U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2015–1166

Prepared in collaboration with the Canadian Wildlife Service, Crane Trust, 

Platte River Recovery Implementation Program, and U.S. Fish and Wildlife Service

Whooping Crane Stopover Site Use 

Intensity Within the Great Plains
Cover photograph. Migrating whooping cranes in Beadle County, South Dakota, April 3, 2015. 

Photograph taken by Chris Bailey.
Whooping Crane Stopover Site Use 

Intensity Within the Great Plains

By Aaron T. Pearse, David A. Brandt, Wade C. Harrell, Kristine L. Metzger, 

David M. Baasch, and Trevor J. Hefley

Prepared in collaboration with the Canadian Wildlife Service, Crane Trust, Platte 

River Recovery Implementation Program, and U.S. Fish and Wildlife Service

Open-File Report 2015–1166

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

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Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

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Suggested citation:

Pearse, A.T., Brandt, D.A., Harrell, W.C., Metzger, K.L., Baasch, D.M., and Hefley, T.J., 2015, Whooping crane stopover 

site use intensity within the Great Plains: U.S. Geological Survey Open-File Report 2015–1166, 12 p., http://dx.doi.

org/10.3133/ofr20151166.

ISSN 2331-1258 (online)
iii

Acknowledgments

Logistic, administrative, and financial support was provided by the Canadian Wildlife Service, 

Crane Trust, U.S. Fish and Wildlife Service, and the Platte River Recovery Implementation Program, with additional support from the Gulf Coast Bird Observatory, International Crane Foundation, and Parks Canada. F. Chavez-Ramirez, B. Strobel, M. Bidwell, M. Harner, G. Wright, and 

T. Stehn were integral to the initiation and continuation of this work. We thank B. Hartup, M. 

Folk, S. Herford, W. Wehtje, and J. Dooley for assistance and support during crane capture or 

other aspects. J. Fieberg and E. Vander Wal provided comments to earlier versions of this report. 

The U.S. Geological Survey–Platte River Priority Ecosystems Program provided partial financial 

support. Northern Prairie Wildlife Research Center supported logistical and administrative 

aspects of this work.
iv

Contents

Acknowledgments........................................................................................................................................iii

Abstract...........................................................................................................................................................1

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

Methods...........................................................................................................................................................2

Study Area..............................................................................................................................................2

Field Methods and Locations..............................................................................................................2

Analytical Methods...............................................................................................................................4

Results .............................................................................................................................................................6

Discussion.......................................................................................................................................................9

Summary........................................................................................................................................................11

References Cited..........................................................................................................................................11

Figures

1. Map showing migration corridor of whooping cranes, including primary breeding 

areas in Wood Buffalo National Park and wintering areas near Aransas National 

Wildlife Refuge..............................................................................................................................3

2. Photograph showing how platform transmitting terminals were secured to the tibiatarsus of captured whooping cranes with a two-piece leg band. Leg bands were 

secured with blind rivets and glue.............................................................................................4

3. Graph showing cumulative proportion of grid cells containing 1 or more whooping 

crane stopover sites (PO) by migration (M; spring 2010 = 1), assuming that an 

average of 20.7 transmitters (T) remain active ........................................................................6

4. Graphs showing cumulative proportion of grid cells (proportion area) that contained 

whooping crane stopover site volume (SV) within the migration corridor and 

crane day volume. An exponential function provided a means to identify the 

critical volume where the slope of this association was 1.0.................................................7

5. Map showing areas within the migration corridor of whooping cranes identified 

with varying levels of stopover site use intensity ...................................................................8

6. Graph showing percentiles of distances (kilometers) from the whooping crane 

migration corridor centerline for all grid cells with stopover sites (circles) and a 

subset of grid cells identified as having greater intensity of use (triangles)......................9

Tables

1. Categories of stopover site use intensity used to identify areas of conservation 

priority within the migration corridor of Aransas-Wood Buffalo whooping cranes .........5

2. Number of grid cells within the U.S. portion of the study area by stopover site use 

intensity category that contained no wind turbine or at least one wind turbine as 

of July 2013 ....................................................................................................................................9
v

Conversion Factors

International System of Units to Inch/Pound 

Multiply By To obtain

Length

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd)

Area

hectare (ha) 2.471 acre

square kilometer (km2) 247.1 acre

hectare (ha) 0.003861 square mile (mi2)

square kilometer (km2) 0.3861 square mile (mi2)

Velocity

meter per second (m/s) 3.281 foot per second (ft/s)

kilometer per hour (km/h) 0.6214 mile per hour (mi/h)

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

Datum

Horizontal coordinate information is referenced to the North American Datum of 1983 

(NAD 83).

Abstract

Whooping cranes (Grus americana) of the AransasWood Buffalo population migrate twice each year through the 

Great Plains in North America. Recovery activities for this 

endangered species include providing adequate places to stop 

and rest during migration, which are generally referred to as 

stopover sites. To assist in recovery efforts, initial estimates 

of stopover site use intensity are presented, which provide 

opportunity to identify areas across the migration range used 

more intensively by whooping cranes. We used location data 

acquired from 58 unique individuals fitted with platform 

transmitting terminals that collected global position system 

locations. Radio-tagged birds provided 2,158 stopover sites 

over 10 migrations and 5 years (2010–14). Using a grid-based 

approach, we identified 1,095 20-square-kilometer grid cells 

that contained stopover sites. We categorized occupied grid 

cells based on density of stopover sites and the amount of 

time cranes spent in the area. This assessment resulted in four 

categories of stopover site use: unoccupied, low intensity, core 

intensity, and extended-use core intensity. Although provisional, this evaluation of stopover site use intensity offers the 

U.S. Fish and Wildlife Service and partners a tool to identify 

landscapes that may be of greater conservation significance to 

migrating whooping cranes. Initially, the tool will be used by 

the U.S. Fish and Wildlife Service and other interested parties 

in evaluating the Great Plains Wind Energy Habitat Conservation Plan.

Introduction

The whooping crane (Grus americana) is a well-recognized endangered species endemic to North America. The 

only self-sustaining and wild population of whooping cranes, 

the Aransas-Wood Buffalo population, nests at Wood Buffalo 

National Park and surrounding lands near the border of the 

Northwest Territories and the Province of Alberta, Canada. 

Birds from this population migrate nearly 4,000 kilometers 

(km) through the Great Plains and winter along the Gulf Coast 

of Texas at Aransas National Wildlife Refuge and surrounding 

lands (Stevenson and Griffith, 1946; Allen 1952). Persistence 

and recovery of the species is contingent on continued growth 

of the Aransas-Wood Buffalo population, because attempts 

to reestablish migratory or resident populations from captive 

birds have been largely unsuccessful to date (Canadian Wildlife Service and U.S. Fish and Wildlife Service, 2007).

Twice yearly movements between summering and wintering areas serve as defining periods for migratory birds, and 

these migrations can be energetically taxing and risky (Newton, 2008; Stehn and Haralson-Strobel, 2014). For whooping 

cranes and other diurnally migrating birds, successful completion of migration requires suitable sites for birds to rest and 

reside for one to multiple nights, which are generally referred 

to as stopover sites. Opportunistic whooping crane sightings 

have largely shaped understanding of routes and timing of 

migrating cranes, as well as provided documentation of a wide 

variety of wetland and upland habitats used (Johns, 1992; 

Austin and Richert, 2005; Tacha and others, 2010). Although 

efforts to identify and prioritize areas of importance along the 

migration corridor using opportunistic sightings have provided 

useful insights, studies using these data have included caveats 

related to potential biases inherent with opportunistic sightings 

(Austin and Richert, 2005; Belaire and others, 2014; Hefley 

and others, 2013, 2014, in press). 

Expansion of wind-energy development and related 

infrastructure within the whooping migration corridor has 

been identified as a conservation concern (Canadian Wildlife 

Service and U.S. Fish and Wildlife Service, 2007; Belaire and 

others, 2014). Collisions with manmade obstacles, mainly 

power lines, have been listed as significant threats to seven 

species of cranes, including whooping cranes (Harris and 

Mirande, 2013). Identification and assessment of risk factors during migration informs development of conservation 

and management programs targeted at minimizing risk and 

identifying potential areas in need of protection. In response 

to large-scale planned development of wind-energy projects 

across the migration corridor of whooping cranes, multiple 

companies began developing a habitat conservation plan 

Whooping Crane Stopover Site Use Intensity 

Within the Great Plains

By Aaron T. Pearse,1 David A. Brandt,1 Wade C. Harrell,2 Kristine L. Metzger,3

David M. Baasch,4 and Trevor J. Hefley5

1

U.S. Geological Survey.

2

U.S. Fish and Wildlife Service, Ecological Services, Austwell, Texas.

3

U.S. Fish and Wildlife Service, Division of Biological Services, Albuquerque, New Mexico. 

4

Headwaters Corporation, Kearney, Nebraska.

5

Department of Fish, Wildlife, and Conservation Biology, Colorado State 

University, Fort Collins, Colorado.
2 Whooping Crane Stopover Site Use Intensity Within the Great Plains

in 2009 under Section 10 of the Endangered Species Act 

(16 U.S.C. §1531 et seq. 1973). The Great Plains Wind Energy 

Habitat Conservation Plan covers whooping cranes as well as 

other threatened and endangered species. The overall goal of 

the plan is avoidance, minimization, and mitigation of risks to 

covered species to the maximum extent practical while providing regulatory certainty for the plan’s developers. The U.S. 

Fish and Wildlife Service has been in consultation with those 

developing this plan and will be charged with evaluating the 

final plan. This report provides the U.S. Fish and Wildlife Service and other interested parties assistance in evaluations and 

negotiations related to the Great Plains Wind Energy Habitat 

Conservation Plan. 

Strategic conservation of habitats that provide resources 

throughout the annual life cycle of the whooping crane will 

assist in species recovery (National Resource Council, 2005; 

Canadian Wildlife Service and U.S. Fish and Wildlife Service, 

2007). Locations of individuals based on telemetry data provide one example of information that could be used to prioritize conservation and management (Sawyer and others, 2009; 

Wilson and others, 2009; Corre and others, 2012; Grecian 

and others, 2012). The purpose of the report is to provide a 

simple, objective, and transparent examination of the distribution of stopover sites used by radio-tagged whooping cranes. 

Specifically, we used whooping crane locations acquired during 2010–14 to (1) estimate intensity of stopover site use by 

radio-tagged whooping cranes along their migration corridor 

in the Great Plains and (2) identify categories of stopover site 

use intensity for prioritizing conservation and management 

actions. We included data from 2010 to 2014, yet data collection continues during 2015 and will likely extend into 2016; 

therefore, we consider results presented here as provisional. 

Also, analytical methods for describing telemetry-based data 

have increased in the past decade; thus, we acknowledge 

methods applied herein are but one of many potentially appropriate ways to describe patterns in intensity of use within the 

migration corridor. This report represents the first analyses of 

these data, with additional work expected to follow that may 

supersede results presented in this report. 

Methods

Study Area

Most whooping crane sightings during migration have 

occurred within Canadian Provinces and U.S. States in the 

Great Plains (Texas, Oklahoma, Kansas, Nebraska, South 

Dakota, North Dakota, Saskatchewan, and Alberta; Johns, 

1992; Tacha and others, 2010). The study area included a 

region encompassing all stopover locations collected during 

migration, which included parts of the previously identified 

States and Provinces, and also areas within Missouri, Iowa, 

Minnesota, Montana, Manitoba, and the Northwest Territories 

(fig. 1).

Field Methods and Locations

We captured cranes and attached platform transmitting terminals with global position system (GPS) capabilities (North Star Science and Technology LLC, Baltimore, 

Maryland) at Wood Buffalo National Park and sites along 

the Texas Gulf Coast. Capture teams consisted of individuals with experience handling endangered cranes, including a 

licensed veterinarian. We captured pre-fledged juvenile cranes 

at breeding sites by locating family groups from helicopters 

and positioning personnel nearby for ground pursuit and hand 

capture (Kuyt, 1979). We captured cranes in Texas using leg 

snares that enclosed on the lower tarsus upon triggering. 

Transmitters were mounted on two-piece leg bands (Haggie 

Engraving, Crumpton, Md.) placed on the tibia-tarsus 

of captured birds (fig. 2). Transmitters had solar panels integrated on three exposed surfaces and were expected to provide 

a 3- to 5-year lifespan. The transmitter and leg band weighed 

approximately 75 grams (g), which represented approximately 

1 percent of body weight of adult whooping cranes. Transmitters were programmed to record 4–5 GPS locations daily at 

equal time intervals, which provided daytime and nighttime 

locations. Capture and marking procedures were approved 

by the Animal Care and Use Committee at Northern Prairie 

Wildlife Research Center. 

We initially inspected GPS locations from all birds to 

remove locations collected or transmitted potentially with 

error. We performed multiple assessments to determine plausibility of locations and omitted locations outside the expected 

time sequence, with an implausible rate of displacement 

(greater than 100 kilometers per hour [km/h]), or forming an 

acute angle (less than 5 degrees [°]) at distances greater than 

50 km (distance/angle; see Douglas and others, 2012). After 

removing potential errors, we retained only GPS locations 

recording an instantaneous velocity of less than 2.1 meters per 

second (m/s), which we assumed was indicative of locations 

acquired while the crane was on the ground rather than flying.

We identified each point as being collected during 

migration (spring and fall) based on manual inspection of 

movement patterns with respect to time of year (Krapu and 

others, 2011). Each migration season, we identified specific 

stopover sites for individual whooping cranes by identifying 

clusters of locations based on distance, movement pattern, 

and manual inspection. Unique stopover sites were identified 

if birds moved more than 15 km between nighttime locations 

from one day to the next, although we occasionally deviated 

from this rule based on expert opinion. After identifying a set 

of locations as a unique stopover, we calculated its centroid 

and enumerated total days that the animal remained at each 

stopover site. In some instances, information indicated only 

daytime use at a site. We identified these as daytime stopovers 

and assigned a value of 0.5 day duration of stay.

Locations were originally collected in degrees of latitude 

and longitude using the World Geodetic System 1984 datum. 

We projected locations to a Transverse Mercator projection with a central meridian of 102°W, which serves as the 
Methods 3

!

!

!

!

!

!

!

!

Source: US National Park Service

130° 120° 110° 100° 90° 80° 70°

60°

50°

40°

30°

figure01.ai

Base map from Esri and is used herein under liscence (500 meter resolution).

Univeral Transverse Mecator projection, zones 13–14 N

North American Datum of 1983 (NAD 83)

Map data modified from Esri, U.S. Fish and Wildlife Service, and

U.S. Geological Survey digital data, various resolutions.

Saskatoon

Regina

Bismarck

Pierre

Grand 

Island

Oklahoma City

Wichita

Dallas

Aransas National Wildlife Refuge

Wood Buffalo 

National Park

0 200 400 600

200 400 600

800 MILES

0 800 KILOMETERS

Study area 

boundary

MONTANA

NORTH

DAKOTA

MEXICO

UNITED 

STATES

UNITED STATES

CANADA

SOUTH

DAKOTA

WYOMING

IDAHO

OREGON

WASHINGTON

NEVADA

UTAH

CALIFORNIA

ARIZONA NEW

MEXICO

COLORADO

TEXAS

OKLAHOMA

KANSAS

NEBRASKA IOWA

MINNESOTA

WISCONSIN

ONTARIO

QUEBEC

MANITOBA ALBERTA

BRITISH

COLOMBIA

SASKATCHEWAN

ILLINOIS

KENTUCKY

TENNESSEE

GEORGIA

FLORIDA

ALABAMA MISSISSIPPI

OHIO

PENNSYLVANIA

NORTH

CAROLINA

SOUTH

CAROLINA

VIRGINIA

NEW

YORK

WEST

VIRGINIA

MISSOURI

INDIANA

ARKANSAS

LOUISIANA

MICHIGAN

Figure 1. Migration corridor of whooping cranes, including primary breeding areas in Wood Buffalo National Park and wintering 

areas near Aransas National Wildlife Refuge.
4 Whooping Crane Stopover Site Use Intensity Within the Great Plains

boundary between Universal Transverse Mercator zones 13 

and 14. This projection allowed for 98 percent of locations to 

be included within 10° of the central meridian (fig. 1). We also 

converted locations to the North American Datum of 1983. 

Analytical Methods

To explore stopover site use intensity, we estimated a utilization distribution (Worton, 1989), which provided relative 

densities of stopover sites used by radio-tagged cranes over 

the entire migration corridor. Using a grid-based approach, we 

divided the study area into grid cells and summarized information about stopovers within each cell (Adams and Davis, 

1967; MacDonald and others, 1980; Powell, 2000). We used 

this approach rather than techniques that smooth observed data 

(kernel density estimation) because we desired a relatively 

simple and transparent measure of stopover density and distribution. We combined years (2010–14) and migration seasons 

(spring and fall) and justified pooling data by reasoning that 

many conservation and management actions would be based 

on factors irrespective of the calendar year or migration season 

in which stopovers occurred.

Although we avoided applying smoothing techniques, we 

acknowledge that the grid-based method implicitly smoothed 

data, where size of grid cells determined smoothing level. 

We therefore considered multiple factors when identifying an 

appropriate cell size. Generally, analysts may consider ecological context and intended use when choosing a cell size or 

other smoothing parameters (Powell, 2000). In the context of 

migration ecology, selection of a stopover site best fits within 

a hierarchy of habitat selection choices (Johnson, 1980). 

Because we summarized multiple locations at a stopover site 

into a single centroid, we were inherently interested in where a 

bird chose to reside within a larger landscape. Of the locations, 

95 percent were within 6.8 km of stopover centroids and 99 

percent were within 12.5 km, which corresponded to circular 

Figure 2. Platform transmitting terminals were secured to the tibia-tarsus of captured whooping cranes with a two-piece 

leg band. Leg bands were secured with blind rivets and glue. Photograph taken by David Baasch, Platte River Recovery 

Implementation Program.

Figure02
Methods 5

areas of 14,520 and 48,063 hectares (ha), respectively. More 

importantly, the intended application was to identify potential 

co-occurrence with commercial wind towers, facilities, and 

associated infrastructure. Average 

area of wind tower arrays built within the study area as of 

July 2013 was 12,083 ha (median = 8,210 ha; Diffendorfer and 

others, 2014). When including a 6.5-km buffer surrounding 

towers, which was the average distance of wintering sandhill 

cranes (Grus canadensis) locations less than 10 km from 

established wind towers in Texas (A.T. Pearse, U.S. Geological Survey, unpub. data, 2015), average footprint increased 

to 50,783 ha (median = 44,774 ha). Based on these assessments, we identified a cell size of 20 square kilometers (km2

) 

(40,000 ha) as best fitting specific requirements. 

We initially considered each stopover site used by a 

radio-tagged crane with equal weight and, in a secondary 

classification, we included duration of stay to further identify 

grid cells that had greater stopover site intensity and greater 

longevity of use. We calculated number of days individual 

cranes spent at a particular stopover site and enumerated 

number of days for all stopovers within grid cells (crane days). 

We first identified occupied grid cells (greater than or equal to 

[≥] 1 stopover site). For those cells, we enumerated stopovers, 

enumerated crane days over all stopovers within the cell, identified the first migration year in which a stopover was detected 

(for example, fall 2010), and determined during which migration season(s) stopovers occurred (fall only, spring only, or 

both seasons). We ranked grid cells by stopover density and 

determined cumulative sum and proportion of stopovers found 

within cells (volume and cumulative proportion volume). We 

used volume metrics to identify areas of more intense stopover 

site use. Specifically, we used a method described by Vander 

Wal and Rodgers (2012), where utilization distribution area 

and volume were plotted against one another (Powell, 2000). 

We used a fitted exponential model to estimate this association and determined the point at which the slope equaled 1.0. 

The volume at this inflection point represented a transition 

where, at cumulative volume values above, proportion of 

occupied area increases at a greater rate than probability of 

use. This method uses characteristics of the data to define 

a critical value rather than an analyst selecting an arbitrary 

volume to define core or intensely used regions compared with 

peripheral areas of lower intensity. We determined this method 

required some modification because the critical volume could 

fall within a group of cells with equal density (for example, 

two stopovers per cell). If this occurred, we identified the next 

greatest difference in cell density to serve as delimiter between 

categories (for example, transition between two and three 

stopovers per cell). We further divided identified core intensity 

grid cells into two categories based on volume of crane days. 

We used the same procedures as above, but considered only 

core intensity cells.

Our analyses allowed for identification of four mutually 

exclusive categories for grid cells (table 1). Unoccupied cells 

had no identified stopover sites. Low intensity cells were occupied but at densities above the cumulative stopover volume 

described above. Core intensity cells had densities of stopover 

sites below the cumulative stopover volume. These sites also 

had lower densities of crane days, as compared with the final 

category, extended-use core intensity, which had high intensity 

stopover site use and numerous crane days. 

We calculated a corridor centerline of stopover sites for 

illustration and comparative purposes. First, we grouped cells 

into 36 equally spaced categories based on northing coordinates (latitude) and calculated median northing and easting 

coordinates from grid centroids, weighting by number of stopover sites in grid cells. The 36 derived vertices formed a line 

that represented the central tendency of stopover sites within 

the migration corridor. Weighting by number of stopovers in 

each cell, we calculated distance percentiles for all stopover 

sites and those associated only in cells identified as core intensity or extended-use core intensity. 

We intersected spatial databases with results as a preliminary evaluation of protections and risks faced by migrating 

whooping cranes. We used the Protected Areas Database of 

the United States and the Conservation Areas Reporting and 

Tracking System to identify lands under some type of conservation or land protection (U.S. Geological Survey, Gap Analysis Program, 2012; Canadian Council on Ecological Areas 

2015). We also included locations of wind turbines constructed 

within the United States as of July 2013 to characterize one 

potential and relevant hazard (Diffendorfer and others, 2014). 

Table 1. Categories of stopover site use intensity used to identify areas of conservation priority within the migration corridor of 

Aransas-Wood Buffalo whooping cranes.

[≥, greater than or equal to; >, greater than; ≤, less than or equal to]

Category Criterion Description

Unoccupied 0 stopover sites Cell lacks evidence of use.

Low intensity ≥ 1 stopover sites

> critical cumulative stopover volume

Cell has evidence of use and low stopover site use intensity.

Core intensity ≤ critical cumulative stopover volume

> critical cumulative crane day volume

Cell contains density of stopovers identified as high use intensity 

and crane days of lower intensity.

Extended-use core intensity ≤ critical cumulative stopover volume

≤ critical cumulative crane day volume

Cell contains high use intensity of stopovers and crane days.
6 Whooping Crane Stopover Site Use Intensity Within the Great Plains

We were not able to obtain specific wind tower locations for 

Canada. We determined percentage of all occupied and core 

(core intensity and extended-use core intensity categories) grid 

cells that contained protected lands and calculated percentage 

of grid cell area identified as protected lands, because most 

cells contained a mix of protected and unprotected lands. For 

wind turbines, we determined the number of grid cells within 

the United States that contained ≥1 wind turbine and summarized information for each stopover use category. We tested the 

null hypothesis that turbines were randomly associated with 

stopover use categories using a chi-square test (Iman, 1994).

Finally, we were interested in examining how cell occupancy changed with increased data collection. We explored the 

trend in accumulation of occupancy (≥1 stopover site identified) of grid cells during 10 migrations in spring 2010 (S10) to 

fall 2014 (F14). We fit this function using linear regression and 

logit transforming proportion of occupied cells. Explanatory 

variables included season (sequentially increasing integers) 

and number of transmitters deployed per season. We extrapolated results to include an additional 10 migrations, which 

assumed a similar number of active transmitters would be 

monitored.

Results

We marked 68 unique whooping cranes with transmitters 

between December 2009 and February 2014, including 

35 juvenile and 33 subadult or adult birds. Two birds were 

recaptured and fitted with new transmitters after original transmitters failed. Of the 68 marked cranes, 58 unique cranes provided migration locations for analyses (10 in 2010, 23 in 2011, 

33 in 2012, 33 in 2013, 27 in 2014). The average number of 

cranes monitored during each migration was 20.7. Radiotagged cranes provided data for an average of 3.6 migrations 

and an average of 37 stopover sites (6–84 stopover sites, 

median = 40). Overall, we identified 2,158 stopover sites; the 

average number of sites identified per year was 432 (110 in 

2010, 321 in 2011, 527 in 2012, 676 in 2013, 524 in 2014). 

More stopovers occurred during spring (54 percent, n = 1,163) 

than fall (46 percent, n = 995), whereas a larger percentage 

of crane days occurred during fall (57 percent, n = 3,680.5) 

compared with spring (43 percent, n = 2,792.5).

Our study area included 5,431 grid cells, 20 percent 

(n = 1,095) of which contained ≥1 stopover site. Of the occupied grid cells, 30 percent received only fall stopover use, 

47 percent exclusively spring use, and 23 percent had use 

during both migration seasons. Percentage of occupied cells 

increased each migration as we accumulated more data 

(fig. 3). Newly occupied cells continued to be identified after 

cessation of marking new birds before migrations in 2014. 

During the 10 migrations included in analyses, an average of 

120 (standard error = 15.3) new grid cells per migration were 

identified as used. Provided that a similar sample of transmitters continued to be monitored, the cumulative percentage of 

cells that may be identified as occupied would be expected to 

continue increasing and reach approximately 34 percent 

(95 percent prediction interval: 22–55 percent ) in 5 additional 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.8

Spring

2010

Fall

2010

Spring

2011

Fall

2011

Spring

2012

Fall

2012

Spring

2013

Fall

2013

Spring

2014

Fall

2014

1+ 2+ 3+ 4+ 5+ 6+ 7+ 8+ 9+ 10+

Proportion occupied cells

Migration

Best-fit line

Extrapolated 

prediction 

interval

ln(PO/1 - PO) = - 5.485 + 1.656 x ln(M) + 0.017(T)

Coefficient of determination (R 2

) = 0.99

Figure 3. Cumulative proportion of grid cells containing 1 or more whooping crane stopover sites (PO) by migration (M; spring 

2010 = 1), assuming that an average of 20.7 transmitters (T) remain active. 
Results 7

migrations and 46 percent (95 percent prediction interval: 

29–64 percent ) in 10 migrations (fig. 3).

Stopover sites per occupied cell ranged from 1 to 29 

(average = 2.0, standard deviation = 2.0). Of the occupied 

cells, 56 percent contained only one stopover site. The association of cumulative percent stopover volume and area was fit 

with an exponential model and the resulting critical value used 

to delineate core cells was 0.614 (fig. 4A). This percentage 

volume was located among cells of equal intensity; therefore, 

we located a crucial value at the first available difference in 

intensity (less than or equal to 0.484), which corresponded 

with an intensity difference of 2 and 3 stopovers per cell. 

Based on this critical value, 233 grid cells were identified as 

core intensity grid cells. 

Within the 233 cells identified above, crane days per cell 

varied from 2.5 to 631 and averaged 16.6 (standard deviation = 46.8; median = 6). The association between cumulative 

percent crane day volume and area was fit with an exponential 

model and the resulting critical value was 0.698 (fig. 4B). This 

percentage volume was located among cells of equal intensity; 

therefore, we located a crucial value at the available difference 

in intensity (less than or equal to 0.691), which corresponded 

with an intensity difference between 18 and 19 crane days per 

cell. Based on this critical value, 44 grid cells were identified 

as having core intensity of crane days. 

Using four defined mutually exclusive categories 

(table 1), 4,336 grid cells were included in the unoccupied 

group (80 percent), 862 in low intensity (16 percent; 79 percent of occupied), 189 in core intensity (3 percent; 17 percent 

of occupied), and 44 in extended-use core intensity (1 percent; 

4 percent of occupied). Cells identified as low intensity 

included 52 percent of stopover sites and 40 percent of crane 

days. Core intensity grid cells included 35 percent of stopovers 

and 18 percent of crane days. Cells identified as extended-use 

core intensity represented 13 percent of stopover sites and 

42 percent of crane days. Geographically, grid cells identified 

in the core intensity category were generally centrally located 

in the migration corridor, as identified by the centerline 

(fig. 5). Latitudinally, these cells were present throughout the 

corridor, except extended-use core intensity cells, which were 

more frequent in the northern part of the corridor in Saskatchewan, North Dakota, and South Dakota.

The average distance of stopovers to the centerline 

was 43.8 km (median = 27.5; SD = 31.4; max = 480.4). The 

75th percentile was 58.8 km, 85th percentile was 82.3 km, and 

95th percentile was 144.1 km (fig. 6). Including only stopovers 

within grid cells identified as core intensity or extended-use 

core intensity, average distance was 22.2 km (median = 17.1; 

SD = 51.2; maximum = 144.1). The 75th percentile was 

31.4 km, 85th percentile was 41.2 km, and 95th percentile 

was 74.5 km (fig. 6).

Of grid cells with ≥1 stopover site, 75 percent contained 

at least one tract of protected land, and 83 percent of core grid 

cells (core and extended use designations) contained protected 

land. Protected lands covered a varying area of grid cells and, 

on average, 13 percent of land within all occupied cells and 

32 percent of land within core grid cells had some measure of 

protection. Thus, lands with some type of protection covered 

approximately 10 percent of the migration corridor used by 

whooping cranes and approximately 27 percent of the core 

corridor. 

We estimated that 7 percent of grid cells within the U.S. 

study area contained ≥1 wind turbine (9,765 turbines). To 

test the null hypothesis that wind turbines occurred within 

Figure 4. Cumulative proportion of grid cells (proportion area) that contained A, whooping crane stopover site volume (SV) 

within the migration corridor and B, crane day volume. An exponential function provided a means to identify the critical volume 

where the slope of this association was 1.0. 

0.0

0.2

0.4

0.6

0.8

1.0

A B

0 0.2 0.4 0.6 0.8 1.0 0 0.2 0.4 0.6 0.8 1.0

Proportion of area

Proportion of stopover volume Proportion of crane days volume

ln(PA) = ln(0.0033) + 5.6823 x NV

= 0.99

ln(PA) = ln(0.0535) + 2.9874 x SV

= 0.99

figure04.ai

Coefficient of determination (R 2) Coefficient of determination (R 2)
8 Whooping Crane Stopover Site Use Intensity Within the Great Plains

130° 120° 110° 100° 90° 80° 70°

60°

50°

40°

30°

Whooping crane migration corridor

EXPLANATION

Centerline

Low intensity

Core intensity

Extended-use core intensity

0 200 400 600

200 400 600

figure05.ai

800 MILES

0 800

Base map from Esri and is used herein under liscence (500 meter resolution).

KILOMETERS

Univeral Transverse Mecator projection, zones 13–14 N

North American Datum of 1983 (NAD 83)

Study area 

boundary

MONTANA NORTH

DAKOTA

MEXICO

UNITED 

STATES

UNITED STATES

CANADA

SOUTH

DAKOTA

WYOMING

IDAHO

OREGON

WASHINGTON

NEVADA

UTAH

CALIFORNIA

ARIZONA NEW

MEXICO

COLORADO

TEXAS

OKLAHOMA

KANSAS

NEBRASKA IOWA

MINNESOTA

WISCONSIN

ONTARIO

QUEBEC

MANITOBA

ALBERTA

BRITISH

COLOMBIA

SASKATCHEWAN

ILLINOIS

KENTUCKY

TENNESSEE

GEORGIA

FLORIDA

ALABAMA MISSISSIPPI

OHIO

PENNSYLVANIA

NORTH

CAROLINA

SOUTH

CAROLINA

VIRGINIA

NEW

YORK

WEST

VIRGINIA

MISSOURI

INDIANA

ARKANSAS

LOUISIANA

MICHIGAN

Figure 5. Areas within the migration corridor of whooping cranes identified with varying levels of stopover site use 

intensity (category definitions shown in table 1).
Discussion 9

stopover site use categories independently, we first combined core intensity and extended-use core intensity categories because we observed no turbines in the latter category 

(table 2). Observed grid cell frequencies by stopover site use 

category were not different from what would be expected by 

chance alone (chi-square = 3.2; degrees of freedom = 2; probability value = 0.10).

Discussion

Various reports have defined the migration corridor of 

Aransas-Wood Buffalo whooping cranes (Allen, 1952). To 

date, corridor boundaries have been based on observational 

sightings and one radio telemetry study from the early 1980s 

(Kuyt, 1992; Johns, 1992; Tacha and others, 2010). The results 

support earlier findings regarding the migration corridor’s general location (fig. 5). By comparison, intensity of use within 

the migration corridor has received less attention. Using all 

migration data available as of spring 2008 within the United 

States, Tacha and others (2010) identified migration corridor 

width using a distance-from-centerline approach and determined that 75 percent of stopover sites occurred within 

48 km of the centerline, 85 percent within 80 km, and 

95 percent within 136 km. The Tacha and others (2010) 

analysis provided greater definition of where the majority of 

stopovers occurred within the U.S. (approximately 60 percent 

of the entire migration centerline). We determined strikingly 

similar results for the entire migration corridor and, based on 

the derived centerline, 75 percent of stopover sites occurred 

within 59 km, 85 percent within 82 km, and 95 percent within 

144 km of the centerline. This similarity provides initial 

Figure 6. Percentiles of distances (kilometers) from the whooping crane migration corridor centerline for all grid 

cells with stopover sites (circles) and a subset of grid cells identified as having greater intensity of use (triangles).

25

50

75

100

125

150

0 20 40 60 80 100

Percentile (P)

0

25

50

75

100

125

150

Kilometers from centerlnie (D)

ln(D) = ln(3.86) + 3.70 x P

= 0.99

ln(D) = ln(3.70) + 3.15 x P

= 0.99

figure06.ai

Coefficient of determination (R 2)

Coefficient of determination (R 2

)

Table 2. Number of grid cells within the U.S. portion of the study 

area by stopover site use intensity category that contained no 

wind turbine or at least one wind turbine as of July 2013.

[≥, greater than or equal to]

Category No wind turbines ≥ 1 wind turbine

Unoccupied 2,543 196

Low intensity 469 33

Core intensity 120 5

Extended-use core intensity 22 0

Total 3,154 234
10 Whooping Crane Stopover Site Use Intensity Within the Great Plains

evidence that observation data, even with known biases, may 

have value in this type of coarse and large-scale evaluation.

Using a grid-based approach, we summarized density of 

stopover sites to identify areas of greater use intensity. Areas 

with greater stopover site intensity were present through the 

migration corridor, with conspicuous clusters along the border 

of Texas and Oklahoma, north-central Nebraska, south-central 

and north-central South Dakota, northwestern North Dakota, 

the border of North Dakota and Saskatchewan, and south-central Saskatchewan (fig. 5). These results refine earlier efforts 

and represent one of the first estimates of site use intensity 

across the entire migration range not relying upon observational data. 

Principal strengths of this work were data quality, data 

quantity, and a simple analytical approach. Transmitters 

provided locations across a vast landscape selected by cranes, 

presumably to meet a complex set of behavioral, physiological, and nutritional needs during migration. The GPS locations 

were more precise (10–100 meters) and not subject to observation biases as compared to previously reported opportunistic 

observations during migration (Tacha and others, 2010). 

The sample of whooping cranes represented approximately 

20 percent of the population, a sampling proportion not often 

possible in wildlife or ecological research. This sample size 

allowed for identification of 2,158 stopover sites, which is a 

9-percent increase from those identified in the United States, 

1975–2008 (1,981; Tacha and others, 2010). We examined 

stopover site locations using relatively simple methods, which 

provided straightforward interpretations. These methods 

subdivided the entire migration corridor into categories for 

identifying potential conservation priority (table 1, fig. 5). 

Despite these strengths, certain limitations of our study 

design require qualified interpretation. Location data identified 

stopover sites of radio-tagged individuals without providing information regarding accompanying unmarked cranes. 

Whooping cranes generally migrate in small groups depending on age and social status (Howe, 1989; Austin and Richert, 

2005), although individual whooping cranes are known to 

migrate alone or with sandhill cranes. Juvenile cranes are 

accompanied by at least one and most likely both parents during the entirety of their first fall migration and most of their 

first spring migration (Howe, 1989; Kuyt, 1992). Understanding of associations of older-aged radio-tagged birds with 

unmarked birds relied on limited reported sightings. Thus, 

radio-tagged birds could represent an unknown multiple of 

birds, which could have provided another weighting factor at 

identified stopover sites. Group size information is generally 

available from observational whooping crane sightings. Hefley 

and others (in press) used group size information to model 

distribution of migrating whooping cranes in Nebraska and 

determined that variation in group size did not provide useful 

additional information; hence, lacking this information may 

be of only slight consequence. Also, the analytical method 

provided only a coarse identification of stopover site intensity 

over the entire corridor. This coarseness was driven by choice 

of grid cell size, which we justified based on multiple criteria, 

but we acknowledge that cell size limits the scale at which 

identification of priority areas can be discerned. Results were 

not intended to provide fine-scale site evaluation of potential 

whooping crane use in the migration corridor and would be 

poorly suited at that task. Site-level evaluations generally 

require modeling of landscape and local-scale characteristics 

that may influence site choice (Armbruster, 1990; Belaire 

and others, 2014; Hefley and others, 2014). In addition, the 

analytical approach provided interpretation of stopover density 

without regard to potential spatial association of stopover sites 

larger than the selected grid cell size. Identifying larger landscapes with clusters of stopover sites may be useful for certain 

applications. We plan to explore techniques that incorporate 

local and landscape covariates and spatial associations among 

stopover sites in future efforts. 

As stated previously, we view results as provisional. The 

investigation of increasing evidence of used or occupied grid 

cells over time provides insight into how additional data might 

update results. Based on the predicted trend, monitoring birds 

for additional migrations will likely continue to identify new 

areas (fig. 3). We acknowledge that this function could asymptote more quickly than predicted, which would bias predictions. Fieberg (2007) noted a similar potential for home range 

analyses, where increasing study duration would likely lead to 

estimating larger home ranges as animals explored new areas 

over time. Although we did not investigate how core intensity 

cells might change with increasing sample size, we suspect 

these cells to remain more stable over time. Thus, we caution 

using occupancy (low intensity category) alone to prioritize 

conservation, because we believe it to be most sensitive to 

further data acquisition. 

A significant percentage of the core migration range 

was identified to be under some measure of land protection 

(27 percent) in comparison to occupied grid cells (10 percent) 

and the study area overall (10 percent). Ownership, nature, and 

longevity of protection varied among lands within the migration corridor. Federal, State, tribal, and private ownerships 

were included, lands had access policies that ranged from open 

to restricted, and protection longevity varied from perpetual 

to time-limited easements. Given this variation, we consider 

the estimate of protected lands within the core migration area 

of whooping cranes as optimistic. Moreover, most protections 

were not put in place for the conservation of whooping cranes 

specifically, and therefore represent collateral benefits of other 

efforts to protect areas with natural, cultural, societal, and 

wildlife values. 

Nearly 10,000 wind turbines have been constructed 

within the U.S. study area, occupying 7 percent of grid cells. 

Most grid cells with wind turbines did not contain whooping crane stopover sites (84 percent), and only 2 percent of 

cells were identified as core migration areas. The estimate of 

spatial overlap during migration provides a baseline that can 

be compared with future wind-energy development scenarios 

and future increases in the whooping crane population (Butler 

and others, 2013). The relatively low level of spatial overlap 

was not likely due to avoidance of turbines by cranes because 
References Cited 11

we could not reject the hypothesis that wind turbines occurred 

within stopover use categories in proportion to availability. To 

maintain minimal spatial overlap of cranes and wind turbines, 

efforts could be made to place turbines in locations expected 

to have a low probability of crane use (Fargione and others, 

2012).

Our evaluation over the entire corridor provided opportunity to compare use intensity relative to all other areas. This 

approach is in contrast to regional products available (Johns, 

1992; Tacha and others, 2010; Belaire and others, 2014; Hefley and others, in press). This approach allows for flexibility 

in prioritizing areas for conservation and management of 

migrating whooping cranes. When using defined categories 

(table 1), practitioners may wish to scale or weight categories 

based on specific objectives. Because prioritization decisions 

will be fundamentally objective-driven, we do not attempt to 

identify a single set of scaling factors for determining conservation priority for migrating whooping cranes.

Summary

The whooping crane (Grus americana) is a well-recognized endangered species endemic to North America. For 

whooping cranes and other diurnally migrating birds, successful completion of migration requires suitable sites for birds to 

rest and reside for one to multiple nights, which are generally 

referred to as stopover sites. The purpose of the report is to 

provide a simple, objective, and transparent examination of 

the distribution of stopover sites used by radio-tagged whooping cranes. We used location data acquired from 58 unique 

individuals fitted with platform transmitting terminals that 

collected global position system locations. Radio-tagged birds 

provided 2,158 stopover sites over 10 migrations and 5 years 

(2010–14). Using a grid-based approach, we divided the study 

area into 20-square-kilometer grid cells and summarized 

information about stopovers within each cell. The study area 

included 5,431 grid cells, 20 percent (n = 1,095) of which 

contained 1 or more stopover sites. We identified 233 cells as 

having greater intensity of use and 44 of those as receiving 

numerous days of use by cranes. Areas with greater stopover 

site intensity were present through the migration corridor, 

and these results refine earlier efforts, representing one of 

the first estimates of site use intensity across the entire migration range not relying upon observational data. Lands with 

some type of conservation protection covered approximately 

10 percent of the migration corridor used by whooping cranes 

and approximately 27 percent of the core corridor. Most grid 

cells that contained wind turbines as of July 2013 did not 

contain whooping crane stopover sites (84 percent), and only 

2 percent of cells were identified as core migration areas. 

Results will be used initially by the U.S. Fish and Wildlife 

Service and other interested parties in evaluating the Great 

Plains Wind Energy Habitat Conservation Plan.

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Pearse and others—Whooping Crane Stopover Site Use Intensity Within the Great Plains—Open-File Report 2015–1166

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http://dx.doi.org/10.3133/ofr20151166