# Environmental Management

A large-scale, multiagency approach to defining a reference network for Pacific Northwest Streams --Manuscript Draft--

Manuscript Number:
Full Title: A large-scale, multiagency approach to defining a reference network for Pacific Northwest Streams Article Type: Environmental Assessment Funding Information:

Abstract: Aquatic monitoring programs vary widely in objectives and design. However, each program faces the unifying challenge of assessing conditions and quantifying reasonable expectations for measured indicators. A common approach for setting resource expectations is to define reference conditions that represent least disturbed or most natural state of a resource characterized by the range of natural variability across a region of interest. However, identification of reference sites often relies heavily on professional judgment, resulting in varying and unrepeatable methods. Establishing standardized methods for data collection, site characterization, and reference site selection facilitate greater cooperation among assessment programs and development of assessment tools that are readily shareable and comparable. Here we illustrate how we created a consistent and transparent reference network for multiple stream survey organizations through the Pacific Northwest and Northern California. We provide a case study that offers a simple example of how reference sites can be used, at the landscape level, to link upslope management practices to specific in-channel response. We found management practices, particularly areas with high road densities, have more fines under 6mm than areas with fewer roads. While this example uses data from only one of the partner agencies, if data were collected in a similar manner they can be combined and create a larger, more robust dataset. Wehope that this starts a dialogue regarding more standardized ways through interagency collaborations to evaluate data. Creating more consistency in physical and biological field protocols will increase ability to share data. Corresponding Author: Stephanie Miller Bureau of Land Management Corvallis, OR UNITED STATES Corresponding Author Secondary Information: Corresponding Author's Institution: Bureau of Land Management Corresponding Author's Secondary Institution:
First Author: Stephanie Miller First Author Secondary Information:
Order of Authors: Stephanie Miller

Peter Eldred

Ariel Muldoon

Kara Anlauf-Dunn

Charles Stien

Shannon Hubler

Leslie Merrick

Nick Haxton

Chad Larson

Powered by Editorial Manager® and ProduXion Manager® from Aries Systems CorporationAndy Rehn

Peter Ode

Jake Vander Laan Order of Authors Secondary Information:
Author Comments: Suggested Reviewers: Thomas Whittier whittier.thom@epa.gov Develops and uses reference sites for aquatic systems in Pacific Northwest Ryan Hill hill.ryan@epa.gov Did a very through review of reference programs throughout the world. Expert on using sites to make predictive models. Alan Herlihy herlihy.alan@epa.gov Expert of developing and using reference sites.

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### 1 A large

- scale, multiagency approach to defining a reference network for Pacific Northwest Streams 

### 1

Stephanie Miller 1 ,8 , Peter Eldred 1

, Ariel Muldoon 2

, Kara Anlauf

- Dunn 3 , Charlie Stein 3 , Shannon Huble r 4

, 

### 2 Lesley Merrick 4

, Nick Haxton 4

, Chad Larson 5

, Andy Rehn 6 , Peter Ode 6

, Jake Vander Laan 7 

### 3

1 Aquatic and Riparian Monitoring Program, US Bureau of Land Management and Forest Service, 3200 SW 

### 4 Jefferson Way, Corvallis, OR 97333 

### 5

2 College of Forestry, Oregon State University, 3200 SW Jefferson Way, Corvallis, OR 97333 

### 6

3 Department of Fish and Wildlife, State of Oregon, Hwy 34, Corvallis, OR 97333 

### 7

4 Department of Environmental Quality, State of Oregon, Hillsboro , OR 97124 

### 8

5 Environmental Assessment Program , Washington State Department of Ecology
, Lacey , WA 98503 

### 9

6 Aquatic Bioassessment Laboratory, California Department of Fish and Wildlife, State of California, CA 

### 10

7 Department of Watershed Sciences, Western Center f or Monitoring and Assessment of Freshwater 

### 11 Ecosystems, and Ecology Center, Utah State University, Logan, Utah 84322

- 5210 

### 12

13

Corresponding Author: Stephanie Miller, smiller@blm.gov , 541

- 750

- 7017 

### 14

Keywords: reference site, minimally disturbed, aquatic, stream, monitoring 

### 15

16

Abstract 

### 17

Aquatic monitoring programs vary widely in objectives and design. However, each program faces the 

### 18

unifying challenge of assessing conditions and quantifying reasonable expectations for measured 

### 19
indicators. A common approach for setting resource expectations is to define reference conditions that 

### 20

represent least disturbed or most natural state of a resource characterized by the range of natural variability 

### 21

across a region of interest. However, ide ntification of reference sites often relies heavily on professional 

### 22
judgment, resulting in varying and unrepeatable methods. Establishing standardized methods for data 

### 23

collection, site characterization, and reference site selection facilitat e g reater coope ration among 

### 24

assessment programs and development of assessment tools that are readily shareable and comparable. Here 

### 25
we illustrate how we created a consistent and transparent reference network for multiple stream survey 

### 26

organizations through the Pacific No rthwest and Northern California. W e provide a case study that offers a 

### 27

simple example of how reference sites can be used, at the landscape level, to link upslope management 

### 28
practices to specific in

- channel response. W e found management practices, particular ly areas with high 

### 29 Manuscript Click here to view linked References

### 2 road densities, have more fines under 6mm than areas with fewer roads. While this example uses data from 

### 30

only one of the partner agencies , if data were collected in a similar manner they can be combined and 

### 31
create a larger, mor e robust dataset. We hope that this starts a dialogue regarding more standardized ways 

### 32

through inter

- agency collaborations to evaluate data. Creating more consistency in physical and biological 

### 33

field protocols will increase ability to share data. 

### 34

35

36

Intro duction 

### 37

One of the greatest challenge s in assessing stream condition is quantifying reasonable expectations for 

### 38

measured indicators ( Hughes et al. 1986 ; Stoddard et al. 200 6 ). A common approach for setting 

### 39

appropriate expectation levels is to define reference cond ition s ( Herlihy et al. 2008; Whittier e t al. 200 7
, 

### 40

Pollack et al. 2012; Ode et al. in press ). Ideally, reference conditions represent a pristine benchmark for 

### 41
ecosystem condition. In reality, few or no areas exist that a re tr uly free of human disturbance. As such, 

### 42

r eference conditions are a representation of the least disturbed or most natural state of a resource 

### 43
characterized by the range of natural variabilit y across a region of interest . Deviation from this range of 

### 44

expectations is used as evidence for anthropogenic degradation

. In using a reference condition approach, 

### 45
the practitioner is asking whether human activities are altering the biological, physical, or chemical 

### 46
properties of an ecosystem beyond a lev el observed at reference sites . To reasonably answer this question, 

### 47

human disturbances must be factored out as much as possible

. 

### 48

Despite a common lexicon for defining biological expectations ( see Stoddard et al. 2006 ) , refer ence 

### 49

conditions are often established using different methods and assumptions . Such methods can be based on 

### 50
data, professional opinion, or a combination of both

. While working toward standardizing interpretation of 

### 51

data, individual programs and agencies often use similar basic approaches yet lack common criteria that 

### 52

would allow for comparison among such groups. A n internal review of several federal and state agencies 

### 53
perform ing stream assessments within the Pacific Northwest revealed that many use Geog raphic 

### 54
Information Systems (GIS) landscape scale analyses coupled with local best professional judgment to 

### 55

### 3 define reference conditions. However, the GIS layers used, scale of interpretation, and thresholds that 

### 56

defined reference condition varied among prog rams. Thus, the implied quality of the reference sites defined 

### 57
independently by each organization remained questionable (Hawkins et al

. 2010). These inconsistencies 

### 58

make it difficult to share reference data or to accept assessments of stream con ditions across agencies. 

### 59

Further, variability in how reference sites were defined can influence the accuracy and precision of how 

### 60
benchmarks are quantified and predicated (Hawkins et al. 2010). 

### 61

Specifying conditions that ensure high

- quality reference sites while m aintaining adequate sample sizes to 

### 62

characterize biological , chemical, and physical habitat expectations can be challenging, particularly in 

### 63

variable landscapes (Stoddard et al

. 2008) or areas with exte nsive land

- use (Paulsen et al. 2008 ; Ode et al. 

### 64
in pr ess ). S electing reference sites in relatively undisturbed streams is optimal if the biological 

### 65

measurements taken at these locations represent the natural variability across the area of interest. For large



### 66
scale assessments across regions of high environm ental heterogeneity
, using reference site selection criteria 

### 67

that includes adequate numbers of uniforml y least or minimally disturbed sites while representing natural 

### 68

variability can be challenging. Under

- representation reduces our ability to accura tely and precisely set 

### 69
expectations (Cao and Hawkins 2011)

. Creating a larger pool of candidate reference sites from sites visited 

### 70

by several programs across the landscape increases the sample size and likelihood that reference sites will 

### 71

represent stream reac hes across the landscape

. Data from reference sites on uniformly defined thresholds 

### 72
can then be shared among the various organizations and have wide use
, even for outside of participating 

### 73

agencies since these data are often used to set goals and objectives for many types of programs

. 

### 74

The ability to discern repeatable patterns across broad geographic regions will likely increase when data are 

### 75

shared across monitoring programs

. This paper describes such a cross

- organizational effort to compile a 

### 76

network of reference sites. The effort was precipitated by the federal Northwest Forest Plan Aquatic and 

### 77
Riparian Effectiveness Monitoring Program (AREMP) , which monitors the effectiveness of the NWFP 

### 78

Aquatic Conservation Strategy (Reeves et al. 200 6 ) across a lar ge geographic extent
, ranging from 

### 79
Washington into northern California (Figure 1). AREMP overlaps several federal and state boundaries and 

### 80

monitoring programs that use reference conditions to set indicator or bioassessmant tool benchmarks

. 

### 81

AREMP initiated this reference site delineation project to define large

- scale stream reference conditions
, 

### 82

### 4 partnering with t he Oregon Department of Fish and Wildlife Aquatic Inventories Project ( ODFW ) , the 

### 83

Oregon Department of Environmental Quality Laborat ory (ODEQ) , Washington Department of Ecology 

### 84

(WD O E), and California Department of Fish and Wildlife Aquatic Bioassessment Laboratory (CDFW). 

### 85

This study bu ild s from the experiences in developing reference criteria shared among the partnering groups 

### 86

to produce a network of regionally consistent and shar e able reference sites

. It is our hope that the 

### 87
experiences gained by this project will be beneficial to other regional based monitoring programs, and 

### 88

highlight many of the challenges and benefits associate d with this type of exercise. Further, we provide an 

### 89

example of how these reference sites were used to link landscape level upslope land management activities 

### 90

to in

- channel sediment by comparing highly managed sites to reference conditions. Common reference 

### 91

sites are a first step toward data and assessments that are shareable and comparable across multiple 

### 92

agencies. This increased cooperation among aquatic monitoring programs at state, regional, and national 

### 93
scales would provid e multiple benefits to all agencies . These reference data are the framework used to 

### 94

advance empirically defined benchmarks to more accurately and precisely inform aquatic conditions across 

### 95
resource management agencies and are fundamental to the development sound environmental policy and 

### 96

management directives

. 

### 97

M ethods 

### 98

Study Area 

### 99

The study region spans portions west of the Cascade Mountains in three states of the United States Pacific 

### 100

Northwest, California, Oregon, and Washington (Figure 1). The study region is diverse
, encompassing 11 

### 101

Level III E coregions (Omernik 1987; Bailey 1980, Bailey et al. 1994 ) , varied geologic history, and 

### 102

numerous environmental gradients. Anthropogenic land

- use is pervasive and varied in degree and history

. 

### 103

Much of the Pacif ic Northwest is actively harvest ed for timber ; road construction and maintenance for this 

### 104
purpose are common. Further, many lower elevation valley bottom areas have been converted to 

### 105

agricultural or urban land

- use through out much of the region . Natural variation also exists a cross the 

### 106

landscape
, where the frequency and rate of episodic events vary (e.g. wildfires and landslides vary )

. 

### 107

Sampling Sites 

### 108

### 5 While each monitoring program has different objectives and sampl ing designs (see Reeves et al. 200 6
, 

### 109

Olsen and Peck 2008 )
, each evaluates stream conditions by benchmarks set through describing reference 

### 110

conditions and/or expert opinion (see Larsen et al. 2001; Stoddard et al 2006; Bailey et al. 2004) . Prior to 

### 111
this project, each group individually set reference conditions using a variety of methods that were 

### 112

inconsistent among programs . Further, reference sites de fined early in the inception of each program used 

### 113

what today would be considered outdated GIS data

. Indicator benchmarks based on the distributions of 

### 114
reference sites warrants periodic reevaluation as newer technologies become available. 

### 115

Each program has sampled streams within their respective areas , for a combined total of 5516 site visits

. 

### 116

The sample frame for each program differed in the National Hydrography Database ( NHD ) 

### 117

( http://nhd.usgs.gov/ ) stream layer used to generate sample points (e.g. 1:24 ,000 versus 1:100 ,000 ). For 

### 118

programs whose sample surve y points were generated using different NHD layers , each sample point was 

### 119
registered t o the medium resolution NHD ( 1:100,000 ) when appropriate. Points that di d not fall on the 

### 120

NHD 1:100,000 layer were removed from the analysis ; as such we only infer reference site designation 

### 121

from this group of streams

. For all site s, an upstream catchment polygon was generated using 30 m digital 

### 122

elevation models (DEMs) and flow direction from NHDplus version2

. C atchment size ranged from 1 to 

### 123

766
, 842 ha

. Over the last 10 years
, AREMP has sampled in over 200 5 th field hydrological units (Seaber et 

### 124

al. 1987) and visited more than 1600 sites

. During the same time, WD O E has sampled over 500 sites 

### 125
throughout Washington , approximately half of which fall within the NWFP area

. The ODEQ and ODFW 

### 126

combined have sampled over 3400 Oregon sites that include three Evolutionary Significant Units ( ESU’s ) 

### 127

for salmonids . Combined
, these Oregon state programs have visited more than 200 watersheds ( 5th field 

### 128
hydrologic unit (HU) ) in the NWFP area since the early 1990s. CDFW have sampled 330 sites in the 

### 129

NWFP area

. 

### 130

Human Disturbance and Natural Variability Data 

### 131

We aggregated several publicly available spatial ly data
, incorporat ing local data when consistent at the 

### 132

larger scale. Regionally consistent data layers with high reliability were used to assess human activities 

### 133

associated with land

- use, hydrological alteration, and mining (Table 1) . Data layers to assess natural 

### 134

variabilit y included geology and mineral content, climatic and atmospheric deposition, as well as variables 

### 135

### 6 associated with catchment size and groundwater contributions. We aggregated data from several sources to 

### 136

create consistent and the most accurate datasets acro ss the region (Table 1). Data were summarized at two 

### 137

levels: catchment and 2km catchment (local) scale (Figure 2)

. The smaller local catchment limits the 

### 138

summarization to the area in the catchment 2km upstream from the downstream survey point. T he 2km 

### 139

catc hment represents more localized disturbance with the greatest capacity to directly impact survey site 

### 140

conditions

. 

### 141

Roads are commonly used in reference site delineation ( Al

- Chokhachy et al. 2010 ) but GIS road layers are 

### 142

rarely accurate across large geographic extents. For the most accurate representation of roads across the 

### 143

landscape we compiled the most updated local derived data into a single consistent dataset. On federal 

### 144

lands, Forest Service local districts main tain independent road data that can then be linked to a regional 

### 145
transportation database, while the Bureau of Land Management (BLM) requires each district to maintain 

### 146
data in a corporate database. Though the BLM includes all lands in their database and co nstantly updates 

### 147
data on BLM lands, they infrequently update data on non

- BLM lands. We used GIS to select all roads on 

### 148

BLM lands, and used the update d roads on Forest Service land using local district information across the 

### 149
region. In California
, we combi ned Forest Service roads with a roads layer compiled by CA State 

### 150

University Chico for CDFW that included roads across all ownerships
, collected from a variety of sources
, 

### 151
including private timber lands . Data were error checked to remove duplicated roads in the Chico State 

### 152
layer, but no edge mapping was performed. Unfortunately, few data were available for private lands. We 

### 153

investigated the use of commercially available road layers but found little dif ference in these layers to the 

### 154
BLM layer for private land; the BLM layer was more accurate for rural lands while the commercial layers 

### 155

were better for urban areas (analysis not shown) . We felt that urban areas would be represented through 

### 156

land cover data and instead chose to use the BLM road layer to represent non

- federal lands in Oregon and 

### 157

Washington , with the Chico State layer represent ing non

- federal lands in California. The road layer was the 

### 158
only customized layer and served as the basis for all road density and road stream crossing calculations. 

### 159

Several anthropogenic disturbance parameters were used to characterize disturbance across the study area. 

### 160

The Multi

- resolution Land Characteristics Consortium ( www.mrlc.g ov ) National Land Cover Database 

### 161

( MRLC ) was used to assess land cover disturbances specifically: developed open space, agriculture, urban 

### 162

### 7 lands

. From the NHD layer, we calculated the percent of canals above a site and distance to nearest 

### 163

downstream dam. Mine data were used to determine the nearest distance to non

- gravel m ine s and gravel 

### 164

mines separately ( http://mrdata.usgs.gov/)

. 

### 165

Screening Criter ia 

### 166

Both spatial scales (catchment and local) were evaluated and used to identify both reference and degraded 

### 167

sites

. The 25 th and 90 th percentiles of each disturbance variable were calculated for the population of 

### 168

probability based survey catchments (Table 1 ) where the lower percentiles have fewer disturbances than 

### 169
higher percentiles (except distance to dam) . We used the 25 th percentile for each screening metric as the 

### 170

threshold where all sites with values below the 25 th percentile for all metrics were defined as reference
, or 

### 171

“least disturbed”. Conversely, values above the 90 th percentile represent ed “most disturbed”. 

### 172

These exercises were limited to entire population of probabilistic surveys sites visited by each of the 

### 173
various organizations ; as such
, we only attempt ed to characterize expectations where we had adequate 

### 174

statistical sample sizes ; because of this, we are unable to make inference to areas where reference sites 

### 175
were unavailable (e.g. areas with high rates of urbanization and agricultur e ) . Threshold sensitivity to 

### 176

screening criteria was explored by holding each threshold constant and examining the number of sites that 

### 177

passe d differing threshold levels. This exercise allowed us to understand which disturbance variables were 

### 178
most sensitive to screening thresholds , as well as which stressors most frequently occur on the landscape

. 

### 179

All reference sites were further screened using a final visual screening process

. E ach site was scanned for 

### 180

catchment and local level stressors that may have been missed during the initial GIS screening process

. 

### 181

Using an easily accessible landscape level program, Google Earth 2009 (GE) , we imported reference sites 

### 182

with respective catchment and local boundarie s and over laid these on aerial photographs from the time 

### 183

period closest to the date on which sites were sampled . We then assessed the level of disturbance across 

### 184

multiple classes (agriculture, roads, timber harvest, mining, etc.) for each site by rating both frequency and 

### 185
proximity cat egories

. Frequency was defined as the percentage of the entire catchment scale for which a 

### 186

disturbance occurred

. Proximity was defined as the relative rate of occurrence for a particular disturbance 

### 187

within the local scale. All observations at the catchment and local scales produced qualitative ranks and 

### 188

### 8 scores. The visual screening score for an individual disturbance class was then calculated based on the 

### 189

combination of frequency and proximity scores (Table 2 ). Scores for all disturbance classes were summed 

### 190
and values exceeding the 95 th percentile were eliminated from inclusion as reference candidates. 

### 191

Examining the Range of Natural Variability 

### 192

Our goal was to produce a reference network that adequately represented as much of the landscape as 

### 193

po ssible. To verify our success, we compared the ranges of various natural gradients (e.g., climate, 

### 194

elevation, gradient, etc.) in both the reference and non

- reference populations

. Several administrative 

### 195
boundaries exist across the region but these boundar ies are rarely useful for partitioning biological 

### 196

variability. While information such as ecoregion have been generally useful in categorizing regions of 

### 197

si milarity (See Miller et al. 2011 ) they may not always represent the natural gradients important to stream 

### 198

systems (Snelder et al. 2004; Cao et al

. 2007)

. Here i ntrinsic environmental data were calculated and 

### 199

summarized for each catchment including geology, climate, and vegetation to investigate the range of 

### 200
natural variability across the region (Table 3 ). 

### 201

As an exploratory analysis, we visually inspected boxplots
, as well as means
, standard deviations and 

### 202

ranges of individual intrinsic variables (Table 4) to assess whether the sites we designated as reference 

### 203

occurred across the same range of values for non

- reference sites ( Table 3 )

. Since the vast majority of non



### 204
reference reaches were generated using a random and spatially balanced survey design, the population 

### 205

represents the extent of the wadeable stream network across the Pacific Northwest. Whil e individual 

### 206

evaluation of natural gradients helps inform whether we are adequately covering the range of each variable
, 

### 207
it does not allow for an understanding of the multivariate nature of the landscape. Here , we ordinated a 

### 208

subset of multivariate normal standardized intrinsic variables using principle component analysis

. We 

### 209

visually assessed ordinated data and summarized sites in frequency tables to ensure adequate representation 

### 210

for the distribution of ordinated environmental gradients

. V isual assess ment allowed use to determine 

### 211

whether adequate representation among the frequency categories was achieved , in particular whether all 

### 212

ecoregions had adequate representation

. 

### 213

AREMP Case Study: Responsiveness of in

- channel sediment to management 

### 214

### 9 In order to test the responsiveness of the this set of reference sites to management practices , we compared 

### 215
the means of “most disturbed” (sites above the 90 th percentile for GIS screens ) and reference sites (sites 

### 216

under the 25 th percentile for GIS and less than 95 th percentile for GE) using the indicator of in

- channel fines 

### 217

less than 6mm . Only AREMP data from the first rotation of its panel design (2002

- 2009) was used in this 

### 218

exercise

. 

### 219

Excessive fine sediment is commonly used as a measure of pollution ( e.g., total maximum daily load) and 

### 220

is regulated by the Environmental Protection Agency and individual states. Excessive sediment particles 

### 221

finer than 6mm can infiltrate the interstitial spaces between cobbles and boulders
, which can prevent or 

### 222

reduce the emerge nce of salmonid fry (Lisle 1989; Kondolf 2000) and suffocate developing eggs 

### 223
(McHenry et al

. 1994). Further, reduction of space between streambed gravels can reduce s the habitat 

### 224

available for benthic invertebrates and fish, reducing both food and cover. T he higher levels of sediment in 

### 225

“most disturbed” stream s are likely the result of current and historical management practices that include 

### 226

road construction and maintenance (Furniss et al

. 1991), timber harvest (Dose and Roper 1994), and mining 

### 227

activ ity (T rombulak and Frissel 200 0 ; Daigle 2010). 

### 228

Using data collected by AREMP (Miller et al. 2015) , w e tested for differen ce between groups with an 

### 229

ANOVA
, using the MIXED procedure in SAS (SAS Institute, 20 11 ). Reference or “most disturbed” was 

### 230

the categorical variable used as main treatment effect, and catchment served as the random factor. To 

### 231

account for differences in particle size as a function of site location within the stream system, gradient was 

### 232

used as a continuous covariate. Gradient is oft en used as a covariate in fines assessment (Al

- Chokhachy et 

### 233

al

. 2010) and was available for all locations, where other measure such as stream flow and power were 

### 234

unavailable. We graphically assessed assumptions of constant variance and normality o f residuals

. A 

### 235
square root transformation of fines was needed to meet assumptions of normality and homoscedasticity of 

### 236

residuals

. Once assumptions were adequately met, we tested whether interactions existed between our 

### 237

main effect and covariate. If ther e was no evidence of an interaction, main effects were interpreted

. Since 

### 238
considerable variability exists in in

- channel physical habitat data, we chose to use an alpha of 0.10 when 

### 239

interpreting the statistical results. 

### 240

241

### 10 Results 

### 242

Screening and Examining the Range of Natural Variability 

### 243

Combined among all agencies, 5 516 individual sites with corresponding catchments were used to calculate 

### 244

disturbance percentiles for each of the variables (refer to table) . Using the 25 th percentile of each 

### 245

disturbance vari able to set reference thresholds resulted in 725 GIS

- screened candidate reference sites 

### 246

(13% , Figure 1 ) . Catchment level screens eliminated 4559 sites, while 232 more sites were eliminated 

### 247

following local screens. “Most disturbed” sites (above the 90 th percentile for all disturbance variables ) 

### 248

totaled 1104 (20%) . Road density was the strongest contributor to defining reference
, eliminating 75% of 

### 249

sites ; 8% more were removed due to a high number of roads crossings. Combined, d eveloped open space 

### 250

( land

- use code 21), mining, the distance of a site to a dam, and kilometers of c anals accounted for removal 

### 251

of the remaining 4% of sites. In the final visual inspection screen ing process , based on additive frequency 

### 252

and proximity of disturbance
, 45 sites a bove the 95 th percentile were removed . Following both GIS and 

### 253
visual (Google Earth) screens, a final total of 680 sites pa ssed all screen ing levels ( F igure 1 )

. 

### 254

Comparison of means and standard deviations of individual natural gradient metrics grouped by reference 

### 255

or non

- reference showed overlap in range for nearly every variable (Table 3 ). Non

- reference sites showed 

### 256

slightly higher values of erodibility (KFCT) than reference sites. Overall, values of TMI N (minimum 

### 257
annual air temperature) were lower for reference sites. Therefore, reference sites may not full y capture all 

### 258

possible values that we know to exist for erodibility or low temperatures across the landscape. The 

### 259

reference sites do, however, appear to adequately capture the range of variability for all other catchment 

### 260

level metrics and site level metrics such as gradient and stream bankfull width throughout the AREMP 

### 261

sampl ing frame. Principal component analysis of standardized natural gradient attributes reveal ed 

### 262

tremendous overlap between reference and non

- reference sit es (Figure 2 ). Spatially, five ecoregions had no 

### 263

reference sites: Strait of Georgia, Puget Lowlands, Columbia Plateau (within NWFP area), Willamette 

### 264

Valley
, and the Central Cali fornia Foothills and Coast Mountains. The Eastern Cascade Slopes and 

### 265

Foothills ecoregion ha d only eight reference sites. 

### 266

AREMP Case Study: Responsiveness of in

- channel sediment to management 

### 267

### 11 Using in

- chan ne l data collected by AREMP, sites categorized as either reference or “ most disturbed ” 

### 268

showed a statistically significant difference in fines under 6mm as a related to disturbance ( F 1,561 = 6.22, p = 

### 269

0.01 )

. After controlling for gradient, “ most disturbed ” catchments were estimated to have 9.5% more 

### 270

percentages of fines greater than 6mm relative to reference catchments (90% CI: 3.69 to 4.27 ). 

### 271

Discussion 

### 272

Using consistent and repeatable methodology, we created a network of reference sites sampled by several 

### 273

programs across the Pacific Northwest. By including sites from multiple organizations
, we greatly 

### 274

increased the sample size of the network, as well as environmental representation. While using sites that 

### 275

represent low levels of human disturbance was a primary goal, understanding and characterizing the natural 

### 276

environmental gradient s across these sites should also greatly enhance our ability to evaluate stream 

### 277

condition more accurately and with greater precision (Cao and Hawkins 2011 ) within areas of adequate 

### 278

sample size

. Further, using all survey sites visited by each organization this effort was comprehensive, 

### 279

streamlined, and a less biased and transparent process than each participating agencies previous ref erence 

### 280

site selection process. It is important to acknowledge that despite reducing bias, we did not completely 

### 281

eliminate it using this process. For example, selecting the percentiles to define thresholds was selected 

### 282
based on professional judgment from the group. 

### 283

In this study, we us ed fairly conservative (low) thresholds to define areas with minimal disturbance. 

### 284

However, in regions with much higher density of human land uses, thresholds may need to be relaxed. The 

### 285

thresholds were based on percentiles acquired using our global dataset ; these values are more conservative 

### 286
than those typically found in the literature ( Stoddard et al 2006 ). For example, many of the sites in this 

### 287

analysis were eliminated due to impairment associated with roads ; r oad densities as high as 2km /km 2 are a 

### 288

commonly used threshold ( Ode et al. 2005 ; Grant et al. 2008 ). Our thresholds may eliminate sites that may 

### 289
still be adequate as reference sites. However, regional stressor s typically found throughout the Pacific 

### 290

Northwest are often as sociated with ro ads (e.g. timber harvest, mining)

. To investigate the propensity of 

### 291

our methods in eliminating sites that were actually reference and as an additional check on sites we 

### 292
delineated as reference, we used Google Earth images for the time period that the site was visited to verify 

### 293

categorization; all 5516 sites were reassessed. We found that only a single reference site was mis

- 

### 294

### 12 categorized due to missing the catchment delineation and only 14 sites failed the GIS screens due to 

### 295

inaccurate road densities ; this e liminated the 14 sites for consideration as reference. 

### 296

In the Northwest Forest Plan area, w ith the exception of the Willamette Valley and Puget Lowlands
, 

### 297
agricultural and urban stressors are less common than those found in areas such as the Central Valley of 

### 298

California. Based on the stressors typical of the Pacific Northwest, we feel confident that we have 

### 299

minimized human disturbance, though we have not c ompletely eliminated it. T he restrictive criteria used in 

### 300
this procedure have reduced the ability to use this network to create expectations for more highly altered 

### 301

areas. R elaxation of thresholds to accommodate representation in these areas may create exc essive 

### 302

variability that would hinder any predictive evaluation (Cao and Hawkins 2011). For discriminating among 

### 303

more highly impacted areas , this analysis could be repeated focusing on these areas separately, using a 

### 304

higher disturbance threshold

. However, this would require separate assessment tools and thresholds for 

### 305
these regions. This could also be used as a tool to identify areas for restoration activities or targeted 

### 306

monitoring , by identifying those areas with the greatest amount of disturban ce and thus the greatest 

### 307

potential for recovery

. 

### 308

Results from our sediment case study represent a simple application of this reference network to a 

### 309
landscape level analysis of disturbance. Here, we used the categories reference versus “ most disturbed ” to 

### 310

evaluate whether a landscape level relationship exists across the Northwest Forest Plan area as it relates to 

### 311

the level of fine s ediments in the stream. Our analysis illustrates how measures of management ac tivities, 

### 312
as represented by “most disturbed”, within a catchment can be combined with field data to predict in



### 313

channel conditions at the landscape level ( Anlauf et al

. 2011)

. 

### 314

Improvements in the accuracy and availability of GIS derived spatial data have enhanced our ability to use 

### 315

these resources as a mechanism to screen for impairment and, more generally, quantify disturbance across 

### 316
the landscape. However, as with processing higher levels of data at large spatial scales, we urge caution; 

### 317

these data likely underestimate impairment proximate to str eam reaches and include stressors not captured 

### 318

in our screens (Yates and Bailey 2010). Unique environmental characteristics, such as some types of 

### 319
geologies, were likely missed. Deviations from reference expectation should warrant further investigation 

### 320

into the causal mechanism
, as the reference network only serves to show that a deviation exists. We expect 

### 321

### 13 that data availability will improve over time and strongly encourage reevaluation of reference sites 

### 322

periodically . For this effort, most partners have agreed to an annual assessment to evaluate any new or 

### 323
problematic sites

. Incorporating site

- level field based screens for disturba nce help s to identify areas where 

### 324

GIS analysis missed disturbance
, or to add sites where we think GIS screens were erroneously high. 

### 325

Assessment tools developed from this reference network create a commonality that can be employed by 

### 326

organizations assessin g streams and catchments through the Pacific Northwest. Physical and biological data 

### 327
collected, using similar protocols, can be shared for maximum efficiency. Further, the potential exists for 

### 328

tool development using these reference sites that can be shared among various organizations. The AREMP 

### 329

case study examined here is just one example of how these reference sites can be used, at the landscape 

### 330
level, to link upslope management practices to a specific in

- channel response. We found evidence that 

### 331

managemen t practices, in particular areas with high road densities , have a negative response to the amount 

### 332
of fines under 6mm in the system regardless of stream gradient. While this example uses only AREMP 

### 333

data, if data from different programs were collected in a sufficiently similar manner they could be 

### 334

combined to create a larger, more robust dataset. Macroinvertebrate collection methods across these Pacific 

### 335
Northwest Agencies were standardized by 2002. Further, each agency uses a probabilistic survey design to 

### 336

s elect sites. As r eference sites and macroinvertebrate data among organizations were defined and collected 

### 337

in the same standardized manner, they can thus be combined and used to inform macroinvertebrate 

### 338
assessment tools such as observed to expected models or to devel op multimetric indices (Miller et al. in 

### 339

prep). Using a consistent network sh ould, potentially , allow for greater shar e ability of standards or 

### 340

evaluation criteria as defined by expectations se t by reference sites of simila r environmental characteristics; 

### 341
thus, improving our ability to leverage information among agencies to better inform environmental policy 

### 342

and management directives. 

### 343

T his effort serves to create consistency and transparency in the establishment of a reference network for 

### 344

multiple stream survey organizations through out the Pacific Northwest and Northern California

. We hope 

### 345

that this starts the dialogue regarding more standardized ways to evaluate data. Here we have established a 

### 346

framework that that allows us to move towards advancing the accuracy and precision of benchmarks that 

### 347

we used to inform aquatic conditions

. Creating more consistency in physical and biological field protocols 

### 348

### 14 will increase shar e ability of data. However, even more important is whether the bioassessment tools used 

### 349

by these organizations
, built using the same reference network
, ultimately show similar results and 

### 350
performance (Cao and Hawkins 2011). Future work to compare individual components and overall results 

### 351

of bioassessment tools developed by each organization using this reference network will be a n important 

### 352

benefit to understanding reference network performance. 

### 353

354

Acknowledgements 

### 355

We thank Chuck Hawkins and Scott Miller of the Utah State Western Monitoring Center and Aquatic 

### 356
Ecology Lab for constructive comments throughout the development of this manuscript , Raphael Mazor 

### 357

provided R code for PCA graphics
, and Steve Wilcox constructed maps and performed reference final 

### 358

sc reening. This work was funded by the Bureau of Land Management, Forest Service (Regions 5 & 6), and 

### 359
the Environmental Protection agency through interagency support of the Northwest Forest Plan Aquatic 

### 360

and Riparian Effectiveness Monitoring Program. 

### 361

362

363

364

365

366

367

368

369

370

371

372

373

374

### 15 References 

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### 376 Habitat in Headwater Streams within the Interior Columbia River and Upper Missouri River 

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### 379

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### 380 (2011) Explaining spatial variability in stream habitat using both natural and management

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Bailey RC, Norris RH , and Reynoldson TB (2004) Bioassessment of Freshwater Ecosystems: Using the 

### 389 Reference Condition Approach. Springer US. pp 1

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### 391 improves the accuracy and precision of diatom

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### 394 methodological issues. Journal of the North American Benthological Society 30:680

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### 398 gaps: A literature review. BC Journal of Ecosystems and Management 10(3):65

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### 400 Meehan W R , editor. Influences of forest and rangeland management on salmonid fishes and 

### 401 their habitat s. Amercian Fisheries Society, Sp ecial Publication 19, Bethesda Maryland. 

### 402

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### 409
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### 410 consistency in a national assessment: The challenges of applying a reference condition approach 

### 411 on a continental scale. Journal of the North American Benthological Society 27:860

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### 413 health potentials. Environmental Management10:629

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Kondolf GM (2000) Assessing Salmonid Spawning Gravel Quality. Trans. Am. Fish. Soc. 129:262

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### 16 Larsen DP
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### 416 scale trends. Bioscience 51 : 1069

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### 417

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### 418 California. Water Resourc es Resea rch 25(6): 1303

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### 419

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### 423

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### 424 seed transfer zone? Evidence from a common garden study w ith five native species. Restoration 

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### 426

Ode P , Rehn AC , and May JT (2005) A quantitative tool for assessing the integrity of southern 

### 427 coastal California streams. Environmental Management 35: 493

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### 428 429 Ode P , Rehn AC , Mazor PD
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### 430 and Hawkins CP

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### 431 assessments in environmentally complex regions. 

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### 433 Journal of the North American Benthological Society 27(4):822

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### 434
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### 435 American Geographers 77:118

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### 437 Hawkins CP , Herlihy AT , Kaufmann PR , Barbour MT , Larsen DP , and Olsen AR (2008) 

### 438 Condition of stream ecosystems in the US: an overview of the first national assessmen t. Journal 

### 439 of the North American Benthological Society 27(4): 812

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Pollack MM , Beechie TJ and Imaki H (2012) Using reference conditions in ecosystem restoration: an 

### 441 example for riparian conifer forests in the Pacific Northwest. Ecosphere 3(11):98 

### 442

Reeves GH , Williams, JE, Burnett KM, and Gallo K (2006) The aquatic conservation strategy of the 

### 443 Northwest Forest Plan. Conservation Biology 20 (2): 319

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### 444 445 SAS Institute Inc ( 20 11) Cary, NC

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### 446

S eaber PR
, Kapinos FP, and Knapp GL (1987)Hyrdologic Unit Maps: US Geological Survey (USGS), 

### 447 Water Supply Paper 2294, pp 63. 

### 448

Snelder TH , Cattanéo F , Suren AM, and Biggs BF (2004) Is the River Environment Classification an 

### 449 improved landscape

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### 450 Society 23(3):580

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### 451

Stoddard JL , Larsen DP , Hawkins CP , Johnson RK , and Norris RH (2006) Setting expectations for the 

### 452 ecological condition of running waters: the concept of reference condition . Ecological 

### 453 Applications 16:1267

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### 454

Stoddard JL
, Herlihy AT , Peck DV , Hughes RM , Whittier TR , and Tarquinio E (2008) A 

### 455 process for creating multimetric indices for large

- scale aquatic surveys. Journal of the North 

### 456 American Benthological Society 27:878

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### 457 458

### 17 Trombulak SC and Frissell CA (2000) Review of ecological effects of roads on terrestrial and aquatic 

### 459 communities. Conservation Biology 14 (1): 18

- 30. 

### 460 461 Whittier TR, Stoddard JL
, Larsen DP, and Herlihy AT (2007) Selecting reference sites for stream 

### 462 biological assessments: Best professional ju dgment or objective criteria. Journal of the North 

### 463 American Benthological Society 26:349

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### 464

Yates AG and Bailey RC (2010) Selecting objectively defined streams for bioassessment programs. 

### 465 Environmental Monitoring and Assessment 170:129

- 140 

### 466

467
468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506

### 18 Table 1. Stressor metrics percentiles used to discriminate between impaired and reference sites. 

### 507 Metrics were calculated at the catchment scale where the lowest downstream point was an individual site. 

### 508 Sites that fell within the 1 st and 25 th percentile for all stressors were defined as “reference”. This was used 

### 509 as the first screening process. Sites above the 90 th percentile were defined as “impaired”. * For Distance to 

### 510 Dam, the 75 th percentile was used to de f ine “reference”, while the 10 th percentile was used to define “most 

### 511 disturbed”. 

### 512

513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540

a Custom dataset complied from Forest Service, BLM, and Chico State University data 

### 541 b M ulti

- resolution Land Characteristics Consortium ( www. mrlc .gov ) dataset 

### 542

c USGS Mine data (www. mr data

. usgs .gov) 

### 543 d USGS N ational Hydrography Dataset (http://nhd.usgs.gov/) 

### 544 545

546

547

548

549

550

551

552 Percentile

Reference Most disturbed

Metric Unit 25th 90th

ROAD DENSITY a km/km 2 1.347 3.87

STREAM CROSSING a count / km 2 0.241 1.013

AGRICULTURE b % 0.047 2.735

DEVELOPED OPEN SPACE b % 1.996 7.821

MINES c % 0.209 26.640

GRAVEL MINES c mines / km 2 0.005 0.059

CANALS d % 1.703 29.273

75th 10th

DISTANCE TO DAM * d km 20.559 3.555

### 19

Table 2. Visual screening scoring criteria used to validate sites that passed the initial GIS screening metrics. 

### 553

Frequency: percentage of the disturbance at the catchment scale Proximity: how common the disturbance occurs at the local scale (2

- km catchment) None Low Moderate High Absent Rare Infrequent Common None 0 0 1 3 10 Low >0

- 10% 1 1 3 10 Moderate 11

– 30% 3 3 10 10 High > 30% 10 10 10 10 

### 554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

### 20

Table 3

. Intrinsic n atural gradient metrics used for examining the range of natural variation among reference 

### 576 sites. Metrics were calculated at the catchment scale where the lowest downstream point was an individual 

### 577 site. Two site level metrics, gradient and stream bankfull width (m) were also analyzed. 

### 578

Range Metric Description Unit Reference Non

- Reference
PPT_00_09 2000

- 2009 mean annual precipitation a mm 4

- 47 3.4

- 40.8

TEMP_00_09 2000

- 2009 mean monthly temperature a °C 1.5

- 15.3

- 0.80

- 15.5

TMEAN Mean annual air temperature a °C 2.4

- 14.8 2.05

- 14.9

TMAX Maximum annual air temperature a °C 1.5

- 35 16.5

- 35.0

TMIN Minimum annual air temperature a °C

- 0.01

- 4.4

- 9.4

- 4.3

PSUM Mean annual precipitation a mm/100 4.7

- 49 3.5

- 48.1

PMAX Maximum monthly precipitation a mm/100 0.62

- 8.7 0.55

- 8.5

PMIN Minimum monthly precipitation a mm/100 0.009

- 1.1 0

- 1

AtmCa 1994

- 2006 annual weighted atmospheric mean Calcium c mg/L 0.02

- 0.04 0.02

- .0.04

AtmMg 1994

- 2006 annual weighted atmospheric mean Magnesium c mg/L 0.008

- 0.07 0.008

- 0.07

AtmSO4 1994

- 2006 annual weighted atmospheric mean SO4 c mg/L 0.1

- 0.3 0.1

- 0.3

XWD 1961

- 1990 annual mean number of wet days c # days 70

- 233 62

- 229.9

MAXWD 1994

- 2006 annual mean maximum number of wet days c # days 10

- 27 9

- 27

CaO Calcite mineral content b % 0.5

- 12.2 0.5

- 13.2 MgO Magnesium oxide mineral content b % 0.5

- 39.3 0.5

- 34.4

N Nitrogenous mineral content b % 0.00000002

- 0.0005 0.00000004

-

0.001

P Phosphorus mineral content b % 0.000009

- 0.0006 0.000002

- 0.0009
S Sulphur mineral content b % 0.01

- 0.33 0.001

- 0.48

PCT_GNEISS Percent gneiss geology in catchment b % 0

- 100 0

- 100

PCT_GRANITE Percent granitic geology in catchment b % 0

- 100 0

- 100

PCT_MAFIC Percent mafic/ultramafic geology in catchment b % 0

- 100 0

- 100

PCT_QUATERNARY Percent quaternary, geology in catchment b % 0

- 100 0

- 100

PCT_SEDIMENTARY Percent sedimentary geology in catchment b % 0

- 100 0

- 100

### 21

PCT_VOLCANIC Percent volcanic geology in catchment b % 0

- 100 0

- 100

PrdCond Predicted reference condition conductivity d uS/cm 27

- 283 26

- 335

UCS Catchment mean unconfined Compressive Strength e MPa 1.6

- 196.5 0.1

- 206.4

BDH Catchment mean bulk density e g/cm 3 0.09

- 1.6 0.09

- 1.6

KFCT Catchment mean soil erodability (K) factor e None 0.005

- 0.3 0.007

- 0.48

PRMH Catchment mean soil permeability e In/hour 0.04

- 18.0 0.04

- 16.4

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

- 2.6

- 1.8

- 0.002

- 2.3



### 579

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

### 580 b Olson and Hawkins (in Review) 

### 581

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

### 582 d Olson and Hawkins, 2012 

### 583

e Baker et al. 2003. Olson and Hawki ns (in R eview) 

### 584 585

586

### 22 Table 4

. Mean and standard deviation for individual metrics for reference and non

- reference sites

. 

### 587

Metric Reference Mean ( SD) Non

- Reference Mean (SD)

PPT_00_09 19.27 (6.58) 17.27 (6.13) TEMP_00_09 8.29 (2.77) 9.93 (1.88) TMEAN 7.61 (3.03) 9.47 (2.04) TMAX 23.44 (3.66) 24.76 (2.71) TMIN

- 2.92 (3.24)

- 0. 80 ( 2.40) PSUM 21.75 (7.80) 19.58 (7.41) PMAX 3.59 (1.31) 3.23 (1.26) PMIN 0.28 (0.18) 0.24 (0.14) AtmCa 0.03 (0.004) 0.04 (0.004) AtmMg 0.026 (0.01) 0.03 (0.0003) AtmSO4 0.21 (0.06) 0.23 (0.06) XWD 153.96 (33.18) 153.30 (32.49) MAXWD 19.76 (3.14) 19.76 (3.24) CaO 5.19 (2.41) 4.30 (2.63) MgO 5.18 (5.90) 3.61 (3.38) N 0.00003 (0.00005) 0.00003 (0.00007) P 0.0001 (0.00007) 0.0001 (0.00007) S 0.05 (0.04) 0.06 (0.05) PCT_GNEISS 3.23 (17.44) 0.21 (4.12) PCT_GRANITE 5.56 (21.41) 2.25 (13.53) PCT_MAFIC 9.08 (27.65) 3.76 (17.44) PCT_QUATERNARY 0.14 (3.77) 1.31 (10.46) PCT_SEDIMENTARY 41.47 (47.37) 54 .31 (47.90) PCT_VOLCANIC 40.25 (48.06) 37.02 (46.64) PrdCond 73.84 (37.19) 81.62 (38.58) UCS 113.42 (35.14) 100.68 (34.17) BDH 1.24 (0.18) 1.23 (0.17) KFCT 0.17 (0.04) 0.20 (0.05) PRMH 4.17 (2.24) 3.39 (1.91) LPREM

- 0.82 (0.47)

- 0.66 (0.45) 

### 588

589

### 23 Figure 1. Spatial distribution of reference sites across the study area by agency

. 

### 590

591

592

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### 25 597
Figure 3 . Principal Component Analysis using standardized natural gradient metrics (Table1). Reference 

### 598 sites are denoted by larger circles and colors consistent with ecoregion, Standardized natural gradient 

### 599 vectors based on the first two principal components are included to the right of the graph, below the region 

### 600 legend. 

### 601

602