United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

General Technical Report RMRS-GTR-246WWW

September 2010

Monitoring Ruffed Grouse in the Black 

Hills: Protocol and User’s Manual for the 

Occupancy Spreadsheet Program

Christopher P. Hansen

Mark A. Rumble

Joshua J. Millspaugh
Hansen, Christopher P.; Rumble, Mark A.; Millspaugh, Joshua J. 2010. Monitoring 

ruffed grouse in the Black Hills: Protocol and user’s manual for the occupancy 

spreadsheet program. Gen. Tech. Rep. RMRS-GTR-246WWW. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

36 p.

Abstract

Monitoring ruffed grouse (Bonasa umbellus) in the Black Hills National Forest is 

a priority for forest managers due to the bird’s status as the management indicator 

species for quaking aspen (Populus tremuloides) and its value to hunters and other 

recreational groups. We conducted drumming surveys, estimated occupancy, and 

assessed the influence of sampling and site variables to determine benchmark 

levels of precision for ruffed grouse occupancy and detection probability estimates. 

Using these estimates and simulations, we developed a monitoring protocol for 

ruffed grouse in the Black Hills. We then created a user-friendly program that 

uses monitoring data in Microsoft Excel to calculate ruffed grouse occupancy and 

detection probability estimates. (The program is available at http://www.fs.fed.

us/rm/forest-grassland-lab/products/ruffed-grouse-occupancy.) The user’s manual 

herein briefly describes the theory behind occupancy modeling and explains 

how to enter and analyze data and interpret results from drumming surveys. 

Additionally, we provide recommendations on which type of occupancy and 

detection probability estimates should be calculated, depending on the needs of the 

investigator. The program does not provide precision estimates for heterogeneous 

occupancy or detection probabilities.

Keywords: detection probability, indicator species, monitoring, occupancy, quaking 

aspen, ruffed grouse

Authors

Christopher P. Hansen is a Research Associate with the Department of Fisheries 

and Wildlife Sciences, University of Missouri in Columbia, MO.

Mark A. Rumble is a Research Wildlife Biologist with the U. S. Forest Service, 

Rocky Mountain Research Station in Rapid City, SD.

Joshua J. Millspaugh is a Distinguished Professor with the Department of Fisheries 

and Wildlife Sciences, University of Missouri in Columbia, MO.

Cover photo by Martin Tarby.

Available only online at http://www.fs.fed.us/rm/pubs/rmrs_gtr246.html

Rocky Mountain Research Station

Publishing Services

Telephone (970) 498-1392

FAX (970) 498-1122

E-mail rschneider@fs.fed.us

Web site http://www.fs.fed.us/rm/publications

Mailing address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526
Contents

Part I.

Introduction....................................................................................... 1

Sample Site Determination ................................................................ 2

Determining Survey and Site Requirements ................................ 2

Cost............................................................................................ 3

Site Identification........................................................................ 3

Survey Strategy ........................................................................... 4

Calculating Occupancy and Detection Probabilities................... 4

Part II.

Overview ........................................................................................... 6

Description................................................................................. 6

Occupancy Modeling................................................................. 6

Detection Histories..................................................................... 7

Covariates .................................................................................. 7

Calculating Constant Occupancy................................................ 8

Calculating Occupancy as a Function of Strata........................... 8

Calculating Occupancy as a Function of 

 Vegetation Covariates.......................................................... 9

Viewing Qualitative Occupancy and Detection 

 Probability Trends................................................................ 9

Opening and Starting the Program..................................................... 9

Microsoft Excel 2003................................................................ 10

Microsoft Excel 2007................................................................ 10

Using the Program ............................................................................11

Title Worksheet..........................................................................11

Detection History Worksheet.................................................... 12

Covariates Worksheet ............................................................... 14

Occupancy Worksheet ............................................................. 16

Literature Cited................................................................................ 24

Appendix A. Sample Sites for the 2007 and 2008 

Ruffed Grouse Drumming Surveys in the 

Black Hills National Forest ................................................... 26

Appendix B. Data Sheet for Ruffed Grouse Surveys in the Black 

Hills National Forest............................................................. 35

Appendix C. Instructions to Calculate Vegetation Areas for 

Ruffed Grouse Sample Sites in the Black Hills 

National Forest ..................................................................... 36
USDA Forest Service RMRS-GTR-246WWW. 2010. 1

Part I.

Monitoring Protocol for Ruffed Grouse in the 

Black Hills National Forest

Introduction

Ruffed grouse are a popular game species that depend on the health and availability 

of specific habitats (Barber and others 1989, Madison 1969). Ruffed grouse inhabit early 

successional deciduous forests with inclusions of mature vegetation (e.g., aspen [Populus 

spp.]) for cover from predators and acquisition of food (Kubisiak 1985, Maxson 1989, 

Sharpe and others 1997). Because ruffed grouse depend on specific vegetation (usually 

aspen) for food and dense woody cover, they are considered an important indicator species for the condition and abundance of aspen communities (Barber and others 1989). As a 

result, ruffed grouse were selected as the management indicator species for quaking aspen 

(Populus tremuloides) in the Black Hills National Forest (BHNF) (U.S. Forest Service 

1997).

Fire suppression, cattle grazing, and pine (Pinus spp.) expansion for timber harvest 

have caused extensive changes in the vegetation composition in the BHNF (Froiland 1990, 

Bartos and Shepperd 2003). In particular, aspen communities have declined and have been 

replaced by either white spruce (Picea glauca) or ponderosa pine (Pinus ponderosa). Thus, 

management strategies such as burning, cutting, and fencing have been used in the last 20 

years in an attempt to halt and reverse aspen declines (U.S. Forest Service 1997, Bartos and 

Shepperd 2003). To evaluate whether aspen management and associated strategies have 

been effective in increasing the extent of aspen and ruffed grouse in the BHNF, monitoring 

is a necessity. Monitoring will provide managers with information on ruffed grouse status, 

trends, and habitat associations as it relates to implementation of the BHNF Forest Plan.

Past monitoring activities of ruffed grouse in the BHNF have not effectively evaluated 

ruffed grouse population trends because of inconsistent surveys, bias from conducting surveys only in vegetation types where ruffed grouse were expected to occur, and the failure 

to consider imperfect detection (SAIC 2005, M. A. Rumble, U.S. Forest Service, personal 

communication). Occupancy modeling is one alternative for monitoring ruffed grouse in 

the BHNF that overcomes some of these deficiencies. Occupancy models have recently 

become popular because they do not assume all individuals are detected, only require the 

investigator to determine the presence or absence of the species from repeated surveys, and 

can be robust predictors of the proportion of the study area occupied when appropriate predictor variables are considered (MacKenzie and others 2002, Crossland and others 2005, 

MacKenzie and others 2006). Additionally, using a multi-year design, an examination of 

the metapopulation dynamics of the species is possible by evaluating local extinction and 

colonization probability trends in the study area (Hanski 1994, MacKenzie and others 

2003, MacKenzie and others 2006). These trends might also be useful for future monitoring purposes as well as prescribing effective management strategies.

Drumming surveys (Petraborg and others 1953, McBurney 1989) paired with occupancy modeling were used to estimate ruffed grouse occupancy and detection probabilities 

in 2007 and 2008 (Hansen and others in press). Those estimates were used in multi-year 

sample site simulations to determine the required number of sites and surveys to meet occupancy precision benchmarks (Hansen 2009). We used the results from Hansen (2009) to 
2 USDA Forest Service RMRS-GTR-246WWW. 2010.

develop a monitoring protocol that will provide statistically defensible results on the status 

and trends of ruffed grouse in the BHNF. We describe how to complete this protocol below.

Sample Site Determination

To use an occupancy modeling design for monitoring, the investigator must define the 

sampling unit (i.e., site) and decide how the presence or absence of the species will be 

determined at each site (MacKenzie and Royle 2005). The most widely used monitoring technique for ruffed grouse is roadside drumming counts (Petraborg and others 1953, 

McBurney 1989). This technique involves stopping at established points along roads during the spring to listen for male ruffed grouse drumming. This is an efficient and effective 

method because male ruffed grouse drum consistently throughout the early morning (approximately every 2-4 minutes), making it easy to determine the presence of a ruffed grouse 

at a listening point (McBurney 1989). This technique is also particularly well suited for use 

in the BHNF given the high road densities of 3.2 km/km2 (Rumble and others 2005).

For drumming surveys, the most appropriate sampling unit is the radial area surrounding 

an established listening point. The radius is determined as the farthest distance at which 

a ruffed grouse can be heard. During 2007 and 2008 ruffed grouse drumming surveys in 

the BHNF, Hansen (2009) found that the farthest distance at which he could hear a ruffed 

grouse was 550 m. Thus, investigators should consider the 550 m radius surrounding each 

survey point (95 ha area) as the site.

Determining Survey and Site Requirements

Estimating the number of repeat surveys and the number of sites to survey during a 

monitoring season necessitates prior knowledge of the occupancy and detection probability 

of the study species in the study area (MacKenzie and Royle 2005, MacKenzie and others 

2006). Using occupancy and detection probability estimates from 2007 and 2008 (Hansen 

and others in press), Hansen (2009) found that three repeat surveys were the most efficient 

when detection probabilities were >0.3 during the monitoring season. Because the probability of detecting ruffed grouse in the BHNF was 0.27 (Hansen and others in press), and 

because this probability will increase if surveys are completed during optimal detection 

conditions, investigators should conduct at least three surveys at each site during future 

monitoring practices.

To calculate the appropriate number of sites to survey, the investigator must first decide 

upon a sampling design. Hansen (2009) found that conducting the same number of repeat 

surveys at each site and surveying the same sites each year was the most efficient monitoring design for ruffed grouse in the BHNF. Next, the investigator must determine whether 

sites will be stratified throughout the study area or be randomly distributed. In the BHNF, 

Hansen and others (in press) found that ruffed grouse occupancy probabilities differed 

across strata defined by extent of aspen (Map 1). Therefore, it will be most efficient to 

stratify the sample and calculate separate site requirements for each stratum.

Using occupancy estimates for each stratum, an average detection probability, and three 

repeat surveys, the investigator may estimate the number of sites necessary to achieve 

the desired precision by entering the appropriate values into the multi-season application 

in Program GENPRES (Bailey and others 2007; http://www.mbr-pwrc.usgs.gov/software/

presence.html). Hansen (2009) found that 70, 171, and 455 sites should be surveyed each 

year for at least four consecutive years in high, medium, and low strata, respectively, to 

achieve an occupancy estimate within ± 50% of the true population occupancy, 95% of the 

time (given occupancy and detection probability estimates in the BHNF in 2007 and 2008). 

Ultimately, the sampling requirements will be determined by the desired amount of precision and the occupancy and detection probabilities of ruffed grouse in the BHNF.
USDA Forest Service RMRS-GTR-246WWW. 2010. 3

Cost

The primary costs associated with ruffed grouse monitoring will be manpower and vehicles. For future monitoring, we assume surveys will be completed from 1 May to 1 June 

and surveys will be conducted 5 days each week. Thus, there will be a maximum of 21 days 

available to complete surveys, assuming surveys will not be completed on days with inclement weather. If the number of sites recommended by Hansen (2009) (70, 171, and 

455 sites in high, medium, and low strata, respectively) are surveyed three times each, then 

the necessary number of survey events will equal:

Map 1. Vegetation strata in the Black Hills National Forest. Strata were delineated by district with slight 

modifications to district boundaries. Strata represented low, medium, and high proportions of quaking aspen.
4 USDA Forest Service RMRS-GTR-246WWW. 2010.

3 surveys * (70 high strata sites + 171 medium strata sites + 455 low strata sites) = 

2,088 survey events

If each technician/volunteer can complete 10 surveys each day (Hansen 2009), then the 

estimated number of technicians/volunteers needed to complete all survey events will be:

2,088 survey events / (21 survey days * 10 surveys/day/technician) ≈ 

10 technicians/volunteers

Each technician/volunteer will also need either a Forest Service or personal 4x4 vehicle 

to reach survey sites. During 2007 and 2008 surveys, each technician drove an average of 

80 miles/day, depending on the distance to the survey routes (Hansen 2009). If 10 technicians drive 80 miles/day for 21 days, then 16,800 miles will be driven each year.

Finally, there will be equipment and maintenance costs such as All-Terrain Vehicles, 

Global Positioning Systems units, wind meters, thermometers, tire repair, oil changes, and 

so on. Total costs for a monitoring season will depend upon the amount technicians are paid 

and the number of volunteers recruited, the cost of gas and reimbursement rate, and the cost 

of equipment each season.

Note: These sample size estimates are on the high side. Our sample size estimates are based on occupancy and detection probability estimates from 2007 

and 2008. Further, sample size estimates assume occupancy estimates are 

within ± 50% of the true population occupancy, 95% of the time (Hansen 2009). 

Managers might consider several sampling strategies given the numerous tradeoffs among available personnel, sample size estimates, and monitoring costs. For 

example, annual monitoring in the high and medium strata will offer insight into 

the general status of ruffed grouse in the BHNF. Because monitoring in the low 

strata requires substantially more sampling effort than the medium and high strata, 

managers might consider relaxing the precision for monitoring this stratum. While 

site requirements and cost are high to achieve the benchmarks used by Hansen 

(2009), the estimated number of sites to achieve a particular level of precision 

will decline if occupancy of ruffed grouse increases. Sample site estimates and 

cost will also reduce if detection probability is maximized by monitoring during the 

month of May, during low wind speeds, near sunrise, and during good weather 

conditions (Hansen and others in press). Finally, multiple years of monitoring will 

reduce sample size estimates and cost, thus, a long-term commitment to monitoring is necessary. In summary, if fewer sites are surveyed, then fewer technicians/

volunteers will be needed and less fuel will be consumed, which will reduce overall 

costs.

Site Identification

Once the sample size has been estimated, the investigator must then decide how and 

where to distribute the sites throughout the study area. To increase efficiency for 2007 

and 2008 ruffed grouse surveys, Hansen (2009) placed survey sites along secondary (i.e., 

gravel) and primitive (i.e., dirt) roads using a stratified random sampling design, without replacement. Strata were defined by the proportion of aspen (low, medium, and high) 

compared to other vegetation, and a simple random sample was drawn within each stratum. Sites were not included along primary roads (i.e., paved and upgraded gravel roads) 

because traffic could interfere with the ability to hear drumming ruffed grouse. Further, 

Hansen (2009) constrained survey sites to be >1 mile apart to ensure independence among 

sites. Assuming the maximum distance a ruffed grouse can be heard is 550 m (Hansen 

2009), approximately 91% of the BHNF is represented alongside these road systems.

We recommend that survey sites be selected from a random sample of the sites in each 

stratum monitored during 2007 and 2008 (Hansen 2009; Appendix A). There is a high 

degree of confidence that these sites are accessible and there is already two years of data 

on these sites, which will make occupancy trends more robust. However, if more sites are 
USDA Forest Service RMRS-GTR-246WWW. 2010. 5

necessary in a stratum than what was previously sampled, sites should be randomly placed 

along secondary and primitive roads at least one mile apart using ArcGIS (Environmental 

Systems Research Institute, Redlands, California, USA). We also recommend dividing 

sites into regions and routes for organizational purposes and to maximize efficiency while 

monitoring. Region and route information are included for each site surveyed during 2007 

and 2008 (Appendix A).

Survey Strategy

Survey conditions

It is important to conduct ruffed grouse drumming surveys during optimal detection 

conditions. In the BHNF, the optimal conditions for sampling ruffed grouse were between 

1 May and 1 June, during wind speeds <10 mph, early in the morning (near sunrise), and 

when no precipitation (rain or snow) was occurring (Hansen and others in press). Thus, 

drumming surveys should be completed during May from half an hour before sunrise until 

5 hours after sunrise and should not be conducted during poor weather such as high winds 

(>10 mph) or moderate to heavy precipitation.

Drumming surveys

At each site, the investigator should listen for drumming ruffed grouse for 5 minutes on 

each survey and record, on provided data sheets (Appendix B), whether or not a grouse 

was detected drumming. A standardized 5 minute survey is used because ruffed grouse 

typically drum every 2-4 minutes during the breeding season (McBurney 1989). If one 

or more grouse are detected, the investigator should record a “1” under the “presence/absence” column for the corresponding survey on the data sheet. If grouse are not heard, the 

investigator should record a “0.” Other data that may be recorded at each site include date, 

time, temperature, weather conditions, and average wind speed (using a hand-held wind 

meter) during each survey. Although it is not necessary for monitoring, there is also space 

on the data sheet to record the azimuth and estimated distance (<50 m, between 50 and 150 

m, >150 m) to the drumming ruffed grouse.

All sites should be surveyed once before the second survey is initiated. This order of 

surveys has some flexibility, but overall this should be the protocol. In total, at least 3 repeat surveys should be conducted at each site. Repeat surveys at individual sites should be 

spread across the sampling season (early May, mid-May, late May) and each repeat survey 

should be conducted at different times of the morning (at least an hour apart) to ensure 

that the time of survey does not influence detection of ruffed grouse. Additionally, repeat 

surveys should be conducted by different individuals to remove observer bias that may be 

associated with experience or ability to hear ruffed grouse drum.

Calculating Occupancy and Detection Probabilities

Once all surveys have been completed at all sites, the investigator should calculate occupancy and detection probability estimates using the Occupancy Spreadsheet Program 

described in Part II or other free software such as Program PRESENCE (MacKenzie and 

others 2006) or Program MARK (White and Burnham 1999). The Occupancy Spreadsheet 

Program calculates estimates for occupancy and detection probability, provides the ability to view results from previous years, and offers guidance for how to interpret results. It 

does not, however, calculate occupancy and detection probability trends using regression 

methods such as estimating the percent incline or decline within a certain confidence limit. 

If the investigator desires to calculate occupancy and detection probability trends using 

regression methods, we suggest consulting literature (e.g., Sauer and Droege 1990, Dixon 

and others 1998) to determine which method of trend estimation is most appropriate to 

meet the needs of the investigator.
6 USDA Forest Service RMRS-GTR-246WWW. 2010.

Part II.

User’s Manual for the Ruffed Grouse 

Occupancy Spreadsheet Program

Overview

Occupancy and detection probability estimates can typically be calculated using free 

software such as Program PRESENCE (MacKenzie and others 2006) and Program MARK 

(White and Burnham 1999). However, learning how to use these programs can be time 

consuming and difficult. To simplify the calculation of occupancy and detection probability estimates, we have developed a spreadsheet program in Microsoft Excel that makes all 

the necessary calculations. This program calculates occupancy and detection probability 

estimates for ruffed grouse in the Black Hills National Forest (BHNF) using data from annual monitoring that is recorded into the spreadsheet. The user may download the program 

at http://www.fs.fed.us/rm/forest-grassland-lab/products/ruffed-grouse-occupancy.

Description

The occupancy modeling spreadsheet program uses a maximum likelihood estimation 

approach and multinomial likelihood framework to calculate single-season occupancy and 

detection probability estimates for ruffed grouse in the BHNF using detection histories 

(presence/absence data) and covariate (variable) values recorded during each survey at the 

survey site. The model can be executed in either Microsoft Excel 2003 (XP) or 2007, and 

the majority of functions are executed using Visual Basic for Applications (VBA) code in 

modules attached to the Excel workbook. All covariates included in the model (e.g., date, 

wind speed, time, precipitation, area of quaking aspen [Populus tremuloides] and white 

spruce [Picea glauca] surrounding the site) are the parameters that were most influential on 

occupancy and detection probability during 2007 and 2008 spring ruffed grouse drumming 

surveys (Hansen and others in press).

This program includes separate worksheets for entering detection histories, sampling 

covariates that influence detection probability, and site covariates that influence occupancy. 

Each of these worksheets includes the 402 sites that were surveyed during 2007 and 2008 

ruffed grouse drumming surveys (Hansen 2009) and 101 extra sites in case sample size 

increases in the future. Associated with each site are four survey events, allowing the user 

to complete up to four repeat surveys at each site, which was consistent with the level 

of sampling used and suggested by Hansen (2009). Each worksheet has a “Continue” or 

“Back” button which allows the user to move forward or back through the worksheets in 

the program. Also, each worksheet has either a “Help” (?) or “Interpretation” option if 

directions or results are confusing. Finally, the “Read Me” section on the title worksheet 

provides further information about the program and occupancy modeling if the help and 

interpretation options in the program are not sufficient.

Occupancy Modeling

To properly estimate occupancy, two critical aspects of sampling animal populations 

must be accounted for: detectability and spatial variability in occupancy (MacKenzie and 

others 2006). Occupancy models assume the species of interest is detected imperfectly 
USDA Forest Service RMRS-GTR-246WWW. 2010. 7

(MacKenzie and others 2002). In other words, it is assumed that animals are not detected 

during each survey, even if they are present. Thus, to estimate occupancy accurately, repeat 

surveys are necessary at each site to obtain estimates of detection probability. Repeat visits 

to a survey site produce “detection histories.” These detection histories form the basis for 

analysis because they allow the investigator to estimate a detection probability as well as 

an occupancy probability where the likelihood (L) can be written as:

∏=

Ψ =

n

i

L p X Xn Xi

1

1 ( , ,..., ) Pr( ) (1)

where

Ψ is the occupancy probability,

p is the detection probability, and

Xi

, i =1,..., n are the detection histories for n number of surveyed sites (Mackenzie 

and others 2006).

To account for spatial and temporal variability in occupancy and detection probability, 

covariates may be included in the analysis. It is important to appropriately model covariates that cause heterogeneity in occupancy or detection probabilities because neglecting to 

do so might result in biased estimates of occupancy and detection probabilities and reduced 

inference about factors that influence these metrics (MacKenzie and others 2006).

Detection Histories

To calculate occupancy and detection probability estimates using this program, the user 

must first enter the detection histories for each site sampled in the “Detection History” 

worksheet, entering a “1” if a ruffed grouse was detected at the site during the specific 

survey, a “0” if a ruffed grouse was not detected, or a “-” if a survey was missed or a site 

was not surveyed.

Covariates

Next, the user may enter values for sampling covariates that influence detection probability estimates. During 2007 and 2008 surveys, Hansen and others (in press) found the 

factors that had the highest influence on the probability of detecting ruffed grouse were 

date, wind speed, time, and precipitation. Thus, the user may enter the date (mm/dd/yyyy), 

average wind speed (mph), time at the beginning of the survey (hh:mm), and whether or 

not precipitation occurred during the survey (coded as “1” for precipitation and “0” for 

no precipitation) at each site during each survey in the worksheet. The program automatically converts the date to an ordinal date and time to number format (for computational 

purposes) and then standardizes the date, wind speed, and time values into Z-scores using:

Z score x x

v - = -

t

r (2)

where

x is the value,

xr is the mean value, and

vt is the sample standard deviation (Donovan and Hines 2007).

The program standardizes these values because if the values are too high or if there is 

too large a range in values, the program can have a difficult time converging on a solution. 

By standardizing, 99% of the data fall between -3 and +3 (Donovan and Hines 2007). No 

standardization is necessary for the precipitation data because precipitation is a categorical 

variable.
8 USDA Forest Service RMRS-GTR-246WWW. 2010.

The user may also enter site covariate values that influence occupancy on a separate 

worksheet (“Site Characteristics”). Hansen and others (in press) found that the spatial extent of aspen and spruce occurring within 550 m of the sample site was related to ruffed 

grouse occupancy. As a result, these covariates were included in the worksheet. In this 

worksheet, there are no repeat surveys because it is assumed that vegetation characteristics do not change throughout the sampling season. Thus, each site should have one value 

for each vegetation type for that monitoring season. Currently, the spatial extent of each 

vegetation type for sites from 2007 and 2008 surveys (Hansen 2009) are entered into the 

worksheet. However, vegetation attributes will change from year to year, so when ArcGIS 

BHNF vegetation layers have been updated, the user should update the values in the worksheet (see Appendix C). Once entered, these data are also automatically standardized into 

Z-scores. Using the detection histories and covariate values that were entered by the user, 

the program calculates occupancy and detection probability estimates by using the “Solver” 

option in Excel to maximize the likelihood of acquiring a particular detection history, given 

the histories and covariate values provided.

Calculating Constant Occupancy

Occupancy and detection probability estimates may be calculated for various situations, 

depending upon the biology of the study species, characteristics of the study area, and 

the needs of the investigator. If the investigator hypothesizes that site attributes are not 

influencing the presence of the study species throughout the study area, then it may be 

appropriate to only calculate one occupancy estimate for the study area. Similarly, if the 

investigator assumes that date, weather condition, observer, and so on have no influence on 

detecting the species, then one estimate of detection probability may also be appropriate. 

For this reason, this program provides the option of calculating both a constant occupancy 

and detection probability. Conversely, the presence of the species at a site might be influenced by the attributes of that site, and the probability of detecting the species might be 

influenced by sampling variables such as time, date, and weather conditions. In this case, 

the probability of the site being occupied will vary among sites and the probability of detecting the species on successive surveys will also vary. As a result, this program provides 

two options for calculating occupancy and detection probabilities that are a function of 

covariates. Because ruffed grouse select some vegetation attributes over others and the 

probability of detecting a ruffed grouse is influenced by time and sampling conditions 

(Hansen and others in press), calculating constant occupancy and detection probability 

estimates might not be the most appropriate for this species.

Calculating Occupancy as a Function of Strata

When calculating sampling effort for ruffed grouse surveys in the BHNF, Hansen and 

others (in press) hypothesized that ruffed grouse occupancy would depend upon the extent 

of aspen vegetation surrounding the site. As a result, he stratified the BHNF into high, 

medium, and low aspen proportion strata. Hansen and others (in press) also found that 

the probability of detecting ruffed grouse was a function of date, wind speed, time, and 

precipitation. This program gives the user the option to calculate occupancy as a function 

of vegetation strata and detection probability as a function of date, wind speed, time, and 

precipitation. If this option is selected, the model outputs an occupancy estimate for each of 

the three strata in the BHNF and an average detection probability estimate. Also, the user 

may view survey-specific detection probability estimates and graphs that display how date, 

wind speed, time, and precipitation influenced the probability of detecting ruffed grouse.
USDA Forest Service RMRS-GTR-246WWW. 2010. 9

Calculating Occupancy as a Function of Vegetation Covariates

Hansen and others (in press) discovered that the area of aspen and spruce within 550 m 

of a survey point influenced ruffed grouse occupancy. Consequently, this program allows 

the user to calculate occupancy as a function of the spatial extent of aspen and spruce surrounding the survey point. This option provides the user with an occupancy estimate for 

each site as well as an average occupancy value. Also, this option assumes detection probability is a function of date, wind speed, time, and precipitation; thus, the user is provided 

with a detection probability estimate for each survey and an average detection probability 

estimate. This option is more robust than the previous two because it allows the user to estimate the probability that each site is occupied and determine at what times and conditions 

detection probability is maximized.

Viewing Qualitative Occupancy and Detection 

Probability Trends

The previous three options for calculating occupancy and detection probability provide 

the user with a “snapshot” of the state of ruffed grouse in the BHNF. Because trend information is typically much more useful to managers, the program is equipped with an option 

that allows the user to view the trend in occupancy and detection probability estimates 

since 2007 (“View Occupancy and Detection Trends”). This option will be useful because 

managers will easily have a qualitative view of whether occupancy and detection probabilities of ruffed grouse have been increasing, decreasing, or remaining stable, which will 

aid in prescribing effective management strategies. This option does not provide estimates 

of precision for occupancy and detection probability; thus, the user must use other occupancy software such as Program PRESENCE or Program MARK if precision estimates are 

desired.

Opening and Starting the Program

Download the program from http://www.fs.fed.us/rm/forest-grassland-lab/products/

ruffed-grouse-occupancy.

When opening the program from Windows Explorer or through the “file open” option 

in Excel, a security warning screen will appear. On this security screen, “Enable macros” 

should be selected to ensure all modules are available for use. If there are problems opening the program, the security level might be set too high on the computer.

This program uses the “Solver” option in Microsoft Excel to calculate occupancy estimates. Thus, “Solver” must be added to the Excel spreadsheet and referenced in VBA for 

this calculation to be possible.

Instructions for enabling macros, adding “Solver,” and referencing “Solver” in VBA 

differ between versions of Microsoft Excel. Thus, the user should use the following instructions, conditional on the version of Excel he/she is using.

Microsoft Excel 2003

To check the security level in Microsoft Excel 2003, select “tools,” “macro,” and “security.” Click the “Security” tab, and select “medium.” This will ensure access to the macros 

available in this program.
10 USDA Forest Service RMRS-GTR-246WWW. 2010.

To add “Solver” in Microsoft Excel 2003, select “Tools” and “Add-ins” from the dropdown window once the spreadsheet has been opened. Then, check the box next to “Solver 

Add-in.”

To reference “Solver” in VBA for Microsoft Excel 2003 (once the spreadsheet has been 

opened):

-Click on the Tools tab, point to “Macro,” and select “Visual Basic Editor”

-Click on Tools, then References

-Click Browse and navigate to:

C:\Program Files\Microsoft Office\Office10\Library\SOLVER

-Under “Files of type:” select “Microsoft Office Excel Files”

-Select SOLVER

-Click “Open”

-Make sure “SOLVER” is checked under “Available References”

-If “MISSING:SOLVER.XLA” is checked under “Available References”, uncheck it

-Click “OK,” close Visual Basic Editor, and return to the Excel spreadsheet.

Microsoft Excel 2007

To check the security level in Microsoft Excel 2007 (once the spreadsheet has been 

opened):

-Click on the Microsoft Office button in the top left hand corner of the screen

-Click “Excel Options”

-Click “Trust Center” on the left side of the screen

-Click “Trust Center Settings”

-Click “Macro Settings”

-Select “Enable all macros” to ensure access to the macros available in this program

-Click “OK”

-Click “Trust Center Settings” again

-Click “ActiveX Settings”

-Select “Enable all controls without restrictions and without prompting”

-Click “OK” and exit out of the “Trust Center” and “Excel Options” windows

To add “Solver” in Microsoft Excel 2007 (once the spreadsheet has been opened):

-Click on the Microsoft Office button in the top left hand corner of the screen

-Click “Excel Options”

-Click “Add-Ins” on the left side of the screen

-In the “Manage” box near the bottom of the screen, select “Excel Add-ins”

-Click “Go”

-In the “Add-Ins Available” box, check “Solver Add-In”

-Click “OK”

To reference “Solver” in VBA for Microsoft Excel 2007 (once the spreadsheet has been 

opened):

-Click on the Microsoft Office button in the top left hand corner of the screen

-Click “Excel Options”

-Check “Show Developer tab in the Ribbon” under “Top options for working with Excel”

-Click “OK”

-Select the Developer tab at the top of the screen

-Select Visual Basic (located underneath the Microsoft Office button)

-Click on “Tools,” then “References”

-Click Browse and navigate to:

C:\Program Files\Microsoft Office\Office12\Library\SOLVER

-Under “Files of type:” select “Microsoft Office Excel Files”

-Select SOLVER.XLAM

-Click “Open”
USDA Forest Service RMRS-GTR-246WWW. 2010. 11

-Make sure “SOLVER” is checked under “Available References”

-If “MISSING:SOLVER.XLA” is checked under “Available References,” uncheck it

-Click “OK” and return to the Excel spreadsheet

Warning: Occasionally “Solver” encounters errors and needs to be uninstalled 

and reinstalled. This may be the case if the message “Solver encountered an error 

in a target or constraint cell” appears. To uninstall “Solver,” follow the directions 

above to install solver; however, uncheck the “Solver” box and click “OK.”. Then, 

navigate back to the “Solver” box, re-check it, and click “OK.”. This should refresh 

the “Solver” tool and permit its use.

Note: If buttons in the program do not work after reducing the security level and 

referencing Solver in VBA, try saving, closing, and restarting the program.

Using the Program

Title Worksheet

The first worksheet that is displayed after opening the program is the title worksheet, 

which shows a ruffed grouse drumming and reads, “Ruffed Grouse Occupancy in the Black 

Hills National Forest.” This worksheet also contains two buttons: “Start” and “Read Me!” 

(Figure 1). Before beginning, we advise the user to review the “Read Me” to ensure he/she 

understands how to use the program and how to interpret occupancy and detection probability estimates. Selecting “Start” will prompt the user with a question, asking whether 

he/she has read the “Read Me” or user’s manual. If “No” is selected, the program will not 

allow the user to continue. However, if “Yes” is selected, the program will automatically 

direct the user to the “Detection History” worksheet.

Figure 1. View of the title worksheet of the ruffed grouse occupancy spreadsheet program. The user 

may select “Start” to begin the program or select “Read Me!” to learn more about the program.
12 USDA Forest Service RMRS-GTR-246WWW. 2010.

Detection Histories Worksheet

The “Detection Histories” worksheet (Figure 2) is the location where presence/absence 

data are entered. Provided in this worksheet is the strata and site identifications for 402 sites 

that were previously monitored (Hansen 2009). The strata identified in the previously surveyed sites represent three vegetation strata (high, medium, and low proportions of aspen) 

that largely represent the Bear Lodge, Northern Hills, Mystic, and Hell’s Canyon Ranger 

Districts. Therefore, each site in the worksheet has a unique ID, starting with an H, M, or L, 

which represents the vegetation strata (high, medium, or low) where the site resides. These 

sites were located along secondary (gravel) and primitive (dirt) roads at least one mile apart 

in the portion of the BHNF north of Highway 16. Sites south of Highway 16 were not surveyed because of the extremely low occurrence of aspen and ruffed grouse (M. A. Rumble, 

U.S. Forest Service, personal communication). Also provided in this worksheet are 101 

sites named “Extra.” If the user surveys more sites than were surveyed in 2007 and 

2008, he/she may use these “Extra” sites to enter the remaining data. The user should 

then give the new sites a unique identification in the “Site ID” cell and enter the name 

of the strata in which the site resides in the “Strata” cell.

Sites should be surveyed multiple times because the probability of detecting a species 

during a survey is almost always less than 100% (MacKenzie and others 2002). Hansen 

(2009) found that conducting three repeat surveys was the most efficient for ruffed grouse 

drumming surveys in the BHNF when detection probability was >0.3, and conducting four 

repeat surveys was the most efficient when detection probability was <0.3. Because the 

probability of detecting ruffed grouse was 0.27 in 2008 (Hansen and others in press), we 

have provided space in the “Detection Histories” worksheet to input data for up to four 

repeat surveys.

Occupancy studies require the observer to determine the presence or absence of a species by detecting any sign of the species of interest (MacKenzie and others 2002, 2006). 

This protocol relies on drumming surveys of ruffed grouse. Therefore, to complete the 

“Detection Histories” worksheet, the user must record whether a ruffed grouse was detected (1) or not (0) at a site for each survey. If more than one ruffed grouse are detected 

at a site, only record “1” (this value does not signify the number of grouse heard, 

rather the fact that grouse were detected at that site). If a site was missed or not all 

four surveys were completed for the site, a “-” should be entered in the spreadsheet cell for 

those surveys. For example, in Figure 2, site H-115 has the detection history: 0111. Thus, a 

ruffed grouse was not detected drumming during the first survey, but was detected during 

survey 2, 3, and 4. Detection histories must be completed for each site surveyed; otherwise, 

occupancy estimates will be inaccurate.

Note: It is not necessary to survey all sites for occupancy estimates to be calculated correctly. The user must simply enter “-” in each of the survey cells for the 

sites not surveyed (e.g., site H-114 in Figure 2).

Warning: Only “1”, “0”, or “-” should be entered into cells in this worksheet. If 

any other numbers or symbols are entered into these cells or cells are left blank, 

occupancy estimates will not be computed correctly. Also, accurate occupancy 

estimates assume that the data for sites and strata are entered correctly. If entered 

incorrectly, an error message will appear that will read, “Occupancy estimates 

have not been calculated correctly! Check detection histories and covariates for 

empty cells or detection history values other than ‘1’, ‘0’, or ‘-’.”

After entering detection histories, the user may select “Continue,” “Back,” or “Clear.” 

Selecting “Continue” will direct the user to the next worksheet. Selecting “Back” will 

move the user back to the title worksheet and selecting “Clear” will clear all the detection 
USDA Forest Service RMRS-GTR-246WWW. 2010. 13

histories from the worksheet. After selecting “Clear,” the user is prompted with the question “Are you sure you want to clear all detection histories?” Selecting “OK” will clear 

all detection histories and automatically enter “-” in each of the cleared cells. Selecting 

“Cancel” will return the user to the “Detection Histories” worksheet without any alterations to the data.

Figure 2. View of the “Detection Histories” worksheet in the occupancy spreadsheet (with example 

data). Users are provided with 402 unique sites and 101 extra sites with four repeat surveys 

for each site. An entry of “1” represents a ruffed grouse drum was heard during the survey, “0” 

represents no drum was heard, and “–” represents a survey that was not completed.
14 USDA Forest Service RMRS-GTR-246WWW. 2010.

Covariates Worksheet

Selecting “Continue” directs the user to the “Covariates” worksheet, which provides the 

option of entering the date, average wind speed, time, precipitation, and site characteristics 

(Figure 3). Selecting “Date” will direct the user to a worksheet in which the date of each 

survey at a site may be entered (Figure 4). The user must enter the date in the format mm/

dd/yyyy and enter the year surveys were conducted underneath the cell labeled “Year.” If a 

site was not surveyed, or less than 4 surveys were completed at a site, the user must enter 

a “-” for that cell.

Covariates

Site characteristics

Precipitation

Time

Wind

Continue

Back

?

Date

Date

Year

H-0 5/8/2008 5/9/2008 5/28/2008 - 2008 

H-1 4/29/2008 5/9/2008 5/21/2008 - 

H-10 5/6/2008 5/15/2008 5/20/2008 - 

H-100 5/17/2008 5/23/2008 5/28/2008 - 

H-101 5/20/2008 5/29/2008 6/3/2008 - 

Clear

Back ?

Site Id Survey 1 Survey 2 Survey 3 Survey 4 

Figure 3. View of the “Covariates” worksheet that displays the five options for covariates to enter 

(date, wind, time, precipitation, and site characteristics).

Figure 4. View of the “Date” covariate worksheet in the occupancy spreadsheet (with example data) 

in which users enter survey dates for up to four surveys at 503 sites and the year surveys were 

conducted.
USDA Forest Service RMRS-GTR-246WWW. 2010. 15

Selecting “Wind” will direct the user to a worksheet in which the average wind speed 

(mph) may be entered for each survey at each site (Figure 5). Average wind speeds should 

be collected during each survey using a hand-held wind meter. If a site was not surveyed, 

or less than 4 surveys were completed at a site, the user must enter a “-” for that cell.

Wind

H-0 0 0.3 0 -

H-1 0.3 0 0.1 -

H-10 0.9 1.2 0 -

H-100 0 0.5 0 - 

H-101 1.3 0 0.4 - 

Clear

? Back

Site Id Survey 1 Survey 2 Survey 3 Survey 4 

Figure 5. View of the “Wind” covariate worksheet in the occupancy spreadsheet (with example data) 

in which users enter average wind speeds (mph) for up to four surveys at 503 sites.

Selecting “Time” will direct the user to a worksheet in which the time of the beginning 

of the survey may be entered for each site (Figure 6). The user must enter the time in the 

format hh:mm. If a site was not surveyed, or less than 4 surveys were completed at a site, 

the user must enter a “-” for that cell.

Time

H-0 07:38 09:39 06:16 -

H-1 06:58 08:38 08:12 -

H-10 06:17 09:46 08:21 -

H-100 06:11 09:13 09:48 - 

H-101 09:26 05:35 09:21 - 

Clear

Back ?

Site Id Survey 1 Survey 2 Survey 3 Survey 4 

Figure 6. View of the “Time” covariate worksheet in the occupancy spreadsheet (with example data) 

in which users enter the time of the beginning of the survey for up to four surveys at 503 sites.

Selecting “Precipitation” will direct the user to a worksheet in which the user may 

enter whether or not precipitation (e.g., mist, rain, or snow) occurred during the survey 

(Figure 7). If precipitation occurred at any time during the survey, the user should enter 

“1” and if no precipitation occurred, the user should enter “0.” If a site was not surveyed, 

or fewer than four surveys were completed at a site, the user must enter a “-” for that cell.

Precipitation

H-0 0 0 0 -

H-1 0 0 0 -

H-10 0 0 0 -

H-100 0 1 0 - 

H-101 0 0 0 - 

Clear

Back

?

Site Id Survey 1 Survey 2 Survey 3 Survey 4 

Figure 7. View of the “Precipitation” covariate worksheet in the occupancy spreadsheet (with 

example data) in which users enter whether precipitation occurred during the survey for up to four 

surveys at 503 sites. A “1” signifies precipitation occurred during the survey and a “0” signifies no 

precipitation occurred.
16 USDA Forest Service RMRS-GTR-246WWW. 2010.

Selecting “Site Characteristics” from the covariates worksheet directs the user to a 

worksheet for entering the acreage of aspen and spruce within 550 m of the site (Figure 8). 

Instructions for calculating vegetation areas within 550 m of the site can be found in 

Appendix C. For any sites that do not contain a vegetation type, a “0” should be entered 

in the cell. However, for sites that were not surveyed, a “-” should be entered in each cell.

Figure 8. View of the “Site characteristics” covariate worksheet in the occupancy spreadsheet (with 

example data) in which users may enter the acreage of aspen and spruce within 550 m of a site for 

up to 503 sites.

Site Characteristics 

H-0 0 0

H-1 0 0

H-10 9.708087042 0

H-100 37.67108317 0

Back

Clear

?

Site Id Aspen (acre) Spruce (acre)

Occupancy Worksheet

After completing the covariates worksheets, the user must select “Continue,” which 

will direct him/her to a worksheet with four options for calculating occupancy: “Calculate 

Constant Occupancy,” “Calculate Occupancy as a Function of Strata,” “Calculate 

Occupancy as a Function of Covariates,” and “View Occupancy and Detection Trends” 

(Figure 9). Each of these options uses the “Solver” tool within Microsoft Excel to find 

beta values for occupancy and detection probability that maximize the log-likelihood of 

obtaining the detection histories entered. Upon selecting one of these options, “Solver” 

will converge upon a solution and, after a few seconds, a message box will appear, reading: “Solver has converged to the current solution. All constraints are satisfied.” Clicking 

“OK” on this message box will direct the user to the occupancy and detection probability 

estimate output worksheets.

Note: If, after one of the occupancy options is selected, the solver message box 

appears and says “Solver encountered an error in a target or constraint cell,” the 

solver may not have been added to the spreadsheet or the solver needs to be 

uninstalled and re-installed. To do this, the user should refer to the “Opening and 

Starting the Program” section above.
USDA Forest Service RMRS-GTR-246WWW. 2010. 17

Figure 9. View of the “Occupancy” worksheet that provides four options for calculating occupancy: 

“Calculate Constant Occupancy,” “Calculate Occupancy as a Function of Strata,” “Calculate 

Occupancy as a Function of Covariates,” and “View Occupancy and Detection Trends.”

Occupancy

View Occupancy and Detection Trends

Calculate Occupancy as a Function of Covariates

Calculate Occupancy as a Function of Strata

? Back

Calculate Constant Occupancy 

“Calculate Constant Occupancy” option

The assumption when calculating a “constant” occupancy and detection probability is 

that occupancy and detection probabilities do not vary in time, space, or weather conditions 

(MacKenzie and others 2002). Selecting the “Calculate Constant Occupancy” option will 

provide the user with one occupancy and detection probability estimate, two measures of 

precision (standard error [SE] and the coefficient of variation [CV]), and the total number 

of sites surveyed (Figure 10). This option is usually not appropriate for ruffed grouse because ruffed grouse select aspen vegetation for life requirements and they typically drum 

during specific times and conditions (Gullion 1966, Zimmerman and Gutiérrez 2007, 

Hansen and others in press). However, the advantage of calculating constant occupancy 

and detection probabilities is that it is possible to calculate a measure of precision for occupancy estimates. There is not a way, using this program, to calculate precision estimates for 

occupancy or detection probabilities that are not constant. For this reason, we recommend 

the user only use this option if estimates of precision are required.

Note: Estimates of precision can be obtained when occupancy and detection 

probability are heterogeneous (i.e., not constant) using free software such as 

Program PRESENCE (<http://www.mbr-pwrc.usgs.gov/software/presence.html>) 

or Program MARK (<http://welcome.warnercnr.colostate.edu/~gwhite/mark/mark.

htm>).

“Calculate Occupancy as a Function of Strata” option

In the BHNF, sites were divided among three strata based on the extent of aspen described previously (Hansen 2009). Thus, selecting “Calculate Occupancy as a Function 

of Strata” provides the user with an estimate of occupancy for each stratum (Figure 11). 

This option also assumes detection probability is a function of date (quadratically), wind 

and time (linearly), and precipitation (categorically) because these variables influenced 

the probability of detecting ruffed grouse during 2007 and 2008 monitoring (Hansen and 

others in press). As a result, survey-specific detection probabilities can be estimated and 

viewed by selecting “View Survey-Specific Detection Probabilities.” This option also provides a graph of the distribution of detection probabilities throughout the sampling period 
18 USDA Forest Service RMRS-GTR-246WWW. 2010.

(Figure 12). Finally, by selecting “View Detection Probability Graphs” the user may view 

four graphs demonstrating the influence of date, wind speed, time, and precipitation on 

detection probabilities (Figure 13).

To calculate sample size estimates for future ruffed grouse monitoring, it is necessary 

to have an occupancy estimate for each stratum. Therefore, we recommend using this 

occupancy calculation option when sample size calculations for each stratum are necessary. Unfortunately, this program cannot calculate estimates of sample size for multi-year 

monitoring. However, estimates of sample size can easily be calculated using Program 

GENPRES (Bailey and others 2007), which can be downloaded for free from: http://www.

mbr-pwrc.usgs.gov/software/presence.html.

Note: Because occupancy and detection probabilities were not constant using 

this option, SE and CV cannot be calculated for occupancy estimates using this 

program.

Figure 10. View of example output after selecting “Calculate Constant Occupancy.” One estimate of 

occupancy and detection probability is provided, including a standard error (SE) and coefficient of 

variation (CV) estimate.

Occupancy Probability 0.128 0.028 0.220 401 

Detection Probability 0.285

Interpretation Back

Estimate SE CV No. Sites 

Figure 11. View of example output after selecting “Calculate Occupancy as a Function of Strata.” 

Estimates of occupancy for each stratum and average detection probability are provided, including 

the options for viewing survey-specific detection probabilities and detection probability graphs.

Estimate

Occupancy Probability_High Strata 0.228 136 

Occupancy Probability_Med. Strata 0.160 149 

Occupancy Probability_Low Strata 0.053 116 

Detection Probability 0.251

Interpretation Back

View Detection Probability Graphs

View Survey-Specific Detection Probabilities

No. Sites 
USDA Forest Service RMRS-GTR-246WWW. 2010. 19

Figure 12. View of example survey specific detection probability (pn

) estimates and detection 

probability distribution bar graph; provided after selecting “View Survey Specific Detection 

Probabilities” on the “Occupancy as a Function of Strata” output worksheet (Figure 11).

Survey-Specific Detection Probabilities 

Site Id p1 p2 p3 p4

H-0 0.255601 0.252945 0.463989 -

H-1 0.041323 0.286134 0.498862 - 

H-10 0.127988 0.28892 0.508502 - 

H-100 0.478124 0.443142 0.463989 - 

H-101 0.332442 0.445722 0.272367 -

H-102 0.017784 0.225289 0.492201 -

H-107 0.14803 0.509054 0.492201 -

H-108 0.255601 0.274785 0.463989 - 

H-109 0.069539 0.362187 0.492201 -

H-11 0.286134 0.46692 0.492201 -

Detection Probability Distribution

0

100

200

300

400

500

0-0.1

0.1-0.20.2-0.3

0.3-0.40.4-0.50.5-0.60.6-0.70.7-0.80.8-0.9

0.9-1.0

Detection Probability 

Number of Survey Site 

Visits

Back

Interpretation
20 USDA Forest Service RMRS-GTR-246WWW. 2010.

Figure 13. View of example graphs representing the influence of date (a.), wind speed (b.), time (c.), 

and precipitation (d.) on the probability of detecting ruffed grouse during drumming surveys. These 

graphs can be viewed after selecting “View Detection Probability Graphs” on the “Occupancy as a 

Function of Strata” output worksheet (Figure 11).

a. b.

c. d.

0.6

0.5

0.4

0.3

0.2

0.1

Detection Probability

0

4-Apr 24-Apr 14-May 3-Jun

Date

0.6

0.5

0.4

0.3

0.2

0.1

Detection Probability

0

0

Wind speed (mph)

1 2 3 4 5 6 7 8 9 10

0.6

0.5

0.4

0.3

0.2

0.1

Detection Probability

0

04:48 06:43 08:38 10:33

Time

0.6

0.5

0.4

0.3

0.2

0.1

Detection Probability

0

Precipitation No Precipitation

“Calculate Occupancy as a Function of Covariates” option

Occupancy estimates that incorporate site covariates provide a robust view of the heterogeneity and distribution of occupancy probabilities across the study area (MacKenzie 

and others 2006). Selecting “Calculate Occupancy as a Function of Covariates” provides 

the user with ruffed grouse occupancy estimates in the BHNF that are a function of the area 

of aspen and spruce within 550 m of the survey site. Also, similar to “Calculate Occupancy 

as a Function of Strata,” selecting this option provides detection probability estimates that 

are a function of date in a quadratic form, wind and time in a linear form, and precipitation categorically. On the output worksheet, average estimates of occupancy and detection 

probability are provided (Figure 14). Also, by selecting “View Site- and Survey-Specific 

Occupancy and Detection Probabilities” the user is provided with estimates of occupancy 

at each site and the detection probability during each survey (Figure 15). Selecting “View 

Detection Probability Graphs” displays four graphs that demonstrate the effects of date, 

wind speed, time, and precipitation on detection probability, similar to those created for 

the “Calculate Occupancy as a Function of Strata” option (Figure 11). Selecting “View 

Occupancy Probability Graphs” displays a graph that demonstrates the influence of the 

area of aspen and spruce on occupancy probability (Figure 16).

Including the covariates described above improved the performance of the ruffed grouse 

occupancy model and reduced bias in occupancy estimates (Hansen and others in press). 

That is, including the covariates explained more variation than other candidate models that 

excluded the covariates. As a result, we recommend using this option because it will provide occupancy and detection probability estimates with the least bias and provide crucial 

information about when and during what conditions the probability of detecting ruffed 
USDA Forest Service RMRS-GTR-246WWW. 2010. 21

grouse is maximized. By maximizing detection probability, sample site estimates for future 

surveys will reduce (Hansen 2009).

Note: Because occupancy and detection probabilities were not constant using 

this option, SE and CV cannot be calculated for occupancy estimates using this 

program.

Figure 14. View of example output after selecting “Calculate Occupancy as a Function of Covariates.” 

Average estimates of occupancy and detection probability are provided, including the option 

of viewing site- and survey-specific probabilities, detection probability graphs, and occupancy 

probability graphs.

Estimate No. sites 

Occupancy Probability 0.111

Detection Probability 0.272

Interpretation Back

View Occupancy Probability Graphs

View Detection Probability Graphs

View Site- and Survey-Specific Occupancy and Detection Probabilties

401
22 USDA Forest Service RMRS-GTR-246WWW. 2010.

Figure 15. View of example site-specific occupancy and survey-specific detection probability (pn

) 

estimates and occupancy probability distribution bar graph; provided after selecting “View Siteand Survey-Specific Occupancy and Detection Probabilities” on the “Occupancy as a Function 

of Covariates” output worksheet (Figure 14). A detection probability distribution graph is also 

provided, which resembles the graph presented in Figure 12.

Site- and Survey-Specific Probabilities

Site Id p1 p2 p3 p4 Occupancy Probability 

H-0 0.295489 0.288311 0.473545 - 0.072611126

H-1 0.05732 0.325651 0.515211 - 0.118830882

H-10 0.153127 0.307425 0.527056 - 0.132270644

H-100 0.50253 0.454215 0.473545 - 0.287458286

H-101 0.342325 0.454774 0.27781 - 0.112687671

H-102 0.023363 0.264902 0.503143 - 0.316509391

H-107 0.177321 0.52193 0.503143 - 0.096486505

H-108 0.295489 0.312925 0.473545 - 0.407428459

H-109 0.082186 0.369348 0.503143 - 0.054990694

H-11 0.325651 0.478038 0.503143 - 0.079318934

H-111 0.084242 0.264902 0.325651 0.503143 0.139192552

H-114 0.084242 0.055405 0.083296 - 0.108538026

H-115 0.054236 0.136985 0.529821 0.473545 0.071251813

Occupancy Probability Distribution

0

20

40

60

80

100

120

140

160

180

200

0-0.1

0.1-0.20.2-0.3

0.3-0.4

0.4-0.50.5-0.60.6-0.7

0.7-0.80.8-0.90.9-1.0

Number of Observations (Sites)
USDA Forest Service RMRS-GTR-246WWW. 2010. 23

Figure 16. View of an example graph demonstrating the influence of the area of quaking aspen and 

white spruce within 550 m of a survey point on ruffed grouse occupancy probability. This graph 

can be viewed after selecting “View Occupancy Graphs” on the “Occupancy as a Function of 

Covariates” output worksheet (Figure 14).

1

0.8

0.6

0.4

0.2

0

Occupancy Probability

0

Acres

25 50 75 100 125 150 175 200

Quaking Aspen

White Spruce

“View Occupancy and Detection Probability Trends” option

The reason for monitoring management indicator species is to evaluate trends in occupancy over time. As a result, we’ve included a worksheet that displays the trends in 

occupancy and detection probability. Selecting “View Occupancy and Detection Trends” 

directs the user to a worksheet that includes a table and graph with occupancy and detection 

probability estimates from previous years of surveys (Figure 17). To include the current 

year’s occupancy and detection probability estimates, the user may select “Add Current 

Year.” Selecting this option will calculate occupancy and detection probability estimates 

using the “Calculate Occupancy as a Function of Covariates” option because this option 

provided estimates with the least bias. If the user desires precision estimates for occupancy 

and detection probability trend data, we recommend using either Program PRESENCE or 

Program MARK (see above for URL addresses).

Note: This worksheet is meant to provide a qualitative analysis of trends in occupancy and detection probability over time. If the user desires to calculate 

occupancy and detection probability trends using regression methods (e.g., Sauer 

and Droege 1990, Dixon and others 1998), other software such as Program R 

(http://www.r-project.org/) or Statistical Analysis System (SAS, http://www.sas.

com/) should be used.

Warning: After entering detection histories, covariate values (date, wind speed, 

and so on), or estimating occupancy trend data, the spreadsheet must be saved; 

otherwise, all the data will be lost.
24 USDA Forest Service RMRS-GTR-246WWW. 2010.

Figure 17. View of example data after selecting “View Occupancy and Detection Trends” 

on the “Occupancy” worksheet (Figure 9). Data from the current year may be added by 

selecting “Add Current Year.”

Literature Cited

Bailey, L. L., J. E. Hines, J. D. Nichols, and D. I. MacKenzie. 2007. Sampling design trade-offs in 

occupancy studies with imperfect detection: examples and software. Ecological Applications 

17:281-290.

Barber, H. L., R. Kirkpatrick, J. Kubisiak, D. Rusch, F. A. Servello, S. K. Stafford, D. F. Stauffer, 

and F. R. Thompson III. 1989. The ecological niche. Pages 15-20 in S. Atwater and J. Schnell, 

editors. The Wildlife Series: Ruffed Grouse. Stackpole Books, Harrisburg, Pennsylvania, USA.

Bartos, D., and W. Shepperd. 2003. Aspen regeneration in the Western States. U. S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. Fort Collins, Colorado, USA 

<http://www.fs.fed.us/rm/aspen/>. Accessed 1 Sept. 2006.

Crossland, M. R., D. I. MacKenzie, and S. Holzapfel. 2005. Assessment of site-occupancy modeling 

as a technique to monitor Hochstetter’s frog (Leiopelma hochstetteri) populations. Doc Research 

& Development Series 218, Science & Technical Publishing, Wellington, New Zealand.

Dixon, P. M., A. R. Olsen, and B. M. Kahn. 1998. Measuring trends in ecological resources. 

Ecological Applications 8:225-227.

Donovan, T. M., and J. Hines. 2007. Exercises in occupancy modeling and estimation. http://www.

uvm.edu/envnr/vtcfwru/spreadsheets/occupancy.htm.
USDA Forest Service RMRS-GTR-246WWW. 2010. 25

Froiland, S. G. 1990. Natural history of the Black Hills and Badlands. The Center for Western 

Studies, Sioux Falls, South Dakota, USA.

Gullion, G. W. 1966. The use of drumming behavior in ruffed grouse population studies. Journal of 

Wildlife Management 30:717-729.

Hansen, C. P. 2009. Occupancy modeling of ruffed grouse in the Black Hills National Forest. Thesis, 

University of Missouri, Columbia, Missouri, USA.

Hansen, C. P., J. J. Millspaugh, and M. A. Rumble. In press. Occupancy modeling of ruffed grouse 

in the Black Hills National Forest. Journal of Wildlife Management.

Hanski, I. 1994. A practical model of metapopulation dynamics. The Journal of Animal Ecology 

63(1):151-162.

Kubisiak, J. 1985. Ruffed grouse habitat relationships in aspen and oak forests of Central Wisconsin. 

Department of Natural Resources, Technical Bulletin No. 151, Madison, Wisconsin, USA.

MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A. Langtimm. 

2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 

83(3):2248-2255.

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site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 

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Wildlife Series: Ruffed Grouse. Stackpole Books, Harrisburg, Pennsylvania, USA.

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editors. The Wildlife Series: Ruffed Grouse. Stackpole Books, Harrisburg, Pennsylvania, USA.

Petraborg, W. H., E. G. Wellein, and V. E. Gunvalson. 1953. Roadside drumming counts: A spring 

census method for ruffed grouse. Journal of Wildlife Management 17:292-295.

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area. Intermountain Journal of Science 11:10-24.

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DC, USA.

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Forest Service Black Hills National Forest, Littleton, Colorado, USA.

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umbellus) for field studies. American Midland Naturalist 139:39-48.

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plan for the Black Hills National Forest. U.S. Department of Agriculture, Forest Service, Custer, 

South Dakota, USA.

White, G. C., and K. P. Burnham. 1999. Program MARK: Survival estimation from populations of 

marked animals. Bird Study Supplement 46:120-138.

Zimmerman, G. S., and R. J. Gutiérrez. 2007. The influence of ecological factors on detecting 

drumming ruffed grouse. Journal of Wildlife Management 71:1765-1772.
26 USDA Forest Service RMRS-GTR-246WWW. 2010.

Appendix A. Sample Sites for the 2007 and 2008 

Ruffed Grouse Drumming Surveys in the 

Black Hills National Forest

Strata Region Route Site UTM_East UTM_North

High BL H17 H_0 546706 4933132

High BL H10 H_1 559467 4903268

High NW H5 H_10 581629 4909010

High BL H7 H_100 571965 4918210

High NW H2 H_101 573943 4903874

High BL H20 H_102 548454 4950335

High BL H14 H_107 541355 4933705

High BL H17 H_108 547249 4934825

High BL H13 H_109 538334 4930583

High BL H14 H_11 541266 4937794

High BL H20 H_111 548687 4952679

High BL H3 H_114 566831 4906745

High BL H10 H_115 559201 4907096

High BL H14 H_116 540992 4935831

High NW H5 H_117 583424 4914497

High BL H7 H_119 567957 4922052

High BL H3 H_12 567071 4908465

High BL H11 H_121 547417 4923205

High BL H16 H_122 554847 4935764

High BL H20 H_124 550639 4950124

High NW H1 H_126 571506 4900597

High NW H1 H_128 571753 4896403

High BL H18 H_13 553134 4939327

High BL H19 H_131 550271 4946512

High NW H1 H_134 571170 4892597

High BL H18 H_135 555074 4940278

High BL H10 H_136 557923 4903959

High NW H4 H_137 571832 4908837

High BL H6 H_14 573552 4918453

High BL H20 H_140 553274 4947585

High BL H16 H_141 550821 4937061

High NW H5 H_142 578104 4912916

High BL H3 H_143 564423 4911387

High BL H14 H_145 540304 4931116

High BL H9 H_146 573138 4911292

High BL H21 H_147 548663 4956769

High NW H4 H_148 577064 4911610

High BL H3 H_15 568771 4907085

High BL H7 H_151 568540 4924102

High BL H21 H_152 550882 4956559

High BL H3 H_153 568095 4905317

High NW H4 H_154 573505 4909400

High BL H21 H_156 549678 4955264

High BL H14 H_158 543608 4939274
USDA Forest Service RMRS-GTR-246WWW. 2010. 27

High NW H4 H_159 579240 4909434

High NW H2 H_16 572734 4901993

High BL H10 H_160 560036 4911206

High BL H3 H_161 564492 4909427

High BL H18 H_162 548732 4942577

High BL H20 H_164 551911 4948536

High BL H15 H_165 546288 4939467

High BL H16 H_166 553044 4931407

High BL H10 H_168 562309 4905971

High NW H5 H_169 577537 4914651

High BL H20 H_170 550471 4953330

High BL H21 H_171 551993 4958151

High BL H11 H_173 541793 4922252

High BL H17 H_174 543841 4929345

High BL H11 H_176 548780 4921557

High NW H5 H_177 580937 4914711

High NW H2 H_179 573254 4905559

High BL H17 H_18 542305 4928268

High BL H12 H_180 546037 4928810

High NW H1 H_182 569610 4899377

High NW H4 H_19 575193 4907502

High BL H15 H_20 543696 4936709

High BL H10 H_21 560488 4906193

High NW H5 H_22 582323 4913029

High BL H21 H_23 551280 4960002

High BL H10 H_24 559742 4909414

High BL H9 H_25 567255 4912017

High BL H20 H_26 550249 4948158

High BL H14 H_27 542061 4930648

High NW H2 H_28 573712 4897311

High NW H1 H_3 570972 4894850

High NW H4 H_31 572679 4907411

High NW H1 H_32 570597 4898398

High BL H9 H_33 569668 4909929

High BL H12 H_35 544921 4925684

High BL H18 H_36 550358 4938945

High BL H17 H_37 546407 4930570

High BL H8 H_38 569010 4915587

High NW H1 H_4 575018 4894768

High BL H12 H_40 548790 4924575

High BL H6 H_42 574911 4916676

High NW H2 H_43 573091 4900297

High BL H18 H_44 550454 4941373

High BL H21 H_45 547367 4954824

High BL H16 H_46 547542 4929320

High NW H2 H_47 570137 4904080

High BL H13 H_48 539864 4928940

High BL H10 H_49 557706 4906222

High BL H12 H_5 544007 4927278

High BL H15 H_50 543133 4934455

Strata Region Route Site UTM_East UTM_North
28 USDA Forest Service RMRS-GTR-246WWW. 2010.

High BL H3 H_51 564332 4907441

High BL H9 H_52 565667 4913363

High BL H13 H_53 541526 4925949

High BL H11 H_54 545939 4920408

High NW H5 H_55 583094 4911376

High BL H8 H_56 571064 4912207

High BL H16 H_58 552839 4933109

High BL H21 H_59 549625 4958124

High BL H12 H_6 547049 4927322

High NW H2 H_60 570527 4906459

High BL H6 H_61 574511 4922644

High BL H8 H_62 570220 4913686

High NW H1 H_63 573372 4893802

High BL H14 H_64 543554 4932378

High BL H15 H_65 545802 4933828

High NW H4 H_67 576423 4908965

High BL H16 H_68 552476 4937066

High BL H7 H_69 571573 4922398

High BL H16 H_70 548924 4930539

High BL H8 H_71 568049 4913367

High BL H17 H_72 548253 4937685

High BL H3 H_73 564697 4905216

High BL H7 H_74 570534 4921211

High NW H2 H_75 575113 4905531

High BL H15 H_76 545529 4932055

High BL H11 H_77 548687 4919844

High BL H6 H_78 575387 4913298

High NW H5 H_8 579180 4914359

High BL H16 H_80 549742 4934814

High BL H16 H_82 550989 4931977

High NW H4 H_83 578683 4907888

High BL H19 H_84 553777 4945311

High NW H1 H_86 570100 4902282

High BL H8 H_87 566806 4918398

High BL H8 H_88 569350 4911997

High BL H12 H_89 545183 4923915

High NW H2 H_9 571996 4904258

High BL H11 H_92 545158 4922256

High BL H19 H_93 552519 4945540

High BL H18 H_94 551387 4942917

High NW H4 H_96 574790 4910849

High NW H5 H_98 580614 4910641

High BL H6 H_99 575091 4920947

Low SW L10 L_0 582365 4863400

Low SW L8 L_10 580824 4888263

Low SW L12 L_100 592716 4874143

Low SE L21 L_102 624603 4879583

Low SW L30 L_103 594198 4881839

Low SW L30 L_104 592012 4879990

Low SE L22 L_105 627870 4887619

Strata Region Route Site UTM_East UTM_North
USDA Forest Service RMRS-GTR-246WWW. 2010. 29

Low SE L23 L_106 630641 4873423

Low SW L16 L_107 599933 4858021

Low SE L27 L_108 619455 4869585

Low SE L1 L_109 617815 4849060

Low SW L10 L_11 577059 4861980

Low SW L7 L_111 586329 4854511

Low SW L3 L_112 602871 4849564

Low SE L24 L_113 624835 4866153

Low SW L5 L_114 586485 4843873

Low SW L13 L_115 596047 4869393

Low SW L7 L_116 584740 4857507

Low SE L2 L_117 607049 4848700

Low SE L28 L_118 609026 4879542

Low SW L5 L_119 584359 4844949

Low SE L20 L_12 625702 4886217

Low SE L1 L_120 616186 4849890

Low SE L23 L_121 631384 4876755

Low SE L29 L_122 609763 4867414

Low SE L21 L_123 620966 4881839

Low SE L19 L_124 612113 4881065

Low SE L24 L_125 633316 4865795

Low SW L14 L_126 595089 4861300

Low SW L17 L_127 601694 4865725

Low SW L17 L_128 600648 4863125

Low SW L11 L_129 585058 4882972

Low SW L10 L_13 578483 4865524

Low SW L13 L_130 594740 4872952

Low SW L9 L_131 577029 4870470

Low SW L7 L_132 586070 4856588

Low SW L11 L_133 587324 4882712

Low SE L20 L_14 620084 4886202

Low SE L24 L_15 628219 4865893

Low SW L7 L_16 582621 4858397

Low SW L4 L_18 596835 4844717

Low SW L8 L_19 576774 4889438

Low SE L23 L_2 629247 4875417

Low SW L13 L_20 592913 4868231

Low SW L12 L_21 592559 4872493

Low SW L8 L_22 577199 4882386

Low SE L1 L_23 619993 4848592

Low SW L30 L_24 600776 4884552

Low SE L18 L_25 600688 4879502

Low SW L5 L_26 588722 4842642

Low SW L6 L_27 583346 4853882

Low SE L21 L_3 622585 4880514

Low SW L15 L_30 592228 4857340

Low SW L9 L_31 581631 4874667

Low SW L10 L_32 577945 4856913

Low SE L2 L_33 606547 4846792

Low SE L27 L_35 616452 4873825

Strata Region Route Site UTM_East UTM_North
30 USDA Forest Service RMRS-GTR-246WWW. 2010.

Low SE L24 L_36 629944 4868702

Low SE L19 L_37 612746 4886914

Low SW L3 L_38 600338 4850252

Low SW L15 L_39 595569 4856275

Low SW L8 L_4 584166 4887818

Low SE L25 L_40 632705 4853778

Low SE L18 L_41 600950 4877333

Low SW L9 L_42 582502 4870817

Low SW L5 L_43 588826 4848004

Low SE L19 L_44 610869 4885419

Low SE L2 L_45 609556 4846911

Low SW L10 L_46 578403 4868323

Low SW L15 L_47 589399 4860631

Low SE L2 L_48 609253 4850697

Low SW L11 L_49 590558 4883686

Low SE L2 L_5 607074 4852705

Low SW L9 L_50 579593 4876524

Low SW L14 L_51 588843 4863854

Low SE L29 L_52 606313 4861000

Low SW L16 L_54 597870 4854180

Low SW L16 L_55 599309 4855062

Low SE L2 L_58 604603 4849448

Low SE L29 L_59 610240 4864700

Low SE L29 L_6 604842 4866783

Low SW L12 L_60 589123 4871559

Low SE L23 L_61 630453 4870474

Low SW L13 L_62 597332 4867092

Low SW L9 L_64 579895 4872759

Low SE L20 L_66 615700 4886616

Low SW L9 L_68 577154 4876521

Low SW L10 L_69 579249 4858699

Low SW L12 L_7 589789 4875759

Low SW L4 L_70 599930 4845780

Low SE L27 L_71 618763 4871522

Low SW L15 L_72 594038 4858579

Low SW L6 L_73 581057 4852368

Low SW L14 L_74 590622 4863303

Low SE L19 L_76 617466 4879615

Low SW L15 L_77 594941 4854487

Low SW L11 L_78 587212 4886379

Low SE L28 L_79 611994 4875295

Low SW L5 L_8 587420 4846391

Low SE L26 L_80 613734 4862499

Low SE L20 L_82 622976 4886248

Low SW L11 L_83 589690 4881821

Low SE L19 L_84 611015 4886853

Low SW L7 L_86 584495 4859986

Low SE L25 L_87 629133 4854779

Low SE L19 L_88 612412 4883697

Low SW L8 L_89 576739 4887047

Strata Region Route Site UTM_East UTM_North
USDA Forest Service RMRS-GTR-246WWW. 2010. 31

Low SE L29 L_9 604742 4870069

Low SE L25 L_90 630930 4863403

Low SE L23 L_91 624641 4874984

Low SE L23 L_92 632923 4875103

Low SW L3 L_93 603890 4846134

Low SE L29 L_94 605439 4864034

Low SE L18 L_96 604090 4874787

Low SW L14 L_97 592754 4859998

Low SE L29 L_99 606403 4865416

Medium NW M23 M_1 584182 4910122

Medium NE M10 M_10 612551 4891646

Medium NW M29 M_100 601562 4916916

Medium NE M21 M_101 613824 4913209

Medium NE M10 M_102 606282 4893423

Medium NE M31 M_103 620880 4898758

Medium NW M2 M_104 589581 4892012

Medium NE M14 M_105 607110 4898973

Medium NE M14 M_106 611239 4898167

Medium NE M11 M_109 620207 4888601

Medium NW M23 M_110 583842 4904929

Medium NW M1 M_111 581859 4893327

Medium NE M16 M_112 622235 4901121

Medium NE M12 M_113 619891 4894861

Medium NW M30 M_114 593685 4900061

Medium NE M18 M_115 616535 4910550

Medium NW M6 M_116 590351 4903705

Medium NE M12 M_117 612383 4896289

Medium NE M21 M_118 613878 4916265

Medium NE M16 M_120 618463 4901682

Medium NW M2 M_121 587209 4889616

Medium NE M15 M_122 618330 4899426

Medium NW M26 M_123 579951 4924419

Medium NW M2 M_124 590326 4890318

Medium NE M10 M_125 612764 4892836

Medium NE M22 M_126 593536 4893739

Medium NE M15 M_127 614198 4902680

Medium NE M12 M_129 614625 4894519

Medium NW M4 M_13 589244 4893840

Medium NE M7 M_131 610220 4888938

Medium NW M4 M_132 586278 4897660

Medium NW M26 M_133 580222 4922260

Medium NE M19 M_134 612383 4905721

Medium NE M3 M_135 598861 4887557

Medium NW M1 M_137 575888 4901028

Medium NW M1 M_138 582460 4895329

Medium NE M20 M_14 610635 4916403

Medium NW M26 M_140 579407 4916391

Medium NE M16 M_141 623543 4900459

Medium NE M14 M_144 609109 4901542

Medium NW M24 M_145 593335 4905924

Strata Region Route Site UTM_East UTM_North
32 USDA Forest Service RMRS-GTR-246WWW. 2010.

Medium NE M13 M_147 605057 4902936

Medium NE M21 M_148 614773 4915104

Medium NE M13 M_149 604276 4901092

Medium NW M30 M_15 596507 4899227

Medium NW M6 M_150 588419 4902974

Medium NW M28 M_151 592043 4914133

Medium NE M7 M_152 606326 4885067

Medium NW M5 M_153 580368 4904295

Medium NW M27 M_154 588321 4919729

Medium NE M7 M_156 608973 4885838

Medium NW M5 M_157 577239 4906145

Medium NE M18 M_16 616451 4912411

Medium NW M2 M_160 586237 4892292

Medium NE M12 M_161 622227 4895529

Medium NW M24 M_162 595305 4902627

Medium NE M31 M_164 629360 4896361

Medium NE M15 M_165 615720 4901704

Medium NE M11 M_166 622875 4892592

Medium NE M13 M_167 605799 4897484

Medium NE M19 M_168 608187 4909238

Medium NW M27 M_169 586331 4923454

Medium NW M26 M_17 580182 4920603

Medium NW M30 M_170 597331 4901836

Medium NE M9 M_171 597078 4896173

Medium NW M30 M_172 598661 4898180

Medium NE M19 M_173 608207 4904607

Medium NE M17 M_174 618863 4905700

Medium NW M27 M_175 584609 4922160

Medium NW M4 M_176 586013 4894410

Medium NW M29 M_178 599162 4916707

Medium NW M1 M_18 579847 4895157

Medium NW M25 M_180 581763 4921718

Medium NE M31 M_181 622622 4899009

Medium NW M4 M_182 585652 4896319

Medium NE M11 M_183 619020 4890662

Medium NE M11 M_184 625379 4890795

Medium NE M18 M_19 621416 4912500

Medium NE M9 M_21 603636 4895500

Medium NE M9 M_22 601438 4896895

Medium NE M19 M_23 606863 4907042

Medium NW M24 M_24 600595 4906240

Medium NE M12 M_25 618423 4892647

Medium NE M10 M_26 610624 4894553

Medium NE M17 M_28 615968 4907985

Medium NE M8 M_29 599764 4893472

Medium NE M21 M_3 611335 4910292

Medium NW M5 M_30 580162 4902159

Medium NE M13 M_31 601385 4903176

Medium NW M25 M_32 583716 4924280

Medium NW M25 M_33 581849 4916572

Strata Region Route Site UTM_East UTM_North
USDA Forest Service RMRS-GTR-246WWW. 2010. 33

Medium NW M24 M_34 597218 4903811

Medium NW M25 M_35 583900 4912927

Medium NE M21 M_36 612872 4910774

Medium NE M22 M_38 590474 4896347

Medium NE M13 M_39 601096 4899879

Medium NE M10 M_4 614661 4888902

Medium NE M7 M_41 608203 4883176

Medium NW M4 M_42 587509 4896420

Medium NE M9 M_43 599739 4896006

Medium NE M8 M_44 602544 4889012

Medium NE M31 M_45 627316 4896994

Medium NW M23 M_46 583772 4908124

Medium NE M7 M_48 607565 4890375

Medium NW M28 M_49 592303 4919916

Medium NE M8 M_5 601557 4890735

Medium NE M9 M_50 603264 4897642

Medium NW M6 M_51 589696 4901869

Medium NE M10 M_52 609147 4892585

Medium NE M3 M_53 599065 4890696

Medium NW M28 M_55 595909 4915487

Medium NE M3 M_56 600949 4886922

Medium NE M20 M_57 609657 4919675

Medium NW M26 M_58 579646 4918793

Medium NW M24 M_59 592737 4904320

Medium NE M19 M_6 606261 4904027

Medium NW M27 M_60 586589 4918753

Medium NW M23 M_61 587656 4904491

Medium NW M23 M_62 584655 4906328

Medium NE M3 M_64 593014 4889212

Medium NE M21 M_65 610290 4907296

Medium NW M2 M_66 584502 4892856

Medium NE M22 M_67 591840 4894878

Medium NE M13 M_68 603711 4903478

Medium NE M9 M_69 596123 4894577

Medium NE M15 M_7 615737 4899498

Medium NE M22 M_70 591977 4897408

Medium NE M7 M_71 609161 4887878

Medium NE M14 M_72 615590 4896450

Medium NE M10 M_73 610535 4892366

Medium NE M17 M_74 623105 4904901

Medium NE M17 M_77 621143 4907171

Medium NE M10 M_78 609277 4894746

Medium NW M29 M_79 602231 4920416

Medium NW M6 M_8 587298 4901343

Medium NE M14 M_80 613881 4899091

Medium NE M14 M_81 608410 4902487

Medium NE M11 M_82 621606 4890396

Medium NE M18 M_83 615888 4909457

Medium NE M12 M_85 616379 4893278

Medium NW M1 M_87 575562 4898049

Strata Region Route Site UTM_East UTM_North
34 USDA Forest Service RMRS-GTR-246WWW. 2010.

Medium NW M4 M_88 587558 4893512

Medium NE M7 M_89 603866 4888169

Medium NE M8 M_91 603957 4891564

Medium NW M24 M_92 599302 4905049

Medium NW M6 M_94 591292 4899643

Medium NE M20 M_95 610210 4913550

Medium NE M12 M_97 624906 4894096

Medium NE M11 M_98 622152 4887960

Strata Region Route Site UTM_East UTM_North
USDA Forest Service RMRS-GTR-246WWW. 2010. 35

Appendix B. Data Sheet for Ruffed Grouse Surveys in the 

Black Hills National Forest
36 USDA Forest Service RMRS-GTR-246WWW. 2010.

Appendix C. Instructions to Calculate Vegetation Areas 

for Ruffed Grouse Sample Sites in the Black Hills 

National Forest

To determine the area of vegetation types at each site, the user must first download the 

vegetation layer provided at the website: www.fs.fed.us/r2/blackhills/. Once at the website, select “Maps and Publications,” then “Geospatial Maps and Data,” then “Existing 

Vegetation (Shapefile).” After downloading, add this layer to a new project in ArcGIS. 

Next, the user must add the sites from Appendix A by copying and pasting the sites into 

Microsoft Excel, saving them as a Database IV file, and importing them into ArcGIS (the 

UTM coordinates should be projected onto the map using the NAD 1983 UTM Zone 13N 

coordinate system).

Next, the user will need to make a 550 m buffer around each point and clip the buffered 

region out of the vegetation layer (the buffer and clip tools are found in the Arc Toolbox 

under “Analysis Tools”). After clipping, the area of polygons (“Acres”) must be recalculated within the clipped region; otherwise, polygon areas will be inaccurate. To recalculate 

polygon areas, the user can simply open the attribute table of the clipped layer, right click 

over the “acres” attribute, select “Calculate Geometry,” select “Area,” the coordinate system of the data source, and “Acres US.”

Next, the user must add the site attributes such as ID, strata, and region to the clipped 

layer table. To do this, a spatial join should be completed by joining the buffered site layer 

(with all the site attribute info) to the clipped layer (with all the vegetation info). When 

completing this step, the user should select the option that allows each polygon to attain the 

attributes of the joined polygon it falls within. Now, the user is provided with a new layer 

that contains the correct vegetation attributes and site ID for each polygon in the clipped 

layer.

Note: This step can also be completed by using the “Intersect” tool also found in 

the “Analysis Tools.” To do this, the user may select to intersect both the buffered 

site layer and the clipped layer. This will provide the user with another layer that 

contains both the vegetation data and site ID data in the attribute table.

Last, the user must sum the acres of each vegetation type within the 550 m radius surrounding each site. The easiest way to do this is to export the final joined table from the last 

step into Microsoft Excel and use the Pivot Table Wizard. When using the pivot table, the 

user should place the site ID in the row section, the “Cover Type” in the column section, 

and the “Acres” in the data section. This will provide a table with the acres of each vegetation type around each of the sites. The only vegetation types that are needed are aspen 

(TAA) and spruce (TWS).

Once the vegetation acres have been calculated around each site, all that is necessary is to copy and paste the values into the “Site Characteristics” worksheet (Figure 8). 

Currently, the vegetation area data for 2007 is entered in the worksheet. When vegetation 

layers for the BHNF are updated on the website described above, vegetation areas should 

be recalculated.
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Rocky

 Mountain

 Research Station

The Rocky Mountain Research Station develops scientific information 

and technology to improve management, protection, and use of the 

forests and rangelands. Research is designed to meet the needs of 

the National Forest managers, Federal and State agencies, public and 

private organizations, academic institutions, industry, and individuals. 

Studies accelerate solutions to problems involving ecosystems, range, 

forests, water, recreation, fire, resource inventory, land reclamation, 

community sustainability, forest engineering technology, multiple use 

economics, wildlife and fish habitat, and forest insects and diseases. 

Studies are conducted cooperatively, and applications may be found 

worldwide.

Station Headquarters

Rocky Mountain Research Station

240 W Prospect Road

Fort Collins, CO 80526

(970) 498-1100

Research Locations

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Logan, Utah

Ogden, Utah

Provo, Utah

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its 

programs and activities on the basis of race, color, national origin, age, disability, 

and where applicable, sex, marital status, familial status, parental status, religion, 

sexual orientation, genetic information, political beliefs, reprisal, or because all 

or part of an individual’s income is derived from any public assistance program. 

(Not all prohibited bases apply to all programs.) Persons with disabilities who 

require alternative means for communication of program information (Braille, 

large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 

720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, 

Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, 

DC 20250-9410, or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA 

is an equal opportunity provider and employer.

Flagstaff, Arizona

Fort Collins, Colorado

Boise, Idaho

Moscow, Idaho

Bozeman, Montana

Missoula, Montana