**Integrated Status and Effectiveness Monitoring**

**John Day Pilot Program**

**2005 ANNUAL REPORT**

**DRAFT**

***Prepared by:***

**Nicolaas Bouwes**

> ![](media/image1.wmf){width="1.4777777777777779in"
> height="1.929861111111111in"}

***Prepared for:***

**NOAA Fisheries**

***Prepared for and funded by:***

**Bonneville Power Administration**

**Integrated Status and Effectiveness Monitoring**

**John Day Pilot Program**

**2005 ANNUAL REPORT**

**DRAFT**

Prepared for

Chris Jordan

Project Leader

**NOAA/NMFS/NWFSC - CB Division**

Newport Research Station 950 NAL

2032 SE OSU Drive

Newport, Oregon 97365

and

Roy Beaty

COTR

Bonneville Power Administration

Portland, OR

Compiled and edited by

Nicolaas Bouwes

**Eco Logical Research, Inc.**

456 South 100 West

Providence, UT 84332

Contributors:

+-----------------+-----------------+-------------------+--------------+
| Chris Jordan    | Russel Faux     | Ian Tattam        | Nicholas     |
|                 |                 |                   | Weber        |
| Carol Volk      | Hiram Li        | Joseph Feldhaus   |              |
|                 |                 |                   | John         |
| Steve           | Peter Bayley    | M.Sc. Luis        | Faustini     |
| Rentmeester     |                 |                   |              |
|                 | Guillermo       | Francisco         | Don Butcher  |
| Mimi D'Iorio    | Giannico        | Madriñán          |              |
|                 |                 |                   | Phil Rogers  |
| Jim Ruzycki     | Scott Heppell   | Seth White        |              |
|                 |                 |                   | Kristin      |
|                 |                 |                   | Swoboda      |
|                 |                 |                   |              |
|                 |                 |                   | Michael      |
|                 |                 |                   | Newsom       |
+-----------------+-----------------+-------------------+--------------+

December 19, 2005

Citation: **Bouwes, N. ed.** 2005. Integrated status and effectiveness
monitoring John Day pilot program, 2005 draft annual report. Compiled
and edited by Eco Logical Research, Inc. Providence, UT for NOAA
Fisheries Service and the Bonneville Power Administration.

**[Table of Contents]{.underline}**

[**1.0 Executive Summary** 1](#__RefHeading___Toc125779667)

[**2.0 Introduction** 6](#__RefHeading___Toc125779668)

[**3.0 Summary of Coordination Activities in 2005**
8](#__RefHeading___Toc125779669)

[**3.1 establish contacts with RME personnel in the John Day Basin**
8](#__RefHeading___Toc125779670)

[**3.2 CURRENT RME EFFORTS IN THE JOHN DAY BASIN**
12](#__RefHeading___Toc125779671)

[**3.2.1 Status and Trend Landscape and Habitat Monitoring**
12](#__RefHeading___Toc125779672)

[**3.2.1.1 Landscape Changes and Patterns Project**
12](#__RefHeading___Toc125779673)

[**3.2.1.2 John Day Basin Sediment Assessment**
14](#__RefHeading___Toc125779674)

[**3.2.1.2.1** *Field-Based Assessment*
14](#__RefHeading___Toc125779675)

[**3.2.1.2.2** *GIS Analysis and Sediment Modeling*
16](#__RefHeading___Toc125779676)

[**3.2.1.3** **John Day River Basin Airborne TIR Remote Sensing &
Spatial Data Analysis Project** 18](#__RefHeading___Toc125779677)

[**3.2.1.3.1** *Archived Data Sets* 18](#__RefHeading___Toc125779678)

[**3.2.1.3.2** *Airborne Thermal Infrared Imagery*
19](#__RefHeading___Toc125779679)

[**3.2.1.3.3** *Light Detection and Ranging (LiDAR)*
20](#__RefHeading___Toc125779680)

[**3.2.1.4** **PIBO Effectiveness Monitoring Program**
21](#__RefHeading___Toc125779681)

[**3.2.1.4.1** *Field Sampling Protocols*
23](#__RefHeading___Toc125779682)

[**3.2.1.5 ODFW Habitat and Riparian Survey**
23](#__RefHeading___Toc125779683)

[**3.2.1.6 EMAP- Western Pilot Project**
25](#__RefHeading___Toc125779684)

[**3.2.1.7 Other Habitat Monitoring** 26](#__RefHeading___Toc125779685)

[**3.2.2 Water quality monitoring** 26](#__RefHeading___Toc125779686)

[**3.2.2.1 John Day TMDL Project** 26](#__RefHeading___Toc125779687)

[**3.2.2.2 Other water quality monitoring**
29](#__RefHeading___Toc125779688)

[**3.2.3 Status and Trends of Juvenile and Adult Salmonids**
30](#__RefHeading___Toc125779689)

[**3.2.3.1 ODFW Freshwater Production Monitoring**
30](#__RefHeading___Toc125779690)

[**3.3 Compilation of Historical and recent data**
33](#__RefHeading___Toc125779691)

[**3.3.1 Compilation of GIS and Tabular Datasets**
33](#__RefHeading___Toc125779692)

[**3.3.2 The Protocol Manager/Library**
36](#__RefHeading___Toc125779693)

[**4.0 Information Needs** 37](#__RefHeading___Toc125779694)

[**5.0 Reliability of Monitoring Information**
42](#__RefHeading___Toc125779695)

[**5.1 Comparison of satellite remote sensing information collected at
different levels of resolution.** 42](#__RefHeading___Toc125779696)

[**5.2 Comparison of Aquatic Survey Protocols Used to Measurement of
Physical Habitat Attributes within the John Day Basin Oregon.**
43](#__RefHeading___Toc125779697)

[**5.3 Protocol tests and development of the Juvenile Salmon and
Steelhead Production project** 44](#__RefHeading___Toc125779698)

[**5.3.1** *Devising less stressful fish capturing techniques*
44](#__RefHeading___Toc125779699)

[**5.3.2** *Diver Effects on Fish Behaviors*
45](#__RefHeading___Toc125779700)

[**5.3.3** *Calibration of Fish Sampling Techniques.*
45](#__RefHeading___Toc125779701)

[**5.3.4** *Validating the precision and accuracy of the fat meter*
46](#__RefHeading___Toc125779702)

[**5.3.5** *Tests of heat shock protein induction*
47](#__RefHeading___Toc125779703)

[**6.0 Monitoring Design** 47](#__RefHeading___Toc125779704)

[**6.1 Status and trend monitoring sample size**
49](#__RefHeading___Toc125779705)

[**6.2 Watershed Classification** 49](#__RefHeading___Toc125779706)

[**6.3 Reach Classification and Sample Effort**
50](#__RefHeading___Toc125779707)

[**7.0 Developing Baseline Causal Relationships and Descriptive
Indices** 50](#__RefHeading___Toc125779708)

[**7.1 Linking Landscapes to Riparian Process: Exploring Landscape Scale
Influences on In-Stream Water Temperature Using Remote Sensing**
51](#__RefHeading___Toc125779709)

[**7.2 Juvenile Production Project** 52](#__RefHeading___Toc125779710)

[**7.3 Invertebrate Productivity Monitoring**
55](#__RefHeading___Toc125779711)

[**8.0 Limiting Factors** 58](#__RefHeading___Toc125779712)

[**8.1 EDT Assessment of Limiting Factors**
58](#__RefHeading___Toc125779713)

[**8.2 Synthesis of Causal Mechanisms Explored in the John Day Basin.**
59](#__RefHeading___Toc125779714)

[**9.0 Determining the Effectiveness of Restoration Actions**
64](#__RefHeading___Toc125779715)

[**9.1 Intensively Monitored Watershed Studies (Work Element 1)**
64](#__RefHeading___Toc125779716)

[**9.1.1 Potential IMW locations** 69](#__RefHeading___Toc125779717)

[**9.1.1.1** *Bridge Creek* 69](#__RefHeading___Toc125779718)

[**9.1.1.2** *The South Fork of the John Day River*
80](#__RefHeading___Toc125779719)

[**9.1.1.3** *Upper Middle Fork of John Day*
83](#__RefHeading___Toc125779720)

[**9.2 Effectiveness monitoring** 83](#__RefHeading___Toc125779721)

[**10.0 Provide Rationale and Suggestions on how to Change Current
Monitoring Designs (Work Element 2).** 85](#__RefHeading___Toc125779722)

[**11.0 Literature Cited** 87](#__RefHeading___Toc125779723)

[**Appendix A** 90](#__RefHeading___Toc125779724)

[**Appendix B** 92](#__RefHeading___Toc125779725)

[**Appendix C** 99](#__RefHeading___Toc125779726)

[**Appendix D** 105](#__RefHeading___Toc125779727)

[]{#__RefHeading___Toc125779667 .anchor}**1.0 Executive Summary**

The goal of the Tributary Federal RME program is to, first describe the
'health' of fish population processes and their habitat so that it can
be determined whether mitigation or changes to current management are
required to ensure their survival under the Endangered Species Act
(ESA). The program then needs to identify and prioritize restoration
actions to increase freshwater production as a means of increasing
overall life-cycle survival based on the initial 'health' assessments of
populations and their habitat. Finally, the program needs to quantify
the degree to which restoration actions are achieving its goals. To
determine the most efficient ways to meet these three specific needs, -
the Integrated Status and Effectiveness Monitoring Program is developing
and testing monitoring and evaluation approaches through as series of
pilot scale efforts in the Wenatchee, John Day, and Salmon subbasins.
The pilot projects approach has been adopted as a cost effective way to
develop a variety of tools prior to their more general implementation
across the Columbia River basin.

In the John Day, the pilot project has developed a framework for a RME
program to address the three programmatic goals outlined above. An
explicit goal of the RME program development is that it must deliver
reliable and relevant information that is logistically feasible and
affordable to collect. The second part of this statement is, of course,
a constraint to every RME program that most everyone is aware of. The
first part of this statement, however, is often not stated or tested.
Estimates and comparison of the reliability, or the accuracy and
precision, of information collected through different protocols is
rarely determined. Also, RME programs often do not demonstrate that the
metrics collected are relevant to questions related to project goals or
that all relevant information is being collected. For example, many
monitoring programs are implemented under the pretext that the
information collected will be used to inform the above goals for ESA
listed fishes. Many of these programs collect macroinvertebrates
samples; however, the metrics that are derived from these samples are
mainly used to describe water quality. Little research has been
conducted that demonstrates that these invertebrate metrics correlate to
fish production even though that is justification for this monitoring.
Therefore, in the John Day pilot project, much emphasis has been place
on determining and developing monitoring protocols that produce reliable
and relevant information. Identification of information that should be
collected and programs to do this, are another component of the program.

Synthesis of this research will be captured in the hierarchical
framework to describe the causal relationships of ecological processes
across several scales throughout the John Day Basin. This information
will be used to identify and prioritize stream reaches that would
produce the greatest response for the level of effort in restoration.
This information can also be used to design mitigation experiments and
form the expectation of responses from these actions. As a means to test
these approaches, the pilot project has initiated and is developing
Intensively Monitored Watersheds studies. The synthesis of information
collected from current RME activities are used to help describe limiting
factors of watersheds in the John Day. These studies will test a set of
restoration actions in a watershed in an experimental management
framework. Intensive monitoring and research will be used to describe
the impacts of these actions on fish and their habitat. The steps in
executing an IMW are similar to the steps required to implement a
comprehensive and well coordinated basin-wide RME program, and thus the
IMW will be extremely informative to the pilot project efforts.

Development of the John Day RME pilot project is a long term process
whereby lessons learned through research and the Intensively Monitored
Watershed studies will be used as the rationale to suggest changes to
current monitoring efforts. Monitoring programs are already in place in
the John Day and we are working closely with the personnel implementing
these monitoring programs. Changes to these programs will likely not be
accepted unless we can demonstrate a strong justification of why this
would be beneficial to the management of salmonids in basin. Thus, the
current approach in John Day pilot project is the development of a
framework that describes the multiple tasks that must be completed
before the final RME plan will be determined and agreed upon by the
multiple entities conducting monitoring in the basin.

Briefly, the following are the steps we are undertaking in the John Day
pilot project to develop a long term RME plan.

1.  **Establish working relationships with RME personnel, characterize
    current RME activities, compile historic information in the John Day
    Basin.** The John Day Basin is large and crosses multiple
    jurisdictional boundaries. There are well over 120 people from over
    30 agencies and institutions working on monitoring, restoration, and
    management associated with fisheries in the basin. Historic and
    current monitoring programs have collected a large amount of
    information on fish and their habitat. One goal of the pilot project
    is become well aware of the different efforts in the basin and to
    begin to develop a working relationship with RME personnel. In 2005,
    we continued meetings with the Analytical Framework group, a group
    of 30 people involved in John Day or Columbia River Basin RME
    efforts established in 2004, to aid in the design of the RME program
    in the John Day. Also, in 2005 we began to meet with staff
    responsible for actual implementation of RME programs in the John
    Day to become more aware of ongoing projects and available
    information, and to relay our intentions of the pilot project.
    Another objective of the pilot project is to house this information
    and the protocols used in databases that will be accessible to these
    agencies and others. Substantial progress has been made on this over
    the past year.

2.  **Determine what information we need to inform goals.** While there
    are a large number of agencies and people conducting monitoring and
    research in the John Day, the shear size and complexity of the basin
    assures that large information gaps will exist. The goal of the
    pilot projects is to determine which information is lacking, yet is
    essential for informing the goals. We have collaborated with members
    of the Analytical Framework group, local biologist and researchers,
    and reviewed the subbasin plan to not only understand what
    information is currently being collected, but what information is
    lacking. We plan to continue to query these people on their
    opinions, as well as use current research and analyses of
    information obtained to help identify further information needs.

3.  **Determine the accuracy and precision of information we need
    collected through different protocols.** The ability for a sample to
    provide accurate and precise information depends heavily on the
    protocol used to collect that sample. Therefore, monitoring programs
    need to adopt protocols that are the most informative yet are still
    affordable (e.g. time, money, effort, etc.). This requires testing
    the accuracy and precision of several protocols. Currently, there
    are multiple monitoring protocols to measure the same general
    characteristic. Often these protocols differ enough that, although
    they are attempting to describe the same general characteristic,
    they produce different information. These differences produce
    inconsistencies that make detection of changes difficult or
    information that is not even comparable across areas where multiple
    protocols are implemented. Effort is being spent in the John Day to
    use protocols that provide reliable information and are reasonable
    to implement. Many protocols that are used in the current RME
    program were tested in 2005. Testing of protocols will continue
    throughout the life of this project but to a much lesser degree than
    has already occurred.

4.  **Given what we learned about the accuracy and precision of
    different protocols, develop a sampling design.** While accuracy and
    precision of a sample is needed, the ability to extrapolate a
    collection of sample to provide an accurate assessment at the
    appropriate scale is dependent on the sampling design. The sampling
    design describes, when, where, and how much to sample. The design is
    highly dependent on the protocols used to collect the information
    and how the information will be used. In addition, we have conducted
    analyses to classify watersheds and reaches to use in stratify
    sampling efforts and extrapolate results to other areas with similar
    characteristics. Based on the results of the testing of protocols
    and information from current monitoring efforts, we are assessing
    whether these efforts are sufficient to detect defined differences.
    Status and trend monitoring information has only been collected by
    ODFW for 2 years so the ability to address sources of variability
    and its implication on sample designs is limited. Therefore, we will
    continue to refine sample designs over the life of this project as
    new information becomes available.

5.  **Establish causal relationships between ecological processes that
    control fish production.** If we can understand the relationships
    between fish and their environment, then we are much more likely to
    succeed in managing these populations. This will help us understand
    the mechanism limiting freshwater fish production as well as methods
    and limits to which production can be manipulated. Also, defining
    the causal relationships will aid in the developing metrics and
    indices that should be monitored to capture ecological processes.
    Examples of this research in the fisheries literature is abundant
    and can help inform potential relationships in the John Day Basin.
    We are fortunate to have a wealth of past and current intensive
    research in the John Day Basin, in part funded by other agencies
    (e.g. BOR), to also define these causal relationships across
    multiple scales. In addition, some monitoring in the John Day
    simultaneously collects fish and habitat information that can test
    these relationships on a broader scale, or generate new hypotheses
    that should be further explored. We expect that RME efforts and the
    IMW studies conducted over the next 3 years in the John Day will
    continue to discover and refine causal relationships. This is an
    ongoing endeavor for all fisheries scientist. Our goal is to stay
    current with the best available science.

6.  **Determine factors limiting populations in order to prioritize and
    develop restoration programs.** Literature and current research are
    defining causal relationships. These relationships have to be put
    together in a framework (e.g. models) that allows us to assess
    factors limiting fish production in the John Day. There are few
    examples of effective restoration actions, and this is partially
    because the action does not necessarily address the limiting factor
    (Roper et al. 1997). This should be a required step before the
    implementation of a restoration action. These models are being
    developed and tested in the John Day Basin. In addition, we are
    working with others that participated in the John Day subbasin
    planning process that have implemented the Ecosystem, Diagnostic,
    and Treatment (EDT) model that can be used to assess limiting
    factors. EDT results have been summarized for each watershed
    in 2005. TMDL efforts are expected to completed in 2007 and provide
    addition information on limiting factors in the John Day Basin.

7.  **Determine the effectiveness of restoration actions through
    effectiveness monitoring or in an experimental management framework,
    such as the Intensively Monitored Watershed studies.** Restoration
    actions are being implemented in the John Day on an opportunistic
    basis. Restoration is often applied to degraded habitat under the
    assumption that this will have a benefit to fish. However, this
    assumption is rarely based on a limiting factors analysis and is
    rarely followed up on with monitoring to evaluate restoration
    effectiveness. Many people working on RME projects in the John Day
    agree that effectiveness monitoring is sorely lacking. The pilot
    project is developing Intensively Monitored Watershed studies to
    design and test the impacts of restoration as outlined in the
    Tributary Federal RME program. In addition to testing restoration
    effectiveness, the comprehensive approach of IMWs will also address
    steps 1-6 therefore inform whether this plan to develop a basinwide
    RME program needs modifications. In 2005, we identified a set of
    potential IMWs. The South Fork of the John Day IMW began in 2005 and
    will continue for a minimum of three more years. Progress on this
    study will be reported annually. We are submitting a proposal for
    implementation of an IMW in Bridge Creek by January 10, 2006.
    Further work on this is dependent on subsequent funding.

> We believe there are also opportunities for developing effectiveness
> monitoring programs for existing and planned restoration actions. A
> large effort has been made in 2005 and is continuing to inventory
> these actions. We are developing methods, such as comparative studies,
> to address action effectiveness.

8.  **Given what we learned from above, provide rationale and
    suggestions on how to change current monitoring designs to best
    address goals.** While we are working on completing the above steps,
    we will be working with current monitoring efforts to determine near
    term alterations that may need to be implemented to provide
    necessary information. Ultimately, we would like to apply what we
    have learned through completing the above steps to develop a well
    coordinated and comprehensive monitoring program in the John Day
    Basin as an example of how other basins should develop an RME
    program. The success of the pilot project will be dependent on the
    ability to develop and maintain working relationships with those who
    will ultimately be conducting the RME program. Collaboration and
    demonstration of how the RME process can be improved will likely
    result in an adoption of these suggestions. While this is not a fast
    track process, we believe that this will lead to a longer term and
    better coordinated RME program than if we immediately insisted on
    implementation of a theoretical RME plan. We have worked with
    several people conducting RME programs in the John Day Basin
    in 2005. Current programs constitute the current pilot project RME
    program with additional programs initiated in 2005. We will continue
    to work with these people to determine how to improve RME efforts.

    []{#__RefHeading___Toc125779668 .anchor}**2.0 Introduction**

A federal research, monitoring, and evaluation (RME) program for the
Columbia River Basin, has been called for under the US National Oceanic
and Atmospheric Administration Fisheries (NOAA-Fisheries) 2000 Federal
Columbia River Power System Biological Opinion (BiOp) and the Federal
Columbia River Salmon Recovery Strategy (All-H Strategy). Implementation
of this regionally coordinated, programmatic approach to tributary RME
is being tested through NOAA-Fisheries Integrated Status and
Effectiveness Monitoring Program pilot projects in the Wenatchee, John
Day, and Salmon subbasins, prior to extending it to larger areas. This
program is developing two novel monitoring and evaluation programs: (i)
subbasin-scale pilot status and trend monitoring efforts for anadromous
salmonids and their habitat, and (ii) effectiveness monitoring for
suites of habitat restoration projects in selected watersheds within the
three target subbasins. This report focuses on the status of the John
Day Integrated Status and Effectiveness Monitoring Program pilot
project.

The goal of the Tributary Federal RME program is to, first describe the
'health' of fish population processes and their habitat so that it can
be determined whether mitigation or changes to current management are
required to ensure their survival under the Endangered Species Act
(ESA). The program then needs to identify and prioritize restoration
actions to increase freshwater production as a means of increasing
overall life-cycle survival based on the initial 'health' assessments of
populations and habitat. Finally, the program needs to quantify the
degree to which restoration actions are achieving its goals. To
determine the most efficient ways to meet these three specific needs, -
the Integrated Status and Effectiveness Monitoring Program is developing
and testing monitoring and evaluation approaches through as series of
pilot scale efforts in the Wenatchee, John Day, and Salmon subbasins.
The pilot projects approach has been adopted as a cost effective way to
develop a variety of tools

The John Day the pilot project has outlined a plan to potentially modify
current RME efforts in the basin to address local agency goals as well
as the three programmatic goals outlined above. The project relies on
the collaboration of researchers and managers to: consolidate current
and historic information; define information needs; design research and
monitoring programs to establish status and trends, and casual
relationships; synthesize RME information to determine limiting factors
to prioritize and design restoration actions. A major component of the
pilot project is to design methods to evaluate the effectiveness of
monitoring programs. The John Day pilot project is planning on
evaluating project effectiveness in an experimental management
framework. Factors limiting fish production in watersheds will be
assessed, restoration actions will be designed to address these
problems, actions will be implemented as a large scale experiment (e.g.
BACI design), and intensive monitoring and research will evaluate the
success of the action. This study is referred to as the Intensively
Monitored Watershed (IMW) study, and is not only a powerful approach to
evaluate action effectiveness, but, on a smaller scale, is an example of
the process required to develop an RME program.

The development of the John Day RME pilot project is expected to be a
long term process whereby lessons learned through research and the
Intensively Monitored Watershed studies will be use to suggest changes
to current monitoring efforts. Monitoring programs are already in place
in the John Day and we are working closely with the personnel
implementing these monitoring programs. Changes to these programs will
likely not be accepted unless we can demonstrate a strong justification
of why this would be beneficial to the management of salmonids in basin.
Thus, the current approach in John Day pilot project is the development
of a framework that describes the multiple tasks that must be completed
before the final RME program will be determined and agreed upon by the
multiple entities conducting monitoring in the basin. This document
summarizes the current RME efforts and the framework we see to, if
possible, build a more collaborative and comprehensive RME process in
the John Day basin. Below are the steps that outline the plan for
developing for John Day Integrated Status and Effectiveness Monitoring
Pilot Program.

1.  Establish working relationships with RME personnel in the John Day
    Basin characterize current and historic RME activities, and compile
    a database to house this information.

2.  Determine what information we need to inform goals.

3.  Determine the accuracy and precision of information we need
    collected through different protocols.

4.  Given what we learned about the accuracy and precision of different
    protocols develop a sampling design.

5.  Establish causal relationships between ecological processes that
    control fish production.

6.  Determine factors limiting populations in order to prioritize and
    develop restoration programs.

7.  Test the effectiveness of restoration actions through effectiveness
    monitoring or in an experimental management framework, such as the
    Intensively Monitored Watershed studies.

8.  Given what we learned from above, provide rationale and suggestions
    on how to change current monitoring designs to best address goals.

    []{#__RefHeading___Toc125779669 .anchor}**3.0 Summary of
    Coordination Activities in 2005**

    []{#__RefHeading___Toc125779670 .anchor}**3.1 establish contacts
    with RME personnel in the John Day Basin**

The Subbasin Plan was a process that began to coordinate RME activities
in the John Day Basin. As indicated in the Plan additional technical
support beyond the subbasin coordination team will be needed to develop
GIS and database designs and data management, statistical design and
analysis, and modeling (John Day Subbasin Plan 2005). The Analytical
Framework Group was created in 2004 to provide some of this support and
to begin to develop the John Day pilot project. The original intention
of the Analytical Framework Group (AFG) was to provide a forum to
discuss the various RME strategies and needs, ensure continuity between
the different projects, reduce duplication of efforts, integrate RME
results, and develop appropriate mitigation experiments. Most members of
the AFG were either currently involved in research within the basin or
had expertise in designing RME programs. This collective knowledge has
been useful in outlining feasible RME activities. During FY2005, the AFG
met three times to discuss the development of RME activities in the John
Day.

Although the AFG members were quite helpful and informative, it became
apparent that further coordination with agency personnel directly
involved in monitoring and restoration in the John Day basin would be
essential for the success of this program. Implementation of long term
monitoring program will need to be adopted by those employed in the John
Day Basin. Currently, there are logistic constraints to implementing
further monitoring in the region, such as the ability to hire, manage
and accommodate field personnel. Awareness of these constraints requires
collaboration with people facing those constraints. In addition, this
project needs to demonstrate that it will be useful and long lived
enough to justify further time commitment of staff that are already
working, at the least, full time capacity. Therefore, in 2005 a shift in
the structure pilot project related meetings was necessary. Eco Logical
Research, Inc. (ELR) and NOAA Fisheries (NOAAF) met with local personnel
to get a better understanding of current and past RME and restoration
efforts in the John Day. Although there is not presently a large scale
forum in which monitoring personnel in the John Day basin can coordinate
on-the-ground activities, we hope to identify key individual to ensure
all active groups are represented in the planning and design of RME
efforts, much like the AFG but with more local in participation.

In May 2005, a meeting hosted and organized by Julie Conley of the
Monument Soil and Water Conservation District (MSWCD), was attended by
the NOAAF, ELR, Bureau of Reclamation (BOR), Oregon Department of
Environmental Quality (ODEQ) members of the AFG. In addition, personnel
from US Forest Service (FS), Confederated Tribes of the Warm Springs
(CTWSRO_JDBO), Confederated Tribes of the Umatilla (CTUIR), Oregon State
University (OSU) Experiment Station, Oregon Water Trust, National Park
Service (John Day Fossil Beds), ODEQ, Oregon Watershed Enhancement Board
(OWEB), and The Nature Conservancy (TNC), Columbia Intertribal Fisheries
Commission (CRITFC) attended the meeting allowed us become aware of
several projects that are occurring and to introduce our intentions to
others working in the basin.

NOAA Fisheries, BOR, and ELR have met and collaborated with personnel
from several agencies to develop a database to house current and
historic information. See *Compilation of Recent and Historic Data*
section for further details. This effort is an example of how this
program can help provide a valuable resource for those participating in
RME efforts.

ELR and NOAA Fisheries have also made several visits to the basin to
meet with other agency personnel. This included a meeting at the
Murderers Creek research site. The meeting was conducted with BOR,
Oregon Department of Fish and Wildlife (ODFW), OSU, NOAAF, Environmental
Protection Agency (EPA), and CTWSRO_JDBO, Individual meetings were held
with other personnel throughout the John Day Basin.

ELR and NOAAF have also met with others to discuss broader scale issues
that are conducted as part of the NOAA-Fisheries Integrated Status and
Effectiveness Monitoring Program. This includes two meetings of the FRME
program conducted by BPA using the pilot projects as a springboard for
development of the FRME program. We have attended meetings to further
understand and develop remote sensing techniques that are or could
potentially be used in the John Day Basin. This includes a meeting by
NOAA Fisheries in Seattle, WA and with PNAMP in Portland, OR. ELR also
provided an overview of the NOAA-Fisheries Integrated Status and
Effectiveness Monitoring Program, with specific emphasis on the John Day
pilot project, to other forums such as the CSMEP, American Fisheries
Society, and Lemhi RME Technical Oversight Committee.

Also, ELR and NOAAF have conducting meetings with researchers from OSU,
WS, ODFW, EPA, and NMFS Science Center about IMW designs. See IMW
section for further design details. Related to this, we have met with
Grant County SWCD, MSWCD, North Fork Watershed Council, ODFW, The Nature
Conservancy, CTWSRO_JDBO, and BOR to observe potential sites for IMWs.

ELR has also designed and oversees the testing if an invertebrate
monitoring program to develop an invertebrate metric that relates to
some measure of fish production (e.g. fish growth). ELR oversees this
project funded through NOAA

Table 1. Salmonid and habitat Status and trend monitoring programs
currently implemented in the John Day Basin. '\*' denotes projects
initiated by the John Day pilot project.

+-------+-----------+-------------+-------------+-----------+-------+
| **A   | **General | **Specific  | **Sampling  | **P       | **Sp  |
| gency | characte  | i           | frequency** | rotocol** | atial |
| &     | ristics** | ndicators** |             |           | sc    |
| Monit |           |             | **Design**  |           | ale** |
| oring |           |             |             |           |       |
| Pr    |           |             |             |           |       |
| ogram |           |             |             |           |       |
| Ti    |           |             |             |           |       |
| tle** |           |             |             |           |       |
+-------+-----------+-------------+-------------+-----------+-------+
| ODFW- | Adu       | Escapem     | Steelhead-  | adult,    | sa    |
| Sal   | lts/redds | ent/Number, | 50 random   | redd, and | mple- |
| monid |           | Di          | spatially   | carcass   | reach |
| Pro   | steelhead | stribution, | balance     | surveys   | /wate |
| ducti |           | age         | (17every    |           | rshed |
| vity, | Chinook   | structure,  | yr, 16      |           |       |
| E     |           | sex ratio,  | every 4yrs, |           | esti  |
| scape |           | size,       | 17 new/yr)  |           | mate- |
| ment, |           | genetics    | & long term |           | subb  |
| T     |           |             | index sites |           | asin- |
| rend, |           |             |             |           | basin |
| and   |           |             | Chi         |           |       |
| Ha    |           |             | nook-census |           |       |
| bitat |           |             | over known  |           |       |
| Monit |           |             | extent,     |           |       |
| oring |           |             | random      |           |       |
| in    |           |             | beyond know |           |       |
| the   |           |             | extent      |           |       |
| John  |           |             |             |           |       |
| Day   |           |             |             |           |       |
| Basin |           |             |             |           |       |
+-------+-----------+-------------+-------------+-----------+-------+
|       | Parr/     | Abundance,  | 50 random   | snorkel   | sa    |
|       | Juveniles | di          | spatially   | survey    | mple- |
|       | -steelhea | stribution, | balanced    | where     | reach |
|       | d/redband | re          | sites       | possible  |       |
|       | trout     | lationships |             | else      | esti  |
|       | ,Chinook, | with        | (17every    | electr    | mate- |
|       | cutthroat | habitat     | yr, 16      | oshocking | reach |
|       |           | features    | every 4yrs, |           | to    |
|       |           |             | 17 new/yr)  |           | sub   |
|       |           |             |             |           | basin |
|       |           |             | same sites  |           |       |
|       |           |             | as habitat  |           |       |
|       |           |             | survey      |           |       |
+-------+-----------+-------------+-------------+-----------+-------+
|       | Smolts-   | Production, | Sampled     | smolt     | subb  |
|       | s         | abundance,  | daily       | trap at   | asin- |
|       | teelhead, | size,       | during      | base of   | basin |
|       | Chinook   | genetics    | ou          | s         |       |
|       |           |             | tmigration, | ubbasins, |       |
|       |           |             | estimated   | mark-     |       |
|       |           |             | annually,   | recapture |       |
|       |           |             | for SF, NF, | to        |       |
|       |           |             | MF and      | estimate  |       |
|       |           |             | lower       | capture   |       |
|       |           |             | mainstem    | e         |       |
|       |           |             |             | fficiency |       |
+-------+-----------+-------------+-------------+-----------+-------+
|       | SAR-      | smo         | per cohort, | PIT tag   | subb  |
|       | s         | lt-to-adult | tag a       | fish      | asin- |
|       | teelhead, | survival    | minimum of  | captured  | basin |
|       | Chinook   | rates       | 5,50        | in smolt  |       |
|       |           |             | 0/annually, | traps,    |       |
|       |           |             | minimum of  | detect    |       |
|       |           |             | 500 per     | adults    |       |
|       |           |             | subbasin    | returning |       |
|       |           |             | (SF, NF,    | at        |       |
|       |           |             | MF, lower   | B         |       |
|       |           |             | JDR)        | onneville |       |
|       |           |             |             | dam       |       |
+-------+-----------+-------------+-------------+-----------+-------+
|       | Habitat   | LWD, pools, | 50 EMAP     | see       | r     |
|       | status    | riparian    | random      | h         | each- |
|       | and trend | cover,      | spatially   | ttp://osu | basin |
|       | m         | gravel,     | balanced    | .orst.edu |       |
|       | onitoring | fines       | sites       | /Dept/ODF |       |
|       |           |             |             | W/freshwa |       |
|       |           |             | (17every    | ter/inven |       |
|       |           |             | yr, 16      | tory/pdff |       |
|       |           |             | every 4yrs, | iles/habm |       |
|       |           |             | 17 new/yr)  | ethod.pdf |       |
|       |           |             |             |           |       |
|       |           |             | same sites  |           |       |
|       |           |             | as habitat  |           |       |
|       |           |             | survey      |           |       |
+-------+-----------+-------------+-------------+-----------+-------+
| F     | M         | Specie      | annual      | kick net  | Reach |
| S/ODE | acroinver | s/Community |             | samples   | /Site |
| Q/OSU | tebrates- |             |             |           |       |
|       | localized |             |             |           |       |
| C     | as        |             |             |           |       |
| TUIR/ | sessments |             |             |           |       |
|       |           |             |             |           |       |
| C     |           |             |             |           |       |
| TWSRO |           |             |             |           |       |
+-------+-----------+-------------+-------------+-----------+-------+
| O     | Measure   | T           | 100+        | LiDAR,    | sa    |
| DEQ - | Clean     | emperature, | habitat     | FLIR,     | mple- |
| TMDL  | Water Act | alkalinity, | surveys,    | v         | reach |
| Pro   | c         | bod,        | LiDAR,      | egetation |       |
| ject, | ompliance | chlorophyll | TIR,7 days  | morp      | esti  |
| Am    |           | a, cod,     | for 2 yrs   | hological | mate- |
| bient |           | co          | at 5 sites  | surveys,  | reach |
| Monit |           | nductivity, | all other   | temp      | to    |
| oring |           | DO, e.      | parameters, | loggers,  | sub   |
|       |           | coli, fecal | TMDL model  | hyrolabs, | basin |
|       |           | coliform,N, | for NF, MF, | model     |       |
|       |           | P, pH,      | SF,         | te        |       |
|       |           | turbidity   | mainstem    | mperature |       |
|       |           |             |             | load      |       |
+-------+-----------+-------------+-------------+-----------+-------+
| CT    | Water     | T           | Hydrolabs   | h         | Site  |
| WSRO, | quality-  | emperature, | sample      | ydrolabs, |       |
| C     | localized | alkalinity, | hourly,     | te        |       |
| TUIR, | as        | co          |             | mperature |       |
| \*    | sessments | nductivity, | site        | loggers   |       |
| SWCDs | of water  | DO, pH,     | samples     |           |       |
|       | quality,  | turbidity   | hourly to   |           |       |
|       |           |             | annually    |           |       |
|       | 3         |             |             |           |       |
|       | hydrolabs |             |             |           |       |
|       | by        |             |             |           |       |
|       | CTWSRO,   |             |             |           |       |
|       |           |             |             |           |       |
|       | 3         |             |             |           |       |
|       | hydrolabs |             |             |           |       |
|       | by SWCDs  |             |             |           |       |
+-------+-----------+-------------+-------------+-----------+-------+
| USFS  | habitat   | Channel     | GRTS        | protocols | sa    |
| -PACF | m         | morphology, | sampling    | are       | mple- |
| ISH-I | onitoring | riparian    | design      | described | reach |
| NFISH |           | vegetation, | through     | in        |       |
| Biolo |           | substrate,  | Columbia    | Henderson | e     |
| gical |           | macroin     | Basin       | et al.    | stima |
| Op    |           | vertebrates |             | (2005)    | te-su |
| inion |           |             | 5 site in   |           | bbasi |
| (     |           |             | JDB every   |           | n-mul |
| PIBO) |           |             | year, 55    |           | tiple |
| Eff   |           |             | sites every |           | b     |
| ectiv |           |             | 5 yrs       |           | asins |
| eness |           |             |             |           |       |
| Monit |           |             |             |           |       |
| oring |           |             |             |           |       |
| Pr    |           |             |             |           |       |
| ogram |           |             |             |           |       |
+-------+-----------+-------------+-------------+-----------+-------+
|       | imple     | riparian    | Field Units | protocols | sa    |
|       | mentation | vegetation, | (USDA 2003) | are       | mple- |
|       | m         | stream      |             | described | reach |
|       | onitoring | bank, and   | 51 sites in | in        |       |
|       | to        | bankfull    | JDB         | Henderson | e     |
|       | evaluate  | width       |             | et al.    | stima |
|       | impacts   | m           |             | (2005)    | te-su |
|       | of        | easurements |             |           | bbasi |
|       | grazing   |             |             |           | n-mul |
|       |           |             |             |           | tiple |
|       |           |             |             |           | b     |
|       |           |             |             |           | asins |
+-------+-----------+-------------+-------------+-----------+-------+
| ODEQ  | habitat   | Channel     | 100+ EMAP   | EMAP      | sa    |
| -EMAP | and       | morphology, | random      | protocols | mple- |
| We    | b         | riparian    | spatially   |           | reach |
| stern | iological | vegetation, | balanced    |           |       |
| Pilot | m         | substrate,  | sites       |           | esti  |
| Pr    | onitoring | macroinv    |             |           | mate- |
| oject |           | ertebrates, | 20          |           | reach |
|       |           | other       | reference   |           | to    |
|       |           | biological  | sites,      |           | sub   |
|       |           | indicators  | sampled     |           | basin |
|       |           |             | between     |           |       |
|       |           |             | 2000-2004   |           |       |
+-------+-----------+-------------+-------------+-----------+-------+
| Sub   | EDT       | reach       | nearly 1300 | expert    | r     |
| basin | co        | assessment  | reaches set | opinion   | each- |
| Pla   | mpilation | of survival | up in EDT   |           | basin |
| nning | of expert | impacts of  | to describe |           |       |
| Pr    | opinion   | 49          | current and |           |       |
| ocess |           | en          | historical  |           |       |
|       |           | vironmental | conditions, |           |       |
|       |           | variables   | nearly      |           |       |
|       |           |             | 160,000     |           |       |
|       |           |             | \'data\'    |           |       |
|       |           |             | entries     |           |       |
+-------+-----------+-------------+-------------+-----------+-------+
| \     | remote    | a           | 1980-2000   | Remote    | water |
| *UW/N | sensing   | griculture, | every 4     | sensing   | shed- |
| OAAF/ | of        | urban,      | yrs, 30m    | data      | basin |
|       | landuse   | logging,    | pixel for   | derived   |       |
| BOR   | and       | vegetation  | Columbia    | from the  |       |
| -Land | landscape | type and    | River Basin | LANDSAT 5 |       |
| scape | patterns  | health,     |             | Thematic  |       |
| ch    | changes   | riparian    |             | Mapper    |       |
| anges | through   | vegetation, |             | sensor    |       |
| and   | time      | roads       |             |           |       |
| pat   |           |             |             |           |       |
| terns |           |             |             |           |       |
+-------+-----------+-------------+-------------+-----------+-------+
| \     | landscape | relative    | 72 EMAP     | use GIS   | sa    |
| *EPA/ | analysis  | bed         | random      | inf       | mple- |
| NOAAF | of        | stability   | spatially   | ormation, | reach |
| -John | sediment  | (RBS)       | balanced    | instream  |       |
| Day   | loading,  |             | sites       | channel   | esti  |
| Sed   | reach     |             |             | charac    | mate- |
| iment | scale     |             | 21 sites,   | teristics | reach |
| Asses | sediment  |             | sampled     | to        | to    |
| sment | a         |             | between     | predict   | sub   |
|       | ssessment |             | 2000-2003   | RBS       | basin |
+-------+-----------+-------------+-------------+-----------+-------+
| \     | LiDAR and | riparian    | LiDAR- one  | LiDAR     | reach |
| *Wate | thermal   | vegetation  | time census | (measures | -wate |
| rshed | infrared  | height and  | for large   | laser     | rshed |
| Scie  | remote    | cover,      | reach or    | points of |       |
| nces- | sensing   | channel and | watershed,  | ref       |       |
| Air   | of        | valley      | ap          | lectance) |       |
| borne | w         | morphology  | proximately |           |       |
| LiDAR | atersheds | chara       | 15 cm pixel | Thermal   |       |
| and   |           | cteristics, |             | Infrared  |       |
| TIR   |           | l           | TIR - one   | (TIR)     |       |
| R     |           | ongitudinal | time census |           |       |
| emote |           | thermal     | for large   |           |       |
| Se    |           | profiles    | reach or    |           |       |
| nsing |           |             | watershed,  |           |       |
|       |           |             | 0.5-1.5m    |           |       |
|       |           |             | pixel       |           |       |
+-------+-----------+-------------+-------------+-----------+-------+

Integrated Status and Effectiveness Monitoring Program and has
coordinated with OSU to have this research integrated with the intensive
research in the South Fork John Day IMW (see *Invertebrate Productivity
Monitoring* section).

ELR and NOAAF stay in contact through email and phone conversations with
these agencies and organizations to foster a relationship and further
discuss RME opportunities. Although the benefits of developing these
contacts is difficult to immediately demonstrate, this has taken a great
deal of time that will likely benefit in the longer term success of this
program. See Table 1, Appendix A for list of individuals that ELR or
NOAAF have been in contact with in FY2005.

[]{#__RefHeading___Toc125779671 .anchor}**3.2 CURRENT RME EFFORTS IN THE
JOHN DAY BASIN**

Currently, several RME efforts are already underway in the John Day
Basin (Table 1). In 2004, BOR and NOAAF worked in partnership to help
coordinate and develop some of the RME efforts. This information was
reported in the Analytical Framework Document (Bouwes 2004).
Collectively, these project represent the current basinwide RME program
until further information becomes available that can be used to suggest
changes to this program. Here we provide a short summary of the
different research groups and projects that are ongoing and scheduled to
occur over the next few years. We address the general methods,
objectives, questions, and the scale that each project will focus. In
general, these are presented in order from larger to smaller scale
projects.

[]{#__RefHeading___Toc125779672 .anchor}**3.2.1 [Status and Trend
Landscape and Habitat Monitoring]{.underline}**

[]{#__RefHeading___Toc125779673 .anchor}**3.2.1.1 Landscape Changes and
Patterns Project**

The ability to evaluate changes in landscapes is rapidly expanding with
recent technological advances of aerial and space borne remote sensing
(RS), global positioning system (GPS) satellites and precision
navigation and surveying, and management and analysis of geographic
information using geographic information systems (GIS). Exploring unique
linkage between these three tools and their related application to
Pacific salmon freshwater life history is the foundation of this
project.

While there are clear patterns in the correlations between land use and
land cover at a landscape scale with salmon population trajectories,
these correlations are often too general for extrapolating mechanistic
connections between habitat type and condition, and salmon population
status for two reasons. First, most studies that attempt to relate gross
habitat attributes with population trends, use static geospatial data
layers, whereas population tends are evaluated over a times series.
Second, currently used geospatial data layers of land use and land cover
and classification of remote sensed imagery is not specific to Pacific
salmon habitat requirements.

This study will attempt to develop applications in the use of remote
sensed imagery classifications with the objectives that:

-   compare the ability of various types of remote sensed imagery to
    identify habitat features relevant to anadromous salmonids, and

-   track land use and land cover change over time.

The central questions to be answered by the study are:

1)  Can remote sensed data of different spatial and spectral resolutions
    be used in combination to generate high spatial resolution habitat
    classifications?

2)  Can pattern recognition or texture analysis be used to enhance
    classification of high spatial resolution/low spectral resolution
    remote sensed data?

3)  How much of field or ground surveyed information can be gathered
    using remote sensed data?

4)  What are the limitations of various remote sensed data layers with
    respect to habitat feature delineation?

5)  How much of remote sensed imagery classification process can be
    automated?

6)  Are riparian and wetland habitats accurately classified using
    LANDSAT ETM+ remote sensed data?

7)  Is it feasible to use change detection of LANDSAT ETM+ remote sensed
    data in habitat models?

8)  Does a time series of land use and land cover improve the fit of
    fish habitat models?

Remote sensing data derived from the LANDSAT 5 Thematic Mapper sensor
imagery and IKONOS high resolution multispectral imagery are being
employed to assess relationships between land cover and fish habitat
throughout the Columbia River Basin. Using BOR's time series (1984,
1988, 1992, 1996, and 2000) of raw LANDSAT 5 TM imagery change in land
cover and vegetation patterns will be quantified over six main subbasins
of the CRB, including the John Day, Wenatchee, Willamette, Yakima,
Salmon and Grande Ronde. Temporal variability in vegetation cover, as
well as spatial metrics of patch composition and structural
heterogeneity are calculated from the time series of spectral data to
evaluate the relative intensity of vegetation cover and the connectivity
between land cover types in relation to known indicators of fish
habitat. Designed to improve modeling efforts for predicting suitable
and sustainable fish habitat, this research offers a new perspective on
the analysis of land cover in providing a temporal dataset that defines
not only the variability in thematic cover types, but the indices and
spatial pattern metrics of land cover that may more directly influence
habitat distribution.

In addition the researchers will review existing efforts to classify
land use and land cover in the United States, such as U.S.Geological
Survey (USGS) National Land Cover Database; USGS Land Use and Land Cover
Program, National GAP Analysis Program; and the Northwest Habitat
Institute Current and Historic Wildlife-Habitat Types Program.

Several project objectives have been completed in 2005. For more project
details visit <http://faculty.washington.edu/mimid/index.html>

Project Investigators: Mimi D'Iorio and Miles Logsdon of the University
of Washington, Seattle, and Blake Feist and Chris Jordan of NWFSC,
Seattle.

[]{#__RefHeading___Toc125779674 .anchor}**3.2.1.2 John Day Basin
Sediment Assessment**

Elevated water temperature and excess deposition of fine sediments are
thought to be major causes of biological impairment in the John Day
Basin (JDB), especially restricting the spatial and temporal patterns of
key salmonid species. Many of the streams and rivers in the JDB are
listed as water quality limited for temperature and sediment on the
State-Federal 303(d) list of impaired waters. This project seeks to
evaluate the occurrence and potential impacts of excess fine sediments
throughout the JDB using a combination of field sampling and GIS-based
modeling tools. The field-based assessment makes use of a probability
survey of 72 sites on wadeable perennial streams in the JDB sampled
between 2000 and 2003 as part of the U.S. EPA's Environmental Monitoring
and Assessment Program--Western Pilot project (EMAP-WP). Targeted
sampling of 21 additional sites occurred during the summer of 2005. The
modeling component of the study has initially focused on surface (rill
and interrill) soil erosion. It applies a set of GIS-based erosion and
sediment delivery modeling tools developed by the EPA Landscape Research
Group in Las Vegas based on existing sediment models, in combination
with analysis of remote sensing (RS) imagery to characterize land cover
changes, with the aim of generating spatially explicit estimates of
sediment delivery to streams associated with anthropogenic land cover
change.

[]{#__RefHeading___Toc125779675 .anchor}**3.2.1.2.1** *Field-Based
Assessment*

An index of relative bed stability, log *RBS*, was computed from the
EMAP field sampling data as the log of the ratio of observed geometric
mean particle size to the estimated critical diameter (largest mobile
diameter) at bankfull flow. Large negative values of log *RBS* indicate
frequent bed mobility that may be the result of excess fine sediments.
In addition, we computed an index of excess fine sediments, *I~fs~*, as
the residual from a regression of the percent of stream bed surface
sediments \<2 mm (*PCT_SAFN*) against critical diameter. Many other
metrics of channel habitat condition, including channel morphology and
complexity, were also computed and examined as potential indicators of
channel response to anthropogenic disturbance. Metrics of riparian
vegetation structure and indices of anthropogenic riparian disturbance
were also computed from the field data.

Preliminary results from our analysis of stream channel substrate in
relation to watershed and riparian anthropogenic disturbance were
presented at the American Fisheries Society Oregon Chapter meeting in
Corvallis (Feb. 2005) and at the Association of American Geographers
annual meeting in Denver (April 2005). These results showed only weak
associations between log *RBS* and other in-channel response metrics
with watershed disturbance based on land cover/land use (LCLU)
information derived from the National Land Cover Database (NLCD), which
in part motivated the sediment modeling and land cover characterization
efforts. A stronger \[negative\] association was found between log *RBS*
and riparian disturbance derived from the field survey data. A
subsequent analysis using more detailed disturbance measures from a
separate study on a subset of 27 sites in the JDB revealed a strong
negative relationship between log *RBS* and the frequency of upstream
road/stream crossings (#/km). Our analysis also reveals that, as
expected, geology is a key control on stream channel sensitivity to
disturbance---streams underlain by relatively erodible sedimentary rocks
or unconsolidated deposits appear to be much more likely to exhibit low
*RBS* values or high levels of excess fine sediments than those
underlain by resistant rock such as basalt.

Because the field data come from a probability sample, we are able to
estimate the proportion of the stream channel network with particular
habitat characteristics (e.g., *PCT_SAFN* \> 20 or log *RBS* \< -2) and
have developed cumulative distribution functions (CDFs) for selected
metrics. However, the paucity of relatively undisturbed reference sites
among those sampled in the JDB makes determination of excess fine
sediments (e.g., cut-off values for 'impaired' vs. 'unimpaired' values
of log *RBS*) difficult. To address this issue, a reanalysis using an
expanded set of reference sites from the surrounding region is currently
underway.

In addition to the 72 probability survey sites, an additional 21
hand-picked sites were sampled in the JDB this summer. These included 9
sites (plus three previously sampled EMAP sites) sampled as part of a
multi-agency protocol comparison study sponsored by the Pacific
Northwest Aquatic Monitoring Partnership (PNAMP), four additional sites
in the Bridge Creek watershed (to get an increased sample of incised
channels), and 8 additional sites in the South Fork John Day (SFJD)
watershed. The SFJD sites were located up- and downstream of a series of
landslides and debris flows that entered the SFJD River during an
intense July 2004 thunderstorm to assess channel response to this pulse
input of sediments one year later.

[]{#__RefHeading___Toc125779676 .anchor}**3.2.1.2.2** *GIS Analysis and
Sediment Modeling*

The erosion and sediment delivery model we selected to use in the
initial phase of this project incorporates the Revised Universal Soil
Loss Equation (RUSLE) (Renard et al., 1996) to estimate potential
surface soil erosion from hillslopes and the SEDMOD model (Fraser, 1999)
to estimate sediment delivery to stream channels. These models quantify
rates for modeled sediment erosion and delivery processes using
empirical coefficients rather than detailed process simulations, which
allows them to be run using readily available geospatial input datasets
without collecting extensive site-specific parameterization data, making
them suitable for application at the scale of the JDB. The EPA's
Environmental Sciences Division Laboratory in Las Vegas developed a
modeling package incorporating the RUSLE and SEDMOD models bundled with
needed input datasets (including DEM, climate, soils, and NLCD land
cover). For this project, however, we needed higher resolution and
greater flexibility, so we worked with the Las Vegas lab to customize
the modeling package. Key model refinements included allowing use of 10
m DEM data and a 10-m model grid resolution, and permitting
user-specified stream network and land cover data layers and
user-specified parameter values (e.g., for user-supplied land cover
data).

Because of the need for increased resolution and the emphasis in this
project on sediment delivery to the stream channel network (as opposed
to past applications which focused on identification of sediment source
areas on the landscape), several iterations of model refinement and
testing have been needed. Problems with the riparian sediment delivery
module of the model identified during the most recent round of testing
are currently being corrected in what is anticipated to be the final
iteration of model code refinements needed to achieve project
objectives.

One issue identified from testing of the sediment model was the mismatch
between the available digital stream layer and the 10-m National
Elevation Dataset (NED) DEM used in the model. Therefore, we developed a
stream layer based on the NED DEM using Arc Hydro with a 0.1 km^2^
drainage area threshold. This yielded a stream network that was
topographically consistent with the DEM and that approximated the
maximum extent of the stream network on 1:24K USGS topographic maps,
although with a higher overall stream density.

As noted above, we generally found only weak associations between log
*RBS* or other in-channel characteristics and measures of watershed
disturbance based on the NLCD land cover data. However, the NLCD
reflects land cover around 1992, approximately a decade before the EMAP
field sampling occurred (2000-2003). In comparing the NLCD
classification to digital orthophoto and satellite imagery, it became
apparent that in many areas the NLCD layer did not accurately reflect
current land cover, due to a combination of inaccurate classification of
some land cover types and subsequent changes in land cover. In
particular, many recent clearcuts were not represented in forested areas
with active logging. In addition, the NLCD "evergreen forest" class
includes everything from sparse juniper steppe woodlands to open
ponderosa pine forests to dense spruce-fir forests, a wide range of
forest cover that might be expected to have varying degrees of
susceptibility to surface erosion. Therefore, we utilized Landsat 7
imagery (2000-2001) and supplemental data layers and GIS analysis to
update and modify the NLCD land cover for the JDB.

To address these issues, a "disturbance index" (DI) was developed to
quantify the normalized spectral distance of any given pixel from a
"mature forest" class to a "bare soil" class using ERDAS Imagine 8.7
software. The DI was calculated from tasseled cap
(brightness-greenness-wetness) indices derived from Landsat 7 TM data.
An unsupervised classification yielded 15 classes, which were
subsequently aggregated into four classes generally corresponding to
decreasing forest cover:

1.  dense mature forest

2.  open forest (e.g., ponderosa pine forest) and young or thinned
    stands

3.  young forest re-growth and very low density stands (e.g., juniper),
    and

4.  non-forest (e.g., clearcuts, meadows or other non-forested areas).

This reclassification was applied only to areas classified as forest in
the NLCD dataset. In addition to identifying up recent clearcuts and
distinguishing between young/open and mature/dense forest types, the DI
proved to be effective at identifying burned areas and distinguishing
between stand-replacement and lower intensity fires, as a comparison
with USFS fire maps revealed.

Another issue identified in the NLCD land cover was the
misclassification of recent or recovering clearcuts as
"grasslands/herbaceous" in some forested areas. This was a particular
concern for modeling purposes, because an established meadow would be
expected to have very low erosion potential, while a recently clearcut
area would have a much higher erosion potential. Therefore we used a
DEM-based analysis using ArcGIS 9 software to develop a topographic
filter based on slope and a topographic position index (TPI) to identify
flat valley bottom areas on the landscape where meadow grasslands were
likely to occur. This topographic filter was used to reclassify NLCD
grasslands into "meadow grasslands" and "non-meadow grasslands" that
could be assigned different parameter values in the sediment model.

A final important shortcoming of the NLCD land cover is the absence of
most roads (except for major transportation corridors such as interstate
highways), which can have a significant impact on potential surface
erosion. To incorporate roads (predominantly rural roads) into the
revised NLCD land cover layer, the Wessex digital line road layer was
rasterized at a resolution of 10 m, which reasonably approximates the
effective width of many rural (forest) roads. The modified NLCD layer
(which has a resolution of 30 m) was then resampled at a resolution of
10 m and the two datasets were merged. Road cells were assigned to
either a "paved road" (primary and secondary roads) or "rural road"
(unpaved) classification depending upon road attribute information from
the Wessex road layer.

Current efforts focus on (1) finalizing and testing the sediment
delivery model code, (2) coming up with defensible parameter values for
the modified NLCD land cover classes, and (3) developing a "potential"
(i.e., "undisturbed") land cover layer based on existing land cover,
topographic characteristics, and "potential natural vegetation" derived
from level 4 ecoregion information (Thorson et al., 2003).

Once model development and testing is complete, the model will be run
using both existing and potential land cover to estimate the spatially
distributed changes in sediment erosion and delivery to stream channels
associated with anthropogenic land cover changes. These changes in
sediment erosion and delivery will then be related to log *RBS* and
other field-based indicators of channel habitat condition that might be
expected to respond to changes in sediment supply. The results of this
analysis will then be combined with a GIS-based analysis of topographic
controls (e.g., slope and valley floor width) and geologic controls
(e.g., resistant vs. erodible rocks) to develop a map of stream channels
at greatest risk of adverse impacts from excess fine sediments.

Subsequent efforts will assess the spatial distribution and relative
contribution of mass wasting processes (primarily shallow landslides and
debris flows) to the sediment budget of streams in the JDB.

This work is a collaborative effort between Oregon State University
Department of Fisheries & Wildlife and the U.S. Environmental Protection
Agency (U.S. EPA) funded by NOAA Fisheries and the U.S. EPA. Work on the
project commenced in November 2004 and is being conducted at the U.S.
EPA Western Ecology Division Laboratory in Corvallis Oregon.

Project investigators: John Faustini and Alan Herlihy (Oregon State
University, Corvallis, OR), Philip R. Kaufmann and Phil Larsen (U.S.
EPA, Western Ecology Division, Corvallis, OR), Peter Leinenbach (U.S.
EPA, Region 10, Seattle, WA)

[]{#__RefHeading___Toc125779677 .anchor}**3.2.1.3** **John Day River
Basin Airborne TIR Remote Sensing & Spatial Data Analysis Project**

[]{#__RefHeading___Toc125779678 .anchor}**3.2.1.3.1** *Archived Data
Sets*

Landscape scale surveys within the Upper John Day River Basin were
conducted from 1994 through 1998 by the Department of Fisheries and
Wildlife at Oregon State University. These studies include intensive
habitat surveys, fish pit tagging/tracking, and stream temperature
analysis. The stream temperature analysis consisted of both point
location monitoring using in-stream thermographs and spatially
continuous temperature profiles derived from airborne thermal infrared
(TIR) imagery. These extensive data sets are currently archived at OSU
and can provide a critical baseline for future studies in the upper
watershed.

The past data sets were assembled in order to provide a complete context
for analyzing temperature affects on fish habit quality in the Upper
Middle Fork, Upper North Fork, South Fork John Day Rivers. Archived
temperature data sets (1994 -1998) were provided to NOAA Fisheries and
BOR for integration into a basin-wide spatial database. These data sets
included both temporally continuous point monitoring locations and
sampled temperatures from airborne thermal infrared (TIR) flights from
the same time period.

[]{#__RefHeading___Toc125779679 .anchor}**3.2.1.3.2** *Airborne Thermal
Infrared Imagery*

Airborne TIR surveys provide a complete picture of current thermal
conditions including sources of heating, temperature accumulation,
cold-water refugia, and spatial explicit thermal gradients. The imagery
and derived data allow analysis of how morphology, vegetation, and human
activities influence in-stream temperatures along the stream gradient.

Airborne TIR images were collected on both the upper Middle Fork and
South Fork John Day Rivers on August 15-16, 2003. A GIS database was
produced for both basins which included geo-referenced high resolution
TIR images and color video images, longitudinal temperature profiles,
and a report illustrating and analyzing temperature patterns (i.e.
tributary influences, source heating areas, and thermal refugia). The
report and database were released on October 28, 2003 and provided to
OSU, ODEQ, and BOR. On the upper Middle Fork, the data allowed a
comparison of current conditions to temperature patterns measured in the
mid-90's (94-98). This comparison illustrated how changes in watershed
management (stream fencing, water buy backs and allocations, etc.) may
have influenced spatial temperature patterns and local habitat quality
over the past eight years. On the South Fork, the data provide a map of
current thermal conditions and have been utilized by the *Juvenile
salmon and steelhead production group* (describe below) for on-ground
site selections with respect to thermal habitat.

In 2004, airborne TIR images were collected on the South Fork (August
19), upper North Fork (August 20), and mainstem (August 29-30) John Day
Rivers. The surveys in the South Fork sub-basin included the mainstem,
Murderer's Creek, and Black Canyon Creek. This imagery allowed for
comparison of spatial temperature patterns under two very different
temperature and flow regimes (2003, 2004). On the mainstem John Day
River, the TIR image acquisition extended from the headwaters to the
mouth. The imagery and associated report were released on June 2, 2005.
These data provide a valuable calibration source for ODEQ's Heat Source
Model, which will be utilized in the basin wide temperature TMDL effort.
The TIR image acquisition in the upper North Fork extended previous
acquisitions to include the upper North Fork John Day River, Granite
Creek, and Trail Creek. The data and report from the North Fork surveys
were released on April 24, 2005.

[]{#__RefHeading___Toc125779680 .anchor}**3.2.1.3.3** *Light Detection
and Ranging (LiDAR)*

![](media/image2.png){width="3.4916666666666667in"
height="4.75in"}Airborne LiDAR is a technology that allows high
resolution mapping of topography and vegetation at vertical accuracies
of \< 15cm (RMSE). The LiDAR instrument works by sending a continuous
series of laser pulses across the track of the survey aircraft and
measuring the travel distance and amplitude of the reflected signal. The
instrument has a high pulse frequency (50-100 KHz) resulting in a high
density of returns from the ground and effective penetration of
vegetation. The data are processed to produce detailed topographic maps
that include bare-earth and vegetation features (see inset Figure). A
high resolution true color digital camera is integrated with the LiDAR
system. Together, these sensors allow precise characterization of
channel morphology and riparian vegetation height and structure.

LiDAR data were collected on the South Fork John Day River on March 11,
2005. The data acquisition covered an approximately 500-m wide corridor
along the South Fork (\~24 miles), Murder's Creek (\~13 miles), and
Black Canyon Creek (\~4 miles). The acquisition area covered roughly the
same extent as ground crews analyzing fish population and movement in
the South Fork sub-basin. The raw data were processed to produce a
1-meter digital elevation model, and a "1^st^ return" vegetation model.
Data products were completed on July 10, 2005 and delivered to BOR, OSU,
and ODEQ. High resolution true color digital photos are being
ortho-rectified with delivery planned for February 2006.

On-going analysis of the South Fork data set includes: 1) Mapping of
historic wetlands based on channel complexity/paleo-channels. 2)
Calculation of channel morphology characteristics including stream
gradient, valley segment width, and bank-full width. 3) Calculation of
aspect and slope of near channel uplands (i.e. 300 meters from stream
centerline), and 4) Mapping of habitat types. In addition, the TIR
imagery will be integrated with the LiDAR data to understand links
between observed thermal response and landscape and channel
characteristics.

On September 27-28^th^ 2005, LiDAR data were collected on lower Bridge
Creek and on 12 experimental monitoring sites distributed across the
John Day River Basin. On Bridge Creek, the survey extended from the
mouth upstream to the town of Mitchell. The objective of the Bridge
Creek survey was to provide detailed channel morphology data to support
the study of bank incision and sediment transport that will be studied
in an IMW. The LiDAR data and associated digital photography will
provide baseline engineering data set for planning and tracking stream
restoration efforts. On the 12 experimental sites, the LiDAR data will
provide an assessment of on-the-ground physical habitat monitoring. The
raw LiDAR data are currently being processed to produce standard data
products (i.e. 1-meter DEM and vegetation model) with completion
scheduled for December 2006. Future analysis will focus on generating
stream channel metrics including valley segment widths, bank-full
widths, wetted widths, stream gradient, and riparian height and
structure.

Project Investigators: Russell Faux of Watershed Sciences

[]{#__RefHeading___Toc125779681 .anchor}**3.2.1.4** **PIBO Effectiveness
Monitoring Program**

The PACFISH/INFISH Biological Opinion (PIBO) Effectiveness Monitoring
Program was developed in response to monitoring needs addressed in the
Biological Opinions for bull trout (U.S. Department of the Interior,
Fish and Wildlife Service 1998) and steelhead (U.S. Department of
Commerce, National Marine Fisheries Service 1998). It provides a
consistent framework for monitoring aquatic and riparian resources
within the range of the Pacific Anadromous Fish Strategy (PACFISH) and
the Inland Fish Strategy (INFISH), and will determine whether land
management practices are maintaining or improving riparian and aquatic
conditions at both the landscape and watershed scales on Federal lands
throughout the Upper Columbia River Basin. The PIBO Effectiveness
Monitoring Program officially began in 2001.

The program goal is to determine whether PACFISH/INFISH management
practices are effective in maintaining or restoring the structure and
function of riparian and aquatic systems. The specific objectives are:

1\. Determine whether a suite of biological and physical attributes,
processes, and functions of upland, riparian, and aquatic systems are
being degraded, maintained, or restored across the PIBO landscape.

2\. Determine the direction and rate of change in riparian and aquatic
habitats over time as a function of management practices.

3\. Determine if specific Designated Monitoring Area practices related
to livestock grazing are maintaining or restoring riparian vegetation
structure and function.

While this program was design to address the influence of management
practices of the Forest Service at the scale of the Upper Columbia River
Basin, this information may also be useful in describing status and
trends of watersheds at the scale relevant to fish populations.

The PIBO study area contains 3,547 subwatersheds (sample units) with at
least some FS or BLM ownership. A generalized random tessellation
stratified design (GRTS) was used to select subwatersheds to achieve a
random, nearly regular sample pattern throughout the study area
(Kershner and others 2004). Approximately 1,300 subwatersheds were
selected for sampling during the first 5 years (2001 to 2005). Each of
these subwatersheds will then be resampled on a 5-year rotation
beginning in 2006 (Table 2).

Table 2. The table displays the PIBO sampling design at full
implementation where 250 subwatersheds would be sampled each year from
2001 to 2007. These sites will be resampled every 5 years. An additional
50 watersheds were selected for annual sampling (sentinel sites). The
actual number of subwatersheds sampled at half implementation in 2001
and 2002, and full implementation in 2003 and 2004, are shown in
parentheses.

  ------------ ---------- ---------- ---------- ---------- ---------- ---------- ----------
  **Sampling   **2001**   **2002**   **2003**   **2004**   **2005**   **2006**   **2007**
  design                                                                         
  category**                                                                     

  Sentinel     50(38)     50(26)     50(48)     50(50)     50         50         50

  Group 1      250(152)                                               250        

  Group 2                 250(106)                                               250

  Group 3                            250(233)                                    

  Group 4                                       250(241)                         

  Group 5                                                  250                   
  ------------ ---------- ---------- ---------- ---------- ---------- ---------- ----------

To address the first objective, an "integrator" reach is sampled in each
randomly chosen subwatershed. These sites were chosen because they are
the most likely location to show integrated effects from upstream
management actions, and 83 percent of these sites are located in the
most downstream response reach (defined as having a stream gradient less
than 3 percent) on Federal land with the remaining sites (17 percent) at
the downstream most transport reach (stream gradient between 3 and 5
percent) on Federal lands. The design also requires at least 50 percent
Federal ownership upstream of the site. A suite of physical stream
habitat attributes, riparian vegetation characteristics, and
macroinvertebrate samples are collected at these sites.

To address the second objective, 25 reference and 25 managed integrator
sites were randomly selected for annual sampling (sentinel sites).

The third objective required sampling in Designated Monitoring Areas
within each selected subwatershed where cattle grazing occurs within the
riparian area. The location of the Designated Monitoring Area is
determined by the Field Units and is used for annual "Implementation"
monitoring (USDA 2003). This link between implementation and
effectiveness monitoring provides an adaptive management feedback
process. Only riparian vegetation, stream bank, and bankfull width
measurements are collected at these sites.

The PIBO program samples on both Forest Service and Bureau of Land
Management lands within the John Day River Basin. We have sampled 60
Integrator reaches (objective 1) within the John Day River basin between
2001 and 2005. Five of these sites are sampled annually (objective 2).
Fifty-one Designated Monitoring Areas were sampled during this 5 year
period. Re-sampling of these sites will begin in 2006 allowing status
and trends in the resource condition to be determined. Five new
integrator reaches and 15 new Designated Monitoring Areas will be added
between 2006 and 2010. See Henderson and others (2005) for a broader
overview of the PIBO program and accomplishments.

[]{#__RefHeading___Toc125779682 .anchor}**3.2.1.4.1** *Field Sampling
Protocols*

In 1997 an interagency team was convened to determine which physical and
biological attributes should be measured to answer the program
objectives. The original sampling methods we used to measure the
attributes came from a variety of sources. Since 1998 we have continued
to evaluate and refine each of the methods based on feedback from field
crews and results from quality assurance tests.

The stream habitat protocol includes methods for assessing channel
cross-sections, gradient, habitat units, large wood, sinuosity,
streambed substrate, streambank parameters, water temperature, and
aquatic macroinvertebrates (Dugaw and others 2004). The riparian
vegetation protocol describes methods for sampling the species
composition along the greenline and across the riparian area
(Coles-Ritchie and others 2004). Both protocols for the 2004 field
season can be downloaded from our Web site at
[www.fs.fed.us/biology/fishecology/emp](http://www.fs.fed.us/biology/fishecology/emp).

[]{#__RefHeading___Toc125779683 .anchor}**3.2.1.5 ODFW Habitat and
Riparian Survey**

ODFW has extended the structure and methods employed by the Oregon Plan
for Salmon and Watersheds Monitoring Program to the John Day subbasin of
the Columbia Plateau. This approach, successfully implemented in
Oregon's coastal watersheds, applies a rigorous, Tier-2 sampling design
to answer key monitoring questions, provides integration of sampling
efforts, and has greatly improved coordination among state, federal, and
tribal governments, along with local watershed groups.

ODFW has collected two years of habitat survey information paired with
juvenile snorkel surveys under the same sampling design. In each
subbasin, ODFW, in cooperation with co-managers and other interested
parties, will refine the sampling universe for habitat and juvenile
surveys based on updated information. The current sampling domain is
defined at the upper ends of watersheds by perennial streams accessible
to anadromous salmonids and at the lower end by the capability of field
crews to snorkel the sample reach. To balance the needs of status (more
random sites) and trend (more repeat sites) monitoring, we implemented a
rotating panel probabilistic sampling design in the Columbia Plateau
based on recommendations from the EPA EMAP Design Group. The 50 sites
drawn on an annual basis for each subbasin will be assigned to the
rotating panel design as follows:

-   3 panels with different repeat intervals

-   17 of the sites will be sampled every year

-   16 sites will be allocated to a 4 year rotating panel (sites visited
    once every 4 years on a staggered basis)

-   17 sites will be new sites each year

With this sampling strategy, 50 sites were drawn the first year and 33
new sites will be drawn in subsequent years because 17 of the originally
drawn sites will be repeated each year. ODFW experience on coastal
watersheds has demonstrated that a target sample size of 50 sites will
meet the precision targets for habitat and juvenile sampling. Over the
first 3 years of the study, ODFW is evaluating the influence of sample
size on meeting/not-meeting/exceeding target precision levels and make
recommendations for adjusting the sample size accordingly.

Channel habitat and riparian surveys are conducted as described by Moore
et al. (1997) with some modifications. Modifications include: survey
lengths of 500-1000 m and measurement of all habitat unit lengths and
widths (as opposed to estimation). Survey teams collect field data based
on stream, reach, and channel unit characteristics. Each field crew is
comprised of two people with each member responsible for specific tasks.
The \"Estimator\" will focus on the identification of channel unit
characteristics. The \"Numerator\" will focus on the counts and relative
distribution of several unit attributes and will verify the length and
width estimates for a subset of units. The \"Estimator\" and
\"Numerator\" share the responsibility for describing reach
characteristics, riparian conditions, identifying habitat unit types,
and for quantifying the amount of large woody debris. The methods and
indicator variables collected with this protocol can be viewed at
<http://osu.orst.edu/Dept/ODFW/freshwater/inventory/pdffiles/habmethod.pdf>.

The variables described are indicators of habitat structure, sediment
supply and quality, riparian forest connectivity and health, and
in-stream habitat complexity. The specific attributes include but are
not constrained to:

> Density of woody debris pieces (\> 3 m length, \>0.15 m diameter)
>
> Density of woody debris volume (\> 3 m length, \>0.15 m diameter)
>
> Density of key woody debris pieces (\>10 m length, \>0.6 m diameter)
>
> Density of wood jams (groupings of more than 4 wood pieces)
>
> Density of deep pools (pools \>1 m in depth)
>
> Percent pool area
>
> Density of riparian conifers (\>0.5 m DBH) within 30 m of the stream
> channel
>
> Percent of channel shading (percent of 180 degrees)
>
> Percent of substrate area with fine sediments (\<2 mm) in riffle units
>
> Percent of substrate area with gravel (2-64 mm) in riffle units

While these attributes do not describe all of the conditions necessary
for high quality

salmonid habitat, they do describe important attributes of habitat
structure within and adjacent to the stream channel. The attributes are
also indicative of streamside and upland processes.

[]{#__RefHeading___Toc125779684 .anchor}**3.2.1.6 EMAP- Western Pilot
Project**

The EMAP-Western Pilot Project is a partnership between the United
States Environmental Protection Agency and the Oregon Department of
Environmental Quality.  Project design and organization is under
direction of the USEPA, while field operations within John Day Basin are
managed by ODEQ's Laboratory Division--Watershed Assessment Section. The
project was implemented over 2000-2004 field seasons.  The EMAP program
was created because conventional water quality surveys were criticized
regarding their inability to adequately assess conditions of the
nation's surface waters.  In response, the EMAP program was designed by
scientists within the USEPA to provide a scientifically defensible
method for assessing ecological integrity.  EMAP differs from
traditional water quality surveys by applying a more holistic approach. 
EMAP utilizes a suite of indicators spanning the chemical, physical, and
biological components of aquatic systems. The goals of EMAP program
nationally and in the John Day are to classify the conditions of aquatic
resources across larger scales or regions (watersheds, basins, states,
etc.).  The main objective is to identify status and trends in
ecological conditions across the study areas.

 

To draw larger scale inferences, EMAP relies on a probability based
survey design.  Sites were randomly selected across the scale of
interest, with each site representing a fraction of the total resource. 
In this way, the results of a single site represented a portion of a
larger population.  The strength of the EMAP program is in combining
results from single sites into a larger overall picture of ecological
integrity within a region. ODEQ measured several geomorphic and riparian
habitat variables in the John Day including flow, stream channel
profiles, slope, sediment composition, shade, vegetative layers-density
and complexity, and invasive plants (see Kaufmann et al. 1999 for more
details).

 

In addition, ODEQ monitored water chemistry including nutrients (C, P,
N), physical properties (pH, conductivity, temperature, etc.),
biological characteristics such as periphyton, macroinvertebrates,
aquatic vertebrates, and human disturbance at the watershed scale (GIS)
and reach scale.

The Western EMAP pilot project personnel, Shannon Huber, attended AFG
meetings to provide AFG participants of an overview of the information
collected and expected analyses that are currently underway. To date,
compilation of the results from this effort is ongoing and expected to
be completed in early 2006 and available to the public.

[]{#__RefHeading___Toc125779685 .anchor}**3.2.1.7 Other Habitat
Monitoring**

US Geological Survey -- USGS currently monitors stream discharge in the
mainstem John Day River at McDonald Ferry (RM 21), Service Creek (RM
157), Picture Gorge (RM 205), John Day (RM 253) and Blue Mountain Hot
Springs (RM 275). Discharge are also monitored in the North Fork at a
station near Monument (RM 16), in the Middle Fork near Ritter (RM 15),
and two stations on the South Fork, near Dayville (RM 7) and Izee (RM
34). Discharge is also monitored in the tributaries: Mountain Creek,
Lone Rock Creek, Butte Creek, Murderer's Creek, Deer Creek, Canyon
Creek, Strawberry Creek, Camas Creek and Bridge Creek.

[Confederated Tribes of Warm Springs]{.mark}

[Other FS monitoring]{.mark}

[]{#__RefHeading___Toc125779686 .anchor}**[3.2.2 Water quality
monitoring]{.underline}**

[]{#__RefHeading___Toc125779687 .anchor}**3.2.2.1 John Day TMDL
Project**

The Pendleton, Oregon office of the Oregon Department of Environmental
Quality has been conducting monitoring in the John Day Basin since 2002
in support of Clean Water Act Total Maximum Daily Load (of pollutants)
establishment. Total maximum daily loads (TMDL) are target water quality
standards.  Standards typically contain threshold values for water
quality conditions such as pollutant concentration, pH, dissolved
oxygen, or temperature.  The temperature standard numeric criteria in
the John Day is based on salmonid life cycles as the most sensitive
beneficial use of Basin waters. TMDL monitoring in the John Day Basin
has been designed to address the following concerns (Clean Water Act
303(d) Listings):

-   North Fork Subbasin: temperature, sediment, biological criteria
    (macroinvertebrates)

-   Middle Fork Subbasin: temperature

-   Mainstem: temperature, pH

-   Upper mainstem subbasin: temperature, dissolved oxygen

-   South Fork of the John Day River: temperature

One of the main objectives of the TMDL in the John Day is to quantify
the conditions leading to high temperature.  Generally, the numeric
criteria of the standards cannot be met even under the best estimate of
more natural conditions.  In this case, the default target of the TMDL
is the best estimate of natural conditions. Accordingly, for
temperature, the TMDL process assesses the existing and estimated
natural potential heat loads.  From this, land use authorities are
allocated a maximum allowable heat load (best estimate of natural) and
the amount of heat reduction required to achieve this.  Further, these
heat loads are translated into more intuitive measurable targets such as
percent effective shade and channel width.

In order to implement this process, a calibrated model is needed that
will compute the following:

-   flow volume and velocity, wetted channel width and depth, and
    effective shade (daily solar radiant energy blocked by vegetation
    and topography)

-   temperature, temporally and spatially, based on all relevant heat
    transfer processes including evaporation, bed conduction,
    convection, mass transfer, short wave (direct and diffuse) and long
    wave radiation

In addition to providing jurisdictionally allocated goals to address
water quality, model output allows rapid longitudinal viewing of where
heat loads are furthest from potential, and to what process the excess
heat is attributable.  Other studies in the John Day pilot project will
develop relationships between reach temperature distributions and salmon
and steelhead productivity so that the TMDL can be used to evaluate how
alternative habitat restoration effort may affect stream temperatures
and ultimately fish productivity.

 

Monitoring and assessment of vegetation, hydrology, morphology and
meteorological conditions will provide input for temperature modeling
(one-dimensional thalwag characterization). The temperature model will
be calibrated spatially to airborne thermal infrared data and temporally
to instream temperature dataloggers. Based on historical information
including aerial photography, literature, local knowledge, and current
undisturbed conditions, system potential channel shape and vegetation
will be assessed. These estimated conditions will provide the basis for
simulation of a more natural shading and temperature. One key output
will be summer afternoon longitudinal temperature profiles. Temperature
will be simulated for a variety of flow scenarios as well. This
information will inform key questions:

-   What is controlling temperature in the Basin and to what extent is
    this anthropogenic?

-   What is a more natural longitudinal temperature profile in the
    summer afternoon?

-   What is the summer diel temperature range and how much has it been
    increased through human activities? Where?

-   Can the seasonal time-frame of elevated temperatures be reduced?

Specific analytical methods can be found in *Analytical Methods for
Dynamic Open Channel Heat and Mass Transfer* (Boyd, Matthew and Kasper,
Brian, 2002, [www.heatsource.info](../www.heatsource.info)), and are
summarized in TMDLs (e.g., Klamath, Umatilla) that can be found on DEQ's
website: <http://www.deq.state.or.us/wq/TMDLs/TMDLs.htm>. Ground Level
monitoring methods include Rosgen Level II Inventory (Rosgen, 1996),
Wolman Pebble Counts (Wolman 1954); Solar Pathfinder^TM^, continuous
temperature monitoring (Watershed Assessment Mode of Operations Manual,
ODEQ, 2004 --
<http://www.deq.state.or.us/lab/qa/DEQ03-LAB-0036-SOP.pdf>)

The overall TMDL data set has been aided through the efforts of numerous
organizations and the DEQ laboratories, as well as other DEQ Departments
such as the biomonitoring Section. Data collection and organization for
the TMDL analysis is nearly complete. The analysis is expected to
commence in the Spring of 2006. Where feasible, the TMDL-specific data
collection was designed to fill-in where other organizations weren't
monitoring (geographic area or monitoring parameter). The geographic
boundary for this effort it the John Day Basin, and the focus has been
on the John Day River and the South, North and Middle Forks, and the
temperature and flow at the mouths of major tributaries to these rivers.

In 2002, roughly ten temperature dataloggers (Vemco^TM^) were deployed
July through October along the North and Middle Forks. TIR flight data
was contracted and implemented as well, for 120 mile swaths along these
two Rivers, covering their lengths from their mouths to a little ways
above Hwy 395. This complements a 1998 TIR data set that overlaps this
and covers up to the headwaters. One-time summer low flow was measured
at 31 sites, approximately during the TIR flights.

In 2004, vegetation and morphology surveys were completed for the
mainstem and the North, Middle and South Forks, totaling roughly 50
sites. Data include: surveyed cross-sections, abbreviated pebble counts,
description of shade producing vegetation (species and height
measurements, Solar Pathfinder measurements^TM^) Vemco's^TM^ were placed
every 5 to 30 miles along the mainstem, covering June through September.

In 2005, a water quality intensive was conducted. This included 3
monitoring categories: (1) Datasonde Hydrolabs with pH, conductivity,
dissolved oxygen and temperature probes at 7 upper mainstem and 2 north
fork sites; (2) mobile lab *E. Coli* analyses (Colilert method) at 9
upper mainstem sites, and (3) water quality sample collection for
analysis of nutrients, pH, dissolved oxygen, temperature, chlorophyll,
BOD and suspended solids; at all Hydrolabs sites. Timing is as follows,
by category: (1) all sites were monitored simultaneously for 7
consecutive days in August, 2005 and the same will occur in May 2006,
(2) All sites were monitored each day for 5 days over the course of each
selected month: June, August, October of 2005 and will be again in April
of 2006; and (3) water samples were collected twice at each hydrolab
site during the course of their deployment. In addition, in the summer
of 2005 TIR was flow over the mainstem, South Fork and parts of the
North Fork and Granite Creek, and flow monitoring was conducted to
correspond to this data set.

Also in 2005, other ground level data, including field temperature, pH,
conductivity and dissolved oxygen, were collected every sampling day for
each category of monitoring, at each hydrolab, bacteria and lab sample
site.

As mentioned above, remote sensing TIR was collected in swaths covering
the mainstem and all the major tributaries. DEQ has been working with
its TIR contractor, BOR, and Oregon Department of Agriculture to ensure
that sufficient remote sensing of other types is available for
temperature modeling: LiDAR (South Fork), and color DOQ's (North, Middle
and South Fork and Upper mainstem).

Project Investigators: Don Butcher Paul Daniello, and Brian Kasper of
ODEQ

[]{#__RefHeading___Toc125779688 .anchor}**3.2.2.2 Other water quality
monitoring**

The ODEQ have been collecting state-wide water quality information, in
some cases since the 1940s, at their Ambient Monitoring site. Five sites
are located in the basin: in the John Day River at HWY 206, RM 39.5;
John Day River at Service Creek, RM 157.4; North Fork John Day River at
Kimberly, RM 0.2; John Day River u/s Dayville, RM 215.4; and in South
Fork John Day River at Dayville, RM 0.2. These sites are collected 6
times a year with the following parameters collected: alkalinity, bod,
chlorophyll a, cod, conductivity, dissolved oxygen-% saturation,
dissolved oxygen-concentration, *e. coli*, fecal coliform,,
nitrogen-ammonia, nitrogen-nitrate and nitrite, nitrogen-tkn, pH,
pheophytin a, phosphates-dissolved ortho, phosphates-total,
solids-suspended, solids-total, temperature, toc, and turbidity. In
addition, point source monitoring is conducted throughout the basin.
Also, water quality samples were collected as part of the ODEQ's Western
EMAP project (described above).

Confederated Tribes of the Warm Springs. CTWSRO_JDBO has been collecting
water quality information throughout the John Day Basin. Hydrolabs have
been [deployed at ??? sites since ??? and collects]{.mark} including DO,
pH, conductivity, turbidity, and temperature.

Monument SWCD -- MSWCD will deploy and maintain at least 3 YSI
multi-probes to collect water quality data at long-term monitoring sites
identified by John Day pilot project. Grab samples for dissolved oxygen
(DO) have signaled potential problems at the ODEQ Ambient Monitoring
site in Kimberly near the mouth of the North Fork John Day in certain
seasons. Because this site is of concern, is a the long-term water
quality Ambient Monitoring site, and is located near the North Fork
rotary screw trap, this is a likely place to deploy one of the YSIs. A
deployment plan will be developed in collaboration with the NOAAF and
ODEQ to collect year round water quality data including DO, pH,
conductivity, and temperature. Equipment and training have already been
provided via BOR, and DEQ. NOAAF will contract with the MSWCD to perform
this work in 2006.

In addition, MSWCD collects bi-monthly split samples with the ODEQ at
Kimberly taking grab samples for pH, DO, conductivity, and temperature.
These will assist with calibration of multiprobe instruments, provide
credibility to the data of both organizations, and provide a chance for
information exchange. MSWCD will continue to fill crucial gaps in water
and air temperature monitoring, deploying optic stowaway and hobo
devices covering key historic SWCD sites and adding any sites identified
by the pilot project as important (no more than 15 sites).

[]{#__RefHeading___Toc125779689 .anchor}**[3.2.3 Status and Trends of
Juvenile and Adult Salmonids]{.underline}**

[]{#__RefHeading___Toc125779690 .anchor}**3.2.3.1 ODFW Freshwater
Production Monitoring**

The Oregon Department of Fish and Wildlife (ODFW) is largely responsible
for estimating freshwater production, abundance, and distribution of
salmonids in the John Day subbasins.

Specifically, the objectives include:

1.  Monitor trends in abundance of juvenile trout and salmon and status
    and trends in stream and riparian habitats in the John Day River
    subbasin.

2.  Monitor status and trends in steelhead redd abundance and spawner
    escapement in the John Day River subbasin.

3.  Estimate the number and distribution of spring chinook salmon redds
    and spawner escapement in the John Day River subbasin.

4.  Estimate smolt-to-adult survival rates (SAR) and out-migrant
    abundance for spring chinook and summer steelhead and life history
    characteristics of steelhead above Rkm 298 (river mile 185) in the
    John Day River subbasin.

5.  Measure distribution of adult spring chinook holding habitat in the
    John Day River subbasin. Examine if temperature remains a limiting
    factor to freshwater habitat distribution.

6.  Determine population structure of steelhead in the John Day subbasin
    through genetic sampling and analysis.

Basin-wide distribution surveys for juvenile fish are conducted in the
same reaches surveyed for habitat by the ODFW EMAP approach. Snorkel
surveys are the predominant method to quantify the number of juvenile
*O. mykiss* and spring Chinook in these reaches. These surveys involve a
single upstream pass through each pool during daylight along a 1-km
survey reach. Counts of the number of juvenile and adult trout (*O.
mykiss and O. clarki*) and salmon (*O. tshawytscha*) are recorded for
each pool. Trout and salmon are categorized as juvenile (1+ years or
greater), or adult based on size classes. If all pools in a reach have
poor visibilities then as many pools in the reach as possible will be
electrofished. Electrofishing data will be combined with snorkeling data
to determine the presence/absence of juvenile *O. mykiss* and spring
Chinook. The presence/absence data will be analyzed to quantify the
percent of sites where juvenile *O. mykiss* and spring chinook are
present.

Status and trend monitoring for adult steelhead and Chinook are also
conducted to determine abundance and distribution of spawning adults.
Steelhead redd surveys are conducted annually based on standard ODFW
methods. Fifty sites are selected in a random probabilistic design and
visited every two weeks throughout the season to quantify the cumulative
redd count at each site. Each surveyor counts live fish and all redds
are counted and flagged. Population status is indexed through cumulative
redd counts. Hatchery:wild ratios are estimated by observing the
occurrence of adipose fin-clipped and unmarked live fish on spawning
grounds. ODFW managers also conduct annual, steelhead spawner surveys to
compliment trend analysis. Methods are similar to those described above
except that surveys are conducted on stream reaches selected over the
past 50 years to maximize trend data analysis. Spring Chinook spawner
surveys are also conducted annually by ODFW. Adult fish and redds are
counted and carcasses are sampled to determine sex, age, and
reproductive success. These surveys include index and census counts. All
known spawning habitat is surveyed for census counts and supplemented
with randomly chosen survey sites outside of known spawning habitat.
Similar to steelhead, index survey sites were established over the past
50 years to maximize trend data analysis.

This section summarizes the information found in the ODFW FY2003
Statement of Work for estimating freshwater production. Between the
completion of the life history and natural escapement study in 1984 and
the start of this project in 1998, spring Chinook spawning surveys have
not provided adequate information to assess age structure,
progeny-to-parent production values, smolt-to-adult survival (SAR), or
natural spawning escapement. Further, only very limited information is
available for steelhead life history, escapement, and productivity
measures in the John Day subbasin. To meet the data needs to assess the
long-term effectiveness of habitat projects, and to differentiate
freshwater and ocean survival, sufficient annual estimates of spawner
escapement, age structure, SAR, egg-to-smolt survival, and freshwater
habitat use are essential. These needs are beginning to be addressed
through spawning ground surveys initiated for spring Chinook salmon in
1998 and smolt PIT-tagging efforts initiated in 1999. Much of the
information described here will likely be used to compliment research
described in this document.

One objective of the study conducted by ODFW is to estimate
smolt-to-adult survival rates (SAR) and out-migrant abundance for spring
Chinook and summer steelhead and life history characteristics of
steelhead (above Rkm 298) in the John Day River basin. Using methods
developed by Lindsay et al. (1986), ODFW collects Chinook smolts using
beach-seining techniques and rotary screw traps in the lower Mainstem,
South Fork, Middle Fork, and the lower North Fork. This mainstem river
reach used for seining is downstream of all known spawning and rearing
areas and is designed to sample the entire John Day River basin
population of Chinook during the smolt migration period (Lindsay et al.
1986, Wilson et al. 2001). A minimum of 4,000 each, Chinook and
steelhead smolts, captured in either seines or rotary screw traps, are
PIT tagged annually. Estimates of SAR for both species rely on FCRPS
PIT-tag detection facilities at the Bonneville Dam to document returning
tagged adults. The PTAGIS and DART data systems are used to store
PIT-tagged fish information.

To determine productivity (out-migrant abundance) of specific hydrologic
units for Chinook (and management units for steelhead), ODFW estimates
contributions of Upper Mainstem (above river mile 202), Middle Fork,
South Fork, and North Fork reaches to total smolt productivity. Rotary
screw traps are near the downstream extent of each of the four
hydrologic units to measure basin-specific smolt abundance. Traps are
located below known Chinook spawning areas. These same traps are used
for steelhead and are located near the downstream extent of four of the
five steelhead management units defined by Chilcote (2001). The number
of emigrants from above the trap location is estimated using periodic,
mark-recapture trap efficiencies (Thedinga et al. 1994). A minimum of
500 smolts of each species in each basin are PIT tagged to verify their
origin. ODFW is coordinating smolt trapping efforts on the South Fork
with OSU. PIT tag effort and information is shared among groups and an
effort to analyze trap efficiency estimation is currently being
conducted.

SARs have not been estimated for John Day steelhead to help guide sample
size requirements. Assuming a SAR similar to spring Chinook (\~1.2%,
Lindsay et al. 1986) a goal of 5,500 smolts will have to be PIT tagged
to produce an adequate sample of 60 returning adults. SAR will be
determined for each year class using scale analyses of collected
juveniles. Populations will then be apportioned into the varying life
histories and summed across years for SAR.

To determine freshwater life history traits of juvenile Chinook and
steelhead ODFW will subsample steelhead juveniles for age determination
and use the above trap efficiency and abundance estimate efforts for
migration timing in the John Day subbasin. In addition, ODFW is
evaluating the distribution of adult spring Chinook holding habitat in
the John Day River subbasin to examine if temperature remains a limiting
factor to freshwater habitat distribution.

To determine the freshwater habitat use by adult spring Chinook salmon
during critical warm-water periods, snorkel surveys have been conducted
in the Middle and North Fork subbasins. During summer months when water
temperatures are greatest (July and August), streams are surveyed
longitudinally to determine the presence/absence of adult Chinook. Water
temperatures are monitored using point measurements and with
thermistors. Thermistors are placed at the downstream extent (presumably
warmest experienced temperatures) of observed fish distributions. Adult
Chinook are counted, their residing habitat is qualitatively described,
thermal refugia are noted, and all pre-spawning mortalities (carcasses)
are counted and examined for age, sex, size, and fecundity.

Limited genetics data has been identified by the Interior Columbia Basin
Technical Recovery Team (TRT) as a primary factor contributing to
uncertainty in defining steelhead populations in the Mid-Columbia
steelhead ESU. To determine population structure of steelhead in the
John Day River subbasin ODFW is collecting tissue samples from naturally
produced *O. mykiss* from five populations of steelhead identified by
Chilcote (2001). Samples and associated data will be provided to the
NOAA Genetics Laboratory in Seattle

[]{#__RefHeading___Toc125779691 .anchor}**3.3 Compilation of Historical
and recent data**

[]{#__RefHeading___Toc125779692 .anchor}**3.3.1 Compilation of GIS and
Tabular Datasets**

###### NOAAF has been responsible for the comprehensive collation of both historical and recent GIS and tabular datasets. The assembly of existing data sources and locations allows the identification of both spatial and content gaps in current monitoring efforts and will provide a framework for designing supplemental monitoring projects within the basin. Inventories of the GIS data available for the John Day and Wenatchee have been assembled (ongoing). An extensive collection of historical tabular datasets and current monitoring programs have been inventoried for the John Day Basin. Datasets supporting the conceptual plans of potential John Day IMWs were the priority for collection efforts and assimilation into databases. Local agency knowledge and the Pacific NW Salmon Habitat Project Tracking Database (NOAAF, NWFSC) have been the primary source of restoration project knowledge in the pilot basins. Extensive inventories of GIS, tabular, and restoration project data are available upon request. 

[]{.mark}

Data compilation efforts in the John Day and Wenatchee basins were
prioritized according to IMW and monitoring design data needs. Datatypes
of interest included water quality (temperature, DOC, turbidity, and
nutrients), habitat, fish (redd, juvenile, and adult counts),
macroinvertebrates, stream gages, cross sections, and vegetation. In
2004 and 2005 a comprehensive collection effort of historical tabular
data was compiled for both the John Day and Wenatchee basin (see
Appendix B, Table 1). For the John Day basin over 20 years of water
temperature data, ODFW index reach redd counts (since 1960's), and USFS
in-stream habitat survey data (early 1990's) have been assimilated. The
priority has been to collect and standardize existing data in the
Wenatchee and Salmon basins has been of secondary importance until data
storage and standardization methods have been developed for the John Day
basin.

NOAAF has assimilated a list of available GIS datasets for both the John
Day and Wenatchee basins. The master inventory includes data from
agencies with specific objectives (e.g. US Forest Service harvest
records) as well as datasets from regional projects (e.g. ICBEMP).
Supplemental GIS datasets created by NOAAF, UW, and BOR have also been
accounted for in the inventory. In the Wenatchee basin, contracts with
PBI and Earth Systems Inc. (TRIAD) have also provided GIS data
resources.

In an effort to establish a working base of status monitoring sites in
the John Day Basin, NOAAF compiled a comprehensive collection of
historical monitoring sites utilized in the past 20 years. Data have
been categorized and inventoried according to type: fish,
macroinvertebrates, fish habitat, vegetation, water quality, and water
flow (gage). Collation efforts have also been coordinated with the
Pacific NW Salmon Habitat Project Tracking Database (NOAAF, NWFSC) and
the Middle and North Fork John Day Watershed Council to inventory
existing restoration projects within the John Day basin.

Tabular records of water temperature, habitat cross sections,
macroinvertebrates, and fish have been targeted as the priority for data
collation efforts in the basin. Multiple agencies are responsible for
the collection of these data types and limited communication among
groups has resulted in non-standardized data storage methods and limited
data sharing capacities. To facilitate the data sharing and use process,
these existing data have been collected directly from agencies and
stored in SQL databases until incorporation into the Oracle database
created for the Salmon watershed. Specifically, water temperature data
from more than 800 unique sites and 3,2000 monitoring events across 10
agencies have been collected and stored for the basin. Over 60 cross
section and macroinvertebrate monitoring sites have been recorded
although data collation for these types is still in process. Recent fish
data collected by the Oregon Department of Fish and Wildlife (since
2000) along with the 2004 construction of a MS ACCESS database
containing historical Steelhead redd spawning surveys have been the
initial products of the fish data collation process. Additional fish
data from Oregon State University and ODFW (Corvallis, OR) have been
identified but are not currently housed in NOAAF databases. Data
collection and the inventory process is ongoing but has been simplified
with the establishment of working databases for each datatype.

Data assimilation efforts have also facilitated the coordination of
local agencies and their monitoring designs and current data storage
methods. Current agencies participating in data standardization and
monitoring designs include both local, state, and federal agencies, such
as ODEQ, BLM, USFS, ODFW, USGS, SWCD, CTWSRO, Oregon Water Resources
Division, UW, OSU, National Oceanic and NOAAF, and BOR. A basin-wide
meeting in May 2005 of 10 agencies concluded that there is a need for
basin-wide coordination of monitoring efforts and increased
communication between agencies. We proposed an annual winter monitoring
meeting to discuss upcoming summer monitoring and restoration projects
and continue working towards standardizing data collection and storage
methods.

The assembly of GIS datasets for the John Day basin has included the
collection of agency-produced layers from the USFS, Pacific NW Research
Science lab, Umatilla National Forest (harvest and water quality
records), Malheur National Forest (harvest and fire regime records),
Wallawa-Whitman National Forest (harvest and fire regime records), OSU
(fish habitat, fish use, and temperature) and UW (remote sensing
imagery). Additional datasets from the internet or at a regional scale
have also been assembled by the Biological Opinion 2000 Technical
Recovery Team (BiOp TRT) (Appendix B, Table 2).

Project investigators: Carol Volk and Steve Rentmeester of the Northwest
Fisheries Science Center, Seattle, WA.

[]{#__RefHeading___Toc125779693 .anchor}**3.3.2 The Protocol
Manager/Library**

The Bureau of Reclamation GIS group has been working toward the
establishment of a "Protocol Manager/Library" in which will house the
various data collection protocols used to collect monitoring data within
the John Day Basin and other project basins and areas within the Pacific
Northwest. Protocol Manager will be useful in describing which
collection characteristics and methods are relevant and pertain to
specific attributes and indicators used within various protocols, such
as those defined and practiced by data collection agencies using EMAP
and PIBO.

Our first validation meeting was set for May 11-12, 2005 in Boise, ID.
The purpose of this meeting was to begin the validation process of the
Protocol Manager structure and coordination of protocols within Protocol
Manager. This meeting helped to 1) validate current application terms,
e.g. protocol, indicator groups, indicators, attributes, etc., and
structure. 2) Develop initial use cases and a general road map for
future application development. 3) Define roles of protocol
builder/manager in relevant business scenarios.

Our next step was to validate the actual protocols with their
corresponding indicators and attributes. Using the protocol's monitoring
data collection manual, all attributes that were shown as being
collected were pulled into spreadsheets for further validation with
respective data collection agencies using that particular protocol. For
example, in July we met with ODEQ to begin the in-depth validation
process for the EMAP protocol along with documenting changes that have
occurred from year to year with respect to monitoring data collection
methods and procedures. In August, we met with the Forest Service to
work thru the PIBO protocol along with recording differences between
different collection years, and currently in progress is the validation
of the WDOE protocol. An important aspect of this validation process
with the respective data collecting agencies is to ensure completeness
and accuracy surrounding the data collection process. Not only including
all the attributes and information that is being, (or not being)
collected, but many times process or method of collection is a valid
aspect to document as well. This is important in terms of how various
monitoring data will be translated and entered or retrieved from the STM
(Status-Trend Monitoring) database and Protocol Manger.

While the validation process has been moving forward, we have continued
to meet regularly with Rod Davidson, the STM database consultant for
NOAAF. Our focus in working with Rod on the STM database design and data
loading issues has mainly been in reference to protocols and data from
the Wenatchee subbasin. However, the intent has been to eventually
include loading collected monitoring data from the John Day Basin as
well (currently in process). More undertakings by NOAA Fisheries staff,
includes establishing user-friendly interfaces in which to easily
retrieve the loaded data from the STM database.

Bureau of Reclamation GIS has continued to regularly meet with Spatial
Dynamics for Protocol Builder/Manager prototype design,
protocols/indicators/attributes inputs for EMAP, PIBO, WDOE, added
features and other refinements for a new generation version of the
application. The previous version of Protocol Manager was made available
for review and comments pertaining to form, functionality, and exploring
for future utility online at Streamnet for download, or by obtaining a
copy on CD from the Bureau of Reclamation PN Region office.

In hopes of gaining a wider audience for Protocol Manager and eventually
finding a suitable institution and address for the application to be
useable via internet for potential users, we have continued to meet and
demonstrate Protocol Manger with other interested parties including
Northwest Environmental Data Network (Stewart Toshach), Pacific
Northwest Aquatic Monitoring Partnership (Jen Bayer), Washington
Interagency Committee (Bruce Crawford), StreamNet (Mike Banach), Pacific
States Marine Fisheries Commission (Bruce Schmidt), USGS NBII (Jennifer
Pollock/Lisa Zolly), SWIMTAC (Joy Paulus), and International Association
of Fish and Wildlife Agencies (Russ Mason). Bureau of Reclamation
GIS'ers were also in attendance and participated in the Resources for
Coordinated Monitoring Workshop for developers, contributors, and users
of a future web-based monitoring protocol library and monitoring
locator.

Other miscellaneous tasks include: filling requests for data and
imagery, maps, data management, inventory, and user support.

Project investigators: Kristin Swoboda, Melanie Panquin Boler, and
Michael Beaty of the Bureau of Reclamation, Boise, ID.

[]{#__RefHeading___Toc125779694 .anchor}**4.0 Information Needs**

While there are large number of agencies and people conducting
monitoring and research in the John Day, the shear size and complexity
of the basin assures that large information gaps will exist. During
2005, we have made an attempt to determine what other fish and habitat
characteristics need to be monitored in order to achieve the pilot
project goals. In addition, we have queried several individuals involved
in RME efforts about information they believe is lacking or not feasible
to collect under current workloads. We have begun to document these
information gaps and in some cases we have initiated this monitoring or
begun to identify who might be best situated to incorporate this
monitoring if provided the necessary assistance.

Because of the size of the John Day basin, an increase in monitoring for
habitat and salmonids will always improve estimates of status and
trends. However, the increase in effort is dependent on the goals, the
heterogeneity of the sampled landscape, and the precision of the
protocols. As more information is collected, we will have the ability to
address these issues and provide some suggestions on sample size
requirements (see Monitoring Design section).

[Population structure and distribution.]{.underline} The current
monitoring program for status and trends monitoring for adult salmon and
steelhead was developed prior to the determination of the number of
populations delineated by the Biological Review Team. ODFW believes that
further information (e.g. genetic data) is needed to better define these
populations. Therefore, ODFW has begun to collect tissue samples for
genetic information to help define population structure in the John Day
basin for salmon and steelhead (see Chinook Status and Trends of
Juvenile and Adult Salmonids section above). Once population structures
have been further defined, ODFW believes the current spawner and redd
sampling design should be reconsidered to provide better coverage for
all populations.

[Hatchery/wild fish interactions.]{.underline} Several fisheries
biologist believe that hatchery salmon and steelhead can have negative
impacts on their wild counterparts. This can occur because of genetic
mixing, competition, and increase in incidental fishing mortality of
wild fish mediated through a fishery on hatchery stocks. This impact was
thought to be minimal in the John Day because there is currently no
salmon and steelhead hatchery production in the basin. Hatchery fish are
distinguished from wild fish by observing spawning adults or carcasses
with or without the adipose fin, (fins are generally clipped at the
hatchery). Because spawners and carcasses can be difficult to observe,
the proportion of spawners that are from hatcheries is difficult to
determine. ODFW has been documenting, however, an increase percentage of
stray hatchery salmon and steelhead from other drainages arriving on
spawning grounds in the John Day. In 2005, they found 30% of steelhead
spawners were finned clipped (4 out 12 carcasses recovered). Methods to
monitor the proportion of hatchery strays into the basin need to be
improve to assess the potential problems they may cause for wild fish.

[Fish passage monitoring.]{.underline} The Subbasin Plan states that
there is a need for an inventory of the fish passage barriers in the
basin. ODFW believes that the impact of these barriers needs to be
estimated. The impacts of push-up dams are currently being evaluated in
the South Fork IMW (see South Fork IMW section below). This study may
provide insight on the impact of these structures on fish production;
however, these represent a fraction of the types of obstruction faced by
salmonids throughout the basin.

[Salmon and steelhead parr movement.]{.underline} A life-history
strategy for salmon and steelhead parr that is appearing to be more
common in this system than observed in other drainages is seasonal
movement between mainstem and tributaries during rearing (see *Juvenile
Salmon and Steelhead Production* section). This strategy was also noted
in the Subbasin Plan, and the inability to account for this in the
Ecosystem Diagnostic and Treatment model (EDT, developed more for west
Cascade populations) was identified as a model deficiency. While OSU is
conducting intensive research in the South Fork of the John Day, ODFW
believes that a broader understanding of this strategy throughout the
basin is needed to help understand the impacts of obstructions and
irrigation practices on these populations.

[High resolution remote sensing]{.underline}. LiDAR and FLIR information
is being used for the TMDL process, the IMWs, and research evaluating
causal relationships. As suggested in the Subbasin Plan, we believe
greater coverage of fine resolution riparian vegetation would be useful
in defining these relationships and provide a boarder scale assessment
of habitat condition in the basin. LiDAR and FLIR are appearing to be
useful tools in collecting high resolution data over the scale of a
watershed (see *John Day River Basin Airborne TIR Remote Sensing &
Spatial Data Analysis Project* section above). In addition, this tool is
being compared to multiple protocols of on-the-ground habitat surveys
(see *Comparison of Aquatic Survey Protocols Used to Measurement of
Physical Habitat Attributes* section below). This study may suggest that
this tool can replace some on-the-ground measurements allowing for a
greater time spent collecting information not obtainable from remote
sensing. For example, if this tool can estimate several of the metrics
surveyed in ODFW's *Habitat and Riparian Survey* (see above) then they
have suggested that in areas where LiDAR has been conducted, ODFW
habitat monitoring could be greatly reduced allowing for more time and
coverage of snorkel surveys. LiDAR and FLIR coverage was expanded in
2005. Greater coverage of this remote sensing technique will be pursued
in the near future.

In addition to LiDAR and FLIR, investigations are ongoing as to the
ability to characterize riparian habitat using LANDSAT satellite imagery
(see *Landscape Changes and Patterns Project* section above). This
information may also be useful in relating landuse practices to riparian
habitat condition (see *Linking Landscapes to Riparian Process* section
below).

[Water Quality Sampling]{.underline}. While some monitoring of water
quality is conducted in the basin (see *Water Quality Monitoring*
section above), as identified by the Subbasin Plan, the effort is not
sufficient to capture temporal and spatial variability of this
information. Continuous monitoring hydrolabs can be use to increase
temporal resolution of water quality monitoring. ODEQ and CTWSRO have a
few hydrolabs deployed throughout the John Day. The Monument SWCD has
agreed to implement three more hydrolabs as part of pilot project (see
*Other Water Quality Monitoring* section above) to help increase the
coverage of this continuous information.

[Invertebrate Monitoring.]{.underline} An index of water quality can be
estimated through invertebrate community composition. Invertebrate
samples are collected to a limited extent in the John Day basin by
CTWSRO, ODEQ, PIBO, and OSU Extension. The Subbasin Plan suggests an
increase in invertebrate monitoring to better understand the prevalence
of water quality issues through out the basin. These monitoring efforts
need to be more coordinated to ensure that derived indices are
consistent and comparable.

The invertebrate monitoring that is conducted in the John Day basin and,
for the most part, throughout the Columbia River basin, is used to
describe water quality. However, invertebrates are the major food source
for juvenile and adult salmonids. Because these monitoring programs are
mainly developed to assess salmonid production, effort should be made to
monitor their food resources, perhaps the greatest predictor of salmonid
production. In 2005, the pilot project initiated a study that will
investigate whether invertebrate samples can provide information
relating invertebrate abundance to salmonid production (see
*Invertebrate Productivity Monitoring* section below). This study
collected drift net invertebrate samples, and benthic samples using the
same protocols as PIBO. In addition, PIBO agreed to add drift net
sampling to their invertebrate monitoring protocol for 35 sites
throughout their study domain. This will be used to help determine the
most efficient approach of incorporating invertebrate production
monitoring into standard invertebrate sampling protocols. In addition,
this study evaluated spatial and temporal variability in invertebrate
sampling. This information will potentially be used to develop an
invertebrate monitoring program throughout the Columbia River Basin.

[Predation and food web interactions.]{.underline} Several species of
native and non-native fish species live in the John Day system. Very
little is known about how these species interact with anadromous fish.
Some of these are warm water species that are potential predators to
juvenile salmonids, such as smallmouth bass, catfish, and pikeminnow,
especially in the migration corridor that all anadromous species must
pass. Others, such as dace, shiners, and young pikeminnows, might
compete with juveniles for food resources. If so, parr density alone may
not be the most relevant response variable, if inter-specific
competition is just as important as intra-specific competition. Many
warm water species seem to be expanding their range, which may further
complicate the success of some restoration actions. Conversely,
restoration actions that decrease temperature may decrease warm water
species use of certain habitats and may provide an added benefit to
these types of actions. ODFW and the Subbasin Plan have both suggested
that we develop monitoring programs to provide better insight into these
interactions. We have considered developing studies to determine whether
a monitoring program that includes diets and tissue or blood stable
isotopes samples, and behavioral observations to gain insight into these
interactions. OSU has already initiated some behavioral studies in an
attempt to understand some of these food web interactions. In addition,
the US Fish and Wildlife and USU initiated a study in 2005 to evaluate
the proportion of bull trout production that is result of consumption of
marine derived nutrients from salmon and steelhead adults and parr.

[Sources of Variability.]{.underline} In order to understand the
influence of different sources of spatial, temporal, and sampling
variability of monitoring design requires pilot or initially intensive
protocol tests to estimate the magnitude of these sources. This is
addressed in the *Reliability and Relevancy of Monitoring Information*,
and *The Monitoring Design* sections (below).

[Compliance monitoring.]{.underline} Although not a monitoring component
of the pilot project that has been given much consideration, several
biologist in the region suggest that the largest impacts to fish and
their habitat is due to non-compliance of grazing and irrigation
management strategies. Cattle are often found in the stream valley
bottoms, often fens or wetlands, when they should be on higher land as
prescribe by grazing management guidelines. This results in a compaction
of soil that reduces subsurface/surface water interactions (leading to
increased temperatures) and increased sediment inputs. Currently, there
are two range managers patrolling for grazing compliance on public lands
in the entire John Day basin. Also, irrigation use often exceeds
allotments due to high loss of water to highly permeable irrigation
channels or over-usage. This causes reduced flows and increased
temperature. Further, ODEQ conducts limited point source water quality
compliance monitoring for agriculture runoff, but this may insufficient
to document the potential problems of non-compliance point source
pollution. Further compliance monitoring may provide insight into the
prevalence of these problems, as well as to provide a means to invoke
management practices that should already be implemented.

[Implementation monitoring and project inventory.]{.underline}
Retrospective analyses are often used to evaluate the effectiveness of
restoration actions. Thus, an inventory of restoration actions would
greatly increase the ability to conduct these analyses. This, off
course, also requires knowledge of whether or how long the project was
still functioning, and thus the need for implementation monitoring.
Also, knowledge of where these restoration actions reside can help plan
where large scale restoration experiments can be conducted. The Subbasin
plan identifies inventory of restoration projects as a high priority
data need. Much of this inventory was conducted in 2005 (see *Collation
of GIS and Tabular Datasets* section above). In addition, some of these
sites are being revisited to determine if the projects are still
functioning.

[Effectiveness Monitoring]{.underline}. Perhaps the most common
information need identified by people we queried, is response
information of habitat and salmonids to restoration efforts. An
extremely small proportion of restoration actions have associated
effectiveness monitoring programs to measure whether the actions are
having the intended response. Some habitat effectiveness monitoring is
being conducted by the CTWSRO, BLM, and the FS. Although a majority of
restoration actions are targeted towards increasing freshwater
productivity of salmonids, there are virtually no monitoring programs in
the basin that have the ability to evaluate the impact of stream
restoration on their target population. This is not surprising because
the majority of funding has been allocated to implementing restoration
projects with very limited funding for effectiveness monitoring. The
long term result of this approach could prove to be expensive as we
continue to devote funds to projects we are unsure are beneficial. The
lack of effectiveness monitoring is also unfortunate in that each
project represents an experiment that could have provided insight into
how fish respond to changes in manipulated environmental conditions. A
greater understanding of these relationships will lead to a greater
success in the management of these species.

Developing an RME program to evaluate the effectiveness of stream
restoration projects is the main focus of the pilot projects. The
approaches used to address this goal are discussed below in the
*Determining the Effectiveness of Restoration Actions* section.

[]{#__RefHeading___Toc125779695 .anchor}**5.0 Reliability of Monitoring
Information**

The ability for a sample to provide accurate and precise information
depends heavily on the protocol used to collect that sample. Therefore,
monitoring programs need to adopt protocols that are the most
informative and precise. However, another factor that must be included
in the decision of what sampling protocol to use in a monitoring program
is the associated cost (e.g. time, money, effort, etc.). Therefore,
before a monitoring protocol is implemented a decision should be made to
balance this trade-off. This requires testing the accuracy and precision
of several protocols. Currently, there are multiple monitoring protocols
to measure the same general characteristic. Often these protocols differ
enough that, although they are attempting to describe the same general
characteristic, they produce different information. These differences
produce inconsistencies that make detection of changes difficult or
information that is not even comparable. Effort is being spent in the
John Day to determine or develop protocols that provide the best
information but maintain a reasonable cost to implement.

[]{#__RefHeading___Toc125779696 .anchor}**5.1 Comparison of satellite
remote sensing information collected at different levels of
resolution.**

As part of the *Landscape Changes and Patterns Project* (described
above), remote sensing data derived from the LANDSAT 5 Thematic Mapper
sensor is being employed to assess relationships between land cover and
fish habitat throughout the Columbia River Basin. The LANDSAT 5 TM
collects data from 185 km wide swath at 30m pixel resolution. This
information has been collected over 15 times a year since 1984 for the
entire Columbia River basin. The BOR has purchased 15 scenes for 1984,
1988, 1992, 1996, and 2000 for the entire Columbia River basin. IKONOS
high resolution multispectral imager collects information across an 11
km swath at up to a 1-4m pixel resolution. While this information has
higher resolution, the temporal coverage is limited, and it is much more
expensive than LANDSAT 5 images. BOR has also purchased IKONOS data for
the upper Salmon drainage. LANDSAT 5 imagery is being compared to IKONOS
data where they overlap. The objective of this analysis is to determine
what information is lost by using the coarser resolution yet less
expensive LANDSAT 5 data and whether the coarser resolution is adequate
to describe changes in landscape and landuse patterns evaluations used
in the pilot projects.

[]{#__RefHeading___Toc125779697 .anchor}**5.2 Comparison of Aquatic
Survey Protocols Used to Measurement of Physical Habitat Attributes
within the John Day Basin Oregon.**

Physical stream attributes are collected across multiple habitat
monitoring programs, often within the same basin. These programs often
use different protocols to estimate several stream characteristics.
Although different programs may attempt to describe the same given
characteristic, the protocols often differ enough that the information
obtained is not comparable. A recent review described 429 different
protocols for the measurement of aquatic habitat in the Pacific
Northwest. Each protocol has strengths and weakness in describing the
status and change of habitat conditions but little quantitative
comparative information currently exist. Standardization of aquatic
habitat monitoring protocols would facilitate data sharing, decrease
cost, and increase the ability to detect changes.

To date there have been few comparisons among different habitat
monitoring protocols to determine their precision and accuracy, and how
comparable they are to each other. The objective of this study would
be 1) determine how accurately and precisely different protocols
describe physical stream characteristics, 2) determine if they can
discriminate between streams that have been subject to anthropogenic
disturbance and those that have been minimally disturbed, 3) compare
applicability of protocols over an array of stream types, and 4) compare
fine scale to coarse scale protocols, and 5) determine whether cross
walks can be made between different protocols.

This study was led by the USFS PIBO program and in collaboration with
the Pacific Northwest Aquatic Monitoring Program (PNAMP), and the John
Day pilot project. In this study, several of the most widely used stream
survey protocols within the Columbia River Basin were used to survey a
common set of sites. To ensure sufficient data to evaluate crew
variability, at least three crews from each evaluated protocol collected
habitat and physical information at each site. A total of 12 sites in
the John Day drainage were surveyed with these protocols; four sites
falling into each of the following geomorphic reach types; 1) step-pool,
2) pool-riffle, and 3) plane bed. The work was conducted during the
summer low flow period (mid July to mid September 2005). In addition to
these protocols a more precise method (engineering survey) was conducted
to establish a benchmark or 'truth'. Results from this study will be
used to help RME programs decide on the protocols used to collect
habitat information.

The ability of LiDAR remote sensing to estimate habitat and physical
variables has just begun to be evaluated. Because LiDAR can accurately
measure canopy cover, geomorphic features and cover large areas in a
relatively short time, there is potential for this remote sensing
technique to be highly useful in estimating relevant physical
information (see *John Day River Basin Airborne TIR Remote Sensing &
Spatial Data Analysis Project* section above). However, this method has
not been evaluated against on the ground surveys, and thus the ability
of this tool to measure commonly measured variables to characterize
habitat is unclear. Therefore, in addition to these surveys, LiDAR
surveys were conducted at each site to make these comparisons.

The following groups participated; PIBO, AREMP -- Aquatic Riparian
Effectiveness Monitoring Team (FS), ODFW (protocols used in the John Day
pilot project), ODEQ, WFW -- Washington Fish and Wildlife, CFW--
California Fish and Wildlife, NWIFC--Northwest Indian Fisheries
Commission, R6 -- Region 6 (FS), Upper Columbia Monitoring Program
(protocols used in the Wenatchee pilot project), RMRS -- Rocky Mountain
Research (FS), and LiDAR was conducted by Watershed Sciences.

[]{#__RefHeading___Toc125779698 .anchor}**5.3 Protocol tests and
development of the Juvenile Salmon and Steelhead Production project**

[]{#__RefHeading___Toc125779699 .anchor}**5.3.1** *Devising less
stressful fish capturing techniques*

Capturing salmonids is necessary for tagging, measuring, and
mark-and-recapture population estimations. Three alternative methods of
counting and capturing fishes, believed to be less stressful to the
fish, were compared to electrofishing: (1) visual counts by divers, (2)
divers chasing fish into bag seines (3) chasing fish into bag seines
using an electrical probe that emits an irritating electrical field, but
not one set to stun. The following metrics were used to assess stress:
burn marks, broken backs, recovery time (postural orientation), the
ability of fish to hold in current, and the percent of captured fish
that became moribund. All the new techniques demonstrated to be less
intrusive. OSU then cross calibrated gears so that we could provide
estimates of population abundance with 95% confidence intervals. See
*Calibration of Fish Sampling Techniques* results (below).

[]{#__RefHeading___Toc125779700 .anchor}**5.3.2** *Diver Effects on Fish
Behaviors*

Snorkel surveys are a common method used to observe fish behaviors and
to count fishes. If the presence of a diver affects their behavior, then
the accuracy and precision of this method will impacted. OSU evaluated
the response of RBT parr to divers by placing a remotely controlled
video camera in the stream and waiting 30 minutes before attempting to
enter the stream again. They found that fish will scatter into cover
even when a diver enters the stream two pools below the pool to be
inventoried. Calibration techniques are being used to address this
problem. In addition, OSU is now using video camera to record fish
behavior (resource and interference inter and intra-specific competitive
interactions as a function of density).

[]{.mark}

[]{#__RefHeading___Toc125779701 .anchor}**5.3.3** *Calibration of Fish
Sampling Techniques.*

Different protocols for estimating fish density have different biases
that affect their accuracy. However, different protocols are used to
effectively sample all habitats. OSU conducted a calibration study in an
attempt rectify the biases of different gears to count fishes. Block
nets were used close the upper and lower boundaries of the habitat unit
to fish passage. First, snorkel and visual counts were conducted. Then a
diver was used to herd the fish into a bag seine ("snorkel-herding").
Several passes are made until no trout are captured and the number of
other species drops below a target number. The fish were weighed,
measured, inspected, and marked by partially clipping the caudal fin or
PIT-tagged and then released. After 45 minutes to allow the fish to
redistribute, the same habitat was resample by chasing fishes into the
bag seines with an electrical probe emitting an electrical field at low
settings ("electro-herding"). Counts of the number of marked and
unmarked fish were made, and population size (and 0.95 CI) was estimated
using a Peterson Estimate. Preliminary results suggest that
approximately 25%, 35%, 45% of the estimated population size were
detected by visual counts, snorkel-herding, and electro-herding,
respectively. Moreover, the percentage of underestimated fish especially
by visual counts is greater in habitats supporting more fish, because of
the abundance of cover. OSU is continuing to conduct calibrations of
gear types in different habitats. This information will result in more
accurate estimates of population sizes within various stream reaches and
subbasins.

Rotary screw traps are used throughout the Columbia River basin to
estimate smolt production. ODFW operates rotary screw traps on the
downstream extent of the Upper Mainstem, Middle Fork, South Fork, and
North Fork to measure basin-specific smolt production (see *ODFW
Freshwater Production Monitoring* section above). As a common practice
to estimate capture efficiency of the screw traps (which only sample a
portion of the channel), fish captured in the trap are marked and then
release above the trap. The proportion of those marked fish and captured
again in the screw trap represents the trap efficiency. Because the
capture probability is generally low, this method can be somewhat
imprecise. In addition, the timing and location of release may also
affect the estimate of capture efficiency if these factors influence
fish migration behavior.

In the South Fork John Day, PIT tag antennas have been installed just
above the screw trap (see *South Fork John Day Intensively Monitored
Watershed* section below). OSU is working with ODFW to use the antennas
that have nearly 100% detection probability to calibrate the screw trap
efficiency. Several thousand fish have been PIT tagged above the
antennas and traps in 2004-2005 as part of the *Juvenile Salmonid
Production* project (described below). The proportion of the PIT-tagged
fish detected at the antennas found in the screwtrap (fish in the trap
are scanned with a hand held antenna) immediately downstream will
represent the capture efficiency of the trap. Further, tagged and
non-tagged fish are released in three different treatments: 1) release
occurs as commonly practiced \-- fish are released 1 mile upstream from
the trap the morning they are removed from the trap; 2) release occurs 4
miles upstream to give fish more time to adjust to the potential stress
of handling before encountering the screw trap location; 3) held at the
1mile upstream site in a cage equipped with an automated release system
that can be set to release at night, when juvenile salmonids normally
migrate. Difference in these treatments might suggest that attention to
capture induced behavioral modification can alter trap efficiency
estimate that need to be considered.

[]{#__RefHeading___Toc125779702 .anchor}**5.3.4** *Validating the
precision and accuracy of the fat meter*

Different metrics may be used to characterize a fish's responses to its
environments, such as standing crops, production and physiological
indicators. Fat is an important body constituent and is often used to
evaluate relative health condition of a fish. There is a strong
correlation between fat content (accumulated throughout the summer based
on the foraging success of a fish) and overwinter survival. Insufficient
amounts will lead to high winter mortality rates due cardiovascular
failures (e.g., Biro et al 2004). In the past, the Soxhlet fat
extraction procedure has been the methods used to determine fat content
of fish. This is a time consuming process that requires sacrificing the
fish, bringing it to the lab, and rinsing all body tissues with organic
solvents to separate out the fats. The Distell Fat Meter has been
reported as an accurate, quick, non-lethal means of determining of fat
levels of individual fish in the field (Crossin and Hinch 2005). This
meter would make the determination of fat content as quick and easy as
measuring fish length, allowing the processing of literally thousands of
fish in the field. OSU compared the Distell Fat Meter to the Soxhlet fat
extraction procedure in the laboratory. Unfortunately, they found that,
while it may be accurate and precise on larger fish, it lacked precision
on parr less than 12 cm. Therefore, percent body fat was regressed on
percent dry weight to get a conversion coefficient from which to predict
fat percent fishes in the future. This will save time and money.

[]{#__RefHeading___Toc125779703 .anchor}**5.3.5** *Tests of heat shock
protein induction*

Heat shock proteins acts as a chaperone to keep other proteins
functional by maintaining their configuration and structure. Heat shock
proteins can be used to detect thermal stress in fish. This technique
was used in the *Juvenile Salmonid Production projec*t as a means to
describe the impacts of stream temperatures on the stress of juvenile
*O. mykiss.* However; other factors such as disease infections and other
physical stresses can also induce the formation of the protein.
Therefore, if capturing and handing of the fish also induces heat shock
proteins then the use of heat shock proteins to document whether fish
are stressed from the environment is confounded. OSU found that
electrofishing, handling and tagging did not induce heat shock proteins,
allowing heat shock proteins to be a potential useful descriptor of
whether the environment is stressful to a fish.

In addition, tests of heat shock protein 70 induction were conducted in
controlled laboratory experiments at the Smith Farm Experimental Fish
Laboratory at Oregon State University. Redband trout (*O. mykiss*) held
at 13^o^C were subjected to acute exposure at three higher temperatures.
These are then used as reference points to help interpret heat shock
protein from trout collected in the field. Field data findings appear to
be consistent with the laboratory work.

Because the focal species is listed under ESA, OSU was interested in
developing nonlethal methods to determine relative concentrations of
heat shock proteins being induced. The standard ELISA (Enzyme Linked
Immunosorbent Assay) assay requires lethal sampling. OSU developed the
Western Blot technique, a nonlethal fin clip sample is analyzed instead
of muscle or liver tissue. Not only did the Western Blot produce similar
results as the ELISA approach, it was also found to be more precise.

[]{#__RefHeading___Toc125779704 .anchor}**6.0 Monitoring Design**

While accurate and precise sample protocols are needed, the ability to
extrapolate a collection of sample to provide an accurate assessment at
the appropriate scale is dependent on the sampling design. The sampling
design describes, where, when, and how much to sample. The design is not
only dependent on the protocols used to collect the information but how
the information will be used. For example, to describe the status of a
resource distributed broadly across a landscape, samples need to be
collected across this area in a randomized design to prevent from
inducing biases through deliberate site selection. This sample may
require some stratification strategies to ensure greater representation
of important differences. The EPA's Environmental Monitoring and
Assessment Program (EMAP) has developed a spatially-balanced,
site-selection process developed for sampling aquatic systems (U.S. EPA
2000). A grid system for the U.S. was created to describe potential site
that can be random selected in spatially-balanced design. All grids are
interpenetrating so that a lower density grid is a subset of all higher
density grids. Therefore, the EMAP site-selection approach can be used
to develop sampling designs at varying spatial extents.

Estimation of trends of a resource requires a different sampling design
where sites are revisited to detect change through time. Sampling
designs also have been develop to evaluate both status and trends by
using rotating panels (Stevens 2002). A panel represents a collection of
sites that will have the same revisit schedule over time. In this
design, sites in one panel are selected using the EMAP design and
visited say every year, sites in another panel are selected and
revisited every five years, and so on. This is design employed the ODFW
status and trend monitoring of habitat, juvenile salmonid distribution,
and adult abundance and distribution as well as the PIBO habitat
monitoring design (see above).

Effectiveness monitoring will require yet a different design. Sites will
have to be selected to provide contrast and visited through time to
evaluate trends (Determining the Effectiveness of Restoration Actions
section below).

Determination of when to sample and at what frequency should be relevant
to the dynamics of the resource in question. Obvious examples include
conducting annual adult carcass surveys during the spawning season and
deployment of screw traps when juveniles are migrating. Some responses
are not obvious when they should be sampled. For example, habitat
surveys conducted earlier in the year may produce different information
collected later in the year due to growth of vegetation or a change in
stream discharge. The frequency of sampling should also consider the
relevant temporal variability associated with the resource. Riparian
vegetation may not exhibit much year-to-year change and require sampling
perhaps every 5 yrs. Conversely, some metric change fairly rapidly (e.g.
temperatures) and must be sampled at a much higher frequency to capture
these dynamics. When samples are collected is often simply a result of
logistical constraints, such as the timeframe that a stream is
accessible and the time budgets of a monitoring crew. Where possible,
assessing temporal fluctuations should be considered in the development
of a monitoring program. For example, in the Invertebrate Production
Monitoring project (describe below), samples were taken over three
dates, over a three day period, at three times throughout the day to
evaluate season, daily, and diurnal temporal variability. This may
provide insight as to when to invertebrate monitoring should occur.

[]{#__RefHeading___Toc125779705 .anchor}**6.1 Status and trend
monitoring sample size**

The number of samples that need to be collected is dependent on the
different sources of variability relative to the difference you are
trying to detect. Status and trend monitoring information has only been
collected by ODFW for 2 years so the ability to address sources of
variability and its implication on sample designs is limited. However,
some the above protocol tests may adequately describe different sources
of variability and allow for investigations into required sample sizes.
Based on the results of the testing of protocols and information from
current monitoring efforts, we are assessing whether monitoring efforts
are sufficient to detect defined differences.

In many instances, implementation of a RME program to a given area is
not possible for several reasons, such as land access or limitation of
funds and personnel. Therefore, results from elsewhere have to be used
to form our best judgment for management strategies. Proper sampling
designs allow for a greater ability to extrapolate to other studies.
Different methods of analyzing this information can also increase the
generalities. Other methods can be used to help extrapolate what is
learned from areas intensively study. One method is described in the
next section. The other is understanding causal mechanisms of how
environmental factors interact and how fish interact with these factors.

[]{#__RefHeading___Toc125779706 .anchor}**6.2 Watershed Classification**

In conjunction with the development RME monitoring program within the
target basins, NOAAF staff have initiated a watershed classification
project to group similar 6^th^ field watersheds (Hydrologic Unit Code 6,
HUC 6) in the Pacific Northwest according to watershed characteristics.
MCLUST software will be used to classify spatially continuous GIS data
such as precipitation, temperature, watershed topography, geology, and
hydrography metrics into discrete classes. This regionally continuous
classification of watersheds can be used to determine if the selected
Intensively Monitored Watersheds equally represent the landscape
diversity. Additionally, the classification effort will be useful in
determining where RME IMW pilot basin production outcomes and
restoration effectiveness monitoring designs and protocols will be most
applicable and effective. In addition, this classification scheme may be
provide a way to stratify monitoring sites for analyses, and as a means
to extrapolate the results beyond the boundaries of the study.

Subsampling of census data is another approach to evaluating the least
bias sampling design. For example, NOAAF evaluated the bias of six
common sampling strategies for estimating the total abundance of redds.
Routines simulating the strategies were used to subsample a
georeferenced dataset of a census of Chinook salmon redds from a large
wilderness basin in central Idaho. Subsamples were extrapolated to the
basin and compared against the 'truth', i.e. the census to reveal the
biases of the different strategies.

[]{#__RefHeading___Toc125779707 .anchor}**6.3 Reach Classification and
Sample Effort**

A similar classification approach was applied to the Juvenile Production
Project in the South Fork John Day. In 2004, a census of habitat and the
distribution of fishes was conducted in the SFJD, Black Canyon, Deer,
and Murderers creeks. In addition, data were gathered from FLIR imagery,
LiDAR images depicting vegetation structure, channel morphology and
basin topology, aerial photographs of push-up dams, multispectral
satellite imagery and maps identifying patterns of public ownership.
This information was used to define discrete reaches that formed the
sampling strata that greatly reduced sample variance. Sentinel sites
(defined as a 5 pools sequence) or sites that would be sampled
repeatedly, were established based on this strata. These six sentinel
site represents the range of thermal and habitat characteristics and
allow for contrasts to be made between different habitat features to
elucidate their influence on the different fish responses. In addition,
bootstrapping procedures were used to sample the census information to
determine the number of samples required to produce an accurate picture
of the census area with a defined level of statistical power. This
sample size estimate and design was used as the sampling design in this
study.

[]{#__RefHeading___Toc125779708 .anchor}**7.0 Developing Baseline Causal
Relationships and Descriptive Indices**

Critical to the management of fishes, is an understanding of mechanistic
relationships between fish and their habitat and community and how
larger scale processes provide the template for such relationships
(Fausch et al. 2000). These relationships form the basis of what metrics
and indices need to be collected in a monitoring program. For example,
percent fines of a reach is a metric estimated in most habitat surveys
because fine sediments can potentially influence the spawning success of
salmonids (Tappel and Bjornn 1983, Chapman 1988). Synthesis of these
relationships also can be used to determine limiting factors.
Identification of limiting factors is necessary for prescriptions of
stream restoration efforts and management strategies to enhance fish
production.

Several studies already described in this document are developing
several causal relationships, such as the *Landscape Changes and
Patterns Project, John Day Basin Sediment Assessment, EMAP- Western
Pilot Project, the John Day TMDL process*. Below we describe other
studies also evaluating causal relationships. These relationships will
be further tested by evaluating whether these patterns are still
observed with broader scale information produced from status trend
monitoring, or in the responses to restoration experiments.

[]{#__RefHeading___Toc125779709 .anchor}**7.1 Linking Landscapes to
Riparian Process: Exploring Landscape Scale Influences on In-Stream
Water Temperature Using Remote Sensing**

In-stream water temperatures are regulated by a variety of intrinsic and
extrinsic environmental variables dictated by the natural and
anthropogenic state of the stream system. Changes in riparian
vegetation, irrigation water management practices and channel
engineering are all factors that influence the net heat energy and
amount of water delivered to a stream, but how do environmental patterns
in the surrounding landscape affect in-stream water temperatures? What
can landscape scale variables lend to the accuracy and effectiveness of
existing habitat models for predicting fish productivity and
performance?

Current methods used to assess thermal dynamics in streams focus on the
temperature patterns within the riparian corridor and how they are
regulated by local environmental variables including buffer extent, land
use and water rights, ambient temperatures, and stream channel
morphology. However, the spatial and temporal variation of these
riparian variables is governed at a broader scale by the composition and
configuration of the upland landscape. How can we link what can be
observed at a riparian level to what can be mapped at a landscape scale
using remote sensing? Are there predictable relationships between
patterns in the landscape and processes in the riparian zone?

As part of the *Landscape Changes and Patterns Project* (described
above) an integrated remote sensing and GIS-based approach will evaluate
how landscape scale environmental patterns of vegetation cover and land
use are related to riparian function and measured in-stream water
temperatures. Satellite image-derived indices of vegetation cover and
surface wetness will be used to quantify the temporal and spatial
variability in landscape composition and heterogeneity at a riparian
corridor, sub-watershed and watershed scale. Scaleable metrics that
describe patch complexity across the landscape will be tested for
correlation with riparian corridor variables to explore potential
landscape-riparian linkages. An existing time series of in-stream water
temperature data will be evaluated against the newly derived metrics of
landscape complexity to assess how landscape scale factors relate to
measured in-stream variables.

Satellites provide continuous synoptic images of earth at optimum
resolutions for monitoring landscape scale patterns and processes over
time and space. Understanding how the landscape communicates with and
connects to the riparian corridor can help explain how anthropogenic
processes (irrigation, clear cutting) across a landscape influence
in-stream characteristics (like water temperature) and ultimately
habitat suitability. The goal of this research is to determine how to
best capitalize on landscape scale satellite data to derive useful and
scaleable measures of landscape pattern that are relevant to riparian
process and ultimately to the suitability of fish habitat. Can remotely
sensed variables improve our predictive capabilities with regards to
habitat function, fish performance and restoration potential?

[]{#__RefHeading___Toc125779710 .anchor}**7.2 Juvenile Production
Project**

Ultimately, the response desired from habitat mitigation in the John Day
Basin is an increase in population growth rates of target species.
However, given the tremendous distances traveled by anadromous species
and the range of environments encountered, isolating the signal from
changes in spawning and rearing habitat by only evaluating population
growth rates will likely be impossible. The most informative population
response relative to early life stages is freshwater productivity. Here
freshwater productivity is defined as the number or biomass of smolts
per female. This measure of productivity can be further divided to
growth and survival over different life stages in the spawning and
rearing habitat.

In 2003, OSU initiated a study, funded by the BOR and NOAAF, to develop
an index of carrying capacity of salmonids on the basis of longitudinal
stream temperatures. This study was broaden to evaluate whether other
habitat characteristics can help in indexing carrying capacity as was
summarized in the AFG document (Bouwes 2004). The main objective of this
project was to evaluate whether habitat and temperature attributes can
explain changes in fish behavior (e.g. densities and movement), growth
and other physiological conditions, survival, and ultimately production.
This also included plans to evaluate the effects of push-dams and their
removal on these fish responses. The John Day pilot project has
collaborated with researchers of this project agreeing to use these
studies to comprise the majority of the research of South Fork John Day
Intensively Monitored Watershed study (described below).

This study was conducted in the South Fork John Day and its tributaries
accessible by anadromous salmonids. Information was used to define
discrete reaches that formed the sampling strata (*Reach Classification
and Sample Effort* described above). Sentinel sites (defined as a 5
pools sequence) or sites that would be sampled repeatedly, were
established based on this strata. These six sentinel site represents the
range of thermal and habitat characteristics and allow for contrasts to
be made between different habitat features to elucidate their influence
on the different fish responses (Table 3).

Passive Integrated Transponder (PIT) tags are used to uniquely identify
individual fish so that they can be followed through time and space in
order to document their thermal and habitat experiences and relating
them to these responses. Snorkel surveys are also used to evaluate fish
density as a function of habitat characteristics. Also, physiological
indices such as fat content and heat shock proteins are being compared
against different habitat types. Components of this study have been
conducted year round and have revealed potentially important life
history strategies that would not have been observed if the study were
seasonal (i.e. summer). Finally, a study to develop a metric that
relates invertebrate prey availability to fish growth at the sentinel
sites was initiated by ELR and Utah State University in collaboration
with the John Day pilot project and the OSU study (see *Invertebrate
Production Monitoring* below).

Table 3. Data from redband trout PIT-tagged from 15 sentinel pools in
each of Black Canyon and Murderers creeks allocated in 3 types of valley
segments. L = lowest valley segment, M = middle valley segment and U =
upper valley segment.

+------------------+--------+--------+------+------+--------+------+
|                  | *      |        |      | **M  |        |      |
|                  | *Black |        |      | urde |        |      |
|                  | Canyon |        |      | rers |        |      |
|                  | C      |        |      | Cre  |        |      |
|                  | reek** |        |      | ek** |        |      |
+------------------+--------+--------+------+------+--------+------+
| Valley Segment   | L      | M      | U    | L    | M      | U    |
|                  |        |        |      |      |        |      |
| (5 sentinel      |        |        |      |      |        |      |
| Pools each)      |        |        |      |      |        |      |
+------------------+--------+--------+------+------+--------+------+
| Thermal Infrared | 19     | 14-16  | 11   | 24   | 22     | 19   |
| Temperature      |        |        |      |      |        |      |
| (C^o^)           |        |        |      |      |        |      |
+------------------+--------+--------+------+------+--------+------+
| \% Pool Resident | 22.5   | 16.2   | 15.4 | 20.0 | 10.9   | 10.3 |
| Trout            |        |        |      |      |        |      |
+------------------+--------+--------+------+------+--------+------+
| Summer 04 Growth | 0.08   | 0.17   | 0.17 | 0.11 | 0.24   | 0.25 |
| (mm)             |        |        |      |      |        |      |
+------------------+--------+--------+------+------+--------+------+
| \% PIT-tagged    | 7.5    | 7.1    | 4.3  | 12.1 | 6.9    | 7.1  |
| trout detected   |        |        |      |      |        |      |
| at               |        |        |      |      |        |      |
|                  |        |        |      |      |        |      |
| John Day Dam     |        |        |      |      |        |      |
+------------------+--------+--------+------+------+--------+------+

To date OSU researchers have observed *O. mykiss* standing crops
associated with different habitat features in the SFJD proper, and each
of its major tributaries: Black Canyon Creek and Murderers Creek. This
suggests that each basin segment has different limitations that restrict
the production of *O. mykiss*. Moreover, they found that valley segments
within each subbasin have different expressions of how environmental
factors are associated with *O. mykiss* abundance and yet again further
differences are perceived with an increase in spatial resolution to
explain standing crops in pools among valley segments. This analogous to
finding a location for a house to purchase; a good purchase depends upon
the state, the city, the neighborhood and the block. In short we have
developed a perspective of habitat that is hierarchically arranged as
nested subsets in space. This allows for a select of study sites in
randomized nested design.

In order to remove confounding factor of movement, so that growth,
mortality and production rates from warm and coldwater sites could be
compared, seasonal home ranges and movement patterns were determined by
following fish implanted with radio-tags (40) and PIT-tags (8,000).
Movement patterns varied by tributary and by season. During the summer,
home ranges and movement patterns of *O. mykiss* in tributaries were
small, in the range of 30-60 meters, roughly about three pools. Movement
patterns of *O. mykiss* in the lower SFJD were larger but generally less
than a kilometer. During the fall and winter, *O. mykiss* from Murderers
and Black Canyon Creek either remained in their home pools or migrated
into the lower SFJD and even into the John Day River proper.

Of specific interest is that the *O. mykiss* that grew the fastest
during the summer remained pool residents during the winter; whereas,
those that grew slowly migrated out to larger streams during the winter.
The outmigrants then grew faster than the pool residents and by the end
of winter, were similar in size. The percentage of migrants ranged from
2.3% for the colder tributary, Black Canyon Creek population and 8.9%
for the population Murderers Creek (Table 3). Interestingly, there is
not a clear trend with temperatures and growth rates. Other factors such
as prey availability might be required to determine site productivity.

These results suggest that barriers, such as push-up dams, can inhibit
production of potamodromous or river migrant fishes (i.e. *O. mykiss*
that migrate down into the larger streams during the fall to overwinter
and then migrate back upstream to rear in the spring). The distribution
of *O. mykiss* changes in the mainstem SFJD as spring moves into summer,
but is inconsequential in the tributaries. Fish movement or its lack may
be a contributing factor. The Invertebrate Production Monitoring (see
below) will evaluate *O. mykiss* diets and food to help resolve
ambiguities of site production (i.e. habitat quality) as growth rates
production of energy intake as well as energy expended: high food intake
can offset increased metabolic expenditures at elevated stream
temperatures.

Physiological fish responses were also used to evaluate the effects of
temperature on *O. mykiss.* Laboratory studies examined heat shock
protein 70 levels in fish subjected to different rearing temperatures at
our experimental hatchery. The same fish were tested for percent body
fat. Care was taken to account for sources of artifact that may affect
the study (*Tests of heat shock protein induction*). The results
suggests that temperature stress begins at 22^o^C, based upon the
analysis of heat shock protein 70 (Figure 12). Fish were also collected
at all the sentinel sites described above so that a range of
temperatures could be evaluated against the induction of heat shock
proteins. Field observations matched laboratory results, where heat
shock proteins appear to be induced at about 22^o^C. Also, body fats
appear to be inversely related to heat shock protein 70. Thus, heat
shock proteins may be a useful physiological index for describing a
whether the thermal experience of fish has resulted in stress.

An information need that was identified above is the how the structure
of food webs may alter the production of salmonids. Other species may
enhance or reduce growth rates and production of redband steelhead
trout. For example, winter diets samples collected in 2004, suggest that
redside shiners are prey for potamodromous *O. mykiss* during the winter
and important for their production. Interactions among species may
change among seasons (see Lane 1986). At high temperatures, redside
shiners may have a competitive advantage over *O. mykiss* parr, but at
low temperatures parr can outcompete shiners (Reeves et al. 1988). In
the summer, interspecific competition seems likely, but interestingly,
shiners may reduce intraspecific competition between dominant and
subdominant *O. mykiss*, by acting as "cover" and reducing agonistic
attacks on the less dominant trout (Tinus and Reeves 2001). Information
from this study suggests that the laboratory study of Tinus and Reeves
(2001) also can be observed in the field, but whether or not this
offsets competition for food needs to be determined. As the Independent
Science Group recognized, community processes must be addressed if
salmonids are to be restored in the Columbia River Basin (Williams et
al. 1999, pg. 15) and the setting of the physical habitat affects the
nature and intensity of the interactions.

[]{#__RefHeading___Toc125779711 .anchor}**7.3 Invertebrate Productivity
Monitoring**

The growth rates of fishes are highly correlated with survival and
productivity, metrics that are monitored to evaluate the health of
population. Growth rates are a more proximate metric describing a fish's
experience than are many population metrics conducted in many monitoring
program. Understanding factors influencing growth also provides a
mechanism through which the environment influences a population.
However, measures of growth require intense sampling beyond the scope of
many monitoring programs. Therefore, if other information that is more
feasible to collect can be used to estimate growth then a tool is
available to asses how we determine where to invest resources to restore
stream processes or whether a past restoration project has provided
benefits to a population of interest.

Nearly all major monitoring programs do not evaluate food resources
available for juvenile salmonids, such as invertebrate drift density.
The macroinvertebrate monitoring that is currently conducted is
generally used to evaluate water quality and is based on benthic
community composition. Drift samples, however, sample invertebrates in
the water column that represent the available prey items for drift
feeding fishes such as salmonids. Because these samples collect much
less detritus than benthic samples, drift samples may be relatively
inexpensive to process. The goal of this study is to determine if drift
density could act as a surrogate to secondary production that can be
used to estimate salmonid production.

The objectives of this project are to:

1)  Determine what is a useful and affordable invertebrate metric that
    when included with temperature can predict fish growth.

2)  Determine how this metric could be incorporated into the federal RME
    program (i.e. try to estimate the habitat type, number of required
    samples, etc. to achieve some level of precision and accuracy).

3)  Determine if a crosswalk can be made between this metric and metrics
    estimated in current monitoring programs so that past and future
    monitoring can be used in evaluating fish production.

*Objective 1***.** Approximately 8000 juvenile *O. mykiss* have been PIT
tagged in the South Fork of the John Day. Their movement, thermal
experience, and growth have and will continue to be documented
throughout the summers of 2004-2006. In addition, habitat surveys are
conducted in the study area. Results to date suggest that temperature
alone is not a good predictor of *O. mykiss* growth (see *Juvenile
Production Project* above).

To address the importance of drift density (as well as temperature) in
estimating growth rates of juvenile *O. mykiss*, drift and benthic
samples will be taken at the 3 sentinel sites (sites consisting of 5
pools representing 3 different thermal regimes) located in each
Murderers Creek, Black Canyon, and in one site of Deer Creek (Figure 2)
that are used *Juvenile Production Project*. Several metrics of drift
and benthic invertebrate information will be compared to the growth
rates of juvenile *O. mykiss* at these sites. Such metrics include total
drift or benthic biomass, total drift or benthic volume, and biomass and
volume over different sizes and taxa of invertebrates.

*Objective 2.* To determine the spatial and temporal variability of
drift and benthic invertebrate metrics that must be considered when
developing a monitoring program, samples were take at multiple temporal
and spatial scales. Drift samples were taken with 2 to 4 replicate nets
in each riffle, with 1-3 riffles sampled at each of seven sentinel sites
to describe the spatial variability within riffle, within site and
between sites, respectively. Samples were also collected during the
morning, mid-day, and evening over 3 days consecutive days for 3 sample
dates to describe temporal variability within days, between days, and
throughout the season, respectively. This information will be used to
determine the level of effort (samples size and design) needed for a
monitoring program to estimate relevant stream invertebrate information.

![](media/image3.jpeg){width="6.25in" height="4.6874015748031495in"}

**Figure 2.** The sentinel sites or sample locations (circles) for fish
and invertebrates monitoring in the South Fork of the John Day. Boxes
describe the monitoring designs for *O. mykiss* (RBT) and information
collected at the sentinel sites.

*Objective 3***.** Unfortunately, drift samples have not been sampled in
most monitoring programs. However, if there is relationship between
benthic invertebrate counts and drift biomass then benthic invertebrate
sampling from past and current monitoring programs can be used to
provide an indirect estimate of fish growth. In addition to the drift
samples collected in this project, benthic samples have also been
collected at each sentinel site for each sample date using kick net
methods common in other monitoring programs. In addition, the PIBO
monitoring program, which currently collects kick net samples of benthic
invertebrates, agreed to sample \~ 35 of their sites (2 reps each) to
provide paired samples for this project to develop this relationship.

[]{#__RefHeading___Toc125779712 .anchor}**8.0 Limiting Factors**

In order to prescribe proper restoration actions to increase freshwater
salmonid production, it is necessary to understand the factors limiting
production. In addition to understanding what limits production, the
ability to alter limiting mutable characteristics is necessary.
Therefore, a component of the John Day pilot project will be to conduct
a limiting factors analysis.

There are a couple approaches that can be used to address limiting
factors. One approach to address limiting factors is to review
literature sources that have identified causal relationships between
physical attributes and fish. These relationships would then be used to
predict fish responses to physical habitat data collected from
monitoring programs in the basin. What is perhaps lacking from this
approach is a framework by which these relationships interact to produce
a comprehensive assessment of what factor(s) from a suite of factors is
potentially limiting. Models are often used as a framework in which to
integrate these multiple relationships. The Ecosystem Diagnostic and
Treatment (EDT) model described below, for example, was applied to the
John Day Basin through the Subbasin Plan.

A potential problem with this approach is that the relationship used to
predict a fish response was developed in systems very different than the
one in which it is being applied which can potentially be very
misleading. An alternative is to develop relationships specific to the
system you are assessing for limiting factors. The limiting factors
analysis is then basically a synthesis of all the steps discussed so far
in this document.

[]{#__RefHeading___Toc125779713 .anchor}**8.1 EDT Assessment of Limiting
Factors**

EDT is a complex spatially explicit model relating 46 habitat variables
to survival over several life stages. Inputs to the model are habitat
features based on data or expert opinion and synthesized to describe
"environmental attributes". "Biological rules" are applied to translate
environmental attributes into "survival factors" over the different life
stages. These biological rules are based on literature relationships and
expert opinion. EDT describes mechanistic relationships to estimate
survival factors and ultimately, productivity, carrying capacity, and
diversity, which forms the basis for predictions and implications.

Subbasin planners chose to use EDT to identify population limiting
factors, restoration and preservation priority areas, and the potential
impact of restoration scenarios on abundance and productivity for each
focal species population (see John Day Subbasin Plan for more details).
In the John Day, the EDT model inputs were reach-based ratings
determined by subbasin planners for 1264 reaches for both current and
template (historic/reference) status with respect to fish habitat
quality and environmental condition. This required an input of 160,000
"data" entries, which for the most part was based on expert opinion. The
46 environmental attributes summarize as 16 "limiting" factors (listed
below) and were ranked as having high (or large), medium, low, or no
impact on focal species survival by life-stage and reach.

1\. Flow

2\. Channel stability

3\. Habitat diversity

4\. Key habitat quantity

5\. Obstructions

6\. Withdrawals

7\. Sediment load

8\. Oxygen

9\. Chemical toxins

10\. Temperature

11\. Food

12\. Competition (with hatchery fish)

13\. Competition (with other species)

14\. Predation

15\. Pathogens

16\. Harrassment/poaching

The EDT model was used to determine limiting factors for each anadromous
population within the John Day Subbasin (i.e. summer steelhead for the
North Fork, Middle Fork, South Fork, Upper Mainstem, and Lower John Day
populations, and spring chinook salmon populations in the North Fork,
Middle Fork, Granite Creek, and Upper Mainstem). Phil Rogers of CRITFC
and Alex Conley of the North Fork Watershed Council were members of the
AFG and participated in the Subbasin Plan. They summarized the initial
results of the EDT model estimates of limiting factors for each major
tributary (Appendix C). The results presented here need to be updated
with the latest modeling results.

[]{#__RefHeading___Toc125779714 .anchor}**8.2 Synthesis of Causal
Mechanisms Explored in the John Day Basin.**

The John Day pilot project has initiated or collaborated on several
studies that are evaluating relationships between environmental factors
and/or how these environmental factors govern fish populations across
multiple scales. At the landscape scale, satellite remote sensing
information, summarized in GIS, is being used to describe landscape and
land use pattern change through time on a decadal scale and its
potential impact on in-stream temperatures. Landscape information is
also being used to estimate in-stream sediment loads, habitat quality,
and fish distribution. LiDAR and FLIR remote sensing collects data at a
much higher resolution at a watershed/reach scale and is being used to
inform these analyses as well as the TMDL process. The TMDL project and
the sediment project are relating riparian habitat information to
in-stream temperature and sediment loading, respectively. At the reach
level, the Juvenile Production Project is relating temperature and
habitat characteristics to growth, density, survival, and production of
juvenile *O. mykiss* across different seasons. At this scale, the
invertebrate monitoring project is relating invertebrate abundance to
fish growth. These projects have the potential to be synthesized in a
hierarchical fashion to estimate limiting factors across several scales
(Figure 3). Finally, data through basinwide monitoring can potentially
be used in this integrated set of relationships to produce a basinwide
limiting factor analysis. The above synthesis will require completion of
the above projects and thus will not be available in the near term.
Therefore, other models that focus on a reduced set of potentially
limiting factors (as judged by researchers in the John Day) might be
constructed in a much shorter time.

High temperatures and excessive sediments are thought to be problematic
throughout much of the basin. They also are the culmination of multiple
physical processes and thus useful in detecting multiple sources of
degradation. For example, stream temperature patterns result from the
interaction of important physical and ecological processes acting within
the landscape (Ward 1985). Stream temperatures can be moderated by
groundwater inputs and hyporheic exchange, the quantity of which may be
influenced by the width and composition of the alluvial floodplain
(Stanford and Ward 1993). Stream channel structure influences the extent
of floodplain aquifer recharge during seasonal floods, hyporheic
exchange, hydraulic conductivity, flow, retention time, heat gained by
the stream from solar radiation, and heat lost from the stream through
evaporative processes. Factors that disturb riparian vegetation can
increase insolation and stream temperatures (Beschta et al. 1987).
Aspect and topographic shading affect solar input to the streams.
Vegetative structure and condition will also affect rates of water
infiltration and surface runoff to streams, hydraulic conductivity via
soil porosity and via extensive rooting networks. Landuses such as
grazing, agriculture, and forestry affect many several of these
processes and therefore have impacts on temperature.

The John Day Pilot project is pursuing the development of a model to map
potential fish growth throughout several areas of the John Day by
combining models that estimate heat budgets based on physical inputs and
bioenergetics models that use these heat budgets and invertebrate
abundance information to estimate fish growth.

The HeatSource model used in the TMDL process, uses physical processes
to define a heat budget for a reach. These physical processes (e.g. the
rate solar inputs heat water) are somewhat more predictable than
ecological interactions. Watershed Sciences are developing algorithms to
process LiDAR information that can be used as direct inputs into the
HeatSource model. As is done in the TMDL process, impacts of different
scenarios, such as the increase of the riparian canopy through a
riparian fencing project or increased discharge by purchasing instream
water rights, on stream temperature can be estimated with the HeatSource
model.

The physiological processes responsible for *O.mykiss* growth have been
extensively evaluated in the laboratory and are well understood and
documented. These physiological processes exhibit little variability
among individuals. The rate at which respiration and the maximum
consumption rate changes as a function of temperature has been
determined for several fish species (Hanson et al. 1997). Bioenergetics
models use a mass balance equation to describe growth as:

Growth = consumption -- (respiration + wastes)

Respiration and waste can be further divided into more specific
functions that have been well established in the laboratory (Hewett and
Johnson 1992).

What is less well understood are the ecological relationships
determining the amount of food consumed and thus a prediction of growth.
One potential use of the invertebrate information (drift and/or benthic
samples) collected in the South Fork of the John Day is to develop a
relationship between prey density and temperature dependent consumption
rates (see graph in Figure 4). Relationships between prey density and
percent maximum consumption (as estimated by the bioenergetics model)
have been observed with other fishes with some success (Budy et al.
1996, Budy et al. 2005). This simple relationship could be used to
estimate growth potential of different stream reaches that have
temperature information (Figure 4). Incorporated with the HeatSource
model , which describes temperature regimes under restored and current
conditions, these sets of model could identify where temperature and
invertebrate production limits fish production. Restoration activities
addressing these factors can then be prescribed for these reaches.

**Figure 3.** A schematic of the different research projects in the John
Day River Basin generally arranged from large scale (left) to small sale
(right). Arrows originating through octagons (landscape or habitat
features) and linking ovals (project or workgroups) to other scales or
easures, represent processes. Projects and workgroups are arranged by
the processes they will evaluate. Rectangles are specific features of
reaches that will be given greater consideration. Arrows connecting
different ovals represents information exchange.

[]{#__RefHeading___Toc125779715 .anchor}**9.0 Determining the
Effectiveness of Restoration Actions**

A recent study provided a synthesis on over 37,000 stream restoration
actions that have occurred throughout the U.S. with highest density
found in the Pacific Northwest (Bernhardt et al. 2005). They found that
less 10% of these projects had any associated assessments or monitoring,
and further, most of these lacked the information that would allow for
an evaluation of restoration effectiveness. Recent inventory of
monitoring projects in the CRB, also indicate that effectiveness
monitoring is extremely rare. Restoration actions are being implemented
in the John Day on an opportunistic basis. Restoration is often applied
to degraded habitat under the assumption that this will have a benefit
to fish. However, this assumption is rarely based on an evaluation of
the factors limiting production or monitored to assess restoration
effectiveness. Many people working on RME projects in the John Day agree
that effectiveness monitoring is sorely lacking. The pilot project is
developing Intensively Monitored Watershed studies to design and test
the impacts of restoration as outlined in the Tributary Federal RME
program. In addition to testing restoration effectiveness, the
comprehensive approach of IMWs will also address the steps outlined
above to develop a comprehensive basinwide RME design. We believe there
are also opportunities for developing effectiveness monitoring programs
for existing and planned restoration actions. A large effort has been
made in 2005 and is continuing to inventory these actions. We are
developing methods, such as comparative studies, to address action
effectiveness.

[]{#__RefHeading___Toc125779716 .anchor}**9.1 Intensively Monitored
Watershed Studies (Work Element 1)**

As stated, the Tributary Federal RME program is expected to inform,
identify, implement, and measure responses of mitigation strategies. The
ability to do this at the scale of the Columbia River Basin (CRB) or
even in a subbasin such as the John Day can be cost prohibitive if not
designed properly and require long term development. As a means to
implement this RME program on a manageable scale, several have suggested
conducting intensive monitoring and research on a set of restoration
actions in a watershed or large reach. This approach is referred to as
the Intensively Monitored Watershed studies. Several IMW have been
initiated in western Washington (Bilby et al. 2004). PNAMP has also
written a document on general concepts and criteria for selection of
IMWs to create a network of IMWs throughout the CRB (PNAMP 2005). Other
IMWs are in the process or have been initiated throughout the Columbia
River Basin (Figure 5) and, to some extent, are being coordinated and
designed through regional monitoring workgroups such as PNAMP and CSMEP
(PNAMP 2005, CSMEP 2005). If IMW studies cover enough ecotypes, then the
results of these intensive studies can form the expectations of the
types of impacts certain restoration actions can have in watersheds
throughout the Columbia River Basin. The pilot projects are engaged in
this effort as well. The watershed classification study will also help
characterize watersheds to extrapolate the results from IMWs to other
watersheds. In the John Day, we have initiated an IMW and are planning
others.

![](media/image4.wmf){width="7.363385826771654in"
height="4.3728346456692915in"}

**Figure 5 .** A network of potential IMWs described in PNAMP (2005).
Locations shown in yellow are under consideration for consideration for
the John Day pilot project.

We plan to implement the IMWs as an "ideal" experimental management
action. The steps that are required to implement this are similar to the
steps outlined for development of the RME pilot project and outlined in
Figure 6. The goal of stream restoration in the John Day Basin as called
for under the Biological Opinion, is to provide enough improvement in
the survival of the life stages in the spawning and rearing habitat to
mitigate for the impacts of the mainstem dams in the Columbia River on
overall lifecycle survival. This provides targets for the survival
improvements required under this program.

These goals will generate questions about what factors are limiting
production and what manipulations need to be done improve freshwater
production. These question will need to be refined to form hypotheses
that will be tested as an experiment. The steps required to refine these
questions are to conduct research and a limiting factors analysis to
identify the problem(s) that need to be addressed through restoration.
As described in the *Limiting Factors* section (above), understanding
the mechanisms limiting fish production and the landscape context for
these interactions, basically requires a synthesis of the first five
steps in developing an RME program as outlined in the introduction to
this document. During the process of identifying the problem, status and
trends of fish and their environment from associated monitoring and
research will help form the baseline or pre-treatment information.
Through this process, it will likely become apparent that the original
questions were either not specific enough or the wrong questions to ask,
in which case the process will be iterative until the right questions
are being asked. As questions become more specific they will eventually
be formed into hypotheses to test. Examples of questions are temperature
and sediments are limiting freshwater production. A hypothesis more
specific than this might be stated as: Loss of riparian vegetation
through cattle grazing is resulting in an increase in solar inputs,
resulting in an increase in temperature, and a decrease in parr growth.

**Figure 6**. Diagram of the steps required to develop an experimental
management approach to stream restoration. This the framework for
developing the Intensively Monitored Watershed studies.

Based on the above synthesis we will arrive at specific hypotheses to
test. A manipulation that achieves restoration goals while maximizing
learning will require an experimental design. The experimental design
attempts to maximize our ability to detect responses as a function of
the restoration action. For example, a Before-After-Control-Impact
(BACI; Green 1979, Stewart-Oaten et al. 1986) approach could be used,
where a control and treated watershed would be compared pre- and
post-treatment over time. Underwood (1994) suggests that multiple
control areas be used to account for different trajectories that
naturally can occur between sites. The use of reference sites rather
control sites, or impact vs. reference sites (IVRS) has also been
suggested as a design to detect environmental change (Steward-Oaten and
Bence 2001). For a review of the different parametric statistical
designs see Steward-Oaten and Bence (2001).

Randomized Intervention Analysis (RIA) is another approach similar to
BACI type designs but is not constrained to the distribution assumptions
of a parametric design (Carpenter et al. 1989). With a RIA approach, the
difference between each control and treatment pair (treatment minus
control) over the time series is estimated. The average pre- and
post-treatment differences, D(PRE) and D(POST), are computed (Figure 7).
The test statistic is the absolute value of D(PRE)-D(POST) and is
compared against a distribution of random permutations of the sequence
of control and treatment differences. The treatment and control
watershed are expected to share similar climatic conditions, habitat
characteristics (e.g. geology and vegetation), and crews using the same
protocols to collect the response variables. By looking at the average
differences between these watersheds, the variability of the shared
characteristics is expected to be random with a mean of zero. Thus, all
these sources of variability are in a sense filtered out leaving a
better estimate of the treatment effect. Thus, the benefit of a control
becomes more apparent. Cloutman and Jackson (2003) discuss the strength
and weaknesses of each BACI vs. RIA approaches and suggest using both
techniques to analyze these types of manipulations.

If treatment and control watersheds differ substantially in a
characteristic as to swamp the effect of the treatment then these can be
used as covariates to aid in partitioning these sources of variability
from the differences caused by the treatment. Techniques are available
to select useful covariates from a list of potential covariates measured
through the study period (Milliken and Johnson 2001, Kershner et al.
2004).

All designs have their limitations making some approaches more or less
feasible depending on the situations. Thus, how the information will be
collected before and after the restoration action should be given
careful consideration. In addition, the possible manipulations to test
will likely be constrained by other factor, which, in turn, will dictate
to some extent the experimental design. Factors include the location,
feasibility, costs and support.

The ability to implement an IMW will depend, in part, on the location.
Practical considerations of where to conduct this study includes the
availability of past and current research and monitoring to identify the
problem so that the exploratory steps to experimental management are
largely completed, and the experimental component can proceed in the
near future. Also, access and ownership of the land in potential
watersheds in the John Day is a serious challenge that will affect our
ability to conduct an IMW.

Obviously we have to give consideration to the feasibility of
implementing a given restoration action. Constraints will be cost and
time. We will have to estimate the cost associated with the planned
activity and possibly alternatives. We also should consider the time it
takes to see a response of the restoration action. Future funding is
uncertain so implementing a restoration project that requires 30 yrs to
observe a response is not a likely option. Obtaining permits to
implement a restoration activity such as a NEPA process, may also
increase the time required to conduct the experiment. Finally, the
effort required to conduct an IMW will be large and will require the
input from several parties. We need to identify those parties and get
long term commitments from these researchers.

After the experimental design has been determined, implementation
monitoring (monitoring to ensure the restoration action is still in
place) and effectiveness monitoring programs need to be designed. This
will likely include a combination of fixed and probabilistic randomized
sample sites (e.g. EMAP approach), pre- and post-manipulation in control
and treatment watersheds. Fixed sites will reveal responses more quickly
than randomized sites (Roper et al. 2002), but will likely not detect
some responses such as increases in juvenile distribution. A mechanistic
understanding of how restoration actions influence habitat and fish will
help determine where fixed samples sites will be located. In some
watersheds or reaches, whole census can be conducted negating the need
for a design to select sample sites.

The IMW is an incredible opportunity to develop research that can be
conducted in contrasting conditions to elucidate mechanisms of how fish
interact with their environment, and how management might influence
those interactions. Large scale perturbations have revealed an
incredible amount of information applicable to questions relevant to
management, because they are conducted at the appropriate scale
(Carpenter 1990, Carpenter et al. 1995). Research should be conducted
during an IMW to take advantage of this opportunity. We will need to
determine what research conducted in the John Day will also occur in the
IMW, and what other projects should be added to address remaining
critical uncertainties.

> ![](media/image5.wmf){width="4.136805555555555in"
> height="5.501388888888889in"}

**Figure 7**. A) Hypothetical timeseries of treatment and control
watersheds parr-to-smolt survival rates, with an implementation action
assumed to happen in 2015. B) Difference between treatment and control
watersheds parr-to-smolts survival rates over the timeseries. D(PRE) and
D(POST) represent average survival difference pre- and post-treatment.

[]{#__RefHeading___Toc125779717 .anchor}**9.1.1 Potential IMW
locations**

The AFG used their collective experience to compile potential IMW sites
throughout the John Day Basin. Before we embarked on this exercise we
gave thought to these designs and constraints of implementing an IMW.
The AFG created a list of potential IMW projects that take the take
considerations into account. This information is summarized in Tables 1
through 3 of Appendix C. The John Day pilot project has begun to develop
IMW study designs on a subset of these potential projects.

[]{#__RefHeading___Toc125779718 .anchor}**9.1.1.1** *Bridge Creek*

We propose a pilot restoration project along approximately 4 km of
Bridge Creek, sufficient to cause a population-level impact to the
steelhead that utilize the system. We will monitor the results, treating
Bridge Creek as an intensively monitored watershed/restoration site.
This is consistent with the purposes of IMWs, one of which is to test
the impacts of restoration projects, with the goal of detecting
population-level effects on targeted species (Bilby et al 2004, PNAMP
2005). Monitoring will be designed to gather responses to the
manipulation as well as covariates unaffected by the manipulation. The
results will be analyzed and used to inform whether the restoration
action was beneficial to the population (the original goal of the
Biological Opinion) while revealing important causal mechanisms.

![](media/image6.wmf){width="3.811111111111111in"
height="5.324305555555555in"}The Bridge Creek subbasin is a 710 km^2^
watershed draining directly into the lower John Day River. Bridge Creek
and its tributaries are utilized by a run of Middle Columbia steelhead
that are part of the ecologically distinct Lower John Day population
which occupies the lower, drier Columbia Plateau ecoregion within the
John Day Subbasin, and are listed under the Endangered Species Act
(CBMRC 2005, p. 75). The John Day Subbasin Plan (JDSP) has designated
Bridge Creek as a priority watershed for restoration because its
salmonid production and abundance potential is high (CBMRC 2005, pp. 83,
249). The JDSP also identifies habitat quantity, temperature, sediment
load, habitat diversity and flow as limiting factors in Bridge Creek
(CBMRC 2005, p.83). Analysis of the John Day and other subbasins in the
interior Columbia River basin suggest that incision is a widespread
phenomenon affecting as much as half of all the fish bearing streams in
a watershed (Beechie and Pollock, unpublished data). Historical records
suggest that Bridge Creek and nearby watershed incised deeply in the
early 1900s, shortly after Europeans began to settle the area (Buckley
1992, Peacock 1994). The mainstem incision depth in Bridge Creek
typically

Figure 8. Typical sequence of stream incision in the semi-arid region of
the Columbia River basin, showing the lowering of groundwater levels and
the subsequent loss of riparian forest.

ranges from 1-3 m, sufficient to disconnect the stream from the former
floodplain (Figure 8). This has resulted in the lowering of floodplain
water tables,

the loss of off-channel habitat and riparian forest and a general
simplification of stream habitat (Figure 5.1, Elmore et al. 1994).
Stream temperatures in the summer frequently exceed 27 ^0^C, and late
summer streamflows are less than 5 cfs (Anna Smith BLM Hydrologist,
personal communication). Not surprisingly, it is on the 303(d) list of
temperature impaired streams (CBMRC, 2005 p. 40). Due to the erosive
nature of some of the geologies and in particular, the large number of
incised, failing stream banks, sediment loads are high in Bridge Creek,
especially during peak flow events. Annual suspended sediment loads may
exceed 19,000 m^3^ (Pollock and Beechie, unpublished data).

The purpose of the project is to engage in watershed restoration at a
scale sufficient to cause a detectable, population-level impact to the
steelhead and that utilize this watershed. This project proposes to take
a process-based approach to restoration of Bridge Creek to cause
aggradation of approximately 4 km of incised mainstem stream bed and
lower tributary reaches. Such aggradation should raise floodplain water
tables and lead to increased summer streamflows, decreased stream
temperatures, a narrower and more sinuous stream channel, and a vastly
expanded riparian forest. Thus we propose to restore one process
(aggradation), which in turn will trigger a series of positive feedback
loops that restore other biological and physical processes that maintain
stream ecosystems. Comparison of observed parr densities in Bridge Creek
with densities in nearby healthy watersheds suggests that the restored
reaches will potentially increase steelhead rearing capacity
approximately 30 fold. Using available parr and smolt survival estimates
for the John Day River, we calculate that the project will result in an
additional 79 steelhead adult spawners in Bridge Creek. The existing
population is poorly documented because spawner surveys are infrequent,
but based on the very limited, available spawner survey data from ODFW,
which is the best data we have, we estimate that our efforts would
roughly double the current population.

*9.1.1.1.1 Proposed actions*

Presently, aggradation is already occurring behind reaches where the
small, existing beaver population has constructed dams. Some of these
dams have backfilled completely with sediment, allowing for riparian
vegetation such as willows to colonize (Figure 9) and create a stable,
elevated stream bed and floodplain. High sediment loads and rapid
riparian colonization in this system has resulted in a rapid aggradation
process (\<10 yr). This process has locally elevated the stream bed by
as much as 1.5 m in some places, but more typically by less than 0.5 m.

![](media/image7.jpeg){width="3.4965277777777777in"
height="2.6347222222222224in"}

Figure 9. View of an aggraded reach upstream of a 1.5 m high beaver dam
on Bridge Creek, Oregon. The pond has almost completely backfilled with
(approximately 7500 m^3^) of sediment. Willows, cattails and other
riparian vegetation have colonized the new surface. Additionally,
willows have recently replaced sagebrush on the adjacent terrace where
water tables have risen to within 0.5 m of the surface. The dam is just
beyond the patch of open water in the upper left of photograph.

Unfortunately, many of the beaver dams fail before the upstream sediment
wedge and dam can be stabilized by riparian vegetation. Dams appear to
fail primarily because of the lack of large diameter woody vegetation
(e.g. willow and cottonwood stems) to provide sufficient strength to
withstand high flows (Michael Pollock, personal observation).

We propose 3 restoration structures that mimic strong, long-lasting
beaver dams, in order to accelerate aggradation processes. These
include: (1) Beaver dam assisting devices or post-pile fences: insertion
of vertical posts or poles across the incised trench to provide key
members for beaver to build dams off of (Figure 10); (2) construction of
small diameter log bundles placed perpendicular to the stream in a
series of steps (Figure 11); and (3) construction of a series of rock
steps. Each strategy should result in elevated water tables sufficient
to expand the riparian gallery forests and thus create a long-term
supply of structural material for the construction and maintenance of
more beaver dams. All of these structures should become "invisible" in a
relatively short time period as aggradation and vegetative colonization
occurs. The three strategies range between the relatively low cost
option of enhancing habitat so that beaver will build stable dams versus
the higher cost option of actually constructing sediment aggrading
structures. We have identified two general areas along the mainstem of
Bridge Creek totaling approximately 4 km in length where criteria is
appropriate and the likelihood of success is high (Figure 12). Criteria
selection included stream gradient, incision depth, floodplain width and
presence of upstream sediment supplies.

![](media/image8.wmf){width="2.8229166666666665in"
height="3.6354166666666665in"}[Post-pile fence.]{.underline} This
strategy is simple in concept, and lowest in cost. A series of posts or
poles are pounded into the bed substrate perpendicular to the flow of
the stream (Figure 10), preferably near natural constrictions with
low-gradient upstream reaches. These posts provide key structural
support that can then be used by beaver to build durable dams better
able to withstand high flow events until the area behind the dam can
backfill with sediment and be colonized by woody riparian vegetation. A
series of posts lines will be placed in close proximity to mimic typical
frequencies and heights of beaver dams. An idealized beaver dam sequence
might be a primary dam 1-1.5 m high with a smaller 0.5-1 m dam 20-50 m
downstream of the primary dam and 2-3 intermediate-sized (0.5-1 m) dams
upstream of the primary dam, and spaced perhaps 30-100 m apart,
depending on stream gradient, creating a series of connected pools. We
will also provide piles of

[]{.mark}

Figure 10. Schematic of a post-pile fence design to facilitate beaver
dam construction-a "soft engineering" approach for causing aggradation
of an incised stream bed.

cottonwood and willow branches for beaver during the dam-building season
(summer) to supplement food and building material. We will work with
ODFW to relocate nuisance beaver (of which there is a steady supply) to
our sites. Such a strategy has been successfully employed elsewhere to
trap sediment in streams (Scheffer 1938, Apple 1983).

[Bundled log steps.]{.underline} This strategy employs bundles of small
diameter (30 cm) poles to create a series of steps sufficient to raise
the stream bed to the desired height, usually 1-2 m (Figure 11), to
raise the nearstream water table close to the level (\< 0.5 m) of the
floodplain. The structure is designed such that it will be covered with
vegetation and sediment, particularly once the area

behind it backfills with sediment. Riparian plantings (willow and
cottonwood staking) will be used to aid in the "visual recovery" of the
project. Thus within a relatively short time frame (\< 10 yrs) the
project structure will be invisible, and in a longitudinal profile of
the stream will appear as a locally steepened section (Figure 11). These
structures are designed to mimic (very strong) beaver dams in terms of
size and function. These are much higher in cost than the post-pile
fences, and therefore will be limited to fewer locations.

[Rock steps.]{.underline} This strategy employees the use of large rock
to create a series of steps that will cause aggradation, similar in
design to the bundled log steps (see Figure 11). Basalt rock is a common
feature throughout much of the watershed, so indigenous materials can be
used.

![](media/image9.wmf){width="2.84375in" height="3.6395833333333334in"}

Figure 11. Schematic of an idealized bundled-log complex dam-a "soft
engineering" approach for causing aggradation of an incised stream bed.

Within each of the project areas, we will construct two series or
complexes of aggradational structures. The complexes will be designed
with robust centerpiece structures, either the bundled log steps or the
rock steps, with a series of downstream and upstream post pile fences.

*9.1.1.1.2 Expected impacts on O. mykiss*

The restoration actions implemented should restore floodplain processes
that will result in increased baseflow, lower summer temperatures,
decreased sediment loads and greater habitat complexity such as more
off-channel habitat, more riparian vegetation, and more frequent and
deeper pools. These increases in habitat quality and quantity should
increase the carrying capacity of the system. Greater habitat complexity
also provides refuge from predation, interference competition, and high
velocity current. The expected decrease in temperature should also
reduce predation by warm water species such as the exotic smallmouth
bass found in this system either through thermal displacement or
lowering energetic demand. Decreased temperatures should also provide a
thermal environment closer to the energetic optima of *O. mykiss*,
resulting in an increase in growth rates. In addition, allocthonous
inputs should increase with an increase in floodplain connectivity and
riparian vegetation, boosting primary and secondary production, and
increasing growth rates of fishes. Decreases in energetic expenditures
(e.g. temperature and refuge, increases in energetic inputs (e.g.
production), and decreases in mortality (e.g. predation) are expected to
increase survival and production. Decreased sedimentation should lead to
a decrease in gravel and cobble embeddeness, providing increases in
suitable spawning gravels, and habitat complexity for periphyton,
benthic invertebrates, and parr. Egg survival is also expected to
increase as entombment of eggs by sediments is decreased.

To evaluate whether fish are responding to changes in habitat as
expected we will monitor several response variables: 1) Spatial
distribution (juvenile steelhead, and other fish species); (2) smolts
per redd or per spawner (steelhead); (3) migratory timing and size
(steelhead parr and smolts); (4) population abundance (steelhead parr,
smolts, and other fish species); (5) parr-to-smolt survival (steelhead
); (6) smolt-to-adult ratio (SAR- steelhead); and (7) recruiting adults
(R/S- steelhead). The experimental and monitoring program will be
designed to address the hypotheses whether changes in these response
variables are a result of the restoration actions, after accounting for
the number of spawners, natural disturbances, climate indicators, and
habitat conditions not-impacted by the actions (i.e. covariates).

*9.1.1.1.3 Experimental Design*

The experimental design attempts to maximize our ability to detect
responses as a function of the restoration action. We plan to do this by
comparing a time series of responses prior to the manipulation to a
series after the manipulation. Intervention Analysis (IA) have been used
for these types of comparisons (Box and Tiao 1975, Steward-Oaten 1986,
Carpenter et al. 1989). A Before-After-Control-Impact (BACI) approach is
an IA with non-manipulated sites used as a covariate, thus the
"controls" are used to reduce variation but are not used to measure it
as do true experimental controls (Steward-Oaten and Bence 2001).
Basically, the difference between the manipulated and the control
responses are calculated for every sample event and these are averaged
for the pre- and post-treatment periods, or Ď(PRE) and Ď(POST),
respectively. The test statistic is [׀]{dir="rtl"}Ď(PRE)- Ď(POST)
[׀]{dir="rtl"} and is compared to a theoretical distribution (e.g. BACI;
Steward-Oaten et al. 1986) or a distribution of random permutations of
the observed sequence of treatment and control differences (e.g. a
Randomized Intervention Analysis; Carpenter et al. 1989). The latter of
these is not constrained by the assumptions of parametric statistics
(for a comparison of BACI and RIA see Cloutman and Jackson 2003). By
evaluating the difference in treatment and control, shared
characteristics (e.g. climatic conditions, geology and vegetation types,
and monitoring crews) tend to cancel out. Thus, the benefit of a control
as a covariate, capturing multiple parameters, becomes more apparent. If
treatment and control watersheds differ substantially in a
characteristic as to swamp the effect of the treatment then these can be
used as covariates to aid in partitioning these sources of variability
from the differences caused by the treatment. Techniques are available
to select useful covariates from a list of potential covariates measured
throughout the study period (Milliken and Johnson 2001, Kershner et al.
2004). Further refinement to statistical models can account for temporal
autocorrelations, cyclic effects, and gradient effects (Draper 1984,
Steward-Oaten and Bence 2001).

We describe what we believe is a powerful design to detect the impacts
of these restoration projects on steelhead, *O. mykiss*. Ultimately,
everything describe below would be implemented, however, reality may
prove otherwise. Because of the scale and complexity of the system we
are proposing to study, we view the gathering of pre-treatment data as
attempt to implement this plan fully and as a scoping process necessary
to determine what is and is not feasible. Potential limitations include
denied permission by land owners, the ability to capture enough juvenile
*O. mykiss* to PIT tag, natural disturbances (e.g. flood events or
debris flows), vandalism, and other unforeseen events.

Depending on feasibility, the experimental approach will be implemented
in the Bridge Creek study over multiple scales: the watershed,
subwatershed, and reach scales (Figure 12). At the watershed scale,
Bridge Creek will be compared to nearby Murderer's Creek, where ongoing
monitoring of steelhead populations is already occurring. Within Bridge
Creek, comparisons will be made between Bear Creek, Gable Creek and the
mainstem. Within the mainstem of Bridge Creek comparisons will be made
between control and manipulated reaches, separated by enough distance to
reduce movement between reaches by parr.

![](media/image10.jpeg){width="4.024305555555555in"
height="2.8409722222222222in"}

Figure 12. Map of Bridge Creek showing approximate locations of proposed
restoration and control reaches and proposed PIT tag detectors and smolt
traps. Smolt and adult traps will measure watershed and subwatershed
abundances, and PIT tag detectors will be used to describe movement
between reaches and survival information.

Habitat surveys will also be collected in the same areas as the fish
surveys (see below). This information will be used in conjunction with
LiDAR information already collected to describe physically distinct
reaches, as has been done to define sentinel reaches in the South Fork
John Day IMW (Bouwes 2005). This will allow us to better pair control
and treatment reaches for this and further experimental designs. A
minimum of 4 reaches, 2 treated (manipulated) and 2 untreated (control)
reaches, would need to be defined in the mainstem Bridge Creek. More
reaches may be defined to provide more control reaches to bracket
treatment conditions or to conduct other specific research projects
attempting maximize contrast.

Although this hierarchical design may appear redundant, we will attempt
this approach for several reasons. First, by including multiple controls
at different scales, we are protecting against the possibility that
something could go wrong with the one control approach, such as a large
scale disturbance. Second, we are uncertain to the degree restoration
may impact downstream reaches. Although a comparison of multiple reaches
may be more powerful because of higher replicability and the ability to
accurately describe a reach versus a watershed or subwatershed, these
sites may not be independent from each other depending on the degree of
movement by *O. mykiss*, and the degree to which physical impacts from
treated reaches propagate into the next study reach. Third, hierarchical
models such as structural equation modeling, may be able to capture
small scale mechanistic relationships and how larger scale process form
the template for these processes (Shipley 2005). This study will provide
the information to parameterize such models. A better understanding of
the ecological processes that act across scales to ultimately impact
fish populations advances fish management (Fausch et al. 2002). Fourth,
we are also evaluating the degree of variability and statistical power
associated with each scale. This will provide insight into the scale at
which future restoration actions should be monitored. This hierarchical
design will also lend itself to the testing and development of causal
relationships pursued in monitoring and research programs currently
being implemented in the John Day RME pilot program.

*9.1.1.1.4 Monitoring Design*

[Steelhead monitoring.]{.underline} After the experimental design has
been determined, implementation monitoring (monitoring to ensure the
restoration action is still in place) and effectiveness monitoring
programs need to be designed. This will likely include a combination of
fixed and probabilistic randomized sample sites (e.g. EMAP approach),
pre- and post-manipulation in control and treatment watersheds. Fixed
sites will reveal responses more quickly than randomized sites (Roper et
al. 2002), but will likely not detect some responses such as increases
in juvenile distribution. A mechanistic understanding of how restoration
actions influence habitat and fish will help determine where fixed
samples sites will be located. In some watersheds or reaches, whole
census can be conducted negating the need for a design to select sample
sites. Described below is the general monitoring program we will take to
estimate before and after treatment fish responses in the control and
treatment areas across multiple scales for each response variable. We
will monitor several life-stages throughout the study areas, including
parr (age 0 to pre-smolt which range in age from 1-4 yrs old), smolts,
and adults.

This study will take advantage of recent advances in PIT tag technology,
including the use of extended length PIT tag detectors that can be
deployed to obtain nearly complete detection efficiency of tagged fish
that pass through the antennas. The placement of PIT tag detectors
between distinct reaches which include treated and untreated reaches. It
is unclear how many parr can be captured and PIT tagged in the different
areas. Up to 3,000 tagged fish per watershed are needed to provide
watershed specific SAR estimates. PIT tag antennas will document the
amount of independence of reaches on fish distribution, habitat use,
growth, and parr-to-smolt survival. In addition, the movement
information will describe the importance of the potomandromous (seasonal
movement between mainstem and tributaries) life history strategy as
observed in the South Fork of the John Day IMW. This strategy is thought
to occur because of an interaction between behavioral thermoregulation
and density dependent interactions, where a percentage of the population
uses cooler tributaries in the summer, migrates out in the fall to rear
in the warmer mainstem John Day, and potentially returns to the
tributaries as mainstem temperatures become too warm in the summer
(Hiram Li, OSU Personal Communication). The use of tributaries for
thermoregulation is a life-history strategy for steelhead that requires
further investigation (CBMRC 2005, Tim Unterwegner and Jim Ruzycki of
ODFW, personal communication).

Parr abundance and distribution will be estimated through a combination
of snorkeling surveys, snorkel-herding and electro-herding
(electroshocker set to a low setting that is irritating but will not
stun) into bag seines. Snorkel- and electro-herding will be used to
capture fish to PIT tag as well as to calibrate snorkel surveys that are
used by ODFW. These methods have been calibrated in the South Fork John
Day IMW (Bouwes 2005). A census using snorkel/herd-seining surveys may
be conducted over the entire study area. In some areas these surveys
will not be logistically feasible, such as in beaver ponds where
disturbance of sediments will preclude visual estimates, or in shallow
waters that cannot be reasonably observed. In such instances, 3-pass
electroshocking or mark-recapture Peterson abundance estimates will be
conducted for each habitat unit. Surveys will be conducted at the
beginning and the end of the summer field season. Habitat surveys will
be conducted at these sites as well. Changes in fish density will be
used to assess changes in habitat quality, with the assumption that fish
select for higher quality habitat as a means to increase fitness. These
surveys can be used to address reach scale comparisons or rolled up to
assess larger scale comparisons. Further sampling may be done to capture
more fish to increase the sample size of PIT tagged fish.

A redd census and carcass survey will also be conducted in Bridge Creek,
Bear Creek and Gable Creek. As adults begin to return from the first
cohorts of PIT tagged juveniles, carcasses will also be scanned for PIT
tags. In addition, fixed reaches and ten 1 km long reaches selected in a
random probabilistic design will be visited every two weeks throughout
the season to quantify cumulative redd counts at each site, as is done
for the ODFW steelhead surveys.

The number of smolts leaving and adults entering a watershed or
subwatershed will be compared in this experiment as this gets to the
most direct measure of interest, freshwater production or smolts per
spawner. A removable two-way trap will capture outmigrating juveniles
and incoming adults. The trap will be deployed daily to once a week and
checked the next day. PIT tag antennas will be deployed above the traps
to describe trap efficiency. The ratio of tagged to untagged fish may
remain rather constant negating the need to deploy the trap on daily
basis (this is currently being evaluated in the South Fork John Day
IMW). All outmigrating juveniles will be scanned using a hand-held PIT
tag detector or tagged if no tag is detected for further survival
estimates (SARs). These traps will be operated during the migration
seasons when possible. The traps will be used to capture adults
migrating upstream to the spawning grounds. Adults will measured, aged,
sexed and scanned for tags as well during the spawning season. This
information will be used estimate recruits per spawner or overall
life-cycle survival.

Cormack-Jolly-Seber (CJS) models of mark-recaptured PIT tag fish will be
used to estimate seasonal parr survival, parr-to-smolt survival, smolt
reach survival in the mainstem John Day and Columbia River, and
smolt-to-adult survival between reaches, subwatersheds and watersheds.
PIT tag detected at PIT tag antennas in the study watersheds, smolt and
adult traps, and John Day and Bonneville dams on the Columbia River will
be used to make these estimates. Multiple CJS type models can be
compared to evaluate influence other covariates such as; time at
release, size, number of times recaptured, and habitat features on
survival rates (White et al. 1999).

The size, timing, and age of out-migration smolts may provide
information about changes to habitat quality. Length and weights of parr
captured for tagging will be measured. Juveniles recaptured at the traps
or in later juvenile surveys will also be measured to describe a change
in biomass or growth. Growth rates will then be compared between
reaches, subwatersheds, and watersheds throughout the study.
Bioenergetics model can be used to further partition these changes in
growth to changes in temperature and prey production. Growth rates and
other information are a more proximate response to habitat quality than
measures of survival allowing for a finer resolution evaluation of the
impacts of these restoration actions on juvenile salmonids. Also,
changes in the amount of time spent rearing in the different study areas
will also provide us a more mechanistic understanding of impacts of
restoration to this population.

[Habitat monitoring]{.underline} --LiDAR and 3-band digital aerial
photography remote sensing has already been conducted for Bridge Creek
and Murderer's Creeks. When analyzed these data will provide baseline
data on stream and riparian habitat conditions within the restored and
control reaches. Using these data, habitat parameters that will be
quantified within the restored and control reaches include: aerial
extent of riparian vegetation, sorted by dominant vegetation type (e.g.
willows, cottonwoods, emergent graminoids), stream geomorphology,
including cross-section geometry, planform sinuosity and longitudinal
gradient profile, and the location and size of beaver dams. Our goal is
to repeat these remote sensing surveys over the study area every 3 yrs
after completion of the project to measure changes in habitat quality
and quantity. Because LiDAR does not easily penetrate water, we will
also measure and monument stream cross-sections at the restored and
control sites so as to provide data on aggradation rates and volumes
behind the structures relative to control sites, as well as providing
detailed information on changes in the channel cross-section geometry.
Additionally, in FY 2006 we (NOAA Fisheries) will be installing water
level monitoring well fields along the proposed restoration and control
sites to measure the anticipated changes in floodplain groundwater
levels upstream and downstream of the aggradational structures,
including "control" locations where beaver dams already exist. Automatic
water level recorders/temperature monitors will be installed in the
wells. In FY 2006 we will also install temperature data loggers within
the restored and control reaches, which will remain in place before,
during and after completion of the restoration project.

[]{#__RefHeading___Toc125779719 .anchor}**9.1.1.2** *The South Fork of
the John Day River*

We are working collaboratively with the BOR (the funding agency) and OSU
(contracted to conduct the majority of the research) to develop the
South Fork John Day (SFJD) as an Intensively Monitored Watershed Study.
The goal of this study is to examine the influence of push-up dams on
the production of redband steelhead trout (*Oncorhynchus mykiss* -
RBT*).* A related goal is to evaluate whether or not Lay Flat Stanchion
Dams (LFSD) will alleviate the putative problems caused by push-up dams.
OSU conducted extensive habitat and fish surveys collected in 2004 and
2005 in the SFJD and its tributaries that are accessible by RBT (see
*Juvenile Production* Project section above) to form the baseline or
pre-treatment information for this study. All push-up dams on the lower
SFJD will be replaced the end of the summer of 2006.

Push-up dams are used by ranchers to divert stream water into irrigated
pastures. The name is derived from the construction practice which is to
take heavy machinery and push up a dam from the stream's substrate. At
some dams, the water holding capacity is increased by lining them with
plastic tarps or polyethylene liners. There are no formal regulations,
but informally, the best management practice is to bulldoze the push-up
dams after the irrigation season and before juvenile salmonids migrate
to overwintering habitats and before they begin their seaward migration.
The dams are resurrected during the early summer each year, presumably
after all the adults have migrated to spawn and the overwintering
juveniles have returned from the overwintering habitats. Replacement of
Push-up dams are important because they create barriers and fish
movement is important for their survival, especially for redband
steelhead trout (*Oncorhynchus mykiss),* a species listed as threatened
by the National Marine Fisheries Service.

This river system exceeds the Total Maximum Daily Loading of 18^o^C
limits for a 7-day period. In situations like this, fish seek refuge in
coldwater habitats such as cooler tributaries and stream reaches that
exchange hyporheic water during the heat of summer conditions of base
flow. They regulate their body temperatures behaviorally.( See Nielsen
et al. 1994, Matthews 1994, Torgersen et al. 1999). Also, as fish grow
they required access to different habitats for specific purposes (e.g.,
overwintering cover). In addition, push-up dams can potentially act as
barriers to adults to spawning grounds and to smolt migration. This
study will evaluate the degree to which impassible barriers such as
push-up dams obstruct movement patterns of fish, and to what these
barriers interact with temperature to impact fish production (see Li et
al. 1994 and Tait et al. for evidence that temperature has a negative
effect on fish standing crops).

The objectives and approach of this study are to:

1.  *[Determine the status of the watershed]{.underline}---how bad is
    the problem?* (BPA's Tier I monitoring).

```{=html}
<!-- -->
```
A.  Identify the location of the push-up dams in relation to the stream
    temperature profiles of the SFJD and its major tributaries. This
    will determine where interference with behavioral thermoregulation
    may be critical. Remote sensing (true color video and thermal
    infrared (TIR) cameras) were used to characterize properties of the
    stream and thermal profiles in 2003 and 2004 in mid-August.

B.  Determine the severity of push-up dams as barriers. The experiment
    design will be based on using an odds ratio to determine the
    efficacy of fish passage (Figure 11).

C.  Determine the amount of habitat that meets Oregon's TMDL standard.
    TIR provided a longitudinal temperature profile. In addition, over
    80 temperature loggers using the TIR as a template for location
    collected temporal changes in temperature. The preliminary
    assessment based upon these criteria are that the SFJD is poor
    rearing habitat as in 2003, 2004 none of its stream reaches meet the
    criteria, but that its tributaries are in relatively good condition
    for redband trout.

> D. Identify habitat quality within the SFJD drainage accessible to
> anadromous salmonids using growth and production as criteria in
> relation to the concept of the Ideal Free Distribution. Extensive
> habitat and fish surveys (every pool and every third riffle); thermal
> infrared remotely gathered imagery of stream temperatures; LiDAR
> images depicting vegetation structure, channel morphology and basin
> topology;, aerial photographs of push-up dams; multispectral satellite
> images; and maps identifying patterns of public ownership were
> collected in 2004 and 2005 in the SFJD and its two major tributaries,
> Black Canyon Creek and Murderers Creek to address this objective.
>
> E. Monitor PIT and radio-tagged fish to determine patterns of
> movement, growth rate, survival and production before push-up dam
> replacement.

2\. *[Determine the effectiveness of LFSD's.]{.underline}* (BPA's Tier
II monitoring)

> A. Compare LFSDs vs. Push-up dams as barriers.
>
> B. Compare patterns of fish movement, growth rate, survival and
> production of fish before and after push-up dam replacement.
>
> C. Determine how barriers affect production of fishes in specific
> habitats.
>
> D. As barriers affect other fishes, determine how interactions with
> other species influence the production of redband steelhead trout.

3.  *[Determine cause-and-effect relations between population response
    and the restoration approach.]{.underline}* (BPA's Tier III
    monitoring)

```{=html}
<!-- -->
```
A.  Determine the impacts of elevated stream temperatures on fish
    production.

B.  *Compare smolt productivity of different stream reaches using
    PIT-tag detectors at Columbia Dams*. Determine differences between
    those that reared in stream habitats that exceeded 18^o^ C in
    comparision with those that reared in cooler temperatures.

C.  Verify whether or not the TMDL temperature standard is
    scientifically sound.

D.  If there are differences, then use the new standard derived from
    research to reclassify the streams.

E.  Determine whether LFSD alleviates or partially alleviates putative
    problems imposed by barriers in streams.

F.  As barriers affect other fishes, determine how interactions with
    other species influence the production of redband steelhead trout.

    []{#__RefHeading___Toc125779720 .anchor}**9.1.1.3** *Upper Middle
    Fork of John Day*

This area might also provide an opportunity for an IMW study as a fair
amount of information has been collected there and access to MF JDA is
possible. Land holdings include: The Nature Conservancy, Confederate
Tribe of the Warm Springs Reservation, Federal lands, and one private
property, who appears willing to work with us. In addition, several
restoration activities have been planned or are currently being
implemented. Evaluating these restoration actions that have already
received funding may prove to be cost effective. ODFW has recently
finished complete habitat surveys of the MFJDA on CTWSR land. In
addition, there are chinook spawning grounds surveys, snorkel surveys,
and screw trap information to get some information on Chinook status.
OSU has also conducted research in this area in the past. FLIR surveys
have also been collected on the Middle Fork on three different years.
Finally, a TMDL model will be available for this reach.

[]{#__RefHeading___Toc125779721 .anchor}**9.2 Effectiveness monitoring**

We believe there are also opportunities for developing effectiveness
monitoring programs for existing and planned restoration actions. A
large effort has been made in 2005 and is continuing to identifying past
actions. Retrospective studies of these actions might describe project
effectiveness. These analyses have an advantage in that they can be done
relatively quickly and inexpensively, since the projects have already
been implemented. Also, they reflect reality in that they evaluate real
effect sizes (as opposed to a modeling approach). However, very little
information is available to conduct these analyses, if changes in some
fish metric is the response variable. In addition, these analyses are
not very powerful because, for the most part, they were not implemented
and monitored with a design that provided contrasts (e.g. BACI design)
or collected supporting information (i.e. covariates).

Comparative studies might help identify changes due to restoration
actions. For example, over 200 miles of fencing projects ranging from 25
yrs to this year since implementation have occurred throughout the John
Day basin. Monitoring various fish and habitat responses around this
diverse set of riparian fencing project might be able to address why
some projects succeed or fail and how long it takes for them to have an
impact. This will likely require a set of covariates, with time as an
obvious one, to address some of the diverse characteristics on this
large sampling landscape. Although requiring more effort than
retrospective analyses since the response still has to be measured, this
approach would still produce results in a much shorted time frame than
an IMW and for less money. Again, these evaluation would reveal real
effect sizes (as opposed to a modeling approach). This approach might be
more powerful than a retrospective approach in that the study can be
designed to provide more contrast and supporting information that can be
used as covariates. Ideally, these sites would be sample through time to
avoid analyses based on a snap shot. However, this approach is still not
as powerful as an IMW design because of all the potential confounding
factors and the lack of pre-treatment information.

Simulation models could potentially be used to compare different
effectiveness monitoring designs to assess their ability to detect
defined differences. This model would be a spatially explicit
description of a system with the spatial units equal to the smallest
sampling unit (i.e. reach). Smaller units can be combined to ask
questions at larger spatial scales (e.g. reaches can be added to form
watersheds). A hypothetical manipulation would be assigned to a reach or
watershed. How the population uses this area (e.g. where and proportion
of the population) would be user defined and could be tested over
different scenarios. An effect size or range of effect sizes for each
response variable, based on literature values, would be defined for each
reach. Different monitoring and experimental designs could then be
implemented. Designs might include common status and trend monitoring
(e.g. screw traps, mark-recapture studies, habitat monitoring);
permanent site selection associated with each project; probabilistic
sampling associated with project; integrator reaches (e.g. PIBO
approach); BACI designs, etc.. The simulation would sample reaches and
apply observation error (determined from protocol tests) using Monte
Carlo simulations for each design. This set of samples can be used to
determine the minimum detectable effect size for a given sampling
design. These simulations could be conducted relatively rapidly,
inexpensively and can test multiple scenarios (relative to lessons
learned from actual implementation of IMWs). These results could be used
to inform effectiveness monitoring designs used in RME programs.
However, this approach lacks realistic effect sizes and may produce
misleading results.

[]{#__RefHeading___Toc125779722 .anchor}**10.0 Provide Rationale and
Suggestions on how to Change Current Monitoring Designs (Work Element
2).**

In the John Day Basin, several RME efforts are currently underway.
Several of these efforts started before the onset of the pilot project
program (ODFW, ODEQ, PIBO monitoring, *Juvenile Production Project*) and
others were initiated by the program (*Landscape Changes and Patterns
Project; John Day Basin Sediment Assessment; John Day River Basin
Airborne TIR Remote Sensing & Spatial Data Analysis Project; Collation
of GIS and Tabular Datasets; Invertebrate Productivity Monitoring*). The
program has made considerable progress at integrating both sets of
projects, yet much work remains. The RME efforts, described in this
document in the *Current RME Efforts in the John Day Basin* section, are
in affect, the current program for the John Day Basin. This program is
fairly comprehensive with up-to-date monitoring designs and novel and
intensive research programs. ODFW, PIBO, and ODEQ, for example, have
been using an EMAP spatially balance probabilistic site selection
process on a rotating panel schedule, the approach suggested by several
monitoring design workgroups and reviewers (PNAMP, CSMEP, FRME, ISRP,
ISAB).

In 2005, the pilot project has made considerable progress in
understanding ongoing efforts, and making contacts and relaying pilot
project intentions with personnel involved in RME efforts in the basin.
The project has also compiled a large amount of information of current
and historical RME programs and restoration projects. This information
has been reviewed and information gaps are being identified. These gaps
are also identified through interviews with RME personnel in the basin.
The pilot project has also initiated and participated in the development
and testing of reliable monitoring protocols and designs. In addition,
ongoing projects and projects initiated by the pilot program are
establishing causal relationship, and we are developing a framework to
use these relationships to address limiting factors. The project has
collaborated with subbasin planners and is using the EDT limiting
factors analysis as an initial guide for RME development and
prioritization of locations for stream restoration actions. Finally, the
pilot project has collaborated with others to identify potential IMW
sites, initiate an IMW in the South Fork John Day, and develop proposals
to initiate other IMWs. The lessons learned from the IMW efforts will
ultimately inform the pilot project and perhaps other regional RME
programs.

The John Day Integrated Status and Effectiveness Monitoring Pilot
Program plan (Work Element 2) is to all use steps outlined above to
potentially provide rationale to improve current RME efforts in the
basin to be more directed, comprehensive, and coordinated to address
local and Federal RME program goals.

[]{#__RefHeading___Toc125779723 .anchor}**11.0 Literature Cited**

Anderson, A.; Kliewer, G.; Dugaw, D. 2004. Effectiveness monitoring
project for streams and riparian areas within the upper Columbia River
basin, annual Report. Logan, UT: U.S. Department of Agriculture, Forest
Service. Annual reports are produced for each of the 20 National
Forests, three Forest Service Regions, and two BLM State Offices.

Bernhardt, S, M.A. Palmer, J.D.Allan, G.Alexander, K. Barnas, S. Brooks,
J. Carr, S. Clayton, C. Dahm, J. Follstad-Shah, D. Galat, S. Gloss, P.
Goodwin, D. Hart, B. Hassett, R. Jenkinson, S.Katz, G.M.Kondolf, P. S.
Lake, R. Lave, J. L.Meyer, T.K. O'Donnell, L. Pagano, B. Powell, E.
Sudduth. 2005. Synthesizing U.S. River Restoration Efforts. Science
308:636-637.

Carpenter, S. R., T. M. Frost, D. Heisey, and T. K. Kratz. 1989.
Randomized intervention analysis and the interpretation of
whole-ecosystems. Ecology 70:1142-1152.

Carpenter, S. R. 1990. Large-scale perturbations: opportunities for
innovation. Ecology. 71(6):2038-2043.

Carpenter, S. R., S. W. Chisholm, C. J. Krebs, D. W. Schindler, and R.
F. Wright. 1995. Ecosystem experiments. Science. 269:324-327..0

Coles-Ritchie, M.; Henderson, R.; Culumber, M.; Kelly, M. 2004b.
Effectiveness monitoring for streams and riparian areas: sampling
protocol for riparian vegetation parameters. Unpublished paper on file
at: <http://www.fs.fed.us/biology/fishecology/emp>. 32 p.

Chapman, D.W. 1988. Critical review of the variables used to define
effects of fines in redds of large salmonids. Transactions of the
American Fisheries Society 117: 1-21.

Dugaw, D.; Anderson, A.; Archer, E. K.; Henderson, R. C.; Kershner, J.
L. 2004. Effectiveness monitoring for streams and riparian areas:
sampling protocol for stream channel parameters. Unpublished paper on
file at: <http://www.fs.fed.us/biology/fishecology/emp>. 62 p.

Fraser, R. H. 1999. Sedmod: A GIS-Based Delivery Model for Diffuse
Source Pollutants (Nonpoint Source Pollution). Ph.D. Dissertation. Yale
University, New Haven, CT.

Henderson, R. C.; Archer E. K.; Bouwes, B. A.; Coles-Ritchie, M. C.;
Kershner, J. L. 2005. PACFISH Biological Opinion (PBIO) Effectiveness
Monitoring Program seven-year status report 1998 through 2004. Gen.
Tech. Rep. RMRS-GTR-162. Fort Collins, CO: U.S. Department of
Agriculture, Forest Service, Rocky Mountain Research Station. 16 p.

Milliken, G.A. and D.E. Johnson. 2001. Analysis of messy data, Volume

785 III: Analysis of Co-variance. CRC Press. Boca Raton, Florida.

Kershner, J. L.; Archer, E. K.; Coles-Ritchie, M.; Cowley, E.;
Henderson, R. C.; Kratz, K.; Quimby, C.; Turner, D. L.; Ulmer, L. C.;
Vinson, M. R. 2004a. Guide to effective monitoring of aquatic and
riparian resources. Gen. Tech. Rep. RMRS-GTR-121. Fort Collins, CO: U.S.
Department of Agriculture, Forest Service, Rocky Mountain Research
Station. 57 p.

Renard, K. G., G. R. Foster, G. A. Weesies, D. K. McCool, and D. C.
Yoder. 1996. Predicting Soil Erosion by Water: A Guide to Conservation
Planning with the Revised Universal Soil Loss Equation (RUSLE).
Agriculture Handbook No. 703, U.S. Dept. of Agriculture.

Roper, B., J.L. Kershner, E. Archer, R. Henderson, and N. Bouwes. 2002.
An evaluation of physical habitat attributes used to monitor streams.
Journal of the American Water Resources Association 38: 1-10.

Stewart-Oaten, A., and J.R. Bence. 2001. Temporal and spatial variation
in environmental impact assessment. Ecological Monographs 71:305-339.

Tappel, P.D. and T.C. Bjornn. 1983. A new method of relating size of
spawning gravel to embryo survival. North American Journal of Fisheries

Management 3:123-135.

Thorson, T.D., S.A. Bryce, D.A. Lammers, A.J. Woods, J.M. Omernik, J.
Kagan, D.E. Pater, & J.A. Comstock. 2003. Ecoregions of Oregon. (2 sided
color poster with map, descriptive text, summary tables, and
photographs). U.S. Geological Survey, Reston, VA. Scale 1:1,350,000.

USDA Forest Service, USDI Bureau of Land Management, US Fish and
Wildlife Service, USDC NOAA Fisheries. 2003. Implementation monitoring
program module for Pacfish/Infish and the 1998 Biological Opinions for
salmon, steelhead, and bull trout -- program manual. On the Web at:
<http://www.fs.fed.us/rm/boise/teams/fisheries/pac_infish/pac_infishhome.htm>.

USDI. Fish and Wildlife Service. 1998. Endangered and threatened
wildlife and plants: determination of threatened status for the Klamath
River and Columbia River distinct population segments of bull trout
final rule. Federal Register June 10, 1998 (Volume 63, Number 111, Pages
31647--31674) 50 CFR Part 17, RIN 1018-AB94.

U.S. Department of Commerce, National Marine Fisheries Service. 1998.
Endangered Species Act - Section 7 Consultation. Biological Opinion,
Land and Resource Management Plans for National Forests and Bureau of
Land Management Resource Areas in the Upper Columbia River Basin and
Snake River Basin Evolutionarily Significant Units., Northwest Region,
Seattle, Washington.

Underwood, A. J. 1994. Beyond BACI: sampling designs that reliably
detect environmental disturbances. Ecological Applications 4:3-15.

[]{#__RefHeading___Toc125779724 .anchor}**Appendix A**

**Table 1. List of Contacts in the John Day Basin related to RME
efforts.**

<table style="width:100%;">
<colgroup>
<col style="width: 13%" />
<col style="width: 1%" />
<col style="width: 6%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 1%" />
<col style="width: 11%" />
<col style="width: 0%" />
<col style="width: 12%" />
<col style="width: 12%" />
<col style="width: 13%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="4">Bureau of Land Management (BLM)</td>
<td colspan="5">Division of State lands</td>
<td colspan="2">NOAA Fisheries (NOAAF)</td>
<td></td>
</tr>
<tr class="even">
<td>Anna</td>
<td colspan="3">Smith</td>
<td colspan="3">Nancy</td>
<td colspan="2">Puspis</td>
<td>Chris</td>
<td>Jordan</td>
<td></td>
</tr>
<tr class="odd">
<td>Michelle</td>
<td colspan="3">McSwain</td>
<td colspan="3">Kevin</td>
<td colspan="2">Herkamp</td>
<td>Carol</td>
<td>Volk</td>
<td></td>
</tr>
<tr class="even">
<td>Brent</td>
<td colspan="3">Ralston</td>
<td colspan="5">Environmental Protection Agency (EPA)</td>
<td>Richard</td>
<td>Kang</td>
<td></td>
</tr>
<tr class="odd">
<td>John</td>
<td colspan="3">Morris</td>
<td colspan="3">James</td>
<td colspan="2">Hileman</td>
<td>Steve</td>
<td>Rentmeester</td>
<td></td>
</tr>
<tr class="even">
<td>Rick</td>
<td colspan="3">Demmer</td>
<td colspan="3">Jim</td>
<td colspan="2">Hileman</td>
<td>Michael</td>
<td>Pollock</td>
<td></td>
</tr>
<tr class="odd">
<td>Cynthia</td>
<td colspan="3">Tait</td>
<td colspan="3">John</td>
<td colspan="2">Faustini</td>
<td>Blake</td>
<td>Feist</td>
<td></td>
</tr>
<tr class="even">
<td colspan="4">Bonneville Power Administration (BPA)</td>
<td colspan="3">Phil</td>
<td colspan="2">Larsen</td>
<td>Tim</td>
<td>Beechie</td>
<td></td>
</tr>
<tr class="odd">
<td>Jim</td>
<td colspan="3">Geiselman</td>
<td colspan="5">Grant County</td>
<td colspan="2">Oregon Department of Environmental Quality (ODEQ)</td>
<td></td>
</tr>
<tr class="even">
<td>Roy</td>
<td colspan="3">Beaty</td>
<td colspan="3">Kathy</td>
<td colspan="2">McKinnon</td>
<td>Shannon</td>
<td>Hubler</td>
<td></td>
</tr>
<tr class="odd">
<td>John</td>
<td colspan="3">Baugher</td>
<td colspan="3">Dennis</td>
<td colspan="2">Reynolds</td>
<td>Don</td>
<td>Butcher</td>
<td></td>
</tr>
<tr class="even">
<td>Charlie</td>
<td colspan="3">Paulson</td>
<td colspan="3">Hilary</td>
<td colspan="2">McNary</td>
<td>Curtis</td>
<td>Cude</td>
<td></td>
</tr>
<tr class="odd">
<td colspan="4">Columbia Intertribal Fisheries Commission (CRITFC)</td>
<td colspan="3">Mike</td>
<td colspan="2">Hensley</td>
<td colspan="2">Oregon Department of Fish and Wildlife (ODFW)</td>
<td></td>
</tr>
<tr class="even">
<td>Peter F.</td>
<td colspan="3">Galbreath</td>
<td colspan="3">Lane</td>
<td colspan="2">Burton</td>
<td>Tim</td>
<td>Unterwegner</td>
<td></td>
</tr>
<tr class="odd">
<td>Dani</td>
<td colspan="3">Evenson</td>
<td colspan="5">Grant County Soil and Water Conservation District
(GCSWCD)</td>
<td>Kim</td>
<td>Jones</td>
<td></td>
</tr>
<tr class="even">
<td>Jennifer</td>
<td colspan="3">Brainard</td>
<td colspan="3">Ken</td>
<td colspan="2">Delano</td>
<td>Stephanie</td>
<td>Gunkel</td>
<td></td>
</tr>
<tr class="odd">
<td>Denise</td>
<td colspan="3">Kelsey</td>
<td colspan="5">Gilliam Watershed Council North Fork</td>
<td>Karen</td>
<td>Wegner</td>
<td></td>
</tr>
<tr class="even">
<td>Phil</td>
<td colspan="3">Roger</td>
<td colspan="3">Jordan</td>
<td colspan="2">Maley</td>
<td>Jeff</td>
<td>Zakel</td>
<td></td>
</tr>
<tr class="odd">
<td colspan="4">Confederated Tribes of the Umatilla (CTUIR)</td>
<td colspan="5"><p>Monument Soil and Water Conservation District</p>
<p>(MSWCD)</p></td>
<td>Paul</td>
<td>Sancovich</td>
<td></td>
</tr>
<tr class="even">
<td>Jesse</td>
<td colspan="3">Schwartz</td>
<td colspan="3">Julie</td>
<td colspan="2">Conley</td>
<td>Steve</td>
<td>Starcovich</td>
<td></td>
</tr>
<tr class="odd">
<td>Craig</td>
<td colspan="3">Contor</td>
<td colspan="3">Tami</td>
<td colspan="2">Kilby</td>
<td>Al</td>
<td>Hemingson</td>
<td></td>
</tr>
<tr class="even">
<td>Todd</td>
<td colspan="3">Shaw</td>
<td colspan="5">North Fork John Day Watershed Council (NFJDWC)</td>
<td>Chuck</td>
<td>Moore</td>
<td></td>
</tr>
<tr class="odd">
<td colspan="4">Warm Springs Confederated Tribes (CTWSRO_JDBO)</td>
<td colspan="3">Alex</td>
<td colspan="2">Conley</td>
<td>Jeff</td>
<td>Neal</td>
<td></td>
</tr>
<tr class="even">
<td>Michelle</td>
<td colspan="3">Newman</td>
<td colspan="3">National Park Service (NPS)</td>
<td colspan="2"></td>
<td>Russ</td>
<td>Powell</td>
<td></td>
</tr>
<tr class="odd">
<td>Sue</td>
<td colspan="3">Malaney</td>
<td colspan="3">Ken</td>
<td colspan="2">Hyde</td>
<td>Wayne</td>
<td>Wilson</td>
<td></td>
</tr>
<tr class="even">
<td>Mike</td>
<td colspan="3">Gauvin</td>
<td colspan="5">Oregon Department of Forestry (ODF)</td>
<td>Jim</td>
<td>Ruzycki</td>
<td></td>
</tr>
<tr class="odd">
<td>Jim</td>
<td colspan="3">Crocker</td>
<td colspan="3">Liz</td>
<td colspan="2">Dent</td>
<td colspan="2">ODFW –Streamnet</td>
<td></td>
</tr>
<tr class="even">
<td>Mark</td>
<td colspan="3">Berry</td>
<td colspan="3">Chuck</td>
<td colspan="2">Aylworth</td>
<td>Cedric</td>
<td>Cooney</td>
<td></td>
</tr>
<tr class="odd">
<td>Linda</td>
<td colspan="3">Brown</td>
<td colspan="3">Conlee</td>
<td colspan="2">Colleen</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Scott</td>
<td colspan="3">Toro</td>
<td colspan="3"></td>
<td colspan="2"></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Steve</td>
<td colspan="3">Zehntner</td>
<td colspan="3"></td>
<td colspan="2"></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td colspan="3"></td>
<td colspan="3"></td>
<td colspan="2"></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td colspan="3">Oregon State University (OSU)</td>
<td colspan="3">U S Bureau of Reclamation (BOR)</td>
<td colspan="2">USFS ??</td>
<td colspan="4"></td>
</tr>
<tr class="even">
<td colspan="2">Joeseph</td>
<td>Feldhaus</td>
<td colspan="2">Lanie</td>
<td>Paquin Boler</td>
<td colspan="2">Dan</td>
<td colspan="4">Bennett</td>
</tr>
<tr class="odd">
<td colspan="2">Scott</td>
<td>Heppell</td>
<td colspan="2">Kristin</td>
<td>Swoboda</td>
<td colspan="2">Shawn</td>
<td colspan="4">McKinney</td>
</tr>
<tr class="even">
<td colspan="2">Ian</td>
<td>Tattam</td>
<td colspan="2">Michael</td>
<td>Newsom</td>
<td colspan="2">Paul</td>
<td colspan="4">Hessberg</td>
</tr>
<tr class="odd">
<td colspan="2">Guillermo</td>
<td>Giannico</td>
<td colspan="2">Dana</td>
<td>Weigel</td>
<td colspan="2">Ed</td>
<td colspan="4">Uebler</td>
</tr>
<tr class="even">
<td colspan="2">Seth</td>
<td>White</td>
<td colspan="2">Michael</td>
<td>Beaty</td>
<td colspan="2">Dan</td>
<td colspan="4">Rife</td>
</tr>
<tr class="odd">
<td colspan="2" rowspan="2">Hiram</td>
<td rowspan="2">Li</td>
<td colspan="3">U S Forest Service (FS)</td>
<td colspan="2" rowspan="2">Phil</td>
<td colspan="4" rowspan="2">Howell</td>
</tr>
<tr class="even">
<td colspan="3">Deschutes National Forest</td>
</tr>
<tr class="odd">
<td colspan="2">Luis Francisco</td>
<td>Madrion</td>
<td colspan="2">Mark</td>
<td>Wilcox</td>
<td colspan="2">Robes</td>
<td colspan="4">Parrish</td>
</tr>
<tr class="even">
<td colspan="2">Peter</td>
<td>Bayley</td>
<td colspan="3">Wallowa-Whitman National Forest</td>
<td colspan="2">Don</td>
<td colspan="4">McKenzie</td>
</tr>
<tr class="odd">
<td colspan="2">Judy</td>
<td>Li</td>
<td colspan="2">Paul</td>
<td>Boehne</td>
<td colspan="6">Rocky Mountain Research Station</td>
</tr>
<tr class="even">
<td colspan="3">Oregon State University Extension (OSU Ext)</td>
<td colspan="2">Suzanne</td>
<td>Fouty</td>
<td colspan="2">Bruce</td>
<td colspan="4">Rieman</td>
</tr>
<tr class="odd">
<td colspan="2">Dave</td>
<td>Wooster</td>
<td colspan="3">Malheur National Forest</td>
<td colspan="6">Upper South Fork Watershed Council</td>
</tr>
<tr class="even">
<td colspan="2">Gary</td>
<td>Delaney</td>
<td colspan="2">Perry</td>
<td>Edwards</td>
<td colspan="2">Phil</td>
<td colspan="4">St. Clair</td>
</tr>
<tr class="odd">
<td colspan="3">Oregon Watershed Enhancement Board (OWEB)</td>
<td colspan="2">Alan</td>
<td>Miller</td>
<td colspan="6">US Geological Survey (USGS)</td>
</tr>
<tr class="even">
<td colspan="2">Greg</td>
<td>Sieglitz</td>
<td colspan="2">Mary Lou</td>
<td>Welby</td>
<td colspan="2">Pat</td>
<td colspan="4">Connolly</td>
</tr>
<tr class="odd">
<td colspan="3">Oregon Water Resources (OWR)</td>
<td colspan="2">Tom</td>
<td>Friedrichson</td>
<td colspan="6">Utah State University (USU)</td>
</tr>
<tr class="even">
<td colspan="2">Kyle</td>
<td>Sullivan</td>
<td colspan="2">Rich</td>
<td>Greitz</td>
<td colspan="2">Phaedra</td>
<td colspan="4">Budy</td>
</tr>
<tr class="odd">
<td colspan="2">Ron</td>
<td>Miner</td>
<td colspan="2">Dave</td>
<td>Stockdale</td>
<td colspan="2">Jeff</td>
<td colspan="4">Kershner</td>
</tr>
<tr class="even">
<td colspan="2">Jason</td>
<td>Ecklund</td>
<td colspan="3">Ochoco National Forest</td>
<td colspan="2">Nick</td>
<td colspan="4">Weber</td>
</tr>
<tr class="odd">
<td colspan="2">Oregon Water Trust</td>
<td></td>
<td colspan="2">Barb</td>
<td>Fontaine</td>
<td colspan="6">Eco Logical Research, Inc. (ELR)</td>
</tr>
<tr class="even">
<td colspan="2">Kimberly</td>
<td>Schonek</td>
<td colspan="2">Barb</td>
<td>Franano</td>
<td colspan="2">Nick</td>
<td colspan="4">Bouwes</td>
</tr>
<tr class="odd">
<td colspan="3">Rocky Mountain Elk Foundation</td>
<td colspan="2">Barb</td>
<td>Fontaine</td>
<td colspan="6">University of Washington, Seattle (UW)</td>
</tr>
<tr class="even">
<td colspan="2">Shawn</td>
<td>Robertson</td>
<td colspan="2">Jim</td>
<td>Soupir</td>
<td colspan="2">Mimi</td>
<td colspan="4">D'Iorio</td>
</tr>
<tr class="odd">
<td colspan="2">Terraqua Inc</td>
<td></td>
<td colspan="3">Umatilla National Forest</td>
<td colspan="2">Miles</td>
<td colspan="4">Logston</td>
</tr>
<tr class="even">
<td colspan="2">Mike</td>
<td>Ward</td>
<td colspan="2">Caty</td>
<td>Clifton</td>
<td colspan="6">Watershed Sciences (WS)</td>
</tr>
<tr class="odd">
<td colspan="3">The Nature Conservancy (TNC)</td>
<td colspan="2">Kristy</td>
<td>Groves</td>
<td colspan="2">Russ</td>
<td colspan="4">Faux</td>
</tr>
<tr class="even">
<td colspan="2">Jeff</td>
<td>Fields</td>
<td colspan="2">Dolly</td>
<td>Robison</td>
<td colspan="2">Matt</td>
<td colspan="4">Boyd</td>
</tr>
<tr class="odd">
<td colspan="2">University of Oregon</td>
<td></td>
<td colspan="3">PIBO Monitoring (PIBO)</td>
<td colspan="2"></td>
<td colspan="4"></td>
</tr>
<tr class="even">
<td colspan="2">Patricia</td>
<td>McDowell</td>
<td colspan="2">Brett</td>
<td>Roper</td>
<td colspan="2"></td>
<td colspan="4"></td>
</tr>
<tr class="odd">
<td colspan="2">Hughes</td>
<td>Michael</td>
<td colspan="2">Eric</td>
<td>Archer</td>
<td colspan="2"></td>
<td colspan="4"></td>
</tr>
<tr class="even">
<td colspan="2">Colin</td>
<td>Kelley</td>
<td colspan="2">Boyd</td>
<td>Bouwes</td>
<td colspan="2"></td>
<td colspan="4"></td>
</tr>
<tr class="odd">
<td colspan="2"></td>
<td></td>
<td colspan="2">Rick</td>
<td>Henderson</td>
<td colspan="2"></td>
<td colspan="4"></td>
</tr>
</tbody>
</table>

[]{#__RefHeading___Toc125779725 .anchor}**Appendix B**

**Table 1. Inventory of tabular data in the John Day**

+------------+---------+-------+---------+-------------+------------+
| **Data     | **Data  | **Ye  | **A     | **C         | **Data     |
| type**     | descri  | ars** | gency** | ollection** | location** |
|            | ption** |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| cross      |         | 2004  | MNF     |             | MNF        |
| sections   |         |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| cross      |         |       | PI      |             | PIBO-USFS  |
| sections   |         |       | BO-USFS |             |            |
+------------+---------+-------+---------+-------------+------------+
| cross      |         | 2000  | EM      |             | EMAP-ODEQ  |
| sections   |         | -2004 | AP-ODEQ |             |            |
+------------+---------+-------+---------+-------------+------------+
| cross      |         |       | EPA     |             | EPA        |
| sections   |         |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | Chinook | 1999  | ODFW    | scale card  | ODFW,      |
|            |         | -2003 |         | data        |            |
|            |         |       |         |             | NOAAF      |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 1959  | ODFW    | redd        | ODFW,      |
|            | hinook, | -2003 |         | counts,     |            |
|            | St      |       |         | live fish   | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 2002  | ODFW    | trap        | ODFW,      |
|            | hinook, |       |         |             | NOAAF      |
|            | St      |       |         |             |            |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 2000  | ODFW    | seining     | ODFW,      |
|            | hinook, | -2004 |         |             |            |
|            | St      |       |         |             | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 2000  | ODFW    | snorkel     | ODFW,      |
|            | hinook, |       |         |             |            |
|            | St      |       |         |             | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 2001  | ODFW    | snorkel     | ODFW,      |
|            | hinook, |       |         |             |            |
|            | St      |       |         |             | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 2001  | ODFW    | trap        | ODFW,      |
|            | hinook, | and   |         |             |            |
|            | St      | 2003  |         |             | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 2003  | ODFW    | hook and    | ODFW,      |
|            | hinook, |       |         | line        |            |
|            | St      |       |         |             | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 1959  | ODFW    | redd        | ODFW,      |
|            | hinook, | -2003 |         | counts,     |            |
|            | St      |       |         | live fish   | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 1959  | ODFW    | spawner     | ODFW,      |
|            | hinook, | -1995 |         | count       |            |
|            | St      |       |         | s/estimates | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 1956  | ODFW    | estimated   | ODFW,      |
|            | hinook, | -1999 |         | harvest     |            |
|            | St      |       |         | counts      | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 1959  | ODFW    | redd        | ODFW,      |
|            | hinook, | -2001 |         | counts,     |            |
|            | St      |       |         | live fish   | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 1959  | ODFW    | spawner     | ODFW,      |
|            | hinook, | -1995 |         | count       |            |
|            | St      |       |         | s/estimates | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | C       | 1956  | ODFW    | estimated   | ODFW,      |
|            | hinook, | -1993 |         | harvest     |            |
|            | St      |       |         | counts      | NOAAF      |
|            | eelhead |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| fish       | redband | 199   | OSU     | snorkel     | OSU        |
|            | trout,  | 0s-pr |         |             |            |
|            | ste     | esent |         |             |            |
|            | elhead, |       |         |             |            |
|            | chinook |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| habitat    |         | 1990s | ODFW    |             | www.str    |
|            |         |       |         |             | eamnet.org |
+------------+---------+-------+---------+-------------+------------+
| habitat    |         | 2000  | ODFW    |             | Corvallis, |
|            |         | -2002 | R       |             | OR         |
|            |         |       | esearch |             |            |
|            |         |       | Lab,    |             |            |
|            |         |       | Co      |             |            |
|            |         |       | rvallis |             |            |
+------------+---------+-------+---------+-------------+------------+
| habitat    |         | 1990s | UNF     |             | USFS       |
|            |         |       |         |             |            |
|            |         |       |         |             | NRIS       |
|            |         |       |         |             | database   |
+------------+---------+-------+---------+-------------+------------+
| habitat    |         | 1990s | MNF     |             | USFS       |
|            |         |       |         |             |            |
|            |         |       |         |             | NRIS       |
|            |         |       |         |             | database   |
+------------+---------+-------+---------+-------------+------------+
| habitat    |         | 1990s | ONF     |             | USFS       |
|            |         |       |         |             |            |
|            |         |       |         |             | NRIS       |
|            |         |       |         |             | database   |
+------------+---------+-------+---------+-------------+------------+
| habitat    |         |       | CTWS    |             | CT         |
|            |         |       |         |             | WS-Prairie |
|            |         |       |         |             | City, OR   |
+------------+---------+-------+---------+-------------+------------+
| macroinv   |         | late  | OSU     |             | OSU        |
| ertebrates |         | 1990s |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| macroinv   |         |       | Upper   |             | Upper SFJD |
| ertebrates |         |       | SFJD    |             | Watershed  |
|            |         |       | Wa      |             | Council    |
|            |         |       | tershed |             |            |
|            |         |       | Council |             |            |
+------------+---------+-------+---------+-------------+------------+
| macroinv   |         | 2005  | E       |             | EcoLo      |
| ertebrates |         |       | coLogic |             | gical-Utah |
|            |         |       | al-Utah |             | State      |
|            |         |       | State   |             |            |
+------------+---------+-------+---------+-------------+------------+
| macroinv   |         | 2005  | OWRD    |             | OWRD       |
| ertebrates |         |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| macroinv   |         |       | TNC     |             | TNC        |
| ertebrates |         |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| **Data     | **Data  | **Ye  | **A     | **C         | **Data     |
| type**     | descri  | ars** | gency** | ollection** | location** |
|            | ption** |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| vegetation | mon     |       | TNC     |             | TNC        |
|            | itoring |       |         |             |            |
|            | plots   |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| vegetation | mon     |       | CTWS    |             | CTWS       |
|            | itoring |       |         |             |            |
|            | plots   |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      | water   |       |         |             | STORET     |
| quality    | and air |       |         |             |            |
|            | tempe   |       |         |             |            |
|            | rature, |       |         |             |            |
|            | nu      |       |         |             |            |
|            | trients |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      | June-O  | 1991  | ODFW    | continuous  | Streamnet, |
| quality    | ctober, | -1997 |         | loggers     | NOAAF      |
| (temp)     | 1995    |       |         |             |            |
|            | (42     |       |         |             |            |
|            | sites,  |       |         |             |            |
|            | se      |       |         |             |            |
|            | pt-oct) |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      | June-O  | 1998  | MNF     | continuous  | MNF,       |
| quality    | ctober, | -2003 |         | loggers     |            |
| (temp)     | some    |       |         |             | NOAAF      |
|            | May,    |       |         |             |            |
|            | some    |       |         |             |            |
|            | winter, |       |         |             |            |
|            | 1-6     |       |         |             |            |
|            | sites   |       |         |             |            |
|            | per     |       |         |             |            |
|            | stream  |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      | June-O  | 1993  | TNC     | continuous  | TNC,       |
| quality    | ctober, | -2003 |         | loggers     |            |
| (temp)     | 1-2     |       |         |             | NOAAF      |
|            | sites   |       |         |             |            |
|            | /stream |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      | 1-4     | 1993  | Baker   | continuous  | ONF,       |
| quality    | sites   | -2004 | RD, ONF | loggers     |            |
| (temp)     | /stream |       |         |             | NOAAF      |
+------------+---------+-------+---------+-------------+------------+
| water      | June-O  | 1993  | Blue Mt | continuous  | ONF,       |
| quality    | ctober, | -2004 | RD, ONF | loggers     |            |
| (temp)     | some    |       |         |             | NOAAF      |
|            | May,    |       |         |             |            |
|            | 1-3     |       |         |             |            |
|            | sites   |       |         |             |            |
|            | /stream |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      | June-O  | 1997  | Warm    | continuous  | CTWS,      |
| quality    | ctober, | -2004 | Springs | loggers     |            |
| (temp)     | some    |       |         |             | NOAAF      |
|            | winter, |       |         |             |            |
|            | 1-6     |       |         |             |            |
|            | sites   |       |         |             |            |
|            | /stream |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      | June-O  | 1999  | MSWCD   | continuous  | MSWCD,     |
| quality    | ctober, | -2004 |         | loggers     |            |
| (temp)     | 1-4     |       |         |             | NOAAF      |
|            | sites   |       |         |             |            |
|            | /stream |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      | 1-13    | 2000  | PIBO    | continuous  | PIBO       |
| quality    | sites   | -2004 |         | loggers     |            |
| (temp)     | /stream |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      |         | 2000  | ODFW    | continuous  | ODFW,      |
| quality    |         | -2002 | Bull    | loggers     |            |
| (temp)     |         |       | trout   |             | NOAAF      |
+------------+---------+-------+---------+-------------+------------+
| water      | full    | 2001  | ODFW    | continuous  | ODFW,      |
| quality    | years   | -2002 |         | loggers     |            |
| (temp)     | 1-6     |       |         |             | NOAAF      |
|            | sites   |       |         |             |            |
|            | /stream |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      | M       | 1988  | UNF     | continuous  | UNF,       |
| quality    | ay-Nov, | -2004 |         | loggers     |            |
| (temp)     | some    |       |         |             | NOA        |
|            | winter, |       |         |             | A-Fisheris |
|            | 2-19    |       |         |             |            |
|            | sites   |       |         |             |            |
|            | /stream |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      |         | 1993  | OSU     | FLIR and    | OSU        |
| quality    |         | -2004 |         | continuous  |            |
| (temp)     |         |       |         | loggers     |            |
+------------+---------+-------+---------+-------------+------------+
| water      |         | 1985  | BLM     | continuous  | BLM,       |
| quality    |         | -2003 |         | loggers     |            |
| (temp)     |         |       |         |             | NOAAF      |
+------------+---------+-------+---------+-------------+------------+
| water      |         | 1980  | EPA     | continuous  | STORET,    |
| quality    |         | -2003 |         | loggers     | DEQ, NOAAF |
| (temp)     |         |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+
| water      | J       | 2000  | ODEQ    | continuous  | NOAAF      |
| quality    | une-Dec | -2002 |         | loggers     |            |
| (temp)     | 1-2     |       |         |             |            |
|            | sites   |       |         |             |            |
|            | /stream |       |         |             |            |
+------------+---------+-------+---------+-------------+------------+

**Table 2. Inventory of spatial data assembled to date for John Day
pilot project**

**Basins**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

CRB_US.shp Columbia River Shapefile-polygon 1:24k NOAAF

HUC1_Region_reo.shp OR, WA, CA Shapefile-polygon 1:24k NOAAF

HUC1_Region_streamnet.shp PNW Shapefile-polygon 1:24k NOAAF

HUC2_SubRegion_reo.shp OR, WA, CA Shapefile-polygon 1:24k NOAAF

HUC2_SubRegion_streamnet.shp PNW Shapefile-polygon 1:24k NOAAF

HUC3_Basin_reo.shp OR, WA, CA Shapefile-polygon 1:24k NOAAF

HUC3_Basin_streamnet.shp PNW Shapefile-polygon 1:24k NOAAF

HUC4_SubBasin_reo.shp OR, WA, CA Shapefile-polygon 1:24k NOAAF

HUC4_SubBasin_streamnet.shp PNW Shapefile-polygon 1:24k NOAAF

HUC5_Watershed_reo.shp OR, WA, CA Shapefile-polygon 1:24k NOAAF

HUC6_sr OR, WA, CA ESRI-Grid 30m NOAAF

HUC6_SubWatershed_reo.shp PNW Shapefile-polygon 1:24k NOAAF

HUC6_SubWatershed_sr.shp PNW Shapefile-polygon 1:24k NOAAF

IMW_Total.shp PNW Shapefile-polygon 1:24k NOAAF

JohnDay_Basin.shp John Day Basin Shapefile-polygon 1:24k NOAAF

JohnDay_HUC6.shp John Day Basin Shapefile-polygon 1:24k NOAAF

NHD_Catchments PNW ESRI-Grid 30m NOAAF

**Boundaries**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

Land Ownership PNW Shapefile-polygon USBR

Malheur National Forest Malheur National Shapefile-polygon

Oregon.shp Oregon Shapefile-polygon 1:200k NOAAF

Township/Range Grid Umatilla National Shapefile-polygon

**Climate**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

GrowingDegreeDay PNW ESRI-Grid 2km NOAAF

Precip_MeanAnnual PNW ESRI-Grid 4km NOAAF

Precip_MeanApr PNW ESRI-Grid 4km NOAAF

Precip_MeanAug PNW ESRI-Grid 4km NOAAF

Precip_MeanDec PNW ESRI-Grid 4km NOAAF

Precip_MeanFeb PNW ESRI-Grid 4km NOAAF

Precip_MeanJan PNW ESRI-Grid 4km NOAAF

Precip_MeanJul PNW ESRI-Grid 4km NOAAF

Precip_MeanJun PNW ESRI-Grid 4km NOAAF

Precip_MeanMar PNW ESRI-Grid 4km NOAAF

Precip_MeanMay PNW ESRI-Grid 4km NOAAF

Precip_MeanNov PNW ESRI-Grid 4km NOAAF

Precip_MeanOct PNW ESRI-Grid 4km NOAAF

Precip_MeanSep PNW ESRI-Grid 4km NOAAF

Tempature_Max PNW ESRI-Grid 4km NOAAF

Temperature_Min PNW ESRI-Grid 4km NOAAF

**DEMs**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

10m_DEM Continental US ESRI-Grid 10m NOAAF

30m_DEM PNW ESRI-Grid 30m NOAAF

90m_DEM Continental US ESRI-Grid 90m NOAAF

90m_SRTM PNW ESRI-Grid 90m NOAAF

**Fish Distribution**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

Barriers-Anthropogenic Interior Columbia Shapefile-point NOAAF

Barriers-Natural Interior Columbia Shapefile-point NOAAF

Barriers-ODFW Oregon Shapefile-point

Bull Trout Distribution John Day Basin Shapefile-line 1:24k Streamnet

Bull Trout Distribution Oregon Shapefile-line 1:100k Streamnet

Chinook-Fall Distribution PNW Shapefile-line 1:100k Streamnet

Chinook-Spring Distribution PNW Shapefile-line 1:100k Streamnet

Chinook-Summer Distribution PNW Shapefile-line 1:100k Streamnet

Chum Distribution PNW Shapefile-line 1:100k Streamnet

Coho Distribution PNW Shapefile-line 1:100k Streamnet

Fish Screens John Day Basin Shapefile-line 1:12k USBR

Pink Distribution PNW Shapefile-line 1:100k Streamnet

Rainbow Trout Distribution Oregon Shapefile-line 1:100k Streamnet

Steelhead Distribution Oregon Shapefile-line 1:24k Streamnet

Steelhead-Summer Distribution PNW Shapefile-line 1:100k Streamnet

Steelhead-Winter Distribution PNW Shapefile-line 1:100k Streamnet

**Geology**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

OR_Geology Oregon Shapefile-polygon 1:500k NOAAF

PNW_Geology OR, WA, ID Shapefile-polygon 1:500k NOAAF

Soils - SSURGO Oregon Shapefile-polygon 1:24k NOAAF

Soils - STATSGO Oregon Shapefile-polygon 1:250k NOAAF

**Habitat**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

Aquatic Habitat Inventory John Day-Middle coverage

Aquatic Habitat Inventory John Day-North coverage

Aquatic Habitat Inventory John Day-South Shapefile-line

Aquatic Habitat Inventory John Day-Upper coverage

Chinook and Steelhead Redd Counts John Day Basin Shapefile-line

EMAP Reference Sites PNW Shapefile-point

Habitat Survey John Day Basin Shapefile-line 1:24k

ICB_200mReaches Interior Columbia Shapefile-line 1:100k NOAAF

Salmon and Steelhead Habitat Oregon coverage 1:100k

TMDL Oregon Shapefile-line

**Human Impact**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

Census Oregon Shapefile-polygon

Cities PNW Shapefile-point

Constructed Features Umatilla NF Umatilla National coverage

Culverts John Day Basin Shapefile-point

Diversions John Day Basin Shapefile-point 1:12k USBR

Grazing Allotments OR, WA Shapefile-polygon BLM OR/WA

Historic Roads Umatilla National coverage

John Day Basin Water Rights John Day Basin Shapefile-line USBR/OWRD

Major Highways John Day Basin Shapefile-line

Major Towns Umatilla National coverage

Mines PNW Shapefile-point

Pumps John Day Basin Shapefile-point 1:12k USBR

Roads_GTRN John Day Basin Shapefile-line 1:24k USBR

Water Rights Oregon Shapefile-line

**Hydrography**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

Banks John Day-South Shapefile-line

Listed 303d Streams Oregon Shapefile-line 1:100k

NHD_HighResolution PNW Shapefile-line 1:24k NOAAF

NHDPlus PNW Shapefile-line 1:100k NOAAF

ReachFileVersion3 PNW Coverage 1:100k NOAAF

**Imagery**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

Aerial Photo Imagery John Day Basin ESRI-Grid 1:12k USBR

LANDSAT5: TM data John Day Basin ESRI-Grid 35m USBR

Over 70 LANDSAT images Columbia River ESRI-Grid 30m NOAAF

USGS DOQ John Day Basin ESRI-Grid USGS

**Landscape**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

Ecoregions PNW coverage

Erosion Potential-Agricultural Interior Columbia Shapefile-polygon NOAAF

Erosion Potential-Forested Interior Columbia Shapefile-polygon NOAAF

Land Use Land Cover PNW ESRI-Grid

National Land Cover Dataset 1992 PNW ESRI-Grid 1:24k

Sediment Production-Agricultural John Day Basin Shapefile-line NOAAF

Sediment Production-Forested John Day Basin Shapefile-line NOAAF

**Vegetation**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

Current Vegetation Umatilla National coverage

Current-Historic Fuels Comparison Umatilla National coverage

Fire Frequency/Severity-Current OR, WA, ID ESRI-Grid

Fire History Ochoco National coverage

Fire History Umatilla National coverage

Fire Management Zones Umatilla National coverage

Harvest Malheur National coverage

Harvest Ochoco National coverage

Harvest Umatilla National coverage

Historic Timber Types Umatilla National coverage

Old Growth Stand Umatilla National coverage

Permanent Ecology Plots Blue Mountain coverage

Potential Vegetation Umatilla National coverage

Reforested Lands Ochoco National coverage

Timber Lands Umatilla National coverage

Timber Sales Ochoco National coverage

**Water Quality**

**Dataset** **Spatial Extent** **Data Type** **Scale** **Location**

FLIR 1998, 2002, 2003, 2004 John Day Basin ESRI-Grid ODEQ

John Day Temperature Gages John Day Basin Shapefile-point 1:100k

USGS Stream Gages PNW Shapefile-point 1:100k

Water Quality Station Umatilla National Coverage 1:24k

[]{#__RefHeading___Toc125779726 .anchor}Appendix C

Table 1: Potential Intensively Monitored Watershed studies in the South
Fork of the John Day River.

+-----+------+-------+---------+------+-----+-----+-------+-------+
| su  | loca | hypot | manip   | land | acc | pr  | s     | scale |
| bba | tion | hesis | ulation | o    | ess | e-i | cale- | -size |
| sin |      | (pro  |         | wner |     | nfo | tem   |       |
|     |      | blem) |         | ship |     |     | poral |       |
|     |      |       |         |      |     |     | res   |       |
|     |      |       |         |      |     |     | ponse |       |
+-----+------+-------+---------+------+-----+-----+-------+-------+
| So  | Main | 1.)   | remove  | pri  | sa  | OSU | 1+    | r     |
| uth | stem | pu    | push-up | vate | mpl | res | yrs   | each- |
| F   | SF   | sh-up | dams/   |      | ing | ear |       | 4 km  |
| ork |      | dams  |         |      | acc | ch, |       |       |
|     |      | res   | delay   |      | ess | FL  |       |       |
|     |      | trict | p       |      |     | IR, |       |       |
|     |      | mov   | ush-dam |      |     | Li  |       |       |
|     |      | ement | repl    |      |     | DAR |       |       |
|     |      |       | acement |      |     |     |       |       |
|     |      | 2.)   |         |      |     |     |       |       |
|     |      | pu    |         |      |     |     |       |       |
|     |      | sh-up |         |      |     |     |       |       |
|     |      | dams  |         |      |     |     |       |       |
|     |      | res   |         |      |     |     |       |       |
|     |      | trict |         |      |     |     |       |       |
|     |      | a     |         |      |     |     |       |       |
|     |      | ccess |         |      |     |     |       |       |
|     |      | to    |         |      |     |     |       |       |
|     |      | cool  |         |      |     |     |       |       |
|     |      | water |         |      |     |     |       |       |
|     |      | re    |         |      |     |     |       |       |
|     |      | fugia |         |      |     |     |       |       |
+-----+------+-------+---------+------+-----+-----+-------+-------+
|     | M    | 1.)   | remove  | ODFW | sa  | OSU | 1+    | r     |
|     | urde | pu    | push-up |      | mpl | res | yrs   | each- |
|     | rers | sh-up | dams/   |      | ing | ear |       | 12 km |
|     | C    | dams  |         |      | acc | ch, |       |       |
|     | reek | res   | delay   |      | ess | FL  |       |       |
|     |      | trict | p       |      |     | IR, |       |       |
|     |      | mov   | ush-dam |      |     | Li  |       |       |
|     |      | ement | repl    |      |     | DAR |       |       |
|     |      |       | acement |      |     |     |       |       |
|     |      | 2.)   |         |      |     |     |       |       |
|     |      | pu    |         |      |     |     |       |       |
|     |      | sh-up |         |      |     |     |       |       |
|     |      | dams  |         |      |     |     |       |       |
|     |      | res   |         |      |     |     |       |       |
|     |      | trict |         |      |     |     |       |       |
|     |      | a     |         |      |     |     |       |       |
|     |      | ccess |         |      |     |     |       |       |
|     |      | to    |         |      |     |     |       |       |
|     |      | cool  |         |      |     |     |       |       |
|     |      | water |         |      |     |     |       |       |
|     |      | re    |         |      |     |     |       |       |
|     |      | fugia |         |      |     |     |       |       |
+-----+------+-------+---------+------+-----+-----+-------+-------+
|     | B    | p     | Comp    | Wi   | sa  | OSU | Use   | rea   |
|     | lack | rohib | arative | lder | mpl | res | wood  | ch-16 |
|     | Ca   | iting | chronos | ness | ing | ear | c     | km    |
|     | nyon | gr    | equence | Area | acc | ch, | oring |       |
|     |      | azing | study.  |      | ess | FL  | techn |       |
|     |      | and   | Compare |      |     | IR, | iques |       |
|     |      | all   | with    |      |     | Li  | and   |       |
|     |      | owing | represe |      |     | DAR | histo |       |
|     |      | na    | ntative |      |     |     | rical |       |
|     |      | tural | streams |      |     |     | a     |       |
|     |      | fire  | where   |      |     |     | erial |       |
|     |      | c     | grazing |      |     |     | p     |       |
|     |      | ycles | and     |      |     |     | hotog |       |
|     |      | can   | fire    |      |     |     | raphs |       |
|     |      | lead  | supp    |      |     |     | to    |       |
|     |      | to    | ression |      |     |     | co    |       |
|     |      | gr    | is      |      |     |     | mpare |       |
|     |      | eater | allowed |      |     |     | ch    |       |
|     |      | fish  | and     |      |     |     | anges |       |
|     |      | hab   | d       |      |     |     | in    |       |
|     |      | itats | ocument |      |     |     | lands |       |
|     |      | th    | large   |      |     |     | cape, |       |
|     |      | rough | wood,   |      |     |     | river |       |
|     |      | input | habitat |      |     |     | ch    |       |
|     |      | of    | use and |      |     |     | annel |       |
|     |      | large | fish    |      |     |     | and   |       |
|     |      | wood. | den     |      |     |     | large |       |
|     |      |       | sities. |      |     |     | wood  |       |
|     |      |       |         |      |     |     | struc |       |
|     |      |       |         |      |     |     | tural |       |
|     |      |       |         |      |     |     | attri |       |
|     |      |       |         |      |     |     | butes |       |
+-----+------+-------+---------+------+-----+-----+-------+-------+

Table 1 (cont.): Potential Intensively Monitored Watershed studies in
the South Fork of the John Day River.

<table style="width:100%;">
<colgroup>
<col style="width: 7%" />
<col style="width: 8%" />
<col style="width: 9%" />
<col style="width: 11%" />
<col style="width: 8%" />
<col style="width: 9%" />
<col style="width: 10%" />
<col style="width: 7%" />
<col style="width: 6%" />
<col style="width: 9%" />
<col style="width: 9%" />
</colgroup>
<tbody>
<tr class="odd">
<td>subbasin</td>
<td>location</td>
<td>controls</td>
<td>biological response metric</td>
<td>potential for response</td>
<td>physical response metric</td>
<td>representative</td>
<td>ESA</td>
<td>TMDL ?</td>
<td>cooperators</td>
<td>lead</td>
</tr>
<tr class="even">
<td>South Fork</td>
<td>Mainstem SF</td>
<td>Black Canyon, Deer Canyon, Wind Creek, SF mainstem</td>
<td><p>fish movement (Hughes and Grand), fish growth, fat,
production?</p>
<p>Secondary production (maybe difficult), ppr</p></td>
<td>quick, effect size?</td>
<td>Increase connectivity, decrease barriers.</td>
<td>yes</td>
<td>yes, steelhead</td>
<td>yes</td>
<td>AFG</td>
<td>Hiram, Jim, Tim</td>
</tr>
<tr class="odd">
<td></td>
<td>Murderers Creek</td>
<td>Black Canyon, Deer Canyon, Wind Creek, SF mainstem</td>
<td><p>fish movement (Hughes and Grand), fish growth, fat,
production?</p>
<p>Secondary production (maybe difficult), ppr</p></td>
<td>quick, effect size?</td>
<td>Increase connectivity, decrease barriers</td>
<td>yes</td>
<td>yes, steelhead</td>
<td>yes</td>
<td>AFG</td>
<td>Hiram, Jim, Tim</td>
</tr>
<tr class="even">
<td></td>
<td>Black Canyon</td>
<td>May require comparisons with other similar watersheds with different
fire histories in NFJD, MFJD, and upper mainstem, e.g., Tower fire, Mt.
Baldy fire.</td>
<td>Patterns of habitat use and fish density. Relative distribution and
extent of redds. Standardize for watershed area, habitat size, stream
gradient, valley type, mean annual ppt. and elevation.</td>
<td>Substitute space for time.</td>
<td>Examine factors influenced by fire (e.g., sediment, stream
temperature, large wood distribution, retention time &amp; length of
nutrient spirals)</td>
<td>With careful selection of other sites, may compare how fire policy
can influence habitat restoration.</td>
<td>yes, steelhead</td>
<td>yes</td>
<td>AFG</td>
<td>Hiram, Jim, Tim</td>
</tr>
</tbody>
</table>

Table 2: Potential Intensively Monitored Watershed studies in the Middle
Fork of the John Day River.

+-----+-----+----------+--------+-------+------+--------+-----+-------+
| su  | lo  | hy       | manipu | land  | ac   | pre    | sca | scale |
| bba | cat | pothesis | lation | owne  | cess | -infor | le- | -size |
| sin | ion | (        |        | rship |      | mation | te  |       |
|     |     | problem) |        |       |      |        | mpo |       |
|     |     |          |        |       |      |        | ral |       |
|     |     |          |        |       |      |        | re  |       |
|     |     |          |        |       |      |        | spo |       |
|     |     |          |        |       |      |        | nse |       |
+-----+-----+----------+--------+-------+------+--------+-----+-------+
| Mid | Ma  | loss of  | e      | Warm  | ac   | See    | 15+ | Large |
| dle | ins | riparian | xclude | Spr   | cess | Powe   | ye  | water |
| F   | tem | habitat  | cattle | ings, | to   | rpoint | ars | shed. |
| ork | MF  | has lead | from 5 | TNC,  | w    | S      |     | Many  |
|     |     | to       | miles  | Fo    | hole | lide\" |     | Subb  |
|     |     | disfu    | ri     | rrest | s    | OSU    |     | asins |
|     |     | nctional | parian | R     | tudy | S      |     | are   |
|     |     | stream   | area,  | anch, | area | patial |     | large |
|     |     | system   | Oregon | Oxbow | (but | D      |     | (e.g. |
|     |     | (loss of | Water  | R     | some | ata\", |     | Big   |
|     |     | pools,   | Trust  | anch, | rea  | recent |     | C     |
|     |     | increase | may    |       | ches | ODFW   |     | reek, |
|     |     | temp     | still  | pote  | are  | h      |     | Grani |
|     |     | erature, | have a | ntial | s    | abitat |     | te-Bo |
|     |     | high     | lease  | for   | till | inv    |     | ulder |
|     |     | se       | on     | an    | in   | entory |     | C     |
|     |     | diment). | water  | other | pri  | on     |     | reek, |
|     |     | Grazing, | rights | ranch | vate | entire |     | Camp  |
|     |     | fire     | in a   | to    | ha   | CTWSR  |     | C     |
|     |     | distu    | key    | pa    | nds) |        |     | reek, |
|     |     | rbances, | reach  | rtici |      |        |     | Clear |
|     |     | logging  | in the | pate, |      |        |     | C     |
|     |     | on south | Austin | Pa    |      |        |     | reek, |
|     |     | flowing  | Ju     | ckett |      |        |     | Big   |
|     |     | wa       | nction | pro   |      |        |     | Bou   |
|     |     | tersheds | vic    | perty |      |        |     | lder) |
|     |     |          | inity. | downs |      |        |     |       |
|     |     |          | Steven | tream |      |        |     |       |
|     |     |          | P      |       |      |        |     |       |
|     |     |          | arrett |       |      |        |     |       |
|     |     |          | is     |       |      |        |     |       |
|     |     |          | c      |       |      |        |     |       |
|     |     |          | ontact |       |      |        |     |       |
|     |     |          | at     |       |      |        |     |       |
|     |     |          | Oregon |       |      |        |     |       |
|     |     |          | Water  |       |      |        |     |       |
|     |     |          | Trust. |       |      |        |     |       |
|     |     |          | Po     |       |      |        |     |       |
|     |     |          | ssible |       |      |        |     |       |
|     |     |          | water  |       |      |        |     |       |
|     |     |          | div    |       |      |        |     |       |
|     |     |          | ersion |       |      |        |     |       |
|     |     |          | manipu |       |      |        |     |       |
|     |     |          | lation |       |      |        |     |       |
|     |     |          | on old |       |      |        |     |       |
|     |     |          | V      |       |      |        |     |       |
|     |     |          | idondo |       |      |        |     |       |
|     |     |          | Ranch. |       |      |        |     |       |
+-----+-----+----------+--------+-------+------+--------+-----+-------+
|     | Bri | Removal  | small  | ranch | li   | sp     | 1+  | wate  |
|     | dge | of       | dam    | and   | kely | awning | ye  | rshed |
|     | Cr  | r        | r      | pu    |      | ground | ars | for   |
|     | eek | eservoir | emoval | blic, |      | su     |     | mov   |
|     | to  | would    |        | USFS, |      | rveys, |     | ement |
|     | ma  | increase |        | above |      | BLM    |     |       |
|     | ins | fish     |        |       |      | Prin   |     | reach |
|     | tem | access   |        |       |      | eville |     | for   |
|     | MF  | decrease |        |       |      | Fish   |     | t     |
|     |     | water    |        |       |      | Inve   |     | emper |
|     |     | temp     |        |       |      | ntory, |     | ature |
|     |     | eratures |        |       |      | late   |     |       |
|     |     |          |        |       |      | 1      |     |       |
|     |     |          |        |       |      | 980\'s |     |       |
+-----+-----+----------+--------+-------+------+--------+-----+-------+

Table 2 (cont.): Potential Intensively Monitored Watershed studies in
the Middle Fork of the John Day River.

  ---------- ---------- ------------- ----------------------- ----------- ------------- ----------------- ------------ ------- ------------- ------
  subbasin   location   controls      biological response     potential   physical      representative    ESA          TMDL?   cooperators   lead
                                      metric                  for         response                                                           
                                                              response    metric                                                             

  Middle     Mainstem   upstream/     juvenile                long term   temperature   Must be careful   yes,         yes     CTWSR, TNC,   Alex
  Fork       MF         downstream-   abundance/production,               and increase  to avoid          steelhead,           private land  
                        distance      spawner abundance                   in habitat    confounding       bull trout           owners        
                                                                                        effects,                                             
                                                                                        (rrigation,                                          
                                                                                        grazing, timber                                      
                                                                                        harvest,                                             
                                                                                        channelization,                                      
                                                                                        road                                                 
                                                                                        construction,                                        
                                                                                        mining impacts,                                      
                                                                                        wild fire, water                                     
                                                                                        donations,                                           
                                                                                        passive and                                          
                                                                                        active                                               
                                                                                        restoration,                                         
                                                                                        installation of                                      
                                                                                        instream                                             
                                                                                        structures,                                          
                                                                                        fencing), but                                        
                                                                                        basin is large                                       
                                                                                        and appropriate                                      
                                                                                        contrasts may be                                     
                                                                                        possible within                                      
                                                                                        drainages.                                           

             Bridge     Clear Creek,  movement and            quick,      temperature   somewhat unique   yes,         yes in                
             Creek to   Vincent,      productivity , juvenile effect      and increase  but gets at       steelhead    MF                    
             mainstem   others?       density in reach below  size?,      in habitat    passage and                                          
             MF                       dam, redds/mile         large below               temperature                                          
                                                              dam                       impacts                                              
  ---------- ---------- ------------- ----------------------- ----------- ------------- ----------------- ------------ ------- ------------- ------

Table 3: Potential Intensively Monitored Watershed studies in the North
Fork and Mainstem of the John Day River.

  ---------- ------------ -------------- -------------- -------------- ---------- ----------------- --------------- -----------
  subbasin   location     hypothesis     manipulation   land ownership access     pre-information   scale- temporal scale -size
                          (problem)                                                                 response        

  North Fork Granite      increase in    level mine     FS, private    likely     OSU Spatial Data, 15+             watershed
             Creek to NF  channel        tailings                      high       EMAP site, FLIR                   
                          complexity and                                                                            
                          flood plain                                                                               
                          connectivity                                                                              
                          to increase                                                                               
                          spawing                                                                                   
                          habitat                                                                                   

             Upper        fire and clear natural        FS             likely     OSU Spatial Data, happen 10 yrs   large
             NF/Trail     leads to an    restoration                   high       EMAP site, FLIR   ago, 10+ yrs    watershed
             Creek        increase in                                             1998                              
                          temperature                                                                               
                          for large                                                                                 
                          section of the                                                                            
                          NF                                                                                        

             Desolation   Installation   Evaluate       USFS & private Likely     OSU Research      Depends on type Large
                          of instream    impact of      in-holdings.   high       1989, but sketchy of study.       watershed
                          structures to  brook trout on                           stuff. I          Removal of      
                          increase       bull trout and                           snapshot.         exotics on the  
                          pools, role of red band                                                   production on   
                          which not      trout? What is                                             survival and    
                          evaluated.     the                                                        the degree of   
                                         distribution                                               hybridization   
                                         and abundance                                              of brookies     
                                         of natural vs.                                             with bull trout 
                                         man-made pools                                             may take less   
                                         (too few to                                                than 5 years.   
                                         make a                                                                     
                                         difference).                                                               
                                         Compare                                                                    
                                         functions and                                                              
                                         fish densities                                                             
                                         of natural vs.                                                             
                                         man-made                                                                   
                                         pools? This                                                                
                                         gets hairy.                                                                

  Lower      Bridge Creek channel        sediment traps Park Service   sampling   little to no data 10+ years       reach -
  Mainstem   to MS        inciscion                     and BLM,       access                                       10km??
                          results in                    private                                                     
                          loss of                                                                                   
                          rearing                                                                                   
                          habitat,                                                                                  
                          spawing                                                                                   
                          habitat                                                                                   
                          increase temp                                                                             

             Pine Creek   channel                       Warm Springs,             Wayne Bowers and                  
             to Lower     inciscion                     ranch                     Tim Unterwegner                   
             MS????       results in                                              may have some                     
                          loss of                                                 data as they                      
                          rearing                                                 worked on                         
                          habitat,                                                projects with                     
                          spawning                                                OMSI in that                      
                          habitat                                                 region.                           
                          increase temp                                                                             
  ---------- ------------ -------------- -------------- -------------- ---------- ----------------- --------------- -----------

Table 3 (cont.) : Potential Intensively Monitored Watershed studies in
the North Fork and Mainstem of the John Day River.

  ---------- ------------ ------------- --------------- ----------- -------------- ---------------- ------------ ------- ------------- ---------
  subbasin   location     controls      biological      potential   physical       representative   ESA          TMDL?   cooperators   lead
                                        response metric for         response                                                           
                                                        response    metric                                                             

  North Fork Granite      Bull Run,     increase        large       channel        toxins unique,   bulltrout,   maybe   FS, ODFW      Jeff
             Creek to NF  other?        redds/mile,                 complexity,    mining           steelhead                          Neal,
                                                                    temperature    broadscale                                          ODFW

             Upper NF/    timeseries    productivity    large       temperature    large scale      steelhead    Yes     FS ODEQ       Don
             Trail Creek  appoarch,     and redds/mile              and increase   restoration      and                                
                          Baldy or                                  in habitat                      bulltrout                          
                          Crane                                                                                                        

             Desolation   Pre-post      degree of       Quick?      natural vs.                                                        Michael
                          study.        hybridization               man-made                                                           Pollock
                          Possibility   and relative                pools. Pool                                                        
                          of using      composition of              complexity?                                                        
                          upper         pure vs. hybrid             Additional                                                         
                          mainstem      bull trout?                 pool volume                                                        
                          sites to      Compare                     and habitat                                                        
                          examine the   productivity of             for entire                                                         
                          influence on  natural vs.                 watershed?                                                         
                          brook trout   man-made pools                                                                                 
                          on bull       (must                                                                                          
                          trout,        understand                                                                                     
                          redbands, but patterns of                                                                                    
                          upper         movement for                                                                                   
                          mainstem also this to provide                                                                                
                          has westslope dividends)                                                                                     
                          cutthroat                                                                                                    
                          trout. Might                                                                                                 
                          be able to                                                                                                   
                          use Cable                                                                                                    
                          Creek, a                                                                                                     
                          tributary to                                                                                                 
                          Camas Creek                                                                                                  
                          as a control.                                                                                                

  Lower      Bridge Creek Rock Creek,   fish                        flow,          yes              yes,         no      Park Service, Michael
  Mainstem   to MS        others?       productivity                temperature,                    steelhead            BLM           Pollock
                                                                    sediments,                                                         
                                                                    riparian                                                           
                                                                    habitat,                                                           
                                                                    channel                                                            
                                                                    complexity,                                                        
                                                                    etc.                                                               

             Pine Creek                                                                                                                Michael
             to Lower                                                                                                                  Pollock
             Mainstem                                                                                                                  
  ---------- ------------ ------------- --------------- ----------- -------------- ---------------- ------------ ------- ------------- ---------

[]{#__RefHeading___Toc125779727 .anchor}**Appendix D**

The table below provides a perspective of limiting factors based on the
EDT diagnostics reports for each population using 5^th^ field HUCs to
aggregate information for approximately 850 river reach segments. The
three or four most important HUCs affecting each population are listed.
The final assessment will include final EDT model runs which will
include another 400+ reaches and attribute ratings will be weighted
across reaches for each life stage within the 5^th^ HUC. This will allow
us to account for different reach lengths and to evaluate habitat
impacts from and ecological life history perspective.

Table 1. Qualitative list of priority HUCs and habitat attributes
affecting Chinook populations in the John Day River, OR (based on EDT
runs submitted on 11/02/04).

  --------- ------------- -------------- -------------- -------------- -------------
  SPECIES   POPULATION    PRIORITY\      SIGNIFICANT                   
                          5th HUCs       HABITAT                       
                                         ATTRIBUTES                    

                                         PRIMARY        SECONDARY      TERTIARY

  CHINOOK   Granite Creek Granite Cr.    diversity      sediment\      stability\
                                                        quantity       flow

                          NF JDR Big Cr. quantity       diversity\     
                                                        temperature    

                          NF JDR Potamus diversity\     temperature    
                          Cr.            quantity                      

            Middle Fork   Camp Cr.       temperature\   diversity\     stability\
                                         quantity       sediment       flow

                          Big Cr.        diversity\     stability\     
                                         temperature\   food\          
                                         quantity       flow\          
                                                        sediment       

                          Upper MF JDR   diversity\     stability\     
                                         sediment\      flow\          
                                         quantity       temperature    

            North Fork    NF Big Cr.     diversity\     stability\     
                                         temperature\   flow           
                                         quantity                      

                          NF JDR Potamus diversity\     stability\     
                          Cr.            temperature\   food\          
                                         quantity       flow\          
                                                        sediment       

                          Upper NF JDR   quantity       diversity\     
                                                        temperature\   
                                                        sediment       

            Upper John    Canyon Cr.     diversity      quantity       stability\
            Day                                                        flow\
                                                                       temperature

                          Strawberry Cr. quantity       diversity\     stability\
                                                        temperature    flow
  --------- ------------- -------------- -------------- -------------- -------------

Table 2. Qualitative list of priority HUCs and habitat attributes
affecting steelhead populations in the John Day River, OR (based on EDT
runs submitted on 11/02/04).

  ----------- ------------- -------------- --------------- ------------- -------------
  SPECIES     POPULATION    PRIORITY\      SIGNIFICANT                   
                            5th HUCs       HABITAT                       
                                           ATTRIBUTES                    

                                           PRIMARY         SECONDARY     TERTIARY

                            Laycock Cr.    quantity        diversity     stability\
                                                                         diversity\
                                                                         flow\
                                                                         sediment\
                                                                         temperature

  STEELHEAD   Lower John    Rock Cr.       obstructions\   stability\    
              Day                          sediment\       diversity\    
                                           quantity        flow\         
                                                           sediment      

                            Mountain Cr.   stability\      flow\         
                                           diversity\      pathogens     
                                           temperature\                  
                                           sediment\                     
                                           quantity                      

                            Bridge Cr.     obstructions\   diversity\    stability\
                                           sediment\       temperature   flow
                                           quantity                      

              North Fork    NF JDR Big Cr. temperature\    stability\    
                                           quantity        diversity     

                            NF JDA Potamus diversity\      flow\         
                            Cr.            temperature\    sediment      
                                           quantity                      

                            Cottonwood Cr. sediment\       flow\         
                                           temperature\    diversity     
                                           quantity                      

                            Desolation Cr. sediment\                     
                                           quantity                      

                            Upper Camas    sediment\       stability\    
                            Cr.            quantity        flow\         
                                                           temperature   

              Middle Fork   Camp Cr.       sediment\       stability\    
                                           quantity        flow\         
                                                           temperature   

                            Big Cr.        diversity\      stability\    
                                           temperature\    food\         
                                           quantity        flow\         
                                                           sediment      

                            Long Cr.       quantity        flow\         
                                                           diversity\    
                                                           sediment\     
                                                           temperature   
  ----------- ------------- -------------- --------------- ------------- -------------

Table 2 (cont.)

  --------- ------------- -------------- --------------- ------------- ----------
  SPECIES   POPULATION    PRIORITY\      SIGNIFICANT                   
                          5th HUCs       HABITAT                       
                                         ATTRIBUTES                    

                                         PRIMARY         SECONDARY     TERTIARY

            South Fork    Lower SF JDR   sediment\       flow\         
                                         temperature\    diversity     
                                         quantity                      

                          Murderers Cr.  sediment\       flow\         
                                         quantity        diversity\    
                                                         temperature   

                          JDR Johnson    quantity        diversity\    
                          Cr.                            predation\    
                                                         sediment\     
                                                         temperature   

                          Lower JDR      diversity\      predation\    
                          Muddy Cr.      quantity        temperature   

            Upper John    Canyon Cr.     quantity        stability\    
            Day                                          flow\         
                                                         diversity\    
                                                         sediment\     
                                                         temperature   

                          Strawberry Cr. stability\      stability\    
                                         diversity\      flow          
                                         temperature\                  
                                         sediment                      

                          Beech Cr.      obstructions\   flow\         
                                         sediment        diversity\    
                                                         quantity\     
                                                         temperature   
  --------- ------------- -------------- --------------- ------------- ----------