DRAFT

– May 13, 2015



# Northwest Forest Plan

– The First 20 Years ( 1994

- 20 13 )

Watershed Condition Status and Trend

Stephanie A. Miller , Sean N. Gordon, Peter Eldred, Ronald M. Beloin , Steve Wilcox, Mark

Raggon, Heidi Andersen and Ariel Muldoon

Authors

Stephanie Miller is the pr ogram manager for the Aquatic and Riparian Effectiveness Monitoring Program, U.S. Department of the Interior, Bureau of Land Management, Oregon State Office, 3200 SW Jefferson Way, Corvallis, OR 97333, Peter Eldred is the geographic information system (GIS ) coordinator, Ronald Beloin is the data manager, Steve Wilcox is the GIS cartographer, Mark Raggon and Heidi Andersen are fish biologists for the Aquatic and Riparian Effectiveness Monitoring Program, U.S. Department of Agriculture, Forest Service,

Pacifi c Northwest Region, 3200 SW Jefferson Way, Corvallis, OR 97333; Sean Gordon is a research assistant professor with the Institute for Sustainable Solutions, Portland State University, 1600 SW Fourth Ave., Portland, OR 97204. Ariel Muldoon is a research cons ulting

statistician with the College of Forestry, Oregon State University, Corvallis, OR 97330.DRAFT

– May 13, 2015

A bstract

Miller , St ephanie A .; Gordon, Sean N.; Eldred, Peter; Beloin, Ronald M. ; Wilcox, Steve; Raggon, Mark ; Andersen, Heidi ; Muldoon, Ariel

. 201 4 . Northwes t Forest Plan

– the first 20 years (1994

- 20 13 ): watershed condition status and trend . Gen. Tech. Rep. PNW

- GTR

- XXX. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Xxx p.

We used two data sets to evaluate str eam and upslope / riparian condition for sixth

- field watersheds in each aquatic province within the Northwest Forest Plan (NWFP) area. The stream
evaluation was based on stream sampling data collected from 2002 to the 20 13 ( 21 4 watersheds) as part of a n eigh t year repeating (rotating) sample design. We are currently halfway through our

second rotation of stream sampling, and have repeated 110 watersheds since the second rotation began in 2009 . The analysis presented in this report uses roughly half the number of watersheds as was originally intended by the sample design since re

- visitation will not be completed until 2017. In the meantime, we compare the first rotation of visits (2002

- 2009) to the first four years

of the s econd rotation (2010

- 2013) giving us a general idea of current patterns. To evaluate

stream condition we used a reference condition nearest neighbor statistical approach to calculate

the stream physical habitat scores bas ed on substrate, wood, and pool metrics for each watershed.

Macroinverte brates and water temperature were analyzed separate ly from physical habitat and provide additional assessment of overall watershed condition. Scores for both stream and upslope / riparian conditions were normalized to fall between 0 (poor) and 100 (good).

Fo r both rotations, more than 65 percent of the stream scores fell between 40 and 60 and relatively few (2 percent) were less than 20; no watershed scored above 80 during the second rotation and only 12 percent during the first. For watersheds with scores un der 20 , the pool score component of the physical habitat was often the most influential factor. W e detected improving

status trend s in both aquatic macroinvertebrates scores and water temperature. No trend was

detected in the overall stream physical habita t condition across the Northwest Forest plan area. This result is consistent with expectations based on the Aquatic Conservation Strategy (ACS) (FEMAT 1993), as detection of changes in stream watershed condition s were not expected for several decades. Comp leting the current and future rotations will inform these trends.

U pslope / riparian conditions were evaluated for federal lands in all 1, 974 sixth

- field

watersheds in the NWFP area with at least 5 percent federal ownership. The assessment was

based on facto rs affecting five major aquatic processes : sediment production and delivery (mass wasting) , wood production and delivery , riparian habitat, hydrolog ic processes (specifically peak flows) , and fish passage. Impacts were based on mapped data, including road metrics from U.S.

Forest Service and Bureau of Land Management geographic information system road layers and vegetation metrics derived from satellite imagery.

Upslope/riparian condition scores were calculated for 1993 and 2012, and the difference

between these scores was used to represent trend. In 2012, 26 percent of the overall watershed

area received score s above 80, 68 percent scored between 40 and 80, and only 6 percent scored

below 40

. Less than 1 percent of the area scored below 20 in both 1993 and 2012 status

assessments. Since 1993 , scores in 16 percent of the NWFP area increased by more than 5 percent while only 7 percent declined by a similar magnitude. Though at the plan level the mean score changed little (+1), there were broad scale moderate gains due to vegetation growth and3

larger but more concentrated gains due to road decommissioning

. These gains, which occurred predominantly in the areas most heavily managed prior to the NWFP, were largely offset by high score declines due to large fires

, particularly in reserve areas

. Keywords : Effectiveness monitoring, status and trend monitoring, aquatic ecosystems, riparian ecosystems, watersheds, decision

- support models, Northwest Forest Plan, aquatic conservation strategy, Pacific Northwest.4 Pr eface

The effectiveness monitoring program plan for the Northwest Forest Plan (NWFP) was approved by an Intergovernmental Advisory Committee in 1995 to meet the requirements for tracking status and trend of watershed condition, late successional old growth , population and habitats of northern spotted owls and marbled murrelets, social and economic conditions, and tribal relationships. Monitoring is conducted in 1

- to 5

- or 8

- year intervals depending on the

program

. Monitoring results for the first 10 (Gallo et al. 2005) and 15 (Lanigan et al. 2012) years were documented in a series of general technical reports available online at http://www.fs.fed.us/pnw/publications/gtrs.shtml. This report covers the first 20 years of the plan

.

Summary

The watershed monit oring module (also known as the Aquatic and Riparian Effectiveness Monitoring Program or AREMP) determines if the Northwest Forest Plan’s (NWFP) aquatic conservation strategy is achieving the goals of maintaining and restoring the condition of

watersheds. The NWFP area being evaluated includes USDA Forest Service (FS), USDI Bureau of Land Management (BLM), and USDI National Park Service (NPS) lands. Only the federal portion of sixth

- field watersheds was included when determining watershed condition status a nd trend because federal agency land managers have no jurisdiction over nonfederal lands. Overall results are also broken down by the NWFP land use management allocations and by key versus nonkey watershed designations.

We evaluate d stream and upslope/ripa rian condition for each aquatic province within the

NWFP. The stream evaluation was based on stream data (e.g., substrate, pieces of large wood, percentage of pool tail fines

, water temperature, and macroinvertebrates) sampled from 2002 to

20 13 ( 214 waters heds) as part of a repeating (i.e., rotation) sample design. We are currently halfway through our second rotation of stream sampling, and have repeated 110 watersheds since

the second rotation began in 2009 . This analysis uses roughly half the number of wa tersheds as was originally intended by the sample design since re

- visitation will not be completed until 2017

. In the meantime, comparing the first rotation of visits (2002

- 2009) to the first four years of the second rotation (2010

- 2013) as well as estimat ing the yearly trend in status scores gives a general idea of current patterns. We used a reference condition nearest neighbor statistical approach to calculate the physical habitat scores for each watershed. Macroinvertebrates and

water temperature were analyzed separate from physical habitat and provide additional

assessment of overall watershed condition. Scores for both the stream and upslope/ riparian assessments were normalized to fall between 0 (poor) and 100 (good) (Al

- Chokhachy et al. 2011).

For bo th rotations, more than 65 percent of the stream scores fell between 40 and 60 and relatively few (2 percent) were less than 20; no watershed scored above 80 during the second rotation and only 12 percent during the first. For watersheds with scores under 20, the pool score component of the physical habitat was often the most influential factor.

We detected improving trend s in the yearly status scores for aquatic macroinvertebrates and

in yearly seven day average water temperature s

. F uture sampling will rev eal whether this

increase in aquatic invertebrate assemblages and reduction in mean watershed temperatures5

persists

. No trend was detected in the overall physical habitat stream condition across the Northwest Forest plan area. This is consistent with expec tations b ased on the Aquatic

Conservation Strategy (ACS) (FEMAT 1993), as detection of changes in stream watershed

condition were not expected for several decades. Completing the current and future rotations based on our current sampling design will inform these trends. Since the second rotation is not scheduled to be completed until 2017, any rotational trend results should be considered preliminary as we have not achieved design sample size.

Upslope / riparian conditions were evaluated for federal lands in all 1,974 sixth

- field

watersheds in the NWFP area with at least 5 percent f ederal ownership. The assessment was based on factors affecting five major aquatic processes: sediment production and delivery (mass wasting), wood production and delivery, riparia n habitat, hydrologic processes (specifically peak flows), and fish passage. The status of each process was estimated based on impacts of road densities and vegetation conditions derived from mapped data, including road metrics from U.S.

Forest Service an d Bureau of Land Management geographic information system road layers and vegetation metrics derived from satellite imagery.

In 2012, 26 percent of watershed s scored above 80 , 68 percent scored between 40 and 80, and only 6 percent scored below 40. Less t han 1 percent of the area scored below 20 in 1993 and 2012 status assessments. Since 1993, scores in 16 percent of the NWFP area increased by more than 5 percent while only 7 percent declined by a similar magnitude. Though at the plan level the

mean score changed little (+1), an increase in scores was especially noticeable as a shift from

scores in the low to mid

- range (15

- 50) to the higher range (60

- 90). T here were broad

- scale moderate gains due to vegetation growth and larger but more concentrated gains d ue to road decommissioning. These gains, which occurred predominantly in the areas most heavily managed prior to the NWFP, were largely offset by high score declines due to large fires, particularly in reserve areas.

In terms of the land use allocations se t by the NWFP, upslope /riparian condition scores were highest for Congressionally reserved (CR) areas (mean = 75

, 74 , standard deviation (sd) = 18, 18

for 1993 and 2012 , respectively ), followed by late

- successional reserves (LSR) (mean = 66

, 68

, sd = 20, 19 ) and matrix lands (mean = 62

, 65 , sd = 19, 19 ). Changes in mean scores over the 20

- year period were slight, with CR showing a very slight decline ( mean =

- 1 , sd = 7 ), while LSR and matrix lands had small increases (+2

, + 3 , sd = 8, 6 ). Scores for key waters heds, designated for their current or potential capacity to provide high

- quality habitat or refuge for aquatic

- and riparian

- dependent species, differed little from nonkey watersheds ( key mean = 68

, 68 , sd = 20, 19

versus nonkey mean = 67

, 69 , sd = 20, 19 ).

The spatial distribution of watershed scores showed some noticeable patterns. The highest scores (>8 0 ) were found in the central Olympic Peninsula (Olympic National Park), the north

central Cascades and scattered along the Cascades in Oregon and Washing ton, often corresponding to designated wilderness areas

. Other high

- scoring areas occur in the Siuslaw National Forest, in the northeast and southwest areas of the Rogue River

- Siskiyou National Forest, and in scattered wilderness areas in the Klamath mount ain range in northern California

. Low s cores (<4 0 ) were seen in the southern Olympic region, and along the eastern flank of the Oregon Coast range and western flanks of the Cascade Range in Oregon and Washington. However, these low er

- scoring areas also s howed the most consistent, moderate upward trend in

scores over the Plan area. Growth in vegetation and decommissioning of roads made considerable positive impact on the upslope/ riparian condition scores in these areas.6



## Contents

Chapter 1: Introduction

Ch apter 2: Methods

Chapter 3: Results

Chapter 4: Discussion

Literature cited

Appendix 1 : Natural gradient varia bles

Appendix 2 : Natural intrinsic characteristic variables

Appendix 3 : Sp atial dist r i bution maps of stream model components

Appendix 4 : Upsl ope /riparian process i ndicators scores status and trend maps

Appendix 5 : Contact information7



## Chapter 1: Introduction

In 1994, the Northwest Forest Plan (NWFP) Record of Decision amended 19 national forest and 7 Bureau of Land Management (BLM) resource plan s within the range of the northern spotted owl ( Strix occidentalis caurina ) (USDA and USDI 1994). The NWFP put in place a new approach to federal land management. Key components of the Plan included a new set of land

use allocations

— late

- successional rese rves, matrix lands, riparian reserves, adaptive management areas, and key watersheds. The NWFP standards and guidelines provided direction regarding how these land use allocations were to be managed. In addition, the NWFP put in place a variety of strategi es and processes to be implemented. These included adaptive management, an Aquatic Conservation Strategy (ACS), late

- successional reserve and watershed assessments, a survey and manage program, an interagency executive organization, social and

economic mit igation initiatives, and monitoring.

The monitoring component of the plan provides a means to address the effectiveness of these strategies and compliance with forest management laws and policy. M onitoring is essential and required:

Monitoring is an essen tial component of the selected alternative. It ensures that management actions meet the prescribed standards and guidelines and that they comply with applicable laws and policies. Monitoring will provide information to determine if the standards and guidel ines are being followed, verify if they are achieving the desired results, and determine if underlying assumptions are sound. [USDA and USDI 1994]

Judge Dwyer reinforced the importance of monitoring in his 1994 decision declaring the NWFP legally acceptabl e: “Monitoring is central to the [NWFP’s] validity. If it is not funded, or not done for any reason, the plan will have to be reconsidered” (Dwyer 1994).

An interagency effectiveness monitoring framework was implemented to meet requirements for tracking st atus and trend for watershed condition, late and old forests, social and economic conditions, tribal relationships, and population and habitat for marbled murrelets ( Brachyramphus marm o ratus ), and northern spotted owls. The aquatic and riparian

effective ness monitoring program (AREMP) was developed to implement the effectiveness monitoring component of the NWFP Aquatic Conservation Strategy (ACS). Periodic analysis and interpretation of monitoring data is essential to completing the adaptive management cy cle. This important step was described in the overall monitoring strategy (Mulder et al. 1999) and approved by the Regional Interagency Executive Committee. Beginning in 2005, monitoring reports have been published at 5

- year intervals and made available a t http://www.reo.gov/monitoring/.

The Aquatic and Riparian Effectiveness Monitoring Program ( AREMP ) assesses the status

and trend of watersheds at the sixth

- field hydrological units (HU s) scale

. AREMP employs two different methodologies to evaluate condi tion. The upslope / riparian condition program

component uses geographic information system (GI S) data to evaluate all watersheds with at least 5 percent of area within a watershed on federal land within the Northwest Forest Plan (NWFP) , and the stream condi tion component is based on monitoring stream attributes within randomly selected watersheds with a minimum of 25 p e r cent federal ownership along the 1:100,000 stream layer.8 This “ 20

- year report” evaluat es status and changes in condition under the NWFP aqua tic

conservation strategy during years 199 3

– 2013 . Although this report i s intended to evaluate 20 years of data, we are only able to achieve a full 20 year analysis for the upslope/ riparian portion of the program where data were available since the NWFP in ception. The stream condition monitoring program began in 2002 and is currently on an eight year rotation to visit approximately 214 randomly selected watersheds

. The first rotation was completed in 2009; we are currently in the second rotation and are abo ut halfway through repeating these watershed s

.

Overview of the Aquatic Conservation Strategy

The aquatic conservation strategy (ACS) is a comprehensive, region

- wide strategy designed to maintain, restore, and protect those processes and landforms that c reate good ecological conditions in watersheds, such as high

- quality habitat for aquatic and riparian organisms and good water quality ( FEMAT 1993; USDA and USDI 1994 ). The strategy contains nine objectives that describe general characteristics of function al aquatic and riparian ecosystems that are intended to maintain and restore good habitat (see Reeves et al. 2004 ). This approach was intended to prevent further degradation of aquatic ecosystems and restore habitat over broad landscapes, as opposed to foc using on individual projects or species . Aquatic and riparian organisms evolved in a dynamic environment influenced by natural disturbance. The authors of the strategy believed that stewardship of aquatic resources is most likely to protect biological

dive rsity and productivity when land use activities do not substantially alter the natural disturbance regime to which organisms are adapted. Therefore, the strategy used several tactics to try to maintain the natural disturbance regime in watersheds. The stra tegy also includes standards and guidelines that apply to management activities in riparian reserves and key watersheds. The four components of the strategy were intended to work in concert to maintain and restore the health of aquatic and riparian ecosyst ems:

 Watershed analysis

— used to characterize watersheds and provide a basis (context) for making management decisions.

 Riparian reserves

—used to enhance habitat for riparian

- dependent organisms, to provide good water quality dispersal corridors for terrest rial species, and connectivity within

watersheds.

 Key watersheds

—

provide high

- quality habitat or refuge for aquatic

- and riparian

- dependent species, or would be able to after restoration.

 Watershed restoration

— designed to recover degraded habitat and maint ain existing good

conditions.

Although late

- successional reserves are not listed among the components of the strategy, they provide increased protection for aquatic and riparian ecosystems. Late

- successional reserves contain areas of high

- quality stream ha bitat that serve as refuge for aquatic and riparian organisms and as source areas from which organisms may move to recolonize formerly degraded areas (USDA and USDI 1994).

Monitoring was included in the strategy to achieve three goals: ensure that manageme nt actions follow the standards and guidelines and comply with applicable laws and policies (implementation monitoring), determine the effectiveness of management practices at multiple spatial scales ranging from individual watersheds to the entire NWFP ar ea (effectiveness9

monitoring), and determine whether the assumptions underlying the strategy are sound (validation monitoring) (ROD 1994

– section B

- 32)

. The first goal was accomplished through the implementation monitoring program (Baker et al. 2005). The aquatic and riparian effectiveness monitoring program (AREMP) was developed to reach the effectiveness monitoring goal.

Effectiveness Monitoring Questions

The AREMP is charged with answering questions about the effectiveness of the aquatic

conservation s trategy in achieving its goal of maintaining and improving the condition of watersheds in the NWFP area (Reeves et al. 2004). This report focuses on responding to two questions, the answers to which provide insight for evaluating the success of the aquatic conservation strategy:

1. What is the status and trend of stream conditions?

2. What is the status and trend of upslope/ riparian watershed conditions?10



## Chapter 2: Methods

Each of the two principal monitoring questions is answered using somewhat different data

sources and methods. Due to updates in information and data sources, as well as improvements in analytical techniques, the results in this report are not directly comparable to previous reports. The comparisons to earlier years, in this report, use consis tent and updated methodology throughout all time periods and represent the most current information available as of 2013. W e first describe the common elements of the program, study area and the conceptual model s before moving onto sections providing more details on study designs, data sources, and analytical procedures for the two principal monitoring questions.

Overview of the Aquatic and Riparian Effectiveness Monitoring Program

The AREMP is responsible for the effectiveness monitoring component of the ACS. Its purpose is to assess the effectiveness of the NWFP by periodically determining the status of watershed condition and using this information to track trends in the condition of watersheds through time. Watershed condition refers to a combination of aquatic, riparian, and upslope

characteristics within 6 th field hydrological units (HU). Hydrological units are based on U.S. Geological survey classification of river systems defined using topography and classified into smaller, relatively uniformly size d subunits using a combination of drainage basin or distinct hydrological features (Seaber et al 1987). Sixth

- field HUs are small units (10,000

- 40,000 acres) and thought to have less internal variation and , thus, allow us to more easily detect changes than larger 5 th field units (Reeves et al 2004). HUs are commonly used as a framework for water

resource and related planning and are the basis for defining watersheds in the ACS (Seaber et al. 1987, Reeves et al. 2004). “True watersheds” are defined as topogr aphic surfaces where water drains to a specific point and vary extensively in scale (Omernik et al. 2011). While we acknowledge that hydrological units and “true watersheds” are not always synonymous, the 6 th
field HU provides a discrete unit used as the b asis for AREMP monitoring design. These were the smallest consistently delineated unit available at the time and have undergone minor boundary modifications since the ROD was signed. These 6 th field HUs, henceforth called watersheds, serve as discrete unit s used in these analyses which can be aggregated at multiple spatial scales to make assessments of condition.

The original intent of the AREMP was to combine all these characteristics into a single watershed evaluation (Reeves et al. 2004), but the evalua tion process has evolved to consider

stream condit ion separately from upslope/ riparian condition because of the different data sources and resulting sampling designs. Many GIS data sources are not updated yearly making yearly upslope condition assessments difficult since they often are temporally not in sync and, also computational intensive. Further, the upslope assessments are considered a census whereas the stream program relies on a statistical sample to extrapolate to the region. Stream condition is

ba sed on physical stream data (e.g., substrate, pieces of large wood, and pool tail fines), macroinvertebrates, and water temperature. Upslope/ riparian condition is based on mapped data, e.g., road density and vegetation data.

Stream , and upslope / riparian condition, are determined by integrating multiple sources of information (Reeves et al. 2004).The results are assessed as a distribution of condition scores across the NWFP area. If the NWFP is effective, the distribution of conditions should either stay

t he same or improve over time (Reeves et al. 2004). Note that the authors of the ACS did not11

intend for each of the objectives to be monitored individually, nor did they expect that the objectives would be met in each watershed at all times (USDA and USDI 2 003).

Evaluatin g the effectiveness of the ACS wa s based on measuring changes in the distribution

of stream and upslope/

riparian condition scores through time. The ACS does not describe the baseline condition of streams and watersheds, nor does it define a desired distribution. We infer that if the strategy has been effective in maintaining and improving the condition of watersheds,

then the distribution of stream and upslope/ riparian condition scores should shift in a direction that indicates improvement ( Reeves et al. 2004).

Definition of Watershed Condition

The definition of watershed condition devel oped by the monitoring program wa s based on the goals of the NWFP aquatic conservation strategy and on guidance provided by the aquatic monitoring plan (Ree ves et al. 2004). The NWFP was designed to account for the complex and dynamic nature of aquatic ecosystems resulting from the wide range of physical characteristics, natural disturbance events, and climatic features of the region (Benda et al. 1998, Naima n et al. 1992). Monitoring the se dynamic watershed processes wa s accomplished by linking them to measurable physical attributes (e.g., vegetation structure, road density, water temperature). Reeves et al. (2004) initially identified 90 potential attributes that represent key functions and processes in watersheds. This number of attributes was reduced based on criteria established by Noon et al. (1999). The monitoring program further removed some attributes that were found not to produce useful or consistent information (Lanigan et al. 2007). The remaining attributes represent upslope, riparian, and stream processes.

The condition of a watershed wa s defined as “good” if it supports ecological integrity to the extent that key biotic and physical processes are sustained (Mulder et al. 1999 ; Naiman et al.

1992 ; Reeves et al. 2004 ) . Ma ny of the physical indicators wer e chosen for their relevance to native or desired fish species because of these species’ roles in driving management policies (including the NWFP it self) and the availability of research related to their habitat needs. However, we attempt ed to assess indicators relative to the natural potential of the site to provide biotic habitat. A watershed that naturally wa s outside fish distributions (because o f elevation or other natural conditions) but has little vegetation disturbance, few roads, good pools, and wood should be evaluated positively. If this watershed loses significant vegetation, even from natural causes (e.g., fire), then the condition ratin g will go down (it is below its potential).

This simplified view of condition is a consequence of the fact that indicators taken at one point in time are imperfect measures for dynamic temporal processes. Even a watershed with intact processes may not b e in good condition in terms of providing quality fish habitat at any single assessment period. A fundamental principle underlying the monitoring program is that watersheds are naturally dynamic systems. Individual watersheds will cycle through conditions of high and low habitat quality, and not all watersheds can be expected to be in good condition at any one time (Naiman et al. 1992, Reeves et al. 1995 , Roper et al. 1997 ). Therefore, the most important product of the monitoring program is the overall dist ribution of individual watershed ratings in the NWFP area. Implementing the ACS should result in an overall distribution of watershed condition scores that improves over time since it is assumed they are currently degraded ( FEMAT 1993 )

.12

Study Area

The Nort hwest Forest Plan (NWFP) encomp asses more than 24 million ac of federal lands in western Washington, western Oregon, and northwestern California and includes the entire geographic range of the northern spotted owl ( Strix occidentalis caurina ) ( fig. 1 ). Str eam and riparian habitat conditions differ greatly across the NWFP area because of natural and

management

- related factors. Geologic and climatic history influence topographic relief, landforms, channel patterns, and the dominant erosion processes. Precipit ation ranges from more

than 200 in ches per year in some areas near the coast to less than 20 in ches on the east side of the Cascade Range. Riparian vegetation communities are structured by climate and the disturbance regime of the area, including hydrologi c processes and disturbance s such as forest fires (Benda et al. 1998, Naiman et al. 1992). Many of these critical components of landscape form and function are in distinctive combinations characteristic of each physiographic province in the region. Physiog raphic provinces incorporate physical, biological, and environmental factors that shape

broad

- scale landscapes and therefore reflect differences in responses such as soil development and plant community structure.

P hysiographic provinces are useful in desc ribing both terrestrial and aquatic ecosystems, and different processes dominate the functioning of these ecosystems. Consequently, the Forest Ecosystem Management Assessment Team (FEMAT 1993) used different physiographic province boundaries for aquatic an d terrestrial ecosystems. The physiographic boundaries used in this analysis were developed from those used in the aquatic ecosystem assessment (FEMAT 1993)

, and we re based on broadly drawn precipitation and geologic zones, as well as political

boundaries (state lines). These province boundaries differ from those used by the other effectiveness monitoring components (e.g., the late

- successional old

- growth and the northern spotted owl), which were delineated primarily by vegetation type and political boundar ies. The aquatic province boundaries used by the FEMAT (1993) were not available in a digital format, so their province boundary lines were refined by using level

- four lines described by Omernik in Oregon and Washington (Bryce et al. 1999), Bailey ecologic al subsections lines in California (Bailey et al. 1994), and the Cascade crest derived from the Forest Service Pacific Northwest Region sixth

- field HU watershed layer.

The NWFP area contains eight aquatic physiographic provinces including the Olympic

Penin s ula, North Cascades, Willamette

- Puget Tr ough, West Cascades, Washington

- Oregon Coast, High Cascades, Klamath

- Siskiyou, and Franciscan (fig 1) . L and ownership in the

Willamette

- Puget Trough is predominantly private, and none of the watersheds in this provi nce met the monitoring program minimum criterion of federal land ownership. Consequently, this province was not includ ed in the analysis. Descriptions of the provinces based largely on those presented by FEMAT (1993) are available in Gallo et al. (2005).

B ecause the NWFP applies only to federally managed lands, watersheds must contain a minimum of 25 percent of the total length of the stream (1:100,000 National Hydrography Dataset stream layer) within federal ownership (USDA Forest Service [FS], USDI Bureau of Land Management [BLM], or USDI National Park Service [NPS]) to be considered for sampling and analysis in the stream monitoring program. The ownership criterion was recommended by Reeves et al. (2004) to gauge the influence of the strategy while avoidi ng sampling watersheds in

which the contribution of federal lands to the condition of the watershed was less significant. To

be more consistent with the Forest Service National Watershed Condition Framework (USDA FS 2011) and to include a greater percentag e of Bureau of Land Management land, we increased the13

Figure 1

— Map of the Northwest Forest Plan area. The Northwest Forest Plan (NWFP) encompasses the range of the northern spotted owl and includes seven aquatic provinces used to assess watershed conditio n. Lands being evaluated include USDA Forest Service, USDI Bureau of Land Management, and the USDI National Park Service. Within these federal lands, land use allocations and key and nonkey categories assign different management guidelines and priorities s hared by all federal partners.

amount14

of land area analyzed to watersheds with a little as 5 percent federal lands by area for this upslope / riparian condition analysis. Feedback from the local managers suggested that it would be

more useful to the local units to have more watersheds included in the upslope analysis. The

NWFP area contains 2810 watersheds, of which 2039 contain some land that is federally owned,

and 1,974 have at least 5 percent federal ownership by area . The ownership criterion excludes

about 1 percent of the federal lands in the NWFP area from this analysis. Only the federal portion of watersheds was included when determining watershed condition status and trend because federal agency land managers have no jurisdiction over management of nonfederal

lands.

Land Use Categories

Land use categories provide a key spatial component of the NWFP by assigning different management guidelines and priorities to zones within the NWFP area. We review our two monitoring questions in the context of two types of land classification: the general NWFP land use allocations ( congressionally reserved, late

- successional reserve, matrix) and the NWFP

aqua tic conservation strategy designations of key versus nonkey watersheds. The land use allocation categories presented here are the same as those described by Tuchmann et al. (1996). Boundaries for land use categories did not follow watershed boundaries; consequently multiple land use categories may have been present in individual watersheds. Upslope / riparian an alysis

uses actual boundaries for each land use category. For the stream assessment, classification for

each watershed into a single land use category was based on the category covering the largest

amount of its area. The following paragraphs briefly descr ibe each allocation.

Congressional reserves ( CR ) : lands reserved by the U.S. Congress such as wilderness, wild and scenic rivers, national parks and monuments

.

Late

- successional reserves ( LSR ) : l ands reserved for the protection and restoration of late

suc cessional and old

- growth forest ecosystems and habitat for associated species; including marbled murrelet reserves and northern spotted owl activity core reserves . Adaptive management areas managed under LSR guidelines were included in LSR (see below).

M at rix : l ands not included in one of the other allocations. Scheduled timber harvest activities may take place in matrix lands. For analysis and reporting purposes, we grouped some adaptive management with matrix (see below).

Riparian reserves : these reserve s were not included as a separate land allocation because they have not been mapped; they are included as part of the above land allocation s in which they

fall

. The upslope/riparian assessment approximates riparian reserve areas for a number of its

indicat ors.

Adaptive management areas : areas identified to develop and test innovative management approaches to integrate and achieve ecological, economic, and other social and community objectives (USDA and USDI 1994). They are a mix of lands where timber produc tion can occur and where timber production must follow LSR guidelines. For analysis and reporting purposes, we grouped watersheds in adaptive management areas into either matrix lands or LSR, depending on which allocation covered the largest amount of its area.

Key watersheds : areas intended to “serve as refuge for aquatic organisms, particularly in the

short term for at

- risk fish populations, to have the greatest potential for restoration, or to provide sources of high

- quality water” (Haynes et al

. 2006). Key watersheds were identified as part of15

the ACS and independent of the land use allocations in the NWFP, thus key and nonkey watershed designations overlay the other land use allocations. Key watershed delineation was begun prior to the development of t he interagency standard fif th

- and sixth

- field watershed boundaries, so their boundaries are not always coincident. For this analysis, 520 of our 1,974 watersheds are considered key because they have more than 50 percent of the area designated as key water shed. The remaining 1,454 watersheds are considered as nonkey in this assessment.

Study Design

Assessment of Watershed Condition

For this assessment, models were developed separately for stream and upslope/riparian

condition following the process es defined by the monitoring plan and the data sets available for

each

. Upslope/ riparian evaluations were comb ined in one model because they we re based on the

same data sources ; watershed

- wide mapped data (e.g., road density, canopy cover) derived from

satellite ima gery and other corporate data sets (circa 1993 and 2013, 1, 974 watersheds)

. The

stream status evaluation was based on sampling of stream data (e.g., seven day maximum

average water temperature, physical habitat

, and macroinvertebrates) collected in watersh eds by

AREMP field crews from 2002 to 2013. Each model comprises three basic elements: a list of measurable watershed attributes to evaluate, evaluation criteria for rating each attribute, and a model structure, which de fines how the attribute scores we re aggregated into an overall score.

Data from each watershed were analyzed through the appropriate models to produce scores on a

scale from 0 to 100 , where 0 indicates “ poor ” condition and 100 indicates “good” condition

.

Monitoring Questions

1. What is the sta tus and trend of stream conditions?
Study d esign

At the inception of the Northwest Forest Plan, 1,37 3 watersheds in the sixth

- field watershed coverage (version 1.1, dated 2002) with greater than 25 percent federal ownership were

identified and 250 were ran domly selected using a spatially balanced sampling method (Stevens and Olsen 2003, 2004)

. The original study design called for sampling 50 watersheds per year, with repeat visits to watersheds beginning on the sixth sampling year. Due to funding limitatio ns this goal was not realized. The study design was altered to complete approximately 28 watersheds per year, with repeat visits beginning in the ninth year of sampling. An eight year cycle of visits is referred to as a rotation. As of 2013, we have visit ed a total of 214 watersheds and are halfway through the second rotation; 189 watersheds were visited during the first rotation

and so far 25 new watersheds have been visited during the second rotation ; 110 watersheds have been repeated.

Within each waters hed, stream data were collected at multiple (4

- 11) sites. These sites were also selected using a spatially balanced procedure subject to logistical constraints ( e.g. unable to sample 4 sites minimum, fire, or illegal activity) . Sample p oints were drawn fr om the 1:100,000 National Hydrological Data Layer (dated 2000), where points represent the downstream starting

location for stream surveys. The survey le ngth at each site was determined as 20 times the average
bankfull width, with a minimum and maximum of 160 and 460 meters, respectively. Eleven equally spaced transects over the stream length were surveyed at each site.16 Attributes

Stream attributes were collected at each site; details on data collection methods can be found in the AREMP field protocol (ARE MP 2013). Data for each transect attribute were summarized together at the site scale. Each site level attribute was class ified into one of four metrics: pools, wood, substrate, or macroinvertebrates. Water temperature, collected at the lowest point on

fe deral lands within each watershed, was analyzed separate from the physical habitat and macroinvertebrates. Three metrics, pools (pool tail fines) , wood (medium and large frequency)

, and substrate (percent fines under 6mm) , were used as the basis for calcul ating physical habitat

stream condition score from the site lev el individual attributes ( fig. 2 ). Using these metrics allows for increased comparability of scores across aquatic physiographic provinces and retains

much of the framework for watershed condit ion defined through provincial expert workshops. Ultimately, the attributes selected for inclusion in each model element (fig. 2 ) were those that

were a ble to detect a management signal ( Stoddard et al. 2007, Al

- Chokhacky et al, 2011).

Figure 2

— Stream phy sical habitat condition model evaluation structure included three metrics: pools, wood, and

substrate. Each metric contains individual site level attributes used as the basis for calculating the metric

. Site level stream physical habitat condition values w ere aggregated and analyzed hierarchically at the watershed level. Nearest neighbor intrinsic characteristics used to describe individual attributes can be referenced in table 8.

A change from previous models was to evaluate macroinvertebrates separate ly from physical habitat. Previously, both macroinvertebrates and amphibians data were collected and summarized together into a biological condition score. As of 2012, we no longer sample for amphibians due to unreliability of presence / absence data. Whil e macroinvertebrates are a useful indicator of degradation in a system ( Hawkins et al 2000 ) , reliability on a single indicator of biological condition can lead to erroneous interpretation of health of a system (Barbour et al. 1999). Similar to temperature

, we have chosen to analyze macroinvertebrates separately and not integrate this score into our overall stream condition score. For the macroinvertebrate metric we

are u sing an observed to expected (O/E) index developed specifically for the AREMP sample

fra me (Miller, Miller, Van der Laan, and Hawkins, in prep)

. Instead, macroinvertebrate and17

temperature scores will be assessed separately and then used in concert with the physical habitat index as multiple lines of evidence for the condition of system.

Data a nalysis

While the field

- sampled attributes were expected to remain constant over time, analytical procedures were anticipated to change as new science became available (Reeves et al 2004) . For this report, e ach stream physical habitat attribute was evalu ated and scored using an updated approach from previous reports. Past evaluations relied on a decision support model

, with scoring thresholds taken directly from the literature, expert opinion, and /or data fro m other

studies that had sampling protocols tha t were not comparable to those used by AREMP. In addition, many threshold values did not encompass the range of values collected in the AREMP

data and often the threshold range was smaller than the AREMP measurement error of a given

attribute. Rather than defining new thresholds, which would take information we do not currently have, we chose to use a reference condition approach for scoring.

The reference condition approach is frequently used by bioassessment programs throughout

the world that monitor ec ological condition ( Pont et al. 2006, 2009, Herlihy et. al. 2009 , Whittier

et al. 2007

, Pollock et al. 2012). Here we define reference condition as areas with minimal management that characterize the range of natural variability across the region (Miller e t al in prep). We used GIS and remote sensing data summarized at two spatial extents, true watershed (broad level disturbance) and a smaller 2km polygon watershed above a site (localized disturbance) , to quantify stressor and natural (intrinsic) variables. Over 5 5 00 candidate sites, compiled from 5 agencies, were used to define minimal management. Each watershed was characterized using a suite of land

- use and land

- cover variables that quantified both anthropogenic stressors and natural characteristics. Ref erence was defined as sites that fell

below the 25 th percentile for all disturbance variables ( table 1 ) and then, subsequently passed inspection based on visual assessment of aerial photographs . Approximately 260 AREMP sites passed the screening process.

Table 1

— Reference p ercentile t hresholds

. Candidate r eference sites were defined as sites that fell below the 25 th
percentile for all disturbance variables with the exception of distance to dam where the 75 th percentile * was used.

Candidate reference sites were visually inspected using aerial photos before passing into the reference network used for nearest neighbor analysis. The 90 th percentile was used to define sites with the most disturbances.

Percentile

Disturbance v ariables Source Unit 25th 90th

Road density a km/km 2 1.35 3.87

Stream crossing a count / km 2 0.24 1.01

Agriculture b % 0.05 2.74

Developed open space b % 2 7.82

Mines c % 0.21 26.64

Gravel mines c mines / km 2 0.01 0.06

Canals d % 1.7 29.27

Distance to dam d km 20.56 * 3.5618 a Custom dataset completed from Forest Service, Bureau of Land Management, and Chico State University data

b Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., and Xian, G. 2013. A comprehensive change detection method for updating the Nati onal Land Cover Database to circa 2011

. Remote Sensing of Environment , 132: 159

– 175.

c Mine data (mineral.usgs.gov)

d National hydrology dataset (nhd.usgs.gov)

The reference condition approach to scoring each attribute was based on the deviation of an attribute from an individual site to the expected value estimated from a network of minimally managed sites with s imilar intrinsic environmental characteristics

. Intrinsic environmental characteristics are variables that do not change based on management activities (i.e. geology). Expected values of stream attributes will vary with intrinsic characteristics . For examp le,

intrinsic characteristics such as stream gradient or elevation can strongly influence what we would expect the values of attributes to be in minimally managed systems; as such, these types

of characteristics must be accounted for when using a reference approach (Stoddard et al. 2007 ).

We used a nearest

- neighbor approach (described by Bates Prins and Smith 2007) to account for

intrinsic environmental characteristics , where the “distance” between a site and reference was

calculated based on these intrinsi c environmental characteristics ( app. 1 ) (Yates and Bailey 2010). Expected values of an attribute at an individual site were estimated from its reference network of minimally managed sites “nearest” that site based on intrinsic environmental

characteristic s . The neighbors for a site are not necessarily close in space, but rather close in

similar ity based on these environmental characteristics

. The nearest neighbor approach requires that we select both the number of neighbors that

match a site and the intri nsic characteristics to match on. These were selected for each attribute by finding the combination of the number of neighboring sites and a subset of intrinsic

environmental characteristics that minimized the mean squared error (MSE) of the reference

netw ork chosen as outlined above (Bates Prins and Smith 2007). This procedure was performed separately for each attribute, so the number of neighbors

, the intrinsic environmental

characteristics , and the size of the reference network used varies among attribu tes ( Appendix B ).

Scores were calculated on a continuous scale from 0 to 10 based on the 90 percent prediction intervals around the expected value of an attribute for each site (see Stoddard et al. 2007, Al

Chokhacky et al. 2011).

Individual attribute sco res were averaged within their respective metric to create a metric

score for each site (e.g. pools, substrate, wood). If an attribute was missing within a metric for a

site , only the non

- missing attributes were used to calculate the metric score . If an e ntire metric was missing for a site, no metric score or final score was calculated and the site was not used in any subsequent analys e s . To determine the overall site condition, all physical habitat metrics were averaged together and the final scores scal ed f rom 0 to 100. If a site had no pools, the final score was only based on substrate and wood metric scores. Watershed level stream physical

habitat c ondition scores were calculated as the average site level condition scores within each respective water shed that contained three or more sites using the spsurvey package i n R ( R Core Team 2013, Kincaid and Olsen 2013 )

. Watersheds with fewer than three sites were not used in any analysis.

Macroinvertebrates were assessed at the site level using an observed to expected index developed by the Center for Monitoring and Assessment of Freshwater Ecosystems in Logan,

UT . The O/E model compares the taxa at an observed site to similar reference sites (see Hawkins19

et al 2000 for detail). Sites were grouped into class es based on macroinvertebrate assemblage composition similarity. The expected class membership was predicted using a number of intrinsic predictor attributes (simil ar to intrinsic environmental characteristics used in the nearest neighbor analysis above ). All data were standardized to their appropriate operational taxonomic unit prior to analysis and re

- sampled to a 300 fixed count. O/E scores are interpreted by the value 1 indicating that all expected species were found at a site, while a value of 0 indica ted that no expected species were found. Watershed level macroinvertebrate O/E scores were calculated

based aggregated site level O/E scores using the spsurvey package in R (R Core Team 2013, Kincaid and Olsen 2013)

. Water temperature loggers were deplo yed in early spring at the lowest point on federal lands within each watershed and data collected typically in late fall. Data were co llected hourly and

summarized as the seven day maximum average temperature

. To calculate the seven day maximum average te mperature we defined the season to June 1 to September 15 and then calculated the daily maximum average from the hourly recordings. Temperature data were summarized across watersheds using the spsurvey package i n R (R Core Team 2013, Kincaid

and Olsen 2013 )

.

W e use d descriptive statistics and graphical displays to present stream physical habitat and macroinvertebrate scores, and water temperature data for the entire Northwest Forest Plan area, as well, grouped by physiographic aquatic province, land

- use allocations , key and nonkey

watershed

. M ean overall condition was estimated with 95 percent confidence intervals for each group within each rotation. We tested for differences in the cumulative frequency distributions (cdf) among the levels , as described by the ACS, within the above groups using the contcdf.test in the spsurvey package within the program R (Kincaid and Olsen 2013 , R Core 2013 ). This was

done only within the first rotation and for the Northwest Forest plan overall

. No tests were performed within the second rotation for the above groups since we have limited power to detect true biological differences having not yet reached a sufficient sample size (i.e. second rotation is not complete until 2017)

. To assess whether the overall mean cumula tive frequency distributions (cdf) shifted toward better condition we used a two

- sample t

- test (Sokal and Rohlf 1995). The

ACS is considered effective if the mean frequency distribution of scores shift toward a better condition category (Reeves et al 2006)

. We used a linear mixed model fit with package lme4 (Bates et al. 2014) to test for a linear relationship between the stream metric status score s and time, after accounting for province. Individual y ear and watershed

, as well as provi n ce were used

as rando m effects in this model to account for year, watershed and province variability

. A n F

test with a Kenward

- Roger approximation was used to test significance of linear trend for each

indicator.

2. What is the status and trend of upslo pe/ riparian condition s?
Study d esign

In past assessments , the upslope/riparian analysis used the same criteria as the stream
evaluation (at least 25 percent of stream channels along the 1:100,000 stream layer in federal ownership) to define the scope of watersheds to include. For this report, we broadened th e scope to any watershed with 5 percent or greater federal ownership in order to be more compatible with recent USFS national watershed assessment guidelines ( USDA FS 2011 ) and to include more Bureau of Land Management lands

. Only the federal portion of watersheds was included when20

determining watershed condition since federal agency land managers have no jurisdiction over management of nonfederal lands. The NWFP area contains 1,97 4 watersheds that met this sampling threshold

. We further subdivided these watersheds by the NWFP land use allocations and key/nonkey watersheds, as described above, for reporting purposes.

Riparian reserves were defined in the NWFP to have widths varying from 100

- 300 f ee t based

on a combination of 1 00

- year flood plains, breaks in slope, riparian vegetation, and site potential trees (USDA and USDI 1994), but these boundaries have yet to be delineated. For this report we have delineated riparian areas using a uniform 90 m buffer (~300 ft) on either sid e of the

1: 100,000 stream layer. This wide buffer was chosen g iven the coarse resolution of the satellite vegetation data (30 m eters ) and the uncertain positional accuracy of the stream layer. Higher resolution stream lines (1: 24,000 ) were not used because of uneven density over the Plan area that would make comparability among areas inconsistent

. Upslope attributes were calculated for the entire federal portion of the watershed, including the riparian area. Although this approach may count riparian areas t wice, the upslope/ riparian attributes are assessed as proxies for different processes and multicollinearity is not an issue because we are not statistically estimating the influence of explanatory factors. Watershed

- wide

metrics also avoid the problem of w ide variation in the amount of non

- riparian areas in watersheds, and they tend to be consistent with available studies on watershed impacts (e.g.

, road density wa s typically measured as total watershed density).

Attributes

In past reports, an assessment m odel and associated metrics were developed for e ach aquatic province through regional expert workshops (Gordon and Gallo 2011). This flexibility was

intended to account for broad biophysical differences ( vegetation, geology, precipitation, etc.)

, but it al so decreased the consistency between provinces . For this report, we combined the different models into a single unified model structure based on an analysis of commonalities and

differences. Biophysical differences are now handled by setting vegetation eva luation criteria

relative to appropriate vegetation zones, as developed by Davis et al

. ( in press ), along with the integration of geology, landform, and precipitation layers in the sedimentation metric. Additionally, the attributes are now organized explic itly to represent key watershed processes (Beechie and Bolton 1999, Beechie et al

. 2010) (fig. 3)

. The following sections describe indicators used for each of these key watershed processes. Some indicators we re repeated under different processes and so we r e ef fectively double counted; this wa s an intention al choice to21

Figure 3

— Overall upslope/ riparian condition was based on the combination of 5 process indicators, which were in turn derived from a number of finer grained metrics. Description of e ach process and individual indicator components can be referenced in table 2.

Sediment Production and Delivery (mass wasting)

High rates of sediment delivery to streams from episodic mass wasting events such as

landslides and erosion have been shown to have detrimental effects on salmonids and other aquatic biota ( Cover et al 2008; Jensen et al 2009 )

. Natural rates for these processes are determined by a variety of factors, including slope, concavity, soils, geology, geomorphology, and precipitation. Wi thin the range of the Northern spotted owl, f ederal forest management affects these rates primarily through r oad and vegetation disturbances. To evaluate the process of

sedimentation production and transport , t he AREMP model use d the difference between an

estimated background rate of sediment delivery and the rate estimated given the status of road and vegetation disturbances.

Factors considered in the background risk of sediment delivery were estimate d different ly in

California ( USFS Region 5 ) from Oregon /Washington ( USFS Region 6 ) due to the availability

of differ ing datasets (see table 2 ). O n Region 5 Forest Service lands , background risk was estimated using a simplified version of a Forest Service geomorphic terranes model (Elder 2008).

Forest Service g eologists assigned s ediment delivery multipliers to bedrock geology types

combined with three slope classes. For areas outside the Forest Service geologic mapping, geologists crosswalked the se slope

- geology multipliers to the 10 classes in a state

- wide dee p

seated landslide risk map produced by the California Geologic Survey (Wills et al. 2011).22

Table 2

— Upslope/riparian indicators and evaluation thresholds : (a) roads, (b) vegetation, (c) landslide risk.

(a) Roads

Scoring Thresholds

Metric 0 10 0

Riparian Road Density (miles road per mile stream) ≥ 0.4 ≤ 0.1

Overall Road Density (miles road per sq mile area) ≥ 2.4 ≤ 1

Fish Passage (percent habitat available) 0 100

(b) Vegetation

Overall Vegetation Riparian Vegetation

Canopy Cover (%) DBH (cm) Canopy Cover (%) DBH (cm)

Scoring Thresholds> 0 100 0 100 0 100 0 100

Vegetation Zone

Douglas

- fir 12 64 10 35 11 61 10 35

Grand

- white fir 17 64 16 41 18 64 16 43

Juniper 0 12 0 32 0 11 0 28

Mountain hemlock 42 83 16 41 42 82 15 41

Oak woodland 5 44 7 30 5 41 6 28

Other pine 4 37 11 32 5 40 13 31

Ponderosa pine 2 29 7 31 1 31 6 30

Port orford cedar 17 74 13 38 12 71 13 42

Redwood 58 88 21 40 62 85 20 38

Shasta red fir 8 50 7 35 8 47 7 33

Silver fir 44 85 12 40 42 84 11 43

Sitka sp ruce 34 78 13 35 30 74 11 43

Subalpine 10 67 11 30 10 69 10 31

Tanoak 47 81 14 39 33 79 11 41

Western hemlock 41 77 12 37 31 75 10 40

Western redcedar 5 70 4 37 6 68 3 3823

(c) Landslides / Sediment Delivery Scoring Thresholds

Base Standard

Devia tion 0 100

Oregon / Washington (landslides/km 2 0.45 0.2 0.9

California (cu yds/acre) 1.5 0.75 3.0

Oregon / Washington

Bedrock geology Susceptibility Rating

Unconsolidated 100 high

Volcanic (tuffs,pumice, ash, lahars) 80

Sedimentary 50

Metamorphic/Peridotite 25

Extrusive (andesite/basalt) 0

Intrusive 0 low

Landform Associations

Categorical ratings on 150 types (contact authors for details)

Precipitation Susceptibility Thresholds

0 100 Units

Winter (Dec

- Mar) ≤ 700 ≥ 1200 mm

Storm maxima

Westside (24 hr, 25 yr) ≤ 5 ≥ 10 inches

Eastside (6 hr, 100 yr) ≤ 1.7 ≥ 2.1 inches

Rain on snow zones FALSE TRUE

California

Bedrock geology

USFS

- Quantitative ratings on ~1700 types by 3 slope classes (contact authors for details)

CA Geologic Survey Types Base delivery by slope class (percent)

CGS Susceptibility

Class ≤ 15 15

- 55 ≥ 55

Cascade volcanic, metavolcanic, plutons, sandstones 0 0.0005 0.005 0.01

3 0.005 0.015 0.25

6 0.005 0.02 0.1

sc histose rocks, metasediments, argillite, serp 5 0.005 1.5 4

7 0.05 0.3 1

8 0.05 1 2.5

unconsolidated Q deposits, Galice, qtz

- mica schist 9 0.1 0.5 2

10 0.1 2 4.5

Impact Multipliers (all NW F orest P lan area ) Multiplier

Roads (any road) 20

Veg etation Score 0

- 25 5

26

- 40 2

41

- 55 1.5

55

- 70 1.1

71

- 100 124 In Oregon/Washington, background risk was based on slope steepness and convergence, as

calculated in the Netmap model ( LSDEL parameter; Benda et al. 2007 ), and adjusted using

mult ipliers for geology, landform associations, and three precipitation factors (winter rainfall, storm maxima, and rain

- on

- snow areas) , all based on expert judgment of agency soil scientists and geologists

. The impacts of road and vegetation condition s on la ndslide risk w ere modeled s imilarly across the two regions based on multipliers adapted from the USFS R5 geomorphic terranes

model. Road and vegetation multipliers were applied to the background risk layer and the average risk over the unit recalculated. T he indicator of sediment production was then the risk with roads and vegetation minus the background risk. No explicit thresholds for sediment level impacts on aquatic habitat were found in the literature, so the model uses a range based on the

standard de viations of the background risk. Note that the OR/WA and CA models are based on different units, so the thresholds used also differ.

Wood Production and Delivery

Large wood plays a major role in structuring aquatic habitat in the PNW ( Andrus et al 1988 ).

Reeves et al

. (2004) recommended assessing the wood production and delivery process by measuring forest composition and structure class. Previous reports used expert

- derived thresholds for average tree size and canopy cover set by province (and in a few c ases subprovinces). In this assessment, we transitioned to a more empirical approach. For each NWFP vegetation zone as defined by Davis et al. ( in press ) we calculated a reference distribution for

mean tree diameter and canopy cover from areas with less th an 10 percent disturbance based on historical data ( Landsat 1985 to 2012 ; Kennedy et al. 2007, 2010 ). Each attribute score was then based on the departure from the mean of this reference distribution, with a less than

- 5 percent departure receiving an undi sturbed score of 100 and a greater than

- 45 percent departure receiving a score of 0. The minimum of the size and cover scores was taken as the watershed

wide vegetation indicator score because reference condition departures may be indicated by

either metr ic alone (e.g. early and late seral may share the same cover metric but will differ by size)

. Because a large proportion of stream wood comes from the riparian area

, a separate indicator was calculated explicitly for riparian vegetation condition , effectiv ely giving it equal weight to the overall vegetation condition indicator

.

Riparian Shading and Habitat

Riparian conditions play a key role in a number of aquatic processes, including the effect of shading on stream temperatures, roots on bank stability, a nd the provision of habitat for a number of species ( Naiman and Decamps 1997 ). The AREMP model rates the condition of these processes using the average of two indicators : riparian vegetation condition and riparian road density. Riparian v egetation conditio n was measured as the departure of riparian vegetation from

less than 10 percent disturbed vegetation conditions, as described above unde r Wood Production and Transport

. Riparian r oad density was measured as road miles in th e riparian area per stream

mile and evaluation thresholds were derived as an average of values used by different provinces

in the 15

- year assessment.25 Hydrology

Upslope/ riparian conditions affect the quantity and timing of water reaching the stream system and consequently the habitat of aquatic and riparian biota (Poff et al. 1997 ). No consistent regional data we re available on dams and diversions, so this analysis wa s limited to the influences of road and vegetation changes on peak flows. Grant et al. (2008) attempted to synthesize a div erse set of studies on the effects of forest practices on peak flows. Results

show ed considerable variability among watersheds in the hydrologic response of streams to the same changes in forest cover or road densities. However, most of the drivers of thes e differences are not yet well understood or quantified, so we have based this indicator on average response values. One driver addressed in Grant's synthesis wa s that mid

- elevation "rain

- on

- snow" zones have been found to be particularly sensitive because of the potential fast release of water from accumulated snowpack They divided their results by two zones: rain

- on

- snow and rain

dominated (and additionally reasoned that snow

- dominated zones would behave similarly to

rain

- dominated areas). In the rain

- on

- s now zone, their linear estimate shows a +10 percent change in peak flow at 15 percent area harvested; it reaches +15 percent change in peak flow at a 50 percent harvest level, and culminates at a 25 percent flow change at 100 percent harvested.

These effec ts were expected to double in watersheds with a high percentage of road area (greater

than 2 percent or 5.4 mi/mi 2 ) (Grant et al. 2008) . For rain

- dominated zo nes, their linear estimate

showed a possible effect on peak flow at 15 percent area harvested; it reaches +10 percent change in peak flow at a 50 percent harvest level, and culminated at a 30 percent flow change at

100 percent harvested. All studies in the rain zones contained roads. Additionally, Grant et al. (2008) noted that only low gradient stream s we re likely to be susceptible to peak flow effects.

Using a linear approximation based on the thresholds above , and assuming roads contributed half the total increase, we estimated the percent peak flow increase in rain

- dominant zone from vegetation as 0.14 * [percent of vegetation disturbance]. Increases in the rain

- on

- snow zone were approximately 50 percent higher or multiplier of 0.21. As flow with greater than 2 percent roading approximately doubled, we estimate d a separate roads effect using a linea r interpolation between the origin (0,0) and a point equivalent to the 100 percent vegetation loss at 2 percent
road density (5.4 mi/mi 2 ) for a multiplier of 2.5 in rain

- dominated and 3.8 in rain

- on

- snow zones. The percent increases from roads and vegetat ion were then summed to estimate the overall indicator for peak flow change.

We found little information in the literature on which to base scoring thresholds; only one

indirect estimate of an acceptable or unacceptable level of peak flow was identified

. B eamer et
al. (2003) rated subbasins with more than 50 percent watershed area in hydrological immature vegetation due to land use and more than 2 km of road length per km 2 of watershed area as “very likely impaired.” Based on our multipliers above, this lev el of impact would result in a 36

percent increase in peak flow. Therefore, the AREMP model uses 36 percent increase as the poor threshold (score 0) and a minor increase of 5 percent is used as the good threshold (score 100). In order to adjust the impacts by stream susceptibility, we weighted the overall score against the other processes using the proportion of low gradient stream ( less than 4 percent, based on Grant et al. (2008) and input from specialists). A unit with no low gradient stream w as not coun t ed this indicator, while a unit with 50 percent low gradient stream was weight ed 50 percent compared to

the other indicators.26 Fish Passage

Much of the connectivity of habitat used by anadromous salmonids has been reduced by

man

- made barriers in streams, p articularly dams and culverts used at road

- stream crossings ( Chelgren and Dunham, in press; Kemp and O’Hanley 2010 ; Sheer and Steel 2006; Steel et al. 2004). The AREMP model indicator for this process wa s the percentage of potential salmonid

habitat estima ted to be accessible (i.e. not blocked by a man

- made barrier). Streams with gradient less the 20 percent were assumed to be potential fish habitat based on previous studies and state assessment guidelines (Sheer and Steel 2006) . While a regional Forest Ser vice fish passage database is in preparation, no comprehensive assessment of barriers was available at the time of this report. As such, our assessment used road

- stream crossings generated with GIS layers as an estimate of barriers. Regional databases wer e used to determine crossings that were bridges, and therefore not a fish passage issue. Since the bridge databases we re incomplete, the average catchment size above the bridge crossings was calculated and other crossing catchments that were equal or large r in size w ere also assumed to be bridges. All miles of fish habitat above

a non

- bridge crossing were assumed to be blocked. Because no consistent database of barrier removals was available, only the removal of crossings from road decommissioning were

coun ted. The percentage of habitat available was used directly as the score; no further evaluation criteria were applied.

Data analysis

The AREMP upslope /

riparian assessment uses a multi

- criteria evaluation approach, similar to previous reports, where attribu tes representing each process were scored to a common 0

- 100

scale and then these scores were combined using a weighted average approach (Keeney and Raiffa 1976; Gordon 2014) . Each process was given an equal weight (1). For analysis unit s where a particular attribute wa s missing (e.g. some small land use allocation areas lacked a stream segment and riparian area), only t he remaining attribute scores wer e used. The normalized watershed condition scores range d from 0 to 100 where watersheds in good condition have higher scores than those in poor condition.

Using historical datasets, scores for each of the attributes were determined for two time periods: 1993, before the NWFP, and 2012 using the latest data available. Trend in condition

scores for attributes and the overall watershed condition score was calculated by simply subtracting 1993 scores from 2012 scores. Positive trend scores indicate an improvement in condition and negative scores a decline.

Because data on every watershed in the target population were analyzed, inferential statistics

we re not needed to test the reliability of generalizing results from a sample to a larg er population. All differences we re effectively statistically significant , so what remains for judgment wa s whether differences we re meaningful in terms of biology or management. Nevertheless, there wa s measurement error in the underlying data attributes and model uncertainty in terms of how the composite index wa s composed . Error estimates for the vegetation data can be found in ( Da vis et al ., in press ) and error estimates for the roads

indicators remain the same as detailed in the 15

- year assessment (Lanigan et al. 2011).27



## Chapter 3: Results

Results are presented for each of the key monitoring questions, the answers to which provid e insight for evaluating the success of the aquatic conservation strategy for the entire Northwest Forest Plan (NWFP) area and by land use allocation. As described in the “Methods” section, normalized condition model scores range from 0 to 100.

1. What I s the Status and Trend of Stream Conditions?

Northwest Forest Plan Area and Provinces

Within the Northwest Forest Plan area, stream physical habitat condition status scores varied each year (y

- axis, fig. 4 ).We are currently halfway done with the second rot ation and have completed surveys in 110 watersheds as of 2013; 104 watersheds will be visited over the next 4 years to complete the second rotation. Here we report status for each rotation and as well as an estimate of a linear trend through time.

Figu re 4

— Distribution of stream physical habitat condition status scores (y

- axis) for each year (x

- axis). Mean stream physical habitat scores are represented by the solid line, asterisks represent median values. The number of watersheds visited each year is de noted by n along the bottom of the graph.28 Stream physical habitat scores ranged from 12 to 74 with a mean score of 47.7 with a 95

percent confidence interval (95 percent CI) ranging from 46.6

- 48.8 during the first rotation and

46.8 (95 percent CI = 45.3

– 48.4) during rotation two. Only 2 percent of the scores fell below 20 during either rotation. The majority of stream attribute scores for both rotations fell between 40 and 60 (64 and 67 percent, respectively), and no watershed was above 80 during eithe r rotation ( fig. 4

, 5 ). There was no evidence of an overall linear trend in physical habitat status over time ( table 3 ). Figure 6 displays the spatial distribution of stream physical habitat scores across the plan area for both rotations. Low scores were primarily found in Washington/Oregon Coast province.

Figure 5

— Distribution of estimated physical habitat scores by rotation using weighted density plot.
Scores are weighted based on sampling design attributes as a function of the number of watersheds

ran domly selected and stream density within each watershed where sites were randomly selected.

Rotation 1 denotes 2002

- 2008 and rotation 2 2009

- 2012. Density represents a continuous probability
distribution based on the percentage of watersheds.29

Figure 6

— S patial distribution of stream physical habitat scores for the Northwest Forest Plan (NWFP) area for each

rotation. Rotation 1 denotes 2002

- 2008 and rotation 2 2009

- 2012.30

Figure 7

— Distribution of stream physical habitat condition scores (y

- axis) for eac h rotation (x

- axis) separately for each aquatic province. Mean stream physical habitat scores are represented by the solid line, dashed line represent
median values. Rotation 1 denotes 2002

- 2008 and rotation 2 2009

- 2012.31

Figure 8

— Distribution of st ream pool scores (y

- axis) for each rotation (x

- axis) separately for each aquatic province.

Mean stream pool scores are represented by the solid line, dashed line represent median values. Rotation 1 denotes

2002

- 2008 and rotation 2 2009

- 2012.32

Figure 9

— Distribution of stream wood scores (y

- axis) for each rotation (x

- axis) separately for each aquatic province. Mean stream wood scores are represented by the solid line, dashed line represent med ian val ues. Rotation

1 denotes 2002

- 2008 and rotation 2 2009

- 20 12.33

Figure 10

— Distribution of stream substrate scores (y

- axis) for each rotation (x

- axis) separately for each aquatic province. Mean stream substrate scores are represented by the solid line, dashed line represent median values.

Rotation 1 denotes 200 2

- 2008 and rotation 2 2009

- 2012.34 For the low

- scoring watersheds (scores less than 40), pool score was usually the most

influential in both rotations ( fig. 8 ). Here scores are reported on a scale of 0

- 10 . Only Washington/Oregon Coast Range province ha d mean and median pool scores below 5 during the first rotation. For the Northwest Forest Plan area, as well as, the Washington/Oregon Coast Range, and West Cascade provinces, median and mean pool score values were below 5 during

the second rotation. Mean estimated pools scores also differed between rotations (table 4) and we detected evidence of a negative trend in pool scores across time for the NWFP ( table 3 , fig.

8 ). Both wood and substrate scores were centered around 5 with the exception of the Oregon Coast province where the mean and median substrate scores were below 2.5 (fig. 9 and 10)

. Mean estimated substrate sc ores differed between rotation (table 4) ; however, no difference was detected in wood scores ( table 4

, fig. 9 and 10 ). Evidence of a posi tive trend was detected in

substrate scores for the Northwest Forest Plan area but no tre nd was detected in wood scores (table 3 ).

Table 3

— Results from the trend analysis testing for a linear relationship between the stream metric scores and time,

after ac counting for province. Year, individual watershed, and province were used as random effects in this model. The upper and lower 95% confidence limit was included for each trend estimate. A n F

- test with a Kenward

- Roger approximation was used to test signific ance of linear trend for each indicator. The denominator degrees of freedom (df) are listed (numerator df was two for all tests). Bold p

- values denote significant trend in annual status estimates. The sign for each trend estimate denotes the trend directio n.

Indicator Trend

estimate 95%

LCL 95%

UCL F

test df p

- value

Physical

habitat + 0.10

- 0.26 0.47 0.33 5.69 0.59

Pools

- 0.21

- 0.38

- 0.04 6.22 9.44 0.03

Wood + 0.09

- 0.01 0.18 3.14 7.89 0.11

Substrate + 0.10 0.03 0.16 9.90 5.76 0.02

O/E + 0.01 0.00 0.01 10. 84 5.67 0.02

Temperature

- 0.09

- 0.24 0.08 1.19 6.86 0.3135 For aquatic macroinvertebrates, we found that at least 25 percent of the watersheds had more

stream invertebrate assemblages than expected, as denoted by scores above 1. At the same tim e, approximately 25 percent of the watersheds had scores below 0.6; scores of 0.6 signify 40

percent fewer stream invertebrate assemblages than expected from reference condition . Mean and median OE scores from each province did not fall below 0.7 in either rotation ( fig. 11 ). We found a mean difference in rotations and a positive trend over time ( table 3 ).

Figure 11

— Distribution of stream macroinvertebrate observed to expected values (y

- axis) for each rotation (x

- axis) separately for each aquatic provinc e. Mean stream m acroinvertebrate observed to expected values are represented by the solid line, dashed line represent median values. Rotation 1 denotes 2002

- 2008 and rotation 2 2009

- 2012.36

Figure 12

— Distribution of stream seven day maximum average tempera ture (C) (y

- axis) for each rotation (x

- axis) separately for each aquatic province. Mean stream seven day maximum average s are represented by the solid line,

dashed line represent median values. Temperature represented by C. Rotation 1 denotes 2002

- 2008 and rotation 2

2009

- 2012.

The National Marine Fisheries Service standards for a properly functioning system for anadromous fish is accepted as 1 5 degrees C, while the state of Oregon standard for core temperatures in salmonid habitat is 16 degrees C (based on the seven day maximum average temperature). Mean and median seven day average temperature values ranged from 16 to 19 degrees C over the 13 survey years, indicating that federal lands located in lower reaches within

these watersheds do not meet desired criteria based on both National Marine Fisheries and State of Oregon standards ( fig. 12 ). Temperature was the only metric estimate with a significantly37

negative sloping trend indicating a n improving trend with decrease in mean temperatures over time (tabl e 4 ). The mean estimated distribution scores were significantly diff erent between rotations (table 4 ).

Land Use Category

Congressionally reserved land estimated physical habitat condition score was 50 (95 percent confidence interval (CI) 48.1

- 51.8) in the first rotation and 51.2 (95 percent CI 48.6 to 53.8) during the second rotation. LSR land estimated mean score was 46.5 (CI 44.3

- 48.6) in the first rotation and 44 (95 percent CI 41.5

- 46.5) during the second rotation. Matrix land status score was 47.4 (9 5 percent CI 45.3

- 49.5) in the first rotation and 47 (95 percent CI 43.6

- 50.3) in the second. The congressional reserved lands had the smallest range of scores during the both rotations ( fig. 13 ). Examin ation of overall stream physical habitat score resu lts in the context of land use allocations within rotations shows a statistically significant difference in cumulative frequency distributions between congressionally reserved lands and late

- successional reserve (LSR) and congressionally reserved lands and matrix during the first rotation ( table 3 ). No statistical tests between levels among land use categories were performed for the second rotation since this rotation has not been completed.38

Figure 13

— Distribution of stream physical habitat c ondition scores (y

- axis) for each rotation (x

- axis)
separately for each landuse allocation. Mean stream physical habitat scores are represented by the solid

line, dashed line represent median values. (LSR = late

- successional reserves, Reserved = congressio nal

reserves)

. Rotation 1 denotes 2002

- 2008 and rotation 2 2009

- 2012.

There was a statistically significant difference between the distributions of the key and nonkey categories during the first rotation ( table 5

, fig. 14 ). Again, no statistical tests b etween levels between key and non

- key categories were performed for the second rotation since this rotation has not been completed. The scores for nonkey watersheds in the first rotation only ranged from 12 to 72, which key watersheds ranged from 20 to 73 ( fig. 14 ).39

Figure 14

— Distribution of stream physical habitat condition scores (y

- axis) for each rotation (x

- axis) separately for key and nonkey watersheds. Mean stream physical habitat scores are represented by the solid line, dashed line

represent me dian values. Rotation 1 denotes 2002

- 2008 and rotation 2 2009

- 2012.

The distributions of individual attribute scores (e.g. pools, wood, substrate) were more variable in regard to land use categories ( fig. 15 ). Statistically significant differences in me an cumulative frequency distributions between attribute scores by land use category are summarized

in Table 5 . Pool scores mean estimates were highest in congressionally reserved lands during both rotations ( table 5 ). No evidence of differences in pool or wood score was found between key and nonkey watersheds in the first rotation ( table 4 ). No differences in wood distributions were confirmed between the reserved/LSR/matrix categories during the first rotation. There was

little evidence that substrate sco res distributions differed between key and nonkey watersheds; however, the distribution of substrate scores significantly differed in congressionally reserved lands from LSR and Matrix lands during the first rotation ( table 5 ). Substrate score mean

estimat es in congressionally reserved lands were higher than all other land use categories during both rotations ( table 5 ).40

Table 4

— Estimates for stream metric scores, the number of watersheds used in the analysis (n), standard error (SE), along with the lower (L CL) and upper (UCL) 95% confidence limit, by rotation. Bold t value represents significant difference ( two

- samp le t

- test) testing m ean estimates between rotations for the NWFP.

Indicator Landuse

category n Rotation

1 e stimate SE 95%

LCL 95%

UCL n Rotation

2 estimate SE 95%

LCL 95%

UCL t

Physical Habitat NWFP 182 47.7 0.6 46.6 48.8 110 46.8 0.8 45.3 48.4

- 0.9

LSR 66 46.5 1.1 44.3 48.6 45 44.0 1.3 41.5 46.5

Matrix 73 47.4 1.1 45.3 49.5 37 47.0 1.7 43.6 50.3

Reserved 43 50.0 0.9 48.1 51.8 28 51.2 1.3 48.6 53.8

Nonkey 115 47.3 0.7 45.8 48.7 70 46.1 1.1 44.1 48.2

Key 67 48.3 1.0 46.3 50.4 40 48.1 1.2 45.7 50.4

Pools NWFP 180 5.5 0.1 5.3 5.7 108 4.1 0.1 3.9 4.4

- 8.3

LSR 66 5.4 0.2 5.0 5.9 45 3.6 0.2 3.3 3.9

Matrix 71 5.4 0.2 5.1 5.8 36 4.3 0 .2 3.8 4.8

Reserved 43 5.7 0.2 5.3 6.2 27 4.8 0.3 4.3 5.4

Nonkey 113 5.5 0.1 5.2 5.8 68 4.3 0.2 4.0 4.6

Key 67 5.5 0.2 5.1 5.9 40 3.9 0.2 3.5 4.4

Wood NWFP 184 5.0 0.1 4.8 5.2 111 5.2 0.1 5.0 5.4 + 1.2

LSR 67 4.9 0.2 4.6 5.2 45 5.3 0.2 5.0 5.7

Matrix 73 5.1 0.2 4.7 5.5 37 5.0 0.2 4.6 5.4

Reserved 44 5.0 0.2 4.7 5.4 29 5.3 0.2 4.9 5.6

Nonkey 116 4.9 0.1 4.7 5.2 70 4.9 0.1 4.7 5.2

Key 68 5.2 0.2 4.9 5.5 41 5.6 0.2 5.3 6.0

Substrate NWFP 184 4.4 0.1 4.2 4.6 111 5.0 0.1 4.7 5.3 + 3.6

LSR 67 4.1 0.2 3.7 4.4 45 4.5 0.2 4.0 4.9

Matrix 73 4.4 0.2 4.0 4.7 37 5.1 0.3 4.6 5.7

Reserved 44 5.0 0.2 4.7 5.4 29 5.6 0.2 5.3 6.0

Nonkey 116 4.4 0.1 4.2 4.7 70 4.9 0.2 4.6 5.2

Key 68 4.4 0.2 4.0 4.7 41 5.1 0.2 4.7 5.6

O/E NWFP 183 0.8 0.0 0.8 0.9 108 0.9 0.0 0.9 0.9 + 2.9

LSR 67 0.9 0.0 0.8 0.9 43 0.9 0.0 0.9 0.9

Matrix 71 0.8 0.0 0.8 0.9 36 0.9 0.0 0.9 0.9

Reserved 45 0.8 0.0 0.8 0.9 29 0.8 0.0 0.8 0.9

Nonkey 116 0.8 0.0 0.8 0.9 68 0.9 0.0 0.9 0.9

Key 67 0.9 0.0 0.8 0.9 40 0. 9 0.0 0.9 0.9

Temperature NWFP 165 17.9 0.2 17.5 18.4 130 16.6 0.2 16.1 17.1

- 4.1

LSR 58 18.6 0.3 18.1 19.2 52 17.0 0.4 16.3 17.7

Matrix 70 18.6 0.4 17.8 19.5 47 17.2 0.6 16.1 18.3

Reserved 37 15.6 0.4 14.8 16.3 31 14.9 0.4 14.1 15.7

Nonkey 10 6 18.1 0.3 17.5 18.7 83 16.7 0.3 16.0 17.3

Key 59 17.6 0.3 17.0 18.2 47 16.4 0.5 15.5 17.341

Table 5

— Results from testing mean cumulative frequency distributions (cdf) among the landuse categories and key and nonkey watershed. These tests were performe d only within the first rotation. Si gnificant difference s in cdfs

between categories for physical habitat scores and individual metric elements are noted in bold (α 0.05)

.

Metric Landuse c ategory Wald F DF P Value

Physical h abitat LSR Matrix 0.44 132 0. 65

LSR Reserve 5.39 101 0.01

Matrix Reserve 6.39 108 0.00

Nonkey Key 3.17 174 0.04

Pools LSR Matrix 6.11 131 0.00

LSR Reserve 1.00 101 0.37

Matrix Reserve 3.13 107 0.05

Nonkey Key 0.45 172 0.64

Wood LSR Matrix 0.64 133 0.53

LSR Reserve 2.38 103 0.10

Matrix Reserve 2.16 109 0.12

Nonkey Key 1.80 176 0.17

Substrate LSR Matrix 0.56 133 0.57

LSR Reserve 5.16 103 0.01

Matrix Reserve 4.62 109 0.01

Nonkey Key 1.65 176 0.20

O / E LSR Matrix 1.50 132 0.23

LSR Reserve 0.89 104 0.41

Matrix Reserve 0.64 109 0.53

Nonkey Key 1.26 176 0.29

Examining the distribution of aquatic invertebrate O/E scores by land use allocation indicated that, in the first rotation, landuse categories and key and nonkey watershed did not differ (table 5

, fig. 15, fig. 16 ).

Table 6

— Results from testing mean cumulative frequency distributions (cdf) among the landuse categories and key and nonkey watershed based on seven day maximum average temperature. Data from a climate change vulnerability project were used to augment samples which increased sample size to levels adequate to test for differences within categories

. Si gnificant difference s in cdfs between categories are noted in bold (α 0.05)

.

Rotation Metric Landuse c ategory Wald F DF P Value

1 Temperat ure LSR Matrix 4.47 121 0.01

LSR Reserve 16.06 87 0.00

Matrix Reserve 8.57 99 0.00

Nonkey Key 0.04 157 0.96

2 Temperature LSR Matrix 0.98 93 0.38

LSR Reserve 5.97 78 0.00

Matrix Reserve 5.45 74 0.01

Nonkey Key 0.12 125 0.8942 Se ven day maximum average temperature statistically differed in distributions among all

land use categories during the first rotation ( Table 6

, fig. 15, fig. 16 ). No difference in distributions was seen between key and nonkey watersheds ( table 6 ). Estimated mean temperatures were lowest on congressionally reserved lands in both rotations ( table 6 ).

Figure 15

— Distribution of each individual stream metric included in the physical habitat score scores : (a)
Pools, (b) wood, (c) substrate , as well as (d) macroi nvertebrate observed to expected values (O/E), and (e)
seven day maximum average temperature (C) (y

- axis) for each rotation (x

- axis) separately for each

landuse allocation . Mean stream physical habitat scores are represented by the solid line, dashed line

represent median values. (LSR = late

- successional reserves, Reserved = congressional reserves)

. Rotation

1 denotes 2002

- 2008 and rotation 2 2009

- 2012.

(a)43

(b)44

(c)45

(d)46

(f)47

Figure 16

— Distribution of each individual stream metric included in th e physical habitat score scores: (a) Pools, (b) wood, (c) substrate , as well as (d) macroinvertebrate observed to expected values (O/E), and (e) seven day maximum average temperature (C) (y

- axis) for each rotation (x

- axis) separately for key and nonk ey watersheds

. Mean stream physical habitat scores are represented by the solid line, dashed line represent median values. Rotation

1 denotes 2002

- 2008 and rotation 2 2009

- 2012.

(a)48

(b)49

(c)50

(d)51

(e)52

2. What Is the Status and Trend of Upsl ope/ riparian Conditions?

The conditions of upslope / riparian processes we re estimated by scoring and integrating a variety of indicators derived from remote sensing and other mapped data sets (see “Methods” section and app. 4 for details). Data were aggrega ted by HUC, ownership, and land use

allocation and so we re reported as area

- weighted scores rather than watershed counts (some watersheds contained very little federal land). Data on every watershed in the target population were analyzed. Measurement error inherent in the attributes wa s still an issue . H owever, precise

error estimates for all the attributes we re not known and so were only discussed in general terms in these results (see “Discussion” section and app. 6 for more details).

Figure 17

— Upslope /r iparian status and trend scores.53

Figure 18

— Upslope/riparian status and trend score map s

.54 Overall, there was a very slight positive change in upslope/ riparian condition scores, from a

mean score of 68 in 1993 to 69 in 2012 (standard deviation (sd) = 20

- 19) . An increase in scores (a shift to the right) was especially noticeable as a shift from scores in the low to mid

- range ( 15

- 50 ) to the higher range (60

- 90) ( fig. 17 ). The area in the high ranges ( more than 90) actually decreased slightly. Excludi ng minor changes, which may be due to error inherent in the satellite imagery classification process, we also calculated a conservative estimate looking at only condition score changes of greater than 5 percent (± 5) . U sing this threshold, 16 percent of t he area increased versus 7 percent that decreased.

Figure 19

— Up slope/riparian score distribution s by aquatic province. Mean upslope/riparian scores are represented by the dashed line, solid line represent median values.55 The spatial distribution of upsl ope/ riparian condition scores showed some noticeable patterns (fig. 18 ). The highest scores (>80) were found in the central Olympic Peninsula (Olympic National Park), the north central Cascades and scattered along the Cascades in Oregon and Washington, of

ten corresponding to designated wilderness areas. Other high

- scoring areas occur in the Siuslaw National Forest, in the northeast and southwest areas of the Rogue River

- Siskiyou National Forest, and in scattered wilderness areas in the Klamath mountain ran ge in northern California. Low scores (<40) were seen in the southern Olympic region, and along the eastern flank of the Oregon Coast range and western flanks of the Cascade Range in Oregon and Washington , generally lower elevation areas closer to transpo rtation routes which have been the most heavily roaded and harvested in the past.

The upslope/

riparian condition trend map uses seven categories (instead of five used in the status maps), along with smaller central categories to better discriminate changes in scores since trend scores tended to be more tightly grouped than the status scores. Areas that showed a downward trend included north

- central California, SW Oregon, and patches in the central Oregon Cascades and along the eastern edge of the North Cas cades in Washington. The pattern in positive changes wa s similar to the pattern of lower scores mentioned above: the southern Olympic region, the Oregon Coast range and along the western flanks of the Cascade Range in Oregon and Washington.

Breaking these scores down by aquatic province reveal ed some small differences (fig. 19 ). Most provinces had scores very near the regional average ( mean = 68 , sd = 19 ), although the Olympic , Franciscan and North Cascades showed somewhat higher scores ( mean = 76

, 74

, 73

, sd = 18, 19, 17, respectively) and the Klamath/Siskiyou somewhat lower ( mean = 64 , sd = 20 ) . The Washington/Oregon Coast Range had the largest increase in scores (mean = 61 to 66 , sd = 20, 18)

. In terms of the individual process indicators contributing to the overall upslope/ riparian condition score (fig. 20 ), sediment scores were generally high, with overall mean scores of 77,

78 (1993 and 2012 , sd = 36

, 35 ). The West, High and North Cascades and the Olympic had greater than 50 percent of scores at the max imum level (100). The Franciscan, Klamath/Siskiyou, and Washington/Oregon Coast provinces had higher variability and more scores in the mid to low range.

W ood scores were moderate compared to the other indicators (mean = 67

, 69 , sd = 19

, 19 ). The Olympic p rovince had the hi ghest mean scores (74, 77 , sd = 19

, 17 ) and the High Cascades the lowest ( mean = 59, 61 , sd = 19

, 19 ) . Trends in wood scores resembled overall trends, with

downward trends seen in north

- central California, SW Oregon, the central Oregon Ca scades, and the eastern edge of the North Cascades in Washington and positive trends the southern Olympic region, and along the Oregon Coast range and western flanks of the Cascade Range in Oregon and Washington.

Riparian scores, a combination of riparian vegetation and riparian road indicators, averaged 62, 64 (sd = 25

, 24) across the Plan area. Scores in the Franciscan and Olympic provinces were the highest, while scores in the High Cascades, Klamath/Siskiyou, and WA/OR Coas t were
lower and more variable. S patial pattern showed the distinct effect of roads, with high scores in the Olympic and Northern Cascades and low scores along the eastern side of the Oregon Coast Range and the western and eastern sides of the Cascades from Washington to northern

Califo rnia

.56

Figure 20

— Upslope/riparian proce ss indicator scores by province. Mean upslope/riparian scores a re represented by

the dashed line, solid line represent median values.

(a)57

(b)58

(c)59

(d)60

(e)61 Hydrology scores, derived from overall road density and vegetation condition, were the

highest of th e process indicators (mean = 81, 83 , sd = 26

, 25 ). Scores in the Franciscan, North Cascades, and Olympic were noticeably higher than the other four provinces. Low scores occurred primarily along the eastern margin of the High Cascades. Decreasing trends followed vegetation trends more generally (i.e. wood scores), while concentrated increases were seen in the southern Olympics , the southwestern Cascades in Washington, and the southern end of Mt.

Hood National Forest.

Fish passage had the lowest mean scores and greatest variability of all the indicators (54, 55

, sd = 40

, 40, in 1993

- 2012)

. Scores varied considerably between provinces. The Franciscan, North Cascades, and Olympic all had mean scores great er than 60 ; while the West and High Cascades had the lowest scores ( 46 , 48 and 33, 34 , sd = 38

, 39 and 39

, 39, respectively ). Broader spatial patterns predictably followed road densities, with low scores along the eastern margin of

the Plan area and in the around the central valley between the Coast range and the Cascades. No declines in scores occurred and increases were highly dispersed over the Plan area.

Land Use Category

There were noticeable differences in overall upslope scores between land use all ocations (fig.

21 ): Congressionally reserved (CR) areas had the highest scores (mean = 75, 74 , sd = 18

, 18, for

1993 and 2012 , respectively ), followed by late

- successional reserves (LSR) (mean = 66, 68 , sd =

20

, 19 ) and matrix lands (mean = 62, 65 , sd = 19

, 19 ). Changes over the 20

- year period were slight, with CR showing a very slight decline (

- 1 , sd = 7 ), while LSR and matrix lands had small increases (+2 and 3 , sd = 8 and 6 ).

Looking at the contributing process indicators, average scores for wood, riparia n and hydrology indicators followed the general pattern of resource protection levels (CR > LSR > matrix) ; however, for sediment and passage

, reserved areas still had the highest scores but matrix scores were actually higher than LSR scores. In terms of tr end, matrix lands had the greatest average increases (+3 for hydrology, riparian and wood; +2 for sediment; +1 for passage , sd = 6

- 10 ). LSR areas showed similar gains (+3 for hydrology and sediment; +2 for riparian and wood; +1 for passage , sd = 8

- 13 ). In reserved areas, only passage increased slightly (+1), while riparian showed no change and sediment and wood scores actually declined slightly (

- 2 and

- 1 , sd = 7

- 11 ).

There were o nly very slight difference s in average upslope condition scores between key an d nonkey watersheds in 1993 and 2012 ( mean = 68, 68 versus 67, 69 , sd = 20

, 19 and 20

, 19

, respectively) , but nonkey watersheds did show a slight increase (+2 , sd = 6 ) while key

watersheds did not (fig. 22 ). Wood, riparian and hydrology process indictors a ll were higher in key watersheds (+2 to +4 , sd = 19

- 26 in 2012), while passage and sediment scores were actually higher in nonkey watersheds (+4 and +5 , sd = 40

, 34 ). Hydrology, riparian and passage scores all increased slightly in both designations, but s ediment and wood scores increased only in nonkey

watersheds. None of the indicators showed an overall decline in either designation.62

Figure 21

— Upslope/riparian scores by land use allocation :(a) overall upslope/riparian scores, (b) sediment, (c) wood, ( d) riparian, (e) hydrology, (f) fish passage. Mean upslope/riparian scores are represented by the dashed line,

solid line represent median values.

(a) (b)

(c) (d)

(e) (f)63

Figure 22

— Upslope/riparian scores by key/nonkey watershed designation. Mea n upslope/riparian scores are represented by the dashed line, solid line represent median values.

(a) (b)

(c) (d)

(e) (f)64



## Chapter 4: Discussion

Assessment of watershed condition over such a broad area involves considerable challenges,

such as an adequate level of field sampling, the quality and consistency of available GIS datasets, and the setting of meaningful assessment thresholds for scoring these data. A number of methodological advances were employed for this report since the 15

- year report, including deriving reference conditions for more empirically

- based evaluation of stream and upslope vegetation data, as well as the consolidation of diverse provincial models into common unified approaches for both stream and upslope assessment.

Stream T rend

Stream condition was based on three separate elements: physical habitat, macroinvertebrates, and water temperature. Changes in stream condition will likely only be detectable after multiple rotations are completed, particularly for areas that were hi ghly impacted by disturbance prior to the inception of the Northwest Forest Plan (Reeves et al 2006). Recovery may take decades and, in fact, wa s not expected in fewer than three or four sampling rotations (25 years or more); we are currently in year 13 (R eeves et al 2004). The lack of a statistically significant trend in physical habitat condition may signify that these systems have not fully recovered from a historic disturbance(s) event. These results are consistent with expectation under FEMAT 1993.

Com pleting future rotations should increase our ability to detect if any change is occurring.

Repeat sampling began in 2009 for the stream data. As of 2013, we have completed half of the second rotation. We will not be able to truly estimate any changes in watershed condition

until all watersheds have been revisited in 2017. In this analysis, we assess trend in yearly status estimates rather than repeated watersheds since we have not yet completed all resampling. In the meantime, comparing the first rotatio n of visits (2002

- 2009) to the first four years of the second rotation (2010

- 2013) gives a gen eral idea of current patterns. The number of watershed s visited each year does not represent the number paired for the four year comparison since not all

watershe ds visited during the first four years of the rotation were revisited during the second rotation and vice versa. Events such as wildfires, illegal marijuana plantations , high water events, and other safety issues warranted use of alternate watersheds.

Wat ersheds that scored low were primarily driven by poor pool scores

, particular ly during

the second rotation. W ood and substrate generally increased in nearly all provinces and land use categories; particularly congressionally reserved lands which tended to be less variable than other land use categories. Since substrate scores generally increased between rotations (reduction

in fines less than 6 mm throughout sites), we do not believe that fines overall we re increasing but instead that this phenomenon wa s re lated to pool tail fines . Pools scores were estimated using the amount of fines (less than 2 mm) that accumulate within the downstream portion of pools (pool tailout). T hese are areas often used for spawning by salmon and trout . Sites within each

watershe d were compared to reference sites with similar gradient and bankfull width, coarse level geology, precipitation, mineral content within watersheds of similar size

, and wood density (Appendix B). While, we worked toward creating a reference network of site s with similar environmental variability, we hope to include more fine level variables as they become available in future assessments. Levels of fine sediment that are higher than expected naturally can indicate disturbance in a system. In the case of pool s, high levels of fines can suffocate65

developing eggs (McHenry et al 1994), reduce salmonid fry emergence (Lisle 1989, Kondolf 2000), and reduce habitat available for invertebrates.

In this study, we did not consider other aspects of pools in our pool sco re (e.g. pool frequency, pool spacing, percent of pools) for several reasons. First, pools are very difficult to consistently measure. Many monitoring programs tend to simplify their approach to quantifying pools which likely underestimates the actual numb er of pools (i.e. methodology that only consider channel spanning pools). Second, the mechanisms by which pools are formed vary tremendously within a stream . S ome pools are formed by geological condition while others through wood inputs. While management may impact the amount of wood in a stream, thus impacting pool formation, management is unlikely to have much impact in streams where pools are geologically formed (e.g. step cascade systems). We found no indication that stream wood

differed between rotati ons, yet pool scores did drop. Finally, not all pools are equal. We tend to think about pools in terms of how many are necessary to adequately provide habitat, yet more important to that end is whether pools have the complexity necessary to provide cover, food, and thermal refuge rather than just quantity of pools alone

. Until 2012, AREMP measured only pools that extended across the entire wetted wi d th of the channel, and with the exception of pool tail fines, no other data were collected about each pool. We have implemented additional data collection measures to quantify smaller pools within each reach, as well as the amount, and size of wood pieces within each pool. With these additional parameters we can easily calculate the original attributes for consi stency in comparison over time while also use the new components for a better estimate of pool condition in the future. As with any assessment of condition, it depends on the knowledge base at the time of development. As we refine our understanding of wat ershed process we are able to better assess condition (Reeves et al 2004).

Previous provincial models also included macroinvertebrates and amphibians in the overall stream condition score. In 2012, amphibian surveys were dropped from our survey program d ue to unreliability of presence / absence data. AREMP continues to collect macroinvertebrates but, at present, does not collect any other biological data. While macroinvertebrates are commonly used as measures of environmental health, using a single metric to describe watershed biological integrity can lead to erroneous interpretation of biological condition particularly if that estimate is to represent multiple organisms (Barbour et al 1999, Carlisle and Hawkins 2008). As such, we

report macroinvertebrate s separate from physical habitat condition and temperature to provide additional information to more comprehensively evaluate the system.

We detected a positive trend in the status of observed to expected macroinvertebrate scores

between rotations. For mac roinvertebrates, often the level of biological degradation is determined by the number of sites within an area that fall below a species loss threshold (Barbour et al 1999). In the NWFP area, macroinvertebrate biological integrity wa s quite high

. O nly abo ut 25 percent of sites had scores below 0.6

, indicating only a 40 percent difference in stream invertebrate assemblages as expected from reference . The majority of watersheds with scores below 0.6 occurred in nonkey watersheds. A consistent pattern of 40 p ercent difference in stream invertebrate assemblages from reference expectations may indicate that these systems have not fully recovered from some disturbance. A small percentage of scores were above 1 indicating stream invertebrate assemblages than expec ted. While one could consider this an area of high biological diversity, this score could also represent poor model representation, or an early66

warning sign that the system is moving into a state of disturbance. More investigation is needed

to understand w hy these areas score higher than expected by reference conditions.

In prior provincial models, water temperature carried more weight than other attributes because it was only measured once for each watershed (at the lowest elevation on federal land),

in c ontrast to the other attributes, which were averaged over 4 to 11 sites. Since the placement of the thermographs was separate from the site survey and only reflect ed the downstream point on federal land, we felt that it does not adequate ly characterize the variability of temperature for an entire watershed. Here, we chose to analyze temperature separate ly from physical habitat condition as independent information about watershed condition. Congressional reserved (CR)

lands had the fewest number of watershed s with temperatures exceeding 25C, while matrix lands

had the most in both rotations. T he overall mean trend was significantly negative, reflecting an improvement (decrease) in water temperatures. The negative slope indicates that overall seven day maximum average temperatures decreased between rotations in all lands. This pattern could correspond to higher levels of shading in streams due to increases in vegetation along riparian

reserves (Moore et al 2005)

. Despite the improvement in stream temperatures, we found that

some lower reaches within these watersheds do not meet desired conditions based on both National Marine Fisheries and State of Oregon sta ndards (fig. 1 2 ). While these standards are the current guidelines for evaluating stream temperature it i s important to recognize that a single threshold without environmental context is inadequate for assessment (Moore et al 2005)

. AREMP stream temperature assessment will continue to evolve as new assessment tools become

available (e.g. NorWeST) and can serv e as a baseline of spatially representative sites to evaluate trends (Arismendi et al 2012).

Upslope/ riparian

Although the change in mean upslope / riparian condition scores was negligible, a clear

increase was seen in areas that were more highly impacte d (scores 30

- 60) at the beginning of the NWFP. Looking only at the mean scores, this increase was largely offset by declines in some areas which were relatively pristine at the start of the plan. These declines clearly follow the pattern of large fires dur ing the assessment period, including the Biscuit fire in SW Oregon, the B&B complex in the central Oregon Cascades, and numerous fires along the eastern edge of the North Cascades in Washington. While we evaluate the short term effect of fire as a loss in vegetation and therefore a negative impact, this is a simplistic view. Fires are an essential component of long

- term stream ecosystem dynamics ( Bisson et al. 2003 , Reeves et al. 1995 ). AREMP will continue to work towards adjusting scores to account for th e positive effects of fire in future reports as the science becomes available. In terms of area, and using a co nservative estimate of change ( 5 percent or greater ), increases outweighed declines by 2:1 ( 16 percent

versus 7 percent ). The majority of these m oderate positive changes occurred in areas that had previously been considered the most heavily roaded and harvested, including the southern Olympic region, and along the eastern flank of the Oregon Coast range and western flanks of the Cascade Range in Or egon and Washington. Growth in vegetation and decommissioning of roads made a considerable positive impact on the upslope/ riparian condition scores in these areas.

I n terms of the process indicators, s ediment and fish passage scores showed the broadest

ran ge and drove scores lower in certain areas. Both of these indicators are largely driven by road densities, and so showed considerabl e positive changes in watersheds where roads had been decommissioned, but this effect was small in terms of Plan

- wide averag es. Wood production and67

transport, as the only process weighted more on vegetation than road metrics , did help drive the distinct spatial pattern described above for the overall upslope scores . There were broad, moderate increases in previously impacted a reas and sharp declines in many areas that experienced large wildfires.

In terms of land use allocations, the general pattern of higher scores in the more protected categories still held true, but trends, although slight, continue to move these classes in opposite directions: matrix scores are increasing the most, while Congressionally reserved (CR) scores appear to be declining. Given the dynamic nature of ecosystems, this decline is not unexpected,

and s ince many of these CR lands are at the top of the s coring range, t hey can only maintain

condition or decrease due to disturbance events such as fire

.

Stream versus Upslope/ Riparian

Scores from the stream and upslope evaluations we re not strictly comparable, since they are based on different types of evalua tion thresholds

. Stream scores we re relative to reference conditions, while upslope scores we re a combination of deviation from reference expectations

and expert

- derived impact thresholds. Furth er, the upslope

- riparian model wa s assessed only for

the year s 1993 and 2012, while str eam condition wa s assessed over an eight year rotating pattern; this creates temporal incongruence. The overall distributions of the scores likely reflect this difference, with the majority of stream scores falling between 40

- 60

, while the majority of upslope scores were above 60 . In terms of land use categories, both upslope and stream condition scores generally followed a pattern consistent with the amount of allowable vegetation management (i.e., timber harvest). Mean upslope and stream physical habitat scores were highest in the congressional reserves . In both LSR and matrix lands, no statistical difference was

detected for stream scores
, but the upslope model rated LSR lands higher. No difference was

detected in distributio ns for key versus nonkey watersheds based on stream scores, and upslope key watershed scores were only slightly different ( ± 1). We are currently halfway through the

second rotation of watershed visitations for the stream component of the program and a s res ult,

the reported results are incomplete until the rotation can be finished (2017).

Management Implications

AREMP was design as a broad

- scale monitoring and assessment program

. Broad

- scale land use protections offered by the Northwest Forest Plan and the Aquatic Conservation Strategy are the bedrock of our regional efforts to restore aquatic ecosystems. Change occurs slowly, but will be realized through restoring processes over regional extents, not just features in the stream

channels (Roni et al. 2002)

. In the meantime, active management actions (i.e. wood additions, barrier removal, etc.) are short term solutions but cannot substitute for the broader extent of passive efforts such as land use protections. As such, i t should be realized that restoration actions and local level projects are planned and implemented at finer scales and can provide higher resolution data more sensitive to the local context.

At the regional level under current landscape level aggregated management practices, we did

not detec t any trend in stream physical habitat conditions, but we did detect improvements in

macroinvertebrate score and temperature. Though we did not detect a trend in physical habitat condition, this does not imply that one does not exist, making it difficult to understand whether we are truly “maintaining” condition. Improvement in macroinvertebrate s scores and temperature conditions does suggest positive shifts since the inception of the plan. However,68

understanding whether these positive shifts are a respon se to specific management actions is difficult to ascertain given that the program wa s designed to measure regional trends and not individual projects

. For example, over the last twenty years managers have been using additions of large wood to streams. Yet

, a t the Northwest Forest Plan area scale, we d id not detect a positive trend in large wood frequency. Project specific wood placement is unlikely to be accounted for in the AREMP sample design unless a site happens to fall within a wood placement restorat ion area. Further, placing wood in the stream doesn’t affect the mechanism by which wood enters the stream. The process by which wood enters a stream is typically through trees from the riparian area falling into the stream. Thus, maintain a healthy ripari an area capable of providing wood additions is a key process that does not change through wood additions but rather management or restoration of riparian areas.

To identify whether any relationships exist between specific landscape level management

pra ctices and stream attributes (see Hough

- Snee et al 2014, and Meredith et el. 2014 as examples) , we are working on analyzing existing AREMP data. Here, we are using GIS defined management actions, road density and road/stream crossing, to predict stream sed iment and wood at varying spatial extents. The results of this future analysis could serve to illustrate how well typical measures of GIS defined management actions can predict stream conditions a t varying spatial scales

. When possible, AREMP will continu e to use our regionally collected

field data to focus on these types of iterative explicit hypotheses about large

- scale cause effect relationships to further our understanding of management of stream systems on federal lands (Frissell et al 2014).

Accord ing to the upslope model, sediment and impacts to fish passage drove low scores over the broadest area. Sediment delivery increases with roads and vegetation loss on steeper slopes

and erosion

- prone geologies that are topographically positioned to deliver material to streams. As part of this analysis, AREMP has helped to build a regional landslide risk model, which better

defines these vulnerable areas and could contribute to broader ongoing discussions on the refinement of riparian buffers. Based on our m odel, p rotecting riparian buffers by minimizing vegetation loss and road density are strategies which are likely to increase scores for all of the process indicators. Our estimate of fish passage wa s based simply on the existence of road

stream crossings, so the removal of these crossings wa s the only management action which will

have an effect and benefits are highly conditional

. The beneficial effects of numerous aquatic organism passage projects occurring over the last decade on existing roads was not ac counted

for. However, our metric may be improved in the near future with the completion of regional fish passage databases, which will recognize passable and semi

- passable crossings , account for corrected barriers, and allow more targeted barrier removal strategies. The decommissioning of roads in riparian areas has multiple benefits according to our model by improving both the riparian scores directly and typically the sedimentation scores.

Future of Monitoring

While AREMP was designed with the goal of as sessing the effectiveness of the Northwest Forest Plan as a region, we have actively worked on providing more localized reports for the

individual Forest Service forest s , BLM districts, and National Parks. To do this, we summarize our findings at various local levels and are able to provide customized reports. We are working to ensure that these reports can be used for monitoring requirements under any new planning69

rules or record s of decision as the agencies move forward with revision s of forest and resou rce management plans within the area of the NWFP.

We can draw some management implications from this type of broad

- scale monitoring and assessment, but it must be realized th e intent was to inform at a landscape level. If a local unit has a management ques tion of interest or would like a site level evaluation of stream metrics, the current physical habitat and macroinvertebrate tools are capable of being used to make site level

assessments of condition. However, it is important to recognize that this is dep endent on the goals of the project and the types of processes that the individuals would like to better

understand. The reference network that was developed allows for assessment at the individual site against sites with similar intrinsic environmental cha racteristics. In particular, macroinvertebrate data collected by local units can be directly processed through the AREMP O/E tool and easily assessed for expected aquatic invertebrate assemblages . This tool is available for any organization that collects m acroinvertebrate data using a minimum set of standard sampling requirements. At a minimum, t hese tools can help inform practi ti oners as to whether a site is outside the range of reference expectation. The evaluation capabilities of these tools could

be use d to update ACS resource monitoring objectives by evaluating sites to reference sites within the context of environmental similarity. However, additional site level information would be required as to determine cause if a site deviates from expectation.

Similarly, although the resolution of the upslope/ riparian data wa s coarser than some locally available sources, it wa s well

- suited for forest or district level analyses and even for initial project

- level assessment. The evaluation model itself wa s constructed on a platform (ArcGIS) commonly used by most if not all of the agencies involved in the NWFP, so it can be easily transferred and modified to meet different assessment needs. Further work is anticipated to better integrate AREMP data and result s with the USFS National Watershed Condition Class framework and efforts from other agencies. In particular, compiling the science to set well

justified evaluation criteria for different indicators is important for generating common expectations and goals across agencies. AREMP will also continue to work on improvements to specific indicators, such as landslide risk and vegetation reference conditions, which have utility beyond watershed assessment. Currently, many of the GIS sources that are used by AREMP to
evaluate upslope/

riparian condition are available nation

- wide. Some customized datasets, such as landslide risk can be applied to areas outside the Northwest Forest Plan area. AREMP is investigating ways to extend use of the upslope/ riparian model to a reas throughout FS R egion 6

with PACFISH/INFISH (PIBO), another program that monitor s the effectiveness of the Aquatic Conservation Strategy across FS R egion 6 outside the NWFP area.

Both AREMP and the PIBO have a core set of metrics and evaluate data base d on a reference network framework. Further, both programs used multimetric physical stream evaluation tools

that were normalized to the same scale. The two programs are currently working together to investigat e ways to make integrated region

- wide assessme nts as well as assessments for forests that are partially within the Northwest Forest Plan area . These investigations should illustrate a possible framework for broad scale monitoring for Forest Service forest s served by both

programs pursuing forest plan revisions. AREMP also continues to work with Western River and Stream Assessment program ( WRSA ), a BLM national monitoring program, to collect core metrics. In the summer 2015, we will perform a protocol overlap study that will allow us to

assess differen ces in data collection protocols, but more importantly provide us with the70

framework to integrate data between programs for a BLM wide assessment of streams within the

Pacific Northwest.

AREMP is already working with other organizations (Oregon Depa rtment of Water Quality and the Department of Fish and Game) as well as other federal agency monitoring programs such as PIBO and WRSA to standardize physical habitat data to increase the ability to share and develop high level categorical metrics. Integra ting monitoring programs across the region will allow for a greater understanding of the condition of our aquatic systems at multiple spatial

extents. AREMP’s principal purpose is to evaluate the change in aquatic ecosystems at the regional level (ie., th e area of the NWFP). This is done using data collected from a statistically derived sampling program with sites distributed across the areas of interest and, more recently, with the integration of data from other sources. The former is the primary data s ource, and the amount of data and number of monitoring sites has continually increased since AREMP’s inception. Coordination with entities that collect the latter has increased the potential usefulness of the data to AREMP and expand the amount of availab le data . AREMP now has a robust data set, expertise, and tools from which to assess broad

- scales changes in aquatic ecosystem on federal lands and to provide insights into factors that influence aquatic ecosystems. This will be invaluable for the develop ment and evaluation of new management and policy options for aquatic ecosystems in the Northwest Forest Plan area and elsewhere, including non

- federal lands.71



## Metric Equivalents

Metric Equivalents

When you know: Multiply by: To find:

Inches (in) 2.54 Centimeters
Feet (ft) 0 .305 Meters
Acres (ac) 0 .405 Hectares
Square miles (mi2) 2.59 Square kilometers
Miles (mi) 1.609 Kilometers
Trees per acre 2.47 Trees per hectare
Degrees Fahrenheit 0.55(F

- 32) Degrees Celsius

English Equivalents

When you know: Multiply by: To find:

Centimeters (cm) 0.394 Inches
Meters (m) 3.28 Feet
Hectares (ha) 2.47 Acres
Square kilometers (km2) 0.386 Square miles
Kilometers (km) 0 .621 Miles
Trees per hectare 0.405 Trees per acre
Degrees Cels ius (C) 1.8C + 32 Degrees Fahrenheit72



## Literature Cited

Al

- Chokhachy, R.; Roper, B.B.; Archer, E.K. 2010. Evaluating the status and trends of physical stream habitat in headwater streams within the interior Columbia River and upper

Missouri River basins using an index approach. Transactions of the American Fisheries Society. 139(4): 1041

- 1059.

Andrus, C.W.; Long, B.A.; Froehlich, H.A. 1988. Woody debris and its contribution to pool formation in a coastal stream 50 years after logging. Canadian Journal of Fisheries and Aquatic Sciences. 45(12): 2080

- 2086

Aquatic and Riparian Effectiveness Monitoring Program [AREMP]. 2013

. Field protocol manual: Aquatic and Riparian Monitoring Program. http://www.reo.gov/monitoring/reports/watershed/ Aremp2013FieldProtocolFin al .pdf

Bailey, R.G.; Avers, P.E.; King, T.; McNab, W.H., eds. 1994. Ecoregions and subregions of the United States [map 1:7,500,000]. With supplementary table of map unit descriptions by McNab, W.H.; Bailey, R.G., comps., eds. Washington, DC: U.S. Departm ent of Agriculture,

Forest Service.

Baker, D.; Ferguson, G.; Palmer, C.; Tolle, T. 2005. Northwest Forest Plan

— the first 10 years (1994

– 2003): implementation monitoring, summary of regional interagency monitoring

results. R6

- RPM

- TP

- 04

- 2005. U.S. Departmen t of Agriculture, Forest Service, Pacific Northwest Region. 24 p.

Barbour, M.T.; Gerritsen, J.; Synder, B.D.; Stribling, J.B. 1999. Rapid Bioassessment Protocols for use in streams and wadeable rivers: Periphyton, bentic macroinvertebrates and

fish. EPA 8 41

- B

- 99

- 002. U.S. Environmental Protection Agency, Office of Water, Washington, D.C. Second edition.

Bates, D.; Maechler, M.;Bolker, B.;Walker, S.; Haubo Bojesen, R.; Henrik, C.; Bin Dai, S. ; 2014. C+ Eigen, and LinkingTo Rcpp. "Package ‘lme4’." R Foundati on for Statistical Computing, Vienna

.
Bates Prins , S.C.; Smith , E.P. 2007. Using biological metrics to score and evaluate sites using a

nearest

- neighbour reference condition approach. Freshwater Biology, 52: 98

- 111.

Beamer, E. M.; Beechie, T. J.; Perkowsk i, B. S. and Klochak, J. R. 2003. Appendix C:

Restoration of Habitat

- Forming Processes

— An Applied Restoration Strategy for the Skagit River. In: Beechie, T. J.; Steel, E. A.; Roni, P. & Quimby, E. (Eds.) Ecosystem recovery planning for listed salmon: an in tegrated assessment approach for salmon habitat. Washington DC: U.S. Department of Commerce, National Marine Fisheries Service. NOAA Technical Memorandum Northwest Fisheries Science Center (NMFS

NWFSC

- 58). Beechie, T.; Bolton, S . 1999 . An approach to resto ring salmonid habitat

- forming processes in

Pacific Northwest watersheds. Fisheries

, 24 (4), : 6

- 15. Beechie, T. J.; Sear, D. A.’ Olden, J. D.; Pess, G. R.; Buffington, J. M.; Moir, H. ;Pollock,

M. M . 2010 . Process

- based principles for restoring river ecosyst ems. BioScience

, 60 (3), : 209

- 222.73

Benda, L.E.; Miller, D.J.; Dunne, T.; Reeves, G. H.; Agee, J. K. 1998. Dynamic landscape systems. In: Naiman, R.J.; Bilby, R.E., eds. River ecology and management: lessons from the Pacific coastal ecoregion. New York: Spr inger

- Verlag: 261

– 288.

Benda, L.; Miller, D.; Andras, K.; Bigelow, P.; Reeves, G. and Michael, D. 2007. NetMap: a new tool in support of watershed science and resource management. Forest Science. 53 (2)

:206

- 219

Bisson, P. A.; Rieman, B. E.; Luce, C.; He ssburg, P. F.; Lee, D. C.; Kershner, J. L.; Reeves, G. H. and Gresswell, R. E. 2003. Fire and aquatic ecosystems of the western USA: current knowledge and key questions . Forest Ecology and Management . 178 (1

- 2): 213

- 229.

Bryce, S.A.; Omernick, J.; Lars en, D. 1999. Ecoregions: a geographic framework to guide risk characterization and ecosystem management. Environmental Practice. 1: 141

– 153.

Carlisle, D.M.; Hawkins, C.P. 2008. Land use and the structure of western US stream invertebrate assemblages: predi ctive models and ecological traits. Journal of the North American Benthological Society. 27(4): 986

- 999.

Cover, M.R.; May, C.L..; Dietrich, W.E.; Resh, V.H. 2008. Quantitative linkages among

sediment s upply , streambed fine sediment, and benthic macroinvert ebrates in northern California streams. Journal of the North American Benthological Society. 27(1): 135

- 149.

Davis, R. J.; Ohmann, J. L.; Kennedy, R. E.; Cohen, W. B.; Gregory, M. J.; Yang, Z.; Roberts, H. M.; Gray, A. N. and Spies, T. A. [In press]. North west Forest Plan

– The First 20 Years (1994

- 2013): Status and Trends of Late

- successional and Old

- growth Forests . Gen. Tech. Rep. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station.

Chelgren, N. D. and Dunham, J. B. [In press]. Connectivity and conditional models of access and abundance of species in stream networks. Ecological Applications. DOI: 10.1890/14

- 1108.1

Dwyer, W.L. 1994. Seattle Audubon Society, et al. v. James Lyons, Assistant Secretary of Agriculture, et al. Order on motions for summary judgment RE 1994 Forest Plan. Seattle, WA: U.S. District Court, Western District of Washington.

Elder, D. 2008. Cumulative Watershed Effects Analysis, Shasta

- Trinity National Forest: Quantitative Models for Surface Eros ion, Mass

- wasting and ERA/TOC. On file with: USDA Forest Service, Region 5.

Forest Ecosystem Management Assessment Team [FEMAT]. 1993. Forest ecosystem management: an ecological, economic, and social assessment. Portland, OR: U.S. Department of Agricultur e; U.S. Department of the Interior [et al.]. [Irregular pagination].

Gallo, K.; Lanigan, S.H.; Eldred P.; Gordon S.N.; Moyer C. 2005. Northwest Forest Plan

– the first 10 years (1994

- 2003): preliminary assessment of the condition of watersheds. Gen. Tech.

Re p. PNW

- GTR

- 647. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 133 p.

Gordon, S. 2014. Use of EMDS in conservation and management planning for watersheds. In: Reynolds, K. M.; Hessburg, P. F. & Bourgeron, P. S. (Eds.) Making Transparent74 Environmental Decisions: Applications of the Ecosyste m Management System, Springer. p.

63

- 91

Gordon, S.N.; Gallo, K. 2011. Structuring expert input for a knowledge

- based approach to

watershed condition assessment for the No rthwest Forest Plan, USA. Environmental Monitoring and Assessment. 172(1): 643

- 661.

Grant, G.E.; Lewis, S.L.; Swanson, F.J.; Cissel, J.H.; Mcdonnell, J.J . 2008. Effects of forest practices on peak flows and consequent channel response: a state

- of

- science report for western Oregon and Washington. Gen. Tech. Rep. PNW

- GTR

- 760. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 76 p.

Hawkins, C.P.; Norris, R.H.; Hogue, J.N.; Feminella, J.W. 2000

. Development an d evaluation of predictive models for measuring the biological integrity of streams. Ecological Applications, 10, 1456

- 1477.

Haynes, R.W.; Bormann, B.T.; Lee, D.C.; Martin, J.R., tech. eds. 2006. Northwest Forest

Plan

— the first 10 years (1994

– 2003): synthe sis of monitoring and research results. Gen. Tech. Rep. PNW

- GTR

- 651. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 292 p.

Herlihy A. 2009. Development of an index of biotic integrity for forested Pacific Northwest

stream macroinvertebrates. Unpublished report. On file with: AREMP/RPM, USDA Forest Service R6, 333 SW First Ave, Portland OR 97204. 17 p.

Jensen, D.W.; Steel, E.A.; Fullerton, A.H.; Pess, G.R. 2009. Impact of fine sediment on egg

to

- fry survival of Pacific Salmon: A meta

- analysis of published studies. Reviews in Fisheries Science. 17(3): 348

- 359.

Keeney, R.L.; Raiffa, H. 1976. Decisions with multiple objectives: preferences and value tradeoffs. New York: Wiley. 569 p.

Kemp, P.S.; O’Hanley, J.R. 2 010. Procedures for evaluating and prioritizing the removal of fish passage barriers: a synthesis. Fisheries Management and Ecology. 17: 297

- 322.

Kennedy, R.E.; Yang, Z.; Cohen, W.B. 2010. Detecting trends in forest disturbance and recovery using yearly La ndsat time series 1. LandTrendr

- temporal segmentation algorithms. Remote Sensing of Environment. 114(12): 2897

- 2910.

Kincaid, T. M.; Olsen, A. R. 2013. spsurvey: Spatial Survey Design and Analysis. R package version 2.6. URL http://www.epa.gov/nheerl/arm/.

Lanigan, S.H.; Moyer, C.; Gallo, K.; Gruendike, P. 2007. Aquatic and Riparian Effectiveness Monitoring Program. Interagency monitoring program

– Northwest Forest Plan Area. Annual technical report. On file with: U.S. Department of Agriculture, Forest Servi ce, Resource Planning and Monitoring, Pacific Northwest Region, 333 SW First Ave., Portland, OR 97208.

Miller, D.J.; Burnett, K.M. 2008. Probabilistic model of debris

- flow delivery to stream channels, demonstrated for the Coast Range of Oregon, USA. Geomor phology. 94: 184

– 205.

Miller, S.A.; Eldred, P.; Muldoon, A.; Anlauf

- Dunn, K.; Stein, C; Hubler, S.; Merrick, L.; Haxton, N.; Larson, C.; Rehn, A.; Ode, P. [In preparation]. A large

- scale, multiagency approach to defining a reference network for Pacific No rthwest streams. Environmental Monitoring and Assessment

.75

Miller , S.; Miller, S.A.; Vander Laan, J.; Hawkins, C.P. [In preparation]. AREMP macroinvertebrate bioassessment tool. Journal of Ecological Indicators.

Mulder, B.; Noon, B.; Spies, T.; Raphael, M. G.; Palmer, C.J.; Olsen, A.R.; Reeves, G.H.; Welsh, H.H. 1999. The strategy and design of the effectiveness monitoring program for the Northwest Forest Plan. Gen. Tech. Rep. PNW

- GTR

- 437. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 138 p.

Naiman, R.J.; Beechie, T.J.; Benda, L.E.; Berg, D.R.; Bisson, P.A.; MacDonald, L.H.; O'Connor, M.D.; Olson, P.L.; Steel, E.A. 1992. Fundamental elements of healthy watersheds in the Pacific Northwest coastal ecoregion. I n: Naiman, R.J., ed. Watershed management: balancing sustainability and environmental change. New York: Springer

- Verlag:

127

– 188.

Naiman, R. J. and Decamps, H. 1997. The ecology of interfaces: riparian zones. Annual

Revi ew of Ecology and Systematics: 621

- 6 58

Noon, B.R.; Raphael, M.G.; Spies, T.A. 1999. Conceptual basis for designing an effectiveness monitoring program. In: Mulder, B.S.; Noon, B.R.; Olsen, A.R.; ; Raphael, M.G.; Palmer, C.J.; Olsen, A.R.; Reeves, G.H.; Welsh, H.H. The strategy and design of the effectiveness monitoring program for the Northwest Forest Plan. Gen. Tech. Rep. PNW

- GTR

- 437. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 17

– 36.

Olson, J. R. and Hawkins, C. P. 2012. Predicting natur al base

- flow stream water chemistry in the western United States. Water Resources Research. 48 (2)

Olson and Hawkins [In review]. [Gradient variables: geologic]

Omernik et al. 2011 [“True watersheds” are defined as topographic surfaces where water drains

t o a specific point and vary extensively in scale]

Poff, N. L.; Allan, J. D.; Bain, M. B.; Karr, J. R.; Prestegaard, K. L.; Ri chter, B. D.; Sparks, R. E.; Stromberg, J. C. 1997. The Natural F low Regime. BioScience. 47 (11) :769

- 784.

Pollack, M.M.; Beechie, T.J.; Imaki, H. 2012. Using reference conditions in ecosystem restoration: an example for riparian conifer forests in th e Pacific Northwest. Ecosphere 3(11):98 Pont, D.; Hugueny, B.; Rogers, C . 2007 . Development of a fish

‐ based index for the assessment

of river health in Europe: the European Fish Index. Fisheries Management and Ecology

, 14 ( 6): 427

- 439. Pont, D.; Hughes, R. M.; Whittier, T. R.; Schmutz, S . 2009 . A predictive index of biotic integrity model for aquatic

- vertebrate assemblages of western US str eams. Transactions of the American Fisheries Society

, 138 (2): 292

- 305.

R Core Team. 2013. R: A common language and environment for statistical computing. R Foundations for Statistical Computing, Vienna, Austria. ISBN 3

- 900051

- 07

- 0, URL http://www.R

- project .org/.

Reeves, G.H.; Benda, L.E.; Burnett, K.M.; Bisson, P.A.; Sedell, J.R. 1995. A disturbance

based ecosystem approach to maintaining and restoring freshwater habitats of evolutionarily significant units of anadromous salmonids in the Pacific Northwest. In: Nielson, J.L.; Powers,76 D.A., eds. Evolution and the aquatic ecosystem: defining unique units in population

conservation. Bethesda, MD: American Fisheries Society: 334

- 349.

Reeves, G.H.; Hohler, D.B.; Larsen, D.P.; Busch, D.E.; Kratz, K.; Reynolds, K.; Stein, K.F.; Atzet, T.; Hays, P.; Tehan, M. 2004 . Aquatic and riparian effectiveness monitoring plan for the Northwest Forest Plan. Gen. Tech. Rep. PNW

- GTR

- 577. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Statio n. 70 p.

Roni, P.; Beechie, T. J.; Bilby, R. E.; Leonetti, F. E.; Pollock, M. M.; Pess, G. R . 2002 . A review of stream restoration techniques and a hierarchical strategy for prioritizing

restoration in Pacific Northwest watersheds. North American Journal o f Fisheries Management

, 22 (1): 1

- 20.

Seaber, P. R.; Kapanos, F. P. and Knapp, G. L. 1987. Hydrologic unit maps. United States Geological Survey. Water

- supply Papers 2294.

Sheer, M. B. and Steel, E. A. 2006. Lost watersheds: barriers, aquatic habitat connec tivity, and salmon persistence in the Willamette and Lower Columbia River Basins. Transactions of the American Fisheries Society. 135(6): 1654

- 1669.

Sokal

, R.R.; Rohlf, F.J

. 1995. Biometry: The Principles and Practice of Statistics in Biological

Research. 3rd Edition

. W.H. Freeman Co. New York. 8887pp. Steel, E. A.; Feist, B. E.; Jensen, D. W.; Pess, G. R.; Sheer, M. B.; Brauner, J. B. and Bilby,

R. E. 2004. Landscape models to understand steelhead (Oncorhynchus mykiss) distribution and help prioritize barr ier removals in the Willamette basin, Oregon, USA. Canadian Journal of Fisheries and Aquatic Sciences. 61(6): 999

- 1011.

Stevens, D.L., Jr.; Olsen, A.R. 2003. Variance estimation for spatially balanced samples of

environmental resources. Environmetrics. 14: 593

– 610.

Stevens, D.L., Jr.; Olsen, A.R. 2004. Spatially

- balanced sampling of natural resources. Journal

of the American Statistical Association. 99: 262

– 278.

Stoddard, J.L., D.P. Larsen, C.P. Hawkins, R.K. Johnson, and R.H. Norris. 2007. Setting

expectat ions for the ecological condition of running waters: the concept of reference condition. Ecological Applications 16:1267

- 1276

Tuchmann, E.T.; Connaughton, K.P.; Freedman, L.E.; Moriwaki, C.B. 1996. The Northwest Forest Plan: a report to the President and C ongress. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 253 p.

U.S. Department of Agriculture, Forest Service [USDA FS]. 1995. Inland native fish strategy

environmental assessment decision notice and findi ng of no significant impact: Interim strategies for managing fish

- producing watersheds in eastern Oregon and Washington, Idaho, western Montana, and portions of Nevada [INFISH]. Intermountain, Northern, and Pacific Northwest Regions. [Place of publication unknown]. 39 p.

U.S. Department of Agriculture Forest Service [USDA FS]. 2008. Aquatic and Riparian Conservation Strategy Portland OR: Pacific Northwest Region. 48 pp. http://www.fs.fed.us/r6/fish/documents/Final

- ARCS

- 081308.doc. (June 10, 2011).

U.S. Dep artment of Agriculture, Forest Service [USDA FS]. 2010. Forest Service watershed condition classification technical guide. Washington, DC 24 p.77 http://www.fs.fed.us/publications/watershed/watershed_classification_guide.pdf. (June 10,

2011). 71 p. http://ww w.fs.fed.us/publications/watershed/watershed_classification_guide.pdf

U.S. Department of Agriculture, Forest Service [USDA FS] 2011. Watershed condition

framework. FS

- 977. Washington, DC. 24 p. http://www.fs.fed.us/publications/watershed/Watershed_Conditio n_Framework.pdf

U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior, Bureau of Land Management [USDA and USDI]. 1994. Record of decision for amendments to Forest Service and Bureau of Land Management planning documents in the ra nge of the northern spotted owl and standards and guidelines for management of habitat for late

- successional and

old

- growth forest related species in the range of the northern spotted owl. [Place of publication unknown]. 74 p. [plus Attachment A: standards and guidelines].

U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior, Bureau of Land Management [USDA and USDI]

. 1995. Decision notice/decision record, FONSI, environmental assessment, and appendices for the implementation of i nterim strategies for managing anadromous fish

- producing watersheds in eastern Oregon and Washington, Idaho, and portions of California [PACFISH]. [Place of publication unknown]. 305 p.

U.S. Department of Agriculture Forest Service; U.S. Department of the Interior Bureau of Land Management [USDA and USDI]. 2003. Final supplemental environmental impact statement: clarification of language in the 1994 Record of Decision for the Northwest Forest Plan national forests and Bureau of Land Management districts wit hin the range of the northern spotted owl. Proposal to amend wording about the aquatic conservation strategy. Portland, OR. http://www.reo.gov/library/acs/. (Oct. 21, 2010).

Whittier, T.R.; Hughes, R.M.; Lomnicky, G.A.; Peck D.V. 2007. Fish and amphibian

t olerance values and an assemblage tolerance index for streams and rivers in the western USA. Transactions of the American Fisheries Society. 136: 254

– 271.

Wills, C. J.; Perez, F. G. and Gutierrez, C. I. 2011. Susceptibility to Deep

- Seated Landslides

in Cal ifornia. California Geological Survey. Map Sheet 58. http://www.conservation.ca.gov/cgs/information/publications/ms/Documents/MS58.pdf

Yates , A.G.; Bailey , R.C. 2010

. Selecting objectively defined reference sites for stream bioassessment programs. Environ mental Monitoring and Assessment, 170: 129

- 140.78

Appendix 1. Natural gradient variables used for examining the range of natural variation among reference sites.

Variables were calculated at the true watershed scale where the lowest downstream point was

a n individual site. Five site level variables, latitude, longitude, gradient, elevation, and stream bankfull width (m) were also analyzed. Stream density, watershed area, and conifer wood density were additional watershed level variables included in analysi s.

Intrinsic characteristics Source Unit

2000

- 2009 mean annual precipitation a mm

2000

- 2009 mean monthly temperature a °C

Mean annual air temperature a °C

Maximum annual air temperature a °C

Minimum annual air temperature a °C

Mean annual precipit ation a mm/100

Maximum monthly precipitation a mm/100

Minimum monthly precipitation a mm/100

1994

- 2006 annual weighted atmospheric mean Calcium c mg/L

1994

- 2006 annual weighted atmospheric mean Magnesium c mg/L

1994

- 2006 annual weighted atmospheric mean SO4 c mg/L

1961

- 1990 annual mean number of wet days c # days

1994

- 2006 annual mean maximum number of wet days c # days

Calcite mineral content b %

Magnesium oxide mineral content b %

Nitrogenous mineral content b %

Phosphorus mineral content b %

Sulphur mineral content b %

Percent gneiss geology in catchment b %

Percent granitic geology in catchment b %

Percent mafic/ultramafic geology in catchment b %

Percent quaternary, geology in catchment b %

Percent sedimentary geology in catchment b %

Percent volcanicgeology in catchment b %

Predicted reference condition conductivity d uS/cm

Catchment mean unconfined Compressive Strength e MPa

Catchment mean bulk density e g/cm 3

Catchment mean soil erodability (K) factor e None

Catchment mean s oil permeability e In/hour

Catchment mean log geometric mean hydraulic conductivity e 10 - 6 m/s

a PRISM http://www.prism.oregonstate.edu

b Olson and Hawkins (in Review)

c National Atmospheric Deposition Program National Trends Networ k http://nadp.sws.uiuc.edu/ntn/

d Olson and Hawkins, 2012

e Baker et al. 2003. Olson and Hawkins (in Review)79

Appendix 2. Natural intrinsic characteristic variables used to define nearest neighbor reference network

All natural gra dient variables (Appendix 1 ) were included in nearest neighbor analysis; however, the intrinsic characteristic variables in this table represent those that were best able to define similarity among sites for each individual attribute. K represents the numb er of neighbors (network of reference sites).

Attribute Intrinsic characteristics K

% pool tail fines gradient, percent sedimentary, stream density, wood

density, watershed area, mean precipitation 2000

- 2009, mean soil permeability m ean bankful width, phosphorus mineral content maximum annual air temperature, predicted reference condition conductivity 8

% fines > 6mm gradient, percent sedimentary, site elevation, 1994

- 2006

annual weighted atmospheric mean calcium, stream density, wat ershed area, mean bankful width 5

Wood 12”x 25’ longitude, mean bankfull width, predicted reference

condition conductivity, watershed area 7

Wood 18”x 25’ longitude, mean bankfull width, watershed area, mean

log geometric mean hydraulic conductivity 680

Appendix 3: Spatial distribution maps of stream model components by rotation.

Pool status score map.81

Substrate status score map.82

Wood status score map.83

Macroinvertebrate status score map.84

Temperature status score map.85

Appendix 4 : Upslope

/riparian process indicators scores status and trend maps

Hydrology status and trend score map.86

Sediment status and trend score map.87

Wood delivery status and trend score map.88

Riparian status and trend score map.89

Fish passage status and trend score map.90

Appendix 5 : Contact Information

Want to know more? Please contact:

Stephanie Miller, program

manager 541.750.7017 stephaniemiller@fs.fed.us

Sean Gordon, research

associate 503.808.2698 seangordon@fs.fed.us

Peter Eldred, GIS analyst 541.750 .7078 peldred@fs.fed.us

Ronald Beloin, data manager 541.750.7081 ronaldmbeloin@fs.fed.us

Steve Wilcox, GIS cartographer 541.750.7122 sewilcox@fs.fed.us

Heidi Andersen, fisheries biologist 541.750.7067 hvandersen@fs.fed.us

Mark Raggon, fisheries biologi st 541.750.7017 mraggon@fs.fed.us

Ariel Muldoon, statistician 541.737.6232 ariel.muldoon@oregonstate.edu

Please visit our Web site for more information on publications, presentations, reports, and summer employment: http://www.reo.gov/monitoring/watersh ed

- overview.shtml

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