The Northern Woodland White-tailed Deer (Odocoileus virginianus ssp. borealis) is a species of deer found throughout Quebec, including the St. Lawrence Lowland region (Bernhardt et al. n.d.). Its name comes from the white underside of its tail, which can be seen when the deer is alarmed (Rue 2004).
Within the St. Lawrence region, white-tailed deer can be found in conifer and hardwood forests. These brown-coated mammals are crepuscular, which means that they are most active at dusk and dawn (Feldhamer and McShea 2012).
A white-tailed deer’s diet is primarily comprised of browse and forbs; however, because they are opportunistic, they have been known to eat birds and other small animals. Northern woodland white-tailed deer are among the largest of the subspecies, with adult bucks weighing about 100 kg and adult does weighing about 66 kg. Male deer have antlers that grow annually and are used to defend territory or fight over a potential mate (Geist 1998, cited in Innes 2013). Antlers may also be a secondary sexual trait; a study from 2000 found a link between antler development and pathogen resistance, suggesting that antler development may be a signal of male genetic quality (Ditchkoff et al. 2001). Baby deer, called fawns, are precocial, but they lack the muscle strength to outrun predators. Instead, they have white spots on their back and are almost odourless, which helps them camouflage with the background. As the fawn grows older and stronger, it will develop stronger scent glands and lose its spots, as it will be able to rely on speed to escape from predators (Feldhamer and McShea 2012).
White-tailed deer are not a migratory species; studies have shown that they usually stay within the same range (about one square mile) for most of their lives. In fact, “large numbers [of deer] have starved to death rather than leave a yarding spot to move even a few miles to an area where there may be abundant food” (Rue, 1962). However, their movement varies between summer and winter; the deer retreat to denser parts of the woods during winter to seek shelter. The daily activity of deer primarily consists of searching for food, but during “rutting” or mating season, bucks can be seen sparring with other bucks and marking his territory by rubbing his antlers on a tree and scraping the ground with his hooves (Rue 1962; Rue 2004).
PREVIOUS RESEARCH ON WHITE-TAILED DEER
Aging a deer has always been a difficult task. The most common technique is to look at the development and wear of its teeth. While this practice is widely used and accepted (Severinghaus 1949, cited in Gee et al. 2002), a study on 106 deer jawbone samples reveals that it may not be very accurate. The researchers were unable to assign a specific age to the jawbone samples, but categorized them into 3 basic age classes (fawn, yearling and adult). Thirty-four white-tailed deer biologists then attempted to age the jawbones, and failed 60% of the time for samples greater than two years of age. The results found the method of using tooth wear to indicate age very inaccurate beyond that of the 3 basic age classes (Gee et al. 2002).
Habitat preference and sexual segregation
In 2002, a team of researchers used data from forest maps and field surveys to investigate habitat preference and gender segregation of white-tailed deer. They hypothesized that does would seek out dense, sheltered forest during growing season to protect their fawns. The map analyses did not reveal gender segregation, but the field surveys showed that habitat preferences differed by gender. While both sexes used dense forest in the growing season, the males eventually spread out into more open spaces later in the season. The researchers concluded that aerial maps are not detailed enough to be indicative of habitat preference (Lesage et al. 2002).
Deer density based on aerial surveys and pellet-based distance sampling
In 2008 to 2009, a team of researchers compared aerial surveys and pellet-based distance sampling to estimate deer density in 6 preserved forests near Chicago, Illinois. They compared density estimates obtained from the use of both methods, as well as costs, bias, and precision. It was concluded that collecting accurate data on pellet decay and decomposition rates, using a large enough sample size, would be a more efficient and advantageous way of collecting density data than aerial surveys. Data from pellet samplings were less costly, required less equipment and professional skill, and did not depend on snow covering the ground. To conclude, the team discussed the importance of further research (Urbanek et al. 2012).
Depredation caused by deer diet
White-tailed deer have been known to cause extensive damage to field corn. Up to 80 to 90% of deer diets are comprised of corn, which causes the loss of millions of dollars each year. A research study from 2006 revealed that deer prefer certain corn hybrids based on nutrient content and time of maturity. The deer displayed a preference towards earlier maturing hybrids that contained higher levels of digestible material. Additionally, in a study from 2005, 67% of deer-feeding activity ensued in herbicide-treated areas rather than in untreated areas. As deer mostly feed on the edges of corn fields, farmers could reduce damage to their crops by planting hybrids undesired by deer on the edges of fields to minimize depredation (Delger et al. 2011).
White-tailed deer living in the Morgan Arboretum are constantly surrounded by humans and we want to know if deer activity is affected by human activity (agriculture, cultural services, urban development, etc.). To measure this, we are analysing deer signs (tracks, scats and browse) in relation to a scale of human activity.*
We set up three quadrants for each rank of the scale, for a total of 18 quadrants, throughout the Morgan Arboretum, the McGill bird observatory and the McGill Dean’s cornfields. Thus, our research question is redefined as follows: How do human disturbances (agriculture, cultural services, urban development, etc.) affect the activity of white-tailed deer in the Morgan Arboretum and its surrounding areas during the month of October?
We hypothesize that deer will generally avoid areas disturbed by humans, unless food resources are readily available. Deer activity should be most abundant within the denser forests (ranks 0-2), and scarcer within the areas of greater human disturbance (ranks 3-5). Rank 4 (agricultural area), however, may be an outlier as we hypothesize that this particular human disturbance (i.e. corn fields) will be a source of food for the deer and thus increase deer activity.
For data collection, the 10m x 10m quadrants are set up using measuring tape and marking tape. Within these quadrants, we search for deer signs, which we count and include in our data table. We also note the date, time and temperature of our session, as well as a brief description of the type of vegetation, fauna, soil etc. found inside the quadrant or nearby it. Any deer tracks are then erased to avoid replication of data. For the other two visits, we go back to the quadrants on a different day and search for fresh deer signs.
Video 1. How to set up a quadrant and collect data
*Scale of area studied in relation to human activity:
- 0: Dense forest without human activity (Morgan arboretum)
- 1: Light forest away from human activity (50m+) (Morgan arboretum)
- 2: Forest seasonally used by humans (McGill bird observatory)
- 3: Forest within visual distance (up to 30m) of regularly used paths (Morgan arboretum)
- 4: Agricultural area (McGill Dean’s Cornfields)
- 5: Area near/on roads, parking lots, buildings, etc. (Morgan arboretum)
EXAMPLES OF VLOGS TAKEN DURING DATA COLLECTION
Video 2. Vlog of quadrant 0.3 (Dense forest without human activity)
Video 3. Vlog of quadrant 1.1 (Light forest away from human activity)
Video 4. Vlog of quadrant 2.2 (Forest seasonally used by humans)
Video 5. Vlog of quadrant 3.2 (Forest within visual distance of paths)
Video 6. Vlog of quadrant 4.1 (Agricultural area)
- Bernhardt T et al. n.d. The Canadian Biodiversity Website. The Redpath Museum [Internet]. Available from: http://canadianbiodiversity.mcgill.ca/english/species/mammals/mammalpages/odo_vir.htm
- Delger J, Monteith K, Schmitz L, Jenks J. 2011. Preference of white-tailed deer for corn hybrids and agricultural husbandry practices during the growing season. Human–Wildlife Interactions 5(1):32–46 [Internet]. Available from: http://www.berrymaninstitute.org/journal/spring2011/6__Delger.pdf
- Ditchkoff S, Lochmiller R, Masters R, Hoofer S, Van den Bussche R. 2001. Major-histocompatibility-complex-associated variation in secondary sexual traits of white-tailed deer (Odocoileus virginianus): evidence for good-genes advertisement. Evolution [Internet]. 55(3): 616-625; Available from: http://onlinelibrary.wiley.com.proxy1.library.mcgill.ca/doi/10.1111/j.0014-3820.2001.tb00794.x/pdf
- Elbroch M and Murie O. 2005. The Peterson field guide to animal tracks, 3rd ed. Singapore: Houghton Mifflin Company. p. 282-285.
- Feldhamer G and McShea W. 2012. Deer coat colors. In: Deer: the animal answer guide. Baltimore (MD): The John Hopkins University Press. p. 36-37, 92-93.
- Gee K, Holman J, Causey M, Rossi A, Armstrong J. 2002. Aging white-tailed deer by tooth replacement and wear: a critical evaluation of a time-honored technique. Wildlife Society Bulletin [Internet]. 30(2): 387-393; Available from: http://www.jstor.org/stable/3784495
- Geist V. 1998. White-tailed deer and mule deer. In: Deer of the world: Their evolution, behaviour, and ecology. Mechanicsburg (PA): Stackpole Books. p. 255-414.
- Innes R. 2013. Odocoileus virginianus. Fire Effects Information System [Internet]. Available from: http://www.fs.fed.us/database/feis/animals/mammal/odvi/all.html#131
- Lesage L, Crete M, Huot, J, and Ouellet J. 2002. Use of forest maps versus field surveys to measure summer habitat selection and sexual segregation in northern white-tailed deer. Canadian Journal of Zoology [Internet]. 80(4): 717-726. Available from: http://search.proquest.com/docview/220501969?accountid=12339
- Rue L. 1962. The world of white-tailed deer. Philadelphia (PA): B. Lippincott Company.
- Rue L. 2004. Varieties and distribution. In: The deer of North America. Guilford (CT): The Lyons Press. p. 25-26.
- Urbanek R, Nielsen C, Preuss T and Glowacki G. 2012. Comparison of aerial surveys and pellet-based distance sampling methods for estimating deer density. Wildlife Society Bulletin [Internet]. 36: 100–106. Available from: DOI: 10.1002/wsb.116