Spatial assessment of the effects of white-tailed deer
(Odocoileus virginanus) browse in Sewanee, TN
A Thesis Submitted to the Department of Biology
The University of the South
In Partial Fulfillment of the Requirements for Honors in Biology
by
Meg Armistead
on
May 30, 2041
Certified by:
Thesis Advisor
Abstract:
The impact of white-tailed deer (Odocoileus virginianus) browse on forest structure and
composition has become a major concern of natural area managers throughout the eastern
United States. Browse impacts within a given management area are likely to be
distributed in a heterogeneous pattern due to factors such as deer movement, landscape
features, and habitat fragmentation. Thus, it is important that appropriate techniques be
developed for assessing deer browse at large spatial scales. Our study assessed the
effectiveness of a variety of metrics employed to examine of the spatial distribution of
deer browse across a 2,300-acre, upland oak-hickory forest on the Cumberland Plateau.
We used 46 transects distributed across the study area in a stratified, random design. The
transects were compared to four fenced exclosures established in the study area prior to
2000. Browse intensity varied spatially within the study area. Average sapling density in
the exclosures was significantly higher than average sapling density in transects, and the
range of variation among transects suggested that sapling density as a metric was highly
sensitive to browse. Based on this sensitivity, along with ease of sampling, we
recommend the use of sapling density as a proxy for browse in future assessments.
Latitude and distance to edge habitat were the best predictors of sapling density. This
influence in combination with deer movement patterns, deer browse preference, and
habitat fragmentation created a heterogeneous matrix of browse. To maintain the
biological balance of forest regeneration and whitetail deer interactions, it is important to
use a large spatial assessment with a suitable metric to allocate limited resources to
manage high impact areas, preserving overall forest biodiversity.
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Introduction:
Across the eastern United States, rising white-tailed deer (Odocoileus virginianus)
populations have become a major challenge to the management of natural areas
(DeNicola et al. 2000). Many studies suggest that high levels of deer browse can drive
considerable changes in plant communities and potentially lead to alternate stable states
(Anderson et al. 2002, Côté et al. 2004, Millington et al. 2010, Ruzicka et al. 2010, White
2012). Deer acorn herbivory can cause mast event failure limiting reproductive success
of hardwoods and negatively affect acorn dependent organisms (McShea 2012). Pastor et
al. (1993) found that browse impacts woody plant species composition, affects understory
structure, and inhibits or alters ecosystem processes. Deer can alter understory structure
by decreasing sapling density, which may impact the future composition and density of
overstory trees. Deer over-browse causes changes in stem morphology, stem
development, vegetation spatial distribution patterns, and seedling and sapling survival
rates (Russell 2001). Cardinal et al. (2012) found that in reducing understory cover, deer
indirectly affect songbird abundance, richness, and diversity, but these effects may not be
distributed homogenously.
Deer browse can be spatially heterogeneous due to population densities in certain
habitats, but also due to how deer use these habitats. Edge habitat (Ruzika et al. 2010),
browsing preference (Long et al. 2007, Tremblay et al 2005, Stromayer and Warren
1997), and migration routes (Van Deelen et al. 1998) can cause deer browse to be
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distributed in a heterogeneous matrix. Edge habitat is the optimal foraging habitat for
deer due to low search cost, low predator threat, and a high concentration of younger
stems; browsing pressure from deer increases with proximity to edge compared to the
interior forest habitat (Williams 1985, Ruzika et al. 2010). The deer's preference to
browse in an edge habitat causes a higher concentration of browse impacts in this edge
habitat compared to the continuous forest (Kie et al. 2002). Likewise, browse becomes
concentrated around deer paths because deer migrate within social groups along
consistent pathways (Van Deelen et al. 1998). Complex geographic topography or
urbanized landscapes may also potentially funnel deer movement into heavily used
pathways (Quinn et al. 2013). Lastly, selective browse by deer can cause forest
composition shifts to an alternate stable state dominated by browse-tolerant and deer unpreferred species (Long et al. 2007, Tremblay et al 2005, Stromayer and Warren 1997).
High-intensity foraging in edge habitat and along migration paths combined with
selective browse creates a patchwork of browse patterns across a forested landscape,
which can lead to significant losses in biodiversity.
Natural area managers concerned with maintaining forest regeneration in the presence of
increasing deer populations require the ability to track the relationship of browse and
community change across management units. According to the literature, long-term deer
exclosure studies allow the researcher to understand what the ecosystem would be like in
the absence of deer (Long et al. 2007, White 2012). However, these studies tend to be
costly, time intensive, and establish artificially low baselines for deer assessment. Browse
indices derived from field assessments are generally subjective and are used for assessing
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the effect of deer on their food supply and are therefore not necessarily created to be a
direct predictor indirect browse effects (Aldous 1944, Frerker et al. 2013). A direct
measure of community composition should be used to access community change that is
easily applicable at the landscape level. Rooney et al. (2000) found that sapling (30 cm–300
cm) density decreased as browsing pressure increased. Exclosure studies have also found
significant differences in sapling density between browsed and nonbrowsed areas (Bressette et
al. 2012, Martin et al. 2002, Opperman et al. 2001, Pellerin et al. 2010, Ross et al. 1970). In this
study we compared sapling density, percent cover, percent recruitment, tree richness and
tree diversity as metrics to assess the impact of deer browse from the plant community
perspective across a large landscape. We hypothesize that (1) sapling density will be the
most effective metric to quantify this variation across a landscape, and (2) browse will be
distributed in a heterogeneous matrix across a landscape due to edge habitat, deer
movement patterns, and browse preference.
Methods:
Study Site – The study area was a 2,300-acre tract of upland oak-hickory forest located on
the surface of the Cumberland Plateau on the campus of The University of the South in
Sewanee, TN. White-tail deer in Sewanee were hunted to local extinction during the
Great Depression (1930s). They were reintroduced with great success in the 1940’s
(Cheston 1953). Currently, with no natural predators, the only limiting factors on the
population growth are the annual deer cull and limited food resources. Nate Wilson,
Domain Manager, through distance spotlight sampling has estimated the population to be
117 (95% confidence interval; 37-287) deer mi-2 (McGrath Ecology Class 2012). The
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range for overpopulation of deer in the study site area is greater than 20- 25 deer mi-2
given optimal habitat (K.V. Miller, pers. comm. 2003).
Field Methods:
Fieldwork was performed between June and August 2012. We located 46 randomly
stratified, 20m x 50m box transects. Each transect contained 10 randomly distributed,
circular plots. A plot consisted of two nested circles: 5m diameter circle for saplings
(0.25m – 3m ht) and 1m-diameter circle for seedlings (<0.25m ht). All tree seedlings
and saplings were recorded by species and density and ranked into 3 browse categories
based on percent defoliation by deer (0%, 1-50%, >50%). Average browse rankings per
plant, along with other visual evidence, was then used to assign each plot a overall
browse index (0 -3 low, medium and high browse), and the plot indices within the box
transects were then averaged to assign a single browse index for each of the 46 transects.
A photograph was taken and used in the calculation of percent vegetative cover of each
plot.
Four fenced exclosures (2 – 16m2, 2 – 24m2), established prior to 2000 (by the
Departments of Biology and Forestry and Geology), were used to assess the plant
community condition in the absence of deer within the study site. After excluding a .5m
buffer zone near the fence, the entire area within each exclosure was sampled in a similar
fashion to plots described above. Vegetation in exclosure showed no evidence of browse
and therefore received a browse index of 0.
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We calculated sapling density, percent vegetative cover, percent recruitment, tree
richness and tree diversity, within each plot and then averaged these metrics at the
transect level. Using the photographs taken in each plots, percent cover was determined
using Adobe Photoshop following the procedure of Luscier et al. (2006). Percent
recruitment (
# Sapling
) was an indicator of successful seedling to sapling
# Seedling + # Sapling
transitioning within the community. Tree diversity was calculated using the ShannonWeiner index (Krebs 1985).
Data Analysis
Linear regression (ms-EXCEL) was used to examine the correlation between each of the
community metrics and deer browse in the transects. We tested the effect of deer
exclusion on each of the community metrics using Ttest and characterized the range of
variation using box-plots where outliers were defined as greater than 1.5 times the
interquartile range (R package R Core Team 2013). A useful metric for quantifying
browse needed to be significantly different from the controls with little to no threshold
effect. The spatial variation of each metric across the study area was examined using
ordinary kriging in ArcGIS 10.1 (ESRI 2011).
Because deer non-randomly use the landscape, we evaluated the roles of known or
potential drivers of deer browse habits on landscape patterns on traditionally used metrics
of browse and our most useful community metric of browse pressure. We chose to
evaluate predictors of deer browse such as distance from edge (distance from forest edge
or distance from building), distance from access to the browse area (distance to portal,
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distance to stream), and latitude. Edge was defined as forest edge. A portal was defined
in ArcMap as a location that had easy access for deer travel from the cove onto the
plateau. Necessary factors were a change in topography below the plateau, and gradual
elevation change that allowed for easy access. These parameters were calculated for each
of our sampling points using ArcGIS.
We used an information theoretic approach to evaluate the influence of each of these
parameters on our observed landscape patterns of browse. To avoid multicollinearity, we
did not include the two descriptive parameters for distance to edge (distance to edge,
distance to building) or distance to plateau access (distance to stream, distance to portal)
within the same candidate model. We developed candidate models that evaluated each of
our predictive parameters and those parameters in combination with one another. To
determine the relative plausibility of each model given our data, we used Akaike’s
Information Criteria (AIC, Akaike 1983) adjusted for small sample sizes using the smallsample bias adjustment (AICc; Hurvich and Tsai, 1989). Model weights were calculated
for each candidate model and fit was assessed by ranking these models from highest
(most likely) to lowest (least likely) model (Burnham and Anderson 2002). Models were
only included in the confidence set if they had Akaike weights greater than 10% of the
best fitting model’s Akaike weight (Royall 1997). Model fit was evaluated using the root
mean square error. To evaluate the importance and influence of each predictive
parameter, we calculated importance weights, scaled odds ratios, and scaled unit changes
to assess the biological relevance of each parameter.
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To examine how model predictions compared to observed measures of browse, we
developed a composite model using unconditional parameter estimates that incorporate
model selection uncertainty and parameter uncertainty (Burnham and Anderson 2002).
This composite model was used to develop predictive maps in ArcMap through raster
calculations using the equation derived in the linear models predicting sapling density
and browse index.
Results
Hypothesis 1:
Most of the community metrics showed a significant correlation with browse index:
percent cover (p<0.001, R2=0.43), percent recruitment (p<0.001, R2=0.6), tree richness
(p=0.006, R2=0.16), and sapling density (Fig. 1, p<0.001,R2=0.53). Tree diversity
showed no significant relationship with browse within the study site (p=0.97, R2=0.004).
Transects dominated by sassafras (Sassafras albidum) resulted in a low Shannon Weiner
Diversity Index due to unevenness.
As the exclosure data indicate, there is a wide range of variation in community metrics
inherent in the landscape independent of deer browse effect. Sapling density and Percent
Recruitment had significant mean difference between the transects and exclosures
(Sapling Density: transect
Percent recruitment: transect
= .107 per m2, exclosure
= 32%; exclosure
= .56per m2; Ttest p=0.02;
= 76%; Ttest p<0.001). Sapling
density was the only variable for which the range of variation found within the exclosures
existed above the maximum excluding outliers (Figure 2).
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Indicator species can be a useful metric for quantifying browse. In this study we found
two species that could serve as a metric, red maple (Acer rubrum) saplings and black gum
(Nyssa sylvatica) sapling were significantly sensitive to browse without being completely
eliminated from the landscape suggesting that they could serve as a potential indicator
species for browse (Regression black gum saplings: p<0.001, R2=0.36, red maple:
p=0.001, R2=0.19).
Hypothesis 2:
Although the study area was a continuous upland oak-hickory forest, there was varying
gradient of browse. At a landscape level the highest intensity browse was centered around
development and the surrounding edge habitat (Figure 3). This concentration formed a
steady gradient increasing with the distance to edge. This gradient was consistent with
literature suggesting that deer browse concentrates in low-density development habitat
due to the superior foraging habitat (Williams 1985, Ruzika et al. 2010).
Indices of browse varied relatively consistently across the landscape. Linear models
using parameters to predict browse indices demonstrated fit with variation of 13.9% in
root mean square error for sapling density and 13.0% for browse index (Table 1). For
browse index, the best fitting model included distance to portal, distance to building, and
latitude, which was only 1.07 times more likely than the next best fitting model that
removed the parameter, distance to portal (Table 1). Conversely, for sapling density,
Akaike model weights suggested that the model incorporating distance to edge and
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latitude was 1.8 times more likely than the next best fitting model including distance to
edge and stream and latitude (Table 1). For both browse index and sapling density,
latitude was the most important parameter being equally as important as distance to
building for browse index and 1.2 times more important than distance to edge for sapling
density (Table 2). The distance to plateau access parameters including stream and portal
were less important than distance to edge or latitude for both browse index and sapling
density (Table 1).
Sapling density was positively related to latitude, edge and stream (Table 1). The highest
weighted parameter in the model was latitude and increased on average by 1.18 sapling
per 196m2 with every 110m-increase latitude. Edge was the second most important
parameter and increased sapling density by 1.85 with every 50 meters increase away from
edge (non-forest habitat) (Table 2). Collectively, these results suggest that sapling
density should be highest in northern regions further from edge habitat and plateau access
(Table 2).
Browse index was negatively related to latitude and distance to building and positively
related to distance to portal as the weight of the 95% confidence interval is positive.
Browse Index on average increased by 0.05 with every 110m increase in latitude and
increased by 0.01 with every 50m away from building (Table 2). Collectively, we predict
that the browse index should be minimized in northern areas further from development.
So we assume as distance to building stream and latitude increase browse index decreases
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and as distance to portal and edge increases browse increases; however, these
relationships are weak (Table 2).
The predicted browse index map shows the negative impacts of browse influenced by
distance to portal, latitude and distance to building habitat (Figure 5). This produces more
of a gradient effect due to the related vectors than does the map of predicted sapling
density (Figures 4 and 5). The predicted sapling density map shows the negative impacts
of browse concentrated in low latitude and close to edge habitat (Figure 4).
Discussion
The results supported our hypothesis that deer non-randomly use the landscape in a
heterogeneous way and that sapling density would be the most effective metric to
quantify browse. As in other studies, deer movement patterns, browse preference, and
edge habitat influenced the intensity of browse across the landscape (Williams 1985,
Ruzika et al. 2010, Van Deelen et al. 1998).
Hypothesis 2:
Deer movement is driven by variables such as season, reproductive status, and
availability of food and water (Kie et al. 2002). The high intensity browse found in areas
of potential deer migration routes onto the Cumberland Plateau confirmed greater deer
usage of these areas. Two vectors that quantify migration routes, proximity to portal and
stream, were good predictors of browse index and sapling density. Vectors that quantified
edge habitat were also good predictors of browse. Like migration routes, the high degree
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of browse in edge habitat confirms increased deer usage in these areas. Distance to
building and distance to forest edge were used in the best predictive models of browse
index and sapling density, respectively. Kie et al. (2002) also found that edge density
affected deer movement patterns across a landscape and highlighted the importance of
spatial heterogeneity in determining deer distribution. The fact that browse index is
negatively correlated with distance from edge suggests that perhaps browse index used in
this study is not the best quantifier of landscape level deer impacts.
Latitude was highly predictive of sapling density and browse index; it was also included
in both of the combined best-fit models. Latitude served as a proxy for a number
landscape variables, which changed from south to north. This predictability could be due
to the accessibility of deer to the south side of the study area (the southern side had a total
of 16 portals, whereas the northern side had 8) and the direction of deer movement. This
suggests greater deer activity or densities on the south side of the study area. Saïd and
Servanty (2006) similarly concluded that spatial heterogeneity influenced the distribution
and density of deer populations in their study area. This land use pattern further
highlights the heterogeneous way in which deer use the landscape. Models that
incorporated deer movement, quantified through portals and streams, were better
predictors than those that just considered land use and should be used by managers
throughout their range.
Hypothesis 1:
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Managers need an effective metric to quantify browse that is rapid and applicable across
a large landscape to successfully manage for the effects of over-browse in forest
ecosystems (Frerker et al. 2013). Our research shows that the simple metric of sapling
density will detect these impacts. Frerker et al. (2013) found that tracking changes in the
understory over time, specifically plant density, is also more effective in quantifying
browse than percent cover, plant height, or quantifying reproductive structures. An
effective metric is one in which you have the ability to quantify impacts across varying
intensities of browse. We found that with some metrics (such as percent cover, diversity,
and richness) there is a low browse threshold where the metric drops to zero; this is
problematic because one cannot conclude if the elimination is due to deer effects or other
environmental factors. This was not found to be the case with sapling density. A single
browse event does not result in immediate mortality; it impacts saplings by inhibiting
growth, eventually through repeated browse events saplings die thereby effecting sapling
densities. These multistep outcomes allow sapling density to have a higher threshold and
serve as a more effective metric for varying intensities of browse.
Other metrics were not as effective to quantify browse and were not significantly
different from the exclosures. Percent cover measured all vegetative cover; some of the
vegetation, such as grass, is not affected by deer browse, explaining the low correlation.
Factors such as soil type, microenvironmental gradients, and geometry of land surface
could be confounding variables impacting vegetative cover, percent recruitment, species
richness, and diversity. Tree diversity was the only metric that did not correlate
significantly with browse because when clonal trees such as sassafras dominate the
transect, it affects the evenness of the species representation in the diversity index.
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Other studies have used sapling density to measure deer browse with similar results.
Rooney et al. (2000) found that sapling (30 cm–300 cm) density decreased as browsing
pressure increased. Exclosure studies have also found significant differences in sapling
density between browsed and nonbrowsed areas (Bressette et al. 2012, Martin et al. 2002,
Opperman et al. 2001, Pellerin et al. 2010, Ross et al. 1970). Although we used
exclosures in our study to assess the effectiveness of metrics, we suggest the use of
sapling density because it is less time intensive. Other studies have found significant deer
browse effects with other metrics such as percent cover, tree richness, and tree diversity
(Horsley et al. 2003, Kraft et al. 2004, Alverson et al., 1988 and Stockton et al., 2005).
These results are not consistent with our data and this confirms that these metrics are not
reliable across different landscapes. Conversely, sapling density can be dependent on
other limiting resources such as light (Hubbell et al. 1999). One concern for the use of
this measurement is that some sapling species are not eaten by deer, which can positively
bias sapling density indicating low browse such as found by Trembley (2007), but black
gum sapling density could be used as an indicator species because it is significantly
correlated with browse intensity. We acknowledge the use of black gum may not be
applicable to other regions because plants can respond differently to browse in different
landscapes; therefore, we recommend that managers evaluate the efficacy of different
sapling species to indicate browse intensity (Frerker et al. 2013).
Although both percent recruitment and sapling density were significant, from a
management perspective, sapling density is more useful due to the comparatively rapid
sampling methodology. It is important to have an efficient method so that data can
quickly go from field research to management application (Frerker et al. 2013). Sapling
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density is not dependent on seasonality and can be measured year-round. Sampling
sapling density requires very little training to be adept at the methodology. Due to its
sensitivity, lack of temporal constraints, and ease of sampling, this study recommends the
use of sapling density to quantify browse across the landscape. An effective metric such
as sapling density is not only sensitive to browse but also a direct measure of forest
regeneration. A metric that is practically applicable across a landscape is important to be
able to apply successful management to conserve biodiversity (Cardinal 2012).
Our study found that, due to the complicated and patchy distribution, deer over-browse
can pose a challenge to land management. Kie et al. (2002) found that landscape
heterogeneity affects deer movement and recommended incorporating these factors into
management decisions. To limit negative browse effects on the landscape level, one must
understand how browse impacts interact on a spatial scale. Deer browse impacts can limit
woody plant species composition, affect understory structure, and inhibit or alter
ecosystem processes (Pastor et al. 1993). Alverson et al. (1988) found that as few as 1.5
deer/mi2 can prevent regeneration of woody species. Understanding this inherent
variation of browse distribution is an important guide to co-managing deer densities and
forest sustainability (Millington 2010). It is important to recognize that the deer are
affecting forests and to quantify the severity and distribution in a time efficient manner to
focus limited resources in the high impact areas. Deer have created management
problems for many parks by degrading forest habitat, inhibiting plant succession,
changing plant composition, and preventing forest regeneration (DeNicola et al. 2000).
Further research is needed to determine if lowering deer densities limits negative browse
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impacts in high intensity areas. To maintain the biological balance of forest regeneration
and whitetail deer interactions, it is important to use a large spatial assessment with a
suitable metric such as sapling density to derive successful management plan.
Understanding where the landscape is affected allows managers to protect biodiversity
and increase landscape level resiliency. Using sapling density, kriging methodology, and
predictive linear models to develop maps will allow land managers to apply appropriate
and effective science-driven methods for managing deer populations. There are few cases
of using GIS to spatially map deer browse; it has been only used to map population
distributions (Millington et al. 2009, Massé and Côté 2012). However, using maps allows
managers to assess the distribution and severity of deer impacts and predict where the
impacts will be the most detrimental on the landscape. Because deer also cause humanwildlife conflict by causing economic losses, vehicle collisions, and increasing Lyme
disease prevalence (DeNicola et al. 2000), maps of deer activity via indices of browse
may be used by managers to reduce human-wildlife conflict as well as improve forest
regeneration and diversity.
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Acknowledgments
We would like to acknowledge the University of the South for providing funding and
access to their property for field sampling. I am grateful for the assistance of Dr. Jon
Evans for the assistance in the field and advising on this project. Special thanks for
assistance from Dr. Chris Van de Ven, Dr. Kristen Cecala, Kevin Hiers, Nate Wilson,
Ashley Block and Nathan Bourne.
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Literature Cited
Aldous, S. E. 1944. A deer browse survey method. Journal of Mammalogy 25(2), 130136.
Alverson, W. S., D. Waller, and S. L. Solheim. 1988. Forests too deer: edge effects in
northern Wisconsin. Conservation Biology 2.4: 348-358.
Bressette, J. W., H. Beck, and V.B. Beauchamp. 2012. Beyond the browse line: complex
cascade effects mediated by white‐tailed deer. Oikos 121(11): 1749-1760.
Burnham, K. P., and D. R. Anderson. 1998. Model selection and inference: an
information- theoretic approach. Springer-Verlag, New York.
Cardinal, E., J. Martin, J. Tremblay, and S. Cote. 2012. An experimental study of how
variation in deer density affects vegetation and songbird assemblages of recently
harvested boreal forests. Canadian Journal of Zoology 90(6): 704-713.
Carter, W.K. and T.S. Fredrericksen. 2007. Tree seedling and sapling density and deer
browsing incidence on recently logged and mature non-industrial private
forestlands in Virginia, USA. Forest Ecology and Management 242:671-677.
Cheston, C. 1953. The University of the South Forest. The Forestry Department.
Côté, S.D., T.P. Rooney, J.P Tremblay, C. Dussault and D.M. Waller. 2004. Ecological
Impacts of Deer Overabundance. Annual Review of Ecology, Evolution, and
Systematics 35: 113-147.
De Nicola, A. J., et al. 2000. Managing white-tailed deer in suburban environments.
Cornell Cooperative Extension, Ithaca, New York, USA.
ESRI 2011. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems
Research Institute.
19
Frerker, K., G. Sonnier, and D.M. Waller. 2013. Browsing rates and ratios provide
reliable indices of ungulate impacts on forest plant communities. Forest Ecology
and Management 291: 55-64.
Forest Ecology Class. 2012. University of the South Deer Survey. The University of the
South Sewanee, TN 37383. Unpublished Data.
Horsley, S.B., S.L. Stout, and D.S. deCalesta. 2003. White-tailed deer impact on the
vegetation dynamics of a northern hardwood forest. Ecological Applications
13:98–118.
Hubbell, S. P., Foster, R. B., O'Brien, S. T., Harms, K. E., Condit, R., Wechsler, B., ... &
De Lao, S. L. 1999. Light-gap disturbances, recruitment limitation, and tree
diversity in a neotropical forest. Science, 283(5401), 554-557.
Hurvich, C. M., and C. Tsai. 1989. Regression and time series model selection in small
samples. Biometrika 76:297-307.
Kie , J. G., R. T. Bowyer, M. C. Nicholson, B. B. Boroski, and E. R. Loft 2002.
Landscape heterogeneity at differing scales: effects on spatial distribution of mule
deer. Ecology 83:530–544.
Kraft, L.S., T.R. Crow, D.S. Buckley, E.A. Nauertz, and J.C. Zasada. 2004. Effects of
harvesting and deer browsing on attributes of understory plants in northern
hardwood forests, Upper Michigan, USA. Forest Ecology and Management
199(2): 219–230.
Krebs, C.J. 1985. The experimental Analysis of distributions and Abundance. Ecology
3:521-522.
20
Long, Z. T., T. H. Pendergast IV, and W.P. Carson. 2007. The impact of deer on
relationships between tree growth and mortality in an old-growth beech-maple
forest. Forest ecology and management 252(1): 230-238.
Luscier, J. D., W. L. Thompson, J. M Wilson, B.E. Gorham, and L.D. Dragut. 2006.
Using digital photographs and object-based image analysis to estimate percent
ground cover in vegetation plots. Frontiers in Ecology and the Environment 4:
408 - 413.
Martin, J. L., and C. Baltzinger. 2002. Interaction among deer browsing, hunting, and tree
regeneration. Canadian Journal of Forest Research 32(7): 1254-1264.
Massé, A., & Côté, S. D. 2012. Linking habitat heterogeneity to space use by large
herbivores at multiple scales: From habitat mosaics to forest canopy openings.
Forest Ecology and Management, 285, 67-76.
McShea, W. J. Ecology and management of white‐tailed deer in a changing world. 2012.
Annals of the New York Academy of Sciences 1249(1): 45-56.
Millington, J.D., M.B. Walters, M.S. Matonis, and J. Liu. 2010. Effects of local and
regional landscape characteristics on wildlife distribution across managed forests.
Forest Ecology and Management 259:1102–1111.
Opperman, J. J., and A.M. Merenlender. 2000. Deer herbivory as an ecological constraint
to restoration of degraded riparian corridors. Restoration Ecology 8(1): 41-47.
Pastor J., B. Dewey, R.J. Naiman, P.F. McInnes, and Y. Cohen. 1993. Moose browsing
and soil fertility in the boreal forests of Isle Royale National Park. Ecology 74:
467–480.
21
Pellerin, M., S. Saïd, E. Richard, J.L. Hamann, C. Dubois-Coli, and P. Hum. 2010.
Impact of deer on temperate forest vegetation and woody debris as protection of
forest regeneration against browsing. Forest Ecology and Management 260(4):
429-437.
Pedersen, B. S., and A.M. Wallis. 2004. Effects of white-tailed deer herbivory on forest
gap dynamics in a wildlife preserve, Pennsylvania, USA. Natural Areas Journal
24: 82-94.
Quinn, A. C. D., Williams, D. M., & Porter, W. F. 2013. Landscape structure influences
space use by white-tailed deer. Journal of Mammalogy, 94(2), 398-407.
R Core Team 2013. R: A language and environment for statistical computing. R
Foundation for
Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Ross, B.A., J.R. Bray, and W.H Marshall. 1970. Effects of long-term deer exclusion on a
Pinus resinosa forest in north-central Minnesota. Ecology 1088-1093.
Royall, R. M. 1997. Statistical evidence: a likelihood paradigm. Chapman and Hall, New
York.
Royo, A. A., S.L. Stout, D.S. deCalesta, and T.G. Pierson. 2010. Restoring forest herb
communities through landscape-level deer herd reductions: Is recovery limited by
legacy effects?. Biological Conservation 143(11): 2425-2434.
Russell, F. L., D.B. Zippin, and N.L. Fowler. 2001. Effects of white-tailed deer
(Odocoileus virginianus) on plants, plant populations and communities: a review.
The American Midland Naturalist 146: 1-26.
22
Ruzicka, K. J., J. W. Groninger, and J. J. Zaczek. 2010. Deer Browsing, Forest Edge
Effects, and Vegetation Dynamics Following Bottomland Forest Restoration.
Restoration Ecology 18: 702–710.
Saïd, S., and S. Servanty. 2005. The influence of landscape structure on female roe deer
home-range size. Landscape Ecology 20.8: 1003-1012.
Stockton, S. A., Allombert, S., Gaston, A. J., & Martin, J. L. 2005. A natural experiment
on the effects of high deer densities on the native flora of coastal temperate rain
forests. Biological Conservation, 126(1), 118-128.
Stromayer, K.A., and R. J. Warren. 1997. Are Overabundant Deer Herds in the Eastern
United States Creating Alternate Stable States in Forest Plant Communities?
Wildlife Society Bulletin 25(2): 227-234.
Tremblay, J. P., J. Huot, and F. Potvin. 2007. Density‐related effects of deer browsing on
the regeneration dynamics of boreal forests. Journal of Applied Ecology 44(3):
552-562.
Tremblay, J. P., Thibault, I., Dussault, C., Huot, J., & Côté, S. D. (2005). Long-term
decline in white-tailed deer browse supply: can lichens and litterfall act as
alternative food sources that preclude density-dependent feedbacks. Canadian
Journal of Zoology, 83(8), 1087-1096.
Van Deelen, T. R., H. Campa III, M. Hamady, and J.B. Haufler. 1998. Migration and
seasonal range dynamics of deer using adjacent deeryards in northern Michigan.
The Journal of Wildlife Management 205-213.
23
Williams S.C., A. J. DeNicola, T. Almendinger, and J. Maddock. 2013. Evaluation of
orginized hunting as a managment technique for overabundant white-tailed deer
in suburban landscapes. Wildlife Society Bulletin 37(1): 137-145.
Williamson S.J., D.H. Hirth. 1985. An evaluation of edge use by white-tailed deer.
Wildlife Society Bulletin 13:252-257.
White, M.A. 2012. Long-term effects of deer browsing: Composition, structure and
productivity in a northeastern Minnesota old-growth forest. Forest Ecology and
Management 222–228.
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Tables and Figures
160.0
140.0
Sapling Density
120.0
100.0
80.0
60.0
40.0
20.0
0.0
1
1.5
2
Browse Index
2.5
3
Figure 1: Sapling density in by browse index .5m in diameter plots Sewanee, TN ( p<.001,R2=.53)(
p=6.48x10-5, R2=0.31).
25
Percent Recruitment
P>0.001
Sapling Density
P=0.02
Figure 2: Boxplots showing the variation of the significant metrics in the transects. Green squares
represent control values. Significance values derived from Ttest.
26
Figure 3: Map of density of saplings in study area with transects and controls in Sewanee, TN.
27
Figure 4: Predictive map of browse index in study area with transects and controls based on linear
models in Sewanee, TN
28
Figure 5: Predictive map of sapling density in study area with transects and controls based on linear
models in Sewanee, TN
29
Model
Δ AIC
w
Mean
Error
K
AICc
RMSE
Edge + Latitude
3
280.65
0.00
0.51
21.75
21.01
Edge + Latitude + Stream
4
281.82
1.18
0.29
21.71
20.73
Latitude + Stream
3
284.62
3.97
0.07
22.73
21.96
Latitude
2
285.02
4.38
0.06
23.15
22.63
Browse Index
Portal + Latitude +
Building
4
-75.96
0.00
0.49
0.41
0.39
Latitude + Building
3
-75.83
0.13
0.46
0.41
0.40
Global
6
-71.35
4.61
0.05
0.42
0.39
Sapling Density
Table 1: Akaike importance weights for parameters in confidence sets of linear regression models
for prediction of sapling density and browse index. Bold values identify variables with best-fitting
models.
30
Estimate
Parameter
(SE)
Lower
Upper
95% CI 95% CI
Unit
Scaled
Importance
Change
Estimate
Weight
Sapling Density
Intercept
-93,960
-
(17,537)
128,332
-59,587
-
-
-
0.001
Latitude
1,178 (498)
202
2154
degrees
1.18
0.44
Edge
0.037 (0.013)
0.0064
0.055
50 m
1.85
0.37
Stream
0.015 (0.013)
-0.0097
0.039
100 m
1.50
0.18
1,737 (358)
1,034
2,439
-
-
-
degrees
-0.049
0.39
Browse Index
Intercept
0.001
Latitude
Building
Portal
-49.3 (11.0)
-70.9
-27.7
-0.00028
-
-
(0.00003)
0.00034
0.00022
50 m
-0.014
0.39
0.00027
-
(0.0002)
0.00008
0.00063
100 m
0.027
0.21
Table 2: Modeled average parameter estimates, standard errors (in parenthesis), associated
upper lower 95% confidence limits (Cl) for the best fitting linear regression models of predicting
sapling density and browse index. Unit change was based on type of parameter and effect on
deer behavior.
31