U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

NOAA Technical Memorandum NMFS-NWFSC-58

Ecosystem Recovery Planning

for Listed Salmon:

An Integrated Assessment Approach

for Salmon Habitat

December 2003
NOAA Technical Memorandum

NMFS Series

The Northwest Fisheries Science Center of the National

Marine Fisheries Service, NOAA, uses the NOAA

Technical Memorandum NMFS series to issue informal scientific and technical publications when complete formal review and editorial processing are not

appropriate or feasible due to time constraints. Documents published in this series may be referenced in the

scientific and technical literature.

The NMFS-NWFSC Technical Memorandum series of

the Northwest Fisheries Science Center continues the

NMFS-F/NWC series established in 1970 by the

Northwest & Alaska Fisheries Science Center, which

has since been split into the Northwest Fisheries

Science Center and the Alaska Fisheries Science

Center. The NMFS-AFSC Technical Memorandum

series is now being used by the Alaska Fisheries

Science Center.

Reference throughout this document to trade names

does not imply endorsement by the National Marine

Fisheries Service, NOAA.

This document should be cited as follows:

Beechie, T.J., E.A. Steel, P. Roni, and E. Quimby

(editors). 2003. Ecosystem recovery planning for

listed salmon: an integrated assessment approach for

salmon habitat. U.S. Dept. Commer., NOAA Tech.

Memo. NMFS-NWFSC-58, 183 p.
U.S. DEPARTMENT OF COMMERCE

Donald L. Evans, Secretary

National Oceanic and Atmospheric Administration

Vice Admiral Conrad C. Lautenbacher, Jr. USN (Ret), Administrator

National Marine Fisheries Service

William T. Hogarth, Assistant Administrator for Fisheries

NOAA Technical Memorandum NMFS-NWFSC-58

Ecosystem Recovery Planning

for Listed Salmon:

An Integrated Assessment Approach

for Salmon Habitat

Edited by

Timothy J. Beechie, E. Ashley Steel, Philip Roni, and Ed Quimby

From contributions by the editors and

Eric M. Beamer1

, Cara A. Campbell, Alison C. Cullen2,

Lisa Holsinger, John R. Klochak1

, Martin C. Liermann,

Paul McElhany, Sarah A. Morley, Ben S. Perkowski3

,

George R. Pess, Beth L. Sanderson, Nathaniel L. Scholtz,

and Mindi B. Sheer

Northwest Fisheries Science Center

2725 Montlake Boulevard East

Seattle, Washington 98112

Skagit System Cooperative

PO Box 368

LaConner, Washington 98257

University of Washington

Evans School of Public Affairs

Seattle, Washington 98195

Skagit Watershed Council

407 Main Street, Suite 205

Mount Vernon, Washington 98273

December 2003

1

2

3
Most NOAA Technical Memorandums NMFS-NWFSC

are available online at the Northwest Fisheries Science

Center web site (http://www.nwfsc.noaa.gov)

Copies are also available from:

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone orders (1-800-553-6847)

e-mail orders (orders@ntis.fedworld.gov)

ii
TABLE OF CONTENTS 

List of Figures...............................................................................................................................................v

List of Tables ..............................................................................................................................................vii

Executive Summary .....................................................................................................................................ix

Acknowledgments......................................................................................................................................xiii

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

Purpose and Scope.................................................................................................................................. 2

Overview ................................................................................................................................................ 3

Notes on Terminology............................................................................................................................ 3

An Assessment Approach for Habitat Recovery Planning (By Timothy J. Beechie)...................................5

Restoring Ecosystems to Support Recovery of Listed Salmon .............................................................. 5

Scientific Basis for an Ecosystem Approach ................................................................................... 7

Practical Considerations................................................................................................................... 8

Key Assessments for Habitat Recovery Planning .................................................................................. 9

Sequencing the Assessments................................................................................................................ 14

Implementing Habitat Actions and Updating the Recovery Plan......................................................... 16

Analyses for Phase I Recovery Planning: Setting Recovery Goals (By Beth L. Sanderson, E. Ashley 

Steel, Timothy J. Beechie, George R. Pess, Mindi B. Sheer, and Cara A. Campbell) ...............................18

ESU-Level Analyses for Abundance and Survival Goals.................................................................... 18

Correlation Analyses...................................................................................................................... 20

Current and Historical Potential Habitat........................................................................................ 24

Watershed-Level Analyses for Abundance, Survival, and Population Growth Rate Goals................. 28

Limiting Factors Model and Life Cycle Model ............................................................................. 30

Ecosystem Diagnosis and Treatment............................................................................................. 37

Analyses for Spatial Structure and Diversity Goals............................................................................. 37

ESU-Level Analyses...................................................................................................................... 38

Watershed-Level Analyses ............................................................................................................ 39

Conclusions.......................................................................................................................................... 39

Analyses for Phase II Recovery Planning: Identifying Ecosystem Restoration Actions (By George R. 

Pess, Timothy J. Beechie, Sarah A. Morley, and Eric M. Beamer)............................................................40

Watershed-Level Analyses................................................................................................................... 42

Assessing Degradation of Ecosystem Processes and Functions.................................................... 42

Assessing Biological Integrity ....................................................................................................... 53

Conclusions.......................................................................................................................................... 58

Prioritizing Potential Restoration Actions Within Watersheds (By Philip Roni, Timothy J. Beechie, 

and George R. Pess)....................................................................................................................................60

What Do We Know about Restoration? ............................................................................................... 60

Strategies for Prioritizing Actions........................................................................................................ 62

An Interim Approach ..................................................................................................................... 64

Single Species Approaches............................................................................................................ 66

Alternative Prioritization Schemes................................................................................................ 67

Need for Monitoring and Management Experiments........................................................................... 69

iii
Managing Uncertainty in Habitat Recovery Planning (By E. Ashley Steel, Martin C. Liermann, 

Paul McElhany, Nathaniel L. Scholz, and Alison C. Cullen).....................................................................74

Types of Uncertainty ............................................................................................................................ 75

Prediction Uncertainty ................................................................................................................... 77

Parameter Uncertainty ................................................................................................................... 79

Model Uncertainty ......................................................................................................................... 79

Measurement Uncertainty.............................................................................................................. 80

Natural Stochastic Variation .......................................................................................................... 81

Example 1: Creating a Prioritized List of Restoration Projects............................................................ 82

Example 2: Water Quality and Habitat Recovery Planning................................................................. 84

Using Decision Rules When Empirical Data Are Inadequate.............................................................. 86

Glossary ......................................................................................................................................................91

Citations......................................................................................................................................................97

Appendix A: Issues of Scale in Habitat Recovery Planning (By Cara A. Campbell)...............................127

Hierarchical Nature of Stream Systems ............................................................................................. 127

Transferability Across Scales............................................................................................................. 128

Scale in Recovery Planning................................................................................................................ 130

Population Abundance................................................................................................................. 130

Population Growth Rate............................................................................................................... 131

Spatial Structure........................................................................................................................... 132

Diversity....................................................................................................................................... 134

Summary ............................................................................................................................................ 135

Appendix B: Estimating Chinook Salmon Spawner Capacity of the Stillaguamish River (By Lisa 

Holsinger and George R. Pess) .................................................................................................................137

Methods.............................................................................................................................................. 137

Watershed-Scale Chinook Salmon Spawning Distribution ......................................................... 138

Reach-Scale Chinook Salmon Spawning Habitat........................................................................ 138

Chinook Salmon Capacity in Small Streams............................................................................... 141

Historical Chinook Salmon Capacity in Large Streams .............................................................. 143

Current Chinook Salmon Capacity in Large Streams.................................................................. 148

Sensitivity Analyses..................................................................................................................... 151

Results................................................................................................................................................ 151

Conclusions........................................................................................................................................ 155

Appendix C: Restoration of Habitat-Forming Processes—An Applied Restoration Strategy for the 

Skagit River (By Eric M. Beamer, Timothy J. Beechie, Ben S. Perkowski, and John R. Klochak).........157

Overview of the Restoration Strategy ................................................................................................ 157

Study Area.......................................................................................................................................... 159

Methods.............................................................................................................................................. 161

Results and Discussion....................................................................................................................... 168

Hydrology—Changes in Peak Flow ............................................................................................ 168

Sediment Supply .......................................................................................................................... 171

Riparian Function ........................................................................................................................ 171

Isolated Habitats and Disrupted Channel-Floodplain Interactions .............................................. 174

Generalized Habitat Types........................................................................................................... 178

Identification of Restoration Projects .......................................................................................... 178

Current Limitations and Future Work.......................................................................................... 180

Conclusions........................................................................................................................................ 181

iv
LIST OF FIGURES 

Figure 1. Schematic diagram of linkages among landscape processes, land uses, habitat changes, and 

biological responses.........................................................................................................................6

Figure 2. Locations of major rivers and cities of the Pacific Northwest (upper panel) and Level II and 

Level III ecoregions (lower panel).................................................................................................12

Figure 3. Generalized sequence for Phase I and Phase II assessments, integration of Phase I and Phase II 

information into the recovery plan, and plan updates based on continued inventories and 

monitoring......................................................................................................................................15

Figure 4. Illustration of the kinds of relationships between habitat, landscape, and fish that broad-scale 

analyses can examine.....................................................................................................................23

Figure 5. Stream accessibility for all streams considered in the WLC analysis. .......................................26

Figure 6. Example of the identification of potential spawning habitat that is isolated above dams and 

classification of prime habitat for Chinook rearing or spawning in the Lewis River based on 

gradient thresholds.........................................................................................................................27

Figure 7. Generalized life cycle model illustrating linkages among life stage transitions, habitat 

conditions affecting those transitions, and human actions altering habitat conditions or survival.29

Figure 8. General juvenile salmonid use at the habitat scale. ....................................................................31

Figure 9. Sensitivity of lambda (λ) to changes in model parameters when population dynamics of Puget 

Sound ocean-type Chinook salmon are density independent.........................................................35

Figure 10. Predicted Puget Sound ocean-type Chinook salmon escapement as a function of time for 

density-independent survival (DI), density-dependent survival (DDS), and density-dependent 

movement (DDM) scenarios..........................................................................................................36

Figure 11. Schematic diagram of relationships between controls on watershed processes, effects on 

habitat conditions, and salmon survival and fitness.......................................................................41

Figure 12. A is map of areas in the Skagit River basin where sediment supply has likely increased due to 

land use, based on extrapolation of data from sediment budgets. B is landslide hazard map for a 

portion of the upper Cascade River basin. C is hazard map of U.S. Forest Service roads classified 

by risk of failure.............................................................................................................................48

Figure 13. Illustration of change in riparian function with distance from channel and the Skagit 

Watershed Council’s classification of impaired, moderately impaired, and functioning riparian 

forests.............................................................................................................................................51

Figure 14. Simplified model of landscape controls and watershed processes, and how land use and 

restoration or enhancement can influence habitat and biota. .........................................................61

Figure 15. Flow chart depicting hierarchical strategy for prioritizing specific restoration activities. ....... 65

Figure 16. Prioritization sequence for habitat restoration based on species of interest. ............................68

v
Figure 17. Process for restoration planning, prioritization, and implementation used by the Oregon 

Watershed Enhancement Board, Oregon Department of Fish and Wildlife, and State of Oregon.70

Figure B-1. Historical area of anadromous fish access and area opened to anadromous fish by a fish 

ladder around Granite Falls in the Stillaguamish River basin......................................................139

Figure B-2. Comparison of Chinook redds per kilometer to bankfull width measurements, Skagit River.

..................................................................................................................................................... 140

Figure B-3. Range of historical capacity estimates observed by varying parameters in the capacity

equation for small streams in the North Fork Stillaguamish River..............................................152

Figure B-4. Range of historical capacity estimates observed by varying parameters in the capacity

equation for large streams in the North Fork Stillaguamish River. .............................................153

Figure C-1. Simplified flow chart depicting interactions between watershed controls and processes 

resulting in physical habitat conditions........................................................................................158

Figure C-2. Map A shows land use pattern and map B shows area of historical salmon access in the 

Skagit River basin........................................................................................................................160

Figure C-3. Map A shows subbasins in forested mountain areas of the Skagit River basin where peak 

flow is likely impaired. Exploded view B shows streams in lowland subbasins where peak flow 

will be impaired if current zoning is built out..............................................................................169

Figure C-4. Map A shows sediment supply ratings for mountain subbasins and status of forest road 

inventory on National Forest lands in the Skagit River basin. Exploded view B is example of 

detail for road segments and landslide hazard units in the Bacon Creek watershed....................172

Figure C-5. Map of riparian inventory status and riparian areas likely functioning or impaired in the 

Skagit River basin........................................................................................................................175

Figure C-6. Estimated percentages of riparian categories (functioning, moderately impaired, and 

impaired) for land use categories along nonmainstem channels in the anadromous zone of the

Skagit River basin........................................................................................................................176

Figure C-7. Location of hydromodification and manmade barriers (lower Skagit River basin only). .... 177

Figure C-8. Distribution of generalized habitat types (key, isolated or degraded, important) in floodplain 

areas of the Skagit River basin, with exploded view of the delta region. ....................................179

vi
LIST OF TABLES 

Table 1. Primary questions to answer in developing a habitat recovery plan............................................10

Table 2. Regional differences in dominant ecosystem processes or functions in the Pacific Northwest. . 13

Table 3. Habitat types used for the two types of watershed assessments described in this report.............19

Table 4. List of analyses that address questions pertaining to the four categories of recovery goals for 

salmon............................................................................................................................................21

Table 5. General description of potential analytical approaches for rapidly estimating current and 

historical habitat abundance at the ESU level................................................................................22

Table 6. General description of analysis approaches for estimating current and historical habitat 

abundance at the watershed level...................................................................................................33

Table 7. Examples of methods for rating individual landscape processes.................................................43

Table 8. Summary of contemporary spatial scale classifications. .............................................................54

Table 9. The 10 metrics of the B-IBI and their predicted responses to increasing human disturbance..... 57

Table 10. Typical response time, duration, variability of success, and probability of success for common 

restoration techniques. ...................................................................................................................63

Table 11. Example of list of potential restoration actions within a watershed. .........................................71

Table 12. Example of different order of priorities based on different prioritization methods using 

information in Table 11. ................................................................................................................72

Table 13. Tools and methods for quantifying and reducing uncertainty. ..................................................76

Table 14. Questions to guide the evaluation of predictions.......................................................................78

Table 15. Input information and results of decision analysis for prioritizing restoration actions..............83

Table B-1. Estimates of stream length of small streams under current and historical conditions for the 

Stillaguamish River......................................................................................................................142

Table B-2. Values used to vary biological parameters for current and historical capacity estimates...... 144

Table B-3. Data on number of Chinook salmon redds for repeated spawner surveys along the North Fork 

Stillaguamish River......................................................................................................................145

Table B-4. Estimates of stream area under current and historical conditions for the Stillaguamish River.

..................................................................................................................................................... 147

Table B-5. Percent habitat composition and values used to vary percent of habitat suitable for spawning 

for current and historical conditions. ...........................................................................................149

Table B-6. Summary of published information on redd size. ..................................................................150

vii
Table B-7. Range of adult capacity estimates (number of spawners) for the North Fork Stillaguamish 

Chinook salmon population and the South Fork/mainstem Stillaguamish Chinook population 

where the percent habitat suitable for spawning and combined biological parameter values are 

varied. ..........................................................................................................................................154

Table C-1. Summary of background and methods for rating individual landscape processes. ...............163

Table C-2. Designation of generalized habitat types. ..............................................................................167

Table C-3. Magnitude of peak flows for the lower Skagit River before and after flood storage capability.

..................................................................................................................................................... 170

Table C-4. Distribution of 234 field-sampled riparian sites by GIS-based land cover type. ...................173

viii
EXECUTIVE SUMMARY 

The Endangered Species Act (ESA) requires that a recovery plan be developed for all 

species listed as threatened or endangered. For Pacific salmon, this includes 26 evolutionarily 

significant units (ESUs) of six species—Chinook salmon (Oncorhynchus tshawytscha), chum

salmon (O. keta), coho salmon (O. kisutch), pink salmon (O. gorbuscha), sockeye salmon (O. 

nerka), and steelhead (O. mykiss)—distributed among nine geographic areas along the West 

Coast. Several factors associated with harvest, hatchery, hydropower, and habitat influence the 

decline of salmon populations in the western United States, but the relative importance of each 

factor varies among ESUs. Each of these factors should be addressed in a successful recovery 

plan. 

Previous documents have provided guidance for recovery planning (NMFS 1992, 2000, 

McElhany et al. 2000), but they do not have specific guidance on how to implement the habitat 

portion of a recovery plan. This technical memorandum supplements prior guidance documents 

with information specific to habitat recovery planning. It is not intended that existing habitat 

recovery planning efforts (e.g., at the local watershed level) should be abandoned in favor of 

methods discussed here. Rather these methods should help clarify the specific purposes and 

methods of assessment within the existing approaches. Audiences that may benefit from this 

document include Technical Recovery Teams (TRTs) assigned to each of the geographic 

planning areas, local watershed planning groups, and National Marine Fisheries Service (NOAA 

Fisheries) personnel. 

After the introductory section, this report is divided into five main sections. The first, An 

Assessment Approach for Habitat Recovery Planning, presents a conceptual framework for 

understanding relationships among land uses, watershed functions, habitat conditions, and biota. 

The framework relies on principles of watershed and ecosystem management and organizes the 

habitat-related questions that each recovery plan should attempt to answer. The second, 

Analyses for Phase I Recovery Planning: Setting Recovery Goals, discusses assessments that 

help identify important habitat losses and set recovery goals. The third, Analyses for Phase II 

Recovery Planning: Identifying Ecosystem Restoration Actions, presents more detailed 

assessments to conduct within individual watersheds for identifying causes of habitat loss or 

degradation. How to use Phase I and Phase II information together to help prioritize actions is 

addressed in the next section, Prioritizing Potential Restoration Actions within Watersheds. 

Finally, Managing Uncertainty in Habitat Recovery Planning discusses how uncertainty can 

affect planning decisions and provides guidance and examples for identifying and quantifying 

types of uncertainty. This document also includes three appendices. The following is a 

summary of these sections. 

An Assessment Approach for Habitat Recovery Planning 

Our conceptual approach to habitat recovery planning is based on principles of watershed 

and ecosystem function. Salmon are adapted to local environmental conditions, including 

associated temporal and spatial variability. Those conditions vary in space and time due to 

ix
landscape processes and land use. Because landscape processes (e.g., sediment supply, wood 

recruitment to streams) create and sustain habitats over time, an approach to habitat recovery that 

focuses on preserving or restoring ecosystem processes should provide good quality salmon 

habitat over the long term. This general approach applies to all ecoregions, though the relative 

importance of various landscape processes differs by ecoregion. 

For Phase I recovery planning, a suite of habitat analyses helps identify or clarify certain 

recovery goals (e.g., abundance goals) for ESUs or populations. The Phase I assessments can 

also identify where large habitat losses have occurred and may help identify which habitats limit 

recovery of populations. In Phase II planning, watershed process assessments are the basis for 

identifying causes of habitat loss or degradation as well as ecosystem recovery actions. Results 

from both assessments are used to prioritize restoration actions. New information gained from

future assessments and management experiments is used to update the recovery plan. 

Analyses for Phase I Recovery Planning: Setting Recovery Goals 

The first phase of habitat recovery planning addresses how habitat changes might have 

altered the abundance, survival, population growth rate, spatial structure, and life history 

diversity of ESUs or individual populations. This question can be addressed by assessments and 

analyses conducted at several levels of resolution. First, ESU-wide analyses can provide an 

overall understanding of broad-scale patterns of land use and habitat conditions and relate these 

to the recovery goals (salmon abundance, population growth rate, spatial structure, and 

diversity). Second, watershed-level analyses can elucidate such patterns specific to each 

watershed. Results of these analyses can be used to set biological delisting criteria for each of 

the salmon ESUs and their constituent populations. 

The ESU-level analyses are meant to provide information about a broad geographic area 

in a relatively short period of time using existing data. Consistent methodologies applied across 

an entire ESU enable comparisons of results among populations or watersheds. Correlation 

analyses using existing geospatial data can identify relationships among natural landscape 

attributes, land uses, and salmon populations (e.g., the Salmonid Watershed Analysis Model or 

SWAM). Comparisons between current and historical habitat conditions can help assess 

potential productivities and capacities of salmon populations, identify where large habitat losses 

have occurred, and help identify which habitat losses might have large affects on ESU viability. 

At the watershed level, similar analyses can be conducted to ascertain relationships 

among landscape attributes, land uses, and salmon viability. Existing tools include the simplified 

limiting factors model, the ecosystem diagnosis and treatment (EDT) model, and the dynamic 

life cycle model. All three approaches compare current and historical habitat conditions, but 

differ in their data requirements and representation of the salmon life cycle. The EDT model is 

complex and greatly relies on expert opinion for input data, whereas the other approaches are 

simpler models based primarily on measured data. The simplified limiting factors model has the 

least complete representation of the salmon life cycle, allowing only life stage capacities to 

change. The EDT model allows both life stage capacities and survivals to change. The dynamic 

life cycle model allows life stage capacities and survivals to change and can examine population 

x
growth rates over time in response to habitat actions. These assessments elucidate patterns of 

habitat alteration and highlight areas where such change may have most affected salmon 

viability. Results can be used to identify criteria required to sustain viable populations and 

identify critical uncertainties in our predictions of how populations will respond to habitat 

restoration. 

Analyses for Phase II Recovery Planning: Identifying Ecosystem 

Restoration Actions 

Phase II assessments are primarily intended to identify causes of habitat loss or 

degradation and identify ecosystem restoration actions. Specific inventories and assessments to 

identify altered ecosystem processes can be grouped into distributed processes (i.e., widespread 

non-point such as sediment supply inventories), reach-level processes (e.g., floodplain and 

riparian characterization), and other ecosystem functions not easily described by rates or levels 

(e.g., barrier and flow-diversion inventories). Assessments that identify impaired biological 

integrity (e.g., Benthic Index of Biological Integrity or B-IBI, multivariate model analyses) can 

identify locations where habitat degradation may be altering biological communities as well as 

which ecosystem processes have been disrupted. All these analyses aim to identify the natural 

landscape processes active in a watershed, the effects of land use on natural processes, and the 

causal relationships between land use and habitat conditions. Specific results include locations 

of impaired stream segments, reaches, or subwatersheds and causes of impairment. From these 

assessments, a list of habitat restoration actions can be prepared for each watershed of an entire 

ESU. 

Prioritizing Potential Restoration Actions within Watersheds 

The recovery plan should include a prioritized list of ecosystem restoration actions. 

Many factors influence the prioritization of restoration actions in recovery planning and there are 

many philosophical approaches to recovery of ecosystems and listed species. When little is 

known about habitats that limit recovery of listed ESUs or the causes of habitat degradation, an 

interim prioritization approach based on effectiveness of different types of actions can be used. 

This hierarchical strategy gives priority to actions that have high probability of success, low 

variability among projects (i.e., consistency of results), relatively quick response time, and long 

duration of results. If more is known about habitat limitations and causes of habitat loss, Phase I 

and Phase II information can be combined to prioritize restoration actions necessary for more 

efficient recovery of single species. Cost and time required to implement actions as well as

immediate management needs may also be considered during prioritization. Where more than 

one species is of concern or where habitat recovery goals are more broadly defined, alternative 

strategies that consider multiple species, protection of refugia, and other factors may be used. 

xi
Managing Uncertainty in Habitat Recovery Planning 

Acknowledging, describing, and estimating uncertainty associated with assessments and 

analyses can increase the effectiveness of recovery planning. This process elucidates the full 

range of possible outcomes and the probability of seeing each of these outcomes. Knowing 

where uncertainties exist allows resource managers to develop plans with acceptable risk (i.e., 

plans where the benefits of an action outweigh its costs). 

By recognizing where uncertainty exists, areas that need further clarification (e.g., more 

data, additional expert opinion, better model performance) can be identified. To do this, 

estimates of the magnitude of uncertainty in each of five types of uncertainties—prediction, 

parameter, model, measurement, and natural stochastic variation—must be generated. 

Identification of which types of uncertainty are likely to have the largest effect on predictions 

can suggest areas where improvements in information will be most beneficial. 

Often, decisions need to be made before adequate data are available. Provided that 

uncertainties are identified, several established methods can be used to make decisions based on 

the best available information. These methods aid prioritization of actions and are preferred over 

methods that rely on guesswork, biased data, or data collected at inappropriate scales. Final 

outcomes chosen must be robust to each type of uncertainty identified. These decision strategies 

should assist in creating sound plans in the interim and can be reevaluated as new information is 

obtained. 

Appendices 

Three appendices follow in this guidance document. Appendix A, Issues of Scale in 

Habitat Recovery Planning, examines the concept of scale with particular emphasis on analyses 

to help set recovery goals. It demonstrates how the effects of habit change on the four 

components of population viability can be examined over multiple scales and includes examples 

from the literature to illustrate the types of information obtained at each scale. Appendix B, 

Estimating Chinook Salmon Spawner Capacity of the Stillaguamish River, is an example 

analysis for Phase I recovery planning. This case study estimates the river’s current and 

historical capacity for adult Chinook salmon based on habitat data at the unit scale (e.g., pool, 

riffle, and glide), then extrapolates to the watershed scale to estimate the maximum number of 

adult Chinook that the river historically produced and the system’s potential for production 

today. Appendix C, Restoration of Habitat-Forming Processes: An Applied Restoration Strategy 

for the Skagit River, is an example analysis for Phase II recovery planning. It briefly describes 

the Skagit Watershed Council’s habitat protection and restoration strategy, directed at restoring 

the disturbed habitat-forming processes instead of attempting to build specific habitat conditions,

as well as applications of the methods and preliminary results. 

xii
ACKNOWLEDGMENTS

The editors thank the following people for providing valuable reviews and comments on 

an earlier draft of this document: the members of the Willamette/Lower Columbia and Puget 

Sound Technical Recovery Teams; Jim Karr, University of Washington, College of Ocean and 

Fishery Sciences; Susan Bolton, University of Washington, Center for Water and Watershed 

Studies; Pete Bisson, U.S. Forest Service, Pacific Northwest Research Station; Bruce Rieman, 

Jason Dunham, Russ Thurow, and Dan Isaak, U.S. Forest Service, Rocky Mountain Research 

Station; Derek Poon, Gretchen Hayslip, Michael Rhylko, Lillian Herger, Krista Mendelman, and 

Tom Connor, U.S. Environmental Protection Agency, Region 10; Dudley Reiser, R2 Resource 

Consultants, Inc., Redmond, Washington; David Jensen, Frank Orth & Associates, Kirkland, 

Washington; Dale McCullough, Columbia River Inter-Tribal Fish Commission; Brad Johnson, 

Asotin County Conservation District, Clarkston, Washington; Lloyd Moody, State of 

Washington Governor’s Salmon Recovery Office Salmon Team; Kelly Moore, Oregon 

Watershed Enhancement Board; Barry Thom, Spencer Hovekamp, and Tom Cooney, National 

Marine Fisheries Service; Michelle McClure, Mary Ruckelshaus, and Robin Waples, Northwest 

Fisheries Science Center; Tommy Williams, Eric Bjorkstedt, David Boughton, Steve Lindley, 

and Brian Spence, Southwest Fisheries Science Center; and John Kocik, Northeast Fisheries 

Science Center. The editors also thank Karrie Hanson, independent contractor, and Aimee 

Fullerton, Northwest Fisheries Science Center, for administrative and editorial support. 

In addition, the authors of Appendix C, Restoration of Habitat-Forming Processes, thank 

the Environmental Systems Research Institute, Redlands, California, for providing a substantial 

grant enabling the acquisition of Geographic Information System software for that project and 

the U.S. Forest Service for providing road inventory data for the identification of landslide 

hazard areas. 

xiii
xiv
INTRODUCTION 

One of the main purposes of the Endangered Species Act is “to provide a means whereby 

the ecosystems upon which endangered species and threatened species depend may be 

conserved” (ESA 1973). The ESA consequently requires the development and implementation 

of recovery plans in order to realize the conservation of listed species. Details that recovery 

plans must include are: 

1) a description of such site-specific actions as may be necessary to achieve the plan’s goal 

for conservation and survival of the species, 

2) objective, measurable criteria which, when met, would result in a determination that the 

species be removed from the list, and 

3) estimates of the time and cost required to carry out those measures needed to achieve the 

plan’s goal and take the intermediate steps toward that goal. 

For ESA-listed salmon in the western United States, this requirement is no small task, as salmon 

habitat is ubiquitous and actions that protect or restore the ecosystems on which salmon depend 

are in conflict with many land uses in the region. 

The ESA provides little guidance concerning the content of recovery plans for individual 

species. Therefore, the National Marine Fisheries Service (NOAA Fisheries) provides additional 

scientific guidance on setting recovery goals for evolutionarily significant units (ESUs) of 

salmon and the populations within them (McElhany et al. 2000), based on the concept of viable 

salmonid populations (VSPs). An ESU, equivalent to a “distinct population segment” under the 

ESA, is “a population or group of populations that are 1) substantially reproductively isolated 

from other populations and 2) contribute substantially to the ecological or genetic diversity of the 

biological species” (Waples 1995, Myers et al. 1998). For each ESU, recovery goals generally 

are concerned with identifying how many and which independent populations are necessary for 

ESU viability (McElhany et al. 2000). McElhany et al. (2000) identify four categories of 

recovery goals that must be met for each population within an ESU of listed salmon: population 

abundance (size), population growth rate (and related parameters), spatial structure (within a 

population or group of populations), and diversity (i.e., the distribution of traits within and 

among populations). However, the VSP guidance does not address how to identify specific 

restoration actions for harvest, hydropower, hatcheries, or habitat that are necessary to achieve 

ESU or population viability. 

In addition to the VSP guidance, NOAA Fisheries provides guidance to the Technical 

Recovery Teams (TRTs), which are tasked with developing the technical aspects of a recovery 

plan for each ESU of listed salmon (NMFS 2000). Known as the TRT Guidance Document, that 

guidance has two phases of recovery planning: Phase I identifies the recovery goals (i.e., criteria 

that must be met for delisting) and Phase II identifies restoration actions necessary for recovery. 

However, there is considerable overlap in habitat analyses used for Phase I and Phase II 

planning. The habitat elements of the TRT work program, mainly included in Phase II planning, 

are: 1) describe fish and habitat relationships, 2) identify factors causing decline and limiting 

factors, and 3) identify actions for recovery. The TRT Guidance Document goes on to indicate 

that characterizing habitat/fish relationships includes assessing the spatial distribution of fish 

abundance for each population in the ESU, associating fish abundance with habitat 
characteristics, and identifying human factors that have the greatest impact on key freshwater 

and marine habitats. However, it does not specify appropriate spatial scales or resolution levels 

of data analyses. Moreover, it does not clearly elucidate the questions that such analyses are 

intended to answer, especially with respect to population goals for diversity and spatial structure. 

The TRT Guidance Document also stops short of specific questions for identifying limiting 

factors and identifying habitat restoration actions. As neither the VSP guidance nor TRT 

Guidance Document address how to identify specific habitat actions that would support salmon 

recovery, the Watershed Program of the Environmental Conservation Division at the Northwest 

Fisheries Science Center was asked to develop guidance for the habitat restoration elements of 

salmon recovery plans. In response to that request, this technical memorandum describes an 

approach to developing the habitat elements of a recovery plan for salmon listed under the ESA. 

Purpose and Scope 

The purpose of this document is to help both TRTs and local watershed groups list 

specific recovery planning questions, assemble appropriate methods for answering those 

questions, and utilize that assessment information to identify and prioritize ecosystem restoration 

actions. We focus on analyses that can help identify the restoration actions necessary to recover 

ecosystems that support salmon and help set population recovery goals. However, we do not 

address many other aspects of recovery planning here. These include the Columbia River 

hydropower system, harvest and hatchery practices, exotic species impacts, and the specifics of 

certain regulatory statutes that may be considered programmatic elements of a recovery plan. 

Regulations such as water quality standards, forest practices rules, the Northwest Forest Plan, 

and local growth management ordinances should serve as ecosystem protection actions at a 

minimum, and may serve as passive recovery actions in the best case (e.g., where substantial 

riparian buffers allow natural recovery of riparian processes and functions). 

Here we provide guidance for two main audiences, TRTs and local watershed groups 

(e.g., watershed councils, lead entities) that identify and conduct restoration actions. In general, 

we expect TRTs to focus largely on analyses for Phase I recovery planning (with some overlap 

into Phase II) and local watershed groups to focus mainly on analyses for Phase II recovery 

planning. We recognize that many existing assessment methodologies already incorporate 

aspects of the guidance provided here (e.g., Moore 1997, Quigley and Arbelbide 1997, SWC 

1998, OWEB 1999a, JNRC 2001), and we support existing approaches that favor restoration of 

ecosystem processes and functions. We do not intend that existing methods be abandoned in 

favor of a redesigned assessment, but we believe this guidance will be useful to existing TRTs 

and local watershed groups in clarifying the specific purposes and methods of assessment within 

their existing approaches. In addition, there are many TRTs yet to be formed and many local 

watershed groups that have not yet formulated an approach and methodology for recovery 

planning. 

2
Overview 

This document provides guidance for choosing and conducting analyses to assist in both 

Phase I and Phase II recovery planning for listed salmon. In the next section, An Assessment 

Approach for Habitat Recovery Planning, we present a conceptual framework for understanding 

relationships among land uses, watershed functions, habitat conditions, and biota. The 

framework relies on principles of watershed and ecosystem management and organizes the 

habitat-related questions that each recovery plan should attempt to answer. These questions first 

address how habitat changes might have affected abundance, survival, population growth rate, 

spatial structure, and diversity of salmon populations within an ESU (questions relevant to Phase 

I recovery planning or setting recovery goals). A second group of questions addresses causes of 

habitat change. The answers provide the basis for identifying actions that are necessary to 

restore the ecosystem upon which salmon depend (Phase II recovery planning). 

After listing the important questions, we provide a brief overview of methodologies that 

are appropriate for answering each question. In the Analyses for Phase I Recovery Planning: 

Setting Recovery Goals section, we discuss assessments that create a broad understanding of 

habitat issues affecting salmon populations across an ESU and help set recovery goals. In the 

Analyses for Phase II Recovery Planning: Identifying Ecosystem Restoration Actions section, we 

describe more detailed assessments to conduct within individual watersheds for identifying 

causes of habitat loss or degradation. How to use Phase I and Phase II information together to 

help prioritize restoration actions is addressed in the next section, Prioritizing Potential 

Restoration Actions within Watersheds. Then in Managing Uncertainty in Habitat Recovery 

Planning, we discuss how uncertainty may affect planning decisions and provide guidance and 

examples for identifying and quantifying types of uncertainty. Finally, this technical 

memorandum includes three appendices. Appendix A, Issues of Scale in Habitat Recovery 

Planning, examines the concept of scale in recovery planning with particular emphasis on 

analyses to help set recovery goals. Appendix B, Estimating Chinook Salmon Spawner Capacity 

of the Stillaguamish River, is an example analysis for Phase I recovery planning. Appendix C, 

Restoration of Habitat-forming Processes: An Applied Restoration Strategy for the Skagit River, 

is an example analysis for Phase II recovery planning. 

Notes on Terminology

Because salmon recovery planning draws on many scientific disciplines, there is 

considerable variation in the use of some terminology (e.g., the term productivity has different 

meanings depending on the discipline in which it is used). To help minimize confusion 

surrounding specific terms and clarify their meanings in our usage, we have included a glossary, 

which also defines specialized acronyms. In addition, to help avoid misinterpretation of our 

guidance, we discuss here five terms commonly used in salmon recovery planning—habitat, 

ecosystem, recovery, restoration, and productivity. 

The first two terms, habitat and ecosystem, are often used interchangeably, which creates 

some confusion about their meanings. For the purposes of this document, the term habitat refers 

to the aquatic environment that fish experience and not those landscape processes or attributes 

3
outside streams that alter habitat conditions. In general use, the term ecosystem refers to the 

dynamic and holistic system of all the living and dead organisms in an area and the physical and 

climatic features that are interrelated in the transfer of energy and material. In this document, the 

ecosystem is the aquatic environment and biota, physical and biological processes active in that 

environment, and the landscape processes and land uses that form and sustain the aquatic 

environment and biota. In general, recovery planning will aim to restore habitat attributes that 

support salmon by restoring ecosystem processes that form and sustain those habitats. 

The third term, recovery, in the context of listed populations, means attaining specified 

goals for viable populations and ESUs (abundance, population growth rate, spatial structure, and 

diversity). For watershed processes and habitats, recovery means returning from a disturbed 

state to some prior condition, not necessarily pristine. 

Restoration, the fourth term, in its strictest definition is returning a site to some

predisturbed condition (Gore 1985, NRC 1996). Some practitioners call this full restoration. It 

is generally more holistic or systemic than habitat creation, reclamation, rehabilitation, or 

enhancement, and not accomplished through manipulation of individual ecosystem or watershed 

elements. In contrast, habitat enhancement is the improvement of habitat from its existing or 

previous condition. It does not necessarily seek to restore conditions to some predisturbed state 

or restore disrupted watershed or ecosystem processes and functions such as delivery of water, 

wood, and sediment. Some practitioners call this (and related terminology) partial restoration. 

Here we use the term restoration generically to mean both restoration and enhancement, but we 

distinguish between those activities that restore watershed or ecosystem processes and those that 

enhance habitat. 

The fifth problematic term is productivity. In salmon management and research it has 

four meanings: population growth rate (e.g., McElhaney et al. 2000), number of adult returns per 

spawner (e.g., Moussalli and Hilborn 1986), stage-to-stage survival rate at low population size or 

density-independent survival (e.g., Moussalli and Hilborn 1986, Lestelle et al. 1996), and plant 

and algae biomass produced per unit area per year (e.g., Begon et al. 1986). To reduce the 

potential for misunderstanding, we do not use the term productivity and instead use more 

specific terms as appropriate. 

4
AN ASSESSMENT APPROACH FOR HABITAT RECOVERY 

PLANNING 

In this section we briefly describe an approach to understanding ecosystem functions and 

habitat change, as well as the scientific and practical reasons for choosing it. Our approach is 

based on a simple conceptual framework for understanding relationships among ecosystem

processes, land uses, habitat conditions, and biota. Using this conceptual framework, we 

organize a series of questions that must be answered in developing a recovery plan, identify the 

purpose of each assessment method, and illustrate the relationships among different assessments. 

We also suggest a sequence for the assessments and describe the importance of management 

experiments and monitoring in updating the recovery plan. 

Restoring Ecosystems to Support Recovery of Listed Salmon 

Over the past decade, many scientists have pointed out that the listing of salmon and 

other species as threatened or endangered is largely a result of trying to manage individual 

species and habitat characteristics rather than managing whole ecosystems (e.g., Doppelt et al. 

1993, Frissell et al. 1997). Scientists and resource managers alike have recognized that 

restoration that carefully considers the watershed or ecosystem context is more likely to be 

successful at restoring individual or multiple species and preventing the demise of others 

(Nehlsen et al. 1991, Doppelt et al. 1993, FEMAT 1993, Lichatowich et al. 1995, Reeves et al. 

1995, Beechie et al. 1996, Moore 1997). This conclusion suggests that habitat recovery planning 

will require assessments of disruptions to ecosystem functions and biological integrity, which 

have reduced the productive capacity of Pacific Northwest river systems and are partly 

responsible for declines in salmon abundance. The goal of such assessments is to identify 

alterations of key processes that affect stream habitats and specify the management actions 

required to restore processes that sustain aquatic habitats and support biological integrity (e.g., 

FEMAT 1993, Moore 1997, Quigley and Arbelbide 1997, Beechie and Bolton 1999). In this 

approach, restoring specific salmon populations is subordinate to the goal of restoring the 

ecosystem that supports multiple salmon species. In addition, information on habitat changes or 

conditions that limit specific salmon populations can be useful for identifying actions that may 

have the greatest effect on salmon recovery (e.g., Reeves et al. 1991) or for helping to set 

population and ESU recovery goals. 

In this technical memorandum, the ecosystem approach to salmon recovery planning 

includes two main assessment elements: analysis of landscape and habitat factors to help set 

recovery goals, and analysis of disrupted ecosystem processes to identify watershed and aquatic 

habitat restoration actions. Each element relies on a conceptual framework describing general 

relationships among land uses, landscape characteristics, aquatic habitat, and biological 

responses (Figure 1). This framework illustrates that landscape processes and land uses alter 

aquatic habitats, which in turn alter aquatic communities or populations. Therefore, aquatic 

habitat conditions can be viewed as the link between landscapes and fish populations. Making 

these relationships explicit allows us to organize analyses of ecosystem processes and functions 

in a way that brings greater clarity of purpose to each analysis, as well as a better understanding 

5
Figure 1. Schematic diagram of linkages among landscape processes, land uses, habitat changes, and 

biological responses. (Adapted from Beechie et al. 2003.) 

6
of how the results of each analysis will be used in recovery planning. Four classes of analyses 

are useful in recovery planning. First, for ESU-wide analyses of land use effects on salmon 

populations, landscape and land use factors can be correlated with indicators of population 

performance (e.g., correlation analyses) to indicate where populations have been impacted by 

various land uses. Second, population-level analyses that assess biological responses directly 

(e.g., using a biological indicator) can help identify where ecosystem functions have been 

impaired within watersheds. Third, assessments of habitat loss and resultant salmon population 

declines can be conducted by relating current and historical habitat abundance and condition to 

salmon utilization and survival. Finally, assessing disrupted ecosystem functions and processes 

within watersheds can identify causes of habitat change that result in diminished biological 

integrity and declines in salmon populations. 

Scientific Basis for an Ecosystem Approach 

The scientific basis for this approach can be summarized in two important characteristics 

of salmon and their habitats: 

1. Salmonid stocks are adapted to local environmental conditions (Miller and Brannon 

1982, Healey 1991). 

2. Spatial and temporal variations in landscape processes create a dynamic mosaic of habitat 

conditions in a river network (e.g., Naiman et al. 1992, Reeves et al. 1995). 

These statements imply that salmonid species or populations are adapted to spatially and 

temporally variable habitats (Beechie et al. 1996), and may further imply that such 

environmental variability is important to the long-term survival of populations (Reeves et al. 

1995). Perhaps most importantly, each salmon population (even one located close to another) is 

adapted to the spatial and temporal sequences of habitat conditions found in its watershed, which 

influences life history diversity across an ESU. 

Because salmonids are adapted to spatially and temporally varied habitat conditions, it 

does not make sense to manage for the same conditions in all locations or expect conditions to 

remain constant in any single location. This has been recognized in scientific critiques of many 

management issues in the past decade, including “one-size-fits-all” habitat standards (Bisson et 

al. 1997), not managing for spatial or temporal variation in habitats (Reeves et al. 1995, Bisson et 

al. 1997), and addressing symptoms of a disrupted ecosystem rather than causes (Frissell and 

Nawa 1992, Spence et al. 1996). Those approaches generally do not consider that local 

populations are adapted to the natural potential habitat conditions within their range and that 

those conditions vary in space and time. By contrast, identifying the root causes of degradation 

(i.e., impaired ecosystem processes and functions) focuses restoration on those processes that 

form and sustain habitats. This focus allows each part of the river network to express its natural 

potential habitat and helps conserve and restore the natural spatial and temporal variation of 

habitats to which salmon are adapted.

We stress that identifying the root causes of ecosystem degradation is important for two 

main reasons. First, scientists and resource managers do not understand most of the linkages 

between landscapes, habitats, and salmon populations with any great certainty, and we cannot 

predict exactly how land uses alter habitat conditions or how those habitat changes alter salmon 

populations. In fact, one can argue that even now we are not yet aware of all the aspects of 

7
aquatic ecosystems that significantly affect salmon populations. This lack of knowledge has led 

to significant habitat degradation—such as in the widespread removal of wood debris from

Pacific Northwest rivers. While the practice began over 150 years ago to improve navigation 

(Sedell and Luchessa 1982, Collins et al. 2002), the degradation was exacerbated as recently as 

the 1980s when fish biologists recommended wood removal to help adult salmon migrate 

upriver. Only when they learned of its significant role in rearing habitat formation did biologists 

stop the practice. Had they rather chosen to assume that salmon are adapted to local habitat 

conditions, they could have avoided a significant proportion of the recent habitat losses by 

choosing management actions that preserved riparian forest processes and natural wood 

functions in channels, even without understanding the value of wood in aquatic ecosystems. 

The second reason for identifying root causes is that traditional restoration actions (e.g., 

bank protection, spawning gravel placement) attempt to build habitats that do not move in space 

or time, whereas natural habitats are often created by movement of river channels, wood debris, 

and sediment. Many such restoration actions fail to restore habitats because they do not 

recognize the integrated nature of physical and ecological processes in watersheds (Frissell and 

Nawa 1992, Beechie et al. 1996). This lack of knowledge leads to two main types of failure: 1) 

site-prescribed engineering solutions can be overwhelmed by altered watershed processes that 

are far removed from degraded habitats (e.g., increased sediment supply from upslope sources 

can bury engineered structures and pools), or 2) such measures can prevent habitat formation that 

would otherwise naturally occur (e.g., bank protection prevents formation of new off-channel 

habitats). Avoiding these types of project failures requires that we focus on restoring ecosystem

processes and functions that form and sustain salmonid habitats rather than on the habitats 

themselves. 

Many organizations have recently adopted approaches to salmon habitat restoration that 

have a watershed or process-based approach (e.g., Moore 1997, SWC 1998, OWEB 1999a, 

JNRC 2001), which should help avoid some of the mistakes just described. However, many 

local groups continue to identify restoration projects in an opportunistic fashion, without 

assembling a broader understanding of habitat degradation and decline of listed species. Without 

this larger context, proposed projects are often disconnected from each other and fail to address 

the most important habitat losses. Continued development of holistic assessment approaches at 

the watershed level (e.g., SWC 1998, OWEB 1999a) should help resource managers more 

effectively utilize funding and resources allocated for salmon recovery. 

Practical Considerations 

Systematic watershed assessments can address several parts of a salmon recovery plan. 

Three types of watershed assessments address tasks that are listed in the TRT Guidance 

Document: 

1. Correlation analyses help set population recovery goals (Phase I planning) and identify 

fish and habitat relationships (Phase II planning). 

2. Assessments of current and historical habitat abundance and quality help identify 

recovery goals (Phase I planning) or factors causing decline and limiting factors (Phase II 

planning). 

8
3. Assessments of altered habitat-forming processes help identify causes of habitat loss or 

degradation and actions for ecosystem and habitat recovery (Phase II planning). 

In combination, these assessments provide a broad understanding of actions that are likely to 

improve population performance of listed salmon and form the basis of both regional and sitespecific plans for ecosystem restoration. Beyond the TRT guidance tasks, systematic watershed 

assessments provide agencies a watershed-level understanding of habitat restoration needs, 

which they can use for evaluating habitat conservation plans, programmatic actions, and 

proposed habitat restoration projects. 

These assessments can also help agencies address the Clean Water Act (CWA 1972) by 

identifying causes of various water quality problems, especially those associated with non-point 

pollution sources. The assessments are consistent with more holistic management approaches 

such as watershed management (e.g., Swanson 1981), ecosystem management (e.g., Johnson et 

al. 1985), and managing for biodiversity or biological integrity (e.g., McNeely et al. 1990, Karr 

1991), and can simultaneously help accomplish the habitat-related purposes of the ESA and the 

CWA. The stated purposes of the congressional acts are to “provide a means whereby the 

ecosystems upon which endangered species and threatened species depend may be conserved” 

(ESA 1973), and “restore and maintain the chemical, physical, and biological integrity of the 

nation’s waters” (CWA 1972). The common thread is that management of landscapes and 

ecosystems is a single approach that will produce sustainable clean water and support salmon 

recovery. 

Several watershed assessment approaches recently adopted by a variety of salmon 

recovery groups in the Pacific Northwest (e.g., FEMAT 1993, WDNR 1995, SWC 1998, OWEB 

1999a, JNRC 2001) focus on restoring ecosystems and watershed processes, and should help 

address the parallel goals of the ESA and CWA more efficiently. These watershed assessment 

processes support an ecosystem approach or at least include certain process-based components of 

an ecosystem approach. While some of these assessments were not specifically designed to help 

develop Pacific salmon recovery plans, they provide data that are relevant to understanding 

disruptions to watershed or ecosystem processes. To the extent that these assessments answer 

specific questions relevant to habitat recovery planning, their results can be used within the 

context of the approach described in this report. 

Key Assessments for Habitat Recovery Planning 

Developing the habitat elements of a salmon recovery plan requires understanding how 

land uses have altered landscape processes that form and sustain salmon habitats and how those 

habitat changes might have affected salmon populations. We describe two groups of questions 

that must be answered to develop a habitat recovery plan (Table 1). The set of important 

questions for Phase I recovery planning concentrates on how habitats have changed since 

presettlement times and how those habitat changes have affected salmon and other biota. These 

questions motivate historical reconstructions of habitat types and abundance as well as 

assessments of relationships between habitat and salmon population characteristics. The set of 

important questions for Phase II recovery planning focuses on identifying disruptions to 

ecosystem function and the types of habitat restoration necessary for ecosystem recovery. These 

9
Table 1. Primary questions to answer in developing a habitat recovery plan. 

Question Analysis area Data type 

Phase I questions: Assessing changes in 

habitat availability and potential impacts on 

population characteristics 

How might habitat changes have altered

the abundance of individual populations? 

ESU or watershed ESU: mainly remote sensing 

Watershed: mainly field 

How might habitat changes have altered

the population growth rate of individual 

populations? 

ESU or watershed ESU: mainly remote sensing 

Watershed: mainly field 

How might habitat changes have altered

the diversity of life history patterns? 

ESU or watershed ESU: mainly remote sensing 

Watershed: mainly field 

How might habitat changes have altered

the spatial structure of populations?

ESU or watershed ESU: mainly remote sensing 

Watershed: mainly field 

Phase II questions: Assessing disruptions to 

ecosystem functions and biological integrity 

Where has biological integrity been 

degraded? 

Watershed Field

Where have watershed processes and 

ecosystem functions been impaired?

Watershed Field/remote sensing

10
questions also motivate assessments that identify where the biological integrity of ecosystems 

has been degraded and where specific ecosystem processes or functions are disrupted. 

For organizational purposes, it is useful to diagram the relationships among habitat 

assessments that can be used in recovery planning (Figure 1). The Phase I assessments (those 

regarding changes in habitat and salmon populations) fall into two groups: 1) assessments that 

quantify habitat change and then use habitat-based models to estimate changes in fish 

populations (e.g., limiting factors analysis, life cycle models, and the ecosystem diagnosis and 

treatment or EDT model), and 2) correlation analyses that relate landscape and land use 

characteristics to fish population performance without directly quantifying changes to habitat 

(e.g., the Salmon Watershed Analysis Model or SWAM). Note that neither of these assessments 

directly identifies causes of habitat degradation or specific restoration actions. However, these 

assessments have three important uses in setting recovery goals for the ESU and each population 

within it (i.e., Phase I recovery planning). First, they provide habitat-based estimates of potential 

population size for comparison to estimates from population viability analyses (see McElhany et 

al. 2000 for background on use of population viability analyses in describing VSPs). Second, 

they provide insights into potential changes in life history diversity by identifying losses of 

important habitat types. And third, the ESU-wide correlation analyses can help identify which 

populations are most constrained by habitat loss and therefore may be most difficult to recover. 

The Phase II assessments (those regarding ecosystem functions and biological integrity)

can be separated into two components: 1) screening assessments to identify areas where 

ecosystem processes and functions are most impaired, and 2) specific field inventories to 

diagnose causes of ecosystem impairment (see Appendix C, page 157, for examples). 

Assessments that correlate landscape and land use characteristics with population attributes (e.g., 

SWAM) can indicate which habitat changes are most likely responsible for declines in salmon 

populations, and therefore which broad categories of restoration actions are most likely to result 

in increased salmon populations. Direct assessments of ecosystem processes that form salmon 

habitats (e.g., barrier inventories, riparian condition inventories) identify causes of degradation 

as well as restoration actions that are required to recover ecosystem functions and biological 

integrity. 

It is important to note that, while there are relatively few differences in Phase I 

assessment procedures across the Pacific Northwest, Phase II assessment procedures can vary 

substantially. Pacific Northwest environments have been classified as a nested set of ecoregions 

(CEC 1997) (Figure 2). At the coarsest levels (Levels I and II), these ecoregions denote three 

main areas within which climate, lithology, topography, and ecosystems are generally similar: 

marine northwestern Coastal Forests, drier northwestern Forested Mountains, and semiarid to 

arid Western Deserts (CEC 1997, USEPA 2000). Basic differences in watershed processes 

among ecoregions are shown in Table 2 (see also OWEB 1999a). In general, the same categories 

of assessments must be conducted regardless of ecoregion (e.g., sediment supply, riparian 

functions, isolated habitats), but the specific processes or mechanisms addressed may vary from

one ecoregion to another. For example, sediment supply to the stream network should be 

evaluated in any watershed, but certain processes of sediment supply may be emphasized 

depending on location. Sediment supply is dominated by landsliding in most watersheds of the 

coastal range and Cascade Mountains (e.g., Sidle et al. 1985), so understanding land use effects 

on landslide rates and sediment volumes is critical to identifying restoration actions such as road 

11
1

3

2

4

9

10

15

15

4

5 9

12

12

10

11

11

13 18

1

17

17

Level II ecoregions

Coastal Forests

Western Forested Mountains

Western Deserts

1 -- Coast Range

2 -- Puget Lowland

3 -- Willamette Valley

4 -- Cascade Range

5 -- Sierra Nevada Range

9 -- Eastern Cascades Slopes and Foothills

10 -- Columbia Plateau

11 -- Blue Mountains

12 -- Snake River Basin/High Desert

13 -- Northern Basin and Range

15 -- Northern Rockies

17 -- Middle Rockies

18 -- Wyoming Basin

Level III ecoregions

0 50 100 150 200 Miles

N

W E

S

8" 8"

8"

8" 8"

8"

8"

8" 8"

8" 8"

8"

8"

Seattle

Olympia

Portland

Eugene Bend

Medford

Yakima

Pasco

Spokane

Moscow

Boise Idaho Falls

Pocatello

Willamette

River

Columbia

River

Snake

River

Klamath

River

N

W E

S

State boundaries

Level II ecoregions

Major rivers

8" Cities

0 50 100 150 200 Miles

 Figure 2. Locations of major rivers and cities of the Pacific Northwest (upper panel) and Level II and 

Level III ecoregions (lower panel). 

12
Table 2. Regional differences in dominant ecosystem processes or functions in the Pacific Northwest. 

This table is intended to illustrate that different processes and assessments should be emphasized 

in different ecoregions. Important ecosystem processes vary within ecoregions and watershedlevel assessments should target those processes that are locally important within each watershed. 

(Note that the Columbia River estuary is in the Coastal Forests ecoregion, but also affects 

Columbia River stocks in the Western Deserts and Western Forested Mountains ecoregions.) 

Level II ecoregion 

Watershed process 

or function Western Deserts 

Western Forested 

Mountains Coastal Forests 

Sediment Gullying and surface

erosion (especially in 

agricultural areas) 

Mass wasting and 

gullying 

Mass wasting (surface 

erosion in agricultural 

lowlands) 

Flood hydrology Snowmelt dominated 

flood regime

Snowmelt dominated 

flood regime

Rain and rain-on-snow 

flood regime

Low flow hydrology Diversions and dams

common 

Diversions and dams

common 

Diversions and dams

less common 

Riparian functions Grasses and some 

forest; sediment 

retention a dominant 

function 

Sparse forests, shade a 

dominant function 

Dense forests, wood 

recruitment a dominant 

function 

Habitat connectivity Culverts, dams, and 

dikes common; incision 

and floodplain 

abandonment common 

Culverts, dams, and 

dikes common 

Culverts, dams, and 

dikes common 

Estuary function Not applicable 

(Columbia River estuary

should be assessed in 

relation to freshwater 

habitats) 

Not applicable 

(Columbia River estuary

should be assessed in 

relation to freshwater 

habitats) 

Severe impacts in 

agricultural and urban

areas 

Biological integrity Especially important in 

urban and agricultural 

areas 

Especially important in 

urban and agricultural 

areas 

Especially important in 

urban and agricultural 

areas 

13
decommissioning or reconstruction. By contrast, sediment supply in dry rangelands of the 

Columbia Plateau is more a function of surface erosion and gullying (e.g., Kaiser 1967, Peacock 

1994), especially where soils are bare for some portion of the year due to agricultural practices. 

In these areas, assessing sediment impacts will focus more on changes in surface erosion rates 

and volumes in order to identify where modification of agricultural practices may reduce 

sediment supplies. These and other analyses of watershed processes are described in more detail 

in the Analyses for Phase II Recovery Planning: Identifying Ecosystem Restoration Actions 

section of this document, page 40, and the Prioritizing Potential Restoration Actions within 

Watersheds section, page 60. 

Sequencing the Assessments 

The typical sequence of assessments (illustrated in Figure 3) is: 

1. Phase I, identify recovery goals for populations and ESUs, including goals for 

abundance, population growth rate (and related parameters), spatial structure, and 

diversity. 

2. Phase II, evaluate ecosystem processes and functions to identify the suite of possible 

habitat restoration actions. 

3. Integrate Phase I and Phase II information into a recovery plan. 

Phase I assessments indicate where the greatest habitat losses have been, what types of habitat 

losses have occurred, and which habitats are likely having the greatest affect on individual 

salmon populations. They are not ecosystem assessments and focus on individual species in 

order to establish the four categories of recovery goals. Phase I assessments are typically 

conducted at two levels of resolution: 1) ESU-wide correlations among landscape attributes and 

fish population characteristics (e.g., abundance, life history patterns) using low-resolution 

geospatial data, and 2) more detailed watershed-level assessments of habitat availability for 

different life history stages. The former provides information that is useful in setting goals for 

recovery of the entire ESU, whereas the latter is focused on setting more specific recovery goals 

for individual populations. Typically an ESU-wide assessment will be completed first because it 

is conducted with existing data. These assessments can indicate where the most important 

habitat losses have been and provide some insight into which habitats may be limiting individual 

populations. With reference to Figure 1, these assessments will relate landscape attributes and 

land use practices directly to fish abundance or survival, and ignore causal mechanisms that link 

landscape processes and land uses to habitat change or habitat change to fish population 

response. These ESU-wide assessments can indicate general patterns of population declines 

resulting from different land uses, but the data are generally too coarse to allow detailed analyses 

of habitat change and its effects on fish populations (e.g., Lunetta et al. 1997, Pess et al. 1999a, 

Feist et al. 2003). More detailed assessments for watersheds and individual populations involve 

collection of information on current and historical habitat abundance and quality, and therefore 

take more time to complete. These assessments can provide more detail on the types of habitat 

losses that have occurred and which life stages might be most impacted for individual species or 

populations. 

Phase II assessments identify where ecosystem processes or functions have been 

impaired and therefore where ecosystem rehabilitation or restoration actions are needed. They 

14
Figure 3. Generalized sequence for Phase I and Phase II assessments, integration of Phase I and Phase II 

information into the recovery plan, and plan updates based on continued inventories and 

monitoring. 

15
do not focus on individual listed species, but focus on ecosystem attributes and processes that 

support multiple salmon species. ESU-wide correlation analyses of impaired processes and 

salmon populations are typically part of the Phase I coarse-resolution assessment described 

above, and may require little additional work beyond reinterpretation of outputs from the Phase I 

analysis. However, some additional analysis of landscape or land use variables may be required 

to provide results that more specifically address changes in ecosystem processes. Watershedlevel assessments in Phase II are more detailed and time consuming, and resource managers 

should expect such that such inventories and assessments will take several years to complete. 

These watershed-level assessments have two essential components: 1) a screening component to 

identify where in the watershed each ecosystem process or function is most impaired, and 2) a 

field inventory component to identify specific actions that are needed to restore those processes. 

The screening assessments are primarily intended to help focus field inventories where they are 

most needed, but can also provide a general sense of where different types of restoration actions 

are likely to be focused and how much those actions might cost. 

In recovery domains where a single species is listed, the restoration actions identified in 

Phase II assessments can later be prioritized using the life cycle information from Phase I. In 

such cases, life cycle models indicate which habitats are likely most limiting, and therefore 

where and what types of restoration actions are likely to improve population performance. This 

approach alters the sequence of ecosystem restoration actions, but all actions remain focused on 

restoring landscape processes and functions that sustain salmon habitats over the long term (e.g., 

Beechie and Bolton 1999). The main risk inherent in this approach is that life cycle models 

provide only a “best guess” about which actions will most improve a population. Errors in the 

model may lead managers to focus too heavily on restoration actions that are not in fact limiting 

recovery of a population (see the Managing Uncertainty in Habitat Recovery Planning section, 

page 74). Therefore, it is important to emphasize a number of ecosystem restoration actions 

simultaneously, even where a single species is the focus of a recovery strategy. Where it is not 

appropriate to focus on any single species for recovery planning, other schemes for prioritizing 

actions should be employed (see the Prioritizing Potential Restoration Actions within 

Watersheds section, page 60). 

Implementing Habitat Actions and Updating the Recovery Plan 

The assessments described in this document are part of a larger sequence of steps needed 

to enact long-term strategies for salmon recovery. For most groups, completing all Phase II 

inventories will take many years and reliably predicting most population responses to habitat 

actions is not currently possible. Thus it is important to have a strategy for implementing interim

actions, learning how populations respond to those actions, and updating the recovery plan as 

new information becomes available. Recognizing the long-term nature of inventories, 

restoration experiments, and salmon recovery allows the tasks to be logically sequenced for 

implementation. In essence the steps are: 

1. Identify restoration goals and objectives. 

2. List assessments needed to identify appropriate habitat restoration actions (i.e., the 

assessments needed to complete Phase I and Phase II planning). 

3. Identify and compile existing assessments to identify initial restoration actions. 

16
4. Implement preliminary restoration actions as experiments, conduct remaining 

assessments and inventories to fill in the data gaps, and revise the plan and actions as new 

information becomes available. 

Step 1 describes the overall restoration strategy and types of information required to 

implement it. To some extent, the goals of restoration strategies for salmon recovery are 

constrained by the purposes of the ESA (i.e., conserve the ecosystems upon which listed species 

depend) and the CWA (i.e., protect the biological integrity of aquatic systems). Consistency 

with the purposes of these two acts will simplify the assessments needed to identify habitat 

protection and restoration actions and help avoid conflicting restoration priorities that arise from

differing habitat requirements among species. Examples of restoration goals are to “protect and 

restore the processes that form and sustain habitats to which salmonid stocks are adapted” (SWC

1998), “restoration and protection of habitat conditions and processes upon which the fish 

depend” (LCFRB 2001), or “to have a diversity of habitats and natural processes necessary to 

sustain healthy populations of native species” (WRS 2001). Along with these habitat restoration 

goals, strategies should also have clearly stated near-term and long-term objectives. Near-term

objectives should incorporate those actions that we already know are consistent with 

conservation of ecosystems that support salmon (e.g., removal of migration blockages, habitat 

protection through easements or acquisitions). Longer term objectives should include 

management experiments to clarify which actions are most beneficial to aquatic ecosystems and 

listed species, as well as implementing larger restoration projects that require changes in 

infrastructure or land uses (e.g., modifying levee systems to reopen access to estuary habitats). 

In essence, strategies should describe how different actions will be identified and prioritized, 

how long-term inventories can be incorporated into the recovery plan, and how monitoring 

information will feed back into updates of the recovery plan.

Step 2 should explicitly list the types of assessments required to implement a recovery 

strategy. The complete recovery plan will include assessments for all of the Hs (habitat, 

hydropower, hatcheries, and harvest). This document provides guidance on assessments that will 

be useful in Phase I and Phase II planning for habitat actions (only one of the Hs). 

Step 3 examines the list of assessments needed to identify information that already exists 

as well as those assessments that remain to be conducted. At this stage information can be 

compiled to identify interim habitat restoration actions. Some examples of this stage of

assessment can be found in the Oregon Watershed Enhancement Board assessments (online at 

http://www.oweb.state.or.us/publications/index.shtml) or the Washington Conservation 

Commission habitat limiting factors reports (online at http://salmon.scc.wa.gov/reports/index. 

html). 

Step 4 includes two main components: 1) completing inventories that identify specific 

habitat restoration actions (e.g., barrier inventories or riparian condition inventories), and 2) 

conducting restoration experiments to improve our understanding of which types of actions will 

most benefit salmon in each recovery domain or ESU. Inventory data can be directly

incorporated into a recovery plan to expand the list of actions necessary to restore ecosystem

processes and functions that support salmon recovery. Monitoring of management experiments 

improves our ability to predict the outcome of restoration actions and can be used to adjust 

priorities in the recovery plan. 

17
ANALYSES FOR PHASE I RECOVERY PLANNING: 

SETTING RECOVERY GOALS 

Recovery goals for salmon ESUs and populations might include numerical fish 

population targets, numerical population trend targets, qualitative or quantitative targets for 

spatial distribution of populations and population diversity, habitat quality standards, or 

management outcome targets (McElhany et al. 2000). In this section, we consider how habitat 

analyses at the ESU and watershed scales can inform the development of viability goals for 

salmon populations and ESUs. These analyses also inform Phase II recovery planning by 

identifying fish-habitat relationships and factors causing decline. ESU-level analyses are used to 

examine the quantity and quality of habitat across numerous populations within ESUs, whereas 

watershed-level analyses focus on questions specific to individual populations. 

As ESU and population viability goals are set, we must simultaneously evaluate whether 

current, historical, or “restored” habitat might be sufficient to support populations of the desired 

size, as well as whether the type and distribution of available habitats can support the desired 

spatial structure and diversity of salmon populations. Where current habitats cannot support the 

desired populations, we must identify and prioritize ecosystem restoration actions that will help 

achieve the recovery goals (see the Analyses for Phase II Recovery Planning section, page 40, 

and the Prioritizing Potential Restoration Actions within Watersheds section, page 60). For any 

of these analyses, it is critical to select habitat measures that can be linked to population 

performance and are sensitive to land use changes or restoration actions. Habitat measures 

(physical, chemical, or biological) that meet these criteria facilitate an understanding of how land 

uses or restoration actions change habitats and how those habitat changes in turn create 

population responses in salmon (see Figure 1). This is true whether the analysis is conducted 

with coarse-resolution or fine-resolution data available over entire ESUs or only in certain 

watersheds. 

Because habitats and biota are hierarchically structured, it is also important to view 

habitat data in the context of a hierarchical classification system such as illustrated in Table 3 

(see also Appendix A, page 127, for further discussion of scale issues in Phase I recovery 

planning). With such a classification of habitats, results of analyses across entire ESUs can be 

linked to fine-resolution analyses within individual watersheds. Moreover, this hierarchical 

structure allows one to construct simple predictive models for estimating abundance and 

distribution of fine-resolution habitats based on available coarse-resolution data (e.g., Lunetta et 

al. 1997). 

ESU-Level Analyses for Abundance and Survival Goals

ESU-level analyses differ from watershed-level analyses in that they ask questions and

analyze data that span the area of entire ESUs. The area of existing ESUs ranges from 7,200 to 

38,600 km2

. The number of independent salmon populations within each ESU varies from as 

few as one to more than 30 (see Northwest Salmon Recovery Planning, online at http:// 

www.nwfsc.noaa.gov/trt/). Because these analyses encompass large geographic areas and often 

18
Table 3. Habitat types used for the two types of watershed assessments described in this report. Coarser 

scale habitat types are mapped from topographic maps, aerial photography, and satellite imagery. 

Finer scale habitat types are mapped using a combination of aerial photography (for larger units) 

and field measurements. (Adapted from Beechie et al. 2003.) 

Coarser scale

Habitat type 

Finer scale

Large main stem (>50m bankfull 

width) by channel type based on 

gradient and confinement 

• Midchannel 

• Edge 

• Mid-channel pool 

• Mid-channel glide 

• Mid-channel riffle 

Boulder/cobble 

Cobble/gravel 

• Bar edge 

• Bank edge 

Natural 

Hardened 

• Backwater (alcove) 

Small main stem (10–50 m

bankfull width) and tributaries 

(<10 m bankfull width) by channel 

type based on gradient and 

confinement 

• Pool 

• Riffle 

• Pool 

Scour 

Plunge 

Trench 

Backwater 

• Glide 

• Run 

• Rapid 

• Riffle 

Off-channel habitat within large 

main-channel floodplains 

• Channellike 

Impoundment • Pond-like • Pond < 500 m2

• Pond > 500 m2 and < 5 ha

• Lake > 5 ha 

Palustrine wetland • Forested 

• Scrub/shrub 

Open water area by season 

Riverine tidal wetland • Forested 

• Scrub/shrub 

Open water area by season and tidal 

stage 

Tidal delta wetland • Scrub/shrub 

• Emergent 

Open water area by season and tidal 

stage 

Tidal delta channel • Main stem

• Blind 

• Distributary

19
more than one salmon population, the goals and data needs are inherently different from efforts 

directed at watershed, stream, or reach scales. 

The goal of an ESU-level analysis is to identify how habitat changes might have altered 

the abundance, population growth rate, spatial structure, and diversity of individual populations 

and ESUs. The nature of this question combined with the geographic scale of analyses means 

that ESU analyses will typically correlate landscape characteristics (e.g., land use and land form) 

to characteristics of habitat and fish (Figure 1) in order to describe large-scale patterns of habitat 

change (Table 4). In general, fine-resolution data (e.g., habitat typing, barrier inventories) used 

in watershed-level assessments are not comprehensively available across the large geographic 

areas these analyses must cover. Therefore, ESU-scale analyses use currently available data 

(usually at coarse resolution) to rapidly address questions that span large geographic areas (see 

Table 4 and Table 5). 

Here we describe two approaches to assessing how land uses might be related to salmon 

abundance or survival rates. The first uses simple correlations among landscape/land use 

variables and salmon abundance to evaluate the relative quality of different stream reaches 

within a large study area (e.g., Pess et al. 2002, Feist et al. 2003). The second uses coarseresolution data to estimate current and historical habitat abundance within an ESU. Analyses for 

addressing changes in density-independent survival rates (either for single life stages or for 

returning adults per spawner) will be similar to those for abundance, except that survival 

measures will be used as the response variable rather than abundance. 

Correlation Analyses 

Correlation studies link patterns of land cover and land use to fish abundance, fish 

survival, or instream habitat quality and help identify ESU-level relationships between salmonid 

populations and the physical, chemical, and biological components of their habitat. Correlation 

analyses can utilize a broad range of metrics for population performance (e.g., genetic diversity, 

juvenile abundance, adult abundance, population growth rate, life-stage specific survivals), 

landscape characteristics (e.g., road density, geology, land use), or habitat attributes (e.g., percent 

pools, water temperature, number or concentration of contaminants). These studies can be used 

to make predictions about where habitat conditions might limit or enhance salmon populations, 

generate initial prioritizations of habitat action types and locations, generate hypotheses for 

further testing, and suggest important factors to control when setting up small-scale experiments, 

monitoring projects, or large management experiments (Figure 4). However, they cannot 

identify cause and effect relationships because of correlations among habitat descriptors, 

correlations among landforms and land uses, and the potential for unmeasured variables to 

explain existing patterns. 

One example of this type of analysis is the Salmonid Watershed Analysis Model 

(SWAM), a series of spatial and statistical analyses that relate salmonid population metrics in a 

basin to landscape and land use characteristics derived from existing geospatial data layers. 

SWAM has been used in the Salmon River basin in Idaho (Feist et al. 2003), the Snohomish 

River basin in Washington (Pess et al. 2002), and the Willamette River basin in Oregon (Steel et 

al. in prep.). In these basins, SWAM linked indices of adult fish abundance (redd counts or adult 

20
Table 4. List of analyses that address questions pertaining to the four categories of recovery goals for 

salmon. 

Recovery goal Example of analysis Reference 

Quantification of current vs. historical 

habitats 

McIntosh et al. 2000 

Thompson and Lee 2000 

Thurow et al. 2000 

Effects of irrigation diversions in the 

Salmon River basin 

McClure et al. in press 

Population 

abundance and 

growth rate 

Land use impacts on salmon Bradford and Irvine 2000 

Thompson and Lee 2000 

Paulsen and Fisher 2001 

Quantification of current vs. historical 

habitats 

Thurow et al. 2000 

Regional patterns in fish diversity Frissell 1993b 

Waples et al. 2001 

Associations between fish assemblages and 

habitat 

Waite and Carpenter 2000

Spatial structure and 

diversity

Presence/absence mapping Quigley and Arbelbide 1997 

21
Table 5. General description of potential analytical approaches for rapidly estimating current and 

historical habitat abundance at the ESU level. Note that methods are not readily available for 

estimating historical conditions for most habitat types. Thus ESU-scale analyses most commonly

focus on direct loss of important habitat types. 

Type of habitat change Possible analysis method 

Tributary and mainstem

blockages 

Utilize digital information on known barriers and historical ranges (e.g., 

Quigley and Arbelbide 1997). May also model historical ranges based 

on channel slopes and stream size. 

Channel type (e.g., based 

on Montgomery and

Buffington 1997) 

Estimate from vegetation information, hydrography, and digital 

elevation models (Lunetta et al. 1997). 

Off-channel, wetland, or 

beaver pond areas 

No remote sensing method available (historical data lacking). 

Lakes No remote sensing method available (historical data lacking). 

Estuaries No remote sensing method available (historical data lacking). Use 

existing information where possible (e.g., Bortleson et al. 1980). 

22
Habitat Landscape

Fish

Estuary 20

Tributary 15

Main stem 12

Fish

density

(# / m2)

Habitat

type

Fish density

(# / m2)

Land form Land use

Erosive

geology

15

2

8

Urban

Forested

Agriculture

Nonerosive

geology

12

20

5

Urban

Forested

Agriculture

a.

c. b.

Land use

category

Total 100% 100%

Agriculture 50% % riffles 30% % lost

Forested 30% % riffles 60% % lost

Urban 20% % riffles 10% % lost

Distribution Characteristics Distribution Characteristics

Main stem Tributary

Habitat type

Figure 4. Illustration of the kinds of relationships between habitat, landscape, and fish that broad-scale 

analyses can examine: Table A illustrates how habitat quantity and characteristics are distributed 

across different land uses, Table B illustrates how fish density in three land use categories differs 

in erosive vs. nonerosive geologic settings, and Table C describes how fish density differs across 

habitat types. The numbers in the tables are fictitious and for illustrative purposes only.

23
fish counts at index sites) to multiple descriptors of landscape conditions across the entire 

watershed draining to the index reach, as well as to conditions in the riparian area directly 

associated with the index reach. Alternate population metrics such as juvenile abundances or 

life-stage specific survivals are possible wherever adequate data exists. 

The spatial and statistical analyses involved in the SWAM approach are comprised of 

five steps. First, conceptual, mechanistic relationships between landscape features and 

population abundance during all freshwater life stages are identified from the literature and by 

local habitat biologists. These conceptual relationships define the habitat data layers to use as 

potential predictor variables. Second, spatial heterogeneity in the salmonid population data is 

examined to determine if certain areas in the basin consistently exhibit better population 

performance than other areas. Third, landscape data layers are overlaid with the georeferenced 

fish abundance data (e.g., redd counts). Fourth, a statistical model is developed to describe 

annually consistent relationships between landscape characteristics and fish abundance. And 

finally, the statistical model is used to extrapolate relationships to the entire basin of interest or 

specific locations within the basin (Figure 4). Many combinations of population and landscape 

metrics can be used in this type of analysis and the best choice for a particular basin will be

driven by available data. 

SWAM or SWAM-like analyses provide broad-brush estimates of current or potential 

fish occupancy within a basin and factors affecting abundance. In the Willamette River basin, 

SWAM is used to estimate potential fish occupancy behind barriers, then prioritize barrier 

removal projects. Correlative models relating fish population performance to habitat conditions 

may also help identify the best remaining reaches or subwatersheds in a particular basin or 

identify areas that were historically productive. Ecological insights developed from these 

analyses may suggest likely habitat factors limiting population performance. Experience from

these studies can be used to identify habitat characteristics to control when setting up 

experiments or monitoring and evaluation programs. Predictions of areas likely to support strong 

populations can suggest areas where detailed watershed assessments and habitat inventories 

should be conducted (see the Analyses for Phase II Recovery Planning section, page 40). 

Current and Historical Potential Habitat

A second approach for evaluating the ability of multiple watersheds to support viable 

salmon populations is to examine the distribution and quantity of current and historical habitat 

across multiple watersheds and populations (e.g., comparing 20 populations). These analyses 

use limited habitat survey or historical distribution information supplemented by ancillary 

topographic and hydrologic data. In addition, these analyses rely heavily on geospatial data sets 

such as digital terrain models, Landsat imagery, and regional hydrography to predict physical 

stream features (e.g., Lunetta et al. 1997). The widespread availability of corresponding spatial 

datasets (remotely sensed imagery, Geographic Information System or GIS data, and spatially 

explicit modeling methods) permits the derivation of representative reach-level geomorphic and 

hydrologic information for multiple watersheds (Table 5). This information can be used in 

conjunction with field-based mapping data, such as of anthropogenic modifications (e.g., 

Bortleson et al.1980), to refine estimates of currently and historically available habitat for 

24
different species. Subsequently, methods to refine historical habitat estimates can be used to 

estimate possible levels of historical fish abundance across the watershed. 

A specific example of this type of assessment is an inventory of currently and historically 

available stream reaches and the amount of habitat blocked by migration barriers such as dams, 

diversions, and culverts (e.g., Steel and Sheer in prep.). Where inventories of migration barriers 

are available, they can be used to address several questions for ESUs and their constituent 

populations: 

1. How many kilometers of stream habitat are unavailable or unreachable to fish due to 

anthropogenic barriers?

2. What proportion of historical habitats is currently usable for juvenile and adult 

salmonids?

3. Is current habitat sufficient to support VSPs?

In Phase II recovery planning for identifying and prioritizing actions, these same analyses can 

provide additional information toward developing a list of actions (e.g., which barrier removals 

might provide the greatest habitat and population benefits). 

The first step of the analysis includes stream network acquisition or generation and 

barrier identification. Modeled stream networks can be used to achieve the appropriate data 

resolution and multiple barrier data layers will likely need to be examined for accuracy and 

combined. By comparing historically available habitats to those currently available below 

migration barriers, these analyses describe the type and degree of habitat loss for each 

population. For example, mapping 2,600 barriers in the Willamette/Lower Columbia (WLC) 

ESU and the amount of stream blocked by each shows that certain populations have been more 

strongly influenced by manmade barriers than other populations (Figure 5). 

The second step, using a classification of more detailed information about reach-level 

stream characteristics (e.g., stream gradient, drainage area, or channel width), assesses whether 

accessible or inaccessible stream habitats are or might be useful to fish. The classification 

scheme can be based on existing models, published literature, or interviews with local biologists, 

and reach attributes from field data, aerial photos, or models. In the WLC, stream reaches in 

specific gradient ranges were identified as possible or prime spawning and rearing habitat for 

winter steelhead (Oncorhynchus mykiss), summer steelhead, Chinook salmon (O. tshawytscha), 

and chum salmon (O. keta) based on a survey of local biologists, and these gradients were 

modeled for the WLC domain using a procedure Miller (2003) developed that employs a digital 

elevation model to generate stream reach information (e.g., Figure 6). 

The third step is evaluating whether the amount of usable habitat is sufficient to support 

VSPs across the ESU. In the Puget Sound and WLC ESUs, demographic models were used to 

generate preliminary goals for individual populations (see Northwest Salmon Recovery 

Planning, online at http://www.nwfsc.noaa.gov/trt/). The amount of usable habitat calculated as 

described above was used to verify whether target numbers are realistic given the actual 

proportion of stream kilometers likely to be used by salmon, either currently or historically. Fish 

densities implied by a range of population abundance goals (goals divided by total number of 

prime or possible stream kilometers) can be estimated and evaluated (see Steel and Sheer in prep. 

for additional details). Because the inventory approach is based on multiple GIS data layers, 

25
 Figure 5. Stream accessibility for all streams considered in the WLC analysis. Labels indicate the 

4th field hydrologic basin. 

26
 Figure 6. Example of the identification of potential spawning habitat that is isolated above dams and 

classification of prime habitat for Chinook rearing or spawning in the Lewis River based on 

gradient thresholds. 

27
each with variable precision and accuracy, as well as an estimated classification system, the 

precision and accuracy of the resulting estimates should be assessed (e.g., by comparing 

predictions to field measures as in Lunetta et al. 1997). Continued work on this topic will make 

the inventory approach more useful for decision making. 

Watershed-Level Analyses for Abundance, Survival, and Population 

Growth Rate Goals 

For Phase I recovery planning, watershed-level analyses are designed primarily to assist 

in setting abundance goals for recovery, although they can also shed light on goals for densityindependent survival rates, population growth rates, life history diversity, and spatial structure. 

These watershed-scale analyses provide greater detail on how habitat changes might have altered 

salmon populations than do ESU-level analyses. This additional detail is important in salmon 

recovery planning for several reasons. First, landscape, land use, and habitat differences among 

watersheds prevent generalizations about limiting factors, potential abundance, and habitat 

capacity. Second, populations are locally adapted to watershed-level conditions, so habitat needs 

of salmon populations vary among watersheds. Third, the types of habitat changes that cause 

changes in salmon fitness and survival differ among watersheds. Finally, watershed-level 

analyses answer questions that require fine-scale field data that is only available for particular

watersheds. 

In conducting these analyses, considering how different analysis approaches represent the 

salmon life cycle is important. In a generalized salmon life cycle model (Figure 7), the number 

of surviving fish at the beginning of each life stage is a function of capacity and densityindependent survival in the previous stage. Both capacity and density-independent survival are 

affected by habitat quantity and quality, and the number of smolts per spawner represents the 

combined life stage transitions from spawning to saltwater entrance. Habitat analyses at the 

watershed-level are conducted using one of three life-stage specific approaches: 1) simplified 

limiting factor models, 2) complex expert system models such as the EDT model for evaluating 

limiting habitat conditions, and 3) dynamic life cycle models to estimate population responses 

over time. All approaches are based on the salmon life cycle, and all assess current and 

historical habitat conditions in a watershed to estimate how habitat changes may have altered 

salmon abundance or survival at different life stages. However, the approaches differ in two 

main respects. 

First, each approach emphasizes different aspects of the life cycle and different 

parameters driving stage-to-stage survivorship. Limiting factors models such as the coho salmon 

(O. kisutch) limiting factors model (Reeves et al. 1989) focus on changes in capacity at each 

freshwater life stage and treat density-independent stage-to-stage survivals as constants. The 

EDT model allows both capacity and density-independent survival to change at each life stage, 

but does not estimate population response over time. Therefore, both of these approaches can 

help set abundance goals, but the EDT model can also help assess goals for life stage survivals or 

returning adults per spawner at low population sizes. The dynamic life cycle model allows both 

capacity and survival to vary at each life stage, and also explicitly evaluates population growth or 

28
Figure 7. Generalized life cycle model illustrating linkages among life stage transitions (boxes), habitat 

conditions affecting those transitions (ovals), and human actions altering habitat conditions or 

survival. Expanded panel illustrates a typical density-dependent relationship for one life stage, 

with density-independent mortality at low population size and increasing density dependence as 

population size approaches habitat capacity. (Note however that all life stages may not be 

regulated by density-dependent mechanisms.) 

29
decline over time. Thus it can help evaluate population growth rate goals in addition to 

abundance and survival goals. 

The second main difference among the approaches is in data requirements. The EDT 

model is a relatively rapid assessment technique that relies on expert judgments of current and 

historical habitat conditions for more than 40 individual habitat variables, requiring inputs for 

each reach and each month of the year as well as estimated relationships between habitat and fish 

survival. The empirical limiting factors and life cycle approaches focus on fewer parameters 

measured or estimated from empirical relationships and require longer implementation time due 

to the time required to gather habitat data. 

We review the three approaches to Phase I analyses at the watershed-level in the context 

of setting abundance goals for recovery. For this discussion, we group the limiting factors model 

and dynamic life cycle model together because they both rely on similar reconstructions of 

historical habitat availability. The EDT model is considered separately because of its reliance on 

expert judgment for most of its habitat input data. 

Limiting Factors Model and Life Cycle Model 

The simplest approach to estimating how habitat changes have altered fish abundance at 

the watershed level is to use a simplified limiting factors model to assess current and historical 

habitat availability and production potential (Reeves et al. 1989, Beechie et al. 1994). Many 

studies have quantified juvenile and adult fish use of particular habitat types (e.g., Bisson et al. 

1988, Groot and Margolis 1991, Lichatowich 1999, Montgomery et al. 1999) and general 

patterns of habitat use by different species are largely consistent among studies (Figure 8, Pess et 

al. 2003). Therefore, developing new fish-use relationships for each watershed should not be 

necessary and limited field data should be sufficient to confirm that existing data from nearby 

streams can be used. 

Unlike associations among fish densities and habitat types, habitat data are not 

transferable across watersheds. The natural potential of stream networks and the effects of land 

use on habitat condition vary by watershed (Frissell et al. 1986, Lunetta et al. 1997, Beechie et 

al. 2001, Collins and Montgomery 2001). Therefore, habitat inventories must be conducted 

separately in each river basin or planning area. Assessments for estimating current and historical 

habitat in a particular watershed are conducted in two steps: 1) estimate current and historical 

abundance of habitat types and 2) estimate population responses (simplified limiting factors 

model or dynamic life cycle model). 

Assessing changes in habitat availability 

A habitat classification system suitable for estimating current and historical habitat and 

potential fish production must have two main attributes. First, analysts must be able to associate 

fish abundance and survival with each habitat type. Second, to estimate changes in potential 

production over time, it must be possible to quantify both current and historical areas of each 

habitat type. We recommend a suite of habitat types at two hierarchical scales similar to that 

shown in Table 3. The coarser resolution of habitat types can be mapped from remotely sensed 

data at the reach scale (e.g., topographic maps, aerial photography, or satellite information), 

30
Figure 8. General juvenile salmonid use at the habitat scale. (Compilation of over 60 references.) 

31
whereas the finer resolution of habitat types must be identified in the field at the habitat-unit 

scale (sometimes with the aid of aerial photography). Because these typing systems are nested, 

all reaches within a watershed can be stratified by landscape and land use factors using the 

remotely sensed coarse-resolution data, and reaches within each stratum can be subsampled to 

develop distributions of habitat types within each reach type. This hierarchical system enables 

extrapolation of habitat conditions for unsampled reaches within the watershed. Stratification of 

reach types may include several landscape and land use factors, although a relatively small 

number of strata are desirable to reduce the complexity and number of assumptions and 

calculations. For example, tributary reaches may be stratified simply by slope and land use in 

order to identify changes in pool area as a result of land uses (Beechie et al. 2001). However, the 

same slope classes are not particularly relevant for large rivers, where some combination of slope 

and discharge may be more useful in predicting natural channel patterns (e.g., Leopold et al. 

1964). 

Methods for estimating current and historical habitat abundance differ among habitat 

types. Therefore, describing a single methodology for assessing changes is not possible. 

Instead, we provide an overview of different approaches for assessing a habitat’s present and 

historical conditions with references for greater detail on specific methods (Table 6). 

Assessing population responses 

Once habitat changes have been quantified, changes in potential population sizes can be 

estimated for specific life stages using a simplified limiting factors analysis (e.g., Reeves et al. 

1989) or a dynamic life cycle model (e.g., Emlen 1995, Botsford and Brittnacher 1997, Ratner et 

al. 1997). As described earlier, the limiting factors approach focuses only on juvenile life stages 

and allows habitat capacity to vary while treating survival parameters as constants. By contrast, 

the dynamic life cycle model can incorporate changes in capacity or survival and evaluates 

population responses over long time frames. 

Simplified limiting factors analysis—As described in Reeves et al. (1989), smolt production 

potential from a given habitat type or area is calculated as: 

habitat area × average fish density × survival to smolt (1) 

Comparing the impacts of habitat alterations on smolt production potential requires separate 

estimates for each habitat type. Thus the production potential of a habitat for each life stage 

(e.g., spawning, egg to fry, summer rearing, winter rearing) can be expressed mathematically as: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

×⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = ∑ ∑ = =

k

i

i

n

j

N Aij d

1 1

 (2)

where N is the population estimate, ΣAij is the sum of areas of all habitat units (j =1 through n) of 

type i, and di is the density of fish in habitat type i (Beechie et al. 2003). To compare capacities 

among life stages and identify which habitats may be limiting smolt production, the population 

estimate for each life stage in a given habitat is multiplied by density-independent survival to 

smolt stage so the capacities can be compared in terms of number of smolts ultimately produced 

32
Table 6. General description of analysis approaches for estimating current and historical habitat 

abundance at the watershed level. References provide more detail on methods. 

Habitat type Analysis method Reference 

Reduced off-channel 

or wetland areas 

Historical habitat areas estimated from

historical maps, notes, and photos and often 

field verified by residual evidence of prior 

locations. Present-day areas can be 

measured from aerial photographs and in the 

field. 

Beechie et al. 1994 

Collins and Montgomery 2001 

Lakes Changes to lake areas measured directly

from current and historical maps and 

typically indicate where rivers have been

dammed for hydropower or water supplies. 

Beechie et al. 1994 

Beaver ponds Presettlement beaver pond areas estimated 

based on frequencies of beaver ponds in 

relatively pristine areas or predictive 

methods using stream and valley

characteristics. Present-day pond areas 

within the study area measured using field 

surveys and aerial photography. 

Naiman et al. 1988 

Pollock and Pess 1998 

Tributary and 

mainstem blockages 

Portions of tributaries no longer accessible to 

salmon mapped using inventories of habitat 

upstream of migration barriers. Natural 

barriers to salmon migration must first be

identified to delineate the assessment area. 

Habitat areas upstream of each manmade

barrier must be surveyed to determine how 

much habitat is inaccessible. 

Beechie et al. 1994 

WDFW 1998

OWEB 1999a 

Pess et al. 1999b 

Altered pool 

abundance 

Based primarily on data from reference sites 

within the study area, but may also use 

historical information where available. 

Beechie et al. 1994 

WDFW 1998

OWEB 1999a 

Nickelson and Lawson 1998 

Collins and Montgomery 2001 

33
(Reeves et al. 1989). Equation 2 can also be used to estimate historical spawner capacity (or 

potential population size at other life stages) based on estimates of historical habitat availability 

(Appendix B, page 137). Both spawning and rearing capacities can then be used to develop or 

assess population viability abundance goals and can be incorporated into assessments of factors 

that limit population size. 

Dynamic life cycle model—This second tool for evaluating how habitat changes affect salmon 

abundance and population trends (growth or decline) uses the same current and historical habitat 

data as above. A typical form of habitat-based life cycle model is the Leslie matrix (e.g., Emlen 

1995, Botsford and Brittnacher 1997, Ratner et al. 1997), which can consider how fish move 

from one habitat to another and employ density-dependent or density-independent relationships 

to describe transitions between stages (Greene and Beechie in prep.). Thus each life stage 

transition within this model can be governed by capacity, survival, or movement parameters. 

As with the preceding approaches, there are few estimates of stage-to-stage survivals for 

use in such models and few data to describe the form of relationships among habitat and life 

stages (e.g., density-independent mortality, density-dependent mortality, or density-dependent 

movements to less favorable habitats). However, this approach forces analysts to specify how 

mortality and movements are governed between life stages and address uncertainties stemming 

from lack of data. Therefore, one important use of such models is to begin evaluating which 

assumptions and data most strongly affect model outputs so recovery goals can be set with 

appropriate caution. 

In one example of this approach, Greene and Beechie (in prep.) found that predicted 

improvements in Puget Sound Chinook salmon population growth rate or escapement strongly 

depend upon habitat-specific survival and residency estimates, as well as knowledge of densitydependent mechanisms. Both population growth rate and escapement showed the greatest 

sensitivity to nearshore and ocean survival regardless of the existence and mechanism of density 

dependence (Figure 9). However, simply altering the density-dependence assumptions while 

holding all habitat variables the same altered predicted abundance of Chinook salmon by more 

than a factor of four (Figure 10). 

Such findings have direct implications for setting recovery goals, as well as for 

identifying habitats that may limit recovery of populations. First, these models point out that 

model assumptions and parameters can dramatically alter our predictions of population responses 

to habitat changes, and therefore that abundance goals should be set with these uncertainties in 

mind. Second, such models can identify parameter and model uncertainties (see the Managing 

Uncertainty in Habitat Recovery Planning section, page 74) that substantially alter our 

conclusions about which habitats limit recovery, and therefore shift the focus of restoration 

efforts. Finally, such models can indicate that certain life stage survivals (e.g., nearshore for 

ocean-type Chinook salmon populations) may be most sensitive to habitat change regardless of 

the assumptions about density dependence. However, a sensitivity analysis simply begs the 

questions whether significant habitat changes have occurred and whether restoration is feasible. 

Therefore, such analyses also motivate efforts to characterize poorly understood habitats, their 

effects on salmon abundance and survival, and the extent to which they have been modified. 

34
Figure 9. Sensitivity of lambda (λ) to changes in model parameters when population dynamics of Puget 

Sound ocean-type Chinook salmon are density independent. Each bar represents a percentage 

change in λ resulting from a 5% change in each particular model parameter. Parameters are: 

r = redd survival, sj = juvenile stream survival, dj = tidal delta survival, nj = nearshore survival, 

ox = annual ocean survival, μ = survival through harvest, na = adult survival back through the near 

shore, da = adult survival back through the tidal delta, sa = adult survival back through the stream, 

mx = age-specific fecundity, and ax = age-specific breeding propensity. (From Greene and 

Beechie in prep.) 

35
Figure 10. Predicted Puget Sound ocean-type Chinook salmon escapement as a function of time for 

density-independent survival (DI), density-dependent survival (DDS), and density-dependent 

movement (DDM) scenarios with a spawning capacity of 60,000. (From Greene and Beechie in

prep.) 

36
Ecosystem Diagnosis and Treatment 

EDT, an example of the third approach to watershed-level analyses, is a complex salmon 

production model that has been used throughout the Pacific Northwest for salmon recovery 

planning. The conceptual basis for the EDT model is described in Lichatowich et al. (1995) and 

Mobrand et al. (1997). Details of its structure and use are found in Lestelle et al. (1996). EDT is 

a habitat-based model that relies primarily on expert opinion (best professional judgment) as 

environmental input data. It organizes environmental inputs, estimates habitat condition, and 

predicts fish population performance based on estimated life-stage specific stock-recruit 

functions. Fish population performance is characterized using a “survival landscape,” which is 

created by multiplying the life stage functions together. The survival landscape is typically 

created for environmental states corresponding to two points in time, current and historical, but 

may also be created for other states. The spatial unit of analysis is a reach (e.g., one river mile to 

several river miles). 

There are three levels of information in an EDT analysis. Level 1, the input data, 

includes 45 environmental variables (or some subset thereof), including empirical data (e.g., data 

measured in the field), derived data (e.g., estimated from coarse-scale data), and anecdotal 

information (e.g., best professional judgment). Level 2 is the set of biological rules that drive the 

model. Biological rules are working hypotheses about how a salmonid species at a given life 

stage will respond to a specific environmental variable. The shape of each biological rule is 

estimated from published literature and expert judgment. The level of proof behind each rule is 

ranked based on the information underlying it. The sum of the functions results in a combined 

estimate of the survival of fish throughout the life cycle. Level 3 is the translation of the rules 

into 17 biological performance attributes (variables) that give relative survival or performance by 

life stage. Each attribute is used to modify an idealized or optimum condition that is the 

“historical template.” Survival and performance by life stage are the sum of the 17 biological 

attributes and always a fraction that ranges between 0 and 1. 

EDT has been used to estimate habitat capacity under current, historical, and restored 

conditions. It can help organize existing expert opinion and empirical data to create hypotheses 

about linkages among certain habitat conditions and fish abundance. However, because the 

underlying data and functional relationships are largely untested, the accuracy of any EDT 

outcome is unknown. As noted in Mobrand et al. (1997), this performance measure is an 

indicator of how favorable the environment is or might become for salmon to persist and abound, 

not a predictor of how many will return and when. Currently, the EDT model does not provide 

estimates of the uncertainty (or precision) of the output estimates. Output from EDT will be 

more useful for decision making if estimates of both accuracy and precision can be generated. 

Analyses for Spatial Structure and Diversity Goals 

Both coarse-scale and fine-scale (see Table 3) comparisons of current and historical 

habitat within a watershed can indicate whether the diversity of habitat types available to 

salmonids today are vastly different from the past, whether connectivity between habitat types 

has been altered, and consequently whether spatial structure or life history diversity have been 

37
altered. While changes in distribution and diversity of habitats can be identified through these 

analyses, more detailed work is needed to address patch size and natural dynamics within 

individual systems. In this subsection, we describe existing approaches that relate habitat 

conditions to spatial structure and diversity at multiple scales and suggest how this type of 

information might be used to inform the development of recovery goals. 

ESU-Level Analyses 

Few studies have tried to determine how changes in habitat may have altered the spatial 

structure and diversity of salmon populations and ESUs (Table 4). However, several studies 

have examined genotypic and phenotypic diversity at a variety of scales, and from these studies, 

Healey and Prince (1995) concluded that the majority of genotypic diversity is contained within 

stocks while most phenotypic diversity is greater across populations and landscapes. This 

suggests that ESU-level analyses may find stronger relationships among habitat variables and 

diversity than will watershed-level analyses. 

Several completed large-scale analyses for salmon indicate that habitat changes have 

radically altered spatial structure and population diversity in some ESUs. For example, the 

Interior Columbia Basin Ecosystem Management Project characterized the distribution of 

individual fish species, patterns in fish species diversity and status, and their relationships to 

landscape characteristics (Quigley and Arbelbide 1997). The analyses indicate major declines in 

the number and distribution of salmonid species located in Columbia River subwatersheds. 

Anadromous fish species have been largely extirpated from large portions of their range, and the 

consequences of this for ESU viability need to be addressed. 

In another example of large-scale analysis, Waples et al. (2001) characterized patterns of 

intraspecific diversity for Pacific Northwest salmon species along three major axes: ecology, life 

history, and biochemical genetics. The ecology axis included characteristics of freshwater 

habitats that are of known importance to salmon (i.e., hydrography, temperature, vegetation, 

geology, etc.), and results indicated that diversity within a species was significantly related to the 

ecological diversity experienced by that species. For example, diversity measures were lower for 

pink salmon (O. gorbuscha) and chum salmon, which are limited in distribution to areas directly 

affected by Pleistocene glaciation. In contrast, Chinook salmon and steelhead were broadly 

distributed and exhibited the greatest degree of diversity. Results such as these indicate that 

reductions in species ranges will likely alter spatial structure and diversity within ESUs, and may 

ultimately constrain recovery of some ESUs under certain management scenarios. 

Frissell (1993b) mapped region-wide patterns of extinction and endangerment of native 

fishes in the Pacific Northwest and California using data compiled for inland fishes (Williams et 

al. 1989) and anadromous stocks (Nehlsen et al. 1991). Mapping units were based on a drainage 

basin size of 50–2,000 km2

 and basins were categorized according to the number of species (0–1, 

2–3, 4–5, or 6–8) classified as extinct, endangered, or threatened. Isopleths were then fitted 

between the categories. Results indicated a general increase in endangerment from north to 

south and identified basins in Idaho, Puget Sound, and northern California in which population 

declines were mainly due to large-scale dams and irrigation projects. Dams, logging roads, and 

floods have also damaged habitats and severely threatened faunal diversity in other basins in 

southern Oregon and northern California. 

38
Watershed-Level Analyses 

Smaller scale analyses of the spatial structure and diversity of fish populations are rare. 

One that attempts to bridge this gap is an analysis of bull trout (Salvelinus confluentus) by 

Dunham and Rieman (1999) within the Boise River. Their results indicate that the large-scale 

geometry of catchments can influence the distribution of aquatic species. Bull trout populations 

in larger, less isolated, less disturbed patches are more likely to persist. They conclude that 

disturbance within these habitats should be minimized and speculate that conservation and 

restoration opportunities might best be centered within those patches of intermediate size or 

isolation. 

Conclusions 

Habitat analyses at multiple scales can help set goals for abundance, population growth 

rate, spatial structure, and diversity. These large-scale and small-scale analyses compare current 

and historical habitats to evaluate whether current habitat is sufficient to support VSPs and 

ESUs. ESU-scale analyses apply consistent techniques to examine questions over large spatial 

scales and often rely on remotely sensed information and coarser resolution habitat data. 

Watershed-scale analyses focus on questions relevant to individual populations. 

The analyses discussed in this section are designed to answer questions necessary to set 

biological recovery goals (Phase I). As these goals are set, the next phase of recovery planning 

must address how to reach them. The data and information collected for the analyses can also 

contribute to identification of factors causing decline, quantification of fish-habitat relationships, 

and efforts to prioritize actions for ESUs and watersheds. The next two sections address 

questions pertinent to Phase II recovery planning and prioritizing restoration actions. 

39
ANALYSES FOR PHASE II RECOVERY PLANNING: 

IDENTIFYING ECOSYSTEM RESTORATION ACTIONS 

In this section we describe assessments and inventories that develop site-specific lists of

ecosystem protection and restoration actions within watersheds during Phase II of recovery 

planning. These assessments focus on how land uses have disrupted ecosystem processes and 

functions that support salmon and identify specific actions needed to correct those problems. At 

the ESU-level, analyses described under Phase I identify broad habitat types that are most 

impaired within each watershed (e.g., estuary, main stem, tributary), as well as broad categories 

of habitat recovery actions (e.g., barrier removals). We do not repeat discussion of those 

assessments here. 

Within individual watersheds, two types of assessments can be used during Phase II 

recovery planning. The first type includes screening assessments to focus inventories on areas 

where they are most needed. The second type includes field inventories that identify specific 

locations where habitat protection or restoration actions are needed (see also Figure 3). 

Screening assessments can largely rely on existing data (e.g., geospatial coverages, hydrologic 

data, existing inventories) to map impaired processes, but may also include field assessments of 

biological integrity to help identify potential ecosystem disturbances where causes of habitat 

degradation are more complex (e.g., urban areas). In contrast, specific projects such as fixing a 

culvert barrier or fencing a riparian area can only be identified with field data. Both types of 

assessments are relevant in all ecoregions, although specifics of the assessments and inventories 

may vary depending on which ecosystem processes are most important in the given ecoregion. 

Earlier we presented a conceptual diagram illustrating how natural landscape processes 

and land uses form habitat conditions (Figure 1). Here we expand the conceptual diagram to 

explain linkages among ultimate controls, proximate controls, habitat-forming processes, habitat 

conditions, and biological responses (Figure 11). Ultimate controls are independent of land 

management over the long term (centuries to millennia), act over large areas (>1 km2

), and shape 

the range of possible processes and habitat conditions in a watershed (Naiman et al. 1992, 

Beechie and Bolton 1999). Proximate controls are affected by land management over the short 

term (i.e., years to decades), act over smaller areas, and determine habitat conditions expressed at 

any point in time (Naiman et al. 1992). 

For organizational purposes, we group ecosystem processes and functions into three 

categories: 1) distributed watershed processes (similar to non-point sources, such as supplies of 

sediment and water), 2) reach-level processes that primarily affect the adjacent reach (e.g., 

riparian processes), and 3) other ecosystem functions (e.g., habitat connectivity). Ecosystem

processes are typically measured as rates and characterize what ecosystems or components of 

ecosystems do (SWC 1998). For example, sediment or hydrologic processes in a watershed may 

be characterized by the rates (volume/area/time) at which sediment or water is supplied to and 

transported through specific locations of a watershed. Certain riparian functions such as wood 

recruitment to streams can be viewed similarly. In contrast, other ecosystem functions or 

attributes such as habitat connectivity are not well described as processes. Rather, migration 

barriers reduce the habitat capacity of a system and flow diversions may divert or pump fish into 

40
Figure 11. Schematic diagram of relationships between controls on watershed processes, effects on 

habitat conditions, and salmon survival and fitness (adapted from Beechie and Bolton 1999). 

Dark boxes in upper row are ultimate controls and light boxes are proximate controls. 

41
irrigation systems. Because neither fits neatly into the categories of watershed-level or reachlevel processes, each is considered separately. 

Watershed-Level Analyses 

The purpose of watershed-level assessments is to identify: 1) the natural landscape 

processes and functions in a watershed, 2) the effects of land use on natural processes, and 3) the 

causal relationships between land use and changes to habitat conditions. Ecosystem protection 

and restoration actions resulting from these assessments are directed at protecting and restoring 

beneficial habitat-forming processes rather than attempting to build specific habitat conditions 

(FEMAT 1993, Spence et al. 1996, Moore 1997, Beechie and Bolton 1999). As described 

earlier, the watershed-level assessments systematically identify land use disruptions to habitatforming processes at two levels of resolution. First, screening assessments locate disturbed 

habitat-forming processes using existing GIS data and limited field measurements for groundtruthing (e.g., Lunetta et al. 1997, Quigley and Arbelbide 1997, our Appendix C). These 

assessments identify where processes are most disrupted and focus field inventories on areas 

most in need of restoration. Second, field-based inventories identify specific alterations to flow 

regimes, sediment supply, riparian conditions, habitat connectivity, water quality, and channel 

and floodplain interactions (e.g., OWEB 1999a, our Appendix C). This more detailed 

assessment relies solely on field-based inventories and identifies specific protection or

restoration actions that are required for recovery.

Assessing Degradation of Ecosystem Processes and Functions 

Ecosystem processes and functions to inventory at a minimum should include hydrology, 

sediment supply, riparian functions, channel-floodplain interactions, habitat isolated from salmon 

access, and water quality (Table 7). This suite of inventories is based on current scientific 

knowledge of their effects on salmonid habitat and survival of salmon in freshwater, as well as 

knowledge of how various land use practices affect the processes and functions. The list may 

not include all impacts to salmon in a watershed, but does include those that are clearly 

supported by scientific literature (e.g., Meehan 1991, WDNR 1995, OWEB 1999a) and appear to 

be responsible for a significant proportion of the total loss in salmon production from Pacific 

Northwest river basins. As described earlier in this report, dominant watershed controls and 

natural landscape processes vary by ecoregion, leading to differences in the assessments to be 

conducted for identifying restoration actions (Table 2). 

Watershed-level processes 

Watershed-level processes are those that have multiple, widely distributed sources, 

including sediment supply, hydrology, and inputs of nutrients or pesticides (Table 7). Describing 

how these processes have been disrupted and what restoration actions are required for their 

recovery requires two kinds of assessments. First, process assessments identify the degree to 

which process has been altered by land use and where in each watershed these changes have 

occurred. Second, inventories identify where specific restoration actions must be taken in order 

for recovery to occur. 

42
Table 7. Examples of methods for rating individual landscape processes.

Distributed watershed processes 

Hydrology—disruption of peak flows, low flows, and channel-forming flows 

A change in the magnitude, frequency, duration, and rate of minimum, mean, and maximum flows 

can be examined using the indicators of hydrologic alteration (IHA), which assess the difference in 32 

biologically significant hydrologic parameters of pre- and post-anthropogenic activities such as dam

operations, flow diversion, groundwater pumping, or intensive land use (Ritcher et al. 1997). 

Hydrology—increases in peak flows 

Lowland basins: Hydrologic impairment in lowland basins can be rated based on planned effective 

impervious area (EIA), which is the weighted average EIA upstream of the stream reach under fully

developed conditions. EIA ≤ 3% is considered “functioning,” EIA between 3% and 10% is 

“moderately impaired,” and EIA > 10% is “impaired” (based on Booth and Jackson 1997, and see 

example in our Appendix C). 

Mountain basins: Peak flow ratings for mountain subbasins can be developed based on empirical 

correlations between land use and elevated peak flow in forested basins (Jones and Grant 1996, and 

see example in our Appendix C). 

Sediment supply 

Estimating impairment of sediment supply: Changes in average sediment supply for forested 

subbasins within a watershed can be estimated based on present-day sediment supply rates from

unlogged, clear-cut, and roaded portions of the watershed (Dietrich and Dunne 1978, Paulson 1997, 

Montgomery et al. 1998). 

Surface erosion on croplands and rangelands: Changes in average sediment supply for croplands 

within a watershed can be estimated on crop practices, soil type, rainfall, slope, and other factors 

(Dunne and Leopold 1978).

Routing estimates: In-channel sediment storage assessments identify sediment movement rates and 

can help estimate recovery time of habitats (Madej and Ozaki 1996). 

Inventory—identify sediment reduction projects: Inventories must focus on factors that influence 

sediment supply, identification of landslide hazard areas so forest practices can be avoided or 

modified in sensitive areas (e.g., Montgomery et al. 1998), such as risk of road-related landslides 

(e.g., Renison 1998), crop management practices that increase surface erosion (Wischmeier and Smith 

1965), or grazing practices that alter sediment supply.

Reach-level processes 

Riparian function 

Remote sensing assessment: Use remote sensing classifications of vegetation to assess riparian buffer 

width and type in order to help determine how much and where riparian buffer impairment has 

occurred on a reach or river basin scale. Identify historical conditions using reference locations and 

historical documentation. Compare historical condition to current riparian condition in order to

determine degree of change. 

Field inventory: In addition to documenting forested buffer width, field inventories also classify stand 

types by species mix and seral stage, which gives sufficient information to prescribe generalized 

management regimes for each segment of riparian forest (Collins et al. 1994). Inventories also 

identify areas of livestock access and potential fencing projects. 

Riparian alteration due to grazing: Use similar methods but include indicators such as stubble height 

measurements as indicator of disturbance (Clary and Lenninger 2000, Turner and Clary 2001). 

43
Table 7 continued. Examples of methods for rating individual landscape processes. 

Reach-level processes (continued)

Channel and floodplain interactions 

Floodplain areas can be delineated using 100-year floodplain maps from the Federal Emergency 

Management Agency or U. S. Geological Survey 7.5-minute quadrangles and aerial photographs. 

Habitat connectivity—anthropogenic blockages 

Manmade barriers to anadromous fish habitat are identified through a systematic field inventory of 

channel crossing structures (culverts, tide gates, bridges, dams, and other manmade structures). The 

inventory identifies the type and physical dimensions of structures as well as physical attributes 

necessary for modeling water flow conditions and comparing results to passage criteria for salmonids 

(e.g., WDFW 1998). 

Disrupted water quality 

Contaminants

Association of fish assemblage structure and environmental variables: Compare fish assemblage 

composition to chemical and physical environment (Waite and Carpenter 2000).

44
Identifying altered sediment supply—Sediment supply to streams is altered by many processes 

including changes in mass wasting due to logging and road building (e.g., Sidle et al. 1985), 

increased surface erosion after prescribed burns (e.g., Megahan et al. 1995), increased surface 

erosion from unpaved road surfaces (e.g., Bilby et al. 1989), and surface erosion and gullying 

after grazing (Platts 1991, Elmore 1992, Johnson 1992, Trimble and Mendel 1995). We present 

two main approaches to understanding disruptions to these sediment supply processes: 1) 

budgeting and 2) landscape indicators based on known relationships of certain water and land 

uses to the parameter in question. The budgeting approach is often used for sediment supply, but 

can also be used for inputs of nutrients or pesticides to water bodies. The general budget can be 

stated in equation form: 

∆S = I – O (3)

where ∆S is change in storage, I is input, and O is output (e.g., Reid and Dunne 1996). In 

essence, S is the stream condition for any parameter (e.g., the amount of sediment or of a 

pesticide in the stream), and quantifying changes in inputs or outputs indicates how land uses 

have altered the stream ecosystem. In many cases, it may only be necessary to quantify how 

inputs have been altered by land uses, which is called a partial budget. That is, where changes to 

outputs are negligible, an increased input is approximately equal to the change in storage and to 

the altered stream condition. Therefore, it may not be necessary to understand output processes 

in detail (e.g., sediment transport) in order to calculate change in storage and understand how the 

stream ecosystem has been altered. 

We illustrate the partial budget approach by describing how one might approach two

different altered sediment inputs to streams due to land use: 1) extrapolation of limited empirical 

data for estimating increased fine sediment supply from tilled croplands in the Blue Mountain 

Level III ecoregion, and 2) an empirical approach to estimating changes in landslide rates due to 

forestry activities in the North Cascades Level III ecoregion. Both approaches focus on 

identifying where sediment supplies to streams have been significantly altered and can help 

focus restoration efforts on areas that contribute large amounts of sediment. Note that these 

approaches do not identify the exact locations of restoration actions, which require specific 

inventories of croplands where eroded sediments are delivered directly to streams or road 

segments with high risk of landsliding. For efficiency, these inventories can initially target those 

areas of high sediment supply identified by the partial sediment budgets. 

In the first example, the partial sediment budget for croplands makes use of measured 

erosion rates from soils with and without cover crop. Soils without cover crop erode at rates as 

much as 10 times higher than soils with cover crop (see complete overview of processes and 

rates in Dunne and Leopold 1978). The rate varies with soil type, rainfall, slope, cover type, and 

other factors. Local erosion rates for different soils and cover crops have been measured in 

many areas by researchers and land management agencies. Examples for different soils and 

cover crops can be found in Dunne and Leopold (1978). From known rates, a simple cumulative 

model for basins can be expressed as: 

Ic = (Ac x Ec) + (Anc x Enc) (4)

where Ic is the current total estimated sediment input to the selected reach, Ac is the area of land 

45
with cover crop or other natural vegetation, Ec is the erosion rate per unit area with cover crop, 

Anc is the area of land with no cover crop, and Enc is the erosion rate per unit area without cover 

crop. (Additional parameters may be added for other cover types as needed.) A natural 

background rate of sediment supply from surface erosion can be estimated by applying the 

natural erosion rate (In) to the entire basin: 

In = (Ac + Anc) x Ec (5) 

Calculations of Ic/In for various subwatersheds within a region can then be compared to identify 

those areas where tilling is likely to have significantly altered sediment supply to streams. 

Where soil erosion rates have not been measured, it may be necessary to use predictive equations 

such as the Universal Soil Loss Equation (Wischmeier and Smith 1965, 1978) or Revised 

Universal Soil Loss Equation (Renard et al. 1991, 1997). Dunne and Leopold (1978) provide a 

good overview of the equation and its application, along with charts and tables for estimating 

parameters in the equation, and the original handbooks can be consulted for greater detail on the 

methods. Estimates of local soil parameters are generally available from local USDA Natural 

Resources Conservation Service offices. 

In the second example, a partial budget for sediment supply in coastal forests is 

constructed by conducting landslide inventories from historical aerial photographs and 

estimating contributions of fine sediments from road surface erosion (e.g., Paulson 1997). The 

general approach is similar to that for the cropland erosion example, where the final product of 

the partial sediment budget is Ic/In and comparisons among basins indicate areas where sediment 

supplies have been significantly altered. In these inventories, landslides are enumerated and 

measured on each aerial photograph and volume of each landslide is calculated based on a 

relationship of photo-measured area to field-measured volume for a subset of the recent 

landslides. Land use association is also recorded for each landslide (e.g., clear-cut, road, or 

mature forest), allowing estimation of the aggregate impact of land use on the sediment input, as 

well as identification of the land uses most responsible for changes in sediment supply. Surface 

erosion estimates can be based on characteristics of road surfaces, cut and fill slopes, and 

precipitation (e.g., WDNR 1995). Calculation of In typically assumes there is no surface erosion 

(overland flow is rare in the Coastal Forest ecoregion) and the landslide sediment production rate 

for mature forests can be applied to the entire basin. These sediment budgets can then be 

compared to identify areas where sediment supplies have been most altered and where modified 

timber harvest practices or road modifications have the greatest impact on ecosystem recovery. 

Landscape and land use indicators of altered sediment supplies can be developed from 

such sediment budgets (or field studies of erosion) in order to more rapidly screen large areas for 

disrupted sediment supply. For example, GIS maps of geology, soils, and hillslope angles can 

identify areas that are prone to landsliding (e.g., Montgomery and Dietrich 1994, our Appendix 

C), and overlays of land cover and roads can identify where landsliding has likely increased 

(Appendix C). Results of such screening analyses can then identify areas where inventories of 

potential restoration actions should be focused. 

In general, remote sensing methods and mapping of landscape indicators identify areas 

for passive restoration, but field inventories are required to identify active restoration projects. 

For areas dominated by mass wasting, mapping identifies areas particularly prone to landslide 

46
hazard and sensitive to land uses such as clear-cut logging or road building (Figure 12). Such 

maps identify passive restoration actions (e.g., areas to avoid for future logging), which allow 

recovery of sediment supply rates by preventing or modifying land uses within hazard areas. 

Road landslide hazard inventories identify specific areas for active restoration (e.g., removal of 

hazardous roads). Road inventories should identify road segments at risk of failure (e.g., 

Renison 1998) as well as specific stream crossings, cross drains, or fills likely to fail. Each 

potential failure site can be itemized on project lists for restoration action. These actions can 

then be prioritized based on protection of refugia, potential impact to stream habitat, smolt 

production, cost, and other factors. 

Where surface erosion and gullying are dominant processes, terrain and soil conditions

that lead to severe erosion can also identify areas where modified agricultural practices can 

reduce erosion and sediment supply to streams. Modeling of altered sediment yields by various 

conservation practices can help identify the types of agricultural practices that are greatest 

contributors to erosion (Williamson et al. 1998) as well as those restoration actions that are most 

likely to be successful (Ebbert and Roe 1998). Actions can also be itemized and prioritized 

based on a number of factors including the magnitude of reduced erosion and costs. 

Identifying altered hydrologic regime—Hydrologic processes can be altered by land uses in a 

variety of ways, including increased peak flows from impervious surfaces (e.g., Booth and 

Jackson 1997), livestock compaction (Trimble and Mendel 1995), increased peak flows from

increased snow accumulation and melt (e.g., Zeimer 1981, Harr 1986, Beschta et al. 2000), and 

decreased peak flows or low flows from dams and withdrawals (e.g., Ritcher et al. 1996, Donato 

1998, Spinazola 1998). Assessments of increased peak flows typically utilize landscape 

indicators of changes to watershed processes based on known functional relationships between 

land cover and peak flows (e.g., Booth and Jackson 1997, Beschta et al. 2000, our Appendix C), 

and may include detailed models that project changes in peak flow hydrographs as a result of 

land cover changes (e.g., Booth and Jackson 1997). Assessments of low flow changes typically 

include inventories of total withdrawals and calculation of the proportion of stream flow 

removed (e.g., Donato 1998, Spinazola 1998), as well as indirect estimates based on power 

consumption at pumping stations (e.g., Maupin 1999). 

Known relationships among zoning, impervious surface area, and changes in hydrologic 

processes and biota have been used to indicate changes in hydrologic regime in urban areas (see 

Appendix C). Where impervious surface areas are less than 3% of the watershed area, 

hydrologic regime is not significantly different from one with no impervious surfaces (Booth and 

Jackson 1997). However, where impervious surfaces are more than 10% of the watershed area, 

hydrologic regime has likely been altered to the point where changes in biota are severe 

(Lucchetti and Furstenberg 1992, Moscrip and Montgomery 1997, May et al. 1997). Similar 

analyses can be developed for other watershed processes such as contaminant runoff from

agricultural or urban areas, where high concentrations of compounds that are toxic to salmon 

alter their behavior in ways that could reduce survival (Scholz et al. 2000). More general 

landscape indicators such as percent of a watershed in urban land cover may be greater 

predictors of biological condition, because they are more inclusive measures of anthropogenic 

disturbance and not just a change in one specific watershed process such as hydrologic regime or 

water quality (Karr and Chu 2000, Morley and Karr 2002). Examples of use of land cover 

indices for peak flow changes from rain-on-snow are also included in Appendix C. 

47
Figure 12. A is map of areas in the Skagit River basin where sediment supply has likely increased due to 

land use, based on extrapolation of data from sediment budgets. B is landslide hazard map for a 

portion of the upper Cascade River basin. C is hazard map of U.S. Forest Service roads classified 

by risk of failure. 

48
Indicators of hydrologic alteration (IHA) can also be used to assess the degree of 

hydrologic alteration within a watershed (Ritcher et al. 1996). The method summarizes complex 

hydrologic variation using 32 stream flow parameters that have biologically relevant attributes 

(Ritcher et al. 1996). The hydrologic data is from known sources such as stream gages and 

wells. Each parameter is separated into pre- and post-impact time frames, and the central 

tendency (defined as the mean and median) and dispersion (defined as the variance and 

coefficient of variation) are compared to assess degree of hydrologic perturbation (Ritcher et al. 

1996). Perturbation can include activities such as dam operations, flow diversion, groundwater 

pumping, or intensive land use (Ritcher et al. 1997). The tool is used with other ecosystem

metrics (e.g., biological integrity indices) and can help set stream flow restoration targets, 

identify areas of hydrologic alteration, and measure progress toward quantified conservation 

goals (Ritcher et al. 1996, 1997, 1998). The tool is not meant to predict biological response to 

hydrologic alteration (Ritcher et al. 1996). 

Low-flow impairments can be identified through inventories of diversions and 

quantification of water withdrawals. Data availability varies between large dams and small, 

private irrigation or water supply diversions (Spence et al. 1996, Quigley and Arbelbide 1997). 

For large dams (>2 m high), inventory data are available for regional characterization of water 

withdrawals and cumulative withdrawals can be calculated (e.g., Quigley and Arbelbide 1997). 

However, data for smaller diversions and their effect on stream flows are less readily available 

(Spence et al. 1996), and inventories of low flow changes are needed to systematically identify 

stream reaches where low flows are at issue within individual watersheds.

The existing 303d database from the U.S. Environmental Protection Agency (EPA) 

identifies more than 13,000 km of flow-impaired stream reaches in the interior Columbia River 

basin (Quigley and Arbelbide 1997) as well as additional streams in western Oregon and 

Washington. This database can be used as a preliminary inventory of withdrawals and low-flow 

impairments. More detailed field inventories of withdrawals can be conducted within 

watersheds to list all small dams, diversions, and pump stations that withdraw water from

streams and assess the degree to which stream flows are reduced. State water rights databases 

(e.g., Washington Department of Ecology, Oregon Water Resources Department) provide a 

starting point for such assessments, although field inventories of actual withdrawals are often 

needed to confirm which water rights are currently in use. In general, ranking the proportion of 

stream flow withdrawn in various reaches indicates which reaches deviate most significantly 

from natural stream flows and are most likely in need of increased stream flows. Methods 

available to identify specific locations where water withdrawals impact stream flows include 

source metering using stream flow gauges or other measurement devices (e.g., Donato 1998), 

direct assessment of flow change (e.g., Ritcher et al. 1996), and estimates of water withdrawal 

based on power consumption at pumping stations (e.g., Maupin 1999). 

In areas where hydrologic regime approximates the natural regime, ecosystem

management should focus on protecting current hydrologic processes. These actions might 

include avoiding additional hydrologic changes by preventing new impervious surfaces and 

forestry impacts to peak flows. By contrast, where hydrologic regime deviates significantly from 

the natural regime, restoration actions should be identified. For alterations to peak flows, 

identification of restoration actions may include actions to alleviate impervious surfaces (Holz et 

al. 1998, Maryland DER 2000) and impacts of clear-cuts and roads on peak flow responses. For 

49
low flow impairment, Instream Flow Incremental Methodology (IFIM) or variations of it can 

help identify how much stream flow is necessary to support aquatic ecosystems at both low-flow 

and high-flow periods (Jowett 1997). 

Identifying altered water quality—Water quality parameters can also indicate areas with a high 

likelihood of disruption, especially with regard to temperature and nutrient or pesticide inputs. 

Again, the EPA 303d list provides a useful starting point for identifying disruptions to water 

quality. Many streams throughout the West are listed as water quality impaired, which indicates 

that some type of restoration may be necessary. In general, further field inventories are 

necessary to clarify the exact nature of the problem and identify corrective actions. 

Water quality assessments may include direct measures of water quality parameters and 

relationships among parameters and biotic assemblages may help identify where disruptions are 

most important (Waite and Carpenter 2000). For example, multivariate classification and 

ordination were used to examine patterns in chemical and physical variables in association with 

relative fish abundance in the Willamette River basin (Waite and Carpenter 2000). Patterns of 

fish assemblages were primarily related to water temperature, dissolved oxygen, and stream

channel gradient at the ecoregion scale. However, chemical concentrations of pesticides and 

total phosphorus were more important than physical habitat features in low gradient floodplain 

regions such as the Willamette Valley (Waite and Carpenter 2000). Water quality restoration 

actions may thus need to be a priority in such areas. (Additional biological indicators are 

discussed later in this section.) 

Reach-level processes 

Reach-level processes are those processes that directly affect the adjacent reach (Table 

7), such as riparian functions and floodplain-channel interactions. An extensive body of 

literature describes linkages between riparian forest functions and stream habitat, which in turn 

affect the survival and abundance of salmonids. Riparian functions include supply of wood and 

leaf litter to streams (Naiman et al. 1992), shading (Beschta et al. 1987), and root reinforcement 

of streambanks and floodplain soils (Platts 1991, Elmore 1992). Dominant functions vary by 

ecoregion, though many streams even in the driest ecoregions have or had a forested riparian 

corridor (Platts 1991). Channel and floodplain interactions form a wide array of habitats that 

salmonids historically occupied (Sedell and Luchessa 1982, Peterson and Reid 1984, Collins et 

al. 2002). Many of these habitats are now either destroyed or inaccessible to salmon due to the 

effects of levees, dams, channel incision, or other land uses (Sedell and Luchessa 1982, Beechie 

et al. 1994, Peacock 1994, Shafroth 1999, Pohl 1999, Beechie et al. 2001, Collins et al. 2002). 

Identifying disrupted riparian processes—The level of wood input or other riparian functions 

increases with increasing width of forest buffer on streams (Figure 13), and the proportion of 

function occurring within a given distance of the channel edge varies by function (e.g., Sedell et 

al. 1997). These types of relationships can be used to evaluate the current status of functional 

interaction between a stream reach and riparian area and indicate whether existing levels of 

riparian protection are sufficient to ensure continued function. A similar assessment approach 

can be applied to other types of riparian systems and for different riparian functions. 

50
Impaired

Moderately

impaired

Functioning

Riparian buffer width (m)

0 20 40 60

100

0

50

80

Figure 13. Illustration of change in riparian function with distance from channel (curves adapted from

Sedell et al. 1997) and the Skagit Watershed Council’s classification of impaired, moderately

impaired, and functioning riparian forests. 

51
Two types of assessments are required for reach-level processes. First a coarseresolution assessment identifies where reach-level processes have been disrupted, providing a 

general sense of the change in riparian function from historical conditions (e.g., Lunetta et al. 

1997, OWEB 1999a). Subwatersheds where the current distribution of riparian conditions 

deviates markedly from that expected under a natural disturbance regime are general locations 

where riparian restoration efforts may be appropriate. The same data can also help resource 

managers understand how land use practices differ in degree of impact on riparian functions, 

which can then help assess the potential impacts of large-scale land use policies on salmon 

habitat recovery (e.g., evaluating potential effects of growth management legislation). 

Second, field inventories of riparian sites must be used to identify specific restoration 

actions (Clary and Leninger 2000, see our Appendix C). Field inventories may consist of initial 

measurements and classification from aerial photography combined with field confirmation of 

the riparian vegetation conditions for each stream reach. Newly developed, multispectral 

technologies may also identify riparian conditions with sufficient detail for site-scale planning 

purposes. At a minimum, they should classify riparian conditions by buffer width, stand type, 

and age of vegetation. From the data, managers can identify impaired or moderately impaired 

stream segments in order to determine the likely cause of that impairment and identify required 

restoration actions. In general, impairment is defined with respect to a natural reference 

condition, which is usually based on historical information (e.g., OWEB 1999a, Collins and 

Montgomery 2001, our Appendix C). However, in grazed riparian areas where the natural 

riparian vegetation is not forested, identification of disrupted riparian function may rely on other 

measures to indicate levels of disturbance (Clary and Leninger 2000, Turner and Clary 2001). 

Regardless of current condition of riparian areas, establishing protected areas along the 

channel where natural riparian vegetation can develop through time and interact with the stream

is a necessary component of passive riparian restoration. Active restoration efforts may include 

exclusion of livestock in drier riparian systems with less woody vegetation (Clary et al. 1996, 

Clary 1999), as well as planting desired riparian plant species or manipulating existing 

vegetation to accelerate tree growth and the development of desired stand structural 

characteristics (Berg et al. 1996, Beechie et al. 2000). 

Channel-floodplain interactions—Disruptions to floodplain and channel interactions may also 

dramatically reduce abundance of wood, pools, and off-channel habitats in larger river systems. 

These disruptions may result from altered sediment and wood supplies (e.g., downstream of 

dams, Pohl 1999, Shafroth 1999), installation of dikes and riprap to control channel movement 

(e.g., Beechie et al. 1994, Collins et al. 2002), and channel incision that isolates a channel from

its floodplain (e.g., Peacock 1994). Inventories that can help identify these disruptions include 

measurement of channels no longer accessible to salmon (Sedell and Luchessa 1982, Beechie et 

al. 1994), mapping of dikes, riprap, and disconnected floodplain surfaces (Appendix C), aerial 

photograph inventories of channel and habitat changes downstream of dams (Pohl 1999, 

Shafroth 1999), and inventories of incised channel segments. 

Stream channel classification systems can also help identify disruptions in channelfloodplain interactions. Stream classification systems use a suite of variables such as geology, 

valley floor constraint, and channel slope to determine stream channel type and response of 

channels to changes in inputs such as water, wood, sediment, and energy over an entire 

52
watershed (Table 8). Stream channel classification systems reduce the number of variables and 

measurements needed to differentiate site response to channel-floodplain disruption by 

controlling for some of the site-to-site variation in landscape characteristics. The features used 

to classify channel reaches and valley segments are often relevant for the development of 

channel-floodplain restoration plans, as stream reaches that have similar physical characteristics 

will respond to restoration actions similarly. These classification systems should be used in 

conjunction with the preceding inventory methods. 

Habitat connectivity—impaired fish passage 

Assessing isolated habitats is one of the simplest inventories to conduct, because criteria 

for fish migration blockages are relatively clear and identifying the amount of habitat affected 

involves little subjectivity. The Northwest states developed fish passage criteria for juvenile and 

adult salmonids to use as the basis for identifying fish blockages (WDFW 1998, ODFW 2001). 

Private landowners, watershed groups, and local, state, and federal agencies subsequently 

developed systematic methods to restore habitats through barrier inventory, assessment, and 

allocation of funds to correct the fish passage problems identified (ODFW 2001, WDFW 2001). 

Combining these inventory results with cost estimates for restoration actions allows resource 

managers to rank the cost-effectiveness of individual projects in order to more effectively direct 

the expenditure of limited restoration funds. For example, cost-effectiveness of reconnection 

projects can be estimated based on the habitat area upstream of the project multiplied by the 

average life span of a blockage (≈50 years) and divided by the cost of the project (Pess et al. 

2003). These results allowed natural resource agencies to identify the most cost-effective 

projects for reconnecting blocked tributary habitats based on benefits to multiple salmonid 

species, as well as costs of reconstructing individual stream crossings (Pess et al. 2003). 

Water diversions can also impair fish passage and have been a recognized problem for 

salmonids in the Northwest. There are almost 76,000 permitted water diversions in Oregon 

alone, however, many do not affect ESA-listed salmonids (OPSW 2001). NOAA Fisheries, 

California, Idaho, Oregon, and Washington have all developed criteria to exclude both juvenile 

and adult salmonids from being entrained in water diverted without being impinged on diversion 

screens (NMFS 1995, CDFG 2000, ODFW 2001, WDFW 2001). The criteria developed by state 

and federal agencies allow for the development of inventories because it involves less 

subjectivity. Again, combining these inventory results with cost estimates for restoration actions

allows managers to rank the cost-effectiveness of individual projects in order to more effectively 

direct the expenditure of limited restoration funds. 

Assessing Biological Integrity 

A key component of salmon habitat is the stream biota itself. Invertebrates, amphibians, 

diatoms, and other stream organisms are integral parts of the aquatic food web upon which 

threatened and endangered fish species depend. These assemblages are also sensitive to a variety 

of watershed disturbances expressed over multiple spatial scales, and therefore excellent 

indicators of stream condition. Unlike anadromous fishes that are subject to varied disturbances 

in both the marine and freshwater environments (e.g., migration blockages, interaction with 

hatchery fish, damaged estuarine habitats, or overharvest), less migratory stream organisms often 

53
Table 8. Summary of contemporary spatial scale classifications. (Adapted from Bauer and Ralph 1999.) 

Spatial scale addressed by classification system 

Classification 

system reference 

Ecoregion River 

basin 

Watershed SubwatershedValley

segment 

Stream

reach 

Habitat 

unit 

Bisson et al. 1982 X

Frissell et al. 1986 X X X X X

Seaber et al. 1987 X X X

Paustian 1992 X X X

Maxwell et al. 

1995 

X X X X X X

Rosgen 1994 X X

Montgomery and 

Buffington 1997 

X

Omernik and 

Bailey 1997 

X X X

54
provide a more accurate reflection of site condition. Much research in the field of biological 

assessment (measuring and evaluating biota directly) has focused on benthic invertebrates as 

indicator organisms (Rosenburg and Resh 1993, Merritt and Cummins 1996). Over the past 

century, bioassessment techniques using invertebrates and other assemblages have ranged from

saprobien indexes (Hilsenhoff 1982) to toxicity testing (Buikema and Voshell 1993), indicator 

species abundance (Farwell et al. 1999), diversity indexes (Wilhm and Dorris 1966), and more 

recently to multivariate models (Wright et al. 2000) and multimetric indexes (Davis and Simon 

1995). 

In the context of ecosystem recovery planning, these bioassessment tools can identify 

high quality areas in need of protection and degraded reaches in need of restoration, and assist in 

identifying the specific stressors causing biological impairment (such as factors discussed in the 

preceding subsections of this section). This type of information will help focus assessments of 

disrupted ecosystem processes on those impacts that are most biologically important (Beechie 

and Bolton 1999). Of late, many studies incorporating specific bioassessment tools have 

evaluated the relationships between these measures of instream biological condition and land 

uses/land cover patterns over multiple spatial scales (Steedman 1988, Richards et al. 1996, Allan 

et al. 1997, Wang et al. 1997, Morley and Karr 2002). Some applications of this research include 

linking specific land use impacts and current condition of stream reaches, and setting realistic 

recovery goals given current land use patterns in particular river basins. We anticipate that these 

biological assessments will be most valuable in urban and agricultural areas where multiple 

impacts are likely to have occurred and the array of necessary process assessments may be 

prohibitively expensive without information to help prioritize them. 

Multivariate models 

In this approach a predictive model is developed based on a large (≈200 sites) data set of 

reference (minimally disturbed) sites (Reynoldson et al. 2001). Using multivariate statistical 

analyses, reference sites are matched to a set of habitat descriptors (e.g., stream order, elevation, 

geology, etc.) and classified into groups. Level of impairment at a given sample site is then 

determined by comparison to the appropriate reference group. This approach has been most 

widely applied with the development of RIVPACS (River Invertebrate Prediction and 

Classification System) in England, AUSRIVAS (Australian River Assessment Scheme) in 

Australia, and BEAST (Benthic Assessment of Sediment) in Canada (Wright et al. 2000). In the 

Pacific Northwest, multivariate models have been developed for the Fraser River basin in British 

Columbia (Reynoldson et al. 2001) and with the BORIS (Benthic Evaluation of Oregon Rivers) 

model in Oregon (Canale 1999). Based on benthic invertebrates, BORIS scores a site from 0 

(severe impairment) to 100 (comparable to reference condition). A RIVPACS-type predictive 

model applicable to wadeable streams throughout Oregon, Washington, and Idaho is currently 

being developed (Hawkins and Ostermiller 2001, G. Hayslip1

). 

Multimetric indexes 

Multimetric indexes, such as an Index of Biological Integrity (IBI), integrate empirically 

tested metrics of stream biota (Karr and Chu 1999). This approach was first developed using 

 

1

 G. Hayslip, U.S. EPA, Region 10, 1200 Sixth Ave., Seattle, WA 98101. Pers. commun., March 2002. 

55
fish communities in the midwestern United States (Karr et al. 1986), but has since been modified 

for a variety of assemblages—most commonly fish (Simon 1998), invertebrates (Kerans and 

Karr 1994), and algae (Hill et al. 2000). As with multivariate models, IBIs and other multimetric 

indexes are regionally calibrated based on ecoregion designations and local reference conditions. 

In the Pacific Northwest, an IBI using benthic macroinvertebrates was developed and calibrated 

with data from both Oregon and Washington (Kleindl 1995, Fore et al. 1996, Morley 2000, 

Adams 2001). This index, known as the Benthic Index of Biological Integrity or B-IBI (Karr 

and Chu 1999), is composed of 10 measures of taxa richness, population structure, disturbance 

tolerance, and feeding ecology (Table 9). When scores from these metrics are summed, the 

B-IBI provides a numeric synthesis of site condition that ranges from 10 (poor) to 50 (excellent) 

and can determine five categories of resource condition (Doberstein et al. 2000). Multimetric 

indexes developed in other states differ somewhat in field methods and metrics (Hayslip in 

press). 

Applications 

While many of the current bioassessment protocols in use across the nation were 

developed largely in response to legal mandates under the CWA, these monitoring tools have 

much application to recovery planning under the ESA. In response to the CWA’s legal mandate 

“to restore and maintain the chemical, physical, and biological integrity of the nation’s waters” 

(CWA 1972) and under EPA guidance (Plafkin et al. 1989, Barbour et al. 1999), states 

developed assessment protocols and water quality standards to determine if their water bodies 

are supporting beneficial uses such as recreation, domestic water supply, and—most pertinent to 

recovery planning efforts—aquatic life attainment. Although these assessment protocols and 

water quality standards were traditionally focused primarily on physical and chemical criteria, 

over the last decade there has been increasing incorporation of biological indicators that directly 

measure aquatic life and the maintenance of “biological integrity.”

Biological integrity has been defined in many ways. Here we use it as defined by Karr 

(1991): “the capability of supporting and maintaining a balanced, integrated, adaptive 

community of organisms having a species composition, diversity, and functional organization 

comparable to that of the natural habitat of the regions.” States and other public and private 

entities use information collected under the CWA reporting requirements in much the same way 

that it could be applied to ESA recovery planning, for examples: in watershed assessments that 

inventory biological condition across large areas and quantify level of impairment; as a screening 

tool for identifying areas in need of further biological, physical, or chemical evaluation; in risk 

assessments, pollution permitting, and evaluation of proposed habitat modifications; and in 

prioritizing areas in need of protection and restoration, then evaluating these conservation actions 

(Yoder and Rankin 1998, Karr and Chu 1999, Morley and Karr 2002). In the following 

paragraphs, we describe biological assessment protocols currently in development or in place in 

the western United States. 

State environmental agencies apply both multimetric and multivariate techniques to 

assess and report on the biological integrity of surface waters. The Idaho Department of 

Environmental Quality has developed an ecological assessment framework for wadeable streams 

and larger rivers that is composed of four multimetric indexes based on invertebrates, fish, 

diatoms, and physiochemical parameters (Grafe 2000a, 2000b). The California Department of 

56
Table 9. The 10 metrics of the B-IBI and their predicted responses to increasing human disturbance. 

Metric Description Response

Taxa richness and composition 

Total taxa Richness Decrease 

Mayfly taxa Richness Decrease 

Stonefly taxa Richness Decrease 

Caddisfly taxa Richness Decrease 

Population structure 

Dominance by top 3 taxa Relative abundance Increase 

Long-lived taxa Richness Decrease 

Tolerance and intolerance

Intolerant taxa Richness Decrease 

Tolerant taxa Relative abundance Increase 

Feeding and other habits 

Clinger taxa Richness Decrease 

Predators Relative abundance Decrease

57
Fish and Game uses a 19-metric invertebrate stream bioassessment procedure modified from the 

EPA’s national Rapid Bioassessment Protocols (Barbour et al. 1999). The University of Alaska 

at Anchorage, working in conjunction with the state’s Department of Environmental 

Conservation, is developing an invertebrate multimetric stream condition index for three stream

types defined by gradient and substrate (Major et al. 2001). The Washington Department of 

Ecology and Oregon Department of Environmental Quality currently apply a combination of 

multimetric and multivariate approaches to assess the condition of streams and rivers (Mochan 

and Mrazik 2000, Plotnikoff and Wiseman 2001). All the states discussed above include 

narrative biological criteria in their water quality standards; taking this a step further, Oregon 

will soon incorporate numeric biocriteria in its standards (Hayslip footnote 1). 

Although the state environmental agencies are largely responsible for defining, 

evaluating, and protecting designated uses of water bodies under the CWA, federal, tribal, and 

local regulatory agencies, volunteer, nonprofit, and private organizations, and academic 

institutions also conduct bioassessments to varying degrees. At a regional level, monitoring 

work conducted under the Northwest Forest Plan includes bioassessment of invertebrates, 

periphyton, and fishes (Reeves et al. 2002). In the Puget Sound region, the B-IBI has been 

applied by city and county agencies (King County 1996, Thornburgh and Williams 2000), 

university scientists (May et al. 1997, Larson et al. 2001, Morley and Karr 2002), and volunteers 

(Fore et al. 2001) to track the health of streams over time, screen watersheds for further physical 

or chemical monitoring, and evaluate various restoration and conservation strategies. For 

example, two studies recently conducted in Washington used the B-IBI and other invertebrate 

metrics to evaluate the biological effectiveness of wood placement in forested (O’Neal et al. 

1999) and urban (Larson et al. 2001) basins. 

Tuning restoration efforts to site-specific needs is enhanced by using biology to aid 

detection of primary causes of degradation. Both multimetric indexes and multivariate analyses 

provide a numeric synthesis of the biological dimensions of site condition, but they can also be 

broken down to derive descriptive and potentially diagnostic information from each of the 

component metrics. For instance, in the case of aquatic invertebrates, there are hundreds of 

species throughout the Western states—each with specific life history requirements and varying 

tolerance to specific forms of disturbance (Rosenberg and Resh 1993, Merritt and Cummins 

1996). The groundwork has already been laid for research on biological response signatures: 

“biological community characteristics that aid in distinguishing one impact type over another” 

(Yoder 1991, Yoder and Rankin 1995). What remains for future research is to better link 

biological response variables with physical and chemical manifestations of human disturbance. 

In addition to bioassessment for lotic waters, protocols are being developed for lake (Gerritsen et 

al. 1998), wetland (Adamus et al. 2001), and estuarine environments (Gibson et al. 2000). This 

evolving body of work will enable resource managers to better track ecological health over a 

larger portion of the salmon landscape. 

Conclusions 

Assessments of current and historical conditions of a watershed can greatly improve our 

efforts to plan, implement, and monitor ecosystem restoration for the recovery of Pacific salmon. 

58
Systematically collected habitat data, a more thorough understanding of fish responses to habitat 

change, and a greater understanding of stream biota will allow refinement of the modeling tools 

used to predict fish and other biological response from application of different restoration 

strategies. These refinements will improve estimates of rates and pathways of recovery for many 

salmonid species in any river and assist in prioritizing restoration actions. However, many of 

these refinements are still many years from completion. 

In the interim, systematic inventories of disrupted habitat-forming processes and 

blockages to salmon migration should be conducted to provide a complete river basin overview 

of necessary restoration actions that can be logically prioritized. A minimum set of inventories 

for any river basin should include barrier inventories, erosion inventories, floodplain and riparian 

characterization, channel and valley type classification, flow reduction or peak flow increase 

inventories, water quality inventories, and biological indicator inventories. Some of these data 

are already available for parts of many watersheds. These data provide the basis for identifying 

restoration actions, which can be prioritized by cost-effectiveness, influence on particular 

species, adjacency to existing population centers or centers of biological diversity (commonly 

called refugia, biological hot spots, source watersheds, core areas, key habitat), or other 

strategies. 

There are many sources of uncertainty in these assessments. Uncertainties in assessments 

stem from natural variability in habitat-forming processes, habitat characteristics, and fish 

populations, as well as from errors in assumptions and limitations of data or knowledge. Our 

ability to characterize these types of uncertainty is limited by availability of data on watershed 

processes, habitat conditions, and fish populations over long periods of time. Lack of knowledge 

about current habitat conditions or responses of fish populations to changing habitat conditions 

introduces uncertainty into predictions of fish responses to watershed and habitat restoration. 

Improving the quality of the data reduces uncertainty related to knowledge gaps and improves 

our ability to address the uncertainty related to natural variability in fish response to habitat 

conditions. 

Recovery plans designed to protect and recover processes that create and sustain riverine 

habitats are more likely to recover salmon of all species. Using a comprehensive assessment 

process and developing restoration plans focused on the reestablishment of habitat-forming 

processes minimizes conflicts that can arise with species-centric restoration approaches. 

Restoration of habitat-forming processes targets restoration of the natural array of habitat types 

and conditions within a watershed, which is consistent with the concepts of watershed and 

ecosystem management supported by the scientific community. This approach focuses on the 

natural potential of each watershed, and therefore is most likely to restore the diversity and 

abundance of stocks appropriate to each watershed in Puget Sound. 

59
PRIORITIZING POTENTIAL RESTORATION ACTIONS 

WITHIN WATERSHEDS 

Previous sections outlined habitat analyses for setting recovery goals (Phase I planning) 

and identifying ecosystem restoration actions (Phase II planning). The next step is to incorporate 

Phase I and Phase II information into a recovery plan and develop a prioritized list of ecosystem

restoration actions within watersheds (Beechie and Bolton 1999, OWEB 2001, Roni et al. 2002). 

If models could accurately link changes in watershed processes to habitat changes and

population responses, this would be a simple matter of running the models to see which 

restoration actions most efficiently attain the recovery goals. Because such models do not exist, 

we must rely on a longer term approach to recovery that develops initial hypotheses about which 

actions will be most effective, conducts management experiments to test these hypotheses, and 

monitors effectiveness of different actions to adjust the recovery plan in the future. In this 

section we describe a range of prioritization approaches for recovery planning as well as the need 

for management experiments and monitoring. 

What Do We Know about Restoration?

The term restoration has been used to describe a suite of stream, watershed, and estuarine 

habitat manipulations and improvements. As we said in the Introduction section of this technical 

memorandum, restoration in its strictest definition is returning a site to some predisturbed 

condition (Gore 1985, NRC 1996). Generally it is more holistic or systemic than habitat 

creation, reclamation, rehabilitation, or enhancement, and not accomplished through 

manipulation of individual ecosystem or watershed elements (NRC 1996, Frissell and Ralph 

1998). In contrast, habitat enhancement is the improvement of habitat from an existing or 

previous condition. It does not necessarily seek to restore conditions to some predisturbed state 

or to restore disrupted watershed or ecosystem processes and functions such as delivery of water, 

wood, and sediment. Restoration can also be further classified as passive or active (Kauffman et 

al. 1997). Passive techniques seek to restore processes by halting detrimental land uses, 

protecting areas, and setting up conditions that will allow recovery of the stream (e.g., exclusion 

of cattle from a riparian area). Active restoration generally seeks to create relatively rapid 

habitat changes (within a few months or years) in watershed processes or habitat conditions. 

Active techniques, which may include habitat enhancement, are those that seek to directly 

manipulate and improve watershed processes or habitat, such as thinning riparian areas, 

removing migration barriers, or placing logs in a stream channel to create pools. Here, as we 

said earlier, we use the term restoration generically to mean both restoration and enhancement 

(and related terminology), but distinguish between those activities that restore watershed or 

ecosystem processes and those that enhance habitat (Figure 14). 

Most restoration techniques fall into five general categories—habitat reconnection, road 

improvement, riparian habitat, instream habitat, and nutrient enrichment. Gore (1985), Reeves et 

al. (1991), Slaney and Zaldokas (1997), Cowx and Welcome (1998), OWEB (1999b), and others 

provide descriptions of restoration techniques and information on designing and implementing 

them. Gore (1985), Reeves et al. (1991), and Roni et al. (2002) have reviewed common 

60
Figure 14. Simplified model of landscape controls and watershed processes, and how land use and 

restoration or enhancement can influence habitat and biota. 

61
restoration techniques, their effectiveness, longevity, and whether they restore processes or are 

short-term habitat enhancement (Table 10). Although many authors have discussed the need to 

restore processes and prioritize restoration (Beechie et al. 1996, Minns et al. 1996, Jones and 

Moore 2000, Rieman et al. 2000, Luce et al. 2001, JNRC 2002), specific guidance on which 

techniques to use and how to prioritize restoration is rather limited. The lack of guidance on the 

appropriateness of techniques and how to prioritize restoration actions stems in part from limited 

information on the biological effectiveness of various techniques (Reeves et al. 1991, Chapman 

1996, Roni et al. 2002, 2003). The responses of fishes to watershed and stream habitat 

restoration techniques have not been thoroughly evaluated, and there is considerable debate 

within the scientific community about the effectiveness of these techniques (Reeves et al. 1991, 

Kondolf 1995, Kauffman et al. 1997, Roni et al. 2002). 

Most monitoring has focused on the physical response to instream restoration techniques 

with inadequate monitoring of fish, invertebrates, and other biota. However, the biological 

response to restoration techniques is often the ultimate measure of effectiveness. Monitoring 

juvenile and adult salmonid abundance often requires more than 10 years to detect a response to 

restoration, due to high interannual variability (Bisson et al. 1997, Reeves et al. 1997, Maxell 

1999, Ham and Pearsons 2000, Roni et al. 2003). Moreover, some techniques such as wood and 

boulder placement in streams yield highly variable results (Chapman 1986) and how the results 

of reach-level studies can be interpreted for population level responses remains unclear. 

Therefore, drawing conclusions about the biological effectiveness of various techniques has been 

difficult and that difficulty has hampered efforts to provide scientific guidance on restoration 

activities. 

In the 1990s the notion became widely accepted that restoring watershed processes is the 

key to restoring watershed health and improving fish habitat throughout western North America 

and elsewhere. Beechie et al. (1996), Kauffman et al. (1997), Beechie and Bolton (1999), Roni 

et al. (2002), and others have described restoration and recovery strategies that emphasize 

restoring physical and biological processes that create healthy watersheds and high-quality 

habitats. Yet activities that restore processes (e.g., road removal and improvement, culvert 

removal, and riparian and upslope restoration) are often conducted at the site or reach level. 

Prioritization of restoration actions needs to place site-specific restoration within a watershed 

context. 

Strategies for Prioritizing Actions

Watershed and stream restoration are key components of many land management plans 

and should be an important component of most recovery plans for threatened and endangered 

species. Yet how site-specific actions might fit into a larger context of watershed restoration and 

salmon recovery often remains unclear, and many approaches to prioritizing restoration actions 

are available. Deciding which approach best suits a specific watershed or population may 

depend on many factors including how much is known about habitats that limit recovery of 

populations, the causes of habitat degradation in a watershed, or even the number of listed 

species within a watershed. Therefore, describing a single prioritization scheme applicable to all 

watersheds is difficult. 

62
Table 10. Typical response time, duration, variability of success, and probability of success for common 

restoration techniques. (Modified from Roni et al. 2002.) 

Restoration 

typea Specific action 

Years to 

achieve 

response 

Longevity of action 

(years) 

Variability of 

success among 

projects 

Probability of 

success 

Culverts 1–5 10–50+ Low High

Off channel 1–5 10–50+ Low High 

Estuarine 5–20 10–50+ Moderate Moderate to high

Reconnect 

habitats 

Instream flows 1–5 10–50+ Low High 

Road removal 5–20 Decades to centuries Low High 

Road alteration 5–20 Decades to centuries Moderate Moderate to high

Roads and 

land use 

Change in land 

use 

10+ Decades to centuries Unknown Unknown 

Fencing 5–20 10–50+ Low Moderate to high

Riparian 

replanting 

5–20 10–50+ Low Moderate to high

Rest-rotation or 

grazing strategy

5–20 10–50+ Moderate Moderate

Riparian 

restoration 

Conifer 

conversion 

10–100 Centuries High Low to moderate

Artificial log 

structures 

1–5 5–20 High Low to highb

Natural LWD 

placement 

1–5 5–20 High Low to highb

Artificial log 

jams

1–5 10–50+ Moderate Low to highb

Boulder 

placement 

1–5 5–20 Moderate Low to highb

Instream

habitat 

restoration 

Gabions 1–5 10 Moderate Low to highb

Carcass 

placement 

Nutrient 1–5 Unknown Low Moderate to high

enrichment 

Stream

fertilization 

1–5 Unknown Moderate Moderate to high

Off channel 1–5 10–50+ High Moderate 

Estuarine 5–10 10–50+ High Low

Habitat 

creation 

Instream See various instream restoration techniques above 

a

 The first three categories of restoration (reconnect isolated habitats, roads and land use, and riparian restoration)

are considered process-based or passive restoration; the last three (instream, nutrient enrichment, and habitat

creation) are considered enhancement or active restoration. 

b

 Depends on species and project design. 

63
Here we describe several strategies to prioritize restoration actions, beginning with an 

interim approach to prioritizing site-specific restoration activities in a watershed context. This 

interim approach, based primarily on the effectiveness and longevity of specific types of 

restoration actions, does not require detailed information about which habitats limit population 

recovery. We then describe a second approach for watersheds where only one species is listed 

and priorities may be directed toward those actions that are most likely to benefit the listed 

species. Finally, we compare several alternative approaches (including refugia and multispecies

approaches) to illustrate how priorities might differ under different prioritization schemes. We 

focus on those activities that occur within an individual watershed (U.S. Geological Survey 5th 

or 6th field hydrologic unit or HUC). Other large-scale restoration efforts that may occur at the 

basin or ESU scale, and thus may influence many watersheds, should be prioritized within a 

basin or ESU. We do not discuss such broad-scale priorities here. 

An Interim Approach 

In the absence of detailed knowledge of factors limiting recovery, an interim

prioritization scheme can serve to logically sequence different types of restoration actions based 

on their probability and variability of success, response time, and longevity (Table 10). All else 

being equal (e.g., costs, listed species concerns), those techniques that have a high probability of 

success, low variability among projects, and relatively quick response time should be 

implemented before other techniques. For example, reconnecting isolated off-channel habitats or 

blocked tributaries provides a quick biological response, is likely to last many decades, and has a 

high likelihood of success. By contrast, riparian restoration or road improvement may not 

produce results for many years or even decades for some functions, yet improvements will be 

long lasting. Other techniques such as instream large woody debris (LWD) placement are 

generally effective at increasing coho salmon densities (but less effective for other species), and 

have relatively short longevity. 

Using information summarized in Table 10, Roni et al. (2002) developed a hierarchical 

flow chart that can be used to help guide the selection and prioritization of restoration projects 

based on understanding watershed processes and known effectiveness of techniques (Figure 15). 

Protection of high-quality habitat should be given priority over habitat restoration, as 

maintaining good habitat is typically far easier and more successful than trying to recreate or 

restore degraded habitat. Among restoration options, reconnection of high-quality habitat should 

be undertaken before methods that produce less consistent results. Watershed process restoration 

is typically the next highest priority, and instream actions such as LWD placement should be 

undertaken either after or in conjunction with reconnection of isolated habitats and other efforts 

to restore watershed processes. Manipulation of instream habitat may also be appropriate where 

short-term increases in fish production are needed for a threatened or endangered species 

(Beechie and Bolton 1999). 

While most techniques fit well into this hierarchy, estuarine restoration, carcass 

placement, and nutrient enhancement are relatively new techniques whose places in this 

hierarchy are uncertain. Although comparatively little is known about the effectiveness of 

estuarine restoration, reconnecting isolated estuarine habitats such as distributary sloughs is 

similar to reconnecting isolated off-channel habitats, which has shown to be effective (Table 10). 

64
Habitat not

degraded

Habitat

degraded

Basi

a

n/watershed/subbasin

ssessment

Habitats

connected

Habitats isolated

(culverts, off-channel,

estuarine)

Riparian

processes OK

Restore riparian

processes Low LWD Moderate to

high LWD

Low productivity

potential (>5% slope)

High productivity

potential (<5% slope)

Restore (Add LWD)

Wait for long-term

recovery

Prioritize and

reconnect

Assessment

Restore

processes

(long term)

Restore

habitat

(short term)

Prioritize and

restore roads

Reconnect

isolated

habitats

Increase

escapement

Protect and

maintain

processes

Low salmon

escapement

impairing nutrient

levels

Nutrient levels

Roads impairing not impaired

processes

Roads not

impairing

processes

Riparian

processes not

functioning

(watersheds with salmon)

Basin/watershed/subbasin 

assessment 

Figure 15. Flow chart depicting hierarchical strategy for prioritizing specific restoration activities 

(modified from Roni et al. 2002). Shaded boxes indicate where restoration actions should take 

place. Addition of salmon carcasses or nutrients may be appropriate at various stages following 

reconnection of isolated habitats. 

65
Given the importance of estuaries to anadromous fishes and the success of reconnecting isolated 

off-channel habitats, it is likely that reconnecting estuarine habitat would be effective and should 

be considered at the same time as reconnecting other isolated habitats. The placement of salmon 

carcasses or other nutrients into streams, which may increase fish condition and production in the 

short term, is a form of habitat enhancement that can occur at any stage in the watershed 

restoration process. However, because it does not restore but rather mitigates for a deficient 

process, we suggest that it be considered at the same point in the hierarchy as instream habitat 

manipulation. Similarly, the creation of new estuarine or off-channel habitats does not restore a 

process and the effectiveness of these efforts is unclear. 

A common restoration technique not covered in Roni et al. (2002) is restoration of 

instream flows or natural hydrology either from water withdrawal projects or below large water 

storage projects. Water withdrawal or flow manipulation disrupts hydrologic processes, 

including delivery and routing of sediment and nutrients, and can dramatically impact habitat 

formation, connectivity, and quality (Bednarek 2001). We consider restoring instream flows and 

natural hydrologic patterns part of restoring watershed processes, and therefore do not have a 

separate category for this technique. Upslope activities and land use can also have dramatic 

effects on stream hydrology, sediment delivery, water chemistry, and water quality. Altering 

land use and other upslope restoration techniques not explicitly discussed in Roni et al. (2002) 

also can be included with restoring watershed processes. 

Within the broad restoration categories in Table 10, some techniques are more effective 

than others or more applicable in some provinces than others. For example, we include riparian 

silviculture (e.g., replanting, conifer conversion), fencing, and reduced grazing under riparian 

restoration. Livestock exclusion is a form of riparian protection that has been shown to be 

effective on range and agricultural lands (Platts 1991), while the long-term effectiveness of 

riparian replanting and conversion in forested watersheds is largely unknown. Priorities for 

various types of riparian restoration will differ by region and watershed, as will other specific 

restoration techniques that fall into these broad categories. However, a watershed assessment is 

the important first step to determine the most effective type of restoration within a given 

restoration category for the watershed in question (Beechie et al. 2003). 

The principles outlined above were designed primarily for forest, range, and other 

moderately modified rural lands. However, they are still useful in urban and agricultural lands, 

even though other factors such as large infrastructure (e.g., highways and buildings) may 

constrain certain restoration opportunities. In urban areas, hydrologic and sediment processes in 

streams are highly altered (e.g., increased high flows and channel downcutting). Areas with 

intensive agriculture often have severe water quality problems. Stream channels in both urban 

and agricultural areas are often highly channelized and lack adequate riparian vegetation. Thus 

the framework we outline may need to be modified for use in these highly altered systems where 

some processes cannot be reliably restored, or where water quality or hydrologic changes may 

compromise the effectiveness of many of the common restoration techniques. 

Single Species Approaches 

In watersheds where most Phase I and Phase II analyses have been completed, a more 

detailed prioritization of actions is possible. For this one should know: 

66
1. Which habitats are most impaired or most likely inhibiting recovery of populations (from 

Phase I assessments)? 

2. Which impaired ecosystem processes prevent recovery of the limiting habitats (from

Phase II assessments)? 

The information from Phase I assessments can be used to identify high priority areas for salmon 

recovery. Because habitat use varies by species and life history pattern, the same list of habitat 

recovery areas might be prioritized differently depending on the species of interest (e.g., Beechie 

and Bolton 1999, Figure 16). In general, habitat areas should be classified at a relatively coarse 

level of resolution (e.g., estuary, main stem, overwintering habitats), because the information 

available for evaluating which habitats limit salmon recovery is very sparse and the certainty of 

the answers is very low (see also the Managing Uncertainty in Habitat Recovery Planning 

section, page 74). While EDT analyses have a finer spatial resolution, their results should be 

used only in the most general terms because of the large number of uncertainties inherent in 

1) the collection of information, 2) the models used to estimate fish responses, and 3) the lack of 

model validation. 

Once specific habitat areas have been prioritized, the results of Phase II assessments can 

be examined to determine which restoration actions are required for recovery of the high-priority 

habitat areas. The Phase II assessments identify the causes of habitat degradation by evaluating 

where ecosystem processes have been disrupted and inventorying specific causes of habitat loss

(e.g., barrier inventory, road sediment reduction inventory, riparian inventory). Thus if Phase I 

assessments identify a specific habitat area as a likely limiting habitat (e.g., the estuary or 

tributaries for early rearing), then Phase II assessments identify the causes of degradation within 

that area (see also Appendix C, page 157, for examples). 

It is critical to bear in mind that the prioritization of actions does not alter the types of 

actions that are needed to restore ecosystems that support salmon (Beechie and Bolton 1999). 

Rather the Phase II assessments identify the suite of actions needed to restore ecosystem 

processes and functions, and prioritization alters the sequence in which those actions are taken 

(Figure 16). This of course implies that restoration of ecosystems to support salmon will include 

a wide range of actions affecting the entire life cycles of multiple species. However, where a 

single species is listed, altering the sequence of those actions for most rapid recovery of the listed 

species may be prudent. 

Alternative Prioritization Schemes 

Alternative strategies for prioritizing restoration that incorporate economic, ecological, 

and biological factors have been proposed or used, especially where there are multiple listed 

species and attempting to prioritize actions based on the needs of individual species would lead 

to conflicting priorities. In such cases other approaches to prioritization may be more 

appropriate, such as the refugia approach (Sedell et al. 1990) or a multispecies, cost-effectiveness 

approach (e.g., SWC 1998). Where at least one species appears to be at high risk of extinction, 

the refugia approach may be most appropriate to make sure that individual populations are 

preserved first. By contrast, watersheds with relatively stable populations might embark on a 

longer term, process-based approach to ecosystem recovery. It is likely that most recovery plans 

will incorporate different strategies for different watersheds or populations. 

67
Figure 16. Prioritization sequence for habitat restoration based on species of interest. (Modified from

Beechie and Bolton 1999.) 

68
Sedell et al. (1990), Wasserman et al. (1995), Beechie et al. (1996), Frissell (1993a), 

Frissell and Bayles (1996), and others have outlined restoration strategies that focus on providing 

refugia and protecting high quality habitats. Beechie et al. (1996) outlined a prioritization 

strategy that focused on providing refugia for a depressed steelhead stock in Deer Creek, 

Washington. Other strategies might prioritize actions on potential increase in fish numbers, total 

cost, cost per fish, aquatic diversity, metapopulation attributes, or scoring based on a suite of 

these and other factors (e.g., Beechie et al. 1996, Frissell and Bayles 1996, Doyle 1997, SRSRC 

2002, LCFRB 2002). Some states such as Oregon have developed sequential methodologies for 

conducting assessments and prioritizing and implementing restoration activities (Figure 17). 

These various strategies incorporate management goals beyond simply restoring watershed or 

ecosystem processes and habitat. Thus the sequencing of restoration actions under different 

prioritization strategies will vary. 

We demonstrate how priorities might vary with prioritization schemes by developing a 

hypothetical list of potential restoration actions (Table 11) and ranking those actions using 

different prioritization schemes (Table 12). This analysis indicates that if actions were 

prioritized based on Roni et al. (2002), impassable culverts and reconnection of habitats would 

occur first, followed by road, riparian, and LWD placement. If actions were prioritized by 

whether they were in refugia for an ESA-listed species, instream flow and LWD placement 

would be first, simply because they are in a high priority area. Similarly, different cost, cost/fish, 

and total fish production all produced slightly different prioritization scenarios. This simple 

example illustrates how priorities might differ based on the method, information used, and 

management objectives. In the next section, Managing Uncertainty in Salmon Habitat Recovery 

Planning, we also discuss accounting for uncertainty in prioritizing restoration actions toward 

incorporating the risks and likelihoods of success and failure (physical, biological, or financial) 

into the planning process.

The appropriate method for prioritizing restoration activities within a watershed will 

depend on numerous factors. Our intent here is to discuss how one prioritizes site-specific 

restoration actions within a watershed. However, all else being equal or if limited information is 

available, we recommend a strategy similar to that outlined in Roni et al. (2002, see Figure 15) 

that focuses on reconnecting isolated habitats and restoring watershed processes before or 

alongside habitat manipulations or enhancement. 

Need for Monitoring and Management Experiments 

Reviews of various restoration techniques (e.g., Roni et al. 2002) indicate that knowledge 

about the effectiveness of most techniques is incomplete and comprehensive research and 

monitoring are needed. Even techniques that appear to be well studied such as instream LWD 

placement need more thorough evaluation and long-term monitoring. This emphasizes the need

for comprehensive monitoring and evaluation of both individual and multiple restoration actions

at multiple scales. Many restoration actions should be treated as management experiments and 

accompanied by research and monitoring to determine both physical and biological responses. 

These results, crucial for adaptive management, can then be used to guide future restoration 

actions and more accurately quantify the potential increase in fish production for habitat 

69
Figure 17. Process for restoration planning, prioritization, and implementation used by the Oregon 

Watershed Enhancement Board, Oregon Department of Fish and Wildlife, and State of Oregon. 

(Based on figure in OWEB 1999b.) 

70
Table 11. Example of list of potential restoration actions within a watershed. All stream names and 

numbers are fictitious and for demonstration purposes only.

Site 

ID Site name Action 

RefugiaaKm 

treated

 M2

 treated

Coho 

smoltsb

Chinook

smoltsb

Total 

cost $/coho

A Clark Creek LWD placement 2 3 15,000 3,750 750 50,000 13.30

B Upper

Simpson Creek 

LWD placement 1 2 10,000 2,500 500 32,000 12.80

C Lower

Simpson Creek 

LWD placement 1 2 14,000 3,500 700 35,000 10.00

D Check Creek Fencing/cattle

exclusion 

1 5 60,000 6,000 3,000 20,000 3.30

E Dry Creek Increase

instream flows 

2 20 200,000 20,000 10,000 500,000 25.00

F Big River Reconnect 

estuarine tidal 

channel (e.g.,

dike removal) 

3 1 100,000 10,000 50,000 350,000 35.00

G Big River Excavate new

estuarine slough 

3 2 200,000 20,000 100,000 750,000 37.50

H Clark Creek Culvert

replacement/ 

fish passage 

2 3 15,000 3,750 0 150,000 40.00

I Simpson Creek Road decommissioning 1 20 200,000 20,000 10,000 1,500,000 75.00

J Clark Creek Road resurfacing/sediment 

reduction 

2 10 50,000 5,000 2500 750,000 150.00

K Big River

Slough 

Reconnect 

isolated oxbow 

slough 

3 4 800,000 400,000 40,000 75,000 0.19

a Refugia numbers, based on Beechie et al. 1996, are: 1 = refugia (areas where recovery is relatively predictable); 

2 = key habitat areas or areas that provide for the largest long-term recovery of species of interest, but are sensitive 

to disturbance and more difficult to restore; and 3 = key habitat areas or areas expected to provide the smallest gain

for species of interest. 

b

 Numbers represent expected annual increase in smolt production. 

71
Table 12. Example of different order of priorities based on different prioritization methods using 

information in Table 11. Two of the methods of prioritization, Roni et al. (2002), based on the 

project sequence in Figure 15, and refugia, do not distinguish between projects of the same type. 

Hence there are only four levels and three levels for the two methods, respectively. 

Site ID Potential restoration action Roni et al. Refugia Total cost Cost/coho Total fish

A LWD placement 4 2 4 5 8 

B LWD placement 4 1 2 4 11 

C LWD placement 4 1 3 3 9 

D Fencing/cattle exclusion 3 1 1 2 6 

E Increase instream flows 1 2 8 6 4 

F Reconnect estuarine tidal channel 1 3 7 7 3 

(e.g., dike removal) 

G Excavate new estuarine slough 4 3 10 8 2 

H Culvert replacement/fish passage 1 2 6 9 10 

I Road decommissioning 2 1 11 10 5 

J Road resurfacing/sediment 2 2 9 11 7 

reduction

K Reconnect isolated oxbow slough 1 3 5 1 1 

72
manipulations. Ultimately, monitoring and evaluating these actions will help us prioritize 

restoration opportunities and wisely spend limited restoration and recovery funds for salmon. 

73
MANAGING UNCERTAINTY IN HABITAT RECOVERY 

PLANNING 

The salmon ecosystem recovery planning approach proposed in this guidance document 

requires a complex series of decisions about habitat actions despite large amounts of uncertainty 

in the available information from many sources. This uncertainty can result in risks to habitats 

and populations from inappropriate management advice (Fogarty et al. 1996). Past failures of 

management plans to prevent population declines and collapse are due in part to the failure to 

recognize uncertainty in available information and a lack of procedures for including uncertainty 

in the decision-making process (Wade 2001). Inevitably, decisions will be based on a tapestry of 

models, estimates, expert opinions, myths, predictions, and data. By identifying, quantifying, 

and acknowledging the uncertainty in information used for recovery planning, we can increase 

the likelihood that recovery plans will be successful. The benefits of explicitly accounting for 

uncertainty include capturing all the available information regarding uncertain factors, providing 

the full range of possible outcomes and the probability of observing each, and identifying the key 

drivers of overall uncertainty in model projections (Mishra 2001). In this section we provide 

guidance via quantitative and qualitative examples for managing uncertainties inherent in habitat 

recovery planning.

A brief example illustrates how identifying and quantifying uncertainty can help a 

resource manager make explicit trade-offs between potential positive outcomes and acceptable 

risks. In choosing between two possible culverts for restoring fish passage, one might be given 

information that removal of culvert A is predicted to increase fish capacity by 120 fish while 

removal of culvert B is predicted to increase fish capacity by 100 fish. With no estimates of 

uncertainty, the manager would choose culvert A because it has the highest expected increase in 

fish capacity. However, more complete information might indicate that replacement of culvert A 

would open habitat that was less certain to be occupied (120 ± 70), while replacement of culvert 

B would open wetland habitat with a high degree of certainty (100 ± 10) to be quickly colonized. 

With the additional information, decision makers could then explicitly choose between a higher 

but less likely increase in fish capacity and a lower but more certain increase in fish capacity. In 

this example, neither action is likely to cause harm (a negative change in fish capacity). In other 

situations, actions with a high potential payoff may also contain some risk of being detrimental 

to fish, for example, when deciding whether to use chemical herbicides to remove nonnative 

vegetation from riparian areas. Without an estimate of the magnitude of uncertainty in the 

information on which decisions must be made, decision makers cannot make informed decisions. 

The importance of clearly communicating uncertainty has been repeatedly emphasized in 

the fisheries literature (Francis and Shotton 1997): 

• “Understanding the risk or uncertainty associated with choices could help fisheries 

managers select management strategies, decide which types of risks and uncertainty 

inhibit the effectiveness of management techniques, and finally, recognize which types of 

uncertainty must inevitably remain” (Peterson and Smith 1982). 

• “Point estimates should be accompanied by variance estimates” (USCTC 1997). 

• “The managers’ task may be made easier if uncertainty in a fishery assessment were 

expressed” (Francis 1992). 

74
• “Scientific advice to fishery managers needs to be expressed in probabilistic terms to 

convey the uncertainty about the consequences of alternative harvesting policies” 

(McAllister et al. 1994). 

• “Clearly, when management decisions are to be based on quantitative estimates from

fishery assessment models, it is desirable that the uncertainty be quantified and used to 

calculate the probability of achieving the desired target and/or risk of incurring 

undesirable events” (Caddy and Mahon 1995). 

Such reporting of uncertainty in data and predictions has become common in harvest 

management (Rosenberg and Restrepo 1994). However, uncertainty is not often incorporated 

into salmon habitat recovery planning despite broad consensus that considering uncertainty is 

important and necessary in the conservation and management of species (Mangel et al. 1996, 

Flaaten et al. 1998, Akcakaya et al. 2000, Ralls and Taylor 2000, Wade 2001). 

In this section, we first describe five types of uncertainty embedded in predictions of 

habitat capacity. We follow this with two examples of uncertainty in habitat management issues 

related to recovery planning. In each example, we describe how management decisions might be 

improved by acknowledging, quantifying, and reducing uncertainty in the decision-making 

process. The first example describes qualitative strategies for reducing uncertainties regarding 

chemical contaminants and making structured decisions in the face of limited empirical data. The 

second example describes the use of decision tables for making decisions that incorporate 

uncertainty. The final subsection describes strategies for making decisions when empirical data 

are lacking. Here we distinguish between variability, which is characterized by differences in a 

variable’s value over time, space, or populations, and uncertainty, which is lack of knowledge 

about a true and constant value of a quantity (Morgan et al. 1990, Cullen and Frey 1999). Our 

discussion of methods for reducing uncertainty is purposefully simplified throughout, but 

references are provided for each example so that interested readers can locate more detailed 

information. By omitting site-specific and mathematical details, we intend to express a general 

framework for incorporating uncertainty into decisions. 

Types of Uncertainty 

Precise and accurate predictions are a fundamental goal in the aquatic sciences. 

Improved management of aquatic resources will result from a predictive science that can forecast 

the consequences, costs, and benefits of management actions (Pace 2001). A prediction might be 

a value (e.g., habitat capacity estimate, extinction risk, or survival rate) or a relationship between 

a habitat action and a biological response (e.g., effects of high flows on egg survival, effects of a 

particular restoration technique on fish survival, or projected population trajectories under 

different climate scenarios). Population viability and habitat goals (Phase I recovery planning) 

as well as prioritized project lists and watershed plans (Phase II recovery planning) must be 

developed from these types of predicted values and relationships. Informed plans and decisions 

will be based on both the predictions and the uncertainty surrounding them. 

The five types of uncertainty found in predictions of habitat capacity are predictive 

uncertainty, parameter uncertainty, model uncertainty, measurement uncertainty, and natural 

stochastic variation (Table 13). Evaluating the relative magnitudes of the five types of 

75
Table 13. Tools and methods for quantifying and reducing uncertainty. 

Class of uncertainty Brief definition Habitat example Method for quantifying Po

Prediction uncertainty Difference between 

modeled response and true response. 

Uncertainty of predicting habitat capacity of a given watershed after instreamrestoration. 

Leave-one-out estimates of prediction error rates. Simulation studies comparing conditions where model was built to those in which it is being applied. 

CoconpreDo beywhdev

Parameter uncertainty Difference between true 

parameter (such as an average or a regression coefficient) and parameter as estimated from the data.

Uncertainty of parameters describing change in capacity as a function of changes in watershed condition. 

Statistical theory for model coefficients derived fromdata. Sensitivity analysis for model coefficients estimated from other sources. 

ComoCovar

Model uncertainty Difference between natural

system and the mathematical equation used to describe it. Includes model form and set of predictors. 

Uncertainty in relationship between habitat conditions and fish capacity. Uncertainty in which habitat descriptors are best predictors of fish capacity. 

Statistical descriptions of model fit: Akaike’s information criteria (AIC), Bayesian information criteria (BIC), likelihood ratios, F-statistics. 

ComoCoana

Measurement uncertainty Difference between true 

value and the recorded value. 

Uncertainty inmeasurements of data used to build the predictive model, i.e., fish or redd density under differing habitat conditions. 

Test accuracy of measurement technique against standard method orknown values. 

ImtecIncrepCame

Natural stochastic variation (process uncertainty) 

Inherent randomvariability.

Natural fluctuations in population size, habitat selection, or habitat conditions. 

Variance of the observed data. Variance of the observed data for different sets of conditions. 

Cofor Str

76
uncertainty embedded in a particular prediction is valuable because it tells us where to be 

skeptical. More formally, we may pursue value of information (VOI) analysis to establish which 

additional information is most likely to improve our decision-making position (Raiffa and 

Schlaifer 1961, Raiffa 1997). VOI techniques seek to identify situations in which the cost of 

reducing uncertainty is outweighed by the benefit of the reduction. In some cases, the predictive 

uncertainty turns out prohibitively large and the available empirical data therefore provides little 

guidance for decision making. In such cases, other decision-making processes that do not 

require quantitative predictions can be used (see Using Decision Rules When Empirical Data Are 

Inadequate subsection, page 86).

To a great degree, the five types of uncertainty are nested: prediction uncertainty includes 

parameter and model uncertainty, which each includes measurement error and natural variability. 

Here we start with prediction uncertainty, the broadest form of uncertainty, and work down to 

the underlying natural variation. We provide examples of how each type of uncertainty arises, 

how it might be quantified, and how it might be reduced (Table 13). We conclude each 

subsection with a summary of how decision making can be improved by quantifying and 

acknowledging each class of uncertainty. A series of questions to ask of any prediction is in 

Table 14. 

Prediction Uncertainty 

Predictions include uncertainty from natural stochastic variation of the system being 

modeled, measurement uncertainty of the data used to build the model, uncertainty surrounding 

the form of the model, and parameter uncertainty (components addressed in the following 

subsections). In addition, predictions can include uncertainty that results from applying a model 

to a new situation. For example, a capacity estimate for Watershed X might predict future 

capacity based on current and past data for the same watershed or an estimate of current capacity 

for Watershed X might be based on data collected in other watersheds. Both cases involve 

extrapolating from conditions under which data were collected to new conditions. Uncertainty 

associated with these or similar extrapolations, say from the laboratory to the field, is difficult or 

impossible to quantify but must be considered and described. 

Prediction uncertainty can be evaluated by ground-truthing (i.e., field measurement of 

specific attributes), prediction confidence intervals, and cross-validation simulation studies. 

Ground-truthing will help quantify the accuracy and precision of past predictions about current 

conditions, but can only suggest how well the model may perform under future conditions. 

Prediction confidence intervals can be computed in situations for which the manager does not 

need to extrapolate beyond the original data (Zar 1984). Where there is more than one predictor 

variable, caution should be used in defining the joint sample space beyond which one is 

extrapolating. In cross-validation simulations, the model is constructed and parameterized using 

a subset of the data (Stone 1974). The model is then assessed by how well it predicts that subset 

of data excluded from model construction. Cross-validation simulations do not include 

uncertainty associated with extrapolating from measured to unmeasured conditions. To assess 

how well a model may predict unmeasured conditions requires careful consideration of those 

model components that may be sensitive to expected differences between measured and 

unmeasured conditions (i.e., current vs. future conditions). Models can be compared in their 

77
Table 14. Questions to guide the evaluation of predictions. 

Prediction uncertainty 

How similar are the conditions under which the original information was gathered to those for which 

the prediction is being made? How sensitive is the model (data, mechanism, and parameter 

estimates) to site-specific details?

Parameter uncertainty 

Is the prediction sensitive to small changes in parameter estimates? If so, how precise are the 

estimates of those parameters?

Model uncertainty 

What are the assumptions on which the prediction is based? How sensitive is the prediction to these 

assumptions?

Measurement uncertainty 

Could any of the information on which the prediction is based be biased? How precise and how 

accurate are the data?

Natural stochastic variation (process uncertainty) 

Can measurements be stratified across conditions to reduce the effects of natural variability?

78
relative sensitivity to changing conditions. Models that rely on predictors only correlated with 

the causal factors are particularly likely to have high levels of prediction uncertainty, because in 

new situations the correlations on which the model is based may no longer be coincident with the 

causal mechanism.

Parameter Uncertainty 

Model parameters are necessarily estimated with uncertainty. A statement of the 

uncertainty of these parameter estimates is critical for making informed management decisions. 

Parameters that have biological meaning provide a context for interpreting the associated 

uncertainty. For example, imagine one had created a regression model to estimate smolt density 

as a function of the number of pieces of wood in the stream. The model would include a 

parameter, for example 12.3, that estimated the increase in smolt density for each piece of wood. 

The conclusion from such a model without parameter uncertainty estimates might be to embark 

on a widespread wood placement plan. However, if the parameter estimate had been more 

completely expressed as 12.3 ± 15.1, we might diversify the types of restoration actions used or 

choose a different restoration action with a smaller but more certain fish response and little or no 

risk of an adverse affect. For statistical models, parameter estimates are developed from the data 

and the uncertainty associated with these estimates is relatively easy to compute. For 

mechanistic models, parameters may be estimated from data, from similar models of other 

phenomena, or by expert opinion. When parameters are not estimated from data, the uncertainty 

surrounding them can be difficult or impossible to quantify. If estimates from such models are 

used, the potential uncertainties should be described; the direction and magnitude of the potential 

errors can often be estimated qualitatively. 

Sensitivity analyses can be used to estimate the effect of parameter uncertainty. Nominal 

range or local sensitivity analysis computes the effect on model outputs of systematically varying 

each parameter in the model across its range of plausible values while holding the other inputs at 

their nominal values. Where small changes in parameter values lead to large changes in model 

predictions, the uncertainty of those parameters should be carefully evaluated. Models that are 

extremely sensitive to changes in parameter estimates and have highly uncertain estimates of

those parameters will yield predictions with large uncertainty. Even where models produce 

highly uncertain predictions, they may be useful for quantifying the uncertainty in predictions 

and determining the type and quality of information that would be required to produce 

predictions with acceptable levels of certainty. The sensitivity analysis tells the managers that

predictions are sensitive to particular conditions and that they will either have to increase 

precision of parameter estimates or ensure that management plans are robust to expected 

uncertainty. Increased precision of parameter estimates can be achieved by collecting more data, 

data over a wider range of values, or better data (data with less measurement uncertainty). 

Model Uncertainty 

Nearly all estimates and predictions used in management are explicitly or implicitly 

based on an underlying model. Uncertainty exists about both the model form (e.g., a linear 

relationship vs. a Ricker curve) and which predictor variables to include. Model uncertainty 

results from an incomplete understanding and a simplified representation of ecological systems 

79
and functions (Fogarty et al. 1996). For example, we might have a model that predicts habitat 

capacity as a linear function of several habitat parameters: wood density, pool density, gradient, 

adjacent land use, and water temperature. The default assumption may be to use a simple linear 

regression model. However, we may be uncertain whether the effects of these five habitat 

descriptors are additive or have a linear relationship to habitat capacity, and we may also be 

unsure if these five habitat descriptors are the best set of predictors or if an alternate set might 

perform just as well. Many statistical tools (adjusted R-squared, Akaike’s information criteria or 

AIC, Bayesian information criteria or BIC, F-tests, likelihood ratio tests, cross-validation 

metrics) are available for choosing between models (Burnham and Anderson 1998). In general 

these techniques balance the degree to which the model fits or predicts the data with the 

complexity of the model, usually expressed as the number of parameters. 

Models that fail to describe the ecological process accurately or to include an important 

predictor can have enormous management implications. Model predictions can be of the wrong 

magnitude or even the wrong direction. Resource managers and ecologists have often erred 

significantly by failing to consider model uncertainty. For example, the prevailing model of 

habitat effects on fish survival once assumed that fish survival decreases with increasing 

amounts of instream wood, and as a result, large amounts of wood were removed from streams 

and rivers (Maser et al. 1988). Thus habitat degradation in the Pacific Northwest can in part be 

attributed to a failure to assess the possibility that this model was incorrect (Beechie et al. 1996). 

Model uncertainty is very difficult to quantify because there are an infinite number of 

possible models; none is exactly correct. Simulation studies generate data using a particular 

model, then ask questions about the behavior of those data (Morgan et al. 1990). They can 

quantify the degree to which the structure of the model influences the model’s predictions. 

Averaging predictions from a suite of models can reduce the impact of model uncertainty on 

management predictions (Burnham and Anderson 1998, Cullen and Frey 1999). Beyond these 

tools, reducing model uncertainty is extremely difficult. Schnute and Richards (2001) suggest 

that model uncertainty be managed by keeping an open mind, identifying all assumptions, and 

testing those assumptions continuously. 

Measurement Uncertainty 

Measurement uncertainty or observation error is simply the difference between a true 

value and our recorded observation of it. It results from measurement, sampling, and data 

processing errors (Francis and Shotton 1997). All observations carry some degree of 

measurement uncertainty. This uncertainty may be large and problematic or small and of 

negligible consequence. Some phenomena such as the survival of fish in different habitats are 

inherently difficult to measure. Consequently, the variables associated with these phenomena 

have a high degree of measurement uncertainty. Other phenomena such as stream discharge can 

be measured quite accurately. Uncertainty resulting from sampling error occurs when the 

measured samples are not representative of the population for which inference is being made. 

The incorporation of measurement and sampling errors can obscure or create relationships 

between variables (Ludwig and Walters 1981, Walters and Ludwig 1981). Measurement error as 

defined here can also occur during data processing and storage. 

80
Measurement uncertainty is directly related to both the accuracy and the precision of the 

measurement technique. Accuracy in a measurement technique, the inverse of uncertainty, 

describes the average distance between the measured value and the truth. The precision of a 

measurement describes the variability around that average. Therefore, a measurement tool can 

be highly precise (low variance across repeated measurements) and yet inaccurate (the average 

of repeated measurements is far from the true value). In other words, it is quite possible for a 

measurement to be characterized by little variability but a large degree of uncertainty. While 

there have been many attempts to estimate measurement uncertainty in, for example, habitat 

surveys (Pleus 1995, Roper and Scarnecchia 1995, Poole et al. 1997) or redd surveys (Jones et al. 

1998, Dunham et al. 2001), the known uncertainty in these types of data is rarely included in the 

uncertainty of predictions from models that are based on these types of data. 

Measurement uncertainty can result in systematic error or bias. Bias is a directional error 

that results from measurement using a systematically inaccurate tool. Biased or potentially 

biased measurements might include subjective assessments or incomplete records. A less visible 

form of bias occurs when a measurement technique tends to overestimate in certain conditions 

and underestimate in other conditions. A simple example is helicopter redd surveys. Redds are 

easier to identify where there are fewer trees; therefore the accuracy or uncertainty of the 

measurement may depend on whether there are riparian buffers. If the bias is not corrected, the 

data might erroneously predict increases in redd density with removal of riparian trees. 

Measurement uncertainty can be reduced but not eliminated. Replication is the best way 

to reduce the uncertainty, though it will not remove bias resulting from the use of inaccurate 

measurement tools. Bias can be corrected using unbiased measurements. Measurement 

uncertainty in expert opinion or subjective assessments can be very difficult to assess because no 

actual data exists. Although it may be possible to determine how well experts agree with one 

another (precision), it is impossible to assess or quantify accuracy when there are no accurately 

measured data available for comparison. In such cases, sensitivity analyses (as just described in 

the Parameter Uncertainty subsection) can provide an assessment of the degree to which small 

amounts of measurement uncertainty or bias in the input data might affect predictions (Morgan et 

al. 1990). Measurement uncertainty may also be quantified using repeated measurements or by 

computer-intensive techniques such as resampling or bootstrap methods (Efron and Tibshirani 

1991, 1993). By quantifying measurement uncertainty, the value of collecting more data with 

the same measurement or sampling technique versus a more expensive technique can be 

weighed. 

Natural Stochastic Variation 

Natural stochastic variation is the inherent random variability in ecological systems, such 

as temperature or population fluctuations. It also incorporates the underlying stochastic nature of 

population dynamics (Rosenberg and Restrepo 1994). It contributes to our inability to make 

precise predictions. Increased amounts of natural stochastic variation, often called process 

uncertainty, require increased numbers of observations (either more sites or more replications or 

both) to make estimates of a given precision (Shea and Mangel 2001). Very high levels of 

natural variation can mean that estimates of the required precision are simply impossible to 

obtain (Korman and Higgins 1997). 

81
Identifying and quantifying natural stochastic variation helps us to distinguish between 

situations in which small amounts of additional data should dramatically increase our ability to 

make good decisions and situations in which additional data are unlikely to provide significant 

increases in the accuracy of predictions. This is the heart of VOI analysis discussed earlier. In 

some cases, stratifying the data or redefining the question can reduce the effects of natural 

stochastic variation. For example, we might make separate estimates of in-river survival for wet 

versus dry years. Resource managers would then be able to make more informed decisions about 

the value of habitat restoration plans that potentially have different effects in wet versus dry 

years. Because stochastic variation is a natural phenomenon, it cannot be reduced to increase the 

precision of our predictions. Where it can no longer be reduced by stratification, quantifying and 

acknowledging stochastic variation is the best way to manage it. 

In summary, an informed management decision requires information about the 

uncertainty of the predictions on which that decision will be based (Pace 2001, Regan et al. 

2002). Evaluating the uncertainty in each prediction requires the dissection of that uncertainty 

into its classes. Each class as well as methods for quantifying and reducing uncertainty are 

summarized in Table 13. By asking the questions in Table 14, we can identify critical 

knowledge gaps, improve predictions, and reduce the chances of making poor or uninformed 

decisions because of poor predictions. 

Example 1: Creating a Prioritized List of Restoration Projects 

Once we have a series of predictions with their associated uncertainties, we must 

combine them into an action plan (see Prioritizing Potential Restoration Actions within 

Watersheds section, page 60). In this example, we demonstrate one method of setting up a 

decision table for using predictions and their confidence intervals to develop a project list for a 

habitat recovery plan. Developing a project list is difficult because of uncertainty about how fish 

may respond to changes in the environment. For instance, we may have a list of potential 

actions, each of which is expected to increase pool habitat. There are uncertainties in estimating

the increase in pool area and about the density of fish that can be supported by a given amount of 

pool habitat. By explicitly including the uncertainty in a decision table, we can identify the 

actions with the highest expected final fish density and determine the potential value of reducing 

the uncertainty. Analogous examples have been worked out in the harvest literature (Hilborn 

and Walters 1992). 

The first task in setting up a decision table is to describe the “alternative states of nature” 

and ascribe probabilities to these states. In this example, the alternative states of nature are the 

alternative hypotheses about how many juveniles are supported by a given area of pool habitat. 

Table 15 presents sample hypotheses and associated probabilities. The probabilities associated 

with each hypothesis may be generated in a number of ways. One method that can combine 

multiple types of information is meta-analysis, which pulls together information from multiple 

sources (Liermann and Hilborn 1997, Myers et al. 2001). Other Bayesian analysis techniques 

can also be used to combine disparate sources of information. A trademark of Bayesian analysis 

is the assignment of probabilities to alternative states of nature (Wade 2000). Strengths and 

weaknesses of the Bayesian approach are described by Dixon and Ellison (1996). If only limited 

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Table 15. Input information and results of decision analysis for prioritizing restoration actions. Example 

alternative hypotheses about the states of nature (i.e., density of fish per m2

 of pool habitat) and 

the relative probability that the hypothesis is true are in the first two rows. All probabilities must 

sum to one. Expected outcomes for potential habitat actions (total fish) as a function of each 

hypothesized fish density are displayed below the hypothesis probabilities. Overall expected 

outcomes (increase in total number of fish) of each potential action, given all potential states of 

nature, are in last column. 

Hypothesized fish 

density per pool 5 10 15 20 

Hypothesis probability 0.1 0.3 0.5 0.1

Overall 

expected 

outcome

Remove 

culvert A 

2,744 4,892 5,248 5,786 4,945

Remove 

culvert B 

2,844 3,400 3,858 6,457 3,879

Remove 

riprap 

2,012 4,172 4,260 4,340 4,017

Potential 

action 

Add wood 1,568 3,410 5,963 6,230 4,784

83
or ambiguous data are available, expert opinion can be solicited to assign probabilities to the 

various hypotheses. Numerous texts describe the complexity of selecting a group of experts, 

combining their disparate judgments, and other challenges of this approach (Morgan et al. 1990, 

Cooke 1991). As noted earlier in the section, knowing if expert opinion is correct is impossible 

precisely because we use it in situations for which we have no data. If expert opinion is used to 

assign probabilities to a set of hypotheses, then the prioritized list that emerges from the 

decision-analysis process will be a formalization of those opinions. 

The next step in setting up a decision table is to associate an outcome with each potential 

action, assuming each of the alternative hypotheses about the state of nature is true. For 

example, if the hypothesis that pools can support five juvenile fish per m2

 is true, then the 

number of fish expected from the removal of culvert A might be 2,744 fish. In this example the 

outcome is number of fish, but other appropriate outcome units such as fish per dollar may be of 

interest. This outcome is calculated based on an assessment of the number of pools that would 

be made available after removal of the culvert. More realistic and detailed decision tables might 

include additional information such as the number of riffles, types of pools, depths of pools, or 

quality of expected pool habitat. Table 15 shows potential outcomes in total fish for a number of 

management actions as a function of fish density in pools. 

Finally, we calculate the final expected outcome of each potential action, given the 

probabilities of the states of nature (Table 15). The expected outcome of each action is 

calculated by summing the expected outcome for each state of nature multiplied by the 

probability that the state of nature is true. For example, the expected outcome for removal of

culvert A is (2744 × 0.1) + (4892 × 0.3) + (5248 × 0.5) + (5786 × 0.1) = 4945. Table 15 shows 

the expected outcome for each of the four potential actions. The largest expected increase in 

total number of fish is associated with removal of culvert A. 

This is an extremely simple example. Hypotheses about the states of nature will often 

involve more than a single dimension (e.g., more than pool density). Many types of information 

can be included in the analysis, but there will often be only one or two critical uncertainties that 

drive a decision. Decision tables provide a structured method for including and communicating 

uncertainties and can easily be constructed for many of the examples in this document. For 

example, the methods described in the Prioritizing Potential Restoration Actions within 

Watersheds section, page 60, could be modified to include uncertainty about fish response, 

restoration costs, or habitat quality by using the decision table methodology described here. 

Another tool for making decisions is a logic tree, which models the impact of uncertainties in 

states of nature and in the occurrence of future conditions on possible outcomes (Kessler and 

McGuire 1999). Logic trees are particularly useful when only subjective probabilities about the 

states of nature exist. 

Example 2: Water Quality and Habitat Recovery Planning 

Uncertainty in habitat planning can result from the omission of a key habitat variable, 

such as water quality. The quantity and quality of salmon habitat are both important 

determinants of salmon population viability. Stream temperatures, sedimentation, and water 

84
pollution are all examples of measures of habitat quality. However, empirical data for the 

various forms of water pollution are rarely incorporated into habitat models. Consequently, the 

complex impacts of urbanization, agricultural land uses, and industrial activities on the chemical 

condition of salmon habitat may lead to large levels of uncertainty in habitat recovery planning. 

In this example, we suggest ways to improve habitat decision making by incorporating water 

quality data. We provide nonquantitative solutions to reducing uncertainties that result from the 

omission of key habitat variables. 

Environmental monitoring studies have consistently detected a wide array of metals, 

pesticides, and other toxic substances in the surface water and sediment of salmon habitats, and 

also in the tissues of salmon themselves. These contaminants may affect salmon abundance and 

survival via immediate lethal effects on individual fish. However, such effects are rare compared 

to the vast array of potential sublethal effects that may reduce individual fitness and population 

performance and potential indirect effects such as reductions in the abundance of key prey taxa. 

Despite documented exposure conditions (Wentz et al. 1998, Ebbert and Embrey 2002), the 

impact of environmental contaminants on salmon health or on the biological integrity of aquatic 

systems is poorly understood and habitat-based models for salmon recovery rarely capture the 

biological significance of water and sediment quality. Predictions of salmon population viability 

are likely to have high levels of model and prediction uncertainty if water and sediment quality 

are not included in model development. 

There are several reasons why the specific determinants of chemical habitat quality are 

often excluded from habitat models. First, chemical habitat quality can be difficult and 

expensive to measure. Second, there is a general absence of toxicological data for most of the 

chemicals that have been detected in salmon habitat. Third, many conventional endpoints or 

biomarkers of chemical exposure have no clear or consistent relationship to the survival or 

reproductive success of the exposed animal. Consequently, there is often a disconnect between 

the biological scale at which toxicological studies are conducted and the data requirements for 

current habitat recovery models (Hansen and Johnson 1999a, 1999b). 

Recovery plans that capture broad spatial and temporal patterns of chemical habitat 

degradation, despite incomplete empirical data, will minimize uncertainties around predicted 

outcomes of restoration actions and therefore reduce risks to salmon populations. Contaminants 

occur in complex mixtures whose composition varies in time and space. Salmon habitat 

conditions may reflect current land use activities or activities that were restricted or banned many 

years ago (e.g., persistent chemicals such as DDT). Moreover, water quality at a specific point 

within a watershed may be determined by land use activities that are far removed from the focus 

of restoration efforts. Acknowledging the large spatial and temporal scales at which 

contaminants can affect fish helps identify some of the uncertainty associated with predicting the 

effects of restoration actions. We can surmise, for example, that the uncertainty of predicted 

increases in habitat capacity for a given restoration action is likely higher in areas with high 

levels of past or present on-site or upstream chemical contamination. Likewise, we might expect 

inaccuracy and prediction uncertainty in survival estimates that are extrapolated from a stock 

within a pristine watershed to a stock that migrates through a highly contaminated estuary. 

In many cases, we do have data on chemical contamination but we do not know how to 

incorporate it into habitat recovery planning. A limited number of studies have specifically 

85
addressed the impacts of environmental contaminants on biological processes in Pacific salmon 

that are clearly linked to survival, migratory success, or reproductive success (Kruzynski and 

Birtwell 1994, Arkoosh et al. 1998, Hansen et al. 1999, Heintz et al. 2000, Scholz et al. 2000, 

Rice et al. 2001, Meador et al. 2002). The challenge in estimating the effects of toxic chemicals 

on salmon health is to identify which contaminants are known or suspected to occur in particular 

habitats and pathways of toxicity for these chemicals that have significance for the survival, 

migratory success, or reproductive success of wild salmon. 

Planners or researchers should utilize the primary toxicological literature in the 

development of recovery plans. Answers to the following questions can often be found in the 

toxicological literature and will enable more accurate and precise predictions about the effects of 

specific chemical contaminants on predicted salmon population performance. 

1. What is the evidence that a contaminant or class of contaminants is present in salmon 

habitat?

2. What are the expected environmental concentrations? 

3. How long will exposures last? 

4. What life history stages of salmon are likely to be affected? 

5. What are the primary possibilities for sublethal toxicity in fish? 

From this information it may be possible to estimate the chances that the contaminant is 

currently or may in the future be a significant limiting factor in salmon population viability 

within the geographic area of concern. 

Incorporating toxicological data can improve decisions about the prioritization of water 

quality improvements versus physical habitat restoration. For example, in watersheds where 

insecticides occur (primarily in agricultural and urban areas), it should be possible to estimate the 

potential loss of invertebrate prey, the subsequent reduction in the growth of juvenile fish, and 

the likelihood that salmon from contaminated habitats will have a lower rate of marine survival. 

If environmental monitoring data are unavailable, recovery planners might extrapolate potential 

chemical concentrations from other (monitored) basins with similar agricultural or urban land 

use. Even simple comparisons between reported environmental concentrations and toxicity 

thresholds for aquatic invertebrates can reduce the scientific uncertainty surrounding the 

potential effects of contaminants on salmon population viability. This in turn would improve 

restoration prioritization and watershed management plans. 

For water quality and other habitat characteristics about which less is known, it is clearly 

better to acknowledge the uncertainties and incorporate the available information, no matter how 

limited. In the example of water quality, we can estimate and incorporate the direction of the 

effect even when we are not yet able to quantify the magnitude of that effect. We can also seek 

empirical data from nontraditional sources. Moreover, identifying key uncertainties will help 

establish priorities for ongoing and future research. 

Using Decision Rules When Empirical Data Are Inadequate 

A careful and honest examination of uncertainty in data, predictions, and models will 

inevitably lead to the identification of situations in which adequate empirical data for making a 

86
decision are simply not available. Uncertainty should not lead to inaction. Methods are being 

developed to allow quantitative analysis of the sensitivity of decisions to uncertainties in the 

data. For example, sensitivity analyses were used to demonstrate that the best management 

decision for Hector’s dolphin (Cephalorhynchus hectori) was robust to model uncertainties, and 

thereby removed uncertainty in the scientific data as an excuse for inaction (Slooten et al. 2000). 

In the face of large amounts of uncertainty in empirical relationships, simulation models and 

decision analysis were used to evaluate management actions for listed salmonids in the Snake 

River basin (Peters and Marmorek 2001, Peters et al. 2001). Where empirical data are 

inadequate, we strongly discourage basing decisions on biased or imprecise predictions, 

prioritization systems for which guesswork must be substituted for data, or information that 

becomes inaccurate or imprecise at the scale for which the decision must apply. Instead, we 

suggest that resource managers provide an explicit rationale for the decision that requires 

minimal data. 

The most important characteristics of a decision rule are that it can be documented and is 

robust. Documentation is important because future managers will need to understand the basis 

for the decision. This requirement prevents arbitrary decisions in the face of inadequate data. 

Decision rules that are robust to uncertainties in the information help prevent risky management 

decisions (Schnute and Richards 2001). Decision rules presented in the literature include the 

following two examples. 

The Precautionary Principle can be stated as, “When an activity raises threats of harm to 

public health or the environment, precautionary measures should be taken even if some cause 

and effect relationships are not fully established scientifically” (Raffensperger and Tickner 

1999). Because this principle shifts the burden of proof to those who create risks and does not 

define which risks are most important (Hilborn et al. 2001), it has generated much controversy 

and confusion about its appropriate implementation. However, there are many examples of 

national and international policies that have been based on the Precautionary Principle. 

European environmental law is based on the Precautionary Principle through the 1992 Treaty on 

European Union, and the Rio Declaration from the United Nations Conference on Environment 

and Development binds the United States to implement the Precautionary Principle in 

environmental health protection (Raffensperger and Tickner 1999). While we are not advocating 

this particular decision making rule, we present it as an example of a relatively simple guiding 

principle for high-level decisions in the absence of definitive data. 

Safe Minimum Standard (SMS) is another decision-making rule that has received 

considerable attention. The SMS approach is a collective choice process that prescribes 

protecting a given level of a renewable resource unless the social costs are excessive (Berrens 

2001). This approach to making environmental decisions is usually invoked in settings involving

considerable uncertainty and potentially irreversible losses. It prioritizes social costs over loss of 

renewable resources. We present this approach for comparison to emphasize the importance of 

carefully choosing the decision-making principle and documenting exactly what considerations 

should be involved. The choice of a guiding principle will dictate management decisions until 

improved information is available. 

The choice of a decision-making rule need not be purely theoretical. The Assessment 

Approach for Habitat Recovery Planning section, page 5, discusses the importance of defining a 

87
habitat strategy that includes gathering additional data and taking interim actions. This habitat 

strategy is an excellent example of how a guiding principle can be used for decision making until 

adequate data become available. The Prioritizing Potential Restoration Actions within 

Watersheds section, page 60, presents guidelines for selecting restoration actions before all of the 

habitat data are available. Again, this is a simple and effective method for dealing with 

incomplete information. 

Another common approach to formalizing decision making without adequate empirical 

data or quantitative predictions is a scoring matrix. A scoring matrix can be used to prioritize 

potential actions, project proposals, potential action sites, or information gathering. The 

advantage of a scoring matrix is that ranks can be based on weighted priorities, for example, 

project longevity, proximity to other projects, or land ownership. The decision path can be 

clearly explained and is easily repeatable. As better information becomes available, the matrix 

can be adjusted. A disadvantage of the scoring matrix is that the weights assigned to each 

priority can dramatically alter the outcome and specifying a satisfactory weighting function in 

advance is often difficult. Examples of scoring matrices in current use include the Snake River 

Salmon Recovery Region Comprehensive Project Scoring Matrix (SRSRC 2002), the Lower 

Columbia Fish Recovery Board Interim Habitat Strategy Project Scoring Sheet (LCFRB 2001), 

and the Skagit System Cooperative methodology for rating individual landscape processes 

(Appendix C, page 157). The scoring matrix provided by the Lower Columbia Fish Recovery 

Board dedicates a section to “Certainty of Success,” explicitly including some metrics of 

uncertainty.

In each of the above examples, it is important to consider whether the decision strategy is 

robust to the types of uncertainties that exist. A strategy that would be beneficial under a 

scenario that has a 50% chance of representing reality but detrimental the rest of the time is not a 

robust choice. Strategies should be developed so that the outcome is acceptable given the range 

of possibilities for which there is uncertainty. Again the Hector’s dolphin management plan is an 

example of a strategy that is explicitly robust to the uncertainties in the data (Slooten et al. 2000). 

Using decision-making strategies that require minimal data carries two obligations. First, 

we must evaluate whether improved information would produce a cost-effective improvement in 

decision making (VOI analysis). If so, then a strong attempt to reduce uncertainties by gathering 

more or better information is required. The analyses described in the Types of Uncertainty 

subsection above can identify critical information uncertainties and reduce their impact. Second, 

we must set a time frame for reevaluating the decision. In the best possible scenario, decision 

strategies requiring minimal data serve as interim measures until additional information is 

available. 

In conclusion, we emphasize that estimates of uncertainty—quantitative where possible, 

qualitative for other situations—should be included with all information being considered in a 

decision-making framework. A systematic treatment of uncertainty should include: 

1) identification of uncertain events, states of nature, relationships, and parameters, 

2) determination of the likelihood associated with each potential state or value, 

3) use of data or models to evaluate consequences of each potential state or value, and 

4) examination of the relationship between uncertain inputs and potential outputs to identify 

key uncertainties (Mishra 2001). 

88
Even where a formal analysis of uncertainty is not possible, describing sources and magnitudes 

of uncertainty is important in providing managers with enough information to weigh potential 

risks and benefits of possible actions (Rosenberg and Restrepo 1994). 

A careful examination of the sources and causes of uncertainty will ensure informed 

decisions and make improvements in both precision and accuracy likely. Quantifications of 

uncertainty can be formally incorporated into decision making using decision tables. In other 

situations, simple strategies such as collecting data at multiple scales or incorporating data from

other disciplines will provide for more informed decisions. However, a lack of empirical data 

need not prevent informed decisions from being made in a clear and formal manner. It is 

possible to implement strategies that require minimal data. Such strategies are preferable to 

using biased or imprecise predictions, guesswork disguised as data, or information that is 

inappropriate to the scale of the decision. 

As we said earlier in this technical memorandum, our conceptual approach to habitat 

recovery planning is holistically focused on restoring or preserving watershed and ecosystem

processes to provide good quality salmon habitat over the long term. This implies that 

restoration of ecosystems to support salmon will include a wide range of actions affecting the life 

cycles of multiple species. We began with a conceptual framework for understanding 

relationships among land uses, watershed functions, habitat conditions, and biota as a basis for 

organizing the habitat-related questions that each recovery plan should attempt to answer. We

separated recovery planning into two phases—Phase I planning that identifies recovery goals and 

Phase II planning that identifies causes of habitat loss or degradation and necessary ecosystem

restoration actions. Then we showed how results from both assessments can be used to prioritize 

restoration actions and how incorporating estimates of uncertainty into the decision-making 

process increases the likelihood of success in salmon habitat recovery planning. Finally, new 

information gained from assessments and management experiments should be used to update the 

recovery plan. 

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90
GLOSSARY 

anadromous. Moving from the sea to freshwater for reproduction. 

anthropogenic. Caused or produced by human action. 

bankfull width. Channel width between the tops of banks on either side of a stream; tops of 

banks are the points at which water overflows its channel at bankfull discharge. Compare 

wetted width. 

basin. See watershed. 

benthic. Of, related to, or living in the soil-water interface of a lake or stream. 

B-IBI. For Benthic Index of Biological Integrity. An overall assessment of invertebrate 

condition constructed from various biometrics and represented by a single number. See also

benthic, biological integrity, and IBI. 

biodiversity. Range of different species of plants and animals in an environment or during a 

specific period of time. 

biological integrity. Defined in various ways, here it is the capability of supporting and 

maintaining a balanced, integrated, adaptive community of organisms having a species 

composition, diversity, and functional organization comparable to that of the natural habitat 

of the regions (Karr 1991). 

biota. Flora and fauna of a region. 

catchment. See watershed. 

culvert. Buried pipe or covered structure that allows a watercourse to pass under a road or 

underground. 

CWA. For Clean Water Act. Passed by Congress, its purpose is to “restore and maintain the 

chemical, physical, and biological integrity of the nation’s waters” (CWA 1972). 

density-dependent survival. Occurs when death rates in a population are dependent on the 

number of individuals in the population. Compare density-independent survival. 

density-independent survival. Occurs when death rates in a population are not dependent on 

the number of individuals in the population. Compare density-dependent survival. 

distributary. Branch of a river or stream that flows away from the main channel and does not 

rejoin it. Also called distributary channel. 

disturbance. Introduction of an unwanted condition into a system or interference with a 

habitat’s normal or existing conditions. 

91
ecoregion. Area determined by similar land surface form, potential natural vegetation, land use, 

and soil; it may contain few or many geological districts. 

ecosystem. In general use, it is the dynamic and holistic system of all the living and dead 

organisms in an area and the physical and climatic features that are interrelated in the transfer 

of energy and material. In this document, it is the aquatic environment and biota, physical 

and biological processes active in that environment, and the landscape processes and land 

uses that form and sustain the aquatic environment and biota. Compare habitat. 

EDT. For ecosystem diagnosis and treatment. The EDT model is an analytical tool for 

assessing relationships among stream habitat attributes and salmon population performance. 

EIA. For effective impervious area. An impervious area with direct hydraulic connection to a 

stream. See also impervious surface. 

endangered species. Species in danger of extinction throughout all or a significant portion of its 

range. See also ESA and threatened species. 

enhancement. Improving watershed processes and habitat conditions from an existing state. It 

does not necessarily seek to restore processes or conditions to some predisturbed state. Some

practitioners call this partial restoration. Compare rehabilitation and restoration. 

ESA. For U.S. Endangered Species Act. Passed by Congress, its purpose is to “provide a means 

whereby the ecosystems upon which endangered species and threatened species depend may 

be conserved” (ESA 1973). 

estuary. Semienclosed coastal body of water at the mouth of a river where the saltwater ocean 

tide meets the freshwater current. 

ESU. For evolutionarily significant unit. A population or group of populations that are 1) 

substantially reproductively isolated from other populations, and 2) contribute substantially 

to the ecological or genetic diversity of the biological species (Myers et al. 1998). It is 

sometimes represented as the spatial area encompassing the population(s). 

floodplain. Flat area adjoining a river channel constructed by the river in its present climate and 

overflow at times of high discharge. 

fry. Brief transitional stage of recently hatched fish that spans from absorption of the yolk sac 

through several weeks of independent feeding. 

gabion. Wire basket filled with stones, used for enhancing aquatic habitats or stabilizing 

streambanks. 

GIS. For Geographic Information System. A computer system for assembling, storing, 

manipulating, and displaying geographically referenced information. 

glide. Relatively slow and shallow stream section with moderate water velocities and little or no 

surface turbulence. 

92
ground-truthing. Field measurement of specific attributes that have been predicted from

models or remotely sensed data for the purpose of assessing accuracy and precision of 

predictions. 

gullying. Erosion of soil by formation or extension of gullies from surface runoff. 

habitat. In this document, the term refers to the aquatic environment that fish experience and 

not those landscape processes or attributes outside streams that alter habitat conditions. 

Compare ecosystem. 

habitat unit. Relatively homogenous area of the stream channel that differs from adjoining 

areas in depth, velocity, and substrate characteristics. 

hydromodification. Alteration of streambanks or channel morphology by bank hardening (e.g., 

riprap), dredging, diking, or other mechanical means. 

IBI. For Index of Biological Integrity. A synthesis of various biometrics that numerically 

assesses associations between human activities and biological attributes. See also B-IBI and 

biological integrity. 

impervious surface. Defined in watershed management as a manmade surface such as asphalt 

or concrete paving that prohibits the movement of water from the land surface into the 

underlying soil. 

impoundment. Body of water gathered or enclosed (such as in a reservoir) for irrigation, flood 

control, or similar purposes. 

Landsat. Landsat satellites supply global land surface images and data. 

LWD. For large woody debris. Large piece of woody material such as a log or stump that 

intrudes into a stream channel. LWD is typically defined as wood greater than 10 cm in 

diameter and 1 m in length, but other minimum size criteria are also used.

macroinvertebrate. Animal without a backbone living in one stage of its life cycle, usually the 

nymph or larval stage. Macroinvertebrates are visible without magnification and many are 

benthic organisms (see benthic). 

main stem. Principle stream or channel of a stream network. 

mass wasting. Downslope movement of earth materials under gravity, including such processes 

as rockfalls, landslides, and debris flows. 

natural stochastic variation. Inherent random variability in ecological systems, such as 

temperature or population fluctuations. Also called process uncertainty. 

outlier. In statistics, any data point exhibiting anomalous behavior. 

parr. Young salmonid actively feeding in freshwater. 

93
peak flow. Greatest stream discharge measured over a period of time, such as a season or year. 

pool:riffle:glide ratio. Ratio of the respective surface areas or lengths of pools to riffles to 

glides in a given stream reach, often expressed as the relative percentage of each category. 

population. Group of individuals of a species living in a certain area that maintain some degree 

of reproductive isolation. 

reach. Section of stream between two defined points. 

recovery. In the context of listed populations, attaining specified goals for viable populations 

and ESUs (population abundance, population growth rate, spatial structure, and diversity). 

For watershed processes and habitats, returning from a disturbed state to some prior 

condition, not necessarily pristine. 

redd. Nest in gravel, dug by a fish for egg deposition, and associated gravel mounds. 

reference site. Site in a relatively natural state, at which to measure natural or unmanaged 

conditions. May also refer to a site that serves as an experimental control, which has 

characteristics similar to a treatment site with the exception of the treatment itself.

refugia. Also commonly called biological hot spots, source watersheds, core areas, and key 

habitat, they are population centers or centers of biological diversity. 

rehabilitation. Improving ecosystem conditions, or sometimes more specifically, returning 

ecosystem conditions to a defined level of health. Some practitioners call this partial 

restoration. Compare restoration and enhancement. 

remotely sensed data. Data gathered from a remote station or platform, as in satellite or aerial 

photography. 

restoration. In the strictest sense, returning the ecosystem to some predisturbed condition. 

Some practitioners call that full restoration. In this document, the term is used generically to 

mean both restoration and enhancement (and related terminology). Compare rehabilitation 

and enhancement. 

riffle. Shallow section of a river or stream with rapid current and surface turbulence. 

riparian. Part of the landscape that exerts a direct influence on stream channels or lake margins 

and the water or aquatic ecosystems. 

riprap. Layer of large, durable materials used to protect a streambank from erosion; may also 

refer to the materials used, such as rocks or broken concrete. 

salmonid. Fish of the family Salmonidae, including salmon, trout, and chars. 

scale. In aquatic environment recovery planning, it can be viewed as a hierarchy of spatially 

nested systems. Each of these spatially nested systems can be thought of as an individual 

94
spatial and temporal scale. The result is a system in which development and persistence 

occur at specific temporal scales within each level of the hierarchy such that conditions 

within smaller scale systems are constrained by the larger scale systems that contain them

(Frissell et al. 1986, Urban et al. 1987). 

sediment budget. Accounting of sediment sources and transfer processes in a watershed. The 

complete budget quantifies sediment sources, transport, and storage within a watershed, 

usually tracking each process of sediment production or movement separately. 

sediment supply. Supply of sediment to a river system, where it is carried in suspension 

(suspended load and wash load) or on the bottom (bed load). 

seral. Of, relating to, or constituting a sere (which is a series of ecological communities formed 

in ecological succession). 

side channel. Flood channel or abandoned stream channel connected to a stream or river at 

periods of high flow. It serves juvenile fish as rearing habitat and refuge from floods. 

smolt. Juvenile salmonid in its seaward migrant stage. 

species. Category of biological classification ranking immediately below genus or subgenus, 

comprising related organisms or populations potentially capable of interbreeding. (Note that 

in the context of the ESA, “distinct population segments” of a species may be listed 

separately.)

stochastic. Of or relating to uncertainties or random variables. See also natural stochastic 

variation. 

SWAM. For Salmonid Watershed Analysis Model. A large-scale landscape analysis for 

identifying high priority areas for salmon habitat restoration. 

taxa. Plural of taxon, a taxonomic group or entity. 

thalweg. Line defining the lowest points along the length of a riverbed or valley. 

threatened species. Species not presently in danger of extinction but likely to become so in the 

foreseeable future. See also endangered species and ESA. 

tributary. Stream or river that flows into another stream or river. 

TRT. For Technical Recovery Team. The TRT establishes biologically based ESA recovery 

goals for listed salmonids within a given recovery domain. Members serve as science 

advisors to the recovery planning phase. 

uncertainty. Lack of knowledge about the true value of a quantity or lack of knowledge about 

which of several alternative models best describes the mechanism of interest. Types of 

uncertainty include prediction uncertainty, parameter uncertainty, model uncertainty, 

measurement uncertainty, and natural stochastic variation (also called process uncertainty). 

95
variability. Heterogeneity of values over time, space, or different members of a population. 

VSP. For viable salmonid population. An independent population of any Pacific salmonid 

(genus Oncorhynchus) that has a negligible risk of extinction due to threats from

demographic variation, local environmental variation, and genetic diversity changes over a 

long time frame (McElhany et al. 2000).

watershed. Entire land drainage area of a river. Also called basin or catchment.

wetted width. Width of the water surface within a channel. Compare bankfull width. 

96
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APPENDIX A: ISSUES OF SCALE IN HABITAT 

RECOVERY PLANNING 

The aquatic environment is complex and dynamic, changing continually across space and 

time. Inhabitants have evolved in response to these ever-changing conditions. However, 

anthropogenic alterations to the landscape have disrupted the natural processes within these 

systems and species are often forced to contend with altered or unnatural habitat conditions. 

These alterations can be large or small, influencing expansive areas or more local conditions, and 

the effects can occur immediately or years later. Thus a thorough knowledge of the processes 

structuring the aquatic environment and how these processes interact over multiple spatial and 

temporal scales is critical for understanding the effects of disturbance on aquatic systems and 

their inhabitants. 

This appendix examines the concept of scale in recovery planning with particular 

emphasis on analyses to help set recovery goals. Similar concepts apply to analyses designed to 

identify ecosystem restoration actions (described in the Analyses for Phase II Recovery 

Planning: Identifying Ecosystem Restoration Actions section, page 40), but are not discussed. 

This appendix first describes the inherent hierarchical nature of aquatic systems, specifying the 

need to study both the processes and inhabitants of these systems in a similar, hierarchical 

manner. Next it provides examples of small-scale and large-scale studies that highlight 

difficulties in transferring information across scales. Then these concepts are incorporated into 

examinations of how habitat alterations might influence the four categories of recovery goals: 

population abundance, population growth rate, spatial structure, and diversity (McElhany et al. 

2000). Examples from the literature illustrate the types of information obtained at each scale and 

demonstrate the value of combining studies across scales to provide information on impaired 

processes and recovery potential. 

Hierarchical Nature of Stream Systems 

The aquatic environment can be viewed as a hierarchy of spatially nested systems 

(Frissell et al. 1986, Urban et al. 1987). Implicit in this hierarchical model is the concept of 

scale. For example, reaches (101

 m) are contained within watersheds (103 m) and physical 

conditions within reaches are driven largely by frequent (100

–101 years), low magnitude 

geomorphic events such as floods (Frissell et al. 1986). By contrast, watershed attributes are 

influenced by infrequent (105

–106 years), high magnitude geologic events (e.g., glaciation, 

tectonic movements). The result is a system in which habitat development and persistence occur 

at specific temporal scales within each level of the hierarchy, and conditions within smaller scale 

systems are constrained by the larger scale systems that contain them (Frissell et al. 1986, Urban 

et al. 1987). 

In addition, longitudinal (upstream to downstream) and lateral (stream to terrestrial) 

linkages help shape biological and physical structure at each level, resulting in a predictable 

spatiotemporal gradient of physical and biological conditions from headwaters to mouth 

(Vannote et al. 1980, Frissell et al. 1986, Gregory et al. 1991). For example, stream width, 

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depth, discharge (Platts 1979, Leopold et al. 1964), temperature (Allan 1995), and biological 

diversity (Vannote et al. 1980, Barila et al. 1981) increase with stream size while gradient and 

substrate size decrease (Platts 1979, Leopold et al. 1964). There is often a decrease in terrestrial 

inputs and riparian shading coupled with an increase in organic transport from headwaters to 

estuary (Vannote et al. 1980). Moreover, these conditions are structured by the climate, geology, 

and anthropogenic activity of the specific watershed (Vannote et al. 1980, Frissell et al. 1986). 

As a result, each segment within the system contains a predictable array of habitat conditions 

dependent on the watershed. These habitats, however, are not homogenous—there is simply an 

order to their heterogeneity (Frissell et al. 1986). 

Because habitats are heterogeneous, species distributions are not even across the 

landscape but instead occur in patches. The quality and quantity of each habitat is a product of 

both large-scale and small-scale processes, and quantifying this environmental variation helps to

understand subsequent biotic responses (Cunjak 1996). For example, water temperature varies 

both spatially and temporally and influences salmonid distributions and life history patterns. 

Large-scale temperature patterns influence species ranges and distributions (Meisner 1990, 

Flebbe 1993, Welsh et al. 1995, Keleher and Rahel 1996, Welsh et al. 1998). At the watershed 

scale, elevation, latitude, aspect, and stream size interact to determine annual and seasonal 

temperature cycles. Thus the physical location of a stream within the river network influences 

population life history characteristics such as spawn timing (Gresswell et al. 1997), growth rate 

(Lobón-Cerviá and Rincón 1998, Campbell 1999), and the timing of smolt migration (Whalen et 

al. 1999a). At smaller reach or segment scales, the type and density of riparian vegetation and 

the degree of groundwater input influences the stability of seasonal and diel temperatures (Smith 

and Lavis 1975, Gregory et al. 1991, Allan 1995). These reach-level patterns can influence 

behavior (Fraser et al. 1993), food digestion and assimilation (Cunjak and Power 1987, Cunjak et 

al. 1987), ability to hold position against water current (Rimmer et al. 1985, Graham et al. 1996), 

and interspecific and intraspecific survival and distribution (Torgersen et al. 1999, Harvey et al. 

2002). Finally, at the habitat-unit scale, cool-water inputs from tributaries, intergravel flow 

through river bars, and streamside subsurface sources can thermally stratify individual pools, 

providing cool-water refuges for individuals of multiple age classes (Nielsen et al. 1994). Thus 

species assemblages and distributions are structured by a combination of larger scale geomorphic 

and climatic conditions, as well as biotic and abiotic conditions of the local environment. 

Consequently, biological and physical conditions at any site should be viewed in the context of 

the larger geologic, climatic, and geomorphologic conditions of the system as a whole—a 

multiscale approach. 

Transferability Across Scales 

Smaller scale studies generally focus on identifying physical features used by individuals 

(Bustard and Narver 1975, Cunjak 1988, Nakamoto 1994), how this habitat use changes 

ontogenetically and temporally (Rimmer et al. 1983, 1984, Baltz et al. 1991, Heggenes et al. 

1993, Whalen et al. 1999b), and how these habitat preferences differ by species (Bisson et al. 

1988, Fausch 1993, Heggenes et al. 2002). These types of studies also have identified 

bottlenecks limiting the production of different salmonids (McMahon and Hartman 1989, 

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Tschaplinski and Hartman 1983, Solazzi et al. 2000), and contributed to our understanding of 

both intraspecific (Symons and Heland 1978, Kennedy and Strange 1986, Harvey and Nakamoto 

1997) and interspecific (Hearn and Kynard 1986) competition. Smaller scale studies often 

generate models based on correlations between habitat use or availability and fish abundance, 

incorporating the concepts of optimal and suitable habitat. However, individuals are not always 

found in suitable habitat (Bozek and Rahel 1991) and not all recruits are necessarily produced 

from those habitats with the highest densities (Grossman et al. 1995). Moreover, some

preferences (e.g., nose velocities) are transferable within systems (DeGraaf and Bain 1986, 

Morantz et al. 1987), whereas others (e.g., substrate and depth) vary across systems and partly 

depend on habitat availability (Bozek and Rahel 1992). These habitat-based models may have 

low predictive power across large areas because 1) biological factors such as abundance of prey, 

predators, or competitors are sometimes excluded (Grossman et al. 1995), 2) specific 

mechanisms responsible for the selection (and subsequent consequences on individual fitness) 

are not identified (Grossman et al. 1995), 3) stream level variability may not be incorporated 

(Dunham and Vinyard 1997), and 4) frequent stochastic disturbances common at smaller scales 

make these systems less predictable (Levin 1992). 

Larger scale investigations generally address the shaping of aquatic systems and their 

inhabitants by climate variation (Meisner 1990, Keleher and Rahel 1996), geology and 

geomorphology (Platts 1979, Lanka et al. 1987, Nelson et al. 1992, Richards et al. 1996, Kruse et 

al. 1997), and land use and land cover (Connolly and Hall 1999, Bradford and Irvine 2000, Waite 

and Carpenter 2000, Paulsen and Fisher 2001). Examining larger spatial scales over longer time 

frames produces more generalized models; however, detail is sacrificed (Levin 1992). Such 

models can link species presence, absence, or composition to stream or watershed characteristics 

(e.g., stream size, geology, climate, land use), but often with mixed results. The omission of 

natural temporal variation in population abundance can obscure relationships (House 1995, 

Bradford et al. 1997), and study designs with limited spatial coverage (Baxter et al. 1999, 

Rieman and McIntyre 1996, Pess et al. 2002) or short sampling periods (Rieman and Myers 

1997) may not reliably indicate population trends or associations with habitat or habitat change. 

Also, many larger scale analyses synthesize small-scale datasets created for other purposes. 

Merging such data sets often combines data with inconsistent sampling frequencies, efficiencies, 

and representativeness, and analyses can produce unreliable or inexplicable results (Rieman et al. 

1999). 

In summary, key information can be obtained at any scale of study; however, combining 

this knowledge across scales and disciplines has proven difficult (Levin 1992, Imhof et al. 1996). 

It is generally accepted that occupancy of a given habitat is determined by a combination of 

small-scale biotic and abiotic conditions experienced by an individual within the constraints set 

by the larger landscape. As a result, there has recently been a rise in multiscale investigations 

that seek to understand the larger scale variables structuring aquatic species and, within these 

larger variables, the specific habitat characteristic influencing populations (e.g., Watson and 

Hillman 1997, Baxter and Hauer 2000, Labbe and Fausch 2000, Pess et al. 2002). As this 

research continues, recovery planning should emphasize maintaining a high degree of habitat 

heterogeneity to ensure the availability of sufficient habitat combinations to sustain multiple 

populations and species (Ward 1998). The focus should be on maintaining proper ecosystem

function rather than on managing for specific habitat criteria. This focus requires a thorough 

129
understanding of the linkages between biological and physical processes within and across scales 

(Lewis et al. 1996). 

Scale in Recovery Planning 

Anthropogenic activities can limit viability and persistence of salmon populations by 

affecting the quantity or quality of stream, estuary, and nearshore habitats. For example, forestry 

practices can alter the volume and timing of runoff or sediment delivered to streams and reduce 

the volume of large woody debris, number of off-channel habitats, streambank stability, channel 

roughness, and water quality (Meehan 1991). Agricultural practices often reduce riparian 

vegetation and increase streambank instability, sedimentation, hydromodification, and levels of 

nutrients and pesticides (Waters 1995, Waite and Carpenter 2000). Urbanized areas experience 

increased sedimentation and pollution along with many of the problems associated with 

agriculture (Waters 1995, Waite and Carpenter 2000). Dams and other forms of 

hydromodification can alter natural flow regimes, isolating river channels from their floodplains 

and riparian systems and altering the natural processes of sediment erosion and deposition (Poff 

et al. 1997). 

Individually and in combination, these anthropogenic impacts alter salmonid abundance 

and survival at various life stages and ultimately influence the maintenance and recovery of

populations and evolutionarily significant units (ESUs). Anthropogenic activities generally 

reduce habitat and community complexity (Ward 1998, Gorman and Karr 1978), and species 

with more rigid habitat requirements are more susceptible to habitat degradation and 

displacement by other species (Nelson et al. 1992). Thus understanding how land use activities 

alter natural processes and conditions within stream systems is critical for recovery efforts. The 

following subsections discuss incorporating scale into assessments of habitat factors that prevent 

populations or their parent ESUs from meeting the four categories of recovery goals: population 

abundance, population growth rate, spatial structure, and diversity (McElhany et al. 2000). 

Population Abundance 

The extinction risk faced by a population is inversely related to abundance, making it 

possible to use abundance to define broad risk categories (McElhany et al. 2000). To address 

how habitat changes might have altered fish abundance (and thus population risk), determining 

how abundance changes with land use is necessary. Recent and historical trends in abundance 

can be identified through such measures as redd counts, dam and weir counts, spawner and 

carcass surveys, harvest estimates, and juvenile counts. Once population trends have been 

identified, they can be compared to trends in land use activities for possible correlations. 

For example, Nehlsen et al. (1991) examined anadromous fish stocks in the Pacific 

Northwest and California to identify stocks with high or moderate risk of extinction. Recent 

escapement trends for seven anadromous salmonid species were utilized to assess stock risk. 

One finding was that the native upriver fall Chinook salmon (Oncorhynchus tshawytscha) 

population in the upper Columbia was strong within the Hanford Reach, Washington 

(Huntington et al. 1996 classify this as the farthest inland population in healthy condition), while 

130
native, naturally spawning populations had declined to very low levels within the Snake River. 

Using fish passage counts at individual dams, Dauble and Watson (1997) estimated that fall 

Chinook salmon spawning in the Hanford Reach increased from 60% of the total run above 

McNary Dam in the 1960s to 80% of the run in late 1980s and early 1990s. In contrast, the 

proportion of the run entering the Snake River declined over this period from 40% to less than 

5%. This decline in the mid 1960s and the 1970s was attributed to losses of juveniles passing 

through turbines and delays of migrations in Snake River reservoirs (Raymond 1979). 

Once these population trends were identified, investigating how they might have been 

shaped by habitat changes was possible. Dauble and Geist (2000) examined the spawning 

habitat characteristics of the Hanford and Hells Canyon (Snake River) sites to assess how 

hydroelectric development had influenced spawning habitat availability. Redds were found 

across a greater range of depths and dominant substrate sizes in the Hanford Reach than in Hells 

Canyon. They concluded that the Hanford Reach population has remained viable largely due to 

a geologic template that is highly compatible with its life history requirements. In contrast, the 

Hells Canyon population must contend with poor habitat quality and quantity coupled with the 

elimination of upstream and downstream populations through migration blockage and habitat 

inundation associated with dam construction. 

The focus of abundance examinations is to identify spatial or temporal trends in 

abundance and correlate these trends with landscape features or land use activity across or within 

watersheds. However, the resulting correlations cannot identify causation. Rather, they 

highlight more detailed investigations needed to uncover impaired processes and identify 

necessary recovery efforts. 

Population Growth Rate 

Investigations of population growth rate over broad spatial scales often involve 

identifying gross differences in the growth rate of multiple stocks and the role of climatic 

conditions or marine and freshwater processes in shaping these patterns. For example, Mueter et 

al. (2002) examined the effects of ocean temperature on survival rates of pink salmon (O. 

gorbuscha), chum salmon (O. keta), and sockeye salmon (O. nerka) from Washington to Alaska. 

The growth rate was quantified for each of the 120 wild stocks by computing a survival rate 

(log(R/S)) from eggs to adult recruits (R) after accounting for density-dependent effects of 

spawner abundance (S). Temperature effects were estimated for each stock separately, using a 

generalized Ricker model, then the distributions of parameters from these single stock models 

were examined to identify geographic differences across stocks. The results suggested that 

temperature effects on survival rates are consistent within regions, but that northern and southern 

stocks respond in opposite ways to temperature variations. Similarly, Peterman et al. (1998) 

used a multiple stock approach to examine spatial and temporal characteristics of environmental 

processes influencing the growth rate of 29 sockeye salmon stocks from British Columbia and 

Alaska. Again population growth rate varied at the regional scale, with survival rates of Fraser 

River stocks being influenced by large, stock-specific, interannual variability, while Bristol Bay 

stocks were driven by stronger regional-scale processes acting at both interannual and decadal 

time scales. Thus these larger scale analyses tend to identify growth rate trends for multiple 

stocks spanning wide areas, but cannot link these patterns to specific causal factors. 

131
Focusing investigations on specific populations can identify how growth rate responds to 

a specific anthropogenic activity, but again the causal mechanisms can only be suggested. 

Schaller et al. (1999) examined the response of stream-type Chinook salmon stocks within the 

upper (Snake River and upper Columbia River) and lower Columbia River regions to 

hydropower development. Spawners were estimated for each index area based on expanded 

ground and aerial redd counts or live fish and carcass counts, and age-structured spawners were 

expanded into recruitment. For each index stock, spawner and recruit data were classified into 

two time periods: 1) pre-1970 brood years prior to the completion of the last two Snake River 

dams and 2) post-1974 brood years marking the initiation of mass transportation of smolts 

around the Snake River dams and passage improvements. While all three regions showed a 

general decline in growth rate and survival between the two time periods, the declines in upriver 

stocks (which were most affected by hydropower development) were more severe. Thus 

differences in the growth rate and survival rates between upriver and downriver stocks coincided 

in space and time with the development and operation of the hydropower system. However, 

more detailed analyses are needed to examine potential causal mechanisms for these findings. 

Analyses of stage-specific growth rate (i.e., realized over some discrete portion of the life 

cycle) can also be important information sources, particularly where the dynamics of one life 

stage dominate the dynamics of the entire life cycle (McElhany et al. 2000). For example, 

Nickelson et al. (1992) estimated coho salmon (O. kisutch) smolt production for coastal Oregon 

basins using juvenile density estimates by habitat type for different seasons. Fully seeded 

streams were sampled each season and habitat was classified using a modified version of the 

habitat classification scheme described by Bisson et al. (1982). Results indicated that production 

of smolts in Oregon coastal streams can be limited by the availability of winter habitat. Once 

such bottlenecks are identified, they can guide the recovery planning process toward measuring 

the quantity of critical habitats available in nonimpacted versus impacted sites. Also, historical 

reconstructions can be conducted to quantify the actual losses of these critical habitats in 

impacted areas. For example, Beechie et al. (1994) estimated the magnitude of lost rearing 

habitat and the subsequent loss in coho salmon smolt production by habitat type and form of 

impact within the Skagit River basin. Using a combination of field surveys, maps, and 

orthophotos to estimate current and historical habitat, as well as survival to smoltification rates 

from Reeves et al. (1989), they found a 34% decrease in smolt production capacity of winter 

rearing habitats from historical production. Hydromodification, largely due to diking, accounted 

for 91% of the total smolt production losses for winter rearing areas. This type of information is 

useful in the recovery process, for it can highlight the processes that need to be restored to 

improve the health of the local habitat as well as set recovery priorities based on the degree of 

degradation. See the Analyses for Phase II Recovery Planning: Identifying Ecosystem

Restoration Actions section, page 40, and the Prioritizing Potential Restoration Actions within 

Watersheds section, page 60, for related discussions. 

Spatial Structure 

The spatial structure of a population refers to the distribution of individuals within a 

population and the processes generating that distribution. It is dependent on habitat quality, 

spatial configuration, and the dynamics and dispersal characteristics of individuals in the 

population (McElhany et al. 2000). The heterogeneous stream environment may be viewed as a 

132
series of habitat patches at an array of spatial scales. The likelihood of individuals inhabiting 

each patch is dependent on the quality of habitat in the patch as well as the ability of individuals 

to move between patches. Therefore, an investigation into how habitat changes might have 

altered the spatial structure of a population requires understanding the small-scale and large-scale 

influences on both habitat patch dynamics and salmonid movement. 

Comparing historical presence of a species to current or predicted occurrence can help

describe the distribution of a species and populations across a landscape, their degrees of 

isolation, and their sizes. Rieman et al. (1997) compared historically potential and current 

distribution of bull trout (Salvelinus confluentus) within the interior Columbia River basin, 

representing 20% of the species’ global range. Available information on the presence and status 

of bull trout was summarized and validated, and classification trees were developed to predict 

bull trout presence and status using landscape features and management history. Bull trout were 

widely distributed within their potential range, with known or predicted occurrence in 44% of the 

historical spawning and rearing subwatersheds. Such coarse examinations can highlight the 

distribution of populations across the landscape and give some indication of population strength 

based on size. More detailed work is needed to address patch size and dynamics within 

individual systems. 

Dunham and Rieman (1999) examined 81 patches (≥103 m) within the Boise River to 

identify patterns in juvenile bull trout occurrence. Habitat patches were defined as stream

catchments above 1,600 m elevation with an accessible perennial stream (Rieman and McIntyre 

1995), and patches, road densities, and interpatch distances were estimated using Geographic 

Information System (GIS) methodologies. Trout occurrence, stream width, and gradient data 

were obtained in the field. Results indicated that the large-scale geometry of catchments can 

strongly influence the distribution of aquatic species, though smaller scale factors also affect 

distribution. They concluded that bull trout populations in larger, less isolated, less disturbed 

patches are more likely to persist and that it is critical for disturbance within these habitats to be

minimal. On the other hand, small, isolated, disturbed populations and habitats are at risk. Thus 

they speculate that conservation and restoration opportunities might provide best results if 

centered within those patches of intermediate size or isolation. In a comparison of patch size 

distributions for Lahontan cutthroat trout (O. clarki henshawi) and this bull trout data, Dunham

et al. (2002) found that the size distributions were similar between the two species and skewed 

toward smaller patches, so that very few large patches may be critical to each species. Both 

species are likely to occur when patch size exceeds 104

 ha in area and bull trout are more likely 

to occur in smaller patches that may in turn explain their occurrence in a large percentage of 

suitable habitat. 

Identifying how species are distributed across the landscape and utilize habitat patches is 

a necessary initial step in the recovery planning process. Linking this information to their 

dispersal and migratory behavior can highlight “stepping stone” patches: critical patches 

connecting large habitat areas. Once identification of critical habitat patches is achieved, one can 

examine how land use actions have reduced or eliminated connectivity of these patches as well 

as altered or destroyed the patches themselves. This information can then guide the scale of 

recovery efforts by pinpointing the processes that are in need of restoration and identifying local, 

specific actions. 

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Diversity 

Diversity refers to the distribution of traits within and among populations (McElhany et 

al. 2000). Genotypic and phenotypic diversity occur at all scales; however, the majority of 

genotypic diversity is contained within stocks while most phenotypic diversity is greater across 

populations and landscapes (Healey and Prince 1995). Thus successful conservation must focus 

as much on ensuring habitat quality and connectivity as on genotypes (Healey and Prince 1995). 

The genetic controls are beyond the scope of this document, but it is possible to examine how 

habitat changes influence the phenotypic expression of traits. Differences in these traits can have

adaptive value and should be maintained, even if they do not have a genetic basis, and can be 

expressed as changes in morphology, fecundity, run timing, spawn timing, behavior, smolt age, 

age at maturity, and egg size (McElhany et al. 2000). For example, in Carnation Creek, British 

Columbia, a decline in 0+ coho salmon densities and an accompanying increased growth rate 

following logging resulted in greater rates of 1+ smolt production (Hartman et al. 1996). This 

shift in the age and size composition of smolts resulted in an increase in the variability of adult 

production (Holtby and Scrivener 1989). Unfortunately, long-term databases that can illustrate 

the results of anthropogenic activities are rare. Thus other ways to determine widespread threats 

to diversity are needed. 

One approach is to conduct a regional examination of changes in species diversity under 

the assumption that where species diversity is decreasing, phenotypic and genotypic diversity 

might also be decreasing. For example, Frissell (1993b) expanded on the identification of 

Pacific salmon stocks at risk synthesized by Nehlsen et al. (1991) to map region-wide patterns in 

fish diversity. He used data compiled for inland fishes (Williams et al. 1989) and anadromous 

stocks (Nehlsen et al. 1991), focusing on the Pacific Northwest and California, which highlighted 

species and stocks at risk. Mapping units were based on a drainage basin size of 50 to 2,000 km2

and the basins were categorized according to the number of species (0–1, 2–3, 4–5, or 6–8) 

classified as extinct, endangered, or threatened. Isopleths were then fit between the categories. 

Results indicated a general increase in endangerment from north to south. Regions with 4+ 

species at risk were of particular concern and species declines were attributed to large-scale dams

and irrigation projects. These types of regional examinations can identify where diversity is 

declining, where more detailed examinations are consequently needed to identify processes 

responsible for the decline, and efforts needed to remedy the problem. 

A variety of bioassessment tools can also help identify where anthropogenic activities 

have eroded stream health and threaten diversity. One method is to assess habitat degradation 

using invertebrate species assemblages. For example, the reference-condition approach requires 

the development of a reference database containing invertebrate assemblages and matching 

habitat descriptors for a large number of minimally disturbed reference sites. Invertebrate 

assemblages at these reference sites are described, classified, and related to habitat attributes to 

develop predictive models. The resulting reference models can then be compared to test sites to 

identify impairment. Results will highlight impaired areas, but will not specify the underlying 

causes. See the Analyses for Phase II Recovery Planning: Identifying Ecosystem Restoration 

Actions section, page 40, for a more detailed discussion of these methodologies. 

Another method of examining the effects of land use on stream health and diversity is to 

characterize changes in species assemblages across land use gradients. In affiliation with the 

134
U.S. Geological Survey’s National Water Quality Assessment Program, Waite and Carpenter 

(2000) utilized this approach to identify how natural and land use gradients influence biological 

assemblages within the Willamette River basin in Oregon. Both field data (fish sampling, water 

quality sampling, and habitat measurements) and GIS data (land use, drainage area, and 

elevation) were used, covering reaches within seven major subbasins and three ecoregions. They 

found physical habitat a better descriptor of fish assemblages across ecoregions (i.e., all sites), 

whereas water chemistry was a better descriptor within the Willamette Valley ecoregion. They 

also suggested that the reduced riparian quality and increased water temperatures, nutrient 

supply, and sediment supply found in small agricultural and urban streams cause fish

assemblages to shift from those dominated by native, intolerant species to those dominated by 

introduced or tolerant species. Amount of riffle habitat, quality of riparian cover, and maximum 

water temperature were the overriding variables describing variation among land uses. These 

biological assessments provide an opportunity to monitor long-term changes in community 

composition or stream health (Rabeni 1992) at multiple scales, highlighting areas of possible 

impairment due to land use activities, and identify possible causes that can then be further 

investigated.

Summary 

The foregoing illustrated how the effects of habitat change on the four components of 

population viability can be examined over multiple scales. At large spatial scales, one can 

identify where population characteristics have changed from historical conditions and how they 

relate to population or ESU recovery goals. For example, Rieman et al. (1997) and Dunham and 

Rieman (1999) looked at current distributions of bull trout across several river basins and 

predicted historical distributions. Each study identified increased patchiness of bull trout 

populations over time as well as relative population sizes. Each study also examined the spatial 

isolation of populations to estimate their relative strength, diversity, and degree of risk. 

Similarly, Thurow et al. (1997) examined other native salmonids within the interior Columbia 

River and portions of the Klamath and Great Basins to determine their distribution and status. 

Their work highlighted the proportion of the potential range currently occupied by each species 

and the relative strength of the populations. They found all taxa have narrower distributions, 

fewer areas with high diversity, and lower percentages of strongholds than in their estimated 

potential historical conditions. Strongholds were generally found to be rare and not well 

distributed across the landscape. Thus examining abundance and distribution across space and 

time can illustrate areas retaining historical diversity and ecological structure as well as those 

that have possibly been altered by human influence. 

The large-scale studies described above broadly correlate landscape factors (e.g., land 

use, climate, other species) to population trends and highlight systems that warrant closer 

examination. Examination of finer scale components of a system (i.e., at watershed or smaller 

scales) can then identify causation (e.g., road density, hydromodification, water quality 

degradation), which helps one understand how specific ecological processes have been impaired 

and indicates potential pathways for restoring them. The scale of the analysis constrains 

interpretations of results (Wiens 1986) and increasingly fine scales of assessment obtain more 

detailed information. For example, Nehlsen et al. (1991) noted a decline in summer steelhead 

135
(O. mykiss) within most river systems in California. They identified the Eel River population to 

be at moderate risk of extinction and mentioned how floods in 1964 severely affected 

populations throughout California due to extensive erosion and habitat damage in watersheds 

stressed by poor land management. Harvey et al. (2002) highlighted potential mechanisms

contributing to these declines, speculating that habitat changes caused by the floods altered 

summer temperature regimes and allowed extensive invasion by the nonnative Sacramento 

pikeminnow (Ptychocheilus grandis) decades later. The investigators concluded that 

1) restoration of riparian vegetation within the watershed could reduce the range and ecological 

impact of the pikeminnow, and 2) increased riparian vegetation and improved hillslope 

conditions could enhance native salmonid habitat by moderating the influence of peak flows and 

sediment supply on channel stability, thereby improving thermal regimes and instream habitat. 

Thus results of larger scale work identify a problem, while smaller scale work highlights specific 

anthropogenic activities and ecological impairment underlying the problem and, consequently, 

more specific conservation and restoration strategies. 

As aquatic ecosystems are arranged in an interconnected array of hierarchical systems, 

any study of their processes and inhabitants can be organized in a similar hierarchical manner. 

Species patterns and relations to habitat and anthropogenic activities can be seen at any scale; 

however, the scale examined dictates the level of detail that can be inferred from the results and 

any subsequent interpretations. By examining the abundance and distribution of species and 

associating these with land use activity at multiple scales, patterns in the spatial structuring and 

diversity of populations, and thus their level of risk, can be identified. Such information can then 

be used at fine scales to identify the ecological processes impaired and prioritize conservation 

and restoration activities.

136
APPENDIX B: ESTIMATING CHINOOK SALMON 

SPAWNER CAPACITY OF THE STILLAGUAMISH RIVER 

One important task of Technical Recovery Teams is estimating both current and 

historical capacity of the habitat to support juvenile and adult salmonids. These estimates are 

needed to evaluate whether population goals derived from population viability analyses could be 

supported by currently or historically available habitat. For example, if viability estimates 

indicate that 5,000 fish are needed for a population to be viable, estimates of spawner capacity 

can indicate whether that number is within the bounds of what the watershed may have 

historically supported. 

Riverine habitats function at different spatial and temporal scales, ranging from the 

watershed level down to microhabitats (Frissell et al. 1986). Larger scale systems such as 

watershed and segment generally operate over a 100 to 1,000 m linear spatial area and persist for 

1,000 to 1 million years (Frissell et al. 1986). Extrinsic forces driving these larger systems 

include glaciation, volcanism, tectonic uplift, climatic shifts, earthquakes, and alluvial or 

colluvial valley infilling. Reach-scale systems operate at intermediate scales of about 100 m and 

10 to 100 years, and are driven by events such as debris torrents, landslides, and log input or 

washout. Smaller scale habitat-unit systems are typically controlled by events or processes 

occurring at spatial scales of 1 to 100 m and over shorter time periods (persistence of 1–10 years, 

Frissell et al. 1986). Habitats at this level include pools and riffles as well as glides, rapids, side 

channels, and backwater pools, and have characteristic bed topography, water surface slope, 

depth, and velocity patterns (Bisson et al. 1982). These habitat units are driven by delivery or 

routing of sediment, wood, boulders, etc., small bank failures, flood scour or deposition, thalweg 

shifts, and numerous human activities (Frissell et al. 1986). 

For a case study, we estimated the current and historical capacity of the Stillaguamish 

River for adult Chinook salmon (Oncorhynchus tshawytscha) based on habitat data at the unit 

scale (e.g., pool, riffle, and glide). Detailed estimates of salmonid abundance at the habitat-unit 

scale were extrapolated to the watershed scale to estimate change in total salmonid abundance 

for the basin. This analysis of capacity allowed us to estimate the maximum number of adult 

Chinook that the Stillaguamish River produced historically, as well as the system’s potential for 

production today. 

Methods 

Our approach for estimating capacity essentially had two steps. First we assessed the 

amount of habitat available for Chinook spawning. We mapped where adult Chinook spawn in 

the watershed. Within that area, we identified streams either as large main stems or small main 

stems and tributaries. We quantified habitat area by estimating total stream length for small 

main stems and tributaries and total stream area for large main stems. The large main stems 

were further characterized by estimating how much reach-scale habitat was pool, riffle, or glide 

and how much of each habitat unit was suitable for spawning. 

137
Second we assigned fish numbers to habitats. For small main stems and tributaries, we 

estimated the total number of redds possible by multiplying stream length by an estimate for the 

expected number of redds per kilometer. For large main stems, we calculated how many redds 

would fit into our estimated stream area by assuming an estimate for redd area. Then we 

converted estimates of total redds for each reach-scale habitat type to total adults using an 

estimate for number of adults per redd. 

Watershed-Scale Chinook Salmon Spawning Distribution 

Streams were identified as accessible to Chinook salmon based on the location of barriers 

and stream gradient. The historical distribution of Chinook included areas below natural barriers 

(i.e., waterfalls, cascades) and with streams of low (less than 4%) gradient (Montgomery et al. 

1999). Similarly, current fish distribution included areas below anthropogenic barriers 

(primarily culverts) and natural barriers, and with low-gradient streams. At the upstream end, 

Granite Falls in the South Fork Stillaguamish River was not included in either current or 

historical capacity estimates (Figure B-1). Historically, few Chinook were able to ascend 

Granite Falls. At present a fish ladder allows Chinook to pass upstream, but observed juvenile 

outmigrant abundance is low (K. Rawson2

). We defined the downstream limit of Chinook 

spawning as the upper extent of tidal influence, which occurs at about the confluence of Cook 

Slough and the Stillaguamish River, and Pilchuck Creek with the Stillaguamish River. 

Reach-Scale Chinook Salmon Spawning Habitat 

The main Chinook salmon spawning areas are in larger tributary streams and main stems 

of rivers (Miller and Brannon 1982). Chinook use of these freshwater habitats varies depending 

on the size of bankfull channel widths. Chinook redd density in the Skagit River generally 

increases up to about 25 m bankfull width, then sharply declines (Figure B-2, Montgomery et al. 

1999). Similarly, channel morphology varies at different scales as a factor of channel width and 

large woody debris (LWD) loading. Pool frequency increases with bankfull channel width and 

LWD loading up to about 20–25 meter channel width, then drops off (Montgomery et al. 1995). 

Pools in these smaller streams can be formed by individual pieces of wood, which cross streams 

and form stable obstructions (Abbe 2000). In larger streams, channel morphology is most 

significantly affected by large logjams, which form stable obstructions that create pools but at a 

lower frequency (Abbe 2000). Salmon usually spawn at the transition between pools and riffles 

or in areas associated with a lateral bar deposition (Bjornn and Reiser 1991). 

Because Chinook use of channel types varies with channel width, we classified streams to 

reach-scale habitat types by bankfull width. Small streams (or mainstem/tributary habitat types) 

are from 5 to 25 m bankfull width, and large mainstem habitats are greater than 25 m width. 

Additionally, streams less than 5 m bankfull width were regarded as too small for consistent 

Chinook spawner production, based on data from the Stillaguamish, Snohomish, Skagit, Suiattle, 

 

2

 K. Rawson, The Tulalip Tribes, Natural Resources Dept., 7615 Totem Beach Rd., Marysville, WA 98271. Pers. 

commun., May 2001. 

138
 

 Figure B-1. Historical area of anadromous fish access (dark shading) and area opened to anadromous 

fish by a fish ladder around Granite Falls (lighter shading of south fork) in the Stillaguamish 

River basin. 

139
0

20

40

60

80

100

120

0 5 10 15 20 25 30 35 40

Bankfull width (m)

Chinook redds per km

forced pool-riffle

pool-riffle

plane-bed

step-pool

Figure B-2. Comparison of Chinook redds per kilometer to bankfull width measurements, Skagit River. 

(Based on data in Montgomery et al. 1999.) 

140
and White Rivers (D. Hendrick,3

 J. Doyle4). Chinook are occasionally observed spawning in 

smaller streams (Hendrick footnote 3, Vronskiy 1972). However, we wanted to include stream

habitat where the majority of Chinook consistently spawn. 

We estimated bankfull width for both current and historical stream conditions using 

regression models. For historical conditions, we developed a regression to predict channel width 

using channel width measurements from 1860s General Land Office Survey notes (Collins and 

Montgomery 2001) and basin drainage area (A) for the Stillaguamish River: 

Historical channel width = 10^(–2.4 + 0.54 × log A), Adjusted R2

= 0.70 (6) 

Basin drainage area was derived using a 30-m digital elevation model (DEM). For current 

conditions, a regression model was also developed using channel width data and basin drainage 

area: 

Current channel width = 10^(–1.5 + 0.43 × log A), Adjusted R2

= 0.68 (7) 

Channel widths were measured primarily from aerial photographs, but included field 

measurements as well. 

Using these regression relationships, we developed a Geographic Information System 

(GIS) data layer of channel width for the entire Stillaguamish River watershed based on the 

DEM-derived drainage area data. The channel width predictions were then applied to categorize 

all streams in a 1:24,000 scale hydrography into the three categories: less than 5 m bankfull 

width, from 5 to 25 m width, and greater than 25 m width. 

Chinook Salmon Capacity in Small Streams 

Habitat in small streams of the Stillaguamish River basin could only be described at the 

reach scale due to a lack of data describing the proportions of pools, riffles, and other unit-scale 

habitats. Likewise the data available for estimating Chinook use of small streams (5 to 25 m

width) was expressed at the reach scale (i.e., as number of redds per kilometer of stream length, 

Montgomery et al. 1999). Hence we estimated Chinook spawner capacity (Nadults) in small 

streams as a function of stream length, number of redds per kilometer, and number of adults per 

redd: 

Nadults = (Total stream length × Redds/km) × No. Adults/redd (8) 

In generating our historical capacity estimate, we calculated the total length of small streams 

within historical Chinook spawning areas from the 1:24,000 hydrography data. Additionally, we 

included the total length of small, non-pond-like side channels estimated for historical conditions 

(Pess et al. 1999b). For the current capacity estimate, we excluded those streams and side 

channels that were blocked by culverts or dams or otherwise isolated (Table B-1). 

 

3

 D. Hendrick, WDFW, 111 Sherman St., P.O. Box 1100, La Connor, WA 98257. Pers. commun., May 2001. 4

 J. Doyle, U.S. Forest Service, Mt. Baker-Snoqualmie National Forest, 21905 64th Ave. West, Mountlake Terrace, 

WA 98043. Pers. commun., May 2001. 

141
Table B-1. Estimates of stream length (m) of small streams (5 to 25 m bankfull width) under current and 

historical conditions for the Stillaguamish River. 

Time period Habitat type North Fork South Fork Main stem 

Historical Small streams and tributaries 

Non-pond-like side channels

209,348 

10,939 

87,148 

2,667 

39,572 

28,007 

Current Small streams and tributaries 

Non-pond-like side channels 

207,173 

4,462 

93,198 

0 

45,291 

11,079 

142
For estimates of redds per kilometer, we assumed that channel morphology in small 

streams was historically determined primarily by LWD loading. Such in-channel obstructions 

produce forced pool-riffle habitats—channels in which the majority of pool and bar forms are 

forced by flow convergence, divergence, and turbulent scour associated with obstructions 

(Montgomery et al. 1995). Forced pool-riffle habitats, formed by woody debris, may be 

considered indicative of undisturbed conditions (Lunetta et al. 1997). Therefore, data for number 

of redds per kilometer only include counts from forced pool-riffle habitats (vs. pool-riffle and 

plane-bed) for historical conditions (Montgomery et al. 1999). Redd density data were collected 

from 50 reach-level surveys in five tributaries to the Skagit between 1991 and 1996. The median 

value for redds per kilometer in forced pool-riffle channels was 29.6, and the 10th and 90th 

percentiles of the range are 7.0 and 57.5 redds per kilometer, respectively (Table B-2). 

Spawner survey data are also available describing Chinook use specific to the North Fork 

Stillaguamish River for nine river reaches (Table B-3, Montgomery et al. 1999). However, these 

data were collected over longer time spans (up to 23 years) and it was not certain that habitat 

characteristics (in particular, forced pool-riffle) remained constant throughout the time period 

that Chinook data were collected. These data were useful for our current capacity estimate 

where redd counts were needed across a range of habitat types, which could vary across time and 

space (Table B-3). These average counts were first applied to the tributaries from which data 

were collected (i.e., Boulder River, Squire Creek, etc.). For the remaining small streams, we 

summarized the data from the nine streams (Table B-3) and applied the median (2.5 redds/km), 

10th (0 redds/km), and 90th (13.8 redds/km) percentiles of the range in the current capacity 

estimate (Table B-2). 

We estimated the number of adults per redd using data that describes the number of 

males per female from: 1) carcass recovery survey data from the North Fork Stillaguamish, 

Skagit, and Snohomish Rivers, 2) Sunset Falls counts from the Snohomish River, 3) broodstock 

collection data from the North Fork Stillaguamish and Skagit Rivers, 4) mark/recapture study 

from the North Fork Stillaguamish, and 5) gill drift net test fishery data from the Skagit River 

(Hahn et al. 2001, Hendrick unpubl. data). The ratio of males:females plus one is equivalent to 

the number of adults per redd. The median value for number of adults per redd was 1.9 and the 

10th and 90th percentiles of the range were 1.4 and 3.5, respectively. 

To illustrate the full range of potential current and historical capacity values, we 

calculated estimates using the 10th percentile, median, and 90th percentile ranges of all spawner 

biological variable values (redds per kilometer and adults per redd). Stream length included only 

one measurement each for current and historical conditions. All capacity calculations include 

estimates for the North Fork Stillaguamish River Chinook salmon population and the South 

Fork/mainstem Stillaguamish population. 

Historical Chinook Salmon Capacity in Large Streams 

We estimated historical Chinook spawner capacity in large streams (>25 m bankfull 

width) by determining the amount of area in a river that has habitat suitable for spawning and 

calculating the number of redds that fit in that area. We then estimated the number of adults as a 

function of number of fish per redd. The equation for historical capacity in large streams is as 

follows: 

143
Table B-2. Values used to vary biological parameters for current and historical capacity estimates. 

Redds/kmc

Percentile

Redd sizea

(m2) Adults/reddb Historical Current 

90th 4.9 3.5 57.5 13.8

Median 14.1 1.9 29.6 2.5

10th 27.9 1.4 7.0 0.0

a Data from the North Fork Stillaguamish River. 

b Data from the Snohomish and Stillaguamish Rivers (Hendrick unpubl. data). c Data from the Stillaguamish and Skagit Rivers (Montgomery et al. 1999). 

144
Table B-3. Data on number of Chinook salmon redds for repeated spawner surveys along the North Fork 

Stillaguamish River (Montgomery et al. 1999). 

Location Years of data Channel type 

Average 

redds/km 

Boulder River 

Squire Creek 

Furland Creek 

Ashton Creek 

Browns Creek 

Brooks Creek 

Rollins Creek 

Dicks Creek 

Segelson Creek 

16 

23 

2 

2 

10 

4 

6 

4 

2 

Pool-riffle 

Pool-riffle 

Forced pool-riffle 

Forced pool-riffle 

Forced pool-riffle 

Plane-bed 

Plane-bed 

Plane-bed 

Plane-bed 

4.2 

6.4 

13.8 

8.8 

2.5 

0.0 

0.0 

0.0 

0.0 

145
Nadults = (SA × %Spwn) × No. adults/redd (9)

where SA is stream area and %Spwn is percent of area suitable for spawning. 

To calculate stream area in the Stillaguamish River basin, we first generated an estimate 

of wetted widths across all stream reaches for the time period when Chinook spawn (mainly 

August/September). We measured widths from 1:24,000 digital orthophotos of the watershed. 

The photos were taken in mid-to-late July 1998. Mean monthly flows in July are 25% to 45% 

higher than in September (generally, the peak of spawning), which meant we would overestimate 

spawner abundance by some fraction. However, available spawning area does not increase in 

direct proportion to stream flow (i.e., 25% more flow does not equal 25% more spawning area), 

so overestimation may not be significant. We then developed a regression model to predict 

wetted widths based on cumulative stream lengths (total stream length above each wetted width 

measurement) and stream order data, derived from 30-m DEMs: 

Wetted width = 10^(–2.59 + 0.56logCFL + 0.36SOR), Adjusted R2

= 0.77 (10) 

where CFL is cumulative flow length and SOR is stream order. 

With this regression, we developed a GIS data layer of wetted width for the Stillaguamish 

River basin and associated the wetted width predictions to stream reaches in the hydrography 

layer. We then calculated stream area for each reach as a function of wetted width and stream

length. These stream area estimates were used for calculations of both current and historical 

capacity (Table B-4). In addition, we included estimates of stream area for non-pond-like side 

channels for historical conditions (Pess et al. 1999b). All these side channels are presently 

isolated from streams accessible to anadromous fish. 

To describe the stream area suitable for spawning under historical conditions, we 

conducted field surveys in Western Washington streams that are in relatively undisturbed 

condition (North Fork Sauk River, mainstem Sauk River, South Fork Stillaguamish River, Squire 

Creek, and the South Fork Hoh River). PSSRG (1997) developed criteria for measuring the area 

suitable for spawning by describing habitat characteristics in the North Fork Stillaguamish River 

where redds were observed. Geist et al. (2000) similarly used characteristics of spawning habitat 

measured within local spawning areas to evaluate Chinook salmon habitat suitability in the 

Columbia River. In the Stillaguamish River, substrate diameter averaged 74 mm (range of 45–

120 mm, PSSRG 1997). Depths averaged 0.5 m (range of 0.3–1.5 m), and velocities averaged 

0.6 meters per second (range of 0.3–1.0 m/s). Healey (1991) summarized comparable values 

from the literature for water depth and velocity in Chinook spawning beds. 

In our habitat surveys of reference sites, the average values for substrate and depth were 

the primary variables used to estimate suitable spawning area, in addition to channel bed 

morphology. Channel bed morphology indicates the location of subsurface flow (such as at the 

junction of a pool’s tailout and the head of a riffle), which is important in redd site choice by 

Chinook (Vronskiy 1972, Chapman 1943, Russell et al. 1983). Once we identified the location 

of subsurface flow (i.e., the primary spawning site), we measured the area suitable for spawning 

within a habitat (riffle, pool, glide) by surveying the extent of appropriate average substrate and 

depth values. We calculated historical capacity estimates where percent habitat spawnable was 

146
Table B-4. Estimates of stream area (m2

) under current and historical conditions for the Stillaguamish 

River. 

Time period Habitat type North Fork South Fork Main stem 

Historical Large streams

Non-pond-like side channels 

Totals 

2,616,542 

 70,676

2,687,218 

1,842,123 

 0

1,842,123 

1,318,877 

 98,106

2,416,983 

Current Large streams

Non-pond-like side channels 

Totals 

2,532,976 

 0

2,532,976 

1,696,890 

 0

1,696,890 

1,023,863 

 0

1,023,863 

147
segregated by habitat type (Table B-5). However, we had less certainty that habitat composition 

reflected historical conditions because of the limited size of our reference sites. Therefore, we 

summarized the percent of area suitable for spawning (“percent of habitat spawnable”) across all 

habitat units combined. 

For the capacity estimate, we assumed that redds are uniformly distributed and positioned 

immediately adjacent to one another, without a larger territorial boundary. Geist et al. (2000) 

found that when redd densities were near capacity, clusters or redds were uniformly distributed 

and inter-redd distances ranged from 2 to 5 m. In the Stillaguamish River, median redd size was 

14.1 m2

 (4.9 m2 and 27.9 m2 were the 90th and 10th percentiles, respectively, Table B-2). These 

redd sizes compare well with redd areas reported by others (Table B-6, Healey 1991). If we 

assume redds have an approximately circular shape and radius of 2.11 m, distances between 

redds in the Stillaguamish River would fall roughly within the range observed by Geist et al. 

(2000). 

We calculated a range of spawner capacity estimates using the 10th percentile, median, 

and 90th percentile of values for all variables (redd size, number of adults/redd, percent habitat 

spawnable, Tables B-2 and B-5), except stream area for which we only had one measurement 

(Table B-4). Our best estimate for historical capacity included percent habitat spawnable with 

habitat units combined, but we also calculated capacity estimates with percent spawnable 

evaluated separately by habitat type. 

Current Chinook Salmon Capacity in Large Streams 

To estimate current capacity in large streams, we used field data for habitat distribution 

and redd location and field data characterizing habitat suitable for spawning from river 

kilometers 24 to 48 of the North Fork Stillaguamish River (Hahn et al. 2001). We digitized 

Washington Department of Fish and Wildlife (WDFW) field maps describing habitat units (pool, 

riffle, glide, etc.) into a GIS database and summarized the habitat composition across the 24-km

reach (Table B-5). We also digitized redd locations for the same 24-km stretch from 1998, 1999, 

and 2000 field surveys. 

We estimated current capacity similar to methods for historical capacity, except we first 

described the habitat composition of the river, then estimated the percent of habitat suitable for 

spawning based on those habitat units: 

Nadults = ((SA × %Spwn)pools+(SA × %Spwn)riffles + (SA × %Spwn)glide) × No. adults/redd 

 Redd size (11) 

We estimated the percent of habitat that is suitable for spawning in two ways. First we 

estimated the actual percent of habitat with observed spawning using WDFW redd survey data. 

We used GIS to overlay the combined three years of redd survey data with the habitat-unit maps. 

A buffer was generated around all redd locations to represent area spawned. We assumed a 

radius of 2.11 m, which is equivalent to a 14 m2

 redd size. For each habitat type, the percent of 

habitat spawned was calculated as the area within buffers divided by the area of the habitat type. 

Second we estimated the percent of habitat suitable for spawning using our field survey data 

where spawning habitat was characterized by substrate, velocity, depth, and channel bed 

148
Table B-5. Percent habitat composition and values used to vary percent of habitat suitable for spawning 

for current and historical conditions. For current conditions, percent of habitat with observed 

spawning was calculated using Washington Department of Fish and Wildlife (WDFW) redd data 

from 1998, 1999, and 2000 and National Marine Fisheries Service (NMFS) field measurements 

of available habitat. 

Habitat-unit type 

Data source Parameter Riffle Glide Pool Rapid 

Backwater 

pool 

Habitat 

units 

combined

Historical conditions 

Percent habitat 

composition 

Reference site 26.0 25.8 47.0 —* —* 100.0

data 

Percentile of 

habitat spawnable 

 90th 

 Median 

 10th 

20.0 

17.0 

3.0 

18.0 

1.0 

0.0 

3.0 

2.0 

0.0

— — 9.5 

3.0 

0.0 

Current conditions 

WDFW habitat 

survey data 

Percent habitat 

composition 

46.0 39.0 11.0 3.0 1 100.0

WDFW redd 

survey data 

Percentile of 

habitat spawned 

 90th 

 Median 

 10th 

3.4 

1.8 

0.0 

3.4 

0.2 

0.0 

3.3 

0.0 

0.0

3.8 

0.4 

0.0 

0 

0 

0 

5.5 

0.4 

0.0 

NMFS habitat 

survey data 

Percentile of 

habitat spawnable 

 90th 

 Median 

 10th 

77.1 

18.8 

1.0 

50.2 

12.0 

0.0 

0.0 

0.0 

0.0

0.0 

0.0 

0.0 

0 

0 

0 

50.0 

2.7 

0.0 

*Rapid and backwater pool habitats were not surveyed. 

149
Table B-6. Summary of published information on redd size (Healey 1991). 

Redd area (m2

) 

Source Type

a Range Mean

Chapman 1943 

Burner 1951 

Burner 1951 

Vronskiy 1972

Neilson and Banford 1983

Chapman et al. 1986 

O 

S 

S 

S 

O 

2.4–4.0 

4.8–6.5 

2.4–4.1 

4.0–15.0 

0.5–27.5 

2.1–44.8 

NRb

NR 

NR 

NR 

9.5 

17.0 

a

 Reported if available, S = Stream-type (spring-run) Chinook salmon, O = Ocean-type (fall and summer) Chinook. 

b

 NR = Not reported. 

150
morphology, as described for historical large stream capacity (habitat survey data in Table B-5). 

Both estimates of percent habitat spawned and percent habitat spawnable were calculated by 

habitat type and for all habitat units combined. 

Similar to historical estimates for large streams, current spawner capacities in large 

streams were calculated using the range of values for all variables (redd size, number of adults 

per redd, percent habitat spawnable by habitat type) (Tables B-2 and B-6), except stream area 

where only one measurement was possible (Table B-4). Given the substantial length of stream

area surveyed, our habitat composition estimates are robust. We therefore considered current

capacities with percent spawnable measured by habitat type to be the best estimates. For 

comparison, we also included capacities where percent spawnable was measured across all 

habitat units combined. 

Sensitivity Analyses 

We evaluated the sensitivity of our estimates to parameter values used in the capacity 

equation as demonstrated by Gray and Megahan (1981). We conducted the sensitivity analysis 

on the historical capacity estimate in the North Fork Stillaguamish River. We selected the range 

of values as the 10th and 90th percentile of each variable (Tables B-2 and B-5). The base level 

of capacity was computed using median values for all variables. We then changed each input 

variable across its lower and upper range of values while holding other variables constant. 

Finally, the results were plotted for capacity as a relative percentage of change due to variation in 

each variable (Figures B-3 and B-4).

Results 

Spawner Capacity Estimates 

Capacity estimates based on 90th percentile of all input variables are as much as three 

orders of magnitude larger than estimates using the 10th percentiles for both the North Fork 

Stillaguamish River Chinook salmon population and the South Fork/mainstem population, under 

historical conditions as well as current (Table B-7). The most realistic current capacity estimates 

are probably those determined using the median values for the biological parameters. It is less 

clear which statistic is appropriate for estimating percent habitat spawnable under both current 

and historical conditions. If we were to assume that reference site data underestimate available 

habitat under historical conditions, we would use the 90th percentile for percent spawnable for 

historical capacity. This may be a reasonable assumption, given that some of the reference sites 

(e.g., Upper Sauk and South Fork Stillaguamish Rivers) are not likely as pristine as we expect 

streams were under historical conditions. For current conditions, we would use the 90th 

percentile for percent habitat spawnable with the WDFW redd survey data if we were to assume

that the North Fork Stillaguamish River is presently underutilized. This assumption may also be 

fair, given that overall, higher proportions of the habitat types (in particular, riffles and glides) 

were characterized as suitable for spawning than were actually observed spawned in by Chinook 

(Table B-5). 

151
0

5,000

10,000

15,000

20,000

25,000

30,000

-1.0 -0.5 0.0 0.5 1.0 1.5

Proportional change in input parameter

N

u

m

ber of fis

h

Redds per km

(median=30)

Adults per redd

(median=1.9)

Figure B-3. Range of historical capacity estimates observed by varying parameters in the capacity

equation for small streams (≤25 m bankfull width) in the North Fork Stillaguamish River. 

152
0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

-2 -1 0 1 2 3

Porportional change in input parameter

N

u

m

ber of fis

h

Redd size 

(median = 14 m2)

Percent spawnable

(median=3%)

Adults per redd

(median =2)

Figure B-4. Range of historical capacity estimates observed by varying parameters in the capacity

equation for large streams (>25 m bankfull width) in the North Fork Stillaguamish River. 

153
Table B-7. Range of adult capacity estimates (number of spawners) for the North Fork Stillaguamish 

Chinook salmon population and the South Fork/mainstem Stillaguamish Chinook population 

where the percent habitat suitable for spawning and combined biological parameter values are 

varied. Estimates in bold represent those believed to be the most likely values. 

Range of biological parameters 

Location 

Percent habitat 

spawnable 

90th 

percentile Median 

10th 

percentile Comment 

North Fork Historical conditions 

Stillaguamish 

River 

Reference site data 

 90th percentile 

 Median 

 10th percentile 

228,389 

102,484 

45,342 

46,834 

23,275 

12,582 

14,465 

5,996 

2,152 

Percent habitat 

spawnable calculated 

independent of 

habitat type. 

Current conditions 

WDFW data 

 90th percentile 

 Median 

 10th percentile 

129,513 

25,166 

8,533 

24,141 

4,615 

1,503 

8,874 

1,855 

736 

Percent habitat 

spawnable calculated 

by habitat type 

NMFS data 

 90th percentile 

 Median 

 10th percentile 

1,013,582 

252,106 

16,964 

189,569 

47,080 

3,080 

68,340 

17,120 

1,303 

Percent habitat 

spawnable calculated 

by habitat type 

Historical conditions 

Reference site data 

 90th percentile 

 Median 

 10th percentile 

254,882 

102,182 

32,879 

50,622 

22,048 

9,080 

16,503 

6,232 

1,570 

Percent habitat 

spawnable calculated 

independent of 

habitat type. 

Current conditions 

South Fork 

and mainstem 

Stillaguamish 

River 

WDFW data 

 90th percentile 

 Median 

 10th percentile 

137,179 

25,096 

7,231 

25,027 

4,054 

711 

8,741 

1,202 

0 

Percent habitat 

spawnable calculated 

by habitat type 

NMFS data 

 90th percentile 

 Median 

 10th percentile 

1,086,786 

268,860 

16,286 

202,720 

49,668 

2,406 

72,616 

17,598 

609 

Percent habitat 

spawnable calculated 

by habitat type 

154
The median value for percent spawnable may be more appropriate for capacity estimates 

than the National Marine Fisheries Service’s habitat data set for the North Fork Stillaguamish. 

The results from the NOAA Fisheries data set, which was from a river reach that has some of the 

highest Chinook spawning densities in the entire basin, seem high and perhaps overestimate 

capacity for several reasons. First, data collected on substrate, depth, and flow may not include 

all the necessary variables that are important for spawning site selection. Other variables that 

may be important include adjacent or nearby high quality holding habitat (e.g., deep pools with 

instream cover) and subsurface flow conditions. Second, and perhaps more important, the data 

was collected for a different purpose, so the study design originally used for data collection may 

not suffice to properly estimate capacity for the entire basin. 

Using the above assumptions, the best estimate of historical spawner capacity for the 

North Fork Stillaguamish River Chinook salmon population is about 46,800 adults. Current 

capacity would be in the range of 24,100 to 47,000 adults. For the South Fork/mainstem

population, historical capacity would occur at about 50,600 adults, and current capacity would 

range between 25,000 and 49,700 adults. 

Sensitivity Analyses 

In small streams of the North Fork Stillaguamish River, historical capacity was most 

sensitive to changes in redds per kilometer, followed by changes in adults per redd (Figure B-3). 

In large streams, historical capacity showed the greatest sensitivity to percent habitat spawnable, 

followed by redd size (Figure B-4). This estimate was also moderately sensitive to changes in 

adults per redd. 

Conclusions 

Estimates of spawner capacity for both small and larger streams vary by several orders of 

magnitude. Much of the variation is due to: 1) the assumptions and sensitivity around specific 

variables such as the number of redds per kilometer, adults per redd, percent spawnable area, and 

redd size, and 2) the data used to develop capacity estimates. Assumptions combined with a 

sensitivity analysis help identify the suite of variables that need additional data collection in the 

watershed of interest. Each of the variables identified can be measured with existing methods in 

one to several field seasons. Developing a database on numbers of redds per kilometer, adults 

per redd, percent spawnable area, and redd size that are watershed specific would help identify 

how the variability fluctuates by ecoregion and population size. 

Perhaps a more important conclusion is that utilizing data developed for a different 

purpose and scope may lead to large-scale variations in spawning capacity. Extrapolating such 

data to other parts of the basin could lead to an overestimate in spawning capacity, as noted in 

the preceding example. Developing an adequate sampling design for data extrapolation is an 

important step to incorporate prior to data collection and development in order to reduce the 

amount of variation in spawner capacity estimates. 

155
156
APPENDIX C: RESTORATION OF HABITAT-FORMING 

PROCESSES—AN APPLIED RESTORATION STRATEGY 

FOR THE SKAGIT RIVER 

[Editors’ note: A more detailed description of the strategy and updates of specific results are 

available on the Skagit Watershed Council’s Web site: www.skagitwatershed.org.]

Escapement levels of Pacific Northwest and British Columbia salmon stocks have 

declined dramatically in the past century due to habitat loss, high levels of harvest, and changes 

in ocean conditions. Freshwater habitat losses induced by land use were associated with the 

decline of nearly all the at-risk stocks in a review by Nehlsen et al. (1991). However, the 

recognition of causes of decline and the desire to restore salmon runs has not led to specific plans 

for recouping habitat losses in large watersheds. Rather, most habitat restoration actions have 

been conducted in a relatively unplanned and uncoordinated fashion. 

In 1997 the Skagit Watershed Council (SWC) was formed to support the voluntary 

protection and restoration of salmon habitats in the Skagit River basin of northwest Washington 

State. Today the SWC is comprised of 36 member organizations including private industrial and 

agricultural interests, state and federal agencies, local governments, tribes, and environmental 

groups. In 1998 the SWC adopted a salmon habitat protection and restoration strategy that 

recognizes the influences of land use and resource management activities on natural landscape 

processes, which result in changed habitat conditions (SWC 1998). Since 1998 members of the 

SWC have completed an interim application of the strategy, which identifies causes of degraded 

habitats in the watershed and actions needed to restore habitats over the long term (Beamer et al. 

2000). In this appendix we briefly describe the strategy and methods and present the preliminary 

findings of the analyses. We also discuss costs of the analyses relative to projected costs of 

restoration actions. 

Overview of the Restoration Strategy 

Figure C-1 is a conceptual diagram illustrating how watershed controls (ultimate and 

proximate) and natural landscape processes combine to form various habitat conditions. 

Ultimate controls are independent of land management over the long term (centuries to 

millennia), act over large areas (>1 km2

), and shape the range of possible habitat conditions in a 

watershed (Naiman et al. 1992, Beechie and Bolton 1999). Proximate controls are affected by 

land management over the short term (≤ decades), act over smaller areas than independent 

controls, and are partly a function of independent factors (Naiman et al. 1992). Landscape 

processes are typically measured as rates and characterize what ecosystems or components of 

ecosystems do. For example, sediment or hydrologic processes in a watershed may be 

characterized by the rates (volume/area/time period) at which sediment or water is supplied to 

and transported through specific locations of a watershed. Some riparian-related functions can 

be viewed similarly. For example, large woody debris (LWD) “recruitment” is synonymous 

with the idea of supply while LWD “depletion” is the result of both LWD transport and decay 

157
Figure C-1. Simplified flow chart depicting interactions between watershed controls and processes 

resulting in physical habitat conditions. Shaded boxes represent components that are not 

influenced by land and resource management. 

158
rates. Natural rates of landscape processes are here defined as those that existed prior to 

widespread timber harvest, agriculture, or urban development. 

The SWC’s habitat protection and restoration strategy describes a scientific framework 

and set of procedures for identifying and prioritizing activities that restore or protect aquatic 

habitat (SWC 1998). The scientific framework strives to identify: 1) the natural landscape 

processes active in a watershed, 2) the effects of land use on natural processes, and 3) the causal 

relationships between land use and habitat conditions. It focuses not on the symptoms of 

watershed degradation but on the fundamental causes, and encourages protection and restoration 

of natural landscape processes that formed and sustained the habitats to which salmon stocks are 

adapted. Justification for this approach is based on our understanding from current literature that 

natural landscape processes create and maintain the natural habitat conditions in which native 

aquatic and riparian species have adapted (e.g., Peterson et al. 1992, Doppelt et al. 1993, Reeves 

et al. 1995, Ward and Stanford 1995, Beechie et al. 1996, Kauffman et al. 1997). 

We apply the strategy by systematically identifying land use disruptions to landscape 

processes that form salmon habitat. These processes include peak flow hydrology, sediment 

supply, riparian functions, channel-floodplain interactions, habitat isolated from salmon access, 

and water quality. Using a series of diagnostic screens, we locate disturbances to habitatforming processes and identify actions (i.e., projects) required to correct the disturbances. This 

appendix reports the SWC’s progress in applying its strategy within the range of anadromous 

salmonids of the Skagit River basin. 

Study Area 

The Skagit River basin drains approximately 8,544 km2 of the North Cascade Mountains 

of Washington and British Columbia (Figure C-2, map A). Elevations in the basin range from

sea level to about 3,285 m (10,775 ft) on Mt. Baker. Numerous peaks in the basin exceed 2,500 

m elevation. Average annual rainfall ranges from about 90 cm (35 in) at Mt. Vernon on the 

lower floodplain to over 460 cm (180 in) at higher elevations in the vicinity of Glacier Peak. 

Several vegetation zones occur in the study area. As defined in Franklin and Dyrness (1973), 

most of the lower elevations are in the western hemlock zone. This forest zone typically 

includes western hemlock (Tsuga heterophylla), Douglas fir (Pseudotsuga menziesii), western 

red cedar (Thuja plicata), and sitka spruce (Picea sitchensis). Deciduous species in this zone 

include red alder (Alnus rubra), black cottonwood (Populus trichocarpa), and big leaf maple 

(Acer macrophyllum). Middle elevations are in the Pacific silver fir (Abies amabilis) zone and 

higher elevations are in the alpine fir (A. lasiocarpa) zone (Franklin and Dyrness 1973). 

The Skagit River basin is comprised primarily of mountain drainages with few lowland 

subbasins (low topographic relief and low elevation). The hydrographs of most low-elevation 

forested subbasins are dominated by autumn and winter rainfall floods (Beechie 1992). 

Conversely, spring snowmelt floods typically dominate the hydrographs of high elevation 

subbasins in the eastern Skagit. Most areas of the Skagit River basin are of intermediate 

elevation and exhibit both rainfall and snowmelt floods. Lowland subbasins are generally 

159
Agriculture

Commercial forest

National Park and Wilderness

National Forest

Rural

Urban and industrial

A

Limits of salmon access B

WASHINGTON STATE

Figure C-2. Map A shows land use pattern and map B shows area of historical salmon access in the 

Skagit River basin. 

160
located in the western valley (rain dominated) and usually more highly developed with urban and 

agricultural land use than the forested mountain basins (Lunetta et al. 1997). 

Land development (primarily logging and draining or clearing lands for agriculture) 

began around 1860. About 1,590 km2

 (615 mi

2

, 19%) of the basin is currently in private and 

Washington State ownership. Land uses are dominantly agricultural and urban in the lower 

floodplain and delta areas and upland areas are generally commercial forests. About 3,680 km2

(1,420 mi

2

, 44%) of the basin lies within the federally owned North Cascades National Park, Mt. 

Baker and Ross Lake National Recreation Areas, and Glacier Peak Wilderness Area. The U.S. 

Forest Service controls an additional 1,960 km2

 (755 mi

2

, 24%) of the basin in the Mt. BakerSnoqualmie National Forest. Approximately 1,040 km2

 (400 mi

2

, 13%) of the basin is in British 

Columbia. 

Anadromous salmonids indigenous to the basin include Chinook salmon (Oncorhynchus 

tshawytscha), coho salmon (O. kisutch), pink salmon (O. gorbuscha), chum salmon (O. keta), 

sockeye salmon (O. nerka), steelhead (O. mykiss), cutthroat trout (O. clarkii), and native char 

(Salvelinus sp.). Access to anadromous fishes is generally confined to elevations below 700 m

by natural barriers. Upstream migration to the Baker River system has been eliminated by the 

installation of two hydroelectric dams, but anadromous fish production—primarily coho and 

sockeye salmon—is maintained through trapping and hauling operations in addition to the 

maintenance of sockeye spawning beaches and smolt bypass trapping. The extent of salmon 

upstream migration in the Skagit River basin is shown in Figure C-2, map B. 

Methods 

We analyze disturbances to watershed processes in the Skagit River basin in two phases. 

In the first (interim) phase, we locate disturbed habitat-forming processes using a combination of 

existing Geographic Information System (GIS) data and field-based inventories to identify 

disturbances to peak flows, sediment supplies, riparian functions, channel-floodplain 

interactions, blockages to salmon migration, and water quality. The second phase relies mainly 

on field-based inventories. Both phases rely on GIS to analyze and maintain landscape process 

data over the 8,544 km2

 area of the Skagit River basin. This appendix describes only the 

methods and results from Phase 1. 

We have used more than 30 different GIS themes and partial field inventories to apply 

the landscape process screens identified in the strategy. The existing GIS themes provide lowresolution data covering the entire river basin. These data give us a good overview of habitatforming processes in the entire basin, but can give erroneous answers to our questions about 

specific reach-level sites (102

–104 m linear scale). Field inventories provide high-resolution 

data, but with only limited coverage at present. Because field inventories are more reliable at

specific sites, the SWC members have made a long-term commitment to collecting field-based 

information basin wide. 

We analyzed selected landscape processes that form salmonid habitats in the Skagit River 

basin. We selected these analyses based on current scientific knowledge of their effects on 

salmonid habitat and survival of salmon in freshwater, as well as knowledge of how various land 

161
use practices affect the processes (Table C-1). We recognize that the list may not include all 

impacts to salmon in the watershed; however, it includes those that are clearly supported by 

scientific literature and responsible for a significant proportion of the total loss in salmon 

production from the basin. For each process we developed a series of diagnostics based on rates 

derived from scientific literature and local studies. The diagnostics and methods for estimating 

values are summarized in Table C-1.

We synthesized the ratings for individual landscape processes and functions into a single 

reach classification that we call the generalized habitat types. The importance of identifying 

generalized habitat types for watershed restoration is illustrated by Frissell (1993a) and Doppelt 

et al. (1993), where examples of habitat types are listed along with their biotic objective and 

restoration tactics. To apply this concept in the Skagit, we derived generalized habitat types 

based on simple correlations between our understanding of anadromous fish life history 

strategies and reach-level habitat types (≈102

 to 104 m linear scale, Table C-2). We assume that 

relationships between fish life stages and habitat for each indicator species analyzed adequately 

identifies the habitats to which salmon stocks are adapted. 

Our analysis used five species and four life stages to determine generalized habitat types. 

The stages examined were 1) spawning/egg to fry, 2) summer rearing, 3) winter rearing, and 

4) estuary rearing. Several salmonid species in the Skagit River basin were excluded from the 

evaluation due to lack of data or a spatial bias in their distribution not related to geomorphic 

habitat types. Native char were excluded due to a lack of data describing their habitat 

preferences over complete life history. We know the spawning range of native char is biased 

toward higher elevation headwater tributary basins within the range of historical anadromous fish 

access. Cutthroat trout were excluded because of their spatial bias toward the lower elevation, 

rain-dominated subbasins of the Skagit. Coho salmon habitat preference is similar to cutthroat, 

and the coho range includes all of the anadromous cutthroat range in the Skagit; therefore, we 

assume that coho relationships in our analysis adequately represent cutthroat. Sockeye were 

excluded because the population is limited to the Baker River subbasin of the Skagit. While 

resident rainbow trout (O. mykiss) are found throughout the entire river basin, they are assumed 

to have the same juvenile habitat preferences as steelhead (the anadromous form of O. mykiss), 

which are included in our analysis. 

Under pristine habitat conditions (i.e., natural disturbances only) we define reach-level 

habitat types for anadromous salmonids in the Skagit River as either “key” or “secondary” 

(Table C-2). Key habitat is “critical” for at least one life stage or is a “preferred” habitat type by 

the majority of life stages considered. Secondary habitat does not provide critical habitat for any 

life stage and is not a preferred type by the majority of life stages considered. Classification 

systems described in Hayman et al. (1996), Montgomery and Buffington (1997), Peterson and 

Reid (1984), and Simenstad (1983) were used to define the different reach-level habitat types. 

Local studies used to designate whether the specific reach-level habitat types were “critical,” 

“key,” or “secondary” for a life stage included: Beamer and Henderson (1998), Beechie et al. 

(1994), Congleton (1978), Congleton et al. (1981), Hayman et al. (1996), Montgomery et al. 

(1999), and Phillips et al. (1980, 1981). Data from outside the Skagit (Queets River, 

Washington, in Sedell et al. 1984) were also used to help understand juvenile fish use differences 

between large main channels and off-channel habitats. 

162
Table C-1. Summary of background and methods for rating individual landscape processes. 

Background Method description 

Hydrology—peak flow in lowland basins The degree of urbanization is correlated with increased flooding, degraded habitat, and lower salmonid populations in lowland basins of Puget Sound. When the 2-year flood magnitude under current land use equaled or exceeded the 10-year flood under forested watershed conditions, channels were consistently degraded. Watersheds with effective impervious area (EIA) greater than 10% always had degraded channels whereas watersheds with EIA ≤ 3% had high species and 

habitat diversity and abundance. 

Hydrologic impairment in lowland basins rated based on planned EIA, which is the weighted average EIA upstreof the stream reach under fully developed conditions peland use zoning designation. Weighted average EIA wacalculated using GIS by assigning EIA values to polygobased on land use zoning designations. EIA ≤ 3% is 

considered “functioning,” EIA between 3% and 10% is “moderately impaired,” and EIA > 10% is “impaired.” 

Hydrology—peak flow in mountain basins Increased peak flows result in an increased frequency of channel forming and bed mobilizing flow events, leading tochannel destabilization, less complex habitat, and increased bed scour depths significantly affecting salmonids. Two common land use causes of increased peak flow in forested mountain basins relate to hydrologically immature vegetation and forest road drainage. Hydrologically immature vegetation has relatively low canopy density in winter, allowing increased snow accumulation and melt, resulting in higher runoff rates than areas with hydrologically mature vegetation. Forest road ditches extend the channel network, resulting in more rapid routing of water to main stream channels than basins without road networks. 

Peak flow ratings for mountain subbasins in the Skagit were developed based on an empirical correlation betweland use and elevated peak flow in an adjacent basin because subbasin flow data are limited in the Skagit. Subbasins with more than 50% watershed area in hydrologically immature vegetation due to land use and more than 2 km of road length per km2 of watershed are

are rated “very likely impaired.” Subbasins exceeding oor the other of the criteria are considered “likely impaireSubbasins that do not exceed either criterion are conside“functioning.”

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Table C-1 continued. Summary of background and methods for rating individual landscape processes. 

Background Method description 

Sediment Clear-cutting and forest roads increase landsliding and the supply of coarse sediment (>2 mm diameter) to streamchannels, although fine sediments (< 2 mm diameter) are also delivered by mass wasting. Large increases in coarsesediment supply tend to fill pools and aggrade the channel, resulting in reduced habitat complexity and reduced rearing capacity for some salmonids. Large increases in total sediment supply to a channel also tend to increase the proportion of fine sediment in the bed, which may reduce the survival of incubating eggs in the gravel and change benthic invertebrate production. Landform and land use bothinfluence mass wasting rates. Most sediment from mass wasting originates from inner gorge landforms (steep, streamadjacent slopes). On average these areas cover less than 20% of the mountain basins in the Skagit but produce about 75% of the sediment delivered to streams. Hillslopes >30° are also 

generally unstable, tending to produce shallow, rapid landslides from bedrock hollows or channel headwalls. Hillslopes <30° are generally stable. Deep-seated failures, usually located in glacial deposits or phyllite, have high mass wasting and delivery rates to streams. Compared to mature forest, the increase in mass wasting rates for clear-cut forests and forest road areas averages about 6 and 44 times higher, respectively.

Estimating impairment of sediment supply: Average 

sediment supply for each subbasin estimated based on average sediment supply rates for 13 combinations of geology and vegetation cover (Landsat ’93), which were derived from nine sediment budgets conducted within thbasin. Using GIS we calculated average current sedimensupply for each subbasin and the average increase over tnatural sediment supply for each subbasin (current/naturSediment supply process is considered “functioning” whaverage sediment supply is <100 m3/km2/yr, or where th

average is >100 m3/km2/yr but <1.5 times the natural rat

Sediment supply is “impaired” where average sediment supply is >100 m3/km2/yr and >1.5 times the natural rate

Forest road inventory—identify sediment reduction projects: The inventory rates factors that influence roadrelated landslides and the consequences of landslides. Aratings concerning the likelihood of landsliding are summed, then multiplied by a rating of the likelihood thsignificant stream resources will be impacted. The final value, called the risk rating, ranks roads with respect to tthreat that they pose to salmon habitat. Higher risk ratinindicate greater chance that a road will fail and impact salmon habitat. Final ratings were grouped into three categories of risk. A rating >30 is high, 16 to 30 is moderate, and <15 is low. 

164
Table C-1 continued. Summary of background and methods for rating individual landscape processes. 

Background Method description 

Riparian Function Clearing of riparian forests can alter LWD recruitment to streams, which in turn alters the habitat characteristics of streams. Reduced LWD recruitment persists for several decades, leading to declining LWD abundance in the first few decades and sustained low LWD abundance between 50 and 100 years after the disturbance. A change in LWD abundance alters fish habitat characteristics such as pool spacing, pool area, and pool depth, and this alteration of habitat characteristics causes changes in the salmonid carrying capacity of a stream. 

Remote sensing assessment: Riparian forests that can 

produce ≥80% of potential late-seral LWD recruitment over time (≥40 m wide) are considered “functioning.” 

Riparian forests producing 50% to 80% of the potential late-seral recruitment (20 to 40 m wide), are considered“moderately impaired.” Buffer widths <20 m are considered “impaired.” We estimated the proportion of impaired, moderately impaired, and functioning riparian forests by using Landsat classifications of vegetation. 

Field inventory: Ratings are the same as above. In addi

to documenting forested buffer width, field inventories aclassify stand types by species mix and seral stage, whicprovides sufficient information to prescribe generalizedmanagement regimes for each segment of riparian foresInventories also identify areas of livestock access andpotential fencing projects. 

Channel-floodplain Disconnecting rivers from floodplains changes the ability of rivers to supply, transport, or store one or more inputs of water, sediment, and wood. This constrains the formation and maintenance of habitat within floodplains. Streambank hardening (hydromodification) prevents channel migration, reduces LWD recruitment, and typically narrows and steepens channels, increasing both sediment and water transport rates. Mainstem channels in the Skagit dominated byhydromodification exhibited less diversity in edge habitat types and less edge habitat area than nonhydromodified mainstem reaches. Juvenile Chinook and coho salmon abundance was strongly correlated to wood and other natural cover types when compared to riprap or rubble cover, commonly used for streambank hardening. 

Floodplain areas were delineated where the 100-year floodplain was greater than two channel widths using Federal Emergency Management Agency maps or U. S. Geological Survey 7.5-minute quadrangles and aerial photographs. Reach breaks were based on differences in floodplain width and changes in channel pattern. Hydromodified areas were delineated on copies of aeriaphotos by rafting or jet boating each main channel withifloodplain reaches, then digitized into a GIS arc theme. 

165
Table C-1 continued. Summary of background and methods for rating individual landscape processes. 

Background Method description 

Isolated habitat Isolation of habitat by levees and culverts has dramaticallyreduced carrying capacity of the Skagit River basin over the past 150 years. This includes blockages that impede upstreammigration of adult salmon seeking suitable spawning areas as well as blockages to other life stages such as juvenile rearing habitat in both the freshwater and estuarine environment. Some isolated habitat can be recovered by simply removing the barrier (e.g., rebuilding road crossings that block passage), whereas others will require feasibility studies to determine a range of possible alternatives to accommodate both fish use and existing land use. 

Manmade barriers to anadromous fish habitat are identifthrough a systematic field inventory of channel crossing structures (culverts, tide gates, bridges, dams, and other manmade structures). The inventory identifies the type physical dimensions of structures as well as physical attributes necessary for modeling water flow conditions comparing results to passage criteria for salmonids. 

166
Table C-2. Designation of generalized habitat types. Key habitat is critical (i.e., required for the 

persistence of a dominant life history type) for at least one life stage or preferred by the majority

of life stages considered. Secondary habitat (sec) does not provide critical habitat for any life 

stage and is not a preferred type by the majority of life stages considered. 

Reach-level 

habitat type Chum Coho Chinook

Steelhead Pink 

Number

of life 

stages 

examined 

Percent 

of all life 

stages 

designated key 

or critical

Overall 

designation for 

pristine 

habitat 

Tributary reaches: 

Ponds (including 

beaver ponds and 

other wetlands 

with significant 

open water area) 

sec critical key key sec 10 60 key

Pool riffle key key key key key 10 90 key

Forced pool riffle sec key key key key 10 85 key

Plane bed sec sec sec sec sec 10 0 sec

Step-pool sec sec sec key sec 10 15 sec

Cascade sec sec sec key sec 10 15 sec

Main river reaches: 

Main channel 

floodplain < 2 

channel widths 

sec sec sec key sec 10 15 sec

Main channel 

floodplain > 2 

channel widths 

key sec key key key 10 80 key

Off-channel habitat 

(e.g., ponds, 

sloughs, side 

channels, oxbow 

lakes) 

key critical key sec sec 10 60 key

Estuary: 

Estuarine or tidally

influenced wetland 

key sec critical sec sec 5 40 key

Blind channel key key critical sec sec 5 60 key

Subsidiary channel key key key sec key 5 80 key

Main channel key key key sec key 5 80 key

167
Under disturbed habitat conditions (i.e., both human and natural disturbances), we 

designate reach-level habitat types as: “key” when all landscape screening results are rated as 

functioning, “important” when at least one landscape screen is moderately impaired, “degraded” 

when at least one landscape screen is impaired, “secondary” when channel type is step-pool or 

steeper, “isolated” when upstream of a manmade barrier to fish migration, or “unknown.” Some

reaches are designated as unknown because of high probability of error in rating the riparian 

condition correctly by land cover types. 

Mainstem areas with any of the following conditions are consider degraded: riparian 

buffer is less than 20 m wide, streambank edge is hardened (e.g., riprap), or levee is present 

within 60 m of the bankfull channel edge. All other lower Skagit mainstem areas are considered 

important. In the estuary, hydromodified areas are considered degraded, areas adjacent to levees 

are considered important, and areas at least one distributary channel away from a levee are 

considered key. 

Results and Discussion 

Hydrology—Changes in Peak Flow

We estimate 23% of the mountain subbasins in the Skagit are very likely impaired or 

likely impaired with respect to peak flow hydrology (Figure C-3, map A). In lowland basins, we 

estimate 7% of the streams historically accessible to anadromous salmon will be impaired when

urban and residential areas are fully built out and 18% will be moderately impaired (Figure C-3, 

view B). We use the results shown in Figure C-3 to help evaluate the likelihood of success of 

proposed restoration projects. In general we do not support restoration efforts directly in or 

adjacent to channels that are classified as impaired without evidence that the proposed work will 

succeed in spite of the likely increase to peak flows. We also use the results to identify areas 

currently in good condition that are planned for future development to an extent that hydrology 

will likely be impaired. For these areas we consider protection actions such as rezoning to a less 

intensive land use or acquisition. We also identify areas to investigate for potential restoration of 

hydrologic processes. 

Reduced peak flows as a result of flood control change a channel’s ability to create and 

maintain the suite of diverse floodplain habitats to which aquatic species are adapted (Ward and 

Stanford 1995). Annual peak flows in the Skagit River basin have changed since flow regulation 

through the construction of reservoirs capable of flood storage in the Skagit and Baker Rivers. 

Before flood storage capability, floods in the lower Skagit River commonly approached or 

exceeded 5,500 cubic meters per second (m3

/s), and floods in water years 1815 and 1856 were 

estimated at 11,327 and 8,495 m3

/s, respectively. Since the advent of flood storage capability, a 

flood approaching 5,500 m3

/s has not yet occurred. The number of floods between the 2-year 

and 100-year return period has been reduced by roughly 50% since the dams were built (Table 

C-3). Flood storage on the Skagit has likely impacted channel-floodplain processes in reaches 

downstream of the dams, but we have not yet quantified the effects. Until we have a better 

understanding of these impacts, we view the dams as ultimate controls as shown in Figure C-1. 

That is, they operate independently of our management control because they are licensed for up 

168
Functioning

Impaired

Moderately impaired

Functioning Very likely impaired

Likely impaired

Lowland basin

Non-anadromous, above dam

A

B

Figure C-3. Map A shows subbasins in forested mountain areas of the Skagit River basin where peak 

flow is likely impaired. Exploded view B shows streams in lowland subbasins where peak flow 

will be impaired if current zoning is built out. 

169
Table C-3. Magnitude of peak flows for the lower Skagit River before and after flood storage capability. 

Flood storage is the combined capacity of the Skagit and Baker dams. 

Flood return 

period (years) 

Before flood storage a

(cms) 

After flood storage b

(cms) 

2 3,147 1,830

5 4,735 2,479

10 5,862 2,934

25 7,361 3,540

50 8,528 4,015

100 9,734 4,508

a

 Skagit River near Sedro-Woolley (river km 36), reported in Williams et al. 1985. b

 Skagit River near Mt. Vernon (river km 25), Sumioka et al. 1998.

170
to 50 years and are unlikely to be removed. Accepting that this disturbance will not likely be 

altered during the license period of each dam, the artificial creation of off-channel habitat then 

may be justified in stream reaches where off-channel habitat has been lost due to this 

disturbance. Alternatively, it may be possible to reestablish certain channel-forming flows that 

have been eliminated in the past. 

Sediment Supply 

We estimate that 46% of the area in mountain subbasins of the Skagit has impaired 

sediment supply (Figure C-4, map A). Our evaluation of the accuracy of the method shows that 

it correctly estimated the sediment supply rating for seven of the 10 subbasins where sediment 

budget data were available. It overestimated average sediment supply for two of the 10 test 

basins (i.e., rated them impaired when they are functioning), and underestimated sediment supply 

for one subbasin. Therefore, we recognize that this product should not be used to identify 

potential restoration projects. Rather, it is used for project screening where field-based sediment 

budgets are not available and for general planning of watershed-level sediment reduction 

projects. Project proponents use this map for project screening to determine whether the 

proposed area is likely to have an impaired (i.e., high) sediment supply. For reaches where 

sediment supply is impaired, 1) sediment supply in the watershed should be restored to 

functioning levels before downstream reaches are worked on or 2) evidence demonstrating that 

the proposed work will not fail due to increased sediment supply should be presented.

Specific sediment reduction projects are identified based on the results of forest road 

inventories. We focus on forest roads for sediment reduction projects because mass wasting 

rates from forest roads averaged about 44 times more than mass wasting rates in mature forest 

(Paulson 1997). Currently about 1,300 km of road are inventoried with another 3,000 km

remaining (Figure C-4, map A). Risk ratings from the current inventory show that a significant 

number of forest roads in the Skagit River basin pose a landslide hazard and potentially threaten 

fish habitat. Based on this inventory, we will focus initial sediment reduction projects on the 

high-risk and moderate-risk road segments. 

For example, the Bacon Creek watershed (Figure C-4, view B) has 3.7 km of high-risk 

and 18.6 km of moderate-risk roads. The high-risk road segments cross more landforms

sensitive to disturbance for mass wasting (e.g., inner gorges and steeper hillslopes) than 

moderate or lower risk roads. Sediment reduction projects on these roads would reduce the risk 

of increased sediment supply and therefore increase the level of watershed protection. Specific 

road projects primarily involve stabilizing or recontouring road fills, removing stream crossings 

or improving drainage conveyance, and improving road surface drainage. For basin-level 

planning, we consider subbasins with the lowest total cost of road restoration per kilometer of 

salmon stream as the highest priority.

Riparian Function 

Before interpreting the Landsat classification of riparian forests, we used field inventory 

results from 234 riparian sites to describe the distribution of field-based riparian classifications 

within each satellite-based forest class (Table C-4). All of the sampled late-seral forest sites and 

171
A

B

Subbasin sediment supply

Functioning Impaired

Data not available

Lowland basins, no analysis

Non-anadromous, upstream of dams

Forest road inventory status

Completed

Not completed

Mass wasting map unit type

Naturally unvegetated, low hazard

Hillslope < 30 degrees, low hazard

Inner gorge, high hazard

Hillslope > 30 degrees, moderate hazard

Forest road hazard rating High

Low

Moderate

Figure C-4. Map A shows sediment supply ratings for mountain subbasins and status of forest road 

inventory on National Forest lands in the Skagit River basin. Exploded view B is example of 

detail for road segments and landslide hazard units in the Bacon Creek watershed. (See also 

Figure 12 for additional illustration.) 

172
Table C-4. Distribution of 234 field-sampled riparian sites by GIS-based land cover type. 

Buffer width class 

Late-seral 

forest 

(n = 24)

Mid-seral 

forest 

(n = 13)

Early-seral 

forest 

(n = 24)

Other 

forest 

(n = 96)

Nonforest 

(n = 77)

<20 m forested buffer 

“impaired” 

0% 8% 8% 42% 90%

20–40 m forested buffer 

“moderately impaired” 

0% 0% 4% 15% 6%

>40 m forested buffer 

“functioning”

100% 92% 88% 43% 4%

173
between 88% and 92% of the mid-seral forest and early-seral forest sites met the greater than 40-

m wide riparian buffer criteria, fitting our functioning designation. Conversely, 90% of the areas 

mapped as nonforest had less than 20-m wide riparian buffers, fitting our impaired designation. 

Areas mapped as other forest (ranging from clear-cuts to mature hardwoods) were found 43% 

functioning, 15% moderately impaired, and 42% impaired. 

Based on this analysis, we estimate that 29% of the nonmainstem channels in the 

anadromous zone (by length) are in the nonforest land cover category, and therefore have a very 

high likelihood of being impaired and in need of riparian restoration (Figure C-5). Conversely, 

19% of the nonmainstem channels in the anadromous zone are in the mid- to late-seral forest 

land cover category, and therefore have a high likelihood of being functioning and needing 

protection. While we cannot accurately map stream reach-scale riparian conditions associated 

with channels adjacent to all GIS land cover types, we can estimate with reasonable accuracy the 

total of each riparian category at a larger scale. Based on the results in Table C-4, we estimated 

the percentage of nonmainstem channel length in the Skagit anadromous zone by each land use 

designation (Figure C-6). 

We rely on field inventories to identify actual restoration projects because of the abovementioned limitations in the satellite classification of riparian forests. We conducted field 

inventories of riparian forests by walking all streams accessible to anadromous fish and assessing

the riparian vegetation conditions for each stream reach. We classified riparian conditions by 

buffer width, stand type, and age of vegetation within 60 m of stream channels. From these data, 

we selected all stream segments with forested riparian vegetation less than 40 m wide as 

requiring planting and all segments with evidence of livestock access to the stream channel as 

requiring fencing. Riparian planting and restoration projects have been identified through a 

series of field inventories. The inventories were completed systematically as four separate 

projects between 1995 and 1998 in 24% of the Skagit’s subbasins (Figure C-5). Together the 

inventories identified 130 km of stream corridor for riparian planting and fencing projects. 

Isolated Habitats and Disrupted Channel-Floodplain Interactions 

The inventory efforts through September 1999 identified 229 manmade barriers out of 

572 channel crossing structures with 32% of the anadromous zone inventoried. In tributary 

habitat, 143 km of channel is blocked. In the delta, we estimate 185 km (56%) of the channels 

have been isolated or lost to salmon access under present conditions (Figure C-7). Isolated 

channels are those where a channel and water exist, but juvenile or adult salmon access is 

blocked due to manmade disturbances. Lost channels are those areas that were channels 

historically but currently do not have a distinct channel or water present. 

Because manmade barriers are not evenly distributed throughout the Skagit River basin 

and our inventory efforts have focused in areas where barriers are more common, we anticipate 

that the majority of the isolated habitat in the basin has been found. Based on a subsample of 

111 inventoried structures within subbasins of the Skagit with similar land use intensity as the 

subbasins yet to be inventoried, we found that 14% of the inventoried structures do not meet fish 

passage criteria. Therefore, we expect to find about 150 more blockages in noninventoried areas 

of the basin, blocking about 60 km (4%) of the estimated length of tributary habitat in the 

anadromous zone. 

174
Subbasin

Non-anadromous, upstream of dams

Field inventory completed

Field inventory not completed

Riparian area

Functioning

Impaired

Unknown

Figure C-5. Map of riparian inventory status and riparian areas likely functioning or impaired in the 

Skagit River basin. 

175
Figure C-6. Estimated percentages of riparian categories (functioning, moderately impaired, and 

impaired) for land use categories along nonmainstem channels in the anadromous zone of the

Skagit River basin. 

176
S

S

S

S

S SS

S S

S

S

S SS

SS SSS

S

SS

S

S S

S

S

S

SS

SS

S SS

S

S

S

S

SSSSS

S S

SSSS

S S

S

S

SS

S

SSSS S

S SS SSS S S

SS

S

S SSSSS S

S

S

S

S

S

S

S

S

S

S

S S

S

SS

SS

S

S

S

SS

S

SS S

S

S

SS

S SS

S

SS

S

S

S

S

S

SS

SS

SSS

S

S

S

SSS

S

S

S

S SS

S

S

SS

S

S

SS

S

S

Floodplain and alluvial fan areas

Hardened or diked streambanks (large mainstems only)

Channels in the historical anadromous zone

S Inventoried artificial barriers to fish access

Figure C-7. Location of hydromodification and manmade barriers (lower Skagit River basin only). 

177
Upstream of the Skagit River delta, 46 km of streambanks have been riprapped (Figure 

C-7). In the geomorphic delta, 51 km (62%) of the mainstem channel edge is either hardened, 

diked within 60 m of the channel’s edge, or both. These inventory results provide the basis for 

identifying potential riprap removal (or modification) projects, primarily where hardened banks 

no longer protect capital improvements (e.g., house, road). 

Generalized Habitat Types 

The final result of our analysis is the identification of generalized aquatic habitat types 

throughout the entire river basin, which are based on salmonid habitat preferences combined 

with the results of the landscape process screens. The resulting analysis in the Skagit River basin 

yields a mosaic of reach-level habitat patches (Figure C-8). Key habitat areas have all habitatforming processes functioning at or near historical levels and are targeted for habitat protection. 

Because protection of habitat is generally considered less expensive than restoration, we view 

key habitats as some of the highest priority areas for habitat expenditures. Isolated habitats are 

typically the most cost-effective restoration projects and therefore receive strong consideration 

for funding. Important and degraded habitats are both areas targeted for restoration. 

Secondary habitat is not targeted for restoration under this strategy. That is, we do not 

intend to restore secondary habitat to key habitat. However, it is important to understand how 

secondary habitat may function in a watershed in order to protect or restore the other habitat 

types. For example, the source of degradation may originate in secondary habitat (i.e., the idea 

of contributing critical areas, discussed in Frissell 1993a). In such cases, restoration of processes 

originating in secondary habitat areas may be required in order to restore downstream degraded 

or important habitats. 

Identification of Restoration Projects 

The main objective of the strategy is to identify habitat protection and restoration projects 

based on application of the landscape process screens. Together our analyses have led to the 

identification of more than 400 individual restoration projects. For example, our analysis of the 

U. S. Forest Service road inventory identified approximately 650 km of high-hazard and 

moderate-hazard roads that are candidates for restoration. The total estimated cost for all of 

these roads (which does not include forest roads on state and private lands) is approximately 

$11.6 million. We also identified 122 riparian planting and fencing projects during inventories 

of only 24% of the river basin, with a total cost estimated at $1,687,000. Of these riparian 

projects, 39 are already funded. 

We completed migration barrier inventories in 13 out of 38 subbasins and identified 229 

blocking structures. Some blockage removal projects have uncomplicated designs and relatively 

clear benefits. These projects can each be considered independently of other culverts because 

salmon currently access the culvert sites and repair of the structures will provide benefits 

commensurate with the amount of habitat upstream. By contrast, groups of completely blocking 

structures on the same watercourse should be considered either in combination or sequentially, 

and projects that involve flood protection levees or coordination of numerous landowners require 

feasibility studies to determine suitable restoration actions. Currently we have a list of 36 

178
Sub-basin sediment supply or hydrology impaired

Critical contributing area

Non-anadromous, upstream of dams

 0% - 20% key >20%- 40% key >40% - 60% key >60% - 80% key

>80% - 100% key

Important

Isolated or degraded

Key

Figure C-8. Distribution of generalized habitat types (key, isolated or degraded, important) in floodplain 

areas of the Skagit River basin, with exploded view of the delta region. 

179
isolated sloughs and blind tidal channels that require further assessment for design of appropriate 

solutions. 

Each restoration project is mapped on a GIS theme and relevant data are stored in the 

associated databases. These themes can be updated as new inventories are completed, or as 

project status changes (e.g., design phase, construction, completion). Additionally, we can 

develop related databases for monitoring the effectiveness and costs of different project types. 

Over time the GIS maps and databases will help display progress made in restoring habitats in 

the Skagit River basin and help us modify our actions to more efficiently restore habitat. 

Current Limitations and Future Work 

Both lowland and mountain basin GIS-based results give us operating hypotheses for 

peak flow impairment throughout the river basin. Because subbasin flow data are limited in the 

Skagit, peak flow ratings for mountain subbasins in the Skagit were developed based on an 

empirical correlation between land use and elevated peak flows in the adjacent North Fork 

Stillaguamish River basin. The North Fork Stillaguamish has exhibited a 38% increase in mean 

annual maximum flow between 1928 and 1995 with climatic variables explaining less than 40% 

of the increase, suggesting that changes in the watershed condition have caused the balance of 

the increase (Beamer and Pess 1999). However, future efforts for the mountain basin 

methodology must confirm that correlations between land use and peak flows in the North Fork 

Stillaguamish are a cause-effect relationship, then identify the appropriate thresholds for land use 

before reapplication to the Skagit. The lowland basin methodology should be repeated with land 

cover data that estimates current effective impervious area to complement the results reflecting 

impervious area at fully developed watershed conditions per zoning designations. 

Field-based sediment budgets more accurately estimate the sediment supply in a subbasin 

and describe the relative effects of different land uses on sediment supply. Therefore, they 

provide more accurate information for project screening and planning than do our current GISbased estimates. Field-based sediment budgets from Paulson (1997) were completed for 

approximately 12% of the total area and used to develop the GIS-based estimates. Since Paulson 

(1997), we have completed field-based sediment budgets for 51% of the basin. In lowland 

basins, mass wasting is not a dominant sediment supply process, but increased fine sediment 

supply to channels is directly related to urban, livestock grazing, and agricultural land use. We

anticipate future development of a surface erosion and sedimentation screen for these low-slope 

areas, focusing on quantifying surface erosion from agricultural or developed areas. 

The U.S. Forest Service is continuing its road inventory. Similar road inventories have 

not yet been conducted on state or private timberlands. The inventory method appears to be 

appropriate for identifying segments of road that pose the greatest threat to stream resources. 

However, it does not identify the types and locations of work needed to reduce the landslide 

hazard. We anticipate that some inventories will be more detailed than those used by the U.S. 

Forest Service and better identify the specific work actions required for each segment of forest 

road. 

Satellite data do not provide sufficient information for identifying all riparian protection 

and restoration actions at the reach level. The GIS-based riparian screen is reliable for only late180
seral and mid-seral conifer dominated forest and nonforest areas. Because of the higher 

probability of error in rating stream reaches by the remaining land cover types, they are excluded 

from the interim riparian screen. The screen is thus applied to only about 50% of the 

anadromous zone (based on length). Field inventories are far more reliable than remote sensing 

data and can provide sufficient information for reach-level project planning. Therefore our 

primary task is to complete the field inventory of riparian forest conditions throughout the river 

basin. 

The field-based inventory of manmade blockages to salmon migration has been 

completed for only a portion of the basin. Areas currently identified as isolated are accurately 

characterized as upstream of manmade blockages to salmon because they are based entirely on 

field inventory. We assume that some areas yet to be inventoried are isolated, although 

extrapolation from current inventories suggests that no more than 4% of the remaining channel 

length is likely to be upstream of a manmade blockage to salmon migration. In addition to the 

remaining blockage inventories, we have yet to complete our inventory of wetland habitat losses 

in the delta.

Water quality parameters such as dissolved oxygen, temperature, turbidity, nutrient 

loading, and levels of toxic substances are critical to salmon health and survival. Identifying 

areas where water quality is impaired and the various factors contributing to impairment and 

addressing the causes of water quality degradation is important to restoring salmon habitat in the 

basin. Currently we consider stream reaches, lakes, and estuary areas that are included on the 

Washington Department of Ecology’s Candidate 1998 Section 303(d) Impaired and Threatened 

Water Bodies listings as impaired. These water bodies are known to fail Washington State’s 

surface water quality standards and not expected to improve in the near future. We anticipate 

our future water quality screen to include locations of known point and non-point sources that 

may contribute to water quality degradation in the basin. These land use indicators will identify 

areas where water quality problems may exist and direct further investigation (e.g., water quality 

sampling, benthic invertebrate community analyses) to determine if water quality is actually 

impaired. The continuing objective is to improve the quality and quantity of water quality data 

and land use information available to guide protection and restoration of aquatic habitats. 

The primary limitations in accurately identifying generalized habitat types are incomplete 

natural landscape process screens and the accuracy of individual screens used. The consequence 

of incomplete landscape process screens is an underestimate in the amount of “degraded” and 

“important” habitat and an overestimate of the amount of “key” habitat. However, we have high 

confidence that areas identified as degraded are in fact degraded. That is, there is a very low 

likelihood that areas identified as degraded with this analysis will later be identified as important 

or key habitat. Conversely, some areas identified as key habitat with this analysis will be 

changed to degraded or important as more detailed information becomes available. 

Conclusions 

The SWC first identified its conceptual framework and diagnostic criteria, thus enabling 

systematic application of a strategy supported by all members. Without this step, a systematic 

181
and objective inventory of habitat problems in the Skagit River basin would not have been 

possible. Following development of the strategy, the SWC quickly applied the simplest 

diagnostic criteria over the entire basin with limited funds. This effort identified the most 

obvious project ideas (some socially or politically difficult) and provided a good overview of the 

spatial pattern of disturbance in the basin. By contrast, a haphazard inventory or professional 

judgment system would have produced lists of projects, but would not necessarily have given 

resource managers the tools to be strategic or comprehensive in restoring salmon habitats. In 

other words, managers would have been unable to focus on important biological hotspots or 

impaired landscape processes because they would have lacked a comprehensive understanding of 

the causes of habitat degradation in the basin. 

The strategy recognizes that land use and resource management activities influence 

natural landscape processes, which result in changed habitat conditions (Figure C-1). Therefore, 

protection and restoration actions identified by implementing this strategy should be directed at 

the habitat-forming processes instead of attempting to build specific habitat conditions. 

Focusing actions on “building” habitat for specific species may be to the detriment of other 

species and may not be sustainable due to potential conflicts with natural processes (Frissell and 

Nawa 1992, Kauffman et al. 1997, Beechie and Bolton 1999). Instead, actions implemented by 

this strategy will aim to create the conditions necessary for natural landscape processes to 

reestablish at levels similar to those that existed historically. This should 1) result in a high 

likelihood of long-term project success, 2) protect and restore habitat for all salmonid species as 

well as other native aquatic and riparian-dependent species, and 3) ensure the effective use of 

public and private restoration funds. 

The SWC overcame diverse interests to develop and apply the interim phase of its 

protection and restoration strategy in about a two-year period. The field inventory phase of the 

strategy is still in progress. The cost of all inventories and analyses required to develop a 

restoration plan (including a list of required protection and restoration actions) for the 8,500 km2

basin is only about $1.1 million. This total is less than the cost of opening one large, isolated 

estuary channel and wetland complex ($1.9 million, USACE 1998) or a few culvert blockages on 

local or state highways ($250,000 to $600,000 each, D. Brookings5). The estimated cost of 

potential projects identified during this first phase of applying the strategy is well over $100 

million, suggesting that the total cost of the inventories will be less than 1% of the cost of 

protection and restoration actions. Moreover, development of the restoration plan should save 

millions of dollars by avoiding projects that are not effective at restoring salmon habitats. On a 

per unit area basis, the cost of all interim assessments and final field inventories will total only 

$210 per km2

, assuming that costs remain relatively constant in the near future. 

Application of the strategy gives the SWC the ability to become truly strategic (not 

merely opportunistic) in its protection and restoration efforts by providing a consistent set of 

principles that guide actions and systematically identifying hundreds of projects that can be 

prioritized. Having a complete river basin overview of landscape processes and resulting habitat 

conditions allows the SWC to set goals on how much protection or restoration is needed to meet 

a specific priority. The strategy allows priorities to be based on locally defined objectives such 

 

5 D. Brookings, Skagit County Public Works, 1111 Cleveland Ave., Mount Vernon, WA 98273. Pers. commun., 

Dec. 1997. 

182
as recovery of a certain species or completion of certain types of restoration (Lichatowich et al. 

1995, Beechie et al. 1996). However, prioritization does not alter the types of projects enacted, 

but only the sequence in which projects are completed (Beechie and Bolton 1999). Currently the 

SWC prioritizes projects based on the relative cost-effectiveness of different projects, which 

means that projects protecting or restoring the greatest proportion of anadromous fish habitat 

function per dollar cost are considered higher priority. Additionally, individual restoration 

groups may choose projects from any list of projects in order to fulfill their respective missions. 

183

Most NOAA Technical Memorandums NMFS-NWFSC are available online at the

Northwest Fisheries Science Center web site (http://www.nwfsc.noaa.gov).

Recent NOAA Technical Memorandums NMFS

published by the

Northwest Fisheries Science Center

NOAA Tech. Memo.

NMFS-NWFSC57 Rogers, J.B. 2003. Species allocation of Sebastes and Sebastolobus sp. caught by foreign

countries from 1965 through 1976 off Washington, Oregon, and California, USA. U.S. Dept. Commer.,

NOAA Tech. Memo. NMFS-NWFSC-57, 117 p. NTIS number pending.

56 MacCall, A.D., and T.C. Wainwright (editors). 2003. Assessing extinction risk for West Coast salmon.

U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-56, 198 p. NTIS PB2003-104642.

55 Builder Ramsey, T., et al. 2002. The 1999 Northwest Fisheries Science Center Pacific West Coast upper

continental slope trawl survey of groundfish resources off Washington, Oregon, and California. U.S. Dept.

Commer., NOAA Tech. Memo. NMFS-NWFSC-55, 143 p. NTIS PB2003-104641.

54 Krahn, M.M., et al. 2002. Status review of southern resident killer whales (Orcinus orca) under the

Endangered Species Act. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-54, 133 p. NTIS

PB2003-104520.

53 Waknitz, F.W., T.J. Tynan, C.E. Nash, R.N. Iwamoto, and L.G. Rutter. 2002. Review of potential

impacts of Atlantic salmon culture on Puget Sound chinook salmon and Hood Canal summer-run chum

salmon evolutionarily significant units. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-53,

83 p. NTIS PB2002-108143.

52 Meador, J.P., T.K. Collier, and J.E. Stein. 2001. Determination of a tissue and sediment threshold for

tributyltin (TBT) to protect prey species of juvenile salmonids listed under the Endangered Species Act.

U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-52, 21 p. NTIS PB2002-103161.

51 Emmett, R.L., P.J. Bentley, and G.K. Krutzikowsky. 2001. Ecology of marine predatory and prey

fishes off the Columbia River, 1998 and 1999. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC51, 108 p. NTIS PB2002-101699.

50 Turk, T.A., et al. 2001. The 1998 Northwest Fisheries Science Center Pacific West Coast upper continental

slope trawl survey of groundfish resources off Washington, Oregon, and California. U.S. Dept. Commer., NOAA

Tech. Memo. NMFS-NWFSC-50, 122 p. NTIS PB2002-101700.

49 Nash, C.E. (editor). 2001. The net-pen salmon farming industry in the Pacific Northwest. U.S. Dept.

Commer., NOAA Tech. Memo. NMFS-NWFSC-49, 125 p. NTIS PB2002-100948.

48 Meador, J.P., T.K. Collier, and J.E. Stein. 2001. Use of tissue and sediment based threshold concentrations

of polychlorinated biphenyls (PCBs) to protect juvenile salmonids listed under the Endangered Species Act. U.S.

Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-48, 40 p. NTIS number pending.