOCs during winter months due to logistic
constraints, though we can still tentatively use the data that we
collected regarding VOCs during the summer and an understanding of plant
physiology to provide insight into how VOCs may have influenced deer
preference during winter. As the phenology of plants changed, so too did
their nutritional/anti-nutritional properties (Buxton and Marten 1989,
Albon and Langvatn 1992). Green, leafy biomass is typically more
beneficial for herbivores to consume than woody/twig biomass because it
has higher protein content and is easier to digest (Dickson 1989,
Hebblewhite et al. 2008, Van Soest 2018, Tang et al. 2018). Furthermore,
woody biomass in trees possesses tannins at similar concentrations to
leaves (Matthews et al. 1997, Kraus et al. 2003) meaning woody biomass
has a worse benefit:cost ratio than leafy biomass. Similar to nutrients,
VOC emissions vary with plant phenology (Guenther 1997, Padhy and
Varshney 2005). A large portion of plant-emitted VOCs are generally
produced by either foliar or floral components of vegetation, meaning
that trees without these components emit less VOCs (Guenther 1997,
Niinemets et al. 2004). During winter foraging trials in our experiment,
the leaves of the five deciduous species had senesced, and these food
items likely had little to no VOC emissions due to a lack of leafy or
floral biomass (Guenther 1997, Ameye et al. 2018). Conversely, conifers
(such as pines and redcedars) which already emit a greater proportion of
monoterpenes in their total VOC profile than deciduous trees do
(Guenther et al. 1994), continued to have green leafy biomass.
Therefore, while the green needles of the conifers may have been
advantageous for deer to consume from a nutrient standpoint, they were
likely defended by VOCs/terpenes that deterred foraging by deer. It is
possible that the physical structure (sharp needles or sap) of the
conifers also deterred white-tailed deer and resulted in avoidance.
However, throughout the range of white-tailed deer, conifers are a
common or even preferred part of deer diets (Swihart and Picone 1998,
Rooney et al. 2000, Sauvé and Côté 2007), and deer will consume other,
more physically defended plants such as \emph{Smilax} spp. (Lashley and
Harper 2012, Sample et al. 2023).

While deer can mitigate the costs of consuming tannins through
anti-tannin salivary compounds, they have no known mechanism that will
enable them to mitigate terpenes. Because of their lipophilic nature,
terpenes can become difficult to digest or become toxic to herbivores
when consumed in large concentrations (McLean and Duncan 2006). This
suggests that the ingestion of these compounds is likely far more costly
for white-tailed deer than tannins or polyphenols and explains why high
terpene trees had low acceptability index scores and were avoided
according to Manly's selection ratio during the summer. It also may
explain why we found that deer preferred defoliated trees that likely
had minimal VOC emissions but were considered sub-optimal from a
nutrient/tannin standpoint during winter. Our results that white-tailed
deer were avoiding high terpene plants is consistent with those of
Vourc'h et al. (2002), who found that increasing monoterpene
concentrations deterred foraging by other cervids: Sitka black-tailed
deer (\emph{Odocoileus hemionus sitkensis} ), European roe deer
(\emph{Capreolus capreolus} ), and rusa deer (\emph{Cervus timorensis
russa} ).

The findings of our study suggest that VOCs, especially terpenoids, are
of equal, if not greater importance than nutritional/anti-nutritional
constituents in determining the forage selection of white-tailed deer.
Due to the near ubiquitous presence of white-tailed deer in North
America, further data collection in other regions where white-tailed
deer are found could help to confirm these findings with other taxa of
plants that are regionally specific. We also emphasize that conducting
experiments that compare deer preference between high VOC deciduous
trees and low VOC conifers may help to further tease apart the influence
that VOCs have on deer forage selection. Additionally, in future
experiments, specific volatiles could be added to a common food source,
such as performed by Elliott and Loudon (1987) and Bedoya-Pérez et al.
(2014), which could help elucidate which specific VOCs deter
white-tailed deer. In regions where deer persist at high densities, our
results suggest that high VOC-emitting plants may have the potential to
increase the browse pressure on surrounding low VOC plants due to
association effects making the low terpene plants more palatable to deer
(Champagne et al. 2018). Further investigation into this possibility
could help identify plant species that may become at risk or extirpated
due to excessive and prolonged browsing by white-tailed deer.

\textbf{Data Archiving Statement}

The data collected in this experiment are archived in the Open Access
Kent State (OAKS) repository.

\textbf{Conflict of Interest Statement}

The authors declare no conflicts of interest.

\textbf{Ethics Statement}

All methods involving the use of white-tailed deer were approved by the
Pennsylvania State University Institutional Animal Care and Use
Committee (protocol number: PROTO202001387).

\textbf{Funding Statement}

This work was funded by the Art and Margaret Herrick Endowment at Kent
State University and the Kent State University Graduate Student Senate.

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\textbf{Table 1.} We calculated an acceptability index (number of
foraging encounters/total encounters with a tree) of white-tailed deer
to ascertain if VOCs may be providing deer with scent cues prior to
foraging from a plant. A ratio above 0.5 indicated that a deer preferred
to forage on a plant after sniffing it, a ratio of 0.25 -- 0.5 indicated
neutral preference and a ratio below 0.25 indicated avoidance.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Species & Acceptability index & Acceptability class\tabularnewline
\midrule
\endhead
American elm & 0.579 & Accepted\tabularnewline
Black willow & 0.652 & Accepted\tabularnewline
Eastern hemlock & 0.091 & Averse\tabularnewline
Eastern redcedar & 0.0 & Averse\tabularnewline
Silver maple & 0.417 & Neutral\tabularnewline
White ash & 0.594 & Accepted\tabularnewline
White oak & 0.500 & Neutral\tabularnewline
White pine & 0.0 & Averse\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Generalized linear model with a binomial error
distribution using a logit link function to discern the influence of
crude protein, total non-structural carbohydrates (TNC), tannins in
tannic acid equivalents (TAE) and polyphenols in gallic acid equivalents
(GAE) on the foraging preference of white-tailed deer during summer and
winter. We calculated p-values with a Wald chi-squared test and found
that, during summer, deer preferred plants with a high TNC content.
During winter deer preferred plants with a low crude protein content and
a high tannin content.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Season & Factor & p & \selectlanguage{greek}Χ\selectlanguage{english}\textsuperscript{2} & Avoided & Neutral &
Preferred\tabularnewline
\midrule
\endhead
summer & crude protein (\%) & 0.425 & 0.635 & 0.172 \selectlanguage{ngerman}± 0.009 & 0.217 \selectlanguage{ngerman}±
0.010 & 0.179 \selectlanguage{ngerman}± 0.014\tabularnewline
& \textbf{TNC (mg ml\textsuperscript{-1})} & \textbf{*0.003} &
\textbf{8.952} & \selectlanguage{ngerman}\textbf{0.214 ± 0.009} & \selectlanguage{ngerman}\textbf{0.231 ± 0.010} &
\selectlanguage{ngerman}\textbf{0.351 ± 0.030}\tabularnewline
& TAE (mg ml\textsuperscript{-1}) & 0.148 & 2.091 & 0.0035 \selectlanguage{ngerman}± 0.0002 &
0.0049 \selectlanguage{ngerman}± 0.0012 & 0.0034 \selectlanguage{ngerman}± 0.0003\tabularnewline
& GAE (mg ml\textsuperscript{-1}) & 0.205 & 1.609 & 0.143 \selectlanguage{ngerman}± 0.018 &
0.112 \selectlanguage{ngerman}± 0.027 & 0.082 \selectlanguage{ngerman}± 0.010\tabularnewline
winter & \textbf{crude protein (\%)} & \textbf{*0.031} & \textbf{4.626}
& \selectlanguage{ngerman}\textbf{0.151 ± 0.010} & \selectlanguage{ngerman}\textbf{0.139 ± 0.006} & \selectlanguage{ngerman}\textbf{0.121 ±
0.007}\tabularnewline
& TNC (mg ml\textsuperscript{-1}) & 0.606 & 0.267 & 0.283 \selectlanguage{ngerman}± 0.010 &
0.239 \selectlanguage{ngerman}± 0.025 & 0.241 \selectlanguage{ngerman}± 0.017\tabularnewline
& \textbf{TAE (mg ml\textsuperscript{-1})} & \textbf{*0.032} &
\textbf{4.603} & \selectlanguage{ngerman}\textbf{0.0026 ± 0.0002} & \selectlanguage{ngerman}\textbf{0.0027 ± 0.0002} &
\selectlanguage{ngerman}\textbf{0.0038 ± 0.0004}\tabularnewline
& GAE (mg ml\textsuperscript{-1}) & 0.936 & 0.006 & 0.072 \selectlanguage{ngerman}± 0.007 &
0.081 \selectlanguage{ngerman}± 0.005 & 0.088 \selectlanguage{ngerman}± 0.006\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

\textbf{Table 3.} Spearman's rank correlation to examine the
relationship between VOCs that are emitted by trees and deer
acceptability index scores of the same trees to determine which
compounds are correlated with acceptance after sniffing (acceptability
index of more than 0.5) or aversion to after sniffing (acceptability
index below 0.25). We identified 33 compounds that were correlated with
deer acceptability. We included the compound name along with the
chemical abstracts services (CAS) number for identification purposes. We
also included the compound class of each VOC, Spearman correlation
coefficient, p-value and mean ± standard error for each acceptability
index preference category of each compound. Values that are in bold
indicate which acceptability index group had the greatest mean
proportion of a compound. Unknown compounds are those that we were
unable to confirm a positive identification for.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Compound & CAS \# & Class & Coefficient & p & Avoided & Neutral &
Preferred\tabularnewline
\midrule
\endhead
Cyclopentanone & 120-92-3 & Ketone & 0.5337 & 0.002 & 0.0097 \selectlanguage{ngerman}± 0.0032 &
0.0785 \selectlanguage{ngerman}± 0.0443 & \selectlanguage{ngerman}\textbf{0.0960 ± 0.0232}\tabularnewline
3-Hexen-1-ol & 544-12-7 & Alcohol & 0.4813 & 0.007 & 0.0002 \selectlanguage{ngerman}± 0.0002 &
0.0032 \selectlanguage{ngerman}± 0.0032 & \selectlanguage{ngerman}\textbf{0.0196 ± 0.0092}\tabularnewline
Tricyclene & 508-32-7 & Monoterpene & -0.3699 & 0.044 & \selectlanguage{ngerman}\textbf{0.0018 ±
0.0009} & 0.0 & 0.0\tabularnewline
\selectlanguage{greek}α-\selectlanguage{english}Pinene & 80-56-8 & Monoterpene & -0.7128 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.0460 ± 0.0115} & 0.0 & 0.0\tabularnewline
Camphene & 79-92-5 & Monoterpene & -0.6599 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.0112 ± 0.0036} & 0.0 & 0.0\tabularnewline
Unknown {[}m/z{]}84.57.56.55.83 & Unknown & Unknown & 0.4703 & 0.009 &
0.0014 \selectlanguage{ngerman}± 0.0014 & 0.0105 \selectlanguage{ngerman}± 0.0105 & \selectlanguage{ngerman}\textbf{0.0317 ±
0.0157}\tabularnewline
\selectlanguage{greek}β-\selectlanguage{english}Phellandrene & 555-10-2 & Monoterpene & -0.7757 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.0630 ± 0.0283} & 0.0283 \selectlanguage{ngerman}± 0.0283 & 0.0\tabularnewline
\selectlanguage{greek}β-\selectlanguage{english}Pinene & 127-91-3 & Monoterpene & -0.87 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.2202 ± 0.0436} & 0.0 & 0.0\tabularnewline
(-)-\selectlanguage{greek}α-\selectlanguage{english}Thujene & 3917-48-4 & Monoterpene & -0.4346 & 0.016 &
\selectlanguage{ngerman}\textbf{0.0108 ± 0.0054} & 0.0 & 0.0\tabularnewline
3-Carene & 13466-78-9 & Monoterpene & -0.4346 & 0.016 & \selectlanguage{ngerman}\textbf{0.0454 ±
0.0215} & 0.0 & 0.0\tabularnewline
cis-3-Hexenyl Acetate & 3681-71-8 & Alkene & 0.6271 & \textless{} 0.001
& 0.0019 \selectlanguage{ngerman}± 0.001 & 0.1690 \selectlanguage{ngerman}± 0.0787 & \selectlanguage{ngerman}\textbf{0.1758 ±
0.0650}\tabularnewline
n-Caprylaldehyde & 124-13-0 & Aldehyde & 0.4426 & 0.014 & 0.0009 \selectlanguage{ngerman}±
0.0009 & 0.0128 \selectlanguage{ngerman}± 0.0085 & \selectlanguage{ngerman}\textbf{0.0197 ± 0.0098}\tabularnewline
D-Limonene & 5989-27-5 & Monoterpene & -0.87 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.1832 ± 0.0606} & 0.0 & 0.0\tabularnewline
Unknown {[}m/z{]}93.91.77.57.71 & Unknown & Unknown & -0.6063 &
\textless{} 0.001 & \selectlanguage{ngerman}\textbf{0.0461 ± 0.0132} & 0.0 & 0.0\tabularnewline
\selectlanguage{greek}γ-\selectlanguage{english}Terpinene & 99-85-4 & Monoterpene & -0.5148 & 0.004 & \selectlanguage{ngerman}\textbf{0.0030 ±
0.0011} & 0.0010 \selectlanguage{ngerman}± 0.0010 & 0.0\tabularnewline
Terpinolene & 586-62-9 & Monoterpene & -0.7128 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.0153 ± 0.0062} & 0.0 & 0.0\tabularnewline
(-)-Alcanfor & 464-48-2 & Monoterpene & -0.4346 & 0.016 & \selectlanguage{ngerman}\textbf{0.0040
± 0.0017} & 0.0 & 0.0\tabularnewline
Borneol & 507-70-0 & Monoterpene & -0.4346 & 0.016 & \selectlanguage{ngerman}\textbf{0.0051 ±
0.0024} & 0.0 & 0.0\tabularnewline
Methyl Salicylate & 119-36-8 & Ester & 0.3702 & 0.044 & 0.0224 \selectlanguage{ngerman}± 0.0069
& 0.0523 \selectlanguage{ngerman}± 0.0149 & \selectlanguage{ngerman}\textbf{0.0660 ± 0.0210}\tabularnewline
Unknown {[}m/z{]}93.57.121.136.71 & Unknown & Unknown & -0.3699 & 0.044
& \selectlanguage{ngerman}\textbf{0.0010 ± 0.0007} & 0.0 & 0.0\tabularnewline
Unknown {[}m/z{]}78.91.69.93.81 & Unknown & Unknown & -0.3699 & 0.044 &
\selectlanguage{ngerman}\textbf{0.0062 ± 0.0035} & 0.0 & 0.0\tabularnewline
(-)-Bornyl acetate & 5655-61-8 & Ester & -0.4945 & 0.005 &
\selectlanguage{ngerman}\textbf{0.0580 ± 0.0239} & 0.0 & 0.0\tabularnewline
Anethole & 104-46-1 & Aromatic & -0.3699 & 0.044 & \selectlanguage{ngerman}\textbf{0.0154 ±
0.0118} & 0.0 & 0.0\tabularnewline
\selectlanguage{greek}α-\selectlanguage{english}Isosafrole & 17627-76-8 & Aromatic & -0.3699 & 0.044 & \selectlanguage{ngerman}\textbf{0.0042
± 0.0027} & 0.0 & 0.0\tabularnewline
{[}Bicyclopentyl{]}-2-one & 4884-24-6 & Ketone & 0.7211 & \textless{}
0.001 & 0.0341 \selectlanguage{ngerman}± 0.0061 & 0.1784 \selectlanguage{ngerman}± 0.0652 & \selectlanguage{ngerman}\textbf{0.2304 ±
0.0463}\tabularnewline
\selectlanguage{greek}β-\selectlanguage{english}Elemene & 33880-83-0 & Sesquiterpene & -0.4045 & 0.027 &
\selectlanguage{ngerman}\textbf{0.0028 ± 0.0009} & 0.0000 & 0.0006 \selectlanguage{ngerman}± 0.0006\tabularnewline
Unknown {[}m/z{]}120.161.57.105.91 & Unknown & Unknown & -0.4577 & 0.011
& \selectlanguage{ngerman}\textbf{0.0056 ± 0.0016} & 0.0009 \selectlanguage{ngerman}± 0.0007 & 0.0019 \selectlanguage{ngerman}±
0.0013\tabularnewline
Caryophyllene & 87-44-5 & Sesquiterpene & -0.706 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.0403 ± 0.0060} & 0.0066 \selectlanguage{ngerman}± 0.0041 & 0.0053 \selectlanguage{ngerman}±
0.0025\tabularnewline
Humulene & 6753-98-6 & Sesquiterpene & -0.7527 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.0306 ± 0.0116} & 0.0012 \selectlanguage{ngerman}± 0.0008 & 0.0\tabularnewline
\selectlanguage{greek}α-\selectlanguage{english}Farnesene & 502-61-4 & Sesquiterpene & 0.4179 & 0.022 & 0.0048 \selectlanguage{ngerman}±
0.0028 & \selectlanguage{ngerman}\textbf{0.1405 ± 0.0857} & 0.1279 \selectlanguage{ngerman}± 0.0421\tabularnewline
Unknown {[}m/z{]}161.105.91.119.79 & Unknown & Unknown & -0.3699 & 0.044
& \selectlanguage{ngerman}\textbf{0.0291 ± 0.0086} & 0.0019 \selectlanguage{ngerman}± 0.0019 & 0.0148 \selectlanguage{ngerman}±
0.0074\tabularnewline
Unknown {[}m/z{]}161.119.105.57.91 & Unknown & Unknown & -0.6599 &
\textless{} 0.001 & \selectlanguage{ngerman}\textbf{0.0082 ± 0.0026} & 0.0 & 0.0\tabularnewline
Germacrene D-4-ol & 72120-50-4 & Sesquiterpene & -0.6954 & \textless{}
0.001 & \selectlanguage{ngerman}\textbf{0.0217 ± 0.0093} & 0.0 & 0.0007 \selectlanguage{ngerman}± 0.0007\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

\textbf{Table 4.} Spearman's correlation to examine the relationship
between plant-emitted VOCs and deer foraging preference (as calculated
by Manly's selection ratio). We identified 22 compounds that were
correlated with deer foraging preference and included the compound name
along with the chemical abstracts services (CAS) number for
identification purposes. We also included the compound class of each
VOC, Spearman correlation coefficient, p-value and mean ± standard error
for Manly's selection ratio preference category of each compound. Values
that are in bold indicate which Manly's selection ratio groups had the
greatest mean proportion of a compound. Unknown compounds are those that
we were unable to confirm a positive identification for.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Compound & CAS \# & Class & Coefficient & p & Avoided & Neutral &
Preferred\tabularnewline
\midrule
\endhead
Cyclopentanone & 120-92-3 & Ketone & 0.4859 & 0.006 & 0.0381 \selectlanguage{ngerman}\textbf{±}
0.0208 & 0.0430 \selectlanguage{ngerman}\textbf{±} 0.0239 & \selectlanguage{ngerman}\textbf{0.1070 ±
0.0274}\tabularnewline
3-Hexen-1-ol & 544-12-7 & Alcohol & 0.6332 & \textless{} 0.001 & 0.0001
\selectlanguage{ngerman}\textbf{±} 0.0001 & 0.0037 \selectlanguage{ngerman}\textbf{±} 0.0037 & \selectlanguage{ngerman}\textbf{0.0270 ±
0.0117}\tabularnewline
\selectlanguage{greek}α-\selectlanguage{english}Pinene & 80-56-8 & Monoterpene & -0.5719 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.0345 ± 0.0010} & 0.0 & 0.0\tabularnewline
Camphene & 79-92-5 & Monoterpene & -0.5295 & 0.003 & \selectlanguage{ngerman}\textbf{0.0084 ±
0.0029} & 0.0 & 0.0\tabularnewline
Unknown {[}m/z{]}84.57.56.55.83 & Unknown & Unknown & 0.5030 & 0.005 &
0.0056 \selectlanguage{ngerman}\textbf{±} 0.0046 & 0.0 & \selectlanguage{ngerman}\textbf{0.0436 ± 0.0202}\tabularnewline
\selectlanguage{greek}β-\selectlanguage{english}Phellandrene & 555-10-2 & Monoterpene & -0.6981 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.0596 ± 0.0237} & 0.0 & 0.0\tabularnewline
\selectlanguage{greek}β-\selectlanguage{english}Pinene & 127-91-3 & Monoterpene & -0.6981 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.1651 ± 0.0406} & 0.0 & 0.0\tabularnewline
cis-3-Hexenyl Acetate & 3681-71-8 & Alkene & 0.5372 & 0.002 & 0.0417
\selectlanguage{ngerman}\textbf{±} 0.0280 & 0.1310 \selectlanguage{ngerman}\textbf{±} 0.0757 & \selectlanguage{ngerman}\textbf{0.2108 ±
0.0864}\tabularnewline
D-Limonene & 5989-27-5 & Monoterpene & -0.6981 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.1374 ± 0.0494} & 0.0 & 0.0\tabularnewline
Unknown {[}m/z{]}93.91.77.57.71 & Unknown & Unknown & -0.48642 & 0.006 &
\selectlanguage{ngerman}\textbf{0.0346 ± 0.0111} & 0.0 & 0.0\tabularnewline
Unknown {[}m/z{]}93.91.79.80.77 & Unknown & Unknown & 0.4813 & 0.007 &
0.0 & \selectlanguage{ngerman}\textbf{0.0624 ± 0.0415} & 0.0517 \selectlanguage{ngerman}\textbf{±} 0.0259\tabularnewline
\selectlanguage{greek}γ-\selectlanguage{english}Terpinene & 99-85-4 & Monoterpene & -0.3772 & 0.040 & \selectlanguage{ngerman}\textbf{0.0022 ±
0.0008} & 0.0012 \selectlanguage{ngerman}\textbf{±} 0.0012 & 0.0\tabularnewline
Terpinolene & 586-62-9 & Monoterpene & -0.5719 & \textless{} 0.001 &
\selectlanguage{ngerman}\textbf{0.0114 ± 0.0049} & 0.0 & 0.0\tabularnewline
1,1-Dimethyl-3-methylene-2-vinylcyclohexane & 95452-08-7 & Hydrocarbon &
0.3855 & 0.035 & 0.0 & \selectlanguage{ngerman}\textbf{0.1194 ± 0.0541} & 0.0181 \selectlanguage{ngerman}\textbf{±}
0.0155\tabularnewline
(-)-Bornyl acetate & 5655-61-8 & Ester & -0.3968 & 0.030 &
\selectlanguage{ngerman}\textbf{0.0435 ± 0.0189} & 0.0 & 0.0\tabularnewline
{[}Bicyclopentyl{]}-2-one & 4884-24-6 & Ketone & 0.4367 & 0.016 & 0.0972
\selectlanguage{ngerman}\textbf{±} 0.0333 & \selectlanguage{ngerman}\textbf{0.2054 ± 0.1016} & 0.1757 \selectlanguage{ngerman}\textbf{±}
0.0157\tabularnewline
Caryophyllene & 87-44-5 & Sesquiterpene & -0.4654 & 0.010 &
\selectlanguage{ngerman}\textbf{0.0302 ± 0.0064} & 0.0095 \selectlanguage{ngerman}\textbf{±} 0.0044 & 0.0060 \selectlanguage{ngerman}\textbf{±}
0.0033\tabularnewline
7-Methylpentadecane & 6165-40-8 & Hydrocarbon & 0.3744 & 0.042 & 0.0 &
0.0 & \selectlanguage{ngerman}\textbf{0.0161 ± 0.0124}\tabularnewline
Humulene & 6753-98-6 & Sesquiterpene & -0.5579 & 0.001 & \selectlanguage{ngerman}\textbf{0.0230
± 0.0092} & 0.0013 \selectlanguage{ngerman}\textbf{±} 0.0009 & 0.0\tabularnewline
\selectlanguage{greek}α-\selectlanguage{english}Farnesene & 502-61-4 & Sesquiterpene & 0.5543 & 0.001 & 0.0050
\selectlanguage{ngerman}\textbf{±} 0.0025 & \selectlanguage{ngerman}\textbf{0.2016 ± 0.0939} & 0.1447 \selectlanguage{ngerman}\textbf{±}
0.0509\tabularnewline
Unknown {[}m/z{]}161.119.105.57.91 & Unknown & Unknown & -0.5295 & 0.003
& \selectlanguage{ngerman}\textbf{0.0061 ± 0.0021} & 0.0 & 0.0\tabularnewline
Germacrene D-4-ol & 72120-50-4 & Sesquiterpene & -0.5333 & 0.002 &
\selectlanguage{ngerman}\textbf{0.0163 ± 0.0073} & 0.0 & 0.0010 \selectlanguage{ngerman}\textbf{±} 0.0010\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

\textbf{Figure Captions}

\textbf{Figure 1.} We calculated Manly's selection ratio of white-tailed
deer foraging preference for eight tree species. Selection ratio\emph{±}
95\% Bonferroni-corrected confidence intervals are shown for both summer
and winter. Scores in which the lower 95\% confidence interval does not
intersect with 1 are preferred and scores in which the upper 95\%
confidence interval does not intersect with 1 are avoided. Scores in
which either 95\% confidence interval intersects with 1 are considered
to be of neutral preference.

\textbf{Figure 2.} We conducted distance based redundancy analysis
(dbRDA) to examine Bray-Curtis dissimilarity scores that represented
nutrients during summer (panel A) and winter (panel B) as a function of
tree species. We included three biplots for each panel to display the
scores for the first three dbRDA axes. Tree species is indicated by the
color of a point and deer preference (as defined by Manly's selection
ratio) is indicated by the shape of each point. We overlaid the scores
for each nutrient and overlaid a vector connecting each score to the
center of the biplot (0,0). The nutrients included were crude protein
(CP), total non-structural carbohydrates (TNC), tannin as tannic acid
equivalents (TAE) and polyphenols as gallic acid equivalents (GAE).

\textbf{Figure 3.} We conducted distance-based redundancy analysis
(dbRDA) to examine Bray-Curtis dissimilarity scores that represented
VOCs that were emitted by trees during summer as a function of tree
species. We included three biplots to display the scores for the first
three dbRDA axes. Tree species is indicated by the color of a point and
deer preference (as defined by the acceptability index) is indicated by
the shape of each point.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/oik-10377-File003/oik-10377-File003}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/oik-10377-File004/oik-10377-File004}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/oik-10377-File005/oik-10377-File005}

\end{center}
\end{figure}

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

