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. We

hope 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

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Andy 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

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

Stephanie Miller1,8, Peter Eldred1, Ariel Muldoon2, Kara Anlauf-Dunn3, Charlie Stein3, Shannon Hubler

4

2 , 

Lesley Merrick4, Nick Haxton4,Chad Larson5, Andy Rehn6, Peter Ode6, Jake Vander Laan73

1

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

5 Jefferson Way, Corvallis, OR 97333

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

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

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

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

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

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11 Department of Watershed Sciences, Western Center for Monitoring and Assessment of Freshwater

12 Ecosystems, and Ecology Center, Utah State University, Logan, Utah 84322-5210

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14 Corresponding Author: Stephanie Miller, smiller@blm.gov, 541-750-7017

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

16

17 Abstract

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

19 unifying challenge of assessing conditions and quantifying reasonable expectations for measured 

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

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

22 across a region of interest. However, identification of reference sites often relies heavily on professional 

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

24 collection, site characterization, and reference site selection facilitate greater cooperation among 

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

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

27 organizations through the Pacific Northwest and Northern California. We provide a case study that offers a 

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

29 practices to specific in-channel response. We found management practices, particularly areas with high 

Manuscript

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

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

32 create a larger, more robust dataset. We hope that this starts a dialogue regarding more standardized ways 

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

34 field protocols will increase ability to share data.

35

36

37 Introduction

38 One of the greatest challenges in assessing stream condition is quantifying reasonable expectations for 

39 measured indicators (Hughes et al. 1986; Stoddard et al. 2006). A common approach for setting 

40 appropriate expectation levels is to define reference conditions (Herlihy et al. 2008; Whittier et al. 2007, 

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

42 ecosystem condition. In reality, few or no areas exist that are truly free of human disturbance. As such, 

43 reference conditions are a representation of the least disturbed or most natural state of a resource

44 characterized by the range of natural variability across a region of interest. Deviation from this range of 

45 expectations is used as evidence for anthropogenic degradation. In using a reference condition approach, 

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

47 properties of an ecosystem beyond a level observed at reference sites. To reasonably answer this question, 

48 human disturbances must be factored out as much as possible. 

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

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

51 data, professional opinion, or a combination of both. While working toward standardizing interpretation of 

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

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

54 performing stream assessments within the Pacific Northwest revealed that many use Geographic 

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

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

57 defined reference condition varied among programs. Thus, the implied quality of the reference sites defined 

58 independently by each organization remained questionable (Hawkins et al. 2010). These inconsistencies 

59 make it difficult to share reference data or to accept assessments of stream conditions across agencies. 

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

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

62 Specifying conditions that ensure high-quality reference sites while maintaining adequate sample sizes to 

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

64 variable landscapes (Stoddard et al. 2008) or areas with extensive land-use (Paulsen et al. 2008; Ode et al. 

65 in press). Selecting reference sites in relatively undisturbed streams is optimal if the biological 

66 measurements taken at these locations represent the natural variability across the area of interest. For large67 scale assessments across regions of high environmental heterogeneity, using reference site selection criteria 

68 that includes adequate numbers of uniformly least or minimally disturbed sites while representing natural 

69 variability can be challenging. Under-representation reduces our ability to accurately and precisely set 

70 expectations (Cao and Hawkins 2011). Creating a larger pool of candidate reference sites from sites visited 

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

72 represent stream reaches across the landscape. Data from reference sites on uniformly defined thresholds 

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

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

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

76 shared across monitoring programs. This paper describes such a cross-organizational effort to compile a 

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

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

79 Aquatic Conservation Strategy (Reeves et al. 2006) across a large geographic extent, ranging from 

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

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

82 AREMP initiated this reference site delineation project to define large-scale stream reference conditions,

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83 partnering with the Oregon Department of Fish and Wildlife Aquatic Inventories Project (ODFW), the

84 Oregon Department of Environmental Quality Laboratory (ODEQ), Washington Department of Ecology

85 (WDOE), and California Department of Fish and Wildlife Aquatic Bioassessment Laboratory (CDFW). 

86 This study builds from the experiences in developing reference criteria shared among the partnering groups 

87 to produce a network of regionally consistent and shareable reference sites. It is our hope that the 

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

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

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

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

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

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

94 scales would provide multiple benefits to all agencies. These reference data are the framework used to

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

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

97 management directives.

98 Methods

99 Study Area

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

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

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

103 numerous environmental gradients. Anthropogenic land-use is pervasive and varied in degree and history. 

104 Much of the Pacific Northwest is actively harvested for timber; road construction and maintenance for this 

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

106 agricultural or urban land-use throughout much of the region. Natural variation also exists across the 

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

108 Sampling Sites

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109 While each monitoring program has different objectives and sampling designs (see Reeves et al. 2006,

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

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

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

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

114 what today would be considered outdated GIS data. Indicator benchmarks based on the distributions of 

115 reference sites warrants periodic reevaluation as newer technologies become available.

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

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

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

119 programs whose sample survey points were generated using different NHD layers, each sample point was 

120 registered to the medium resolution NHD (1:100,000) when appropriate. Points that did not fall on the 

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

122 from this group of streams. For all sites, an upstream catchment polygon was generated using 30 m digital 

123 elevation models (DEMs) and flow direction from NHDplus version2. Catchment size ranged from 1 to 

766,842 ha. Over the last 10 years, AREMP has sampled in over 200 5

th 124 field hydrological units (Seaber et 

125 al. 1987) and visited more than 1600 sites. During the same time, WDOE has sampled over 500 sites

126 throughout Washington, approximately half of which fall within the NWFP area. The ODEQ and ODFW 

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

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

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

130 NWFP area. 

131 Human Disturbance and Natural Variability Data

132 We aggregated several publicly available spatially data, incorporating local data when consistent at the 

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

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

135 variability included geology and mineral content, climatic and atmospheric deposition, as well as variables 

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

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

138 levels: catchment and 2km catchment (local) scale (Figure 2). The smaller local catchment limits the 

139 summarization to the area in the catchment 2km upstream from the downstream survey point. The 2km 

140 catchment represents more localized disturbance with the greatest capacity to directly impact survey site

141 conditions. 

142 Roads are commonly used in reference site delineation (Al-Chokhachy et al. 2010) but GIS road layers are 

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

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

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

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

147 data in a corporate database. Though the BLM includes all lands in their database and constantly updates 

148 data on BLM lands, they infrequently update data on non-BLM lands. We used GIS to select all roads on 

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

150 region. In California, we combined Forest Service roads with a roads layer compiled by CA State 

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

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

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

154 investigated the use of commercially available road layers but found little difference in these layers to the 

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

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

157 land cover data and instead chose to use the BLM road layer to represent non-federal lands in Oregon and 

158 Washington, with the Chico State layer representing non-federal lands in California. The road layer was the 

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

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

161 The Multi-resolution Land Characteristics Consortium (www.mrlc.gov) National Land Cover Database 

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

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

164 downstream dam. Mine data were used to determine the nearest distance to non-gravel mines and gravel 

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

166 Screening Criteria

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

sites. The 25th and 90th 168 percentiles of each disturbance variable were calculated for the population of 

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

higher percentiles (except distance to dam). We used the 25th 170 percentile for each screening metric as the 

threshold where all sites with values below the 25th 171 percentile for all metrics were defined as reference, or 

“least disturbed”. Conversely, values above the 90th 172 percentile represented “most disturbed”. 

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

174 various organizations; as such, we only attempted to characterize expectations where we had adequate 

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

176 were unavailable (e.g. areas with high rates of urbanization and agriculture). Threshold sensitivity to 

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

178 passed differing threshold levels. This exercise allowed us to understand which disturbance variables were

179 most sensitive to screening thresholds, as well as which stressors most frequently occur on the landscape. 

180 All reference sites were further screened using a final visual screening process. Each site was scanned for 

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

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

183 with respective catchment and local boundaries and overlaid these on aerial photographs from the time 

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

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

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

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

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

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

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

and values exceeding the 95th 191 percentile were eliminated from inclusion as reference candidates. 

192 Examining the Range of Natural Variability

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

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

195 elevation, gradient, etc.) in both the reference and non-reference populations. Several administrative 

196 boundaries exist across the region but these boundaries are rarely useful for partitioning biological

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

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

199 systems (Snelder et al. 2004; Cao et al. 2007). Here intrinsic environmental data were calculated and 

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

201 natural variability across the region (Table 3). 

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

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

204 occurred across the same range of values for non-reference sites (Table 3). Since the vast majority of non205 reference reaches were generated using a random and spatially balanced survey design, the population 

206 represents the extent of the wadeable stream network across the Pacific Northwest. While individual 

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

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

209 subset of multivariate normal standardized intrinsic variables using principle component analysis. We 

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

211 for the distribution of ordinated environmental gradients. Visual assessment allowed use to determine

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

213 ecoregions had adequate representation.

214 AREMP Case Study: Responsiveness of in-channel sediment to management

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

the means of “most disturbed” (sites above the 90th 216 percentile for GIS screens) and reference sites (sites 

under the 25th percentile for GIS and less than 95th 217 percentile for GE) using the indicator of in-channel fines 

218 less than 6mm. Only AREMP data from the first rotation of its panel design (2002-2009) was used in this 

219 exercise. 

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

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

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

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

224 (McHenry et al. 1994). Further, reduction of space between streambed gravels can reduces the habitat 

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

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

227 road construction and maintenance (Furniss et al. 1991), timber harvest (Dose and Roper 1994), and mining 

228 activity (Trombulak and Frissel 2000; Daigle 2010).

229 Using data collected by AREMP (Miller et al. 2015), we tested for difference between groups with an 

230 ANOVA, using the MIXED procedure in SAS (SAS Institute, 2011). Reference or “most disturbed” was

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

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

233 used as a continuous covariate. Gradient is often used as a covariate in fines assessment (Al-Chokhachy et 

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

235 unavailable. We graphically assessed assumptions of constant variance and normality of residuals. A 

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

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

238 main effect and covariate. If there was no evidence of an interaction, main effects were interpreted. Since 

239 considerable variability exists in in-channel physical habitat data, we chose to use an alpha of 0.10 when 

240 interpreting the statistical results. 

241

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242 Results

243 Screening and Examining the Range of Natural Variability

244 Combined among all agencies, 5516 individual sites with corresponding catchments were used to calculate 

disturbance percentiles for each of the variables (refer to table). Using the 25th 245 percentile of each 

246 disturbance variable to set reference thresholds resulted in 725 GIS-screened candidate reference sites

247 (13%, Figure 1). Catchment level screens eliminated 4559 sites, while 232 more sites were eliminated 

following local screens. “Most disturbed” sites (above the 90th 248 percentile for all disturbance variables) 

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

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

251 (land-use code 21), mining, the distance of a site to a dam, and kilometers of canals accounted for removal 

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

and proximity of disturbance, 45 sites above the 95th 253 percentile were removed. Following both GIS and 

254 visual (Google Earth) screens, a final total of 680 sites passed all screening levels (Figure 1). 

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

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

257 slightly higher values of erodibility (KFCT) than reference sites. Overall, values of TMIN (minimum 

258 annual air temperature) were lower for reference sites. Therefore, reference sites may not fully capture all 

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

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

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

262 sampling frame. Principal component analysis of standardized natural gradient attributes revealed

263 tremendous overlap between reference and non-reference sites (Figure 2). Spatially, five ecoregions had no

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

265 Valley, and the Central California Foothills and Coast Mountains. The Eastern Cascade Slopes and 

266 Foothills ecoregion had only eight reference sites. 

267 AREMP Case Study: Responsiveness of in-channel sediment to management

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268 Using in-channel data collected by AREMP, sites categorized as either reference or “most disturbed”

269 showed a statistically significant difference in fines under 6mm as a related to disturbance (F1,561 = 6.22, p = 

270 0.01). After controlling for gradient, “most disturbed” catchments were estimated to have 9.5% more

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

272 Discussion

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

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

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

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

277 environmental gradients across these sites should also greatly enhance our ability to evaluate stream 

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

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

280 streamlined, and a less biased and transparent process than each participating agencies previous reference 

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

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

283 based on professional judgment from the group. 

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

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

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

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

analysis were eliminated due to impairment associated with roads; road densities as high as 2km /km2288 are a 

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

290 still be adequate as reference sites. However, regional stressors typically found throughout the Pacific 

291 Northwest are often associated with roads (e.g. timber harvest, mining). To investigate the propensity of 

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

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

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

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

296 inaccurate road densities; this eliminated the 14 sites for consideration as reference. 

297 In the Northwest Forest Plan area, with the exception of the Willamette Valley and Puget Lowlands, 

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

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

300 minimized human disturbance, though we have not completely eliminated it. The restrictive criteria used in 

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

302 areas. Relaxation of thresholds to accommodate representation in these areas may create excessive 

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

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

305 higher disturbance threshold. However, this would require separate assessment tools and thresholds for 

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

307 monitoring, by identifying those areas with the greatest amount of disturbance and thus the greatest 

308 potential for recovery.

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

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

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

312 the level of fine sediments in the stream. Our analysis illustrates how measures of management activities, 

313 as represented by “most disturbed”, within a catchment can be combined with field data to predict in314 channel conditions at the landscape level (Anlauf et al. 2011). 

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

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

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

318 these data likely underestimate impairment proximate to stream reaches and include stressors not captured 

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

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

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

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

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

324 problematic sites. Incorporating site-level field based screens for disturbance helps to identify areas where 

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

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

327 organizations assessing streams and catchments through the Pacific Northwest. Physical and biological data 

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

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

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

331 level, to link upslope management practices to a specific in-channel response. We found evidence that 

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

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

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

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

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

337 select sites. As reference sites and macroinvertebrate data among organizations were defined and collected 

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

339 assessment tools such as observed to expected models or to develop multimetric indices (Miller et al.in 

340 prep). Using a consistent network should, potentially, allow for greater shareability of standards or 

341 evaluation criteria as defined by expectations set by reference sites of similar environmental characteristics; 

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

343 and management directives.

344 This effort serves to create consistency and transparency in the establishment of a reference network for 

345 multiple stream survey organizations throughout the Pacific Northwest and Northern California. We hope 

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

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

348 we used to inform aquatic conditions. Creating more consistency in physical and biological field protocols 

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349 will increase shareability of data. However, even more important is whether the bioassessment tools used 

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

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

352 of bioassessment tools developed by each organization using this reference network will be an important 

353 benefit to understanding reference network performance. 

354

355 Acknowledgements

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

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

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

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

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

361 and Riparian Effectiveness Monitoring Program. 

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

378 basins using an index approach. Transactions of the American Fisheries Society, 139(4): 1041-

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380 Anlauf KJ, Jensen DW, Burnett KM, Steel EA, Christiansen K, Firman JC, Feist BE, and Larsen DP. 

381 (2011) Explaining spatial variability in stream habitat using both natural and management –

382 influenced landscape predictors. Aquatic Conservation: Marine and Freshwater Ecosystems 21: 

383 704-714.

384 Bailey RG (1980) Description of the ecoregions of the United States. No. 1391. US Department of 

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

390 Reference Condition Approach. Springer US. pp 1-15 

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392 improves the accuracy and precision of diatom-based indicators. Journal of the North American 

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395 methodological issues. Journal of the North American Benthological Society 30:680-701

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411 consistency in a national assessment: The challenges of applying a reference condition approach 

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416 Larsen DP, Kincaid TM, Jacobs SE, and Urquhart NS (2001) Designs for evaluating local and regional 

417 scale trends. Bioscience 51: 1069-1078.

418 Lisle TE (1989) Sediment transport and resulting deposition in spawning gravels, north coastal 

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420 McHenry ML, Morril DC and Currence E (1994) Spawning Gravel Quality, Watershed Characteristics 

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422 Watersheds. Lower Elwha S'Klallam Tribe, Port Angeles, WA. and Makah Tribe, Neah Bay, 

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425 seed transfer zone? Evidence from a common garden study with five native species. Restoration 

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430 Ode P, Rehn AC, Mazor PD, Schiff KC, Stein ED, May JT, Brown LR, Herbst DB, Gillett D, Lunde K, 

431 and Hawkins CP. in press. Evaluating the adequacy of a reference site pool for ecological 

432 assessments in environmentally complex regions.

433 Olsen AR and Peck G (2008). Survey design and extent estimates for the Wadeable Streams Assessment. 

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436 American Geographers 77:118-125

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

439 Condition of stream ecosystems in the US: an overview of the first national assessment. Journal 

440 of the North American Benthological Society 27(4): 812-821

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

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

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

444 Northwest Forest Plan. Conservation Biology 20 (2): 319-329.

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446 SAS Institute Inc (2011) Cary, NC.

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

448 Water Supply Paper 2294, pp 63.

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450 improved landscape-scale classification of rivers? Journal of the North American Benthological 

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

453 ecological condition of running waters: the concept of reference condition. Ecological 

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456 process for creating multimetric indices for large-scale aquatic surveys. Journal of the North 

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

460 communities. Conservation Biology 14 (1): 18-30.

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

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466 Environmental Monitoring and Assessment 170:129-140

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

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

Sites that fell within the 1st and 25th 509 percentile for all stressors were defined as “reference”. This was used 

as the first screening process. Sites above the 90th 510 percentile were defined as “impaired”. * For Distance to 

Dam, the 75th percentile was used to define “reference”, while the 10th 511 percentile was used to define “most 

512 disturbed”.

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a

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

b

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

c

543 USGS Mine data (www.mrdata.usgs.gov)

d

544 USGS National Hydrography Dataset (http://nhd.usgs.gov/)

545

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552

 Percentile

Reference Most disturbed

Metric Unit 25th 90th

ROAD DENSITY akm/km21.347 3.87

STREAM CROSSING acount / km20.241 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 / km20.005 0.059

CANALS d % 1.703 29.273

75th 10th

DISTANCE TO DAM*

d

km 20.559 3.555

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

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

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576 Table 3. Intrinsic natural gradient metrics used for examining the range of natural variation among reference 

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

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

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

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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/cm30.09-1.6 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

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

b

581 Olson and Hawkins (in Review)

c

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

d

583 Olson and Hawkins, 2012

e

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

585

586

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587 Table 4. Mean and standard deviation for individual metrics for reference and non-reference sites.

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)

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

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Copyrighted page(s) removed. Copyrighted 

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597

598 Figure 3. Principal Component Analysis using standardized natural gradient metrics (Table1). Reference 

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

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

601 legend.

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