Dynamics of un-managed deer populations: database organization and auditing for
the National Park Service
Sarah Wilkinson, SUNY College of Environmental Science and Forestry
Edna B. Sussman Foundation Internship 2011 Final Report
Introduction
Habitat suitability for white-tailed deer (Odocoileus virginianus) in the eastern United
States has increased since the early 20th century due to intensifying land fragmentation,
anthropogenic food sources, and the extirpation of traditional predators. The resultant
chronic overabundance of deer has left many natural resource managers desperate to
alleviate the social, economic, public safety, and ecological problems caused by large
populations of deer. Nowhere is this issue more contentious than at National Park Service
(NPS) sites and other municipal lands with long-standing deer hunting prohibitions.
The NPS was created to preserve protected lands for the enjoyment and education of
the American people. An overabundance of deer in much of the eastern U.S. has forced the
NPS to grapple with the management of natural resources in the context of their cultural
resource mission. NPS sites cannot embark on management actions that significantly affect
the human environment, such as direct deer population reduction, without developing and
presenting relevant data on deer and other biota. Park managers must also try to predict
the impacts of management options on the environment in order to choose the most
prudent management alternative. In anticipation of future management actions, many of
NPS sites established routine deer monitoring programs a decade or more ago. However,
assistance was needed to locate, review, and interpret deer abundance data for the purpose
of informed selection of management actions and long-term management success. The E.B.
Sussman Foundation made it possible for me to not only provide this assistance to the NPS,
but also further my research into the factors contributing to the regulation of high deer
densities and predict deer population responses to rapid reductions
Work performed
From May to August 2011 I interned with the NPS under the supervision of regional
agency scientists. My primary internship objective was to obtain deer and environmental
data from as many NPS sites where such data existed and to audit these data for use with
mathematical models. The ultimate goal of this work was to better discern differences
between park environments and potential deer population responses to lowered
abundance (i.e. management). In addition, my second purpose was to produce data
products for and offer assistance to NPS and state natural resource staff. Many local, state,
and federal agencies are under great pressure to do more with less. Specialized assistance
improving deer data collection, assessing the impacts of deer on forest regeneration, and
producing maps and templates is in short supply.
I started my internship by doing a general assessment of which NPS sites might have
appropriate deer data. This involved contacting parks, working with NPS staff to acquire
data, and identifying data gaps. In either absolute or relative terms, abundance estimation is
fundamental to achieving the aims of conservation and provides a snapshot of the
population without intensive investments of time or money. Prior to 2000, nightspotlighting counts of deer along park roads and trails represented the bulk of historical
deer abundance data. Night-spotlighting surveys produce an index of relative deer
abundance that is specific to a park, prohibiting the comparison of indices between sites.
After 2000, estimates of absolute density became more widely available as parks began
incorporating distance-sampling methods into their night-spotlighting protocols. Distancesampling adds veracity and credibility to counts due to the method’s explicit incorporation
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of the observer’s ability to detect animals at near and far distances. Utilizing both nightspotlighting and distance-sampling data helps provide a clearer picture of deer population
dynamics due to differences in statistical and sampling errors between the two methods.
Once I identified potential sources of data (Figure 1), I worked with park and
regional managers to assess the condition of the data and identify assistance I could provide
parks. In addition to compiling and auditing deer abundance data, I also compiled climate
and landscape data relevant to deer biology for each park. These data will be used to
evaluate the degree to which climate and landscape features influence deer population
dynamics. I selected four landscape variables as proxies reflecting site habitat suitability for
deer, five variables summarizing climate conditions for plant growth, four variables
assessing climate severity, and two variables representing geographic site location. To
calculate site habitat suitability variables for each park deer-monitoring route I used ArcGIS
(v.9.3) and the National Land Cover Database. Climate data (e.g. number of days with ≥ 0.5”
precipitation, number of days with snow depth ≥ 1”, etc.) were collected from the weather
station closest to each study site and pooled for the time period that deer populations were
sampled. Geographic site location data were collected for the towns where the park
headquarters are located. The computation of these environmental variables in addition to
the compilation of deer population data is vital to my research and framing deer
management at NPS sites.
Accomplishments
During my summer with the NPS I successfully reached my internship objectives.
Deer monitoring data existed in a variety of forms from notes scribbled on blank paper, to
standardized data sheets, to complete databases. I streamlined almost 30 datasets (both
night-spotlighting and distance-sampling) to a series of annual statistics (mean, standard
deviation, coefficient of variation, etc.) ranging in length from 4 to 27 years long. Sites I
compiled data from ranged in latitude from Saratoga National Historical Park (New York) in
the north to Arkansas Post National Memorial (Arkansas) in the south (Figure 1). Each site
is different in land cover, climate, terrain, park size, and spatial/social context, all of which
affect resident deer populations. Due to their broad geographic dispersion and longstanding policies prohibiting hunting, these NPS sites are ideal for studying the dynamics of
un-managed deer populations.
To build positive relationships with each park, I produced products for each NPS
site. These products included summaries of my work, suggestions for improving site deer
population sampling, references for future reading, data spreadsheets with summary
graphs, and digitized sampling route maps. By filling critical data and analyses gaps, my
work this summer and masters thesis research will aid the agency’s mandate of science
based resource management.
Supplementing my initial internship objectives, I also assisted a NJ state biologist
conducting vegetation surveys (Photos 1-3). Across the U.S. and at several NPS sites the
connections between intense deer browsing, non-native plant invasion, and poor forest
regeneration are strong. We estimated plant coverage by species as part of an ongoing study
of native forest regeneration in the presence/absence of deer and presence/absence of
invasive plants. Volunteering to do this not only greatly improved my plant identification
skills, but also benefited the biologist who was in need of a field technician. Independently, I
also performed a very small-scale study to evaluate the application rate of white vinegar to
kill invasive Japanese barberry (Berrberi thumbergii) that dominates the forest understory
of many locales in the mid-Atlantic. Based on my limited sample, I found that a minimum of
2-3 cups of vinegar applied directly to the base of 1-8 stems could kill a plant within 2
weeks. At some NPS sites concern over sub-par forest regeneration has resulted in
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addressing non-native plant invasion. Given the potential that white vinegar could have for
park applications (e.g. could be applied by volunteers), I shared my results with NPS
invasive plant biologists and area conservation organizations.
My internship accomplishments not only benefited the NPS, but my personal skill
sets as well. My working knowledge of population modeling, statistical software, and the
finer details of several deer sampling techniques increased exponentially. I met and learned
from biologists/managers and functioned as an equal in a variety of professional settings.
These skills and connections will be invaluable to me as I seek a federal land management
or biologist position.
Discussion of future work
With auditing complete, data are ready to be fit to appropriate population models. I
will model the possible relationships between deer population dynamics, landscape factors,
and climate to understand what makes deer populations perform the same/differently. The
relationship between exhibited dynamics of un-managed deer populations and site
conditions is currently un-tested. My thesis should improve the collective knowledge of how
un-hunted deer populations function across space and time to better inform NPS
management. In addition, I will be utilizing the data to model potential differences and how
each park deer population could respond to an active reduction and whether the results
concur with NPS management goals. My finalized findings and their significance for the NPS
and other entities grappling with deer overabundance will be presented in my master’s
thesis.
Acknowledgements
My thesis research into the dynamics of un-managed deer populations and my
internship with the NPS would not have been possible without the support of the E.B.
Sussman Foundation. I look forward to presenting my findings at regional
conferences, departmental seminars, and to the NPS. The Foundation will be
recognized in all publications and presentations. I am grateful not only for the
monetary support, but also the confidence gained from the encouragement of such a
prominent organization.
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Figure 1. NPS study sites with deer data collected during my internship this summer.
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Photo 1: Identifying tree species within sampling plot.
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Photo 2: Removing Japanese stiltgrass (Microstegium vimineum) within sample plot.
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Photo 3: Removing garlic mustard (Alliaria petiolata) within sample plot.
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