DRAFT – May 13, 2015

Northwest Forest Plan–

The First 20Years (1994-2013)

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 program 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, 

Pacific 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 consulting 

statistician with the College of Forestry, Oregon State University, Corvallis, OR 97330.
DRAFT – May 13, 2015

Abstract

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

Raggon, Mark; Andersen, Heidi; Muldoon, Ariel. 2014. Northwest Forest Plan–the first 20

years (1994-2013): 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 stream 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 2013 (214 watersheds) 

as part of an eight 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 second 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 based on substrate, wood, and pool metrics for each watershed. 

Macroinvertebrates and water temperature were analyzed separately 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).

For 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 under 20, the pool score 

component of the physical habitat was often the most influential factor. We detected improving

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

detected in the overall stream physical habitat 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 conditions were not expected for 

several decades. Completing the current and future rotations will inform these trends.

Upslope/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 factors affecting five major aquatic processes: sediment production and delivery (mass 

wasting), wood production and delivery, riparian habitat, hydrologic 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 scores 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 and 
3

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

Preface

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 monitoring 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 and 

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 evaluated stream and upslope/riparian 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 

2013 (214 watersheds) 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 watersheds 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 estimating 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 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 under 20, the pool score 

component of the physical habitat was often the most influential factor.

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

in yearly seven day average water temperatures. Future sampling will reveal whether this 

increase in aquatic invertebrate assemblages and reduction in mean watershed temperatures 
5

persists. No trend was detected in the overall physical habitat stream condition across the 

Northwest Forest plan area. This is consistent with expectations based 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 federal ownership. The assessment was 

based on factors affecting five major aquatic processes: sediment production and delivery (mass 

wasting), wood production and delivery, riparian 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 and Bureau of Land Management geographic information system road layers and 

vegetation metrics derived from satellite imagery. 

In 2012, 26 percent of watersheds scored 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 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). There were broad-scale 

moderate gains due to vegetation growth and 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.

In terms of the land use allocations set 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 watersheds, 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 (>80) were found in the central Olympic Peninsula (Olympic National Park), the north 

central Cascades and scattered along the Cascades in Oregon and Washington, 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 mountain range 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. 

However, these lower-scoring areas also showed 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

Chapter 2: Methods

Chapter 3: Results

Chapter 4: Discussion

Literature cited

Appendix 1: Natural gradient variables 

Appendix 2: Natural intrinsic characteristic variables 

Appendix 3: Spatial distribution maps of stream model components

Appendix 4: Upslope/riparian process indicators scores status and trend maps

Appendix 5: Contact information
7

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 plans 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 reserves, 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 strategies 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 mitigation 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. Monitoring is essential and 

required:

Monitoring is an essential 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 guidelines 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 acceptable: “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 status and trend for watershed condition, late and old forests, social and economic 

conditions, tribal relationships, and population and habitat for marbled murrelets 

(Brachyramphus marmoratus), and northern spotted owls. The aquatic and riparian 

effectiveness 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 cycle. 

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 at 

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 (HUs) scale. AREMP employs two 

different methodologies to evaluate condition. The upslope/riparian condition program 

component uses geographic information system (GIS) 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 condition component is based on monitoring stream attributes within 

randomly selected watersheds with a minimum of 25 percent federal ownership along the 

1:100,000 stream layer.
8

This “20-year report” evaluates status and changes in condition under the NWFP aquatic 

conservation strategy during years 1993–2013. Although this report is 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 inception. 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 about halfway through repeating these watersheds.

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 create 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 functional 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 focusing 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 

diversity 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 strategy 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 ecosystems:

 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 terrestrial 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 maintain 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 habitat 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 management 

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 area (effectiveness 
9

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 strategy 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 consistent and updated methodology 

throughout all time periods and represent the most current information available as of 2013. We 

first describe the common elements of the program, study area and the conceptual models 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 6th 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 sized 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 5th field units (Reeves et al 2004). HUs are commonly used as a framework for waterresource 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 topographic 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 6th

field HU provides a discrete unit used as the basis 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 6th field HUs, henceforth called 

watersheds, serve as discrete units 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 evaluation process has evolved to consider 

stream condition 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 

based 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 

the same or improve over time (Reeves et al. 2004). Note that the authors of the ACS did not 
11

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 2003). 

Evaluating the effectiveness of the ACS was 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 developed by the monitoring program was based on 

the goals of the NWFP aquatic conservation strategy and on guidance provided by the aquatic 

monitoring plan (Reeves 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, Naiman et al. 

1992). Monitoring these dynamic watershed processes was 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 was 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). Many of the physical indicators were chosen for their relevance to 

native or desired fish species because of these species’ roles in driving management policies 

(including the NWFP itself) and the availability of research related to their habitat needs. 

However, we attempted to assess indicators relative to the natural potential of the site to provide 

biotic habitat. A watershed that naturally was outside fish distributions (because of 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 rating 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 be 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 distribution 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 Northwest Forest Plan (NWFP) encompasses 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). Stream 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. Precipitation ranges from more 

than 200 inches per year in some areas near the coast to less than 20 inches on the east side of the 

Cascade Range. Riparian vegetation communities are structured by climate and the disturbance 

regime of the area, including hydrologic processes and disturbances 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. 

Physiographic 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.

Physiographic provinces are useful in describing 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 and terrestrial ecosystems. The physiographic boundaries used in 

this analysis were developed from those used in the aquatic ecosystem assessment (FEMAT 

1993), and were 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 boundaries. 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 ecological 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 

Peninsula, North Cascades, Willamette-Puget Trough, West Cascades, Washington-Oregon 

Coast, High Cascades, Klamath-Siskiyou, and Franciscan (fig 1). Land ownership in the 

Willamette-Puget Trough is predominantly private, and none of the watersheds in this province 

met the monitoring program minimum criterion of federal land ownership. Consequently, this 

province was not included in the analysis. Descriptions of the provinces based largely on those 

presented by FEMAT (1993) are available in Gallo et al. (2005).

Because 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 avoiding 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 percentage of Bureau of Land Management land, we increased the 
13

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 condition. 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 shared by all federal partners.

amount 
14

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 

aquatic 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 analysis 

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 describe 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): lands reserved for the protection and restoration of latesuccessional 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).

Matrix: lands 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 reserves were not included as a separate land allocation because 

they have not been mapped; they are included as part of the above land allocations in which they 

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

indicators.

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 production 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 of 
15

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 the interagency standard fifth- 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 watershed. 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 processes defined by the monitoring plan and the data sets available for 

each. Upslope/riparian evaluations were combined in one model because they were based on the 

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

satellite imagery 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 watersheds 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 defines how the attribute scores were 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 status and trend of stream conditions?

Study design

At the inception of the Northwest Forest Plan, 1,373 watersheds in the sixth-field watershed 

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

identified and 250 were randomly 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 limitations 

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 visited 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 watershed, 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 points were drawn from the 1:100,000

National Hydrological Data Layer (dated 2000), where points represent the downstream starting 

location for stream surveys. The survey length 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 (AREMP 2013). Data for each transect attribute were summarized 

together at the site scale. Each site level attribute was classified into one of four metrics: pools, 

wood, substrate, or macroinvertebrates. Water temperature, collected at the lowest point on 

federal 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 calculating physical habitat 

stream condition score from the site level 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 condition defined through provincial expert workshops. 

Ultimately, the attributes selected for inclusion in each model element (fig. 2) were those that 

were able to detect a management signal (Stoddard et al. 2007, Al-Chokhacky et al, 2011). 

Figure 2—Stream physical 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 were 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 separately 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. While 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 using an observed to expected (O/E) index developed specifically for the AREMP sample 

frame (Miller, Miller, Vander Laan, and Hawkins, in prep). Instead, macroinvertebrate and 
17

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 analysis

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, each stream physical habitat attribute was evaluated 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 from other 

studies that had sampling protocols that 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 ecological 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 et 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 5500 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. Reference was defined as sites that fell 

below the 25th 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 percentile thresholds. Candidate reference sites were defined as sites that fell below the 25th

percentile for all disturbance variables with the exception of distance to dam where the 75th percentile* was used. 

Candidate reference sites were visually inspected using aerial photos before passing into the reference network used 

for nearest neighbor analysis. The 90th percentile was used to define sites with the most disturbances.

 Percentile

Disturbance variables Source Unit 25th 90th

Road density akm/km21.35 3.87

Stream crossing acount / km20.24 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 / km20.01 0.06

Canals d % 1.7 29.27

Distance to dam dkm 20.56* 3.56
18

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 National 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 similar 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 example, 

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 intrinsic 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 

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

similarity based on these environmental characteristics.

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

match a site and the intrinsic 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 

network 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 attributes (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, AlChokhacky et al. 2011). 

Individual attribute scores 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 entire metric 

was missing for a site, no metric score or final score was calculated and the site was not used in 

any subsequent analyses. To determine the overall site condition, all physical habitat metrics 

were averaged together and the final scores scaled from 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 condition scores were calculated as the average site level condition scores within each 

respective watershed that contained three or more sites using the spsurvey package in 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 Hawkins 
19

et al 2000 for detail). Sites were grouped into classes based on macroinvertebrate assemblage 

composition similarity. The expected class membership was predicted using a number of 

intrinsic predictor attributes (similar 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 indicated 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 deployed in early spring at the lowest point on federal lands 

within each watershed and data collected typically in late fall. Data were collected hourly and 

summarized as the seven day maximum average temperature. To calculate the seven day 

maximum average temperature 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 in R (R Core Team 2013, Kincaid 

and Olsen 2013). 

We used 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. Mean 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 cumulative 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 scores and 

time, after accounting for province. Individual year and watershed, as well as province were used 

as random effects in this model to account for year, watershed and province variability. An Ftest with a Kenward-Roger approximation was used to test significance of linear trend for each 

indicator. 

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

Study design

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 the 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 when 
20

determining watershed condition since federal agency land managers have no jurisdiction over 

management of nonfederal lands. The NWFP area contains 1,974 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 feet based 

on a combination of 100-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 side of the 

1:100,000 stream layer. This wide buffer was chosen given the coarse resolution of the satellite 

vegetation data (30 meters) 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 twice, 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 wide 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 was typically measured as total watershed density).

Attributes

In past reports, an assessment model and associated metrics were developed for each 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 also 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 evaluation 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 explicitly 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 were repeated under 

different processes and so were effectively double counted; this was an intentional choice to 
21

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 each 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. Within the range of the Northern spotted owl, federal forest management 

affects these rates primarily through road and vegetation disturbances. To evaluate the process of 

sedimentation production and transport, the AREMP model used 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 estimated differently in 

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

of differing datasets (see table 2). On Region 5 Forest Service lands, background risk was 

estimated using a simplified version of a Forest Service geomorphic terranes model (Elder 2008). 

Forest Service geologists assigned sediment delivery multipliers to bedrock geology types 

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

geologists crosswalked these slope-geology multipliers to the 10 classes in a state-wide deepseated 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 0 100

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 spruce 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 38
23

(c) Landslides / Sediment Delivery Scoring Thresholds

Base Standard 

Deviation 0 100

Oregon / Washington (landslides/km20.45 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

schistose 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 Forest Plan area) Multiplier

Roads (any road) 20

Vegetation Score 0-25 5

26-40 2

41-55 1.5

55-70 1.1

71-100 1
24

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 

multipliers 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 conditions on landslide risk were modeled similarly 

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. The 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 deviations 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 cases 

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 than 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 undisturbed 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 watershedwide vegetation indicator score because reference condition departures may be indicated by 

either metric 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, effectively 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, and 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 vegetation condition was measured as the departure of riparian vegetation from 

less than 10 percent disturbed vegetation conditions, as described above under Wood Production 

and Transport. Riparian road density was measured as road miles in the 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 were available on dams and diversions, so this analysis was limited to 

the influences of road and vegetation changes on peak flows. Grant et al. (2008) attempted to 

synthesize a diverse set of studies on the effects of forest practices on peak flows. Results 

showed 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 these 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 was 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 raindominated (and additionally reasoned that snow-dominated zones would behave similarly to 

rain-dominated areas). In the rain-on-snow 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 effects were expected to double in watersheds with a high percentage of road area (greater 

than 2 percent or 5.4 mi/mi2) (Grant et al. 2008). For rain-dominated zones, 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 streams were 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 estimated a separate roads effect using a linear interpolation 

between the origin (0,0) and a point equivalent to the 100 percent vegetation loss at 2 percent

road density (5.4 mi/mi2) for a multiplier of 2.5 in rain-dominated and 3.8 in rain-on-snow zones. 

The percent increases from roads and vegetation 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. Beamer 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 km2of watershed area as “very 

likely impaired.” Based on our multipliers above, this level 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 was not counted this 

indicator, while a unit with 50 percent low gradient stream was weighted 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, particularly 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 was the percentage of potential salmonid 

habitat estimated 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 Service 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 were used to determine crossings that were 

bridges, and therefore not a fish passage issue. Since the bridge databases were incomplete, the 

average catchment size above the bridge crossings was calculated and other crossing catchments 

that were equal or larger in size were 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 

counted. 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 attributes 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 units 

where a particular attribute was missing (e.g. some small land use allocation areas lacked a 

stream segment and riparian area), only the remaining attribute scores were used. The 

normalized watershed condition scores ranged 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 

were not needed to test the reliability of generalizing results from a sample to a larger 

population. All differences were effectively statistically significant, so what remains for 

judgment was whether differences were meaningful in terms of biology or management. 

Nevertheless, there was measurement error in the underlying data attributes and model 

uncertainty in terms of how the composite index was composed. Error estimates for the 

vegetation data can be found in (Davis 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 provide 

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 Is 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 rotation 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. 

Figure 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 denoted 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 either 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 

randomly 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—Spatial 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 each 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 stream 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 median values. Rotation 

1 denotes 2002-2008 and rotation 2 2009-2012.
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 2002-2008 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 had 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 scores differed between rotation (table 4); however, no difference was 

detected in wood scores (table 4, fig. 9 and 10). Evidence of a positive trend was detected in 

substrate scores for the Northwest Forest Plan area but no trend 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 accounting 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. An F-test with a Kenward-Roger 

approximation was used to test significance 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 direction.

Indicator Trend 

estimate

95% 

LCL

95% 

UCL

Ftest 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 0.31
35

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 time, 

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 province. Mean stream macroinvertebrate 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 temperature (C) (y-axis) for each rotation (x-axis) 

separately for each aquatic province. Mean stream seven day maximum averages 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 15 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 significantly 
37

negative sloping trend indicating an improving trend with decrease in mean temperatures over 

time (table 4).The mean estimated distribution scores were significantly different 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 (95 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). Examination of overall stream physical habitat score results 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 condition 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 = congressional 

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 between 

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 median 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 mean 

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 scores 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 

estimates 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 (LCL) and upper (UCL) 95% confidence limit, by rotation. Bold t value represents significant 

difference (two-sample t-test) testing mean estimates between rotations for the NWFP. 

Indicator Landuse 

category n

Rotation 

1 estimate 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 106 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 17.3
41

Table 5—Results from testing mean cumulative frequency distributions (cdf) among the landuse categories and key 

and nonkey watershed. These tests were performed only within the first rotation. Significant differences in cdfs

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

Metric Landuse category Wald F DF P Value

Physical habitat 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. Significant differences in cdfs between categories are noted in bold (α 0.05).

Rotation Metric Landuse category Wald F DF P Value

1 Temperature 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 0.89
42

Seven 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) macroinvertebrate 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 the 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 nonkey 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 Upslope/riparian Conditions?

The conditions of upslope/riparian processes were 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 aggregated by HUC, ownership, and land use 

allocation and so were 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 was still an issue. However, precise 

error estimates for all the attributes were 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/riparian status and trend scores.
53

Figure 18—Upslope/riparian status and trend score maps.
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. Excluding 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). Using this threshold, 16 percent of the 

area increased versus 7 percent that decreased.

Figure 19—Upslope/riparian score distributions by aquatic province. Mean upslope/riparian scores are represented 

by the dashed line, solid line represent median values.
55

The spatial distribution of upslope/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, 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 mountain range 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 transportation 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 Cascades in Washington. The pattern 

in positive changes was 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 revealed 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 maximum level (100). The Franciscan, Klamath/Siskiyou, 

and Washington/Oregon Coast provinces had higher variability and more scores in the mid to 

low range.

Wood scores were moderate compared to the other indicators (mean = 67, 69, sd = 19, 19).

The Olympic province had the highest 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 Cascades, 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 Coast were 

lower and more variable. Spatial 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 

California. 
56

Figure 20—Upslope/riparian process indicator scores by province. Mean upslope/riparian scores are 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 the 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 greater 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 allocations (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 and3, sd = 8 and 6). 

Looking at the contributing process indicators, average scores for wood, riparian 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 trend, 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 only very slight differences in average upslope condition scores between key and 

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 all 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 sediment 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. Mean 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 Trend

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 highly impacted by disturbance prior to 

the inception of the Northwest Forest Plan (Reeves et al 2006). Recovery may take decades and, 

in fact, was not expected in fewer than three or four sampling rotations (25 years or more); we 

are currently in year 13 (Reeves 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. 

Completing 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 rotation of visits (2002-2009) to the first four years of the second 

rotation (2010-2013) gives a general idea of current patterns. The number of watersheds visited 

each year does not represent the number paired for the four year comparison since not all 

watersheds 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. 

Watersheds that scored low were primarily driven by poor pool scores, particularly during 

the second rotation. Wood 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 were increasing but 

instead that this phenomenon was related 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). These are areas often used for spawning by salmon and trout. Sites within each 

watershed 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 sites 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 pools, high levels of fines can suffocate 
65

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 score (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 number of pools (i.e. methodology that only 

consider channel spanning pools). Second, the mechanisms by which pools are formed vary 

tremendously within a stream. Some 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 rotations, 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 width 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 consistency 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 watershed 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 due 

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 macroinvertebrates 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 macroinvertebrates, 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 was quite high. 

Only about 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 percent 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 expected. While one could consider this an area 

of high biological diversity, this score could also represent poor model representation, or an early 
66

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

to understand why 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 contrast to the other attributes, which were averaged over 4 to 11sites. Since the placement of 

the thermographs was separate from the site survey and only reflected the downstream point on 

federal land, we felt that it does not adequately characterize the variability of temperature for an 

entire watershed. Here, we chose to analyze temperature separately from physical habitat 

condition as independent information about watershed condition. Congressional reserved (CR) 

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

had the most in both rotations. The 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 standards (fig. 12). While these standards are the 

current guidelines for evaluating stream temperature it is 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 serve 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 impacted (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 during 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 the positive effects of fire 

in future reports as the science becomes available. In terms of area, and using a conservative 

estimate of change (5 percent or greater), increases outweighed declines by 2:1 (16 percent

versus 7 percent). The majority of these moderate 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 Oregon and Washington. Growth in vegetation and decommissioning of roads 

made a considerable positive impact on the upslope/riparian condition scores in these areas.

In terms of the process indicators, sediment and fish passage scores showed the broadest 

range and drove scores lower in certain areas. Both of these indicators are largely driven by road 

densities, and so showed considerable positive changes in watersheds where roads had been 

decommissioned, but this effect was small in terms of Plan-wide averages. Wood production and 
67

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 areas 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 since many of these CR lands are at the top of the scoring range, they can only maintain 

condition or decrease due to disturbance events such as fire.

Stream versus Upslope/Riparian

Scores from the stream and upslope evaluations were not strictly comparable, since they are 

based on different types of evaluation thresholds. Stream scores were relative to reference 

conditions, while upslope scores were a combination of deviation from reference expectations 

and expert-derived impact thresholds. Further, the upslope-riparian model was assessed only for 

the years 1993 and 2012, while stream condition was 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 distributions 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 as result, 

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, it 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 detect 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 macroinvertebrates scores and 

temperature conditions does suggest positive shifts since the inception of the plan. However, 
68

understanding whether these positive shifts are a response to specific management actions is 

difficult to ascertain given that the program was 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, at the Northwest Forest Plan area scale, we did 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 restoration 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 riparian 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 

practices 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 sediment 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 at

varying spatial scales. When possible, AREMP will continue 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). 

According 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 model, protecting 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 was based simply on the existence of roadstream crossings, so the removal of these crossings was 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 accounted

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 assessing the effectiveness of the Northwest 

Forest Plan as a region, we have actively worked on providing more localized reports for the 

individual Forest Service forests, 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 planning 
69

rules or records of decision as the agencies move forward with revisions of forest and resource 

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 the intent was to inform at a landscape level. If a local unit 

has a management question 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 dependent 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 characteristics. 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 macroinvertebrate data using a minimum set of standard 

sampling requirements. At a minimum, these tools can help inform practitioners as to whether a 

site is outside the range of reference expectation. The evaluation capabilities of these tools could 

be used 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 was coarser than some locally 

available sources, it was well-suited for forest or district level analyses and even for initial 

project-level assessment. The evaluation model itself was 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 results with the USFS National Watershed Condition Class 

framework and efforts from other agencies. In particular, compiling the science to set welljustified 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 areas throughout FS Region 6 

with PACFISH/INFISH (PIBO), another program that monitors the effectiveness of the Aquatic 

Conservation Strategy across FS Region 6 outside the NWFP area.

Both AREMP and the PIBO have a core set of metrics and evaluate data based 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 

investigate ways to make integrated region-wide assessments 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 forests 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 differences in data collection protocols, but more importantly provide us with the 
70

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 Department 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. Integrating 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., the 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 source, 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 available 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 development 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 Celsius (C) 1.8C + 32 Degrees Fahrenheit
72

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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 

an 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 analysis.

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 precipitation 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 duS/cm

Catchment mean unconfined Compressive Strength e MPa

Catchment mean bulk density eg/cm3

Catchment mean soil erodability (K) factor e None

Catchment mean soil permeability eIn/hour

Catchment mean log geometric mean hydraulic conductivity e10-6 m/s

a

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

b

 Olson and Hawkins (in Review)

c

 National Atmospheric Deposition Program National Trends Network 

 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 gradient 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 number 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 mean 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, watershed 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 6
80

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 

biologist

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/watershed-overview.shtml

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