Spatial assessment of deer browse impacts on the Domain
Introduction
Meg Armistead, Jon Evans, and Chris Van de Ven
Department of Biology and Landscape Analysis Lab
Percent Cover
Deer browse effects forest communities
•  Impact woody plant species composition, understory structure,
and inhibit or alter ecosystem process (Pastor et al. 1993)
Tree Diversity
Sapling Density
•  Cause changes in stem morphology, stem development, and
reduce seedling and sapling survival rates (Russell 2001)
•  Decrease sapling density and potentially impacting the future
composition and density of overstory trees (Pedersen 2004)
Deer browse is heterogeneous
•  Edge habitat provides optimal foraging due to low search cost,
low threat from predators, and high concentration of younger
stems (Williams 1985)
•  Deer migrate within social groups; migration paths result in
concentrated browse in and around these routes (Van Deelen et
al. 1998)
•  Preferential browsing can cause a shift in a forest composition to
an alternate stable state dominated by browse tolerant and unpreferred species (Long et al. 2007)
Deer overabundance on the Domain
• . Point count sampling has estimated the population to be 117
(+170/-80) deer per square mile which is believed to be five times
the sustainable density
•  To understand and effectively manage for the ecological impacts
of deer overpopulation we need an effective metric that can be
applied across the Domain
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http://coleyork.blogspot.com/2010/12/deer-in-sewanee.html
Percent Recruitment
Tree Richness
Results
Sapling Density (Fig1)
•  Most sensitive metric
•  Metric with greatest range at the unit of measure with no threshold effect
•  Only metric for which there was a significant browse effect (Wilcoxon signed rank p=.003)
Spatial Analysis (Fig 2)
•  Highest intensity browse was concentrated around campus and surrounding edge habitat
•  Browse forms a decreasing gradient as one moves away from campus
Black Gum Sapling (Nyssa sylvatica)
•  Significant correlation with browse index (Linear Regression p<.001, R2=.35 )
•  Could serve as a potential indictor species
p=.003
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Figure 1: Boxplots showing variation of metrics from transect data. Green boxes are control values.
Hypotheses:
•  Sapling density is the most sensitive and effective metric for
assessing deer browse across a large spatial area
•  Deer browse intensity pattern is heterogeneous at a landscape
level
Materials and Methods
Field Methods
•  46 randomly stratified points in plateau habitat out Breakfield Rd.
• 10 circular plots (5m diameter ) along a 50m transect
•  Saplings
•  Seedling (1m diameter nested circle)
•  Recorded seedling(>.25m) and saplings (.25m-3m)
by density and species
•  Browse Index (0-3)
•  Controls
•  4 exclosures (fences to keep deer out)
•  Established previous to 2000 (Biology and
Forestry Depts.)
•  Sampled using same methods as transects
Data Analysis
•  Sapling density: avg. number of saplings per transect
•  Percent recruitment : number of saplings divided by number of
seedling and saplings
•  Tree diversity : Shannon-Weiner index
•  Percent cover : Photoshop
•  Maps : ArcMap using ordinary kriging spherical model
Figure 3: Understory cover in low browse (left) and high browse (right) areas on the Domain
Conclusions
Sapling Density as efficient methodology
•  Sensitive to browse
•  Rapidly assessed
•  Not seasonally dependent
•  Applicable with little training
•  Represents direct measure of tree regeneration
Figure 2: Map of study site location on the Domain showing sapling density (stems per 196m2 )
estimated from transect and control data. Map derived from kriging analysis.
Acknowledgments
Sewanee for providing funding for my summer internship. The assistance in both the field and the lab Nate
Wilson, Dr. Matthew Rudd, Ashley Block and Nathan Bourne. I would also like to acknowledge my advisors Dr.
Jon Evans and Dr. Chris Van de Ven.
Importance of Landscape Heterogeneity
•  Deer browse on the Domain has varying intensity
•  Land managers need to take this spatial variation into account to be able to effectively comanage for biodiversity and deer
•  This GIS mapping allows management to take spatial variation into account