This document summarizes a study on the white-tailed deer population at Sequoyah State Park in Oklahoma. It acknowledges contributors to the study and references other literature. The study aims to determine factors that predict fall deer density by analyzing how fawn recruitment is affected by hunting harvests, drought, flooding, controlled burns, visitors, and disease. Historical data show the park's deer population was above carrying capacity and experienced health issues. The park has monitored deer numbers since 1989 using annual deer drives. Preliminary regression analysis of the data suggests fall density and fawn recruitment may be increasing but the models have high variability. Advanced statistical analysis is still needed to account for more of the variation in yearly herd densities.

1. ACKNOWLEDGEMENTS
• Brent Morgan: OK Dept. of Wildlife
• Josh Mathis & Scott Henderson: US Army CoE
• Monica Deming & Mark Shafer: OK Climatological Survey
• Steve Amburn & Richard Tinker: NOAA & NCDC
REFERENCES
1. Masters RE, Bidwell TG, Elmore DR. 2013. Ecology and Management of Deer in Oklahoma. Oklahoma State
University, Oklahoma Cooperative Extension Service. NREM-9009. 2/1/2013, 01/16/2015.
2. Sams MG, Lochmiller RL, Qualls, Jr. CW, Leslie, Jr. DM.1998. Sensitivity of Condition Indices to Changing
Density in a White-Tailed Deer Population. Journal of Wildlife Disease [print]. [1998 Jan 1] 34(1): 110-125.
3. Logan, T. 1972. Study of White-Tailed Deer Fawn Mortality on Cookson Hills Deer Refuge Eastern Oklahoma.
Proceedings of the annual conference, Southeastern Association of Game and Fish Commissioners. 26, 27-35.
4. Innes RJ. 2013. Odocoileus virginianus. Fire Effects Information System, [Online]. U.S. Department of Agriculture,
Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). [2015 Jan 15]
5. Adams K. The Reality of Doe:Buck Ratios. Quality Deer Management Association [Internet]. 2013, April 23 [cited
2014 Dec 10].
6. Vreeland JK, Diefenbach DR, Wallingford BD. 2004. Survival rates, mortality causes, and habitats of Pennsylvania
white-tailed deer fawns. Wildlife Society Bulletin [online]. [cited 2014, Jan] 32(2):542-553.
ABSTRACT
Monitoring and controlling deer density is important for maintaining
healthy herds. Sequoyah State Park (SSP) in Cherokee county,
OK, houses a nature center which monitors the habitat of 10 km2 of
wildlife, including a white-tailed deer (Odocoileus virginianus)
population at or above carrying capacity. The park has been
monitoring deer density using bi-annual ‘deer drives’ with
Northeastern State University from 1989 through the present.
These ‘deer drives’ have allowed park biologists to reduce parasitic
diseases and tick population through relocation and controlled
harvest of the white-tailed deer. Until now, these decisions have
been based on the raw data, which demonstrates that the herd
density is decreasing at 0.75 deer/km2 each year. We aim to
determine the major factors attributed to predicting fall white-tailed
deer density. We hypothesize that the fall density indicates the
park’s carrying capacity and is dependent upon fawn recruitment to
the herd. Further, through analysis of the factors that affect fawn
recruitment, we will be able to account for a significant amount of
the variation in herd density each year. This will be accomplished
using statistical inquiry to analyze the effects of both county and
park hunting harvest, the Palmer Drought Severity Index (PDSI),
lake elevation/flooding, area of controlled burns, number of park
visitors, and disease prevalence among harvested deer.
BACKGROUND
In 1989 park biologists noted that the deer population was becoming diseased
and began to monitor the herd density in collaboration with Oklahoma State
University . The park began a deer relocation in 1991. However, of the ~74
deer relocated from ‘91-’94 (data missing from ’92), 6 of those deer were
previously tagged and at least two were found swimming the lake to return to
the park. Therefore, SSP discontinued the program until 2007 when they began
controlled archery hunting within the park.
DEER HERD DENSITY
In Oklahoma, good quality habitat can support up to 16 deer/km2 (1). As seen in
Fig. 3, SSP herd density is considerably higher. Therefore, Oklahoma State
University researchers analyzed the health affects of high herd density at SSP
from 1989-1999. They concluded that deer in high density (~70 deer/km2)
regions, deer exhibited classic nutritional stress and high prevalence of
parasitic disease (2).
In Cookson Hills Refuge, OK, high herd densities displayed high fawn mortality
as compared to lower density years and 71% of fawn mortality was due to lone
star tick feeding (3).
Fig. 2. November 2014
deer drive: (a) Leaping
Doe; (b) Drivers moving up
the road; (c) Pre-drive
meeting; and (d) Park
biologist Paula Hanafee
discussing drive protocols
d
c
b
a
DEER DRIVE METHODOLOGY
Fig. 1. (a) SSP is uniquely void of significant predation to deer, because it is a
peninsula that is bordered to the North by Hwy. 51. (b) The area in red
represents the current deer drive area where ‘drivers’ move from West to East
and deer are counted by ‘observers’ as they cross roads bordering the North,
South, and West. The combination of the red and grey areas represent the
drive area from 1989-1999.
Coordinate system: WGS 1984 World Mercator
Hwy. 51
Hulbert, OK
Na b
RESULTS
Fig. 3. Linear regression of preliminary data. (a) This graph has been used as the
primary measurement of density and source for determining the number of hunting
tags in SSP since 2007. (b & c) These graphs split fall and spring density. Fall density
demonstrates much less decline in comparison to spring density. Therefore, fawn
recruitment must be increasing. (d) Palmer Drought Severity Index (PDSI) calculated
from Mar. – Jun. of the preceding spring using the Tahlequah Mesonet station and the
Vanderbilt MATLAB PDSI tool.
CONCLUSION
Each preliminary data set demonstrates a trendline
with a promising correlation (Ex. Fig. (d)). However,
variability in the data (shown by R2) is high.
Advanced statistical measures and determination of
outliers is crucial to reconcile variability.