Evaluations of Duck Habitat and Estimation of 

Duck Population Sizes with a Remote-SensingBased System

by 

Lewis M. Cowardin, Terry L. Shaffer, and Phillip M. Arnold

U.S. Department of the Interior

National Biological Service

Northern Prairie Science Center

Jamestown, North Dakota 58401

Abstract: During 1987-90, we used high-altitude photography, aerial videography, counts, and 

models to estimate sizes of breeding populations of dabbling ducks (Anatinae) and duck 

production and to identify duck habitat on U.S. Fish and Wildlife Service land and easements 

and on private land in the prairie pothole region of the United States. The study area contained 

about 3.1 million wetland basins (28,490 km2). Wetland area (ha per km2) was highest on 

service-owned land; wetland-basin density was greatest on service easements. Temporary and 

seasonal wetlands were underrepresented and lakes were overrepresented on service-owned 

land. Seventy-eight percent of all basins were less than 0.41 ha. Cropland dominated private 

land. Pond density decreased from 4.4/km2 in 1987 to 3.4/km2 in 1990 and pond area, from 7.2 

ha/km2 to 2.7 ha/km2. The density of the blue-winged teal was greatest (3.4 pairs/km2) and was 

followed in magnitude by those of the mallard (2.1 pairs/km2), the gadwall (1.8 pairs/km2), the 

northern pintail (0.8 pairs/km2), and the redhead (0.8 pairs/km2). Duck density was consistently 

highest on service-owned land. The decline of breeding-population sizes in 1987-90 closely 

corresponded to losses of pond numbers and pond area. The density of breeding pairs per pond 

was inversely related to pond density, suggesting that breeding ducks tended to concentrate on 

the remaining ponds as drought intensified. The production of recruits followed the same 

pattern as breeding-population sizes. We estimated that 2.5% of the ducklings hatched on 

service-owned land, which was 1.3% of the study area; 19.6% hatched on service easements, 

which were 14.2% of the study area; and 77.9% hatched on private land, which was 84.6% of 

the study area. Various sources of bias and sampling error and improvements to the system are 

discussed. 

Key words: Ducks, wetlands, population, recruitment, management, models, videography, 

drought. 
This resource is based on the following source (Northern Prairie Publication 0925):

Cowardin, Lewis M., Terry L. Shaffer, and Phillip M. Arnold. 1995. Evaluations 

 of duck habitat and estimation of duck population sizes with a remote-

 sensing-based system. National Biological Service, Biological Science 

 Report 2. 26pp.

This resource should be cited as:

Cowardin, Lewis M., Terry L. Shaffer, and Phillip M. Arnold. 1995. Evaluations 

 of duck habitat and estimation of duck population sizes with a remote-

 sensing-based system. National Biological Service, Biological Science 

 Report 2. Jamestown, ND: Northern Prairie Wildlife Research Center Online. 

 http://www.npwrc.usgs.gov/resource/birds/duckhab/index.htm

 (Version 16JUL97).

Introduction

The U.S. Fish and Wildlife Service has long been involved in monitoring wetland conditions and duck 

populations. Cooperative breeding-ground surveys by the United States and Canada (Martin et al. 1979; 

Reynolds 1987) have provided data essential to management of continental waterfowl populations. 

However, these surveys were designed for obtaining data that are used primarily for setting annual 

hunting regulations and were not intended for obtaining data for the management of national wildlife 

refuges and wetland management districts or for evaluating differences of habitat use by ducks among 

landownership classes. 

To meet the unfilled need for data from local areas, Hammond (1969) developed a system for 

measuring duck use and production on refuges and in waterfowl production areas in the prairie 

pothole region of the United States. That system was frequently modified to meet the needs 

and resources of individual managers, and some of the parameters that were used in the 

procedure were later shown to be erroneous. In addition, the Hammond system did not allow 

comparisons of duck use and production among lands owned by the U.S. Fish and Wildlife 

Service, easements by the service, and private lands. This is a critical need by managers in the 

prairie pothole region. With our system, we attempted to overcome some of the shortcomings 

of the previous system. 

Here we describe a remote-sensing-based system for estimating the number and area of ponds, 

the sizes of breeding duck populations, and the number of young ducks recruited to the 

population each fall in the prairie pothole region of Minnesota, North Dakota, South Dakota, 

and Montana. The area of upland habitat, number of ponds, and areas of ponds in 18 

waterfowl management districts (all districts in the Dakotas and in Minnesota but only the most 

eastern districts in Montana) were estimated. 
Our goals were to design a system that provides consistent estimates among areas and years, 

to maximize the use of existing data, to rapidly execute complex procedures with 

microcomputers, and to develop a database that documents changes in the estimated 

parameters. 

METHODS

Definitions

Definitions of the terms wetland, wetland area, wetland basin, and pond follow Cowardin (1982). We 

define four essential terms for describing nesting ducks: A successful clutch is a clutch of eggs of which 

one or more eggs hatch even if one or more dead young are found at the nest site (see successful, Klett 

et al. 1986). A successful nesting attempt is a hen's attempt to nest when that attempt results in a 

successful clutch. Clutch success is the probability that a clutch will be successful and is directly 

equivalent to nest success as defined by Klett et al. (1986). Hen success is the probability that a hen will 

make at least one successful nesting attempt during a single breeding season.

Design

The remote-sensing-based system differs from previous systems in concept, scope, and objectives. For 

instance, there is a relation between the amount of wetland habitat in an area and the number of ducks 

expected to settle there in spring. Johnson and Grier (1988) presented a rigorous discussion of the 

relation between numbers of ponds and numbers of ducks observed during the cooperative breedingground surveys. A less obvious relation is between the amount and quality of nesting habitat and the 

productivity of ducks (Klett et al. 1988). Direct measures of the breeding population and production are 

difficult and expensive. Measurements of the amount and type of habitat by remote sensing are 

relatively simple and inexpensive. Therefore, models that relate duck numbers and production to 

habitat should increase the precision of surveys without greatly increasing the cost. 

Johnson et al. (1987) developed a mallard productivity model. That model and habitat data 

from a large sample (n=422) of 10.4-km2 plots and nest-survival estimates in those habitats 

(Klett et al. 1988) were used by Cowardin et al. (1988) to simulate results from various 

management scenarios. These plots and the data files from them were used as the basis for the 

remote-sensing-based system. From 1982 to 1986, preliminary compilation of data and tests of 

proposed techniques were conducted in the Arrowwood Waterfowl Management District. The 

study included breeding-pair counts on 10.4-km2 plots and building of baseline regression 

equations for estimating duck numbers from pond data. These regressions were specific to 

areas and years when data were available. They were later modified to account for annual and 

regional variations. We also assessed the adequacy of the regression equations for estimating 

duck numbers and evaluated video cameras. 
Sample Universe 

The remote-sensing-based system was applied in the prairie pothole region of the United States 

in Minnesota, North Dakota, South Dakota, and Montana. This area of glacial landscape is 

bounded on the east by forest land in Minnesota, on the south and west by the limit of 

glaciation in the Dakotas and Montana, and on the north by the Canadian border (Fig. 1). We 

approximated the boundaries by transferring boundaries presented by Hammond (1965) and 

Mann (1974) to 1:500,000 U.S. Geological Survey (USGS) maps with the constraint that 

boundaries must follow townships, which were used as a basis for stratifying sampling units. 

Sample Design

The sampling units for habitat data were 10.4-km2 plots. The plot size was chosen to 

approximate the homerange size of a breeding mallard pair (Cowardin et al. 1988). By 1990, the 

sample of 335 plots in 1987 was increased to 443 plots (Table 1). 

Table 1. Numbers of 10.4-km2 plots (n) for evaluating duck (Anatinae) habitat and estimating numbers 

of ducks and number of plots covered by videography (NV)aduring 1987-90 in the prairie pothole region 

of the United States. 

1987 1988 1989 1990

State N NV N NV N NV N NV

Minnesota 95 87 98 79 128 118 128 128

Montana 14 14 14 13 14 14 14 14

North Dakota 203 202 226 219 226 220 226 223

South Dakota 23 23 23 22 75 74 75 75

All States 335 326 361 333 443 426 443 440

aWeather conditions and time sometimes prevented acquiring video data in the required time interval. 
The original sample was a stratified random sample of 500 plots drawn from a universe that represented 

the United States portion of the prairie pothole region (Cowardin et al. 1988; Fig. 2). The universe was 

divided into 93.2 km2 townships based on 1:500,000 state maps. We defined 3 landownership classes: 

land owned in fee by the U.S. Fish and Wildlife Service (service-owned land); easements consisted of 

tracts for which the service obtained easements to prevent the draining or filling in of wetland and that 

included the surrounding private land in the tract; and private land that included private land and other 

state and federal land not owned by the service. Townships were assigned to 3 strata by the following 

rules: 

1. A low landownership stratum contained 15.5 km2 or less of easements and 0 km2 of land owned 

by the service. 

2. A moderate landownership stratum contained greater than 15.5 km2 of easements and less than 

2.6 km2 of land owned by the service. 

3. A high landownership stratum contained 2.6 km2 or more of land owned by the service. 

We randomly drew 10.4-km2 plots from each landownership stratum to obtain a sample with 

higher sampling fraction in areas with high service landownership or easement because these 

areas were most desirable for simulations of management. At that time, recent color-infrared 

aerial photographs of only 422 of the selected 500 plots were available. The photographs were 

taken in May of a wet year. The final sample had sampling fractions of 0.0045 in the low, 0.0100 

in the moderate, and 0.0824 in the high service landownership strata. Although considered 

sufficiently representative for model simulations, the sample was no longer strictly random 

because of the plots without photographs (Cowardin et al. l988). 

We used the existing 422 sample plots as the basis for the remote-sensing-based system 

because of the large prior expenditure for mapping and digitizing data from those plots. This 

decision created three problems. First, the remote-sensing-based system required estimates by 

wetland management districts that were not considered in the original sample selection. 

Second, the method of stratification resulted in some plots without service landownership in 

the high or moderate landownership strata because townships were assigned to strata and the 

rules did not apply to the plots. Third, when the wetland-management-district boundaries were 

placed over the existing sample, some waterfowl management districts contained few sample 

plots and some strata inside waterfowl management districts were not represented. 
Fig 1. Distribution of 10.4-km2 sample plots used to evaluate duck (Anatinae) habitat and population size 

during 1987-90 in the prairie pothole region of the United States. Waterfowl management districts used 

for analysis duck populations are shaded. Letter codes are: AR, Arrowwood; AU, Audubon; CL, CrosbyLostwood; DL, Devils Lake; JC, J. Clark Salyer; KU, Kulm; LL, Long Lake; ML, Medicine Lake; TE, Tewaukon; 

WB, Waubay. 

We restratified the sample to overcome the first two problems by estimating landownership in 

each 10.4-km2 cell of the sample universe and by assigning each plot to a wetland management 

district. This procedure allowed us to calculate weights for each stratum and thus obtain 

unbiased estimates of each parameter in each waterfowl management district. The 

restratification was accomplished by mapping and digitizing all landownership classes in the 

entire sample universe on 1:250,000 USGS maps and by then overlaying a grid of all 10.4-km2 

plots. Where wetland-management district boundaries lay along rivers, the areas were divided 

into irregularly shaped plots of approximately 10.4 km2 . This grid was digitized, and the digital 

data were merged with the landownership map by The Map Overlay and Statistical System

(Pywell and Niedzweadk 1980) to produce a file with the landownership of each 10.4-km2 cell in 

the universe. Based on these data, all plots in the universe were assigned to strata by the 

following rules: 

1. A refuge stratum included any plot that contained any national wildlife refuge land regardless of 

other landownership in the plot. 

2. A waterfowl-production-area stratum included any plot not classified as a refuge that included 

64.8 ha or more of waterfowl production area. 

3. An easement stratum included any plot not classified as refuge or waterfowl production area 

and 64.8 ha or more of service easement. 

4. A private stratum included any plot not in the previous three strata. 

In addition, each plot in the universe was assigned to the appropriate wetland management district. 
To overcome the third problem, we added additional plots inside waterfowl management 

districts that had insufficient sample size. We required at least two sample plots in each 

landownership in each wetland management district. The sample contained few plots from the 

refuge stratum. Refuges are often larger than 10.4-km2 plots, and the plot size is not well suited 

to obtaining a representative sample. Therefore, for this report, we collapsed refuge (stratum 

1) and waterfowl production area (stratum 2) into a single stratum called service. 

Sample Wetland Basins

Approximately 200 wetland basins were selected from all plots in each wetland management 

district as sites for conducting breeding-pair counts. Sample size was determined according to 

the amount of time available for conducting pair counts rather than by statistical estimation of 

sample size required for a given degree of precision. The purpose of the pair counts was to 

adjust baseline regressions for annual and geographic variation in pair density. Therefore, we 

used an optimum allocation for a stratified random sample and treated the wetland-basin 

classes as strata to obtain a sample throughout the range of wetland-basin sizes and to avoid

oversampling of small basins that are often dry and, therefore, provide no information about 

duck density. Although the technique is intended for minimizing the variance of a stratified 

mean, given the individual strata variances (see Neyman allocation in Cochran 1977), it also had 

the desired effect of reducing the sample of temporary wetlands basins, which had a smaller 

variance than the more permanent wetland-basin classes. Strata variances were estimated 

from a regression model by obtaining estimates of the number of mallard pairs in each wetland 

basin in each plot. The area of each wetland basin was obtained from special maps prepared by 

the National Wetland Inventory. All wetland basins were assumed to contain ponds. Variances 

of the number of mallard pairs among wetland basins within each wetland-basin class in each 

wetland management district were then calculated. 

Habitat Data

Classifications

We required a classification of wetland basins to estimate breeding duck populations and a 

classification of upland and wetland nesting habitat to estimate duck production. Wetland was 

treated different from the other habitat classes. The National Wetland Inventory mapped 

wetland and upland on the plots as a special project. Wetland was mapped according to the 

classification and definitions in Cowardin et al. (1979) and with some exceptions according to 
the current mapping conventions by the National Wetland Inventory. The exceptions were that 

no codes for unknown water regime or mixed classes were allowed. Wetland mapping of the 

plots, except the addition of a unique number for each polygon in each basin, was essentially 

identical to the National Wetland Inventory operational inventory (Wilen 1990). Cowardin 

(1982) illustrated the difference between classifications of wetland area and of wetland basins. 

Available data for constructing pair-wetland regressions were from various basin classifications. 

Therefore, we had to translate the cover classes mapped by the National Wetland Inventory 

into basin classes. The technique was a simplification of the rules by Stewart and Kantrud 

(1971) for forming pond classes from wetland zones. Their pond classes (equivalent to our basin 

classes; Table 2) were derived from the water regime of the zone with most permanence and 

with an aerial cover of 5% or more. Our algorithm first summed the area of all wetland 

polygons in a basin by a unique identifier coded at the time of digitization. It then searched for 

the polygon with the most permanent water regime regardless of polygon size. If two or more 

polygons had the same water regime, the algorithm selected the largest. That polygon became 

the basin-naming polygon and was used to determine the class of the basin (Table 2). If the 

basin contained only one wetland polygon, that polygon became the basin-naming polygon by 

default. 

Table 2. Wetland-basin class definitions based on the water regime of the basin-naming polygon.a

Basin Class Basin-Naming Polygon

Temporary basin Water regime temporary (a)bor saturated (b)

Seasonal basin Water regime seasonal (c)

Semipermanent 

basin

Water regime semipermanent (f)

Lake

System Lacustrine (L) or water regime intermittently exposed (h) or permanent 

(g)

a

The basin-naming polygon is the polygon with the most permanent water regime in a wetland 

basin.

b

Letters in parentheses refer to symbols on National Wetland Inventory maps. 

Upland habitats were those described by Cowardin et al. (1988): grassland, hayland, planted cover, 

cropland, woodland, scrubland, other, right-of-way, and barren land. We modified some of the mapped 

habitats to obtain the nesting-habitat classes based on landownership and to reflect major habitat 
changes since mapping. Grassland was subdivided into grassland and grassland-wildlife. Grassland was 

typical pasture. Grassland-wildlife was nonuse grassland that is typical in waterfowl production areas 

and on refuges. Because these types are difficult to separate on aerial photographs, we defined any 

grassland in waterfowl production areas or refuges as grassland-wildlife. The Conservation Reserve 

Program of the Food Security Act of 1985 (United States Congress 1985) went into effect during our 

development of the remote-sensing-based system. Locations of Conservation Reserve Program tracts on 

10.4-km2 plots were furnished by wetland-management-district managers who obtained the locations 

from the county offices of the Agricultural Stabilization and Conservation Service in each wetland 

management district. We used the class Conservation Reserve Program cover for land in these tracts. 

Prior to analysis, we modified the corresponding habitat files for wetland management districts where 

we estimated duck production. The amounts of planted cover in waterfowl production areas changed 

from the time photographs for the National Wetland Inventory were taken and the time the plots were 

mapped. We obtained maps of current planted cover tracts in each wetland management district from 

the managers in 1987 and modified our data files to reflect current conditions. We used these data for 

all years irrespective of possible minor changes during 1987-90. 

Habitat Maps

Habitat on each plot was interpreted, mapped, and digitized by the National Wetland 

Inventory. The mapping was a special project conducted for us prior to production of standard 

wetland maps by the National Wetland Inventory. Data were from high-altitude (1:63,360), 

color-infrared photographs taken during the late 1970's and early 1980's. All features on the 

plots were delineated with a 5-aught pen on acetate overlays. Wherever possible, areas were 

shown as closed polygons, but some features such as roads, trails, and rock piles had to be 

shown as lines or points because of the small scale. At the time of digitization, a unique basin 

number was added as an attribute to all polygons in a single wetland basin. Polygons, linear 

features, and points were transferred to 1:24,000 USGS quadrangle maps by a Bausch and 

Lomb Zoom Transfer Scope. 
Fig 2. Regression of number of blue-winged teal (Anas dicors), gadwall (Anas platyrhynchos), 

northern shoveler (Anas clypeata), and northern pintail (Anas acuta) pairs on pond size during 

1987-90 in the prairie pothole region of the United States. 

The resulting maps were then digitized on a digitizing tablet and converted to Map Overlay and 

Statistical System files (Pywell and Niedzweadk 1980). A second set of 1:24,000 plot maps 

showing landownership boundaries was prepared from data on file at realty offices of the U.S. 

Fish and Wildlife Service. These maps were also digitized into Map Overlay and Statistical 

System files. The two Map Overlay and Statistical System files were overlaid to produce a file 

with the landownership attributes of all polygons. Text files of each polygon with single records 

were produced from these files. 

Because the remote-sensing-based system required that all habitats have some area, line and 

point features were buffered by multiplying length by average feature width (linear features) or 

by pie times the radius squared (point features). The following average dimensions were 

determined from aerial photographs and were used for buffering linear and point data on 

maps: 8.2 m wide for shelterbelts; 14.6 m diameter for rockpiles and brush or grass areas, too 

small to enclose with a polygon; 14.6 m wide for linear wetland basins; and 15.3 m diameter for 

point wetland basins. Linear road features were buffered for the width of the road surface, 

which we equated to being barren, and for the distance from the road surface to the border of 

the right-of-way. Distances from the center line of the road were 3.1 m to the edge of the road 

surface and 10.1 m to the far edge of the right-of-way on gravel roads and respectively 3.8 m 

and 19.1 m on hard-surface roads and 6.1 m and 19.8 m on railroads. The average width of 

fence rows and vegetated strips between fields was 3.1 m. 

Areas for buffered linear features and points were added to the text files derived from polygon 

data. This inflated the total plot area. All polygon areas were then resealed to the true plot area 

by calculating a correction factor (true plot area ÷ inflated plot area) and by multiplying the 

area of each polygon by the correction factor. 
Aerial Videography

The maps and databases contained data on all wetland basins (wet or dry) when the 

photographs were taken. We assumed that the maps had no errors from omissions or 

commissions. The remote-sensing-based system also required that we know the numbers and 

sizes of all ponds (wetland basins with water) each spring. We selected aerial video taken 

during flights in early May of each year as the technique to obtain these data. Video, although 

lacking the good resolution of photographs, has advantages over photographs (Sidle and 

Ziewitz 1990). Video is less expensive than aerial photography. Because a monitor is in the 

aircraft, the user can guide the pilot to the target area and knows whether the target area was 

recorded. The data are ready for immediate use at the completion of the flight. The Map and 

Image Processing System software (Miller et al. 1990) allows instant capture of the images in 

digital form directly from the video signal (unlike photographs, which must be scanned to 

produce digital data). 

We used a Panasonic D 5000 video camera with a 5.9-mm Angenieux lens, a Panasonic AG 2400 

video recorder, and a Panasonic CT 500V monitor. The camera was controlled by a Panasonic 

WVCR12 controller board. The camera was mounted in a custom-designed aluminum Study 

Area mount that allowed the leveling and rotation of the camera to correct for crabbing of the 

aircraft. The use of the short lens did not allow use of the automatic white-balance and iris. The 

aperture had to be set before the flight, and the camera was white-balanced just before or 

during the flight. 

We used several aircraft including Cessnas 172,172RG, and 182 and a Partenavia Surveyor. A 

variety of camera port sizes and locations was used. Camera ports from 12.7 to 30.5 cm in 

diameter proved adequate, but larger ports were easier to use because the chance of including 

part of the aircraft skin in the video frame is less. Video data were obtained from an altitude of 

3,812.5 m above ground level. This elevation combined with our type of lens permitted a 5.2-

km swath width and adequate room for navigational error. 

Analysis of Image Data

Video data were captured in digital form by a microcomputer with a Targa 16 image-capture 

board and The Map and Image Processing System (MIPS) software. This procedure produced a 

16-bit composite raster. After capture, we classified ponds by the Feature Mapping procedure 

in MIPS. The objective was to classify all areas that are covered by water, including vegetation 

growing in water. The procedure required skill in interpretation and knowledge of local wetland 

conditions. The percent of the wetland basin covered by water was recorded during the 

counting of the breeding pairs and furnished ground truth. Feature Mapping in MIPS can be 

used in either an automated mode or by drawing boundaries of a pond on the screen with a 

mouse. Video data seldom furnish sufficient spectoral separation for completely automated 

classification of a scene. We picked and classified training pixels, known to contain water from 
ground observation, until errors of commission began to appear. It was then necessary to begin 

on-screen interpretation by drawing boundaries around areas interpreted as wet. Where the 

basin contained emergent vegetation, we looked for water along the shore or in openings in 

the vegetation. This was a good indication of vegetation underlain by water. Sun-glint problems 

were resolved by referring to the original video tape and by observing sun-glint move across 

the scene as the aircraft moved over the wetland basin. Cloud-shadow problems were 

overcome by comparing the relation between the darker shading of water and the lighter 

shading of upland in shadows and clear areas. 

Interpretation is subject to errors, and consistency among interpreters is important. Two 

people interpreted the same scenes of most video data to identify errors in the classification of 

amount of water. When inconsistencies occurred, the area was reclassified. 

Duck Populations

We restricted our analysis of annual change in ponds, duck population sizes, and duck production to 10 

waterfowl management districts (Fig. 1) for which we had aerial video data during 1987-90. Although 

video was obtained of other waterfowl management districts as they were added to the remotesensing-based system, the lack of data in some years would have confounded comparisons among years. 

Regression Estimates

Estimates of breeding-population sizes were derived from a regression model. Data for 

constructing baseline regressions for all species except wood ducks (Aix sponsa) were gathered 

at the Arrowwood Waterfowl Management District (1982-84). For wood ducks, we used data 

gathered at the Fergus Falls (1986-87) and Detroit Lakes (1987) waterfowl management 

districts. Baseline regressions were constructed by regressing the number of pairs observed on 

each pond on the size and the square root of the size of the pond. The general form of the 

equations was: 

Wet area in this equation is the wet area in each pond. (Estimates of the regression coefficients 

A and B are presented in Table 3, and the regression curves are presented on Fig. 2). 

Ground Counts
The boundary of each wetland basin where breeding-pair counts were conducted was 

delineated on aerial photographs, and a unique identification number for the naming of the 

polygon of that basin was placed on the photograph. 

Table 3. Baseline regression coefficients used for estimating breeding duck (Anatinae) populations on 

10.4-km2 plots from the area (ha) of individual ponds determined from airborne video in the prairie 

pothole region of the United States, 1987-90. 

Regression 

coefficientsa

Species A B

Mallard 0.0106 0.2899

Gadwall 0.0341 0.2848

American Wigeon 0.0000 0.0193

Green-winged Teal 0.0000 0.0431

Blue-winged Teal 0.0000 0.7376

Northern Shoveler 0.0136 0.1870

Northern Pintail 0.0000 0.1866

Redhead 0.0410 0.2233

Canvasback 0.0064 0.0453

Lesser Scaup 0.0173 0.0528

Ring-necked Duck 0.0022 0.0110

Ruddy Duck 0.0119 0.1226
Wood Duck 0.0064 0.5612

a

Pairs = A x wet area + B x wet area.

The pair count was conducted by a person who walked around each pond. Pairs on lakes and rivers were 

sometimes surveyed from a boat or from a canoe. When dense emergent vegetation was present, the 

observer moved through the vegetation and flushed the birds. Sample ponds were more widely 

dispersed in the remote-sensing-based system than in surveys that are often conducted on plots or 

transects. We assumed that sample ponds were usually far enough apart to prevent the flushing of birds 

from one sample pond to another. Social groups were recorded on field forms designed for data entry 

into a microcomputer. Data were entered soon after completion of the counts. Error checking was done 

by a series of custom-designed programs that were executed at the time of data entry and immediately 

after data entry. The size of a breeding population was expressed as the estimated number of breeding 

pairs, which included observed breeding pairs and pairs that were inferred from other social groups. We 

followed approximately the same techniques described by Hammond (1969) and Dzubin (1969). The 

estimated number of breeding pairs was estimated by the computer at the time of data entry as follows: 

1 observed pair = 1 estimated breeding pair; 1 lone male = 1 estimated breeding pair; 1 lone female 

diving duck = 1 estimated breeding pair; each flocked, male dabbling duck in flocks of five or fewer 

individuals = 1 estimated breeding pair except in wigeons and in northern shovelers for which the 

number of flocked males was not converted to estimated number of breeding pairs; each flocked female 

diving duck in flocks of five or fewer individuals = 1 estimated breeding pair. Flocked birds in flocks with 

more than five individuals were not used to estimate the number of breeding pairs.

Data Analyses

Estimation Procedures

We required two types of estimates: totals (e.g., number of ponds) and ratios of totals (e.g., 

average pond size = area of ponds per number of ponds). Where possible, we also wanted 

variances of these estimates. 

To estimate totals, we treated our sample of 10.4-km2 plots as a random sample and stratified 

by waterfowl management district and U.S. Fish and Wildlife Service landownership. The 

amount of area in a given landownership varied from plot to plot. We employed a combined 

ratio estimator (Cochran 1977) that made use of the fact that the totals that we wanted to 
estimate positively correlated with the amount of area in that landownership. Estimation was 

the computation of an average density by landownership from the sample plots in each 

waterfowl management district and the multiplication of that value by the total amount of area 

of that landownership in each waterfowl management district. The overlay of plots on 

1:250,000 landownership maps, described under stratification, provided estimates of the 

amount of land by landownership in each stratum and in each waterfowl management district. 

The combined totals of all landownerships were estimated by summing the landownership 

totals. Totals by state and their estimated variances were calculated by summing estimates 

from constituent waterfowl management districts. 

Variances of the landownership totals were calculated with the Formula 6.51 from Cochran 

(1977). An upper bound to the variance of the combined total landownership of all ownership 

was estimated from Formula 5.10 (Cochran 1977:93). 

We divided appropriate estimates of population totals of one parameter by totals of another 

parameter to arrive at estimates of population ratios (e.g., pairs per pond). We do not report 

estimated variances of population ratios because exact formulas do not exist and formulas for 

approximations are complex (Mood et al. 1974). 

Estimates of Habitat Parameters

Number of wetland basins, area of wetland habitat, and amount of upland nesting cover were 

obtained from the maps of 10.4-km2 plots prepared by the National Wetland Inventory. The 

number of ponds and the area of wetland covered by water varied from year to year and were 

obtained from the classified video scenes. 

Number of Breeding Pairs

To estimate the size of the breeding duck population, the area of each pond (from the video 

scenes) was entered into the baseline regression of each species to estimate the number of the 

breeding pairs. These by-pond estimates were summed by plot and species and then expanded 

to waterfowl management districts as explained under Estimation Procedures. The resulting 

estimates were based on the assumption that the densities of ducks on ponds of the same size 

remains constant from year to year and from area to area. An adjustment of the differences 

among areas and years in each waterfowl management district was based on counts of the 

sample wetland basins. A correction (G) was defined as: 
To determine the number of predicted pairs in this equation, we required the area of each 

counted pond. The video data did not identify each pond by number. Therefore, we used the 

product of the estimated proportion of the basin that contained water and the mapped area of 

the basin to estimate the area of each counted pond. Finally, the estimated number of 

predicted pairs in the waterfowl management district was multiplied by G to correct for 

differences in pair densities among years and among waterfowl management districts. 

Estimation of Production

The number of young ducks recruited to the population in fall cannot be counted and, 

therefore, we used a model to calculate estimates. The model was deterministic and produced 

a result similar to that obtained from a stochastic mallard productivity model (Johnson et al. 

1987). For our purposes, the deterministic technique was simpler and permitted more rapid 

calculation of estimates than the stochastic model. An example illustrates the method. To 

estimate the number of recruits in each landownership class, we essentially worked backwards 

from the general relation: 

where 2 is a constant based on the assumption of equal sex ratio at hatch, R is the recruitment 

rate as defined by Cowardin and Johnson (1979), and n is the number of breeding pairs. 

The recruitment rate was broken down into the components described by Cowardin and 

Johnson (1979) in the equation: 

where H is hen success, 2 is a constant used because recruitment is defined in terms of only 

females, Z is the proportion of entire broods that survived to time of census, and B is the 

average brood size at fledging. For mallards, we used Z = 0.74 and B = 4.9 from Cowardin and 

Johnson (1979). Brood survival (Z) of 0.74 was also used for gadwalls, blue-winged teals, 

northern shovelers, and pintails. The mallard brood size of 4.9, adjusted for broods of size zero 

(Cowardin and Johnson 1979), was used as baseline to estimate Class-III brood (Callop and 

Marshal 1954) size of other species (Table 4). 

Table 4. Average brood sizes of surface-feeding ducks (Anatinae), derived by scaling mallard (Anas 

platyrhynchos) brood size by relative clutch sizes of mallards and other species during a 1987-90 study in 

the prairie pothole region of the United States. 
Species Clutch Sizea

Average Brood Sizeb

Mallard 8.4 4.90

Gadwall 9.9 5.78

Blue-winged Teal 10.2 5.93

Northern Shoveler 9.9 5.78

Northern Pintail 7.1 4.12

a

After Duebbert and Frank (1984:Table 3).

b

Average brood size = clutch size divided by mallard clutch size x 4.90 

Table 5. Percent of total wetland basins that contained ponds in a study area in North Dakota (Cowardin 

et al. 1985) and estimates of A

a

during 1977-80 in the prairie pothole region of the United States. 

Year Total Basins Ponds % A

1977 1984 224 11.3 0.5918

1978 1984 880 44.4 0.9108

1979 1984 1527 77.0 1.0444

1980 1984 507 25.6 0.8700

a

An index to nesting intensity after Cowardin and Johnson (1979). 

Hen success was determined from the relation: 
where P is clutch success, A is an index to nesting effort (Cowardin and Johnson 1979), and E is 

approximately 2.718. A relation between A and the percentage of basins containing water was 

derived from unpublished data gathered during a study of mallards in central North Dakota 

(Table 5). The technique was the same as that used by Cowardin et al. (1985) for relating A to 

the percentage of semipermanent ponds containing water. The resulting equation was: 

where W is the percentage of basins that contained water in a waterfowl management district, 

estimated from the video data and divided by total number of basins as mapped by the 

National Wetland Inventory. 

The number of produced recruits on a plot can be determined from the previous two equations 

if clutch success in each plot is available. Clutch success and the allocation of nests to the 

landownership classes were computed as in the following simplified example. 

Fig. 3 Hypothetical distribution of duck (Anatinae) habitats and nests on a 10.4-km2 plot with 

two landownerships and two habitats. Landownership classes include land owned by the U.S. 

Fish and Wildlife Service (service) and privately owned land (private) in the prairie pothole 

region of the United States, 1987-90. A and B denote arbitrary land cover type. 
We assumed that the distribution of habitat and landownership (Fig. 3) was two habitats A and 

B and two landownerships, 259.2 ha of service landownership and 777.6 ha of private 

landownership. We assumed that the ducks' preference for habitat A was twice that for habitat 

B. If n nests were on 64.8 ha of habitat B. the number of nests on the other tracts of land could 

be calculated as area of the tract ÷ 64.8 x preference Value (Fig. 3). If clutch success was 

assumed to be PA = 0.50 and PB = 0.10, we could calculate the number of successful nesting 

attempts in habitats A and B (Fig. 4). 

Fig. 4 Numbers of successful nesting attempts by ducks (Anatinae) on a hypothetical 10.4-km2 

plot with two landownership classes include land owned by the U.S. Fish and Wildlife Service 

(service) and privately owned land (private) in the prairie pothole region of the United States, 

1987-90. 

With these assumptions, the total number of nests (Fig.3) is 23n and the number of successful 

nesting attempts (Fig.4) is 7.9n. Therefore, the weighted clutch success in the entire plot (P) is: 

To allocate recruits to the landownership classes, we calculated the proportion of clutches 

hatched in each landownership class. 
RESULTS and DISCUSSION

Habitat Base

The amount of land in the sample universe was 315,980 km2. Most land was in North Dakota (42%), and 

more land was in South Dakota (29%) than in Minnesota (25%) and in Montana (5%). These values 

reflected the proportions of the prairie pothole region in each state except in Montana where our data 

were taken from only one waterfowl management district in the northeastern part of the state and in 

Iowa a state that was not included in the survey. Most of the land in each state was in private 

landownership (Table 6). The difference among states reflected differences in the acquisition of 

easements rather than variation in the amount of wetland. Only about 1% of the land in the prairie 

pothole region is in service landownership. This land, including wetland and upland, has the greatest 

potential for intentional modification of habitat to benefit ducks because the service controls 

management. 

Table 6. Land ownership (%) by state in the prairie pothole region of the United States based on 1986 

data. 

State EasementaFWS Privateb

Minnesota 0.83 1.03 98.14

Montana 1.82 0.87 97.30

North Dakota 14.77 1.26 83.97

South Dakota 9.96 0.53 89.51

All States 9.37 0.97 89.65

a

Includes all land in the easement tract. Only the wetlands are under easement.

b

Includes government land not owned or under easement by the U.S. Fish and Wildlife Service. 
Wetland Habitat

We estimated that about 3.1 million wetland basins covering about 28,490 km2 (Table 7) were 

in the prairie pothole region of the United States. Most basins were in North Dakota (2.0 

million), and more were in South Dakota (0.8 million) than in Minnesota (0.2 million). Our 

sample in Montana (48,800 wetland basins) represented only a small area of that state. 

The estimated area of wetland per km2 was similar among the states except in Montana where 

the area was about half of that in other states. The estimated density of wetland basins varied 

among states. The highest density was in North Dakota. The estimated mean wetland-basin 

sizes were 2.7 ha in Minnesota, 1.2 ha in Montana, 0.6 ha in North Dakota, and 1.1 ha in South 

Dakota. The distribution of wetland basins by size class was highly skewed to the smaller sizes 

(Fig. 5). This distribution was biased because parts of some wetland basins on the plot 

boundary included only the area inside the plot. The bias was greatest in areas of large lakes. 

The estimated wetland area per km2 was greater on service easements than on private land 

and was much greater on service-owned land than in any of the other landownership classes 

because the service tended to buy large wetland basins, especially for refuges. Estimated 

wetland-basin density was highest on service easements (Table 7) because easements were 

taken in areas of high wetland-basin density and the wetland basins were small. 

Fig. 5 Cumulative distribution of wetland basins by size and wetland-basin class in the prairie 

pothole region of the United States, base on photographs from the early 1980s. Wetland-basin 
classes include lake basins (Lake), semipermanent wetland basins (semipermanent), seasonal 

wetland basins (seasonal), and temporary wetland basins (temporary). 

Table 7. Estimated number of basins, area of wetland, density of basins, and area of wetland/km2 by 

land-ownership class in the prairie pothole region of Minnesota, North Dakota, South Dakota, and 

extreme eastern Montana, based on photographs from the early 1980s. 

Number of Basins Area of Wetland Basins/km2 Wetland ha/km2 

State

Ownersh

ip

N/100

0

95% CIakm2 95% CI

N. 

Basin

s

95

%

CI

Hectar

es

95

%

CI

Minneso

ta

Easemen

t

8.6 7.5 9.8 136.1 114.5 157.7 34.7

30.

1

39.

3

21.1

17.

8

24.5

U.S. Fish 

and 

Wildlife 

Service

7.5 5.0 10.1 435.7 402.0 469.4 24.5

16.

3

32.

7

54.7

50.

5

58.9

Private 226.8 188.9 264.7 5,900.3 3,723.8 8,076.9 7.7 6.4 9.0 7.8 4.9 10.6

Total 243.0 204.3 281.7 6,472.1 4,303.6 8,640.6 8.1 6.8 9.4 8.4 5.6 11.2

Montana

Easemen

t

3.1 1.8 4.4 44.4 22.4 66.4 31.0

18.

4

43.

6

17.0 8.6 25.5

U.S. Fish 

and 

Wildlife 

Service

0.6 0.0 2.2 81.2 31.4 131.0 13.5 0.0

45.

4

64.8

25.

0

104.

5

Private 45.0 26.5 63.5 449.3 146.2 752.4 8.4 4.9

11.

8

3.2 1.1 5.4
Total 48.8 29.5 68.0 574.9 210.4 939.3 8.8 5.3

12.

3

4.0 1.5 6.6

North 

Dakota

Easemen

t

449.5 388.9 510.1 2,387.1 2,123.1 2,651.1 59.4

51.

4

67.

4

12.2

10.

8

13.5

U.S. Fish 

and 

Wildlife 

Service

15.6 9.4 21.8 685.8 541.5 830.2 24.2

14.

5

33.

9

41.1

32.

5

49.8

Private

1,524.

1

1,358.

5

1,689.

7

9,475.7 8,172.2

10,779.

3

35.4

31.

6

39.

3

8.5 7.3 9.7

Total

1,989.

2

1,780.

1

2,198.

4

12,548.

7

11,118.

7

13,978.

6

38.8

34.

8

42.

9

9.5 8.4 10.5

South 

Dakota

Easemen

t

144.8 117.0 172.4 1,139.9 905.3 1,374.5 41.1

33.

2

49.

0

12.5 9.9 15.1

U.S. Fish 

and 

Wildlife 

Service

2.9 1.9 3.9 257.1 194.1 320.1 15.3 9.9

20.

8

52.9

39.

9

65.8

Private 662.4 563.7 761.2 7,396.9 4,821.9 9,972.0 20.9

17.

8

24.

1

9.0 5.9 12.2

Total 810.1 693.5 926.7 8,793.9 6,243.8

11,344.

1

22.9

29.

6

26.

2

9.6 6.8 12.4

All States

Easemen

t

606.1 539.4 672.7 3,707.5 3,353.0 4,062.0 53.0

47.

1

58.

8

12.5

11.

3

13.7

U.S. Fish 

and 

Wildlife 

Service

26.7 19.7 33.6 1,459.8 1,291.2 1,628.4 22.4

16.

6

28.

3

47.5

42.

0

53.0

Private

2,458.

4

2,261.

0

2,655.

7

23,222.

3

19,594.

7

26,849.

9

22.5

20.

7

24.

3

8.2 6.9 9.5
Total

3,091.

1

2,847.

8

3,334.

4

28,389.

6

24,731.

3

32,047.

9

25.3

23.

3

27.

3

9.0 7.8 10.2

a

+ or - 2 standard errors. 

The distribution of wetland-basin classes varied by landownership class (Fig. 6). The distributions of 

wetland-basin classes on easements and on private land were similar, except more seasonal wetland 

basins and fewer lakes were on easements than on private land. Temporary and seasonal wetland 

basins were greatly underrepresented and lakes were overrepresented on service-owned land most of 

which is on refuges, which were historically purchased as resting areas for migrating birds and not as 

waterfowl production areas. 

Fig. 6 Distribution of wetland-basin classes by landownership class in the prairie pothole region of the 

United States, based on photography from the early 1980s. Wetland-basin classes include lake basins 

(Lake), semipermanent wetland basins (semipermanent), seasonal wetland basins (seasonal), and

temporary wetland basins (temporary). Landownership classes include land owned by the U.S. Fish and 

Wildlife Service (service) and privately owned land (private). 

Upland Habitat
The estimated area of upland-habitat classes (Table 8) varied by state and landownership class. 

Cropland was the dominant land-cover class on private land and on easements in all four states. 

The distribution of area of land-cover classes on easements and on private land was similar. The 

small amount of cropland on service-owned land was the result of tillage on some refuges and 

in waterfowl production areas where tillage was used to rejuvenate planted cover or to provide 

food plots. These tilled areas appeared as cropland on aerial photographs. 

Grassland was the second most abundant component of the landscape in all states and its 

abundance increased from east to west. The percent of grassland was lowest in Minnesota (6.7) 

and lower in North Dakota (16.3) than in South Dakota (26.0) and in Montana (33.9). The 

distribution of grassland-wildlife (Table 8) was partly an artifact of our definition. No grassland 

was shown on service-owned land and no grassland-wildlife was shown on easements and on 

private land. The occurrences of hayland and other land were minor but were important duck 

nesting habitat (Cowardin et al. 1985). Planted cover was uncommon except on service-owned 

land where it was a major habitat component. 

Annual Change in Ponds

Pond density varied from 4.4/km2 in 1987 to 0.8 in 1990 (Fig. 7). The area of ponds (ha per km2) 

varied from 7.2 in 1987 to 2.7 in 1990 (Fig. 8). Climate in the prairie pothole region occurs in 

cycles of wetness and drought (Kentrud et al 1989). Near-average water conditions were 

present in 1987 and followed by drought in 1988, slight recovery in 1989, and severe drought in 

1990. The highest density of ponds was on easements, especially in 1987 (Fig. 7). The area of 

ponds per km2 was greater on service-owned land than in the other types of landownership 

(Fig. 8). Although these large basins with semipermanent water regimes responded more slowly 

to drought conditions, surface water declined steadily throughout the period (Fig. 8). The area 

of ponds per km2 on easements and on private land fluctuated similarly. The average pond size 

(Fig. 9) increased slightly as the amount of surface water declined because small wetland basins 

dried up entirely. The average pond size in wetland basins on service-owned lands increased 

during 1987-88, contracted as small ponds appeared in 1989, and increased again in 1990 as 

the small ponds again disappeared. The average pond size was less in 1990 than in 1988 

because even the larger ponds were becoming smaller because of severe drought. 

Duck Populations and Production

Breeding Populations

The 13 duck species reported here (Fig. 10) represent 39% of the duck species that breed in 

North America and 73% of the species in the genera Anas and Aythya in North America. During 

1987-90, the density of the blue-winged teal (3.4 pairs/km2) was higher than those of the 
mallard, (2.1), gadwall (1.8), northern pintail (0.8), and redhead (0.8) (Fig. 11). Density was 

consistently highest on service-owned lands primarily because of the large areas of ponds.

Annual Change in Silences of Breeding Populations

The sizes of the breeding populations of the five most numerous dabbling ducks declined 

throughout 1987-90 as drought conditions intensified (Fig. 11 ). The declines corresponded 

closely to loss of ponds (Fig. 7) and pond area (Fig. 8), but the relation of pair density to area of 

ponds differed by species (Fig. 12). We expected species with a high degree of philopatry and 

possibly weak territoriality to concentrate on ponds as the number of ponds decreased because 

of drought. Slopes of the linear regressions of pair density on pond density (Fig. 13) were: -

0.108 (gadwalls), -0.098 (mallards), -0.071 (blue-winged teals), -0.025 (northern shovelers), and 

-0.0004 northern pintails). The ranking is similar to published data on return rates (Anderson et 

al. 1992). Mallards and gadwalls exhibit strong philopatry (Lakemoen et al 1990). Our ranking of 

bluewinged teals is higher and of northern pintails lower than expected according to the

literature. The comparison with the literature can only be approximate because of considerable 

spatial and temporal variations in published return rates, variation of return rates by age and 

sex, and rare correction of published return rates of mortality.

Our data (Fig. 12) may suggest that, where the correlation between the area of ponds and 

breeding population is low, the number of ducks is too low to fill the available breeding habitat. 

However, other explanations are possible, and we found no data that support a depression of 

northern shoveler populations. Johnson and Shaffer (1987) analyzed data from annual surveys 

by the service and concluded that estimated mallard population sizes no longer parallel 

estimated pond numbers. Their first possible explanation was that the number of mallards was 

too low to fill the habitat. 

Change in the total number of pairs per km2 by year varied among landownership classes (Fig. 

14). The highest density was on service-owned land as expected because more wetland and 

more pond area are on these lands than on land in the other landownership classes. The 

decrease in pairs per km2 during 1987-88 was less severe on service-owned lands than on other 

lands, probably because of the greater amount of semipermanent wetland basins on serviceowned land. Birds that return to a landscape in a drought, where the less permanent ponds 

were dry, probably concentrated on large semipermanent wetland basins like those on serviceowned land. The decline of the number of pairs was more apparent on easements than on 

private lands. More wetland basins are on easements than on private lands, but not as many 

large semipermanent and permanent wetland basins are on easements as on private lands (Fig. 

8). 

Duck Production

The number of recruits is the product of size of breeding population and the recruitment rate 

(Cowardin and Johnson 1979). Drought has a negative effect on both (Cowardin et al. 1985, 

Johnson and Grier 1988). We point out that our estimated recruitment rate was more 
dependent on model prediction than on observation and was highly influenced by the 

underlying assumptions of the model. The estimated density of the recruited ducklings (Fig. 15) 

followed the same general pattern as the sizes of the breeding populations among the five 

species (Fig. 10 and Fig. 15) and among years (Fig. 11 and Fig. 16) of the five species for which 

production was estimated. The estimated recruitment rates varied among species and among 

years (Fig. 17). The rates were highest in blue-winged teals and gadwalls and lowest in mallards 

and northern pintails. The annual variation in recruitment rates (Fig. 17) resulted from variation 

in A (a measure of nesting intensity; Table 9). Our estimates of A had a major effect on our 

estimates of hen success (Equation 5). The estimated clutch success in stable populations 

(Cowardin et al. 1985 and Klett et al. 1988) is lower than those presented in Table 10. However, 

the estimates presented in those papers were based on the assumption that A equaled 1. For 

the low A values in this study, higher clutch success is required for recruitment rates of a stable 

population. For mallards, a hen success of 31%, a summer survival of 0.74, and an average 

brood size of 4.9 (Cowardin et al. 1985) equate to a recruitment rate of 0.56 in a stable 

population. This is well above our estimated recruitment rate in mallards (Fig. 17).

We did not have sufficient data for estimating clutch success by year. Greenwood et al. (1995) 

showed that clutch success in the prairies of Canada is depressed by drought. If we had 

estimates of clutch success by year, the variation in our estimated recruitment rate by year (Fig. 

17) would probably have been greater. 

Table 9. Estimates of A, a measure of nesting intensity, by wetland management district and year during 

1987-90 in the prairie pothole region of the United States.a

District 1987 1988 1989 1990

Arrowwood 0.962 0.776 0.711 0.668

Audubon 0.858 0.721 0.655 0.640

Crosby-Lostwood 0.736 0.655 0.729 0.629

Devils Lake 0.745 0.650 0.656 0.642

J. Clark Salyer 0.744 0.658 0.654 0.630

Kulm 0.879 0.673 0.759 0.657

Long Lake 0.920 0.701 0.678 0.640
Medicine Lake 0.820 0.669 0.844 0.697

Tewaukon 0.741 0.751 0.766 0.676

Waubay 0.803 0.684 0.844 0.745

a

A is a linear function of the percentage of wetland basins containing water in each year. 

Table 10. Estimates of average clutch success by species and landownership class during 1987-90 in the 

prairie pothole region of the United States. 

Ownership

Species Easement FWS Private All Owners

Mallard 17 22 17 17

Gadwall 22 27 24 24

Blue-winged Teal 22 23 23 23

Northern Shoveler 21 21 21 21

Northern Pintail 20 21 21 21

All Species 20 24 21 21

a

+or - 2 standard errors. 

Our model predicted that the densities of mallard, gadwall, blue-winged teal, and northern shoveler 

recruits were greater on service-owned land than on easements and private land (Fig. 16). Northern 

pintail recruits were produced at almost equal rates in the landownership classes. The number of 
successful nesting attempts per km2 varied by landownership class (Fig. 18) because of the differences in 

clutch success (Table 10) and the relative preferences of the different species for the various nesting 

habitats (Klett et al. 1988, Table 2) on those lands. Klett et al. (1988) found a higher preference for 

cropland in northern pintails than in other duck species. The densities of successful nesting attempts per 

km2 in mallards, gadwalls, and blue-winged teals were higher on service-owned lands than on other 

lands. The number of successful nesting attempts per km2 by northern pintails and northern shovelers 

was similar among the landownership classes.

CONCLUSIONS and MANAGEMENT IMPLICATIONS

Wetland and Duck Management

Effect of Service-owned Land

Sidle (1983) described how alarm over declining duck populations and destruction of prairiepothole wetland habitat led to the service's small wetland acquisition program (DeBates 1967). 

Our data demonstrated how waterfowl production areas and service easements acquired under 

this program and national wildlife refuges are important. These lands not only prevent further 

destruction of wetland but also provide areas where ducks can continue to reproduce. If these 

lands can be managed so that recruitment rate more than compensates for annual mortality, 

the managed lands may slow or reverse the declines of some duck populations. In 10 waterfowl 

management districts, service-owned land was only 1.3% of the land surface, but 2.5% of the 

five most common dabbling ducks were produced on it. On easements, including the wetlands 

under easement and the private land in the easement tract, 19.6% of the ducks were produced 

on 14.1% of the land surface. Private land on the other hand produced 77.9% of the ducks on 

84.6% of the land surface. These estimates do not include the contribution of service owned 

land and easements where the preserved wetlands contribute to duck production from 

surrounding private uplands. Thus, lands owned in fee and easements taken on wetlands 

increase duck production; however, most ducks are produced on private lands. 
The people who initiated the small wetland-acquisition realized that bigger and wetter wetland 

is not necessarily better for ducks. Kaminski and Weller (1992) summarized results from several 

studies that revealed the Importance of seasonal wetlands to dabbling ducks. These wetlands 

are generally small. The cumulative frequency distribution of wetland basins by size class in the 

prairie pothole region (Fig. 5) has important management and conservation implications. 

Restricting regulatory responsibility or protection to larger basins would leave most of the 

prairie potholes unprotected. For example, 78% of the basins are less than 0.41 ha and most 

have temporary or seasonal water regimes. 

Wetland easements are the main source of protection for wetland basins with temporary and 

seasonal water regimes because these classes are poorly represented on lands owned in fee 

title (Fig. 6). The large lakes on national wildlife refuges may serve as resting areas for migrating 

waterfowl, but their contribution to duck production is minor. 

Drought Effects

Our estimates from the remote-sensing-based system during the first 4 years clearly supported 

the well known fact that drought depresses duck populations and production in the prairie. 

Data gathered during this period revealed only the decline in breeding populations as drought 

conditions increased during 1987-90. Data gathered in future years may document the increase 

in populations and production as water conditions improve on the prairies. Although the large 

wetland basins on service-owned lands contribute little to duck production, our data suggested 

that, because they may remain wet during drought, large wetland basins with permanent water 

regimes may modify drought effects. 

Species Effects

Duck management is often based on mallards because data are more comprehensive on 

mallards than on other duck species. The assumption is that management that is good for 

mallards is good for other ducks. The assumption is probably valid for some dabbling ducks, but 

managers should be aware of species differences. Our analysis agreed with published 

information on philopatry (Lokemoen et al. 1990), which suggests that homing to natal areas is 

stronger in mallards and gadwalls than in the other species we studied. This means that 

intensive management to increase local recruitment would be more beneficial to these species 

than to those that do not home strongly. The analysis of successful nesting attempts per km2 by 

landownership class (Fig. 18) revealed additional important species differences from the 

interaction of preferences for nesting covers, clutch success in the various covers, and the types 

of cover in the landownership classes. Accordingly, management of uplands to benefit mallards 

and gadwalls on service-owned lands may not be beneficial to northern pintails and northern 

shovelers. Upland management probably has limited value for diving ducks, most of which nest 

in wetlands. Management of service-owned lands and easements to preserve wetland may 

have a beneficial effect by providing nesting areas for diving ducks and for some dabbling 

ducks, especially in dry years. 
System Evaluation

Possible Biases

The estimates derived from the remote-sensing-based system may have several biases. Testing 

for these biases requires special studies that were beyond the scope of our evaluation. Typical 

difficulties in estimating duck numbers and production over vast areas may preclude objective 

verification. Our purpose here is to alert the reader to some of the more important possible 

sources of errors. 

The remote-sensing-based system is based on data from remote sensing for the identification 

of wetlands and uplands from aerial photographs and on data from aerial video for measuring 

annual change in the number of ponds and area of water. We made the assumption that 

mapping by the National Wetland Inventory was without errors of omission or commission. 

Although not tested, our experience in this and numerous other studies in the prairie pothole 

region revealed that such errors are few. Errors that we detected were usually errors of 

omission of small temporary wetland basins. These errors lead to underestimation of breedingpopulation sizes. Data on the number of ponds and on the area of water from video 

(sometimes poor quality) may contain errors from the omission of numbers of ponds (L. L. 

Strong, Northern Prairie Science Center, National Biological Service, Jamestown, N. Dak., 

unpublished data). These errors cause the underestimation of breeding-population sizes. They 

also cause the underestimation of A and, therefore, recruitment rate. We also point out that 

our method for estimating A relies on data from only 4 years in one study. We also make the 

assumption that the form of the regression curves for estimating the number of breeding pairs 

is the same among years and areas. This assumption was not examined. Adjustment of 

regressions by G may have introduced errors that we are currently unable to evaluate. 

For the estimates of duck production, we used Conservation Reserve Program cover estimates 

from 1990 and applied them to all years. Conservation Reserve Program contracts were taken 

during 1987-90. Conservation Reserve Program cover is attractive to ducks with relatively high 

clutch success (Kantrud 1993, Reynolds et al.1994); therefore, our estimates of production in 

years prior to 1990 may be high. 

All systems for the estimation of breeding-population sizes of ducks are subject to errors of 

biological interpretation such as determining whether an observed pair of ducks represents a 

resident or migrant pair and whether observed social groups such as lone males represent 

breeding pairs (Cowardin and Blohm 1992). We made no attempt to solve these problems but 

attempted to use methods that correspond to those used in other surveys so that the estimates 

are comparable. 

Sampling Errors
Although our overall sample size (Table 1) was large, samples in some wetland-managementdistrict-landownership strata were small, and variation in habitats among plots was great. 

Confidence limits on most estimates of habitat parameters were large. Cost probably prohibits 

large increases of sample size, although addi tional plots are currently added to the sample 

where sample sizes are minimal. At present, wetland mapping and digitizing in the entire prairie 

pothole region are progressing rapidly, and technology is available for adding uplands to the 

data. When data from the entire prairie pothole region become available, sampling will no 

longer be necessary for estimating numbers of wetland basins and wetland area. Our 

confidence limits for duck-population sizes and duck-production parameters reflect only the 

variation associated with our sample of 10.4-km2 plots. For example, the confidence limits do 

not reflect the variance in the number of ducks or recruitment in individual plots. Thus, the 

confidence limits for these parameters are actually narrower than they should be. 

Suggested Future Modifications

The remote-sensing-based system, although operational, should be considered a prototype that 

can be improved by various modifications and by the addition of new data. One simple 

modification would be a complete inventory of wetlands as described in the previous section. 

Annual estimation of the number of ponds will probably have to remain sample based because 

of cost and logistic problems in obtaining complete remote-sensing data over such a large area 

each spring. Satellite data with sufficient resolution to delineate the small ponds that ducks use 

may become available at a reasonable cost in the future, but obtaining data in the required 

narrow time frame will remain a problem. Advances in aerial videography techniques have 

been made since the remote-sensing-based system was initiated. We recommend that new 

methods be evaluated to increase the accuracy of the annual estimates of the number of 

ponds. We used ratio-estimation methods involving the amount of land area in each 

landownership class to improve our estimates of several parameters. Ratio estimates involving 

area of wetland or numbers of basins in each landownership class could further improve 

estimates of parameters such as area and number of ponds or numbers of breeding pairs. Such 

improvements will be possible when the inventory of wetlands is complete. 

As use of the remote-sensing-based system continues, new data become available for 

improving the regression equations for estimating numbers of breeding pairs from pond areas. 

Furthermore, the classification of wetland basins should be modified to better represent major 

differences in duck use. For example, the current class lakes includes many types of lakes that 

receive different use by ducks. The data should be examined to determine whether enough 

information exists to construct regressions for various kinds of lakes. In addition, the remotesensh~gbased system was designed primarily for estimating numbers of pairs on discrete 

basins. The remote-sensing-based system does not work well for estimating numbers of pairs 

on large lakes and in riverine habitat. Studies should be conducted to determine whether 

shoreline segments of lakes and reaches of rivers are preferable sampling units in these

habitats. Such a change requires gathering data from these units to develop appropriate 

regression models for the remote-sensing-based system. When we designed the remotesensing-based system, we could not link vector data from the National Wetland Inventory maps 
to the raster data from aerial videography. Therefore, ground crews estimated the proportion 

of a wetland basin containing water. These estimates were used to calculate G. The link 

between the two data sets is now available, and we recommend that aerial videography (or 

possibly photography) be used for determining G. 

Overall Assessment of the System

Our first goal with the remote-sensing-based system was consistency of estimates among areas 

and years. We believe that this goal was accomplished, although major differences in habitat 

among waterfowl management districts demand some regional modification of methods. Our 

second goal was maximum use of existing data. We believe that this objective was 

accomplished, but implementation of the remote-sensing-based system clearly pointed out 

that certain data required by the system are scarce or lacking. This is especially true for reliable 

estimates of clutch success in some areas and in some habitats (Shaffer and Newton, in press). 

Our third goal was rapid execution of complex procedures by microcomputer and a resulting 

database that documents changes in the estimated parameters. This goal was partly 

accomplished. Because the remote-sensing-based system has been constantly evolving, 

continued modification of computer programs has been necessary. At the same time, 

technological advances in computer development and remote-sensing techniques have moved 

ahead of those used in the remote-sensing-based system. 

Fluctuation-sometimes violent-of breeding-population sizes and production of prairie ducks 

causes problems for waterfowl managers. The manager usually attempts to manage such things 

with the amount and extent of harvest and the availability and quality of habitat. The success or 

failure of management is usually evaluated in terms of duck numbers and production. Johnson 

and Shaffer ( 1987) demonstrated the difficulty of separating weather factors from the 

numerous other causes of fluctuation in duck population sizes and the need for long-term 

surveys with consistent methodology. The remote-sensing-based system proposed here has the 

potential to provide better evaluation of management by providing data that are essential to 

understanding the interaction of various factors that cause fluctuation in duck population sizes. 

ACKNOWLEDGEMENTS

This study was a cooperative effort by research personnel of the Northern Prairie Wildlife Research 

Center and managers in Regions 3 and 6 of the U.S. Fish and Wildlife Service. Numerous individuals from 

the waterfowl management districts assisted with pair counts and in the later years with conducting the 

aerial video missions. In addition, the regions furnished financial support. Mapping of the plots and 

management of digital files were done by R. Gebhard and H. R. Pywell of the National Wetland 

Inventory. C. R. Luna assembled landownership data on maps that were then digitized by the National 

Wetland Inventory. Lab technicians J. Belant, D. Hertel, P. S. Marshall, and H. T. Sklebar 

photointerpreted video images and maintained data files during development of the remotesensingbased system. In 1990, video interpretation and data management became the responsibilities of the 
Habitat and Population Evaluation Teams of the U.S. Fish and Wildlife Service in Bismarck, North Dakota, 

and in Fergus Falls, Minnesota. R. C. Khan-Malek wrote computer programs and assisted with the 

preparation of figures. H. T. Sklebar processed digital images. D. H. Johnson furnished advice throughout 

the study and initiated work on pair wetland regressions and on the productivity model that are integral 

parts of the remote-sensing-based system. J. E. Austin, N. H. Euliss, D. H. Johnson, W. E. Newton, R. T. 

Reynolds and A. B. Sargeant kindly reviewed earlier drafts of the manuscript.

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Figures 15-18

Fig. 15. Number of produced recruits per km2 by mallards (Anas platyrhynchos), gadwalls (Anas 

strepera), blue-winged teals (Anas discors), northern shovelers (Anas clypeata), and northern 

pintails (Anas acuta) during 1987-90 in the prairie pothole region of the United States, based on 

model projections from aerial videography and ground counts. Landownership classes include 

easements (easement) where the U.S. Fish and Wildlife Service has easements on wetlands to 

prevent draining, filling, or leveling; land owned by the U.S. Fish and Wildlife Service (service); 

privately owned land (private); andall landownerships combined (all). 1987-90. Error bars= 1 

standard error. 
Fig. 16. Number of produced recruits per mi2 of bluewinged teals (Anas discors), gadwalls (Anas 

strepera), mall ards (Anas platyrhynchos), northern shovelers (Anas clypeata), and northern 

pintails (Anas acuta) during 1987-90 in the prairie pothole region of the United States, based on 

model projections from aerial videography and ground counts. Error bars = 1 standard error. 
Fig. 17. Recruitment rate of five species of dabbling ducks during 1987-90 in the prairie pothole 

region of the United States, based on model projections from aerial videography and ground 

counts. Species include mallards (Anas platyrhynchos), gadwalls (Anas strepem), blue-winged 

teals (Anas discors), northern shovelers (Anas clypeata), and northern pintails (Anas acuta) and 

all species combined. 
Fig. 18 Average number of successful nesting attempts per km2 by mallards (Anas 

platyrhynchos), gadwalls (Anas strepera), blue-winged teals (Anas discors), northern shovelers 

(Anas clypeata), and northern pintails (Anas acuta) by landownership class in the prairie 

pothole region of the United States, based on model projections from aerial videography and 

ground counts. Landownership classes include easements (easement) where the U.S. Fish and 

Wildlife Service has easements on wetlands to prevent draining, filling, or leveling; land owned 

by the U.S. Fish and Wildlife Service (service); and privately owned land (private).