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Spotlight and Daylight Cruise Survey of Deer: Collecting and Interpreting Data

Interpreting Data

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The information in the appendix (tables 1-7: download all tables) was collected on the Noble Research Institute's D. Joyce Coffey Ranch located west of Marietta, Oklahoma. Deer spotlight and daylight cruise survey data have been collected on the Coffey Ranch since 1988 but are so voluminous only the 1995 data will be listed.

Survey Summaries and Density Calculations

Tables in the appendix list data from five spotlight surveys and several daylight cruise surveys conducted on the Coffey Ranch in 1995.

The information from tables 2-7 will be summarized for the total numbers of bucks, does and fawns. Now the "unidentified deer" column on the spotlight and daylight cruise survey form becomes important. Buck : doe and doe : fawn ratios should be obtained from totals excluding sighting groups with unidentified deer to eliminate as much sampling bias as possible. However, when the number of deer per acre is calculated, the unidentified deer groups should be included. Daylight cruise survey information should not be used to calculate the number of deer per acre because there are no visibility estimates.

Figures 3 and 4 summarize the number of deer sighted excluding and including unidentified deer.

Next, calculate the total number of acres observed. For simplicity, visibility estimates from only one spotlight survey in 1995 are listed (table 1). The total number of acres observed is needed before the number of deer per acre can be estimated. Totals from the spotlight surveys including that for unidentified deer will be used to calculate density. (Several years ago, we began estimating visibility during each survey; formerly, we estimated it once after the last survey was completed. Multiple visibility estimates allow acreage calculations from the yearly average of all surveys' visibility estimates. Visibility must be estimated each year because of changes in vegetative cover.)

figure 3

figure 4

The following procedure includes data from table 1 to calculate the estimated total number of acres observed. A better estimation results from averaging multiple visibility estimates:

  1. calculate average width of the left side (76 yards)
  2. calculate average width of the right side (58 yards)
  3. calculate average width of the entire route (76 + 58 = 134 yards)
  4. calculate length of the route in yards (7.5 miles [from odometer readings] x 1760 yards/mile = 13,200 yards)
  5. determine square yards in route (134 yards x 13,200 yards = 1,768,800 square yards)
  6. calculate acres observed (1,768,800 square yards/ 4,840 square yards per acre = 365 acres)

To calculate the estimated number of deer per acre, use the following formula:

  1. average number of deer sighted per spotlight survey (figure 4) [(15 + 34 + 23 + 31 + 41)/5] = 144/5 = 29
  2. acres per deer (365 acres/29) = 13

The estimated number of acres observed for all five 1995 spotlight surveys was 345. Divide that number by 29 to estimate acres per deer: 12. The total deer population now can be estimated. The Coffey Ranch is about 2,568 acres; divide that figure by 12 acres per deer for a total estimated deer population of 214 (figure 5).

figure 5

figure 3

Ratios, Composition, and Structure

From the approximate deer population on the Coffey ranch, the numbers of bucks, does, and fawns can be estimated and are best expressed as ratios (buck : doe, fawn : doe), composition (percentage of bucks, does, and fawns), and structure (number of bucks, does, and fawns).

Information from the deer sightings, excluding groups with unidentified deer (figure 3) and daylight cruise surveys is used in the following examples to demonstrate the calculation of population ratios, composition and structure.


Buck : doe and doe : fawn ratios are very useful in deer herd management and, when coupled with other information, help the manager understand yearly changes in the herd. For instance, the Coffey ranch's 1995 fawn : doe ratio was 0.62:1, which is lower than that in previous years (figure 5). The deer population for that year, however, was higher than it had been since 1987; a typical response to the deer population's exceeding carrying capacity is a decline in births.

The buck : doe ratio on the Coffey ranch during 1995 was 0.36:1 (figure 6). Because the herd also exceeded carrying capacity, does might have been overabundant enough to warrant their harvest, depending on the ranch's management goals.

To calculate ratios, use the total number of bucks, does, and fawns listed in the deer sightings, excluding groups with unidentified deer (figure 3). Daylight cruise survey information is included in the totals to increase sample size.
doe : buck ratio = total does divided by total bucks = 98/35 = 2.8 does : buck
fawn : doe ratio = total fawns divided by total does = 61/98 = 0.62 fawns : doe
buck : doe ratio = total bucks divided by total does = 35/98 = 0.36 buck : doe

Population Composition

This calculation estimates composition of the deer herd expressed as the percentage of bucks, does and fawns. The population composition on the Coffey Ranch in 1995, coupled with the above information, suggests an abundance of females as well as a reduced fawn crop.

To calculate population composition, use the total number of bucks, does, and fawns (including that from the daylight cruise surveys) listed in figure 3.
bucks (%) = total bucks divided by total deer sighted = 35/194 x 100 = 18% bucks
does (%) = total does divided by total deer sighted = 98/194 x 100 = 51% does
fawns (%) = total fawns divided by total deer sighted = 61/194 x 100 = 31% fawns

Population Structure

This calculation provides information on estimated actual numbers of bucks, does, and fawns, which a manager can use in conjunction with all other sources of population data to determine an adult-deer harvest quota that is customized to the management goals.

To calculate population structure, multiply the percentage of bucks, does, and fawns obtained from the population composition calculation by the number (214) obtained from the estimated deer population calculation:
number of bucks = 18% x 214 = 39
number of does = 51% x 214 = 109
number of fawns = 31% x 214 = 66

Harvest Quotas

Management goals and population data determine harvest quotas for any given deer herd. White-Tailed Deer — Their Foods and Management in the Cross Timbers gives a good explanation of various population management goals and can be obtained by calling or writing the Noble Research Institute. The publication is highly recommended for anyone managing white-tailed deer.

The Coffey Ranch population management goals are to maintain a deer population at or below carrying capacity, keep buck : doe ratios close to 1:2, and harvest no more than 33% of the bucks. Hunting lessees wanted to harvest bucks, so a quota of one per lessee (11 people) was established, which was less than 33% of the estimated buck population. The doe-harvest quota was set arbitrarilyat 26, more than twice the buck quota.

Assuming an unaltered population, which is purely hypothetical, the 1996 population would consist of 72 bucks (39 bucks plus 33 male fawns) and 142 does (109 does plus 33 female fawns), a doe : buck ratio of 2:1. These 214 deer plus the 1996 fawns would exceed carrying capacity even more than the 1995 population did, but natural deaths help curb herd growth. Still, does must be harvested to ensure smaller populations, and bucks' naturally higher mortality rate necessitates a higher doe-harvest rate if sex ratios are to be maintained. When bucks are harvested on neighboring properties, the doe-harvest rate must be increased commensurately.

Population numbers (figure 5) from total-deer estimates in 1995 were lower than those in 1994, so doe harvests were limited; increased mortality rates are expected when deer populations exceed carrying capacity. Also, because the 1995 doe : buck ratio was higher than desired, the buck harvest quota was conservative. Such adjustments are the "art" portion of the "art and science of wildlife management."

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Russell Stevens served as the strategic consultant manager and a wildlife and range consultant at Noble Research Institute. He received a bachelor’s degree in wildlife biology from the Southeastern Oklahoma State University and a master’s degree in animal science (range and wildlife option) from Angelo State University.