U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2008–1223

Prepared in cooperation with the U.S. Army Corps of Engineers

Missouri River Emergent Sandbar Habitat Monitoring Plan—

A Conceptual Framework for Adaptive Management
A

B

C

Cover photographs: Emergent sandbar on the Missouri River (background photograph); A, field crew collecting fish data by 

hand-trawling; B, technician collecting terrestrial habitat data; and C, least tern nest on a Missouri River 

sandbar. All photographs by M. Sherfy, U.S. Geological Survey.
Missouri River Emergent Sandbar 

Habitat Monitoring Plan—A Conceptual 

Framework for Adaptive Management

By Mark H. Sherfy, Jennifer H. Stucker, and Michael J. Anteau

Prepared in cooperation with the U.S. Army Corps of Engineers

Open-File Report 2008–1223

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2008

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Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Sherfy, M.H., Stucker, J.H., and Anteau, M.J., 2008, Missouri River Emergent Sandbar Habitat Monitoring Plan—a 

conceptual framework for adaptive management: U.S. Geological Survey Open-File Report 2008–1223, 51 p.
iii

Preface

Habitat Monitoring: Scale and Linkage

Understanding habitat as an indicator of animal condition is predicated on (1) an accurate 

understanding of habitat needs of animals, (2) use and distribution of animals in habitats, and 

(3) accurate measures of habitat features and how they are used at the appropriate temporal and 

spatial scales. 

The iterative process of adaptive management requires identifying appropriate measurable 

habitat variables, initiating a management change, monitoring changes resulting from management on habitat, and assessing responses. Throughout the process, it is necessary to evaluate the 

measured variables and techniques deployed in order to identify additional habitat variables or 

include other spatial scales. Therefore, a monitoring plan should be designed with the ability to 

evaluate how appropriate the habitat variables are for predicting wildlife responses.

Processes involved in habitat change on the Missouri River are complex and dynamic. 

However, factors driving change in habitat at the sandbar scale may not describe landscape 

processes at the reach scale, and scale dependent factors may be directly or indirectly related. 

Management actions are applied on individual sandbars, but decisions regarding implementation are made at larger (for example reach) scales. Further, the extent or distribution of managed 

habitats may warrant evaluation of effects at reach or landscape scales. An effective monitoring 

program will provide data that facilitate analysis of biological responses at each of these scales.

This plan is a tool for initiating the process of habitat monitoring for least terns and piping 

plovers at multiple habitat scales (for example nest site, habitat type, river reach, or Missouri 

River system) on the Missouri River. Implementation of this plan will be enhanced by a concurrent commitment to assess spatial distribution of habitats at landscape scales, and to evaluate 

responses by least terns and piping plovers to management change. Parallel studies at Northern 

Prairie Wildlife Research Center (NPWRC) are currently focused on deriving methods for 

monitoring spatial distribution of habitats (NPWRC Task 1: Least tern foraging ecology and 

productivity on the Gavins Point Reach of the Missouri River) and productivity of least terns 

and piping plovers (NPWRC Task 2: Evaluation of procedures for monitoring numbers and 

productivity of least terns and piping plovers on the Missouri River). As these monitoring techniques become refined, they will be incorporated into annual Action Plans.

This plan is intended to serve the long-term needs of the Emergent Sandbar Habitat (ESH) 

Program for monitoring biological responses to management. Advance knowledge of all sites 

where ESH Program activities will be implemented in the future is not possible. Therefore, 

this plan does not identify specific sites or priorities for monitoring that will occur over time. 

What it does provide is a framework for making decisions about where habitat data should be 

collected to accomplish monitoring needs. This framework, illustrated in the accompanying 

flowcharts, is the essential element of the monitoring plan (fig. 1). To illustrate application of 

the design concepts embodied in this framework, the plan relies heavily on the field experience of the U.S. Geological Survey (USGS) habitat monitoring crew during 2006. The USGS 

crew encountered many unique field circumstances that are described to illustrate the manner in 

which the conceptual framework should be applied in decisionmaking. The decisions described 

should be considered unique to the conditions that were encountered in the field, and not as 

universal rules for implementation of the monitoring plan. Rather, the importance of these field 

experiences is that they illustrate how the framework can be implemented under various circumstances. Because each year will present unique circumstances, the framework provides for 

preparation of annual Action Plans that describe specific monitoring objectives for the year and 

establish appropriate response variables and sampling locations.
Define 

Monitoring 

Objectives

Identify 

Need for 

Monitoring

Determine Spatial 

Extent of Monitoring 

Based on Treatment 

Application (Entire vs. 

Portion of Reach)

Assign All Segments 

to Bird Use Strata

Consult with 

Statistician on 

Objectives 

and Design

START

Determine 

Appropriate 

Sampling Unit (for 

example, River 

Segment)

Quantify Historical 

Bird Use for Entire 

Spatial Extent 

Reservoir

Determine Number 

and Bird Use Strata 

Treatment

Segments

Randomly Select 

Identical Number 

of Control 

Segments With 

Similar Bird Use 

History

Can All 

Treatment 

Segments Be 

Sampled?

Randomly 

Select 

Treatment 

Segments to 

Yes Sample

Area a 

Reservoir or 

River?

River Eliminate 

Sandbars from 

Is 

Productivity 

Monitoring 

Planned?

Initiate 

Productivity 

Monitoring at 

Appropriate Scale 

and Intensity

Ensure That 

Productivity Data 

Are Collected By

Segment

Is Color IR 

or Satellite 

Imagery 

Planned?

No

Initiate 

Imagery 

Acquisition 

If Feasible

Yes

No

Yes

Ensure That 

Spatial Data 

Acquisition 

Corresponds to 

Monitored Area

1 2

Is Monitoring Area a 

Reservoir or 

River?

River

Is Sampling 

Segments 

With No 

Sandbars 

Warranted?

Yes

No

Consideration

A Introduction and Planning

Initiation or

Termination

Process

Completion

Data

Collection 

Decision

Link

Report

Preparation 

Consultation

and Planning

Stratification

Sampling

Design

Rivers 

Data

Collection 

Mid-Project

Decisions and

Planning 

Analysis and

Reporting 

Shapes Colors

EXPLANATION

Sampling

Design

Reservoirs 

Figure 1. Conceptual framework for decisionmaking and implementation of habitat monitoring for 

management actions under the Emergent Sandbar Habitat (ESH) Program. The framework includes 

four phases: A, Introduction and Planning; B, Sampling Design; C, Data Collection; and D, Decisions and 

Analysis.

iv
Is Color 

Infrared or 

Quickbird

Imagery 

Available?

Yes Has Habitat 

Mapping Been 

Completed?

Can Habitats 

Be Manually 

Delineated?

No

Obtain Other Imagery 

(such as National 

Agricultural Inventory 

Program)

No

Select Random 

Starting Point in 

Each Segment

Place Transects 

Systematically From 

Starting Point

Select Random 

Points Within 

Segments; Number of 

Points Based on 

Desired Precision for 

Aquatic Habitats

No

Allocate Random 

Points 

Proportionally 

Among Habitat 

Types

Yes

Yes

Place Points 

Systematically 

Along Transects

1 2

Is Sampling 

Terrestrial 

Habitats 

Desirable?

Yes

No

Consult with 

Statistician on 

Objectives 

and Design

3

Is There Other 

Information on 

Habitat Abundance 

(such as Previous 

Random Points)?

Select Random 

Points Within 

Segments; Number of 

Points Inflated To 

Account for Unknown 

Habitat Distribution

No

Select Random 

Points Within 

Segments; Number of 

Points Based on 

Relative Abundance 

of Terrestrial and 

Aquatic Habitats

Yes

Initiate Field 

Work

Prepare 

Annual Action 

Plan

B Sampling Design

Initiation or

Termination

Process

Completion

Data

Collection 

Decision

Link

Report

Preparation 

Consultation

and Planning

Stratification

Sampling

Design

Rivers 

Data

Collection 

Mid-Project

Decisions and

Planning 

Analysis and

Reporting 

Shapes Colors

EXPLANATION

Sampling

Design

Reservoirs 

Figure 1. Continued

v
Navigate To 

Sampling 

Point

Is Terrestrial 

or Aquatic 

Habitat 

Present?

Adequate 

Aquatic Points 

Sampled in 

This Segment?

Terrestrial

No

Yes

Is Fish 

Sampling 

Needed?

3

Yes

Points 

Remaining in 

Segment?

No

Yes

No

Is 

Invertebrate 

Sampling 

Needed?

Yes

No

Will

Continued 

Sampling 

Cause Undue 

Disturbance To 

Birds?

Suspend 

Sampling 

Temporarily

No

Yes

Collect 

Terrestrial 

Habitat 

Data

Collect 

Aquatic 

Habitat 

Data

Collect 

Fish 

Sample

Collect 

Invertebrate 

Sample

Record 

Habitat 

Classification 

for the Point

4

Complete 

Remaining 

Segments for 

the Year

Summarize Productivity 

Data by 

Segment

Obtain Habitat 

Acreage 

Estimates From 

Spatial Data

Advance to 

Decisions 

and Analysis

Collect Habitat 

Data at Nest 

Sites Within 

Segments

Aquatic

C Data Collection

Shapes Colors

EXPLANATION

Initiation or

Termination

Process

Completion

Data

Collection 

Decision

Link

Report

Preparation 

Consultation

and Planning

Stratification

Sampling

Design

Rivers 

Data

Collection 

Mid-Project

Decisions and

Planning 

Analysis and

Reporting 

Sampling

Design

Reservoirs 

Figure 1. Continued

vi
4 Terminate Monitoring

Were PreTreatment Data

Collected? 

Has Management

Been

Implemented? 

Before

Control

(BACI) Design

Control

Impact

Design

Yes Is Management

Implementation

Still Proposed? 

Yes Yes

No

No

Is Additional 

Treatment 

Data Needed?

Collect More 

Data

Yes

Has Status of 

Any Treatment or 

Control Segments 

Changed? 

No

No

Yes

Reservoir

Is Monitoring 

Area a 

Reservoir or 

River?

River

1

Continue PreTreatment Monitoring

for BACI Design 

Yes

Evaluate 

Objectives

Prepare Report 

and 

Conclusions

Summarize 

Habitat Data 

by Treatment

Consult with 

Statistician on 

Data Analysis, 

Objectives, 

Stratification, 

and Design

Adjust 

Number of 

Segments as 

Necessary

Consult with 

Statistician on 

Objectives 

and Adjusting 

Sample Size

2

D Decisions and Analysis

Initiation or

Termination

Process

Completion

Data

Collection 

Decision

Link

Report

Preparation 

Consultation

and Planning

Stratification

Sampling

Design

Rivers 

Data

Collection 

Mid-Project

Decisions and

Planning 

Analysis and

Reporting 

Shapes Colors

EXPLANATION

Sampling

Design

Reservoirs 

Figure 1. Continued

vii
viii

Emergent Sandbar Habitat Monitoring Framework

The approach to monitoring of emergent sandbar habitat that is outlined in this plan can be 

broken down into a framework consisting of four phases. To illustrate these, the flowcharts on 

figure 1 identify the major decisions, processes, and data inputs required for implementing the 

process. The flowcharts are intended to capture the conceptual approach to monitoring and to 

identify the principal elements of the monitoring framework. Because each monitoring objective presents unique questions, priorities, and logistical hurdles, some aspects of the design (for 

example number of units to sample, number of points to measure per unit) cannot be prescribed 

in advance. Rather, the investigator must have the tools available to make decisions on a caseby-case basis that reflect the unique circumstances at hand. Therefore, accompanying each 

flowchart is a description of the monitoring activities conducted by a USGS field crew during 

2006. The purpose of this report is to describe the various circumstances that occurred and the 

decision processes that were used by the principal investigators to address them. An important 

element of this process is consultation with statisticians to determine if the various phases are 

providing a design that adequately addresses the stated objectives.

The four principal phases of the monitoring framework are as follows:

1. Introduction and Planning—The initial phase sets the stage for the monitoring 

process through development of objectives, decisions regarding spatial extent and 

units of sampling, and ensuring that companion data (productivity and spatial data) 

are being collected concurrently (fig. 1). The end products of this phase are a set of 

objectives for the monitoring program, a stratification of the spatial extent based on 

these objectives, and a selection of sampling units to be sampled.

2. Sampling Design—This phase reflects selection of individual points within the 

sampling units for measurement of habitat variables. Selection of points will be 

completely random in some situations, but under most circumstances there should 

be some information available upon which to base allocation of points within the 

sampling unit. On reservoir shorelines, transects perpendicular to the water’s edge 

are useful, while in many riverine situations analysis of satellite imagery can provide 

information on the distribution of major habitat types. The end product of this phase 

is an Action Plan that describes specific objectives of monitoring for the coming 

year and establishes the response variables and sampling locations to be included in 

monitoring.

3. Data Collection—This phase represents the collection of habitat data at the points 

selected in previous phases. It includes elements of disturbance avoidance, invertebrate and fish sampling (as applicable), and summary of productivity and spatial data 

by sampling unit. The end result of this phase is a complete habitat-productivityspatial data set for all areas sampled to address the objectives.

4. Decisions and Analysis—At the conclusion of each field season, this phase is conducted to decide upon direction and focus for the coming years. This phase allows for 

consideration of changing objectives, changing status of the sampling units, refining 

the scale of monitoring, and evaluating the effectiveness of response variables. It also 

provides for consultation with statisticians, formal data analysis, and reporting. 
ix

From Framework to Action: Implementation of the Plan

Implementation of this plan will require annual assessments of the existing and proposed 

ESH projects for which monitoring is necessary. Because continual addition of new projects is 

anticipated, annual program reviews will become increasingly important exercises. Functions 

of these reviews would be to evaluate conclusions from the existing monitoring data, discuss 

scope and nature of upcoming ESH projects, and set priorities and objectives for monitoring 

during the coming year. These functions could all be accomplished in a single interagency 

meeting during the winter, if monitoring data, spatial analyses, and information on upcoming 

projects were simultaneously available.

There are two annual work products that should be produced through this process. The 

first is an annual Action Plan that establishes specific monitoring objectives for the coming year 

and describes proposed monitoring activities. Preparation of annual Action Plans would require 

adequate information on future ESH project locations so that sites for monitoring could be 

selected. Action Plans would provide detailed guidance to field crews on the locations, habitats, 

and response variables that will be included in that year’s monitoring program. The second 

work product would consist of an overview of the previous year’s monitoring activity, including summary of the data. This data summary would be useful in assessing whether sampling 

intensity was adequate to meet the stated objectives and to provide feedback to management 

decisionmaking.

In accordance with the principles of adaptive management, future revision of the conceptual framework outlined in this plan may be desirable. The framework and focus of the monitoring program will evolve as the ESH Program develops and knowledge accumulates regarding 

processes influencing least tern and piping plover habitat use. Accordingly, this plan will be 

periodically revisited to evaluate whether it continues to meet the science needs for monitoring 

of the ESH Program.

Acknowledgments

This plan was prepared in collaboration with the interagency Emergent Sandbar Habitat 

Product Delivery Team, which comprises scientists from the U.S. Army Corps of Engineers; 

National Park Service; U.S. Fish and Wildlife Service; U.S. Geological Survey; Yankton Sioux 

Tribe; North Dakota Game and Fish Department; South Dakota Department of Game, Fish, 

and Parks; Nebraska Game and Parks Commission; Virginia Polytechnic Institute and State 

University; and David Miller & Associates. Field data collection was conducted by J. Kottsick, 

D. Prater, and M. Suchy. The plan was improved by comments on earlier drafts from C. Huber, 

C. Kruse, M. Morris, and J. Remus (U.S. Army Corps of Engineers), M. Olson (U.S. Fish and 

Wildlife Service), David Miller & Associates, P. Hedren (National Park Service), and D. Buhl 

(USGS).
x

Contents

Preface ...........................................................................................................................................................iii

Habitat Monitoring: Scale and Linkage............................................................................................iii

Emergent Sandbar Habitat Monitoring Framework.....................................................................viii

From Framework to Action: Implementation of the Plan...............................................................ix

Acknowledgments........................................................................................................................................ix

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

Scope and General Methodology ......................................................................................................1

Objectives...............................................................................................................................................2

Indicator Variables ...............................................................................................................................2

Approach to Data Collection........................................................................................................................4

Sampling Units.......................................................................................................................................4

Units of Tern and Plover Nesting Habitat..........................................................................................5

A Stratified Sampling Approach to Monitoring ...............................................................................5

Timing of Sampling................................................................................................................................6

Field Data Collection—Habitat....................................................................................................................6

Delineation and Selection of Strata...................................................................................................6

Rivers ...........................................................................................................................................7

Reservoirs ...................................................................................................................................27

Nest Sites....................................................................................................................................27

Sampling Approach............................................................................................................................30

Rivers .........................................................................................................................................30

Reservoirs ...................................................................................................................................32

Nest Sites....................................................................................................................................36

Habitat Variables.................................................................................................................................36

Terrestrial Variables ..................................................................................................................40

Aquatic Variables.......................................................................................................................42

Fish Sampling..............................................................................................................................42

Invertebrate Sampling ..............................................................................................................43

Timing of Habitat Sampling................................................................................................................44

Sample Sizes for Terrestrial Points..................................................................................................46

Field Data Collection—Productivity .........................................................................................................47

Spatial Data...................................................................................................................................................47

Other Species...............................................................................................................................................47

Data Analysis................................................................................................................................................48

Implementation.............................................................................................................................................48

Permits...........................................................................................................................................................49

Selected References...................................................................................................................................49

..

..
xi

Figures

1. Conceptual framework for decisionmaking and implementation of habitat 

monitoring for management actions under the Emergent Sandbar Habitat (ESH) 

Program.........................................................................................................................................iv

2. Map showing the Upper Missouri River system, including locations of 

selected reservoirs and free-flowing river reaches...............................................................3

2–19. Maps showing:

3. Example of 0.4-river-mile segments on the Gavins Point Reach, illustrating 

segments containing constructed emergent sandbar habitat (GAV09) 

and containing unmanaged natural emergent sandbar habitat (GAV08).................11

4. Example of 0.4-river-mile segments on the Gavins Point Reach, illustrating 

segments containing sprayed emergent sandbar habitat (GAV16) and 

containing unmanaged natural emergent sandbar habitat (GAV15) ........................12

5. Segments of the Gavins Point Reach of the Missouri River (RM 752–756) 

on which habitat data were collected for emergent sandbar habitat 

monitoring during 2006......................................................................................................13

6. Segments of the Gavins Point Reach of the Missouri River (RM 757–767) 

on which habitat data were collected for emergent sandbar habitat 

monitoring during 2006......................................................................................................14

7. Segments of the Gavins Point Reach of the Missouri River (RM 768–776) 

on which habitat data were collected for emergent sandbar habitat 

monitoring during 2006......................................................................................................15

8. Segments of the Gavins Point Reach of the Missouri River (RM 777–786) 

on which habitat data were collected for emergent sandbar habitat 

monitoring during 2006......................................................................................................16

9. Segments of the Gavins Point Reach of the Missouri River (RM 788–797) 

on which habitat data were collected for emergent sandbar habitat 

monitoring during 2006......................................................................................................17

10. Segments of the Gavins Point Reach of the Missouri River (RM 801–808) 

on which habitat data were collected for emergent sandbar habitat 

monitoring during 2006......................................................................................................18

11. Segments of the North Dakota portion of Lake Oahe on which habitat 

data were collected for emergent sandbar habitat monitoring during 2006 ...........21

12. Segments of the Fort Randall Reach of the Missouri River that were 

sampled in 2006 to characterize a proposed emergent sandbar habitat 

project near RM 827, at the headwaters of Lewis and Clark Lake............................24

13. Segments of the Kensler’s Bend Reach of the Missouri River on which 

habitat data were collected for emergent sandbar habitat monitoring 

during 2006..........................................................................................................................26

14. Spatial extent of habitat data for a proposed emergent sandbar habitat 

project on “Sonny’s Island,” Lake Sakakawea in 2006, depicting locations 

of transects composed of habitat evaluations every 5 meters..................................28

15. Spatial extent of habitat data for a proposed emergent sandbar habitat 

project on “Randy’s Island,” Lake Sakakawea in 2006, depicting 

locations transects composed of habitat evaluations every 5 meters.....................29

16. Example of distribution of terrestrial and aquatic sampling points within 

two segments of the Gavins Point Reach of the Missouri River................................31
xii

17. Example of distribution of terrestrial and aquatic sampling points within 

two segments of the Gavins Point Reach of the Missouri River, showing 

the distribution of habitat types used to derive point locations.................................33

18. Example of 100 completely random points distributed in a 0.4-river-mile 

habitat sampling segment on Lewis and Clark Lake, for which 

terrestrial habitat was known in advance to be present, but for 

which a habitat classification had not been derived...................................................34

19. Example of 20 completely random points distributed in a 0.4-river-mile 

habitat sampling segment on Kensler’s Bend, for which terrestrial 

habitat was known in advance to be completely lacking...........................................35

20. Schematic of nest site, 3 meter, and 10 meter plot (1-meter-square plots) 

locations for evaluating nest-site and nest-area habitat characteristics ........................37

21. Map showing example of spatial extent of segment habitat data for 

Lake Sakakawea in 2006, depicting locations of nests, random points, 

transects composed of habitat evaluations every 5 meters, and additional 

transects needed for nest-beach scale analysis..................................................................38

22–24. Graphs showing:

22. Least-square geometric mean counts per hour of invertebrates 

captured on sticky-stick traps during four time intervals at Lake 

Sakakawea in summer 2006.............................................................................................44

23. Least-square geometric mean counts per hour of invertebrates 

captured on sticky-stick traps by habitat type (tiers) during four 

time intervals at the Garrison Reach of the Missouri River in 

summer 2006.......................................................................................................................45

24. Least-square mean probability (95-percent confidence interval) that a 

nest site was available for sampling (not inundated) in August for each 

initial habitat classification after controlling for date of initial habitat 

assessment.........................................................................................................................46

Tables

1. Types of monitoring approaches with respect to a management action of 

interest............................................................................................................................................5

2. Average number of piping plover and least tern nests found on the Gavins 

Point Reach of the Missouri River during 2001–05 by 4-river-mile section.........................8

3. Features of 0.4-river-mile segments of the Gavins Point Reach of the 

Missouri River in which habitat data were collected during 2006 .......................................9

4. Total number of least tern and piping plover nests found on the North 

Dakota portion of Lake Oahe during 2001–05 by 4-river-mile section................................19

5. Features of 0.4-river-mile segments of the North Dakota portion of Lake Oahe 

on which habitat data were collected during 2006...............................................................20

6. Total number of least tern and piping plover nests found on the Fort Randall 

Reach of the Missouri River during 2001–05 by 4-river-mile section.................................23

7. Features of segments of the Fort Randall Reach of the Missouri River on 

which habitat data were collected in 2006.............................................................................23

8. Features of segments of the Kensler’s Bend Reach of the Missouri River on 

which habitat data were collected in 2006.............................................................................25
xiii

9. Summary of stratification of Lake Sakakawea, including numbers of 

2-kilometer segments, segments sampled in 2006, and projected number of 

segments in 2007–09 ..................................................................................................................27

10. Habitat types used to allocate sampling effort for emergent sandbar 

habitat monitoring on the Gavins Point Reach of the Missouri River in 2006...................32

11. Numbers of least tern and piping plover nests where habitat characteristics 

were evaluated for reaches of the Missouri River in summer 2006...................................37

12. Daubenmire cover classes and corresponding ranges of percent cover........................39

13. Size range of substrate classes used to describe habitats on the 

Missouri River .............................................................................................................................39

14. Standard classification scheme for weather observations at sampling 

points ............................................................................................................................................40

15. Terrestrial habitat variables recorded at emergent sandbar habitat 

monitoring points on the Missouri River and the relationship with ongoing 

productivity monitoring and remote sensing efforts.............................................................41

16. Aquatic habitat variables measured at emergent sandbar habitat 

monitoring points on the Missouri River and the relationship with ongoing 

productivity monitoring and remote sensing efforts.............................................................43

17. Habitat classes recorded at nest upon discovery (initial) and during 

August revisits for least tern and piping plover nests (n=345) on the 

Garrison Reach of the Missouri River in 2006........................................................................46

18. Distribution of habitat sampling achieved by a three-technician crew 

on the Missouri River during the 2006 field season ..............................................................48

Conversion Factors 

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

square meter (m2

) 10.76 square foot (ft2)

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F-32)/1.8

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88). 

Missouri River Emergent Sandbar Habitat Monitoring 

Plan—A Conceptual Framework for Adaptive 

Management

By Mark H. Sherfy, Jennifer H. Stucker, and Michael J. Anteau

Introduction

Habitat conditions are one of the most important 

factors determining distribution and productivity of least 

terns (Sternula antillarum) and piping plovers (Charadrius 

melodus) in the upper Missouri River system (Ziewitz and 

others, 1992; Kruse and others, 2002). Habitat conditions are 

known to change within and among seasons in response to 

variation in river flows, weather conditions, and management 

actions targeted at providing for the needs of terns and plovers. 

Although these principles are generally agreed upon, there 

is little empirical information available on the quantity and 

quality of tern and plover habitats in this system, particularly 

with reference to the major life history events that must be 

supported (egg laying, incubation, and brood rearing). Habitat 

requirements for these events are composed of two major 

categories: nesting and foraging habitat. In the case of piping 

plovers, these two requirements must occur on the same area 

because plover chicks are constrained to foraging near nesting 

sites prior to fledging (Knetter and others, 2002; Haffner, 

2005). In contrast, least terns chicks are fed by the adults, 

allowing food procurement for broods to occur outside the 

immediate nesting area; however, food resources must be close 

enough to nesting locations to minimize foraging time.

The complexity and dynamics of the upper Missouri 

River system introduce considerable uncertainty into how best 

to manage tern and plover habitats, and how best to evaluate 

the effectiveness of this management. An extensive program of 

habitat monitoring will be needed to address this complexity 

and support the management of least terns and piping plovers 

under the Missouri River Recovery Program. These needs are 

being addressed, in part, through a program of habitat creation 

and management targeted at improving quality and quantity of 

habitats for terns and plovers. Given the momentum of these 

projects and their associated costs, it is imperative that the 

capacity be available to quantify changes in managed habitats 

for least terns and piping plovers, so that management effectiveness can be evaluated.

Extremely high flows and flooding of the Missouri River 

in 1997 created and improved vast amounts of nesting habitat 

for least terns and piping plovers. Since 1998, there has been 

an apparent loss and/or degradation of habitat throughout the 

river system. However, during the same timeframe reservoir 

water levels have declined, exposing extensive piping plover 

breeding habitat. For example, 64 percent of adult piping 

plovers using the Missouri River in 2005 were observed on 

reservoir habitats, and 43 percent were observed on Lake 

Sakakawea (Threatened and Endangered Species Section, 

Omaha District, U.S. Army Corps of Engineers, unpub. data, 

2006). Given the vast dynamics of this river and reservoir 

system, systemwide monitoring of habitat is clearly needed 

for the U.S. Army Corps of Engineers (USACE) to employ 

adaptive management (with respect to river operations) to 

provide most optimal conditions for the maintenance of breeding habitat of least terns and piping plovers. As a result of this 

need, the U.S. Geological Survey, in cooperation with the U.S. 

Army Corps of Engineers, began work on a habitat monitoring 

plan in 2005 as a conceptual framework for adaptive management.

Scope and General Methodology

This report describes a monitoring program for collection of biological data for evaluation of system responses to 

creation and management of emergent sandbar habitat (ESH) 

on the Upper Missouri River system (fig. 2). The variables 

and field methods described were extensively tested by a 

crew of USGS field technicians during May–August 2006 as 

part of a research task funded by the USACE. This crew was 

tasked with testing the conceptual framework under a variety 

of circumstances to ensure that methodology and variables 

were broadly applicable. The decisions made during habitat 

sampling in 2006 are described to illustrate how the conceptual framework for monitoring could be implemented under 

these varied circumstances. None of the specifics encountered 

during 2006 nor the decisions made to accommodate them 

should be considered universal rules for implementation of the 

monitoring program. Rather, the conceptual framework should 

be viewed as providing guidance in future decisionmaking. 

The areas sampled during 2006 included:
2 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

•	 Kensler’s Bend—This is a reach of river that contains 

no existing ESH, and in which future construction of 

several ESH projects is under consideration. Projects 

would likely consist of top-width widening, chute 

excavation, and other means of restoring sandbar 

conditions within the river’s flood plain. This component of the project reflected a preproject evaluation of 

sites for newly created sandbars in a riverine system.

•	 Gavins Point Constructed Islands—ESH complexes 

were created by the U.S. Army Corps of Engineers 

during 2004–05 at river mile (RM) 754.5, 761.5, 

and 770.0. This component of the 2006 monitoring 

reflected a postproject evaluation of newly created 

sandbars in a riverine system.

•	 Gavins Point Vegetation Control—An extensive 

program of mechanical and herbicidal vegetation 

control was implemented on the Gavins Point Reach 

in 2005. This effort was focused on “natural” sandbars 

(defined as those deposited by the river’s hydrology 

rather than mechanical means) on which vegetation 

growth was believed to be hindering use by least 

terns and piping plovers. This component of the 2006 

monitoring reflected a postproject evaluation of vegetation control in a riverine system.

•	 Lake Oahe Vegetation Control— Implementation 

of vegetation control is proposed for sandbars occurring on upper Lake Oahe, between the North DakotaSouth Dakota border and RM 1304. The upper limit 

of this study area (RM 1304) is commonly defined as 

the northernmost extent of Lake Oahe, with reservoir 

conditions existing below RM 1304 and riverine conditions existing above RM 1304. Owing to drought in 

the upper Missouri River basin, the water level in Lake 

Oahe is at an all-time low, causing riverine conditions to extend well below RM 1304. Consequently, 

this component of the 2006 monitoring reflected a 

pre-project evaluation of a vegetation control project in 

both a riverine and a reservoir system.

•	 Garrison Reach—No ESH projects have been implemented or are proposed on this area. However, the area 

was the subject of an intensive productivity monitoring effort by USACE and USGS crews in 2006. As 

part of the USGS effort, habitat data were collected at 

tern and plover nest sites at the time of nest discovery. 

To evaluate change in habitat conditions from early 

to late summer, the habitat monitoring crew revisited 

nest sites and collected additional habitat data during 

August 2006. This data collection reflected use of the 

proposed procedures to evaluate habitat change over 

time.

•	 Lake Sakakawea—Vegetation control is proposed 

for several areas of Lake Sakakawea shoreline to 

improve piping plover nesting habitat conditions. The 

habitat monitoring crew collected habitat data on two 

of the proposed sites for which the specific locations 

were defined. This component of the 2006 monitoring 

reflected a preproject evaluation of vegetation control 

in a true reservoir system.

Objectives

One of the most fundamental goals of ecological 

monitoring is to detect change in a resource of management 

importance (Bailey and Herzog, 2004). Accordingly, design of 

an efficient and useful monitoring program requires baseline 

information on the abundance and/or distribution of those 

resources. It also requires a sampling approach that is of 

adequate intensity and extent to allow detection of changes 

that are important in a management context. With these 

principles in mind, the following objectives will be the focus 

of this monitoring program:

1. Evaluate changes in least tern and piping plover habitats 

in response to construction of ESH under the ESH 

Program.

2. Evaluate changes in least tern and piping plover habitats 

in response to manipulation of ESH through vegetation 

control.

3. Because the ESH Program focuses on providing habitat 

for least terns and piping plovers, these objectives focus 

on the principal information needs to evaluate success 

at delivering the desired habitat features. It is important 

to recognize that these objectives are intended to serve 

the long-term monitoring needs of the ESH Program. 

Through development of annual Action Plans (Phases 1 

and 2 in the monitoring framework), objectives will be 

derived that are specific to the unique circumstances 

encountered each year.

Indicator Variables

Reeves and others (2003) reviewed a suite of 90 potential 

indicators that could be used to assess watershed condition. 

These were distilled into a core set on the basis of the following evaluation criteria:

•	 Changes in the system would be reflected in measurable changes in the indicator.

•	 The indicator must respond quickly enough to disturbance or recovery to provide results in the chosen 

timeframe.

•	 The indicator can be accurately and precisely measured 

or estimated.

•	 The indicator must be generally accepted as a useful 

indicator of ecological condition.
Figure 2. The Upper Missouri River system, including locations of selected reservoirs and free-flowing river reaches. 

Red symbols represent dams.

Garrison Reach

Lake Oahe

Fort Randall Reach

Lewis and Clark

Lake

Gavins Point 

Reach

Lake Sakakawea

NEBRASKA

SOUTH DAKOTA

MONTANA

WYOMING

COLORADO

KANSAS

MINNESOTA

IOWA

108° 106° 104° 102° 100° 98° 96°

48°

46°

44°

42°

40°

NORTH DAKOTA

0 50 100 150 200 MILES

0 50 100 150 200 KILOMETERS

Introduction 3
4 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

•	 Measurement of the indicator must be cost effective at 

the required level of precision and accuracy.

These criteria provide a useful source of guidance for 

selecting indicators to be used in monitoring tern and plover 

habitats on the Missouri River. In developing this plan, we 

evaluated a preliminary set of indicators to quantify habitat 

conditions at each sampling point, using the same indicators 

on both reservoir shorelines and sandbars. A critical component of this evaluation was to determine the utility of these 

indicators for long-term monitoring of tern and plover habitats. 

Thus, it is possible that the list of indicators or the methods 

used to measure them described in this plan might not be 

suitable for future needs, particularly if there is a change in 

Missouri River management objectives or methods.

The intent is that the suite of indicator variables selected 

for monitoring would be useful not only for measuring habitat 

responses to the ESH Program, but also for evaluating nest 

site selection of terns and plovers. Accordingly, this suite 

of variables should be suitable for measurement at tern and 

plover nest sites. Because collection of habitat data at nest 

sites is important for evaluating least tern and piping plover 

use of habitats provided under the ESH Program, the procedures developed here will be simultaneously implemented 

at nest sites by crews conducting companion studies of least 

tern and piping plover productivity. Evaluating nest site 

selection is an important step toward understanding biological function of the Missouri River system. Perhaps more 

important in a monitoring context, this type of analysis would 

provide feedback regarding importance of the variables being 

measured, allowing refinement of habitat monitoring to target 

those variables that most clearly influence habitat use by terns 

and plovers.

Approach to Data Collection

One of the central questions in designing a monitoring 

protocol is where and when to collect data on the indicator 

species. Many different approaches are possible, ranging from 

a few samples collected in managed habitats, to a complex 

array of sampling in a rigorous, statistically valid experimental 

design. These approaches generally differ in the number and 

distribution of samples, with methods at the chosen sampling 

locations being similar among approaches (such as stem 

counts within a 1-meter-square (m2

) quadrat to measure stem 

density of woody vegetation). Decisions about where and at 

what frequency to conduct sampling have substantial bearing 

on the conclusions that can be drawn from the data.

Elzinga and others (2001) describe a continuum of 

data collection methods for monitoring (Types A–F, table 1) 

that represent a range of sampling intensity and therefore a 

range of possible statistical inference. The simplest form of 

monitoring (Type B) consists of data collection on a single 

sampling unit after the management action has been implemented, with no replication, no pretreatment data collection, 

and no experimental controls. This represents the simplest, 

quickest approach to monitoring, but provides very limited 

ability to draw definitive conclusions about the consequences 

of the management action. Advancing from Type B through 

Type F, sampling is intensified by addition of pretreatment 

monitoring (Type C), experimental controls (Type D), and 

replication of sampling units (Types E and F). An additional 

practice not addressed by Elzinga and others (2001) but that 

could aid in further extending inference from the monitoring 

data is random application of management actions among the 

suite of potential sampling units (Type G). The Type G form 

of monitoring is identical to Johnson’s (2002) “manipulative 

experimentation,” as it includes all three elements (experimental control, replication, and randomization) that together 

provide a powerful way to learn about causal mechanisms. 

Elzinga and others (2001) note that statistical analysis of data 

resulting from all of these approaches is possible, but that 

the “observational” approaches (Types A–D, table 1) provide 

limited information about cause and effect. Consequently, 

selection of a monitoring approach must include some consideration about the importance of demonstrating causal relationships from the data being collected.

Sampling Units

In considering replication of treatment and control groups 

in a manipulative experiment, it is important to define the 

sampling unit and the population from which it will be drawn. 

When the units that are sampled are representative of the 

larger population, inference can be extended to the population 

(Cochran, 1977). Sampling intensity dictates the strength of 

conclusions at several scales. First, each unit must be sampled 

at an adequate intensity to characterize its nature. In some 

cases, this may only require a single measurement, and in 

others, it may require multiple measurements. Second, enough 

sampling units must be included in the study to adequately 

characterize the population from which they were drawn. In 

situations requiring multiple measurements to characterize 

each sampling unit, it is tempting to view each measurement as providing information about the nature of the entire 

population. However, this view ignores a critical element 

in the definition of sampling units—they must be independent of each other to be considered separate units. Thus, 

measurements within a sampling unit provide information 

about that unit, and the “average” condition of the sampling 

units provides information about the nature of the population. Hurlbert (1984) emphasized the importance of defining 

independent sampling units and recognizing that repeatedly 

measuring the same unit does not always contribute additional 

information about the population.
Table 1. Types of monitoring approaches with respect to a management action of interest. Values indicate whether each element is 

sampled under a given approach.

[0=no sampling, 1=one sampling unit, >1=minimal replication, and >>1=adequate replication] 

Type Approach

Pre-action Post-action

Control Treatment Control Treatment

A No monitoring 0 0 0 0

B Postmonitoring only 0 0 0 1

C Premonitoring and postmonitoring 0 1 0 1

D Premonitoring and postmonitoring with 

experimental control

1 1 1 1

E Premonitoring and postmonitoring with 

experimental control and minimal replication

>1 >1 >1 >1

F Premonitoring and postmonitoring with 

experimental control and good replication

>>1 >>1 >>1 >>1

G Premonitoring and postmonitoring with 

experimental control, good replication, and 

randomized application of treatments

>>1 

(Random)

>>1 

(Random)

>>1 

(Random)

>>1 

(Random)

Approach to Data Collection 5

Units of Tern and Plover Nesting Habitat

In the context of monitoring habitat conditions, it is 

imperative to define a unit of habitat as the sampling unit. 

Where making inference to a larger population of habitat 

units is desirable, it is critical that the units be definable and 

independent, and that their relationship to the population 

be known. Least tern and piping plover nesting habitats on 

the Missouri River present some unique challenges in this 

regard, because they are usually arranged in discrete units 

that are seemingly appropriate sampling units. However, both 

midchannel emergent sandbars and reservoir shorelines are 

highly dynamic habitats that change dramatically in form and 

extent both within and among seasons. These changes are in 

response to rising and falling water levels, and to redistribution 

of midchannel sand. In many cases, water level fluctuations 

are substantial enough that sandbars are extensive when flows 

are low and completely absent when flows are high. Because 

the amount of exposed sandbar habitat is directly tied to flow 

rate, sandbars do not represent a discrete, definable sampling 

unit. Additionally, the extensive changes in acreage and distribution of sandbars within and among years make it problematic to consider individual sandbars as discrete sampling units.

A similar problem with defining sampling units occurs 

on reservoir shorelines, where the amount of exposed habitat 

is a function of reservoir water elevation. Falling water levels 

generally produce wider areas of unvegetated shoreline, 

whereas rising water levels reduce width of available nesting 

habitat. Changing water levels also alter the overall length of 

shoreline throughout a reservoir, particularly where contours 

are sinuous and irregular.

A Stratified Sampling Approach to Monitoring

Stratified sampling is an approach to collection of data 

that takes advantage of known sources of variation in the 

population of potential sampling units, dividing the group 

of units into several nonoverlapping subgroups or strata. A 

sample of units to be measured is drawn from each stratum. 

This approach produces results of known precision for each 

stratum, and can produce gains in precision of estimates for 

the population as a whole if it reduces a heterogeneous population into several more homogeneous strata (Cochran, 1977). 

Gains in precision are largest when (Cochran, 1977):

1. The population is composed of units that vary widely in 

size.

2. The variables to be measured are closely related to the 

size of the units.

3. A good measure of unit size is available for defining 

strata.

In the case of Missouri River nesting habitats used by 

least terns and piping plovers, considerable heterogeneity in 

use by birds within river reaches is evident. Therefore, condition 1 is satisfied. There also is an implicit assumption that 

variation in habitat condition is one of the principal factors 

leading to heterogeneous distribution of birds. Thus, for 

monitoring of habitat conditions, the variables to be measured 

(habitat variables) are believed to be closely related to the size 

of strata (measured by relative bird use), and condition 2 is 

satisfied. Finally, the existence of a long-term data set on tern 

and plover nest locations provides a measure of bird distribution within reaches, satisfying the need to measure stratum 
6 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

size in condition 3. Thus, stratified sampling would be a useful 

and appropriate approach to monitoring ESH projects.

Width of the river channel is a secondary stratification, 

which may also be important to consider in riverine habitats. 

River channel width varies extensively throughout the Upper 

Missouri River System (Biedenharn and others, 2001; Elliott 

and Jacobson, 2006); it is positively correlated with the 

number of unattached sandbars and the acres of sandbars and 

is a proxy for river geomorphology (Biedenharn and others, 

2001; Elliott and Jacobson, 2006); and is easily measured 

with widely available geospatial/mapping software. Thus, 

river channel width satisfies the above conditions and could be 

useful information for stratification within bird use histories. 

Accordingly, river channel width could be useful for selecting 

segments to be paired for data analyses or for blocking on the 

variability associated with river geomorphology.

Timing of Sampling

A useful design for monitoring habitat change is the 

Before-After-Control-Impact (BACI) approach to sampling for 

projects that have not yet been constructed and that afford an 

opportunity for collection of habitat data prior to construction. 

These included the Kensler’s Bend, Lewis and Clark Lake, 

Lake Oahe, and Lake Sakakawea study areas during 2006, and 

depending on the extent of ESH implementation, may include 

other areas in subsequent years. The BACI design includes 

sampling of both treatment and control sites before and after 

the treatment is applied, which allows treatment effects to be 

separated from temporal effects. Effective implementation 

of this design requires advance knowledge of where treatments will be implemented so that segments can be accurately 

characterized, which is expected to be the case for this study. 

Repeated evaluation of the same sites in multiple years will 

allow detection of responses due to temporal and treatment 

effects. Therefore, the design calls for random selection of 

segments once for the 2006 project sites, and monitoring of 

those same segments in subsequent years. Likewise, for new 

project sites that are identified in future years, new sets of 

segments should be selected to characterize those treatment 

effects.

Duration of monitoring for each segment will depend 

upon the nature of the project being monitored. Vegetation 

control by herbicide application is one of the principal ESH 

management efforts expected to be evaluated under this 

plan. On the basis of preliminary observations of vegetation 

growth patterns on Missouri River sandbars and the anticipated effects of herbicides being implemented by the USACE, 

some herbicide treatment effects may persist into the second 

year posttreatment. Therefore, segments selected to evaluate 

vegetation control projects should be monitored for at least 

2 years after project implementation. Because construction 

projects are more likely to have long-term influence on bird 

use and productivity, a longer monitoring period will be 

necessary on segments selected for evaluation of constructed 

sandbar projects.

It is possible that some confounding of treatment and 

temporal effects might occur. For example, a control segment 

selected for 2006 might be chosen by the USACE in 2007 

for implementation of an ESH project, and thus become a 

potential treatment segment for 2007. The reverse is not true, 

because treatment segments will remain treatment segments 

for a 2-year period after the ESH project. Therefore, if time 

allows, it would be beneficial to increase the number of 

control segments sampled in the event that their status changes 

in subsequent years.

Field Data Collection—Habitat

The methods described in this plan could be used for a 

number of purposes related to ESH quality, focusing on least 

terns and piping plovers. A traditional monitoring context 

would employ habitat data collected at points arranged 

throughout the defined area of interest in a manner that 

ensures that the points are representative of the area of interest.

For many reasons, it is sensible to consider each of the 

major river reaches and reservoirs (hereafter referred to as 

“reach”), as defined in the 2003 Amendment to the Missouri 

River Biological Opinion, as providing a “population” of 

habitats for which monitoring could be implemented. Each 

reach is managed as a quasi-discrete unit, and each has its 

own unique spatial, hydrological, and geological features that 

lead to substantial variation in use by terns and plovers. The 

Opinion identifies specific ESH acreage goals for each reach, 

and a comprehensive long-term monitoring program for each 

reach could provide a useful evaluation of changes in habitat 

quality as those acreage goals are met.

Delineation and Selection of Strata

Given the challenges in defining sampling units in a 

highly dynamic system and the desirable properties of a 

stratified sampling design, considerable emphasis should 

be placed on establishing boundaries for strata within the 

selected study reach. In some situations, particularly those in 

which habitat creation is concerned, sandbars are not a useful 

measurement unit for evaluating pretreatment and posttreatment habitat conditions. This is because, by definition, nesting 

habitat may be absent or limited under pretreatment conditions at an ESH construction site. A more useful experimental 

unit for measurement of habitat variables is a section of river 

or reservoir shoreline, in which the total amount of nesting 

habitat can be quantified prior to and after the management 

action. For example, documenting that tern and plover nesting 

habitat increased by n acres from year x to year x+1 on a river 

section owing to the ESH Program provides a useful basis for 

temporal and spatial comparisons. Another advantage to this 

approach is that tern and plover productivity data are readily 

partitioned into sections, which would allow for analyses of 

bird responses to habitat management. Moreover, sandbars are 
Field Data Collection—Habitat 7

an inappropriate experimental unit for long-term monitoring 

projects because of their ephemeral nature in riverine systems.

Following are descriptions of approaches to stratification of river and reservoir shorelines that would be useful for 

habitat monitoring. The preferred approach is shown using 

examples from field work on the Gavins Point Reach and Lake 

Sakakawea during 2006.

Rivers

One of the initial challenges in this approach was to 

select a section length that is an appropriate unit for quantifying both productivity and habitat variables. A 4-river-mile 

section of river will be a useful unit of measurement for 

productivity data, on the basis of experience monitoring least 

tern and piping plover nests on the Gavins Point and Garrison 

reaches during 2005 and 2006 (NPWRC Task 2: Evaluation of 

procedures for monitoring numbers and productivity of least 

terns and piping plovers on the Missouri River). This section 

length was selected for use during the 2006 field season under 

the assumption that a crew of 4 technicians would be able to 

monitor all available nesting habitat on 2 sections per day. 

Assuming that this standard can be met, a 4-mile river section 

also would be a useful scale at which river reaches could 

be stratified for the purpose of habitat monitoring. Because 

River Miles (RM) are a standard unit of distance measurement on the Missouri River, they were retained as the basis 

of river stratification for this and companion studies. For the 

purposes of monitoring, each 4-mile section was divided into 

ten 0.4–RM segments. On the basis of experience conducting 

accuracy assessments of remote sensing data on the Gavins 

Point Reach during 2005 (NPWRC Task 3: Mapping, monitoring, and estimation of least tern and piping plover habitats on 

the upper Missouri River using Quickbird imagery), this is a 

practical unit of measurement for quantifying abundance of 

major habitat cover types.

The initial step in developing a stratification of the 

Gavins Point Reach was dividing the reach into 4–RM 

sections. The downstream RM marker was used as a unique 

identifier for each segment (for example 754 for RM 

754–758). For each section, USACE productivity monitoring data were used to derive the average number of least tern 

and piping plover nests for the 5 years prior to initiation of 

habitat monitoring studies (2001–05). After determining the 

average number of nests, each section was assigned to a high, 

medium, or low use category. The goal was to assign approximately equal numbers of sections (4–6) to each category, but 

the natural “breaks” in nest count data might not always be 

evident. One to 13 nests were used for the Gavins Point Reach 

to define the low (n=4) category, 19.8–34.2 nests to define 

the medium (n=6) category, and 38.2–71 nests to define the 

high (n=4) category (table 2). These break points are specific 

to Gavins Point and to the objective of identifying three use 

categories. For other reaches and other objectives (such as 

delineating high and low use only), different break points will 

need to be identified.

The next step was to divide each section into 0.4–RM 

segments, and to assign additional attributes to each segment. 

The bird use categories derived for the 4–RM sections 

were applied to each component segment. Thus, section 01 

(RM 754–758) consisted of ten 0.4–RM segments, each 

of which was classified as high bird use. Not all segments 

within the section will have identical histories of bird use. 

However, this approach recognizes that broad areas of the 

river are favored by nesting birds, with interannual changes 

in habitat conditions dictating distribution of nesting effort 

within sections. After this step was complete for Gavins 

Point, 140 segments were available for sampling. Because the 

management programs under evaluation are targeted at ESH, 

segments that did not contain ESH in 2005 were eliminated 

from consideration. Some situations may warrant retaining segments that are devoid of sandbars in the sample to be 

monitored. For example, a segment containing no sandbars 

that was targeted for construction of a new sandbar could be 

selected for preconstruction monitoring. Because sandbars do 

not persist through time, segments containing sandbars at the 

start of a monitoring program might not contain any in later 

years. For Gavins Point in 2006, 104 segments were identified 

that contained sandbars on the basis of 2005 photography.

At the segment level the secondary stratification of river 

channel width could be applied. For example, this stratification was applied to segments of interest on the Gavins Point 

Reach (see following); the river channel width (furthest 

expanse of water perpendicular to flow including “main” and 

“back” channels) was measured at the middle and borders of 

each segment (n=3). The mean of width measurements were 

used to classify each segment as low (<700 meters), medium 

(700–1,000 meters), and high (>1,000 meters) widths (table 3).

There were two distinct goals of the 2006 sampling on 

the Gavins Point Reach that illustrate different approaches to 

selecting a sample of segments for monitoring. Gavins Point 

contains three constructed sandbar complexes and numerous natural sandbars that were treated with herbicide for 

vegetation control during 2005. The monitoring program was 

designed to accommodate both management tools, but each 

has its own unique attributes.

Mechanical construction of emergent sandbars is a 

complex, costly process. Therefore, this management tool is 

applied relatively infrequently. Viewed in an experimental 

context, this application of a “treatment” does not lend itself to 

detection of responses, because it lacks two principal elements 

of a designed experiment (randomization and replication). 

However, it does allow assignment of a treatment status to 

each 0.4–RM segment on the basis of presence or absence of 

constructed sandbar habitat. Sample size will, by definition, be 

low for segments containing constructed habitat. Therefore, it 

is likely that selection of all “treated” segments within a river 

reach will be required to achieve a sample size suitable for 

statistical analysis. In many cases, even this approach will not 

generate a suitable sample size owing to the focused nature of 

these projects. However, a long-term program of monitoring 

following the procedures outlined in this plan will generate 
Table 2. Average number of piping plover and least tern nests found on the Gavins Point Reach of the Missouri River during 2001–05 by 

4-river-mile section.

[RM, river mile; L, low; M, medium; H, high]

Section number Downstream RM Piping plover nests Least tern nests Total nests Use category

01 754 39.2 31.8 71 H

02 758 18 20.2 38.2 H

03 762 .6 1.4 2 L

04 766 5.4 7.6 13 L

05 770 10.4 23.8 34.2 M

06 774 6.8 13 19.8 M

07 778 13 34.6 47.6 H

08 782 .8 0.2 1 L

09 786 17.4 36 53.4 H

10 790 14.4 19 33.4 M

11 794 12 20.8 32.8 M

12 798 10.8 19 29.8 M

13 802 18.4 12.4 30.8 M

14 806 1.6 2.8 4.4 L

8 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

a data set with many replicates of “treated” and “control” 

segments that will be useful in evaluating biological responses 

to ESH construction.

Habitat manipulation on existing sandbars occurs at an 

entirely different scale and intensity than construction of new 

sandbars. For example, vegetation was sprayed on approximately 181 acres of habitat on 23 separate sandbars on the 

Gavins Point Reach in 2005. Management actions of this 

nature provide more desirable statistical properties because 

they provide replication and interspersion of treated and 

untreated habitats. These properties increase the likelihood 

that a favorable sampling design can be established. To accommodate both ESH construction and management projects, 

each Gavins Point segment was assigned to an ESH stratum as 

follows:

•	 Construction segments contained (in 2005) 

constructed ESH (fig. 3).

•	 Sprayed segments contained habitat that had been 

sprayed in 2005 for vegetation control (fig. 4).

•	 Control segments contained neither sprayed nor 

constructed ESH (figs. 3 and 4).

The ESH strata used on Gavins Point in 2006 were 

designed specifically for the circumstances existing at the 

time habitat monitoring began. Although each river reach 

will present a different arrangement of existing and proposed 

treatments, a similar set of guiding principles should be 

followed in selecting segments for monitoring:

•	 Separate selections should be made for treated 

segments in each of the available ESH strata within 

a reach. Where >1 treatment has been applied within 

a reach, it would be improper to pool all treated 

segments and randomly select treated segments from 

the combined pool. Rather, each treatment applied 

within a reach should be considered separate and 

distinct from the other treatments for the purpose of 

segment selection.

•	 The number of treated segments selected will vary 

according to the number that received the treatment of 

interest, the number that can be feasibly sampled, and 

the likely variation in response variables within that 

treatment. For Gavins Point in 2006, it was determined 

that 30 segments were sprayed during 2005, and 12 of 

these were selected for habitat sampling. Five segments 

were determined to contain constructed habitat; 

because availability of these segments was limited, all 

five were selected for sampling.

•	 Where the number of available treatment segments 

requires subsampling, selection should be in a stratified 

random fashion. Of the 30 sprayed segments on 

Gavins Point, 6 were in the low use stratum, 17 in the 

medium use stratum, and 7 in the high use stratum. 

Because of this the stratification was derived so that 

about one-third of the reach fell into each stratum, four 

sprayed segments were randomly selected from each. 

An alternative would be to select segments according 
Table 3. Features of 0.4-river-mile segments of the Gavins Point Reach of the Missouri River in which habitat data were collected 

during 2006.

[ID, identifier; RM, river mile; L, low; M, medium; H, high; Y, yes; N, no] 

Segment ID Downstream RM Upstream RM Historical tern/

plover use River width Vegetation control in 2005?

Created sandbar 

present?

GAV01 754.0 754.4 H L N Y

GAV02 754.8 755.2 H L N Y

GAV03 757.2 757.6 H M N N

GAV04 757.6 758.0 H L N N

GAV05 758.4 758.8 H M N N

GAV06 758.8 759.2 H M Y N

GAV07 759.2 759.6 H M Y N

GAV08 760.4 760.8 H L N N

GAV09 761.2 761.6 H M N Y

GAV10 764.0 764.4 L M N N

GAV11 766.4 766.8 L M N N

GAV12 768.0 768.4 L H N N

GAV13 769.6 770.0 L H N Y

GAV14 770.0 770.4 M H N Y

GAV15 771.6 772.0 M L N N

GAV16 772.8 773.2 M L Y N

GAV17 774.0 774.4 M L N N

GAV18 774.4 774.8 M M N N

GAV19 774.8 775.2 M L N N

GAV20 780.4 780.8 H H N N

GAV21 780.8 781.2 H H N N

GAV22 782.8 783.2 L H Y N

GAV23 784.0 784.4 L H Y N

GAV24 784.4 784.8 L H Y N

GAV25 784.8 785.2 L H Y N

GAV26 788.8 789.2 H M N N

GAV27 789.2 789.6 H M Y N

GAV28 789.6 790.0 H H Y N

GAV29 790.0 790.4 M H Y N

GAV30 796.0 796.4 M L Y N

GAV31 800.8 801.2 M M Y N

GAV32 801.2 801.6 M L N N

GAV33 807.6 808.0 L L N N

GAV34 808.0 808.4 L L N N

Field Data Collection—Habitat 9
10 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

to proportional allocation of treatments among strata. 

That approach would generate 20 percent (6/30) of 

the selected segments in the low stratum, 56 percent 

(17/30) in the medium stratum, and 23 percent (7/30) 

in the high stratum.

•	 The number of control segments should be approximately equal to the number of treatment segments. 

In 2006, 5 control segments were selected to correspond with the 5 constructed habitat segments, and 

12 separate controls to correspond with the 12 sprayed 

habitat segments. However, a preferable approach is to 

select a single set of control segments to be used in one 

analysis with multiple treatment levels (for example 

“sprayed” and “constructed” treatments in 2006).

•	 Selection of control segments should, to the extent 

possible, generate a sample that is representative of 

the strata from which treatment segments were chosen. 

For Gavins Point in 2006, the principal concern was 

with bird use history as a stratification feature. This 

was achieved by randomly selecting the 12 control 

segments for the sprayed habitat in the same proportions as the treated segments (4 each from low, 

medium, and high bird use strata; table 3). Control 

segments for the created sandbar segments also were 

randomly selected in the same proportions as the treatments (1 low, 1 medium, and 3 high bird use strata; 

table 3 and figs. 5–10). However, it is not necessary 

that sample sizes of control and treatment segments 

be identical for each stratum. Rather, the important 

consideration is that the selected control segments 

are representative of the strata from which they were 

chosen.

•	 The power of the BACI design is its ability to distinguish temporal effects from treatment effects. To 

accomplish this, it is critical to monitor treatment and 

control sampling units both before and after implementation of the management action of interest. This, in 

turn, requires advance knowledge of where treatments 

will be implemented so that segments can be properly 

classified. In a multiyear study, this design could 

minimize effects of annual variation if each of four 

possible treatment classes (premanagement control, 

premanagement treatment, postmanagement control, 

postmanagement treatment) were sampled each year.

•	 Definition of treatment segments will depend on the 

implementation of the ESH Program and the defined 

monitoring objectives. For example, it is possible 

that several different herbicides could be used to 

control vegetation growth on sandbars. These could be 

combined into a single “sprayed” treatment to address 

objectives related to the overall system responses 

to vegetation spraying under the ESH Program. 

Conversely, sprayed segments could be assigned 

to two or more treatment categories depending on 

the management tools (such as different herbicides) 

employed to control vegetation growth. Similarly, 

treatments could be assigned on the basis of whether 

post-herbicide management (such as mowing) was 

employed. Increasing the number of treatments will 

increase the complexity of statistical models required 

to analyze the data, but will enhance the ability to 

detect system responses to specific management applications.
Figure 3. Example of 0.4-river-mile segments on the Gavins Point Reach, illustrating segments containing constructed emergent sandbar habit

containing unmanaged natural emergent sandbar habitat (GAV08). Background image is 2005 color infrared photography. Green lines represent types within sampling segments.

96°45′20′′ 96°45′10′′ 96°45′ 96°44′50′′ 96°44′40′′ 96°44′30′′ 96°44′20′′ 96°44′10′′ 

42°40′10′′

42°40′

42°39′50′′42°39′40′′42°39′30′′42°39′20′′

00

500 1,000 1,500 2,000 FEET

200 400 600 METERS

GAV08

GAV09
96°54′50′′ 96°54′40′′ 96°54′30′′ 96°54′20′′ 96°54′10′′ 96°54′ 96°53′50′′ 96°53′40′′ 96°53′30′′ 

42°44′20′′42°44′10′′

42°44′

42°43′50′′42°43′40′′42°43′30′′

00

500 1,000 1,50

200 400 

GAV15

GAV16

Figure 4. Example of 0.4-river-mile segments on the Gavins Point Reach, illustrating segments containing sprayed emergent sandbar habitat (GAis represented by the red polygon) and containing unmanaged natural emergent sandbar habitat (GAV15). Background image is 2005 color infrarelines represent perimeters of habitat types within sampling segments.
GAV01

GAV02

754

755

756

752

753

751

754

755

756

752

753

751

96°44′ 96°42′ 96°40′ 96°38′

42°38′42°36′

0 750 1,500 2,250 3,000 METERS

0 2,500 5,000 7,500 10,000 FEET

Figure 5. Segments of the Gavins Point Reach of the Missouri River (RM 752–756) on which habitat data were collected for emergent sandbar during 2006. Background image is 2005 color infrared photography. Green lines represent perimeters of habitat types within sampling segmentsrepresent river miles.
GA

GAV05

GAV06

GAV08 GAV07

GAV09

GAV10

GAV11

GA

GAV05

GAV06

GAV08 GAV07

GAV09

GAV10

GAV11

763

764 762

765

766

767

759

760

761

763

764 762

765

766

767

759

760

761

96°48′ 96°47′ 96°46′ 96°45′ 96°44′ 96°43′ 96°42′ 

42°42′42°41′42°40′42°39′

0 750 1,500 2,250 3,000 METERS

0 2,500 5,000 7,500 10,000 FEET

Figure 6. Segments of the Gavins Point Reach of the Missouri River (RM 757–767) on which habitat data were collected for emergent sandbar during 2006. Background image is 2005 color infrared photography. Green lines represent perimeters of habitat types within sampling segments, represent river miles.
GA

GAV13

GAV14

GAV15

GAV16

GAV17

GAV19

GAV18

GA

GAV13

GAV14

GAV15

GAV16

GAV17

GAV19

775 GAV18

771

774 772

773

769

770

775

771

774 772

773

769

770

96°57′ 96°56′ 96°55′ 96°54′ 96°53′ 96°52′ 96°51′ 

42°45′42°44′42°43′42°42′42°41′

0 750 1,500 2,250 3,000 METERS

0 2,500 5,000 7,500 10,000 FEET

Figure 7. Segments of the Gavins Point Reach of the Missouri River (RM 768–776) on which habitat data were collected for emergent sandbar during 2006. Background image is 2005 color infrared photography. Green lines represent perimeters of habitat types within sampling segmentsrepresent river miles.
GAV20

GAV21 GAV22 GAV24 GAV23 GAV25

GAV20

GAV21 GAV22 GAV24 GAV23 GAV25

782 781

785

783

784

780

782 781

785

783

784

780

97°04′ 97°02′ 97°00′ 

42°46′42°44′

0 750 1,500 2,250 3,000 METERS

0 2,500 5,000 7,500 10,000 FEET

Figure 8. Segments of the Gavins Point Reach of the Missouri River (RM 777–786) on which habitat data were collected for emergent sandbar during 2006. Background image is 2005 color infrared photography. Green lines represent perimeters of habitat types within sampling segments, represent river miles.
GAV26

GAV27

GAV28

GAV29

GAV30

794

795

796

797

788

789

790

791

792

793

794

795

796

797

788

789

790

791

792

793

97°14′ 97°12′ 97°10′ 97°08′

42°50′42°48′

0 750 1,500 2,250 3,000 METERS

0 2,500 5,000 7,500 10,000 FEET

Figure 9. Segments of the Gavins Point Reach of the Missouri River (RM 788–797) on which habitat data were collected for emergent sandbar during 2006. Background image is 2005 color infrared photography. Green lines represent perimeters of habitat types within sampling segmentsrepresent river miles.
GAV33

GAV34

808

807

805

806

804

803 802

808

807

805

806

804

803 802

97°26′ 97°24′ 97°22′ 97°20′

42°52′42°50′

0 750 1,500 2,250 3,000 METERS

0 2,500 5,000 7,500 10,000 FEET

Figure 10. Segments of the Gavins Point Reach of the Missouri River (RM 801–808) on which habitat data were collected for emergent sandbaduring 2006. Background image is 2005 color infrared photography. Green lines represent perimeters of habitat types within sampling segmentsrepresent river miles.
Table 4. Total number of least tern and piping plover nests found on the North Dakota portion of 

Lake Oahe during 2001–05 by 4-river-mile section.

[RM, river mile; L, low; M, medium; H, high]

Segment 

number

Downstream 

RM

Least tern 

nests

Piping plover 

nests Total nests Use category

01 1232 3 51 54 H

02 1236 0 8 8 M

03 1240 1 14 15 M

04 1244 2 27 29 H

05 1248 14 36 50 H

06 1252 1 1 2 L

07 1256 0 0 0 L

08 1260 0 0 0 L

09 1264 13 14 27 H

10 1268 5 19 24 H

11 1272 1 8 9 M

12 1276 3 5 8 M

13 1280 6 7 13 M

14 1284 9 21 30 H

15 1288 25 33 58 H

16 1292 8 14 22 H

17 1296 0 1 1 L

18 1300 8 4 12 M

19 1304 1 7 8 M

Field Data Collection—Habitat 19

During the 2006 field season, several circumstances were 

encountered on other reaches that illustrate variations on this 

approach to stratification:

Lake Oahe—The upper Missouri River system is 

generally managed as a series of alternating river reaches and 

reservoirs, with a standard RM marker serving as the division 

between adjacent elements. For example, RM 1304 marks the 

division between Lake Oahe and the Garrison Reach. This 

convention standardizes the definition of each reach or reservoir, but does not account for the influence of changing water 

conditions on habitat structure. For example, a pattern of dry 

weather leading up to 2006 generated record-low water levels 

on Lake Oahe that dramatically reduced the lower portion of 

the lake (which truly functions as a lake) and produced riverine conditions in which midchannel sandbars dominate on the 

upper portion.

The area of interest for monitoring in 2006 (the North 

Dakota-South Dakota border to RM 1304) lies within the 

upper portion of Lake Oahe, and was targeted for vegetation spraying during 2007. Because this area was functionally more similar to a free-flowing river than to a reservoir, 

similar procedures were followed to those used on Gavins 

Point to select segments for monitoring. Strata were delineated 

on the basis of total nest numbers from 2001–05 (table 4), 

using 0–2 nests for the low stratum (n=4), 8–15 nests for the 

medium stratum (n=7), and 22–58 nests for the high stratum 

(n=8).

This exercise illustrates an important feature of any stratification developed for ESH monitoring—it should represent 

the spatial extent and target species upon which the management action is focused. Because potential vegetation control 

sites on Lake Oahe were selected from the North Dakota 
Table 5. Features of 0.4-river-mile segments of the North Dakota portion of Lake Oahe on which habitat data were collected during 

2006.

[ID, identifier; RM, river mile; M, medium; H, high]

Segment ID Downstream RM Upstream RM Vegetation control? Site name Bird use stratum

OAH01 1232.0 1232.4 No Control H

OAH02 1234.0 1234.4 No Control H

OAH03 1240.8 1241.2 No Control M

OAH04 1242.8 1243.2 No Control M

OAH05 1243.6 1244.0 No Control M

OAH06 1273.2 1273.6 Yes Hogback Island M

OAH07 1274.4 1274.8 No Control M

OAH08 1277.2 1277.6 No Control M

OAH09 1277.6 1278.0 No Control M

OAH10 1278.8 1279.2 Yes One Horse M

OAH11 1279.2 1279.6 Yes One Horse M

OAH12 1281.6 1282.0 Yes Eckroth Corner M

OAH13 1282.0 1282.4 Yes Eckroth Corner M

OAH14 1282.4 1282.8 Yes Glencoe M

OAH15 1284.0 1284.4 Yes Barrels H

OAH16 1284.8 1285.2 Yes Fire Island H

OAH17 1285.2 1285.6 Yes Fire Island H

OAH18 1286.8 1287.2 Yes Silo H

OAH19 1287.2 1287.6 No Control H

OAH20 1287.6 1288.0 Yes Castillo’s H

OAH21 1290.0 1290.4 No Control H

OAH22 1291.2 1291.6 No Control H

OAH23 1292.4 1292.8 Yes Rifle Range H

OAH24 1293.6 1294.0 No Control H

20 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

portion of the lake, that spatial extent represents the population of segments from which the units to be measured were 

drawn. It also represents the spatial extent upon which the 

stratification was based (table 4). It is possible that 2 or more 

different stratifications might be necessary for a given reach if 

ESH projects of differing scope are planned or implemented. 

For example, if a second set of vegetation control projects was 

simultaneously planned for Lake Oahe, but the potential area 

of implementation was not restricted to North Dakota, then 

a second stratification would be necessary to obtain suitable 

control segments for those projects. Likewise, it is feasible that 

a project could be designed for the primary purpose of benefiting one species (terns or plovers). In this case, a stratification 

based on nesting history of just that species would be necessary.

After the stratification for Lake Oahe was derived, selection of sites for sampling followed a similar sequence to that 

on Gavins Point. It was anticipated that the field crew would 

be able to sample 24 segments. Thus 12 treatment segments 

were randomly selected, and 12 control segments were 

randomly selected with similar bird use histories to the treatment segments (table 5 and fig. 11).
Figure 11. Segments of the North Dakota portion of Lake Oahe on which habitat data were collected for emergent sandbar 

habitat monitoring during 2006. Background image is 2004 U.S. Department of Agriculture National Agricultural Imagery 

Program aerial photography. Green lines represent perimeters of sampling segments, and yellow dots represent river miles.

1232

1237

1238

1239

1240

1242

1244

1233

1234

1235

1236

OAH01

OAH02

OAH03

OAH05

OAH04

1232

1237

1238

1239

1240

1242

1244

1233

1234

1235

1236

OAH01

OAH02

OAH03

OAH05

OAH04

100°38′ 100°36′ 100°34′ 100°32′ 100°30′ 100°28′

46°04′

46°02′

46°00′

45°58′

45°56′

0 750 1,500 2,250 3,000 METERS

0 2,500 5,000 7,500 10,000 FEET

Field Data Collection—Habitat 21
Figure 11. Continued

OAH09

OAH08

OAH14

OAH10

OAH11

OAH12

OAH13

OAH07

OAH15

OAH06

OAH21

OAH16

OAH17

OAH24

OAH23

OAH18

OAH20 OAH19

OAH22

OAH09

OAH08

OAH14

OAH10

OAH11

OAH12

OAH13

OAH07

OAH15

OAH06

OAH21

OAH16

OAH17

OAH24

OAH23

OAH18

OAH20 OAH19

OAH22

1273

1274

1286

1287

1275

1276

1277

1278

1273

1274

1275

1276

1277

1278

1288

1289

1290

1280

1281

1282

1283

1284

1285

1291

1294

1293 1292

1286

1287

1288

1289

1290

1280

1281

1282

1283

1284

1285

1291

1294

1293 1292

100°40′ 100°38′ 100°36′ 100°34′ 100°32′ 100°30′ 100°28′

46°40′

46°38′

46°36′

46°34′

46°32′

46°30′

46°28′

0 5,000 10,000 15,000 20,000 FEET

0 1,500 3,000 4,500 6,000 METERS

22 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management
Table 6. Total number of least tern and piping plover nests found on the Fort Randall Reach of the 

Missouri River during 2001–05 by 4-river-mile section.

[RM, river mile; L, low; M, medium; H, high]

Segment number Downstream RM Tern and plover nests Use category

01 824 15 M

02 828 0 L

03 832 60 H

04 836 22 M

05 840 91 H

06 844 0 L

07 848 84 H

08 852 63 H

09 856 0 L

10 860 7 M

11 864 67 H

12 868 174 H

13 872 9 M

14 876 0 L

Table 7. Features of segments of the Fort Randall Reach of the Missouri River on which habitat data 

were collected in 2006.

[ID, identifier, RM, river mile]

Segment ID Downstream RM Upstream RM Created sandbar planned?

LCL01 826.4 826.8 Yes

LCL02 826.8 827.2 Yes

LCL03 836 836.4 No

LCL04 838.4 838.8 No

Field Data Collection—Habitat 23

Lewis and Clark Lake—Planned construction of a 

sandbar near RM 827 prompted initiation of habitat monitoring in 2006. This site is near the junction between the Fort 

Randall Reach and Lewis and Clark Lake. Although the rivermile designation of the site formally places it within the lake, 

existing conditions more nearly mimic those of the river reach. 

Additionally, the planned project is a midchannel sandbar, 

which is a characteristic habitat type for river reaches. These 

features pointed toward drawing control sites from the Fort 

Randall Reach for this construction project. Thus, sampling 

was based on a stratification of Fort Randall (table 6). Similar 

habitat conditions existed from RM 827 upstream to the 

confluence of the Missouri and Niobrara Rivers at RM 845. 

Thus, sampling of control segments was restricted to sites 

below the Niobrara River. Because the proposed construction site fell within a medium bird use stratum (SEG01; 

table 6), selection of control sites was restricted to the remaining segments in segment 1 and those in segment 4. Because 

the construction site potentially spanned two segments, two 

control segments were randomly selected (table 7 and fig. 12).

Kensler’s Bend—This reach of the river begins near 

RM 734 and extends to RM 754 near Ponca State Park. 

Kensler’s Bend is unique in that several ESH projects have 

been considered, but there are no existing sandbars and there 

is no history of bird use. Thus, the only basis available for 

stratification of the reach is the planned ESH projects. On the 
841 840

839

838

837

836

835

834

833

832 

831

830 829 828

LC

LC04

LC03

841 840

839

838

837

836

835

834

833

832 

831

830 829 828

LC

LC04

LC03

97°58′ 97°56′ 97°54′ 97°52′ 97°50′ 97°48′ 

42°52′42°50′42°48′42°46′ 0 5,000 10,000 15,000 20,000 FEET

0 1,500 3,000 4,500 6,000 METERS

Figure 12. Segments of the Fort Randall Reach of the Missouri River that were sampled in 2006 to characterize a proposed emergent sandbar hRM 827, at the headwaters of Lewis and Clark Lake. Background image is 2005 color infrared aerial photography. Green lines represent perimetesegments, and yellow dots represent river miles.
Segment ID Downstream RM Upstream RM ESH site 

number

744.0 744.4 4

744.4 744.8 4

744.8 745.2 3

745.2 745.6 3

745.6 746.0 3

KEN05 746.0 746.4 3

746.4 746.8 3

746.8 747.2 3

747.2 747.6 3

747.6 748.0 Control

KEN06 748.0 748.4 Control

KEN07 748.4 748.8 2

748.8 749.2 2

749.2 749.6 2

749.6 750.0 2

750.0 750.4 1&2

750.4 750.8 1&2

750.8 751.2 1

751.2 751.6 1

751.6 752.0 1

752.0 752.4 1

KEN08 752.4 752.8 1

KEN09 752.8 753.2 Control

753.2 753.6 Control

KEN10 753.6 754.0 Control

Table 8. Features of segments of the Kensler’s Bend Reach of the Missouri River on which habitat data were collected in 2006.

[ID, identifier; RM, river mile; ESH emergent sandbar habitat]

Segment ID Downstream RM Upstream RM ESH site 

number

734.0 734.4 Control

734.4 734.8 Control

734.8 735.2 Control

735.2 735.6 Control

735.6 736.0 Control

736.0 736.4 Control

736.4 736.8 Control

736.8 737.2 Control

737.2 737.6 Control

KEN01 737.6 738.0 Control

KEN02 738.0 738.4 Control

738.4 738.8 5

738.8 739.2 5

739.2 739.6 5

KEN03 739.6 740.0 5

740.0 740.4 5

740.4 740.8 5

740.8 741.2 5

741.2 741.6 5

741.6 742.0 5

742.0 742.4 5

742.4 742.8 4

742.8 743.2 4

743.2 743.6 4

KEN04 743.6 744.0 4

Field Data Collection—Habitat 25

basis of information provided by the USACE, a determination 

was made whether each 0.4–RM segment of the reach was 

likely to contain a portion of one or more future ESH projects, 

with the remaining segments serving as controls (table 8). 

The most likely scenario was for one of the five projects to be 

constructed, but there was no basis for selecting which was 

the most likely candidate. Thus, one segment was randomly 

selected from each of the five construction sites and five corresponding control segments (fig. 13).
KEN10

KEN09

KEN08

K

KEN03

KEN04

KEN0

KEN07

KEN05

KEN06

746

747

748

749

750

751

752

754

745 743 739

742 740

741

KEN10

KEN09

KEN08

K

KEN03

KEN04

KEN0

KEN07

KEN05

KEN06

746

747

748

749

750

751

752

754

745 743 739

742 740

741

96°42′ 96°40′ 96°38′ 96°36′ 96°34′ 96°32′ 96°3

42°36′42°34′42°32′42°30′

0 5,000 10,000 15,00

0 1,500 3,000 4,500 

Figure 13. Segments of the Kensler’s Bend Reach of the Missouri River on which habitat data were collected for emergent sandbar habitat monBackground image is 2005 color infrared aerial photography. Green lines represent perimeters of sampling segments, and yellow dots represent r
Table 9. Summary of stratification of Lake Sakakawea, including numbers of 2-kilometer segments, 

segments sampled in 2006, and projected number of segments in 2007–09.

[Stratified by historical piping plover nest densities (1998–2005). %, percent]

Strata Number Number (%) sampled in 2006 Number (%) projected sample for 2007–09

Low 510 4 (1) 9 (2)

Medium 105 4 (4) 6 (6)

High 70 9 (13) 15 (21)

Field Data Collection—Habitat 27

Reservoirs

One challenge in stratification of reservoir systems is to 

develop a stratification that is useful for long-term monitoring, throughout potentially extensive changes in water levels. 

During the time when plover productivity has been monitored 

expansively at Lake Sakakawea (1998–2006) there has been a 

large range of lake water levels. Similarly, throughout this time 

period locations of plover “colonies” have shifted slightly; 

10-kilometer (km) sections of Lake Sakakawea shoreline were 

chosen to classify into strata because 10 km appears to be a 

unit of shoreline long enough to encompass “shifts” of colony 

locations through the years 1998 to 2005. We delineated 

Lake Sakakawea by using 2004 color imagery from the U.S. 

Department of Agriculture’s National Agricultural Inventory 

Program (NAIP) and divided the shoreline into 10-km sections 

from a random start point. The boundaries of each section 

were lines perpendicular to the shoreline on either end because 

the length of the shoreline will vary with lake elevations. 

Islands were grouped into sections with respect to location and 

so that grouped islands had shorelines approximately equal 

to 10 km. Historical piping plover nesting data (1998–2005) 

was used to classify each 10-km section into strata: high 

(>40 nests), moderate (10–40 nests), or low (<10 nests). 

However, 10-km sections of shoreline are too large for effective habitat and productivity monitoring and are not a realistic 

size for a single proposed ESH project. Therefore, each 10-km 

section was divided into five 2-km segments; 2-km segments 

were the sampling unit for habitat and productivity monitoring (see companion study [NPWRC Task 5: Habitat selection, 

productivity, and estimation of available nesting habitat for 

piping plovers on Lake Sakakawea]). The goal of the companion study is to develop estimates of habitat use and availability 

for the entire reservoir, but the study design is not tailored 

to detection of outcomes from habitat management projects. 

However, data from the companion study can be used within 

the framework of a monitoring plan to provide site-specific 

data from habitat conditions at ESH project sites and randomly 

located control areas before and after treatments. For example, 

the companion study sampled seventeen 2-km segments 

in 2006 and will sample thirty 2-km segments in 2007–09 

(table 9), these segments will provide ample control areas for 

monitoring proposed ESH projects (those that do not happen 

to be in future ESH project areas).

In July 2006, the USACE planned to move forward on 

two of the seven ESH projects on Lake Sakakawea for fall 

2006 (Sonny’s and Randy’s Islands, figs. 14 and 15). Productivity on these sites was not monitored because notification 

came after the breeding season was largely over, but the 

habitat was intensively sampled on these sites in August 2006. 

Thus, the random segments used for the companion study 

can be considered control areas for the two new ESH sites on 

Lake Sakakawea, making a replicated BACI habitat monitoring design. According to our stratification of Lake Sakakawea, 

Sonny’s and Randy’s Islands were classified into the low 

density strata; thus, they would be compared to randomly 

selected controls from low density strata. Unfortunately, there 

was not more advanced planning so that productivity data 

could be incorporated into this design. This underscores the 

importance of planning and communication between agencies 

involved in the ESH Program. As new ESH projects are intitiated on Lake Sakakawea, they should be paired with randomly 

selected segments within the same strata and landform so 

they can be incorporated into this habitat monitoring plan and 

productivity monitoring.

Nest Sites

Monitoring habitat at nest sites in conjunction with 

monitoring of habitat for the ESH Program should provide an 

important link between habitat quality and breeding productivity. Furthermore, this should provide a better understanding of the habitat characteristics that attract least terns and 

piping plovers to a given area to nest and those that lead to 

the most successful outcomes (hatched nests and fledged 

young). Continued examination of tern and plover selection 

and success on given habitat allows for a monitoring program 

to use the principles of adaptive management as a check to 

ensure that the proper indices of habitat are being measured at 

the appropriate spatial scale or identify new important indices.

During the 2006 field season, USGS field crews 

conducted research studies on least tern productivity (Task 1; 

Gavins Point Reach), evaluation of productivity monitoring 

procedures (Task 2; Garrison Reach), and reservoir shoreline 

habitat (Task 5; Lake Sakakawea). These studies all involved 

searching for and monitoring fate of piping plover and least 

tern nests. Standard practice during these studies was to 

collect habitat data at nest sites at the time of nest discovery. 
102°24′05′′ 102°24′ 102°23′55′′ 102°23′50′′ 102°23′45′′ 

47°57′15′′47°57′10′′

47°57′05′′

0 200 400 

0 50 100 

Figure 14. Spatial extent of habitat data for a proposed emergent sandbar habitat project on “Sonny’s Island,” Lake Sakakawea in 2006, depictintransects composed of habitat evaluations every 5 meters. Background imagery is 2005 U.S. Department of Agriculture National Agricultural Imagphotography.
102°24′40′′ 102°24′30′′ 102°24′20′′

47°56′50′′47°56′40′′

0 200 400 600 FEET

0 50 100 150 METERS

Figure 15. Spatial extent of habitat data for a proposed emergent sandbar habitat project on “Randy’s Island,” Lake Sakakawea in 2006, depictincomposed of habitat evaluations every 5 meters. Background imagery is 2005 U.S. Department of Agriculture National Agricultural Imagery Progr
30 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

Protocols used by field crews conducting these studies were 

identical to those used by the habitat crew that was tasked with 

evaluating procedures for this monitoring plan. This resulted 

in habitat data being collected at 272 least tern nests on the 

Gavins Point Reach, 122 least tern and 245 piping plover nests 

on the Garrison Reach, and 59 piping plover nests on Lake 

Sakakawea. From this experience, it was concluded that rapid 

evaluation of habitat features at nest sites is feasible, and that 

the required adjustments to nest monitoring protocols would 

be fairly minor. Accordingly, the collection of habitat data at 

all piping plover and least tern nest sites should be standard 

practice.

Sampling Approach

After segments of river reaches or reservoir shorelines 

have been selected for sampling, the next step is to decide 

where to sample within the selected areas. As with selecting 

segments, taking advantage of prior knowledge regarding 

habitat conditions within the segment will maximize the 

efficiency of the monitoring effort. There are very different 

considerations which must be made between river, reservoir, 

and nest site sampling, which are illustrated in the following 

sections using examples from the 2006 field season. In all 

cases, the goal of sampling is to characterize the unit being 

sampled, whether it is a river segment, a reservoir shoreline 

segment, or a nest site.

Rivers

Each river segment represents a defined area of fixed 

extent. One way to characterize a segment would be to select 

random points and visit each to determine the habitat type 

present and to quantify features of that habitat (such as stem 

density of herbaceous vegetation). While this approach would 

provide unbiased estimates of habitat variables within the 

segment, it would not capitalize on prior knowledge of habitat 

distribution, and would not minimize variance in measures of 

habitat condition within habitat types.

Stratification was a useful tool for minimizing variance 

in estimates within reaches. Similarly, it also would be a 

useful tool for allocation of sampling effort within segments. 

However, the nature and extent of data upon which to base a 

stratification will vary among river reaches. The approach in 

2006 on the Gavins Point Reach illustrates a desirable set of 

circumstances, because a preliminary map of habitat types 

had been prepared on the basis of 2005 color infrared aerial 

photography (USGS Northern Prairie Wildlife Research 

Center, unpub. data, 2007). The most obvious means to 

initially stratify a river segment is to separate aquatic from 

terrestrial habitats, where aquatic habitats represent open water 

that is devoid of vegetation and exposed sand, and terrestrial 

habitats represent all other cover types. The 2005 habitat map 

provides information on the abundance and distribution of the 

various terrestrial cover types that were used to stratify terrestrial sampling.

Early in the 2006 field season, it was determined that 

a crew of three technicians could sample 1–2 segments per 

day if each segment consisted of 10 aquatic and 50 terrestrial sampling points (fig. 16). Because no further refinement of aquatic habitats is possible, the 10 aquatic points 

were randomly distributed among the available open water 

habitat, as indicated on the 2005 habitat map. The 50 terrestrial sampling points were distributed among the remaining 

habitat types (table 10) in proportion to the abundance within 

the segment. Thus, a habitat that consisted of 20 percent 

of the terrestrial acreage in a given segment would receive 

10 random points (50x0.2) for habitat sampling (fig. 17).

The decision to sample 50 terrestrial and 10 aquatic 

points per segment was based largely on logistical considerations. This level of effort allowed the technicians adequate 

time to sample 34 segments on the Gavins Point Reach in 

approximately 6 weeks, allowing sufficient time for them 

to accomplish other sampling goals on other reaches. Given 

adequate staffing and support (such as vehicles, boats), the 

number of sampling points per segment could be increased. 

Conversely, an extensive monitoring program with limited 

staff and logistical support could necessitate sampling fewer 

segments or fewer points per segment. In all cases, these 

decisions should be viewed in terms of the ability to achieve 

the monitoring program’s stated objectives, and should be 

discussed with a statistician to ensure that statistical implications of altering sample size are known.

Monitoring on Gavins Point during 2006 was conducted 

under favorable circumstances, because recent (2005) color 

infrared aerial photography of the reach was available as was 

a draft habitat classification based on that photography. These 

are two key elements that are present to varying degrees for 

other reaches, although several concurrent research efforts 

under the Missouri River Recovery Program will provide 

greater access to these tools in the future. In the interim, it 

is possible that future monitoring efforts will be initiated on 

reaches for which availability of spatial data is less than ideal. 

Experiences during 2006 on three such reaches illustrate 

potential approaches to these situations:

Lake Oahe and Lewis and Clark Lake—The portion of 

Lake Oahe under consideration for 2006 was not covered by 

2005 color infrared aerial photography, nor were other desirable forms of spatial data (such as Quickbird satellite imagery) 

readily available. The best available data was the NAIP 

photography from 2004. Because these data are less suitable 

for quantifying abundance of habitat cover types, there has 

not been (and probably will not be) any concerted effort to 

derive habitat maps from it. Similarly, Lewis and Clark Lake 

was covered by the 2005 color infrared aerial photography, 

but analysis of that photography to derive habitat polygons 

has not been conducted. Given a need to conduct habitat 

monitoring where a habitat map is not available, there are two 

potential options. One option would be to manually digitize 

habitat polygons from existing photography for the segments 
GAV20

GAV21

781

97°01′ 97°00′50′′ 97°00′40′′ 97°00′30′′ 97°00′20′′ 97°00′10′′ 97°00′ 96°59′50′′ 96°59′40′′ 

42°46′

42°45′50′′42°45′40′′42°45′30′′42°45′20′′42°45′10′′

0 500 1,000 1,

0 150 300 45

Figure 16. Example of distribution of terrestrial (red dots; n=50 per segment) and aquatic (blue dots; n=10 per segment) sampling points within two Gavins Point Reach of the Missouri River. Background image is 2005 color infrared aerial photography. Green lines represent perimeters of samplinyellow dots represent river miles.
Table 10. Habitat types used to allocate sampling effort for emergent sandbar habitat monitoring on the Gavins Point Reach of the 

Missouri River in 2006.

Habitat type Description

Dry sand Dry coarse- to fine-grained sand at the highest elevations on sandbars and 0–30 percent vegetation cover.

Wet sand Wet coarse- to fine-grained sand, silt and clay and 0–30 percent vegetation cover.

Sparsely vegetated Dry sand and wet substrate containing 30–50 percent vegetation cover.

Vegetated Areas with greater than 50 percent vegetation canopy coverage, including herbaceous vegetation woodlands, and 

shrublands.

Wetlands Isolated areas of wet substrate and herbaceous vegetation that are not physically connected to the river. 

Submerged sand Coarse- to fine-grained sand that is under water but visible.

Water Areas of water coverage where no substrate is visible.

32 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

on which sampling was planned. Although this approach could 

produce a general habitat map that would be useful for stratified sampling, it would be extremely labor intensive and is not 

a desirable solution.

A second alternative would be to digitize a single 

polygon corresponding to the segment boundaries, and 

randomly select points within the polygons (fig. 18). Although 

this approach is much more rapid than manually digitizing 

habitat polygons, it does not provide an ability to stratify the 

sample locations according to habitat type. However, data 

from a completely random set of points within the segment 

do provide an unbiased measure of abundance for each of the 

habitat types. An additional drawback to this method is that 

open water frequently dominates river segments, but features 

of this habitat type are not nearly as important to quantify as 

are features of terrestrial habitats. Therefore, the solution in 

2006 was to select twice as many habitat points per segment 

(n=100) as would have been selected using stratification. This 

provides no guarantee that an adequate number of sample 

points will occur in terrestrial habitats. In some situations, 

particularly when the amount of terrestrial habitat is small 

relative to segment size, it may be necessary to increase the 

number of random points.

When random points are used in this fashion, it may not 

always be necessary to collect the full suite of habitat data 

at each random point. Field crews in 2006 were instructed to 

visit random points sequentially, collecting aquatic habitat data 

at the first 10 random points that occurred in the open water 

habitat type, and to collect terrestrial habitat data at all random 

points that occurred in terrestrial habitats. Any random points 

beyond the initial 10 that fell in open water were classified 

according to habitat type, but quantitative habitat data were not 

collected. On the Lewis and Clark Lake sites sampled in this 

manner, technicians found that an average of 20 random points 

per segment fell in terrestrial habitats; whereas, on the Lake 

Oahe sites, an average of 50 random points per segment fell in 

terrestrial habitats. Therefore, additional random points would 

be necessary in future years to achieve 50 terrestrial points per 

segment on Lewis and Clark Lake.

Kensler’s Bend—A slightly different situation occurred 

on Kensler’s Bend in 2006. Current (2005) color infrared 

aerial photography was available for this reach, however, 

habitat mapping had not been completed. Ideally, flood-plain 

terrestrial habitats would have been sampled but because 

these projects had not been formally announced to the public, 

and landowners had not been contacted, these areas were not 

sampled. It was evident, however, that ESH was not currently 

available. Because it was known that all segments would 

consist exclusively of open water, the sampling was tailored 

toward characterization of aquatic habitats. As with the other 

reaches, the goal was to obtain aquatic data on 10 points per 

segment. Given habitat conditions on Kensler’s Bend, which 

contains many features of channeled rivers, there was greater 

uncertainty about finding suitable fish sampling conditions. 

Therefore, 20 random points per segment were generated 

(fig. 19) and technicians were instructed to sample the points 

in sequence, collecting a fish sample at the first point that 

presented suitable sampling conditions (see “Fish Sampling” 

section). As plans for Kensler’s Bend projects come closer 

to construction, it is essential to reallocate effort and sample 

areas to include terrestrial habitats.

Reservoirs

Habitat types are not distributed randomly on reservoir 

systems. They are largely dependent upon the history of 

water levels at that location; commonly specific habitat types 

occur in rings/bands at various elevations. Reservoir systems 

also have large variation in elevations, slopes, and aspects 

relative to riverine systems. Thus, sampling of habitat on Lake 

Sakakawea incorporated a systematic approach of transects 

perpendicular to the shoreline spaced every 100 meters (m), 

and each transect was sampled for habitat characteristics every 

5 m. Moreover, this systematic approach along with measuring elevation at sampling points allows for the distance to 

shoreline and elevation measurements to control for the variation in habitat characteristics dependent on past water levels 
Figure 17. Example of distribution of terrestrial (red dots; n=50 per segment) and aquatic (blue dots; n=10 per segment) sampling points within tw

the Gavins Point Reach of the Missouri River, showing the distribution of habitat types used to derive point locations. Green lines represent perimsegments, and yellow dots represent river miles.

GAV20

GAV21

781

97°01′ 97°00′50′′ 97°00′40′′ 97°00′30′′ 97°00′20′′ 97°00′10′′ 97°00′ 96°59′50′′ 96°59′40′′ 

42°46′

42°45′50′′42°45′40′′42°45′30′′42°45′20′′42°45′10′′

EXPLANATION

Habitat Class

Completely vegetated

Dry sand

Mixed vegetation

Mostly sandyOther barren LandsRiverSubmerged sandTree shadows

Unclassified

Wet sandWetland

0 500 1,000 1,500 

0 150 300 
9

8

7

6

5

4

3

2

1

0

99

98

97

96

95

94

93

92

91

90

89

88

87

86

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84

83

82

81

79

78

76

75

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73

72

71

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69

67

66

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61

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59

58

56

55

54

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52

51

50

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48

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4

39

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30

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26

25

24

23

22

21

20

19

17

15

13

11

10

9

8

7

6

5

4

3

2

1

0

99

98

97

96

95

94

93

92

91

90

89

88

87

86

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82

81

79

78

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61

60

59

58

56

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4

39

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20

19

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15

13

11

10

97°55′ 97°54′40′′ 97°54′20

42°48′

42°47′40′′

LC03

836

0 250 500 1,000 FEET

0 150

750

75 225 300 METERS

Figure 18. Example of 100 completely random points distributed in a 0.4-river-mile habitat sampling segment on Lewis and Clark Lake, for wwas known in advance to be present, but for which a habitat classification had not been derived. The background image is 2005 color infrarThe green lines represent sampling segment boundaries, and the yellow dot represents river miles.
9

7 8

6

5

3

2 1

20

18

17

16

14

1

12

10

9

7 8

6

5

3

2 1

20

18

17

16

14

1

12

10 KEN04

744

0 175 350 700 FEET

0 100

525

50 150 200 METERS

96°36′10′′ 96°36′ 96°35′50′′

42°30′20′′42°30′10′′

Figure 19. Example of 20 completely random points distributed in a 0.4-river-mile habitat sampling segment on Kensler’s Bend, for which teknown in advance to be completely lacking. The background image is 2005 color infrared aerial photography. The green lines represent samand the yellow dot represents river miles.
36 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

and ensures that habitat types distributed in rings/bands are 

sampled.

The first transect in each segment was located on the 

segment boundary and proceeded inland perpendicular to the 

shoreline (beach aspect was determined by visually averaging 

the aspect of the shoreline 25 m either side of the transectshoreline interface). Each segment boundary essentially serves 

as a random start point for the systematic sample because 

the shoreline of the lake was divided into sections and subsequently segments on the basis of a random start point. When 

entire islands were sampled, the start point for the first transect 

on the island was located randomly (established with random 

azimuth from the approximate center to the shoreline of the 

island). Subsequent transects were located 100 m of shoreline 

away from the previous, until the end of the 2-km segment. 

Within each transect, measurements continued (measuring 

habitat characteristics every 5 m) until (1) the terrain sloped 

downward toward the lake (in the case of a point or island; 

figs. 14 and 15), (2) five measurements were considered 

vegetated (>50 percent vegetation cover) and the next 50 m 

along the transect also was vegetated (see figs. 14 and 15), 

or (3) maximum pool elevation (565 m [1,854 feet] above 

NAVD 88) was reached.

Nest Sites

As part of the productivity monitoring on the Gavins 

Point, Garrison, and Lake Sakakawea Reaches, habitat was 

assessed around nests at several spatial and temporal scales 

(table 11). Each reach presented different objectives, opportunities, and limitations to measuring habitat on nesting sites 

(see companion studies [Tasks 7.1, 7.2, and 7.5]). Despite 

reach specific sampling, in all cases habitat characteristics 

were measured within a 1-m2

 quadrat centered about the nest 

(table 11). It is important that sampling is standardized at as 

many levels or spatial scales as possible; in this case all of 

the sampling within a 1-m2

 quadrat, at the nest and at various 

spatial or temporal scales, contained a set of variables that 

were ubiquitous to all reaches.

On the Gavins Point Reach nesting densities of least terns 

were relatively high, and it was important to understand nest 

site selection on a micro scale (such as why a bird selected 

a particular location over others within a small area; hereafter referred to as nest site selection). We measured habitat 

characteristics at the nest and at four plots 3 m away from the 

nest (on cardinal directions; fig. 20 and table 11). Examination of 1-m-square plots that were used by terns versus those 

adjacent but not used will be valuable in refining the nest site 

requirements of terns, and ultimately provide information 

about important nest site habitat characteristics that should be 

included in future habitat monitoring.

Intensive productivity work prevented extensive habitat 

monitoring across spatial scales on the Garrison Reach. 

However, in order to assess the differences at a given site 

within a season, habitat characteristics were measured at least 

tern and piping plover nest sites upon discovery, and nest sites 

were revisited in the postbreeding season (mid- to late August) 

on the Garrison Reach. This approach will be informative in 

determining the value of conducting post-breeding-season 

habitat measurements as an index to the habitat that was there 

when the birds initiated nesting.

The complexity of factors that appear to influence habitat 

availability and selection and concomitant breeding productivity of piping plovers on Lake Sakakawea demonstrate the need 

for an integrated program of monitoring and research. Habitat 

conditions on Lake Sakakawea are highly variable including large variations in substrate, slope, aspect, vegetation, 

and landform (island versus shoreline). This system appears 

much more complicated and there is limited literature available about habitat selection and use. Furthermore, the lake 

has become a very important breeding area for piping plovers; 

recent reports suggest Lake Sakakawea may have become the 

single most used area for plovers in the summer, throughout 

their breeding range (Haig and others, 2005; Threatened and 

Endangered Species Section, Omaha District, USACE, unpub. 

data, 2006). Despite the relatively high use of the lake for 

breeding, nesting densities generally are lower than those on 

the river, seemingly because the areas of habitat on the lake 

are expansive. Therefore, these conditions on Lake Sakakawea 

present both opportunity and necessity to understand what 

habitat characteristics drive habitat selection of piping plovers 

at multiple spatial scales and which characteristics lead to 

successful outcomes for plovers (such as eggs hatched and 

chicks fledged).

Habitat characteristics were measured on the randomly 

selected 2-km segments in order to examine habitat selection 

at a landscape scale on Lake Sakakawea (fig. 21); the following sections of this report examine the characteristics that 

influence the number of pairs of piping plovers using the 2-km 

segments of shoreline habitat. Moreover, the habitat characteristics of the beach (0.5-km linear shoreline distance on either 

side of the nest; fig. 21) that lead to breeding success (nest 

survival, number of fledged young, and age-adjusted chick 

body mass) are examined. In order to understand nest site and 

nest area (such as what site a bird chooses within the selected 

territory) selection, habitat characteristics were recorded at 

eight systematically selected locations within a radius of 10 

m surrounding the nest and at two randomly select reference 

sites within a 100- to 150-m radius of each nest to represent 

available nesting habitat (fig. 21). These eight points were 

positioned 3 and 10 m from the nest or random point on each 

cardinal direction (fig. 20). Examination of multiple plots of 

used and unused areas within the nest area also allows for 

interpretation of site heterogeneity/uniformity, which may be 

an important habitat characteristic for piping plovers.

Habitat Variables

The foregoing sections describe methods for delineating the spatial extent of sampling, determining an appropriate 

sampling unit, and establishing locations for points at which 
Table 11. Numbers of least tern and piping plover nests where habitat characteristics were 

evaluated for reaches of the Missouri River in summer 2006.

[N, number; n, evaluation of 1-meter-square plot centered on nest at time of discovery; 3m, valuations on four 

1-meter-square plots positioned 3 meters from nest on cardinal directions; r, return visit postbreeding season 

to evaluate habitat; 10m, evaluations on four 1-meter-square plots positioned 10 meters from nest on cardinal 

directions; 2rand, two random points where n, 3m, and 10m were evaluated; s, segment of shoreline 1 kilometer 

centered on nest evaluated with transects]

Reach River miles Species N Measurements

Gavins Point 754–795 Least Tern 272 n, 3m

Garrison 1,304–1,388

 Least Tern 122 n, r

 Piping Plover 245 n, r

Lake Sakakawea 1,390–1,508 Piping Plover 59 n, 3m, 10m, 2rand, s

Figure 20. Schematic of nest site, 3 meter, and 10 meter plot (1-meter-square plots) locations for 

evaluating nest-site and nest-area habitat characteristics.

3 m 

North

3 m 

West 

3 m 

East Nest

3 m 

South

3 m

10 m 

North

10 m

10 m 

West 

10 m 

East

10 m 

South

Field Data Collection—Habitat 37
102°14′ 102°13′

47°42′40′′47°42′20′′

0 500 1,000 1,500 2,000 FEET

0 150 300 450 600 METERS

Figure 21. Example of spatial extent of segment habitat data for Lake Sakakawea in 2006, depicting locations of nests (circles), random points (trnests by color), transects composed of habitat evaluations every 5 meters (yellow lines), and additional transects needed for nest-beach scale ancentered on nest; orange lines). Background imagery is 2004 U.S. Department of Agriculture-National Agricultural Imagery Program photography.
Field Data Collection—Habitat 39

habitat data will be collected. Each monitoring effort will 

present a unique set of circumstances that will dictate allocation of points within and among sampling units. Regardless of 

these circumstances, a universal set of habitat features can and 

should be measured at each point. Consistency in collection 

of habitat data throughout the upper Missouri River system 

will generate a data set that could ultimately lend itself to 

valuable meta-analyses. Consequently, a set of habitat features 

are outlined in this report that should be useful in describing 

relative value of sandbar and shoreline habitats as tern and 

plover nesting habitat, as well as indices of foraging habitat 

value. By adhering to this set of variables, both in long-term 

habitat monitoring efforts and in collecting nest-site habitat 

data in productivity studies, analyses of nesting habitat selection by terns and plovers will be enabled.

Measures of substrate composition, vegetation 

abundance, and vegetation structure were used in selecting the 

variables to be included for habitat monitoring because these 

are generally considered to be the principal features driving 

nest site selection by least terns and piping plovers. A suite 

of variables was used that could be readily measured within 

a 1-m2

 sampling frame without additional equipment that 

might impede mobility of technicians and limit their ability to 

sample extensively. Consequently, all of the selected variables 

can be measured either by count or by visual estimate within 

a 1-m2 quadrat. By eliminating measures that require two 

technicians (such as cover boards), technicians were able to 

work independently of each other. This expedited data collection, minimized potential disturbance to terns and plovers, and 

maximized the number of points that could be sampled per 

segment.

Visual estimation of percent cover for vegetation types 

(Nudds, 1977) can be problematic, and is typified by substantial variation in estimates between individuals. A widely 

applied method for minimizing this variation is to assign 

Daubenmire cover classes rather than point estimates of 

percent cover (Daubenmire, 1959). This approach was useful 

for estimating abundance of plants, and also of the various 

components of sandbar substrates. Identical break points 

between classes were used for each variable measured by 

using Daubenmire classes (table 12). Although this approach 

is generally applied only to vegetation, it also was useful for 

classifying substrate composition. Thus, for each substrate 

class (table 13), a Daubenmire cover class was assigned that 

reflected the relative abundance of that substrate type within 

the 1-m2

 quadrat. Using cover classes in this fashion eliminates the need for time-consuming quantitative measures of 

substrate composition, requires no additional equipment, and 

allows for some uncertainty in assignment of cover classes. 

Consequently, it is a useful tool for rapidly assessing substrate 

conditions at the sampling point.

Table 12. Daubenmire cover classes and 

corresponding ranges of percent cover.

[%, percent]

Class % Cover range Midpoint %

0 0 0

1 >0–5 2.5

2 6–15 10

3 16–30 23

4 31–45 38

5 46–70 58

6 71–100 85

Table 13. Size range of substrate classes used 

to describe habitats on the Missouri River.

[<, less than; >, greater than]

Substrate class Size range

Silt <0.125 mm

Sand 0.125–2 mm

Small pebble 2–10 mm

Gravel 10–64 mm

Cobble 64–256 mm

Boulder >25.6 cm
Table 14. Standard classification scheme for weather observations at sampling points.

[%, percent; F, Fahrenheit; mph, miles per hour; <, less than; >, greater than]

Cloud cover Temperature Wind speed Precipitation

Response (%) Response (F) Response (mph) Response Type

0 0–10 0 0–10 0 <4 0 None

1 11–20 1 11–20 1 5–7 1 Fog

2 21–30 2 21–30 2 8–12 2 Drizzle

3 31–40 3 31–40 3 13–18 3 Rain

4 41–50 4 41–50 4 19–24 4 Flurries

5 51–60 5 51–60 5 25–31 5 Snow 

6 61–70 6 61–70 6 32–38 6 Hail

7 71–80 7 71–80 7 >38

8 81–90 8 81–90

9 91–100 9 >90

40 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

Terrestrial Variables

Technicians collected habitat data using Trimble GeoXT 

handheld PC/GPS units, which enabled them to rapidly 

record the necessary data at each preselected sampling point. 

Upon arriving at a terrestrial sampling location, technicians 

recorded the unique identification information for the point 

(reach, segment number, point number, and observer initials), 

as well as a set of standard weather classification variables 

(table 14). The Trimble GPS units were programmed to record 

the Universal Transverse Mercator (UTM) coordinates of the 

sampled location while the technician collected habitat data.

At each terrestrial point, a suite of habitat variables 

reflecting vegetation abundance and cover and substrate 

composition was recorded (table 15). In sampling Lake 

Sakakawea, technicians also recorded elevation, slope, and 

aspect of the sampling location because these factors are 

believed to contribute strongly to nest site selection by piping 

plovers. Elevation reference points were established by driving 

an iron rod into the substrate and recording the UTM coordinates and elevation using RTK GPS. Laser levels were used 

to establish, by difference, the elevation of habitat sampling 

points within range of the fixed reference point. Similar 

measures may be employed on river sandbars, although 

methods for measuring sandbars have not been developed in 

this context.

By definition, the suite of variables for terrestrial points 

could only be recorded where terrestrial habitat conditions 

existed. Technicians were instructed to record the habitat 

conditions that occurred at the time of sampling. Therefore, a 

point expected to be terrestrial but exhibiting aquatic conditions would be recorded as belonging to an aquatic habitat 

class (such as Open Water). Likewise, points expected to be 

aquatic but exhibiting terrestrial conditions would be assigned 

to the appropriate terrestrial habitat class. Because technicians 

did not have any prior expectation of the specific terrestrial 

habitat class of each point, their assessment of habitat class 

provides some measure of accuracy for the image classification analysis being conducted by the USGS (NPWRC Task 3: 

Mapping, monitoring, and estimation of least tern and piping 

plover habitats on the upper Missouri River using Quickbird 

imagery).

Forage habitat likely influences selection of nesting 

habitat and success of brood rearing, and would be important 

to consider when determining why or why not an ESH site 

was successful. Collection of invertebrate data is important 

to understand the availability of forage habitat. The variation among samples from our invertebrate sampling experiment was examined (see “Invertebrate Sampling” section) 

for guidance on sample size for incorporating invertebrate 

sampling into a segment-based monitoring plan. It appears 

that five samples per segment would be adequate to index 

invertebrate availability for piping plovers. These samples 

should be allocated within wet sand terrestrial points because 

wet sand is apparently the preferred foraging habitat for piping 

plovers on the river system (Le Fer, 2006). It remains unclear 

what invertebrate foods piping plovers select and prefer, and 

identification of invertebrate samples can be labor intensive. 

Thus, it would be appropriate to simply count the number of 

invertebrates caught in three different size classes by length 

(small <3 millimeter (mm), medium 3 mm to 1 centimeter 

(cm), and large >1 cm). Given the time required to collect an 

invertebrate sample and the time constraints on sampling in 

areas occupied by terns and plovers, it would be advisable to 

initiate invertebrate sampling at the first five wet sand points 

encountered while sampling a segment, and sticky traps could 
Table 15. Terrestrial habitat variables recorded at emergent sandbar habitat monitoring points on the Missouri River and the 

relationship with ongoing productivity monitoring and remote sensing efforts.—Continued

[ESH, emergent sandbar habitat; PM, productivity monitoring; RS, remote sensing; ✓, rank of importance for each monitoring effort [variables with more 

checks are ranked more important]; >, greater than; m, meter]

Variable Description Type of data Possible values ESH PM RS

Total woody stem density Number of woody stems (all 

species)

Cardinal Any positive integer ✓✓ ✓ ✓✓✓

Cottonwood stem density Number of cottonwood stems Cardinal Any positive integer ✓ ✓ ✓✓

Willow stem density Number of willow stems Cardinal Any positive integer ✓ ✓ ✓✓

Woody vegetation cover class Daubenmire cover class, all woody 

species

Ordinal Table 12 ✓✓✓ ✓✓✓ ✓✓

Terrestrial herbaceous ccver 

class

Daubenmire cover class, all 

terrestrial herbaceous species

Ordinal Table 12 ✓✓✓ ✓✓✓ ✓✓

Wetland herbaceous cover 

class

Daubenmire cover class, all 

wetland herbaceous species

Ordinal Table 12 ✓✓✓ ✓✓✓ ✓

Habitat class Principal habitat class Nominal Table 10 ✓✓✓ ✓✓✓ ✓

Landform Principal landform Nominal Sandbar, sandbar complex, island, island 

complex, shoreline, 

peninsula

✓✓ ✓✓

Mean vegetation height Estimated average vegetation 

height

Ordinal 0, >0– 0.5 m, 0.5–1.0 m, 

>1.0 m

✓✓ ✓✓

Maximum vegetation height Height class of the tallest 

vegetation

Ordinal 0, >0–0.5 m, 0.5–1.0 m, 

>1.0 m

✓✓ ✓✓

Silt percent cover Visual estimate of percent cover Ordinal Table 12 ✓✓ ✓✓

Sand percent cover Visual estimate of percent cover Ordinal Table 12 ✓✓✓ ✓✓✓

Small pebble percent cover Visual estimate of percent cover Ordinal Table 12 ✓✓✓ ✓✓✓

Gravel percent cover Visual estimate of percent cover Ordinal Table 12 ✓✓ ✓✓

Cobble percent cover Visual estimate of percent cover Ordinal Table 12 ✓✓ ✓✓

Boulder percent cover Visual estimate of percent cover Ordinal Table 12 ✓ ✓

Leaf litter percent cover Visual estimate of percent cover Ordinal Table 12 ✓ ✓

Wrack percent cover Visual estimate of percent cover Ordinal Table 12 ✓✓✓ ✓✓✓

Large debris percent cover Visual estimate of percent cover Ordinal Table 12 ✓✓✓ ✓✓✓

Mammal tracks Presence/absence of mammal 

tracks

Ordinal Present, Absent

Cloud cover Visual estimate of cloud cover Ordinal Table 14

Temperature Estimated range of ambient temperatureOrdinal Table 14

Wind speed Estimated range of wind speed Ordinal Table 14

Precipitation Classification of precipitation Nominal Table 14

Elevation1 Elevation of the point above sea 

level

Interval Positive real numbers ✓✓✓ ✓✓✓ ✓✓✓

Slope2 Difference in elevation between 

the point and a point 1 meter 

down slope

Interval Positive real numbers ✓✓✓ ✓✓✓

Field Data Collection—Habitat 41
Table 15. Terrestrial habitat variables recorded at emergent sandbar habitat monitoring points on the Missouri River and the 

relationship with ongoing productivity monitoring and remote sensing efforts.—Continued

[ESH, emergent sandbar habitat; PM, productivity monitoring; RS, remote sensing; ✓, rank of importance for each monitoring effort [variables with more 

checks are ranked more important]; >, greater than; m, meter]

Variable Description Type of data Possible values ESH PM RS

Aspect2 Compass bearing of the transect Interval Real numbers 0–360 ✓✓ ✓✓

Distance to water2 Distance from the point to the 

nearest standing water—

measured in ArcGIS

Interval Positive real numbers

✓ ✓

Invertebrate index Count of invertebrates obtained by 

sticky stick sampling

Cardinal Positive integers ✓✓✓ ✓✓✓

1

Elevation data, although important for understanding vegetation dynamics, water-level management, and patterns of nest initiation and fate, is time intensive 

and requires an accuracy that may prohibit assessment during breeding season. Postseason assessment is likely preferred but potentially less accurate.

2

Variables of interest for ESH projects on reservoirs.

42 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

be collected upon completion of the terrestrial sampling points 

(>30 minutes; recording the time traps were in place).

Aquatic Variables

A similar approach was followed at aquatic sampling 

points. Four principal habitat variables were recorded upon 

arriving at each point (table 16). As with terrestrial points, 

the actual conditions observed at the time of sampling were 

recorded. However, there were few circumstances when points 

expected to be aquatic were actually terrestrial. In contrast, 

points expected to be terrestrial were frequently aquatic when 

visited. Technicians were instructed to record aquatic habitat 

variables for a minimum of 10 points per segment, and record 

just the habitat class (such as open water) for those in excess 

of 10 aquatic points. Technicians also were instructed to visit 

aquatic points in a predetermined (random) sequence, and to 

collect a fish sample at the first point where water depth was 

<1 m.

Fish Sampling

Small fish are prey for least terns; therefore, assessing 

aquatic habitat and the fish community during the breeding 

season is an appropriate component of ESH monitoring. The 

Missouri River fish population assessment effort (Drobish, 

2006) and Benthic Fish Asssessment (Berry and others, 2004) 

provide general background information on status, trends and 

habitat associations of Missouri River fishes, but provide no 

information on local availability of fish during May–June 

(the peak of breeding), or on fish responses to ESH projects. 

Sampling areas adjacent to ESH complements and does not 

replace existing efforts.

Small fish are best caught by using a small mesh net; 

care must be taken to select gear that is suitable for work 

in shallower water (<2 m) and that can be deployed and 

retrieved quickly with personnel and/or boats. A variety of 

gear has been evaluated for sampling fish in riverine environments (Kushlan, 1981; Miranda and Collazo, 1997; Herzog 

and others, 2005). Mini-Missouri benthic trawls (Herzog and 

Hrabik, Missouri Department of Conservation, oral commun., 

2006) are an active fishing gear that can be deployed from a 

boat or by hand, depending on site conditions. The trawl is 

fished upstream from the bow of a boat, trawling in reverse 

when water depth is >1 m. Hand trawling is conducted by two 

people dragging it downstream when water depth is <1 m. For 

either deployment method, trawls should be >100 m in length, 

which can be easily judged using a premeasured float line 

anchored adjacent to the sampling area. Each sample should 

be accompanied by standard measurements from the Missouri 

River Fish Protocols (Drobish, 2006) including start/end depth 

and GPS location, turbidity, flow, and substrate composition. Captured fish should be identified, measured for total 

length, and weighed. Fish should be kept wet until measured 

and returned to the river when measurements are complete; 

deceased individuals should be preserved in 70 percent ethyl 

alcohol after fixing in 30 percent formaldehyde, and labeled 

and retained as voucher specimens. Skill in identifying small, 

juvenal fish species is essential for rapid processing, and 

vouchers are valuable for training. Confirming identities of 

fish through comparison of voucher specimens and photographs with reference collections is essential.

One fish sample per river segment was collected in 

2006 to characterize fish available to foraging least terns. 

However, variation among multiple trawls from the same 

segment (Stucker and Sherfy, U.S. Geological Survey, unpub. 

data, 2006; NPWRC Task 7.1) indicated that a minimum 

of three 100 m trawls would provide an adequate index to 

fish abundance in the segment. To obtain a random sample 

from shallow water habitat, field technicians should visit the 

randomly located aquatic points in numeric order and collect 
Table 16. Aquatic habitat variables measured at emergent sandbar habitat monitoring points on the Missouri River and the relationship 

with ongoing productivity monitoring and remote sensing efforts.

[ESH, emergent sandbar habitat; PM, productivity monitoring; RS, remote sensing; ✓, rank of importance for each monitoring effort [variables with more 

checks are ranked more important]; ≤, less than or equal to; in, inches; cm, centimeter]

Variable Description Type of 

data ESH PM RS

Water depth Reading from incremented staff resting on river bed Interval ✓✓✓ ✓✓✓

Water temperature Temperature of surface water recorded on a digital thermometer Interval ✓ ✓

Velocity Surface-flow rate measured with digital flow meter Interval

Turbidity Turbidity of a surface water-sample measured with a digital turbidity 

meter

Interval ✓ ✓

Total fish count Count by species of all fish captured by hand trawl ≤ 7 cm (approximately 3 in)Cardinal ✓✓✓ ✓✓✓

Total fish biomass Biomass of all fish captured by hand trawl; estimated by length-mass 

relationships

Interval ✓ ✓

Fish species richness Number of fish species caught by hand trawl Cardinal ✓✓✓ ✓✓✓

Field Data Collection—Habitat 43

a fish sample by hand trawling at the first three points where 

water depth is <1 m.

Invertebrate Sampling

Terrestrial and aquatic invertebrates are the primary 

foods for piping plovers (Haig and Elliott-Smith, 2004), 

thus measuring amounts of invertebrate forage for plovers 

should be incorporated into a monitoring program. However, 

sampling plover forage is innately problematic because it is 

unclear exactly what invertebrates piping plovers consume 

and prefer on the Missouri River. Invertebrate forage for 

piping plovers has typically been sampled by taking soil cores 

(active sampling) or using sticky traps/sticky sticks (passive 

sampling; Loegering and Fraser, 1995; Elias and others, 2000). 

Using soil cores or other active sampling is usually preferable because there are few biases associated with activity or 

catchability, as long as the technique is appropriate for the 

invertebrates targeted. However, soil cores seem unlikely to 

catch larger-fast moving invertebrates, which piping plovers 

likely eat, given their typical foraging style of running and 

pecking (Haig and Elliott-Smith, 2004). Moreover, without an 

intense sampling regime, active techniques tend to yield many 

zero counts when invertebrate density is low, which causes 

problems with data analysis (zero inflation), and in effect 

poorly estimates invertebrate densities. Therefore, using sticky 

traps might provide a better index of invertebrate densities, if 

the inherent biases in catchability due to variations in invertebrate activity are understood.

An experiment was conducted in order to examine 

the diurnal variation in catchability of sticky sticks on two 

reaches of the Missouri River (Garrison and Lake Sakakawea). 

Representative locations were selected on the Garrison Reach 

and Lake Sakakawea that contained areas of each of the 

three habitat types: dry sand, wet sand, and vegetated. Four 

exclosures were randomly located within each habitat; three 

sets of sticky sticks were placed at 0800 hours (each set was 

composed of one horizontal and one vertical; Loegering and 

Fraser, 1995; Elias and others, 2000). Every three hours one 

set of sticks was pulled from each exclosure and replaced with 

a fresh set, leaving the other two sets of sticky sticks in place 

for 12 and 24 hours. The experiment was repeated the next 

day; however, on the second day the experiment was disrupted 

by severe thunderstorms on the Garrison Reach, so data were 

not useable. Invertebrates >3 mm long were counted on each 

stick; counts for each set of sticky sticks were as follows: 

count vertical + (2 x count horizontal). The data were analyzed 

separately for each reach, using generalized linear models 

with a Poisson distribution (PROC GLIMMIX; SAS Institute, 

2006).

Larger numbers of invertebrates (>3 mm) were caught 

in the morning time interval (0800–1100) than in any other 

time interval on Lake Sakakawea (fig. 22). There also was 

a weak positive relationship between cloud cover and catch 

rates. Whereas, on the Garrison Reach there was an interaction between time interval and habitat; in dry and wet sand 

habitats the largest number of invertebrates were caught in 

the first two time intervals (0800–1100 and 1100–1400), and 

there was no relationship with time interval in the vegetated 

habitats (fig. 23). Thus, there clearly is diurnal variation in 

catch of sticky sticks, and some evidence that weather also 

is important. Sticky sticks were examined to see if the sticks 

could be left out for 12 or 24 hours to account for this bias, but 
Figure 22. Least-square geometric mean counts per hour of invertebrates (>3 mm) 

captured on sticky-stick traps during four time intervals at Lake Sakakawea in summer 2006.

TIME INTERVAL

8-11:00 11-14:00 14-17:00 17-20:00

INVERTEBRATES (>3 MM) CAUGHT/HOUR

0.0

0.5

1.0

1.5

2.0

A

B

B

C

44 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

the 12- and 24-hour sets caught fewer invertebrates than that 

of the 12 hours of catch summed from four 3-hour intervals. 

This suggests that sticky sticks do not have a consistent catch 

rate for periods 12 hours or greater; they likely get fouled 

with sand or other debris. Therefore, sticky sticks may be a 

useful way to index invertebrate forage for plovers, but to 

minimize bias and unexplained variation, they should be used 

only between the hours of 0800 and 1100, and within a narrow 

window of weather parameters.

Timing of Habitat Sampling

Habitat sampling often is conducted in late summer, 

after the peak nesting season, because the intensity of productivity monitoring leaves little available time for other data 

collection. This approach carries an implicit assumption that 

data collected in late summer are representative of habitat 

conditions at nest initiation. There are two potentials for bias 

(systematically inaccurate or unrepresentative) in sampling 

nesting habitat after the breeding season: first, certain habitats 

may not be available for sampling after the breeding season; 

and second, habitat characteristics within a location of interest 

may change from the time birds initiate nests to completion 

of breeding efforts. To evaluate this assumption and potential 

biases, habitat characteristics were measured and recorded 

habitat class at 345 least tern and piping plover nest sites at the 

time of nest discovery and remeasured habitat at all 345 nest 

sites during mid-August on the Garrison Reach in 2006.

Habitat classes recorded at a nest upon discovery were 

compared to those recorded in August to evaluate the conditions under which habitats selected for nesting would be 

available for habitat sampling in August (table 17). Specifically, the probability was examined that a given nest site was 

available for habitat sampling (for example, not inundated) 

during August in relation to the initial habitat classification 

and the date of the initial habitat assessment (logistic regression, binomial distribution, logit link function, DF = 340; 

PROC GENMOD, SAS Institute, 2002). Date of initial habitat 

assessment was positively correlated with the probability that 

a site would be available for sampling in August (χ2

=21.85, 

P<0.001). The probability that a site would be available for 

sampling in August varied by initial habitat classification 

(χ2

=14.26, P=0.003) and was significantly below 1 for all 

initial habitat classes (fig. 24). Probability of availability was 

lowest for nest sites located in wet sand habitats (n=59; fig. 24 

and table 17).

Changes in vegetation and substrate characteristics were 

examined for nest sites that were available to be sampled (for 

example, not inundated) in August (n=219). The midpoint 

percentage associated with each Daubenmire classification was used (table 12) for all of the following calculations 

and analyses. The change in vegetation was calculated by 

subtracting the initial midpoint percentage from the postbreeding-season assessment. The change in each substrate characteristic was calculated with the following formula:
Figure 23. Least-square geometric mean counts per hour of 

invertebrates (>3 mm) captured on sticky-stick traps by habitat 

type (tiers) during four time intervals at the Garrison Reach of 

the Missouri River in summer 2006.

TIME INTERVAL

8-11:00 11-14:00 14-17:00 17-20:00

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

8-11:00 11-14:00 14-17:00 17-20:00

INVERTEBRATES (>3 MM) CAUGHT/HOUR

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

8-11:00 11-14:00 14-17:00 17-20:00

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

VEGETATED 

WET SAND

DRY SAND

A

A A

A

AB

A

BC

C

AB

A

BC

C

Field Data Collection—Habitat 45

%∆ = − ,

∑ ∑

Substrate

Substrates

Substrates

Substrates

PBS

PBS

I

I

where %Δ is percent change, PBS is post-breeding-season 

classification, I is initial classification, and Σ Substrates is the 

sum of midpoint percentages from silt, sand, small pebble, 

gravel, cobble, and boulder. Separate t-tests were used for each 

characteristic of interest (PROC GLM, SAS Institute, 2002) 

to examine if the mean change was different from zero. Mean 

herbaceous vegetation cover (estimate=3.591, SE=0.713, 

T=5.04, P<0.001), woody stem counts (estimate=0.717, 

SE=0.33, T=2.17, P=0.031), and percent cover of silt (estimate=6.935, SE=2.305, T=3.01, P=0.003) increased between 

breeding season and August measurements. Percent cover of 

woody vegetation, sand, small pebble, gravel, and cobble were 

similar between measurements (all Ps>0.05)

Implications for timing of nest-site habitat evaluation—Data indicate that on the Garrison reach during 2006 

there was a 0.29–0.57 probability (depending on the initial 

habitat classification; fig. 24) that a nest found during the 

breeding season would not be available to be sampled (for 

example, inundated) in mid-August. If nest-site habitat were to 

be sampled postbreeding season, data suggest that the sample 

of available nests would be 29–57 percent smaller, and would 

be biased by initiation date and habitat class. However, these 

data are from only one year and might represent extreme 

events (such as water dynamics/management) occurring in 

2006; nonetheless, these data demonstrate the possibility of 

a substantial bias in availability of habitat, at a minimum. 

Moreover, there were subtle changes in herbaceous vegetation 

cover and substrate that would further bias estimates of nestsite habitat if measurements in August were used. Therefore, if 

an unbiased measurement of nest-site habitat is needed, clearly 

the habitat should be assessed upon nest discovery to obtain 

accurate estimates of nesting habitat used by least terns and 

piping plovers.

Implications for timing of other habitat measurements—If comparisons between treatment and control 

segments (before and after the treatments are administered) 

are the only analysis of interest, then measuring habitat during 

the postbreeding season may provide an adequate index for 

comparison among these groups. However, depending on 

river flows and water levels, key foraging habitats available to 

plovers during the breeding season might be unavailable for 

sampling during the postbreeding season (fig. 24). Moreover, 

if estimates of the amount of suitable habitat created/managed 

are required, then measurements during the breeding season 

would be desirable because measurements taken during the 

postbreeding season would be biased low, underestimating 

amount of habitat present at start of breeding.

Understanding why areas are used or not used is an 

important step in the adaptive management process to ensure 

that the ESH Program is providing the best habitat for the 

money spent. Measuring habitat during the breeding season 

would provide better information about availability of habitat 

in relation to the habitat used (nest-site measurements), and 

thus would provide better information on why an area was 

used or not used.
Table 17. Habitat classes recorded at nest upon discovery (initial) and during August revisits for least tern 

and piping plover nests (n=345) on the Garrison Reach of the Missouri River in 2006.

August habitat class

Initial habitat class

Wet sand Dry sand Sparse vegetation Vegetated

Open water 32 81 8 5

Wet sand 17 37 5 0

Dry sand 5 99 5 0

Sparse vegetation 3 31 8 2

Vegetated 2 2 0 3

Figure 24. Least-square mean probability (95-percent confidence interval) that a nest site 

was available for sampling (not inundated) in August for each initial habitat classification 

after controlling for date of initial habitat assessment.

INITIAL LAND-COVER CLASS

WET SAND DRY SAND SPARSE

VEGETATION

VEGETATED

PROBABILITY OF AVAILABILITY FOR SAMPLING

0

0.2

0.4

0.6

0.8

1.0

n=59 n=250 n=26 n=10

46 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

Sample Sizes for Terrestrial Points

The rationale for “on the ground” terrestrial habitat 

sampling is to measure the quality of habitat available for least 

terns and piping plovers on emergent sandbars and shorelines along the upper-Missouri River System. To assess the 

variability of various habitat characteristics of interest, 50 data 

collection points were randomly allocated proportionally 

within the following habitats on the Gavins Point Reach (see 

“Sampling Approach, Rivers” section) in 2006: wet sand, dry 

sand, sparsely vegetated, vegetated, wetland, and submerged 

sand (table 10). However, 98.1 percent of tern and plover nests 

(n=673) USGS technicians found, throughout the upperMissouri River System in 2006, were located on wet sand, dry 

sand, or sparsely vegetated habitat classes. Therefore, monitoring of habitat quality should be focused on wet sand, dry sand, 

and sparsely vegetated habitat classes.

Preliminary analyses were done on habitat data from 

the Gavins Point Reach in 2006 to assess variability and the 

ability to detect potential differences in key habitat characteristics among control segments and segments containing created 

sandbars or vegetation removal. These analyses were limited 
Field Data Collection—Productivity 47

monitoring adult populations and reproductive success of least 

terns and piping plovers.

Productivity monitoring is conducted by the USACE on 

all parts of the Missouri River system that would be subject to 

ESH monitoring under this plan. Additional productivity data 

may also be available from concurrent NPWRC studies on 

least tern ecology (Gavins Point and Lewis and Clark Lake).

Spatial Data

Acquisition and storage of spatial data, and documenting metadata, from satellite or digital orthophotography, are 

essential for visually documenting habitat condition. Data 

resulting from interpretation of spatial data are integral in 

comprehensive analyses of management actions on habitat and 

bird productivity. Spatial data should be collected at a time 

coinciding with nesting and brood rearing (late May through 

mid-July). Habitat data collected closest to the acquisition date 

can be used as ground truthing points for aiding interpretation of spatial data, but may need to be supplemented with 

more intensive local surveys. NPWRC is currently conducting 

research (NPWRC Task 7.3: Inventory, mapping, estimation, 

and monitoring of least tern and piping plover habitats on the 

upper Missouri River using Quickbird imagery) with a primary 

goal of developing a cost-effective method to inventory, map, 

estimate, and monitor least tern and piping plover habitats of 

the upper Missouri River using remotely sensed imagery.

Other Species

The sampling scheme outlined in this plan readily 

accommodates additional components with little additional 

expense. For example, surveys may be easily incorporated to 

document occurrence and distribution of other species to meet 

project related or regulatory objectives. Species that should 

be considered for inclusion in the monitoring effort include 

documenting presence of State and federally listed species, 

predator presence, colonial or secretive waterbird occurrence, 

and recreational disturbance.

Creation and modification of habitat may have the 

unintended consequence of providing opportunities for 

establishment or proliferation of invasive or unintended 

species. Documentation of these and similar species are 

important for evaluation of current management and initiation of new management efforts for control. Species listed by 

State weed boards and http://www.invasivespeciesinfo.gov/

can be monitored; of concern for the Missouri River area are 

expansion in range and extent of purple loosestrife (Lythrum 

salicaria) and saltcedar (Tamarix spp), and extensive growth 

of yellow sweet clover on sandbars. Additionally, spread of 

epizootics within high density colonial situations may necessito points that fell in the a priori habitat classes of wet sand, 

dry sand, and sparsely vegetated; including 52.5 percent of 

terrestrial points sampled (mean of 27 samples per segment). 

A control segment was randomly paired with created segments 

within bird use and river width strata, so that there were five 

segments containing created habitat each paired with a control 

with a similar bird use history and river width. Analysis of 

variances (ANOVAs) were done including all segments (n=34) 

each assigned to a treatment level (such as control, created, 

vegetation modification; table 3). The result of the paired 

and the complete analyses were that means of key habitat 

variables of interest (such as percent cover of small pebbles, 

herbaceous vegetation, and woody vegetation) were estimable 

with standard errors small enough to detect appreciable differences among treatment classes. However, these data consist 

only in a control-impact design; if pretreatment data were 

available, the ability to detect differences would be expected 

to increase because preexisting site-to-site variability would 

be controlled in the model. Therefore, the sampling design, 

including 30 terrestrial points, allocated proportionally among 

wet sand, dry sand, and sparsely vegetated habitats would be 

an adequate sample to ensure that appreciable differences in 

habitat characteristics are statistically detectable. However, it 

is equally important that an adequate number of control and 

treatment segments be sampled to provide precise estimates of 

means.

Field Data Collection—Productivity

Monitoring productivity enables a direct and accurate 

evaluation of biological response by birds (number of nests, 

nest fate, nest success, chicks fledged) to management actions. 

Monitoring least terns and piping plovers on the Missouri 

River to assess productivity is essential to accurately evaluate 

management actions and meet reporting requirements under 

the 2003 amended Biological Opinion for Missouri River 

Operations (U.S. Fish and Wildlife Service [USFWS] 2003). 

In light of the Federal Endangered Species Act listing for 

both species and data requirements in the Biological Opinion, 

particular attention to accurate and valid productivity monitoring procedures is warranted. In order to provide the highest 

value data, productivity monitoring must follow standardized 

procedures that eliminate subjectivity, ensure frequent nest 

visits, and collect data that are geospatially and temporally 

accurate. Geospatial and temporal linkages between habitat 

and productivity data are critical for an accurate understanding 

of the relationship between habitat and productivity. Northern Prairie Wildlife Research Center is conducting research 

(NPWRC Task 7.2) to review and evaluate ongoing monitoring efforts by the USACE on the Missouri River. Planned 

products from this research include a field manual that 

describes scientifically valid procedures for collecting data for 
Table 18. Distribution of habitat sampling achieved by a three-technician crew on the Missouri River during the 2006 field season.

Reach Number of segments Terrestrial points Aquatic points 

(with habitat data)

Aquatic points 

(classified only)

Total points 

sampled

Gavins Point 34 1,444 337 313 2,094

Kensler’s Bend 10 0 100 0 100

Lewis and Clark Lake 4 78 40 322 440

Lake Oahe 18 900 180 896 1,976

Garrison 0 364 0 0 364

Lake Sakakawea1 19 6,044 0 0 6,044

Total 85 8,830 657 1,531 11,018

1

Four crews of two technicians sampled Lake Sakakawea.

48 Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management

tate consideration for monitoring health of piping plovers and 

least terns. All carcasses should be collected after consultation 

with the USFWS and submitted to the USGS Wildlife Health 

Lab in Madison, Wis., for analysis. Susceptibility of these 

species to various diseases is unknown, but disease concerns 

include: aspergillosis, avian botulism, avian influenza, 

Newcastle disease, avipoxviruses, and West Nile virus.

Data Analysis

Data collected in accordance with this plan will be wellsuited for analysis using mixed model ANOVA to evaluate 

variation in monitoring indicators relative to ESH management. When feasible (for example, advance knowledge of 

project sites is adequate to allow preproject monitoring), these 

models should follow a BACI design. However, analyses 

to detect these responses should be limited to the subsegments that were selected for that purpose. For example, on 

the Gavins Point Reach in 2006, 24 segments were selected 

to evaluate vegetation control projects and 10 segments were 

selected to evaluate sandbar creation projects. The ANOVA 

for vegetation control would be limited to those 24 segments, 

and a separate ANOVA for sandbar creation would be generated using the other 10 segments. In these situations, the 

BACI design would not apply because no pretreatment data 

are available. However, for other sites (such as Lake Oahe, 

Lake Sakakawea, Lewis and Clark Lake, Kensler’s Bend), 

this design would be feasible. For all ANOVAs, the segments 

should be considered the primary experimental unit.

Following the initial season of data collection on any 

monitoring effort, a power analysis should be conducted to 

evaluate the adequacy of sampling efforts to detect meaningful effect sizes between treated and untreated segments. This 

analysis should be used to adjust number of points sampled 

per segment and/or number of segments sampled per study 

area. However, this approach is only likely to be useful for 

large-scale applications of vegetation control (such as on the 

Gavins Point Reach in 2006), in which there is an abundance 

of treated segments from which to make random selections. 

In other cases, such as construction of a single sandbar on a 

river reach, ability to increase the number of segments will be 

constrained by spatial extent of the treatments.

Implementation

The preceding sections outline the approach to habitat 

monitoring that was conceived and evaluated during the 2006 

field season. Although distribution of effort might vary from 

year to year, the level of field effort expended in 2006 should 

provide guidance on expected quantity of data that can be 

obtained in one field season. The field crew consisted of three 

people (one leader and two technicians) whose exclusive duty 

was to collect habitat data in accordance with this plan. The 

crew was equipped with a four-wheel drive truck, an 18-foot 

tunnel hull jon boat with a 75-horsepower outboard motor, and 

three Trimble GeoXT units. They conducted their sampling 

beginning on June 2, 2006, on the Gavins Point Reach, and 

ending on August 8, 2006, on Lake Sakakawea. A total of 66 

riverine and 19 lake segments were visited, providing habitat 

data at over 11,000 point locations (table 18). Because one 

field crew sampled all of these reaches, a portion of the field 

season was devoted to travel, setting up new bases of operations, and becoming familiar with new surroundings. A greater 

number of points could be sampled if multiple field crews 

were established, eliminating the need to mobilize a single 

field crew to all locations.
Selected References 49

Permits

State and Federal permits are required for work in 

habitats of, or with, listed species. It is essential that monitoring work comply with existing permits, or that permits are 

sought/amended to meet regulatory requirements. Permits 

usually include conditions that are designed to protect the 

species of concern. Typical conditions for work near nesting 

least terns and piping plovers include limits on time spent by 

investigators in nesting colonies. Strict compliance with time 

limits in colonies is essential, even in the absence of specific 

permit conditions providing limits. It is imperative that the 

habitat monitoring effort be coordinated with other concurrent 

research or monitoring projects in the same area, so that potential cumulative effects of multiple investigator visits to nesting 

colonies are avoided.

Habitat monitoring does not require direct interactions 

with nesting birds that are a necessary component of productivity monitoring. Nevertheless, efforts to conduct habitat 

assessment during the nesting season require a cautious 

approach with particular attention to avoiding direct and 

indirect impacts to birds. It is essential that field personnel 

monitoring habitat be trained to recognize nests and behavioral 

cues of birds that suggest nests are nearby, and that they cease 

sampling efforts within appropriate time limits. Given this 

training and guidance, in 2006 it was possible for the NPWRC 

habitat crew to assess >10,000 points during the nesting 

season.

Selected References

Bailey, D., and Herzog, F., 2004, Landscape monitoring, in

Wiersma, G.B., ed., Environmental monitoring: Boca Raton, 

Florida, CRC Press, p. 307–355.

Barbour, M.T., Gerritsen, J., Snyder, B.D., and Stribling, J.B., 

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Sherfy and others—Missouri River Emergent Sandbar Habitat Monitoring Plan—A Conceptual Framework for Adaptive Management—OFR 2008–1223

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