The Effect of the Proximity of Permanent Water Sources on White-Tailed Deer Population Distribution

Running head: EFFECT OF WATER SOURCES ON DEER DISTRIBUTION 1

The Effect of the Proximity of Permanent Water Sources on White-Tailed Deer Population Distribution Andy Huang, Eshi Kohli, Sreeram Pillai, Vivikta Rao Period 1 Thomas Jefferson High School for Science and Technology

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Abstract White-tailed deer overpopulation affects not only Northern Virginia, but the whole continental United States. We chose to focus our study on determining how deer group in proximity to water, as this information could be used to control deer populations. Although the assumption that deer group more around water seems logical, in our research we found that previous studies failed to elaborate on the specifics of deer distribution relative to major water sources as well as lacking focus on white-tailed deer (the species of deer currently overpopulating Northern Virginia). We hypothesized that there would be more white-tailed deer, or ​Odocoileus virginianus, ​around water sources. To study this topic, our group conducted field tests in Elizabeth Hartwell Mason Neck National Wildlife Refuge, located in Lorton, Virginia. Over the course of four different trips, we cleared a total of 76 plots four times each, to look for deer pellets. We counted deer pellets and recorded their location by plot to determine deer distribution. The body of water we used to determine proximity to deer was the Potomac River.

After using an ANOVA test at the 0.05 significance level on our data, we failed to reject the null hypothesis and thus found no correlation between deer population distribution and permanent bodies of water. The study of deer spread must continue in order to control the growth of the deer population.

Introduction Starting in the 20​th​century the U.S. white-tailed deer population grew from less than a million deer to over 30 million nationwide (VerCauteren, 2003). ​Most likely attributed to the lack of natural predators, deer overpopulation disrupts the essential balance of forest ecosystems (White, 2012). ​For example, ​in northern Minnesota, researchers found that decades of deer

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overbrowsing has led to the decline in the diversity of tree species, causing canopy dominance to shift to less ecologically productive trees (White, 2012). Overpopulation of white-tailed deer can lead to major changes in their habitat, harming their ecosystem as a whole.

In the last 40 years, white-tailed deer populations also grew exponentially i​n Virginia, from around 575,000 deer in 1987 to 900,000 deer statewide in 2018 (Knox & Lafon, 2018).

N​atural predators are no longer able to keep the deer population in check, and wildlife management programs are not doing enough to solve the problem. Deer inflict millions of dollars in damage to crops, trees, and gardens and are a safety hazard to drivers. The economic impact of deer hunting in Virginia is over $500 million annually ("Virginia Deer," 2015). ​To address this, local wildlife management programs promoted deer hunting. The initiative saw some success in reducing the deer population, as hunters killed 190,636 white-tailed deer during the 2018-19 deer hunting season (“2018–2019 Deer Kill Summary”, 2019). Elizabeth Hartwell Mason Neck National Wildlife Refuge, a home for white-tailed deer located in Lorton, VA also promoted the culling of deer herds through various programs to minimize the effects of overpopulation on the rest of the refuge (“Deer Hunting”, 2018). Finding ways to control and measure the white-tailed deer population is a major factor in solving the problems that come with deer overpopulation.

The first step in white-tailed deer population management is finding the distribution of deer. Researchers use several survey methods to track deer such as mobile daylight surveys, spotlight surveys, camera surveys, and deer pellet counting. Mobile daylight and spotlight surveys consist of setting a route to drive through and then counting the number of deer seen on the route during the whole day (Collier, Ditchkoff, Raglin, & Smith, 2010). However, these survey results would not represent the true deer population as they do not take into account areas

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away from roads. Another method of deer tracking is the camera survey, in which a camera monitors activity a few feet away near an area with set up bait (Deblinger & Alldredge, 1991).

This survey permits the researcher to control data collection amounts and does not rely on eyesight, however the presence of other species can affect visitation as the cameras often use motion sensors. Finally, deer pellet counting is a simple and effective method to find deer population. However, taking counts in sections of an area, rather than the whole area, may result in inaccurate population estimates, such as if the deer tend to group around water and the researcher takes pellet counts in more wooded areas, resulting in a population estimate that would vary from the true population. By understanding where deer prefer to group, solutions for controlling the overpopulation of deer become more easier to carry out. Thus, in determining whether deer tend to cluster around bodies of water, researchers can more effectively regulate deer populations.

Literature Review Water is a vital source for ​Odocoileus virginianus (​white-tailed deer), but researchers are still unsure of how the surrounding bodies of water affect the distribution of the deer populations.

Ramos-Robles, Gallina, and Mandujano (2013) investigated the distance between white-tailed deer to bodies of water in Tehuacán-Cuicatlán Biosphere Reserve located in Mexico. They concluded that no significant relationship existed between deer density and their proximity to bodies of water. However, the researchers recognized that there may have been unintentional miscalculations due to both the use of pellet group counts to measure density and the possible effects of hunting season on the data.

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On the other hand, other researchers found evidence to counter this outcome. Webb, Hewitt, and Hellickson (2007) researched the effect of permanent water sources on the movements and home ranges of white-tailed deer. Their results supported that deer expand their home ranges to include permanent water sources. However this conclusion is very general as it expresses that water sources affect deer habitat size, rather than determining where deer are most prevalent. Additionally, Deblinger and Alldredge (1991) found that there tends to be a higher concentration of ​Antilocapra americana​(pronghorn antelope) in areas near bodies of water. We can draw comparisons between the pronghorn antelope and white-tailed deer, since they have similar needs and behaviors. This implies that antelope and white-tailed deer population groupings are alike in proximity to bodies of water. Similarly, Boroski and Mossman (1987-1988) hypothesized that the mule deer (​Odocoileus hemionus​) tend to be less than 3.2 kilometers away from a water source. The researchers found on average, deer were 1.38 km away from water sources, therefore supporting their hypothesis. For our research we decided to a similar method to discover where deer prefer to live by using the mean distance from a water source. We believe this would be more effective in finding solutions to containing white-tailed deer populations.

The contrast between the results of the various studies showed that there were still discrepancies in the scientific community. We wished to clarify these variations and confirm the distribution of deer populations relative to water sources, which motivated us to conduct this study.

Hypothesis

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The white-tailed deer population drastically rose in the past decade, and the need to control the growth of this species is crucial for a balanced environment. Attempts to reduce the population of deer include methods such as hunting, several deer tracking surveys, and studying deer behavior. For example, many authors researched the distribution of deer in relation to water sources and some found a correlation (Ramos-Robles, 2013; Webb, 2007; Deblinger 1991; Boroski and Mossman, 1987, 1988). To further our understanding, we hypothesized that if the plot was farther from the nearest body of water, there would be a lower concentration of deer in that area. We found the distance between specific plots and the nearest body of water, the Potomac River, in kilometers. The number of piles of feces per plot determined the population of deer in that plot. This meant that the more piles of feces found, the higher the population of deer would be. We hypothesized that plots farther away from water would have less deer because water is vital to the survival of white-tailed deer. We sought to find an association between these variables because if it exists, then it would be in our great interest to prevent construction on areas near water, as construction would disturb the deer living there. Such an association could also help determine areas in which to contain deer.

Site Description We conducted research at the Elizabeth Hartwell Mason Neck National Wildlife Refuge (EHMNNWR), commonly known as Mason Neck. The Mason Neck refuge is situated in Lorton, VA, 29 kilometers south of D.C at (38°38'45.5"N 77°10'15.0"W). The refuge is southeast of Kanes Creek and northwest of the Potomac

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River. It is also east of Belmont Bay and northeast of Occoquan Bay (Figure 1.1). The wildlife refuge contained approximately 901.2 hectares of land. Some of the habitats present in the refuge included upland hardwood forests and freshwater marshes. Mason Neck has over 30 species of trees, which make up 762.0 hectares of forest in Mason Neck. Most notable of the tree species was the Virginia pine (​Pinus virginiana)​ which can grow up to 18 meters long.​​Mason Neck also housed 31 species of mammals, including the white-tailed deer (​Odocoileus virginianus​)(Figure 1.2), the focus of our research.

Procedure We set up 76 plots in EHMNNWR (Figure 2), adjacent to Sycamore and Anchorage Roads. Setting up a single plot entailed finding pre-existing PVC pipes markers (two per plot) and laying out a 22.5 meter measuring tape between these pipes to verify the length of a plot. Then, we placed the end of a 3.5 meter string at each PVC pipe to mark the width of the plot. Four surveyors flags at the corners of the plot acted as reference points when clearing the plot. To clear out a plot, all four of our group members started at one corner of the plot and worked along the length of the plot, moving leaves out of the way and removing existing deer pellets from the plot.

When we reached the far end of the plot, we turned around and repeated the process until the plot was completely cleared. After clearing the plot, we replaced the leaves (put the displaced leaves back to

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their original areas) over the plot to minimize any disturbances we caused to the environment.

This same method was used to collect deer pellet groups in previous years as well. In total, we cleared 76 total plots (1-40 on Anchorage Road and 41-76 on Sycamore Road) (Figure 1.3) of any existing deer pellet groups on October 18, 2018. We revisited the plots on November 1, 2018 for the first pre-deer hunt data collection, recorded each plot’s deer pellet counts, and cleared all feces from all the plots. We repeated this same procedure on December 6, 2018 and December 20, 2018, both of which were the post-deer hunt visits. All pellet counts were used to determine deer population density in the area.

Results Table 1 Statistics Summary Table for the Effect of Proximity to Water Sources on Mean Deer Pellet Group Counts Intervals of distance from Potomac River (m) 0-200 200-400 400-600 600-800 800-1000 1000-1200 1200-1400 Mean pellets per interval

0.1

0.375

1.455

0.5

0.444

4.333

0.25

Sample Standard

0.345

0.363

2.007

1.363

0.430

0.942

0.5

10

16

11

10

15

8

4

Deviation Number of trials ANOVA

F = 1.8129

Df(between) = 6

α = 0.05

EFFECT OF WATER SOURCES ON DEER DISTRIBUTION F of 1.8129 < 2.2373

Df(within) = 67

9 p = 0.1098

We chose to do an ANOVA test because there are more than 2 levels of the independent variable, and using a one-way ANOVA test with an alpha value of 0.05 allowed us to assess the difference between 7 sample means. The data shows a mean of 0.3 pellets found per plot with 2 peaks in the data at 400-600 m and 1000-1200 m. These peaks in the data show that deer prefer some areas over others when picking locations for home ranges. The trial results within each level of the independent variable is spread similarly, showing little irregularity between each level of the IV. The degrees of freedom within is 67 and the degrees of freedom between is 6.

The alpha value is 0.05. The F Value of the ANOVA is approximately 1.81, and the p-value is 0.1098. The difference between the sample means is not statistically significant, so we fail to reject the null hypothesis, which states that all sample means were the same. However, this data does not support our hypothesis of if there are water sources, deer will tend to group near them.

Discussion The purpose of this study is to assess the relationship between the location of permanent water sources and white-tailed deer distribution in EHMNNWR. We hypothesize that as the distance between the plot and the body of water increases, the white-tailed deer population near the plot will decrease. However, our data does not support our hypothesis, as our critical value F is greater than the p-value (Table 2). Errors in our procedure, variability, or confounding variables may have attributed to this conclusion.

Similar to Ramos-Robles, Gallina, and Mandujano (2013), we believe that the procedure of finding the deer pellets may have been faulty. For example, our group had no control over how students in different groups looked for deer pellets. There is the definite possibility that of

EFFECT OF WATER SOURCES ON DEER DISTRIBUTION 10 human error while counting. Students could easily have miscounted deer pellets, messing up the data. Although the sample was sufficient, we only visited the plots four times, all within a timespan of three months, so not many deer may have visited these plots in this timeframe. In future experiments, researchers should go through each plot twice to account for all deer pellets.

To increase the accuracy of the results, researchers should also collect data during winter and spring.

In the case that deer do not obtain water from the Potomac River, other water sources exist around EHMNNWR. Many temporary and/or unknown water sources, such as Kanes Creek, could have affected deer proximity to the Potomac, which would explain the seemingly average average pellet counts for the plots. This supports our speculation that deer would tend to group around water, however our hypothesis limited itself to the Potomac river.

The containment of the deer population is crucial to protect the stability and balance of the environment. Examining the behavior of deer and their preferences of habitat could aid in finding a solution for deer overpopulation. Unlike Ramos-Robles (2013), Webb (2007), Deblinger (1991), and Boroski and Mossman (1987), whose results supported the correlation between water sources and deer distribution, our results suggest that there is no relationship between the distance of a plot from the nearest body of water and the concentration of deer in that area. For further study, groups should focus on more than just deer pellet grouping and proximity to water. Future studies should widen their focus so that analysis occurs not only on bodies of water, but also on surrounding flora, fauna, and proximity to humans. Since our hypothesis focused on a very specific topic, our results do not address the overarching issue of

EFFECT OF WATER SOURCES ON DEER DISTRIBUTION 11 deer population containment. Later research needs to focus on which types of habitat deer prefer, information of which would be significant for deer management programs.

Acknowledgements We could not have written this paper without the support of many wonderful people who helped us with our study. First, we would like to give a big thanks to the EHMNNWR manager, Amanda Daisey, biologist Christopher Wicker, maintenance Charles Henschel, and administrator Stacie Allison. We would also like to thank the people who made it possible to go on the field study trips: our bus driver, Brian Means, and the volunteer chaperones that accompanied us. We are also very grateful for Robert Greene and Brenna Courtney for helping us review and revise this paper. Finally, we thank our teachers and counselor, Ms. Smith, for being the support we needed to complete this project. Not only did they help supervise the trips but they also gave us time to work on the study, answered our questions, provided materials, and helped to review our paper.

EFFECT OF WATER SOURCES ON DEER DISTRIBUTION 12 Appendix A Table 2 Mean Pellet Count per Interval Distance from Potomac River Intervals of

Average

distance from

Plot number

pellets per

Potomac River

(with average pellet count per plot for all dates visited)

interval

31(0), 32(0), 33(0.667), 34(1), 35(0), 36(0), 37(0.333), 38(0),

0.1

(m) 0- 200

39(0), 40(0) 200-400

23(0.667), 24(0), 25(1), 26(0), 27(0.667), 28(0), 29(0),

0.375

30(0.333), 69(0.333), 70(0.667), 71(0.333), 72(0), 73(0.333), 74(0), 75(0.667), 76(1) 400-600

19(0), 20(0.333), 21(0), 22(0.667), 62(5.667), 63(1.333),

1.455

64(2.667), 65(0.333), 66(0), 67(0.333), 68(4.667) 600-800

13(0), 14(0), 15(0), 16(0), 17(0), 18(0), 57(0), 58(0),

0.5

59(0.333), 60(4.333) 800-1000

5(1), 6(0), 7(0.333), 8(0), 9(0), 10(0.333), 11(1), 12(1.333),

0.444

50(0.667), 51(0.333), 52(0.333), 53(0.667), 54(0), 55(0.667), 56(0) 1000-1200

1(0), 2(0), 3(0.667), 4(0), 45(0), 46(2.667), 47(1), 48(0)

4.333

1200-1400

41(0), 42(1), 43(0), 44(0)

0.25

EFFECT OF WATER SOURCES ON DEER DISTRIBUTION 13 References 2018–2019 deer kill summary. (2019). Retrieved February 21, 2019, from Department of Game & Inland Fisheries website: https://www.dgif.virginia.gov/wildlife/deer/harvestsummary/ Boroski, B. B., & Mossman, A. S. (1996). Distribution of Mule Deer in Relation to Water Sources in Northern California. ​The Journal of Wildlife Management​, ​60​(4), 770-776.

Retrieved May 6, 2019 from http://www.jstor.org/stable/3802376 Collier, B., Ditchkoff, S., Raglin, J., & Smith, J. (2007). Detection Probability and Sources of Variation in White-Tailed Deer Spotlight Surveys. ​The Wildlife Society,​​71(​1), 277-281.

Retrieved May 6, 2019 from https://doi.org/10.2193/2005-728 Deblinger, R. D., & Alledredge, A. W. (1991). Influence of Free Water on Pronghorn Distribution in a Sagebrush/Steppe Grassland. ​Wildlife Society Bulletin​. Retrieved May 6, 2019 from JSTOR database.

Deer hunting. (2018, July 31). Retrieved February 21, 2019, from Elizabeth Hartwell Mason Neck website: https://www.fws.gov/refuge/mason_neck/visit/deerhunt.html 5 Survey Methods for Deer Management. (2014, August 17). Retrieved February 18, 2019, from Deer Management at Buck Manager website: https://www.buckmanager.com/2014/08/17/5-survey-methods-for-deer-management/ Google Maps [Map]. (2017, May). Retrieved May 6, 2019 from https://goo.gl/maps/Fx7mzxmjK4wRiPM79 Knox, M., & Lafon, N. (2007). Deer management in Virginia. Retrieved February 19, 2019, from Rockingham - Harrisonburg Chapter The Izaak Walton League of America website: https://www.iwla-rh.org/html/DGIF_articles/deer_management.html

EFFECT OF WATER SOURCES ON DEER DISTRIBUTION 14 [Map of plots on Anchorage Road and Sycamore Road] [Map]. (2019). Retrieved May 6, 2019 from https://mapmakerapp.com?map=5c9ce9d23e803951577711f64c40 National Wildlife Refuge System. (2015).Wildlife & Habitat - Elizabeth Hartwell Mason Neck U.S. Fish and Wildlife Service. (2015). Retrieved May 6, 2019 from https://www.fws.gov/refuge/Mason_Neck/wildlife_and_habitat/index.html Ramos-Robles, M., Gallina, S., & Mandujano, S. (2013). Habitat and Human Factors Associated with White-Tailed Deer Density in the Tropical Dry Forest of Tehuacán-Cuicatlán Biosphere Reserve, Mexico. ​Tropical Conservation Science,​​6.​Retrieved May 6, 2019 from https://doi.org/10.1177/194008291300600109 VerCauteren, Kurt C., "The Deer Boom: Discussions on Population Growth and Range Expansion of the White-Tailed Deer" (2003). USDA National Wildlife Research Center Staff Publications. 281. Retrieved May 6, 2019 https://digitalcommons.unl.edu/icwdm_usdanwrc/281 Virginia Deer Management Plan, 2015-2024. (2015). Retrieved May 8, 2019, from Department of Game and Inland Fisheries website: https://www.dgif.virginia.gov/wildlife/deer/management-plan/ Virginia Department of Game and Inland Fisheries. (2016). Virginia deer management program [Virginia Deer Management Program]. Retrieved February 12, 2019, from DEPARTMENT OF GAME & INLAND FISHERIES website: https://www.dgif.virginia.gov/wildlife/deer/deer-management-program/ Webb, S. L., Hewittt, D. G., & Hellickson, M. W. (2007). Effects of Permanent Water on Home Ranges and Movements of Adult Male White-Tailed Deer in Southern Texas. ​Texas

EFFECT OF WATER SOURCES ON DEER DISTRIBUTION 15 Journal of Science​. Retrieved May 6, 2019 from http://link.galegroup.com/apps/doc/A177860078/GPS?u=tjhs_e&sid=GPS&xid=7986c09 3 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.​Retrieved May 6, 2019 from https://doi.org/10.1016/j.foreco.2011.12.043