DISCLAIMER 

The information in this document has been funded wholly by the U.S. Environmental Protection 

Agency (EPA). Deborah Reusser was partially funded through AMI/GEOSS IAG #DW-14-

92231501-0 from the U.S. EPA. The publication was subjected to review by the National Health 

and Environmental Effects Research Laboratory’s Western Ecology Division and the U.S. 

Geological Survey (USGS), and is approved for publication. However, approval does not signify 

that the contents reflect the views of the U.S EPA or the USGS. The use of trade, firm, or 

corporation names in this publication is for the information and convenience of the reader; such 

use does not constitute official endorsement or approval by the U.S. Department of Interior, the 

USGS, or the EPA of any product or service to the exclusion of others that may be suitable. 

Preferred Citation 

Lee II, H. and Brown, C.A. (eds.) 2009. Classification of Regional Patterns of Environmental 

Drivers And Benthic Habitats in Pacific Northwest Estuaries. U.S. EPA, Office of Research and 

Development, National Health and Environmental Effects Research Laboratory, Western 

Ecology Division. EPA/600/R-09/140.

ACKNOWLEDGMENTS 

The authors wish to thank the following for their contributions to this report: 

Dynamac, Inc. for field and laboratory work; Steve Rumrill and Alicia Helms, South Slough 

National Estuarine Research Reserve, for Coos Estuary salinity data; Ralph Garono, Earth 

Designs, Inc., Steve Rumrill and Derek Sowers, South Slough National Estuarine Research 

Reserve, and Mark Trenholm and Don Reynolds, Tillamook Estuaries Partnership, for reviewing 

the aerial photomap classification; George Priest, Oregon Department of Geology and Mineral 

Industries, for providing bathymetric data; Peter Eldridge (deceased), U.S. EPA, for providing 

isotope data from the Salmon River Estuary; Steven Ferraro, U.S. EPA, for information on food 

webs; Robert Lackey, formerly of the U.S. EPA, for salmon information; Tony Olsen, U.S. EPA, 

for assistance in sampling design; Tad Larsen of Raytheon Information Solutions for GIS 

support; staff of the Integrated Stable Isotope Research Facility, U.S. EPA, for analyzing the 

stable isotope samples; and Jimmie K. Cheney and Karen Ebert, Senior Environmental 

Employment Program with U.S. EPA, for proof reading the document. Peter Eldridge, Melanie 

Frazier, Jody Stecher, Katharine Marko, Allison Aldous of The Nature Conservancy, and Jeff 

Weber of the Oregon Department of Land Conservation and Development reviewed an earlier 

version of this document and provided insightful suggestions. 

 
 ii

TABLE OF CONTENTS 

List of Figures................................................................................................................................ vi 

List of Tables ................................................................................................................................ xii 

EXECUTIVE SUMMARY .......................................................................................................... xv 

CHAPTER 1: INTRODUCTION AND FRAMEWORK FOR THE PACIFIC NORTHWEST 

REGIONAL CLASSIFICATION PROJECT................................................................................. 1 

1.0 Scope.................................................................................................................................... 1 

1.1 Problem Statement............................................................................................................... 1 

1.2 Regulatory Framework for Estuarine Classifications.......................................................... 2 

1.3 Overview of Eutrophic Conditions and Nutrient Dynamics in the Pacific Northwest........ 4 

1.4 Biotic Response Measures in PNW Estuaries ..................................................................... 5 

1.5 Design and Objectives of the Pacific Northwest Regional Study........................................ 8 

CHAPTER 2: REGIONAL CLASSIFICATION OF PACIFIC NORTHWEST ESTUARIES BY 

WETLAND AND LAND COVER PATTERNS ......................................................................... 10 

2.0 Introduction........................................................................................................................ 10 

2.1 Estuarine, Wetland, and Landscape Data Sources and Methods ....................................... 11 

2.1.1 Estuary Definition and Inventory................................................................................ 11 

2.1.2 Wetland Habitats Based on NWI................................................................................ 13 

2.1.3 Watershed Delineation................................................................................................ 22 

2.1.4 Land Cover Patterns, Impervious Surfaces, and Population Density ......................... 23 

2.1.5 Classification Strategy and Methods .......................................................................... 23 

2.1.6 Ecological Resources in Pacific Northwest Estuaries ................................................ 30 

2.2 Inventory of Estuaries on the Pacific Coast....................................................................... 31 

2.3 Ecological Services of Small Pacific Northwest Estuaries................................................ 34 

2.4 Classification by Geomorphology and Oceanic Exchange................................................ 41 

2.4.1 Coastal Lagoons and Blind Estuaries ......................................................................... 42 

2.4.2 Tidal Coastal Creeks/Tidally Restricted Coastal Creeks............................................ 43 

2.4.3 Marine Harbors/Coves................................................................................................ 45 

2.4.4 Drowned River Mouth and Bar-Built Estuaries.......................................................... 45 

2.4.4.1 Tide- and River-Dominated Drowned River Mouth Estuaries ............................. 46 

2.5 Classification of Estuaries by NWI Wetland Classes........................................................ 53 

2.6 Watershed Classification ................................................................................................... 61 

2.6.1 Watershed Classes ...................................................................................................... 61 

2.7 Crosswalk of Wetland and Watershed Classifications ...................................................... 65 

2.8 Watershed Alterations and Population Patterns................................................................. 74 

2.8.1 Flow-Path Analysis of Impervious Surfaces................................................................ 79 

2.9 Synthesis of Classification Approaches and Research Needs ............................................ 80 

CHAPTER 3: ENVIRONMENTAL CHARACTERISTICS OF THE SEVEN TARGET 

ESTUARIES ................................................................................................................................. 82 

3.0 Introduction........................................................................................................................ 82 

3.1 Pacific Northwest Climate ................................................................................................. 82 

3.2 Coastal Upwelling in the Pacific Northwest...................................................................... 82 

3.3 Criteria for Choosing Target Estuaries and Previous Estuarine Classifications................ 83 

3.3.1 Management Classification of Oregon Estuaries........................................................ 83 

3.3.2 Geomorphological/Hydrological Classifications........................................................ 83 
 iii

3.4 Estuary and Watershed Sizes............................................................................................. 85 

3.5 Watershed Characteristics and Land Cover....................................................................... 86 

3.5.1 Watershed Slopes........................................................................................................ 86 

3.5.2 Population Characteristics .......................................................................................... 86 

3.6 Estuarine Hydrology .......................................................................................................... 90 

3.7 Nutrient Loading................................................................................................................ 91 

CHAPTER 4: WATER QUALITY SURVEYS IN SEVEN TARGET ESTUARIES................ 96 

4.0 Introduction........................................................................................................................ 96 

4.1 Methods.............................................................................................................................. 97 

4.1.1 Water Quality Cruises.................................................................................................. 97 

4.1.2 Datasonde Deployment............................................................................................... 98 

4.2 Riverine Nutrient Inputs During the Wet Season .............................................................. 99 

4.3 Coastal Ocean and River Conditions During Dry Season Sampling............................... 100 

4.4 Estuarine Water Quality................................................................................................... 102 

4.4.1 Dry Season Chlorophyll a and TSS Patterns ............................................................ 102 

4.4.2 Dry Season Nutrient Patterns.................................................................................... 107 

4.4.3 Dry Season Salinity Fluctuations.............................................................................. 109 

4.5 Comparison of Water Quality Data to EMAP Criteria.................................................... 110 

4.6 Synthesis .......................................................................................................................... 113 

CHAPTER 5: ZONATION OF OCEAN AND RIVER DOMINANCE IN SEVEN TARGET 

ESTUARIES ............................................................................................................................... 115 

5.0 Introduction...................................................................................................................... 115 

5.1 Stable Isotope Sampling .................................................................................................. 117 

5.2 Yaquina Estuary............................................................................................................... 118 

5.2.1 Transport Model........................................................................................................ 118 

5.2.2 Conservative Mixing Model ..................................................................................... 124 

5.3 Alsea Estuary ................................................................................................................... 128 

5.4 Salmon River Estuary ...................................................................................................... 134 

5.5 Umpqua River Estuary..................................................................................................... 137 

5.6 Coos Estuary .................................................................................................................... 140 

5.7 Tillamook Estuary............................................................................................................ 144 

5.8 Nestucca Estuary.............................................................................................................. 150 

5.9 Synthesis and Uncertainties in Zonation.......................................................................... 154 

CHAPTER 6: AERIAL MEASURES OF ESTUARINE INTERTIDAL AND SHALLOW 

SUBTIDAL ZOSTERA MARINA COVERAGE........................................................................ 158 

6.0 Introduction...................................................................................................................... 158 

6.1 Methods............................................................................................................................ 158 

6.1.1 Aerial Photography Sampling Frame and Design .................................................... 158 

6.1.2 Estuaries Surveyed.................................................................................................... 162 

6.1.3 Ground Surveys ........................................................................................................ 162 

6.1.4 Classification Accuracy Assessments....................................................................... 163 

6.1.5 Estuary Bathymetry .................................................................................................. 163 

6.2 Results.............................................................................................................................. 164 

6.2.1 Accuracy Assessments of Photomap Image Classifications..................................... 164 

6.2.2 Among-Estuary Comparison of Zostera marina Coverage...................................... 164 

6.2.3 Bathymetric Distribution of Zostera marina ............................................................ 165 
 iv

6.2.4 Within-Estuary Distribution of Zostera marina ....................................................... 171 

6.3 Synthesis .......................................................................................................................... 172 

CHAPTER 7: AMONG AND WITHIN ESTUARINE DISTRIBUTIONS OF SEAGRASSES 

AND ECOLOGICALLY IMPORTANT BENTHIC SPECIES IN PACIFIC NORTHWEST 

ESTUARIES ............................................................................................................................... 173 

7.0 Introduction...................................................................................................................... 173 

7.1 Methods............................................................................................................................ 174 

7.1.1 Probabilistic Sampling Frame and Design................................................................ 174 

7.1.2 Data Analysis............................................................................................................ 175 

7.1.3 Field Methods ........................................................................................................... 175 

7.1.4 Comparison of 0.25 m2 versus 2.5 x 2.5 m Quadrats ............................................... 189 

7.2 Across-Estuary Patterns................................................................................................... 189 

7.2.1 Sediment Grain Size and TOC in the Target Estuaries............................................. 189 

7.2.2 Among-Estuary Distribution of Zostera marina....................................................... 193 

7.2.3 Among-Estuary Distribution of Zostera japonica .................................................... 194 

7.2.4 Composition and Seasonality of Benthic Green Macroalgae ................................... 196 

7.2.5 Among-Estuary Patterns of Benthic Macroalgae ..................................................... 199 

7.2.6 Among-Estuary Patterns of Burrowing Shrimp........................................................ 205 

7.2.7 Bare Habitat .............................................................................................................. 208 

7.2.8 Multivariate Analysis of the Target Estuaries .......................................................... 208 

7.3 Within-Estuary Distribution of Seagrasses and Other Benthic Resources ...................... 211 

7.3.1 Within-Estuary Distributions of Seagrasses .............................................................. 212 

7.3.2 Within-Estuary Distributions of Benthic Macroalgae .............................................. 215 

7.3.3 Within-Estuary Distributions of Burrowing Shrimp ................................................ 215 

7.4 Benthic Macroalgae as Diagnostic Indicator of Cultural Eutrophication........................ 218 

CHAPTER 8: LOWER DEPTH LIMIT OF ZOSTERA MARINA IN SEVEN TARGET 

ESTUARIES ............................................................................................................................... 219 

8.0 Introduction...................................................................................................................... 219 

8.1 Methods............................................................................................................................ 220 

8.1.1 Selection of Sampling Points.................................................................................... 220 

8.1.2 Sampling ................................................................................................................... 221 

8.1.3 Light Attenuation Coefficients ................................................................................. 222 

8.1.4 Epiphyte Biomass ..................................................................................................... 223 

8.2 Results.............................................................................................................................. 223 

8.2.1 Sampling Points/Sampling Errors............................................................................. 223 

8.2.2 Depth Distribution .................................................................................................... 224 

8.2.3 Kd vs. Distance from Estuarine Mouth ..................................................................... 229 

8.2.4 Maximum Zostera marina Depth, Kd and Light Relationship.................................. 233 

8.2.5 Epiphyte Patterns and Impact on Light..................................................................... 234 

8.3 Discussion........................................................................................................................ 236 

8.3.1 Sampling Method...................................................................................................... 236 

8.3.2 Depth Distributions................................................................................................... 236 

8.3.3 Water Clarity Relationships...................................................................................... 237 

8.3.4 Zostera marina Light Criteria................................................................................... 239 

8.3.5 Yaquina Estuary Zostera marina Light Criteria ....................................................... 240 

CHAPTER 9: EMERGING PACIFIC NORTHWEST PARADIGM....................................... 242 
 v

9.0 Overview of the Pacific Northwest Environment and Regulatory Implications ............. 242 

9.1 Environmental Conditions ............................................................................................... 242 

9.2 Classification of Pacific Northwest Estuaries.................................................................. 246 

9.3 Regulatory Implications................................................................................................... 247 

9.4 Future Directions ............................................................................................................. 247 

REFERENCES ........................................................................................................................... 248 

APPENDIX A: MAPS OF THE DISTRIBUTION OF ZOSTERA MARINA BASED ON 

AERIAL SURVEYS................................................................................................................... 267 

APPENDIX B: DETAILED DESCRIPTION OF QUALITY ASSURANCE 

PROCEDURES........................................................................................................................... 275 
 vi

List of Figures 

Figure 2-1. Overview of the generation of the PNW estuary inventory and the wetland and 

landscape data synthesis and analysis..................................................................................... 12 

Figure 2-2. Estuaries along the Washington coast from Cape Flattery to the Columbia River... 14 

Figure 2-3. Estuaries in northern Oregon. ................................................................................... 15 

Figure 2-4. Estuaries in southern Oregon .................................................................................... 16 

Figure 2-5. Estuaries in Northern California, north of Cape Mendocino. ................................... 17 

Figure 2-6. Size distribution of all 230 coastal estuaries in California, Oregon, and Washington 

identified by the presence of a NWI estuarine polygon.......................................................... 32 

Figure 2-7. Size distribution of the 103 PNW coastal estuaries identified by the presence of a 

NWI estuarine polygon........................................................................................................... 32 

Figure 2-8. Distribution of estuarine subtidal and intertidal habitats in the 103 PNW estuaries. 33 

Figure 2-9. Distribution of the projected historical number of Coho smolts by estuary size 

classes in 29 Oregon estuaries. ............................................................................................... 41 

Figure 2-10. Relationship between freshwater inflow normalized by estuarine area and 

normalized by estuarine volume ............................................................................................. 50 

Figure 2-11. Salinity versus distance from the mouth for the wet and dry seasons for estuaries 

with area-normalized freshwater inflow ranging from about 11 to 695 m3 m

-2

 year

-1............ 51 

Figure 2-12. Cluster analysis of 31 PNW estuaries at the 5% significance level based on the area 

of the consolidated NWI habitats............................................................................................ 55 

Figure 2-13a. Cluster analysis of 31 PNW estuaries at the 10% significance level based on the 

area of the consolidated NWI habitats. ................................................................................... 56 

Figure 2-13b. Cluster analysis of 31 PNW estuaries at the 20% significance level based on the 

area of the consolidated NWI habitats. ................................................................................... 56 

Figure 2-13c. Cluster analysis of 31 PNW estuaries at the 40% significance level based on the 

area of the consolidated NWI habitats. ................................................................................... 57 

Figure 2-13d. Cluster analysis of 31 PNW estuaries at the 60% significance level based on the 

area of the consolidated NWI habitats. ................................................................................... 57 

Figure 2-14. Cluster analysis of 31 PNW estuaries at the 5% significance level based on the 

relative proportion of the consolidated NWI habitats............................................................. 58 

Figure 2-15a. Cluster analysis of 31 PNW estuaries at the 10% significance level based on the 

relative proportion of the consolidated NWI habitats............................................................. 59 

Figure 2-15b. Cluster analysis of 31 PNW estuaries at the 20% significance level based on the 

relative proportion of the consolidated NWI habitats............................................................. 59 

Figure 2-15c. Cluster analysis of 31 PNW estuaries at the 40% significance level based on the 

relative proportion of the consolidated NWI habitats............................................................. 60 

Figure 2-15d. Cluster analysis of 31 PNW estuaries at the 60% significance level based on the 

relative proportion of the consolidated NWI habitats............................................................. 60 

Figure 2-16. Cluster analysis of 31 PNW estuaries at the 5% significance level based on the 

areas of the land cover classes in the NOAA 2001 watershed data........................................ 62 

Figure 2-17a. Cluster analysis of 31 PNW estuaries at the 10% significance level based on the 

areas of the land cover classes in the NOAA 2001 watershed data........................................ 63 

Figure 2-17b. Cluster analysis of 31 PNW estuaries at the 20% significance level based on the 

areas of the land cover classes in the NOAA 2001 watershed data........................................ 63 
 vii

Figure 2-17c. Cluster analysis of 31 PNW estuaries at the 40% significance level based on the 

areas of the land cover classes in the NOAA 2001 watershed data........................................ 64 

Figure 2-17d. Cluster analysis of 31 PNW estuaries at the 60% significance level based on the 

areas of the land cover classes in the NOAA 2001 watershed data........................................ 64 

Figure 2-18. Cluster analysis of 31 PNW estuaries at the 5% significance level based on the 

relative proportions of the land cover classes in the NOAA 2001 watershed data................. 66 

Figure 2-19a. Cluster analysis of 31 PNW estuaries at the 10% significance level based on the 

relative proportions of the land cover classes in the NOAA 2001 watershed data................. 67 

Figure 2-19b. Cluster analysis of 31 PNW estuaries at the 20% significance level based on the 

relative proportions of the land cover classes in the NOAA 2001 watershed data................. 67 

Figure 2-19c. Cluster analysis of 31 PNW estuaries at the 40% significance level based on the 

relative proportions of the land cover classes in the NOAA 2001 watershed data................. 68 

Figure 2-19d. Cluster analysis of 31 PNW estuaries at the 60% significance level based on the 

relative proportions of the land cover classes in the NOAA 2001 watershed data................. 68 

Figure 2-20. Cluster analysis of the 31 PNW estuaries based on six metrics of watershed 

alteration. ................................................................................................................................ 78 

Figure 2-21. Percent impervious surfaces in the Yaquina watershed for six buffer zones of 

different distances from the shoreline of the Yaquina Bay or Yaquina River........................ 79 

Figure 4-1. Flood-tide water temperature at Yaquina Estuary and outermost high tide station 

from 2004 dry season classification cruises.......................................................................... 101 

Figure 4-2. Flood-tide water temperature at Yaquina and Coos estuaries and outermost high tide 

stations from 2005 dry season classification cruises. ........................................................... 102 

Figure 4-3. Water temperature versus NO3

-

+NO2

-

 relationship generated using data from a 

station on the shelf and classification high tide outermost stations...................................... 103 

Figure 4-4. Water temperature versus PO4

3- relationship generated using data from a station on 

the shelf and classification high tide outermost stations....................................................... 103 

Figure 4-5. Dry season chlorophyll a versus salinity for all estuaries with filled symbols 

representing high tide cruises and hollow symbols representing low tide cruises ............... 106 

Figure 4-6. Median dry season chlorophyll a versus freshwater inflow normalized by estuary 

volume for eight PNW estuaries........................................................................................... 106 

Figure 4-7. Dry season phosphate versus salinity for all classification water quality stations.. 108 

Figure 4-8. Dry season nitrate+nitrite versus salinity for all classification water quality 

stations. ................................................................................................................................. 108 

Figure 4-9. Dry season salinities near the mouths of estuaries sampled in 2004 and data 

collected in Nestucca during 2006 to fill data gap from lost instrument.............................. 111 

Figure 4-10. Dry season salinities near the mouths of estuaries sampled in 2005. ................... 111 

Figure 4-11. Difference between high and low tide dry season cruise salinity at station closest to 

estuary mouth versus area-normalized freshwater inflow .................................................... 112 

Figure 5-1. Natural abundance nitrogen isotope ratio for various nitrogen sources.................. 117 

Figure 5-2. Location map for Yaquina Estuary showing the locations of datasondes, water 

quality stations, isotope samples, and WWTF...................................................................... 119 

Figure 5-3. Macroalgae isotope data at five stations (N1-N5) for Yaquina Estuary with red boxes 

indicating dry seasons........................................................................................................... 120 

Figure 5-4. Comparison of modeled and observed isotope ratio at Station N2 in Yaquina Estuary 

during 2004. .......................................................................................................................... 122 
 viii

Figure 5-5. Comparison of modeled and observed isotope ratio at Station N3 in Yaquina Estuary 

during 2004. .......................................................................................................................... 122 

Figure 5-6. Modeled contribution of each source of dissolved inorganic nitrogen in Yaquina 

Estuary during 2004.............................................................................................................. 123 

Figure 5-7. Location map for Alsea Estuary showing the locations of datasondes, water quality 

stations, isotope samples, and WWTF.................................................................................. 128 

Figure 5-8. Monthly average observed isotope ratio at 5 locations in Alsea Estuary. .............. 129 

Figure 5-9. Location map for Salmon River estuary showing the locations of datasondes, water 

quality stations and isotope samples..................................................................................... 136 

Figure 5-10. Location map of Umpqua River Estuary showing the locations of datasondes, water 

quality stations, isotope samples, and WWTFs. ................................................................... 138 

Figure 5-11. Location map of stations in Coos Estuary showing the locations of datasondes, 

water quality stations, isotope samples, and WWTFs. ......................................................... 143 

Figure 5-12. Location map for Tillamook Estuary showing the location of datasondes, water 

quality stations, isotope samples, and WWTFs. ................................................................... 146 

Figure 5-13. Median dry season salinities in Tillamook Estuary. ............................................. 149 

Figure 5-14. Location map of Nestucca Estuary showing the locations of datasondes, water 

quality stations, isotope samples, and WWTF...................................................................... 152 

Figure 6-1. Intertidal and shallow subtidal distribution of Z. marina from digital image 

classification of aerial photos of Yaquina Estuary taken in April 2004. .............................. 160 

Figure 6-2. Track of lower margin of surface-visible distribution of Z. marina in the nearsubtidal zone and of upper margin in the Yaquina Estuary.................................................. 161 

Figure 6-3. Sectors of Yaquina Estuary covered by bathymetric surveys conducted by the U.S. 

Army Corps of Engineers between 1998 and 2002. ............................................................. 163 

Figure 6-4. Comparison of substrate elevations from a bathymetric model and independent total 

station survey measurements within Yaquina estuary.......................................................... 167 

Figure 6-5. Interpolated bathymetry and Z. marina distribution for a section of the lower 

Yaquina Estuary.................................................................................................................... 167 

Figure 6-6. The percentage distribution of intertidal and shallow subtidal Z. marina habitat area 

classified from aerial photography that occurs in one foot intervals predicted by the 

bathymetric model for Yaquina Estuary. .............................................................................. 168 

Figure 6-7. The percentage distribution of intertidal and shallow subtidal Z. marina habitat area 

classified from aerial photography that occurs in one foot intervals predicted by the 

bathymetric model for Alsea Estuary. .................................................................................. 168 

Figure 6-8. The percentage distribution of intertidal and shallow subtidal Z. marina habitat area 

classified from aerial photography that occurs in one foot intervals predicted by the 

bathymetric model for Tillamook Estuary............................................................................ 169 

Figure 6-9. Frequency of occurrence values for Z. marina within one-foot depth intervals around 

MLLW in Yaquina, Alsea, and Tillamook estuaries. ........................................................... 170 

Figure 7-1. Locations of the probabilistic sampling strata and the Z. marina lower limit surveys 

in the Alsea Estuary. ............................................................................................................. 176 

Figure 7-2. Locations of the probabilistic sampling strata and the Z. marina lower limit surveys 

in the Coos Estuary. .............................................................................................................. 177 

Figure 7-3. Locations of the probabilistic sampling strata and the Z. marina lower limit surveys 

in the Nestucca Estuary......................................................................................................... 178 
 ix

Figure 7-4. Locations of the probabilistic sampling strata and the Z. marina lower limit surveys 

in the Salmon River Estuary. ................................................................................................ 179 

Figure 7-5. Locations of the probabilistic sampling strata and the Z. marina lower limit surveys 

in the Tillamook Estuary....................................................................................................... 180 

Figure 7-6. The locations of the probabilistic sampling strata and the Z. marina lower limit 

surveys in the Umpqua River Estuary. ................................................................................. 181 

Figure 7-7. The locations of the probabilistic sampling strata and the Z. marina lower limit 

surveys in the Yaquina Estuary............................................................................................. 182 

Figure 7-8. Diagram of the intertidal sampling site layout with the 2.5 x 2.5 m quadrat and the 

0.25 m2

 plant and shrimp quadrats used in the probabilistic surveys. .................................. 185 

Figure 7-9a. Front of the field sheet used to record estimates of habitat characteristics and 

burrowing shrimp hole counts within the 2.5 x 2.5 m quadrat. ............................................ 186 

Figure 7-9b. Back of the field sheet used to record plant cover, point intercepts, shoot counts, 

and blade widths in the 0.25 m2 plant quadrat and burrowing shrimp hole counts in the 0.25 

m

2

 shrimp quadrat................................................................................................................. 187 

Figure 7-10. Cumulative distribution function of the percent fines in the intertidal zone of the 

seven target estuaries. ........................................................................................................... 191 

Figure 7-11. Cumulative distribution function of the percent total organic carbon in the intertidal 

zone of the seven target estuaries.......................................................................................... 192 

Figure 7-12. Average percent cover values of benthic green macroalgae at six sites in the 

Yaquina Estuary during 1999 ............................................................................................... 197 

Figure 7-13. Average biomass values of benthic green macroalgae at six sites in the Yaquina 

Estuary during 1999.............................................................................................................. 197 

Figure 7-14. Average proportions of green benthic macroalgal biomass relative to the September 

peak measured in Yaquina Estuary during 1999 .................................................................. 198 

Figure 7-15. Cumulative distribution function of the % cover of benthic macroalgae estimated 

from the point-intercept method using the 0.25 m2

 quadrats................................................ 201 

Figure 7-16. Percent of the intertidal area covered by benthic macroalgae by density class. . . 202 

Figure 7-17. Cumulative distribution function of the unadjusted benthic macroalgal biomass.203 

Figure 7-18. Cumulative distribution function of the seasonally adjusted benthic macroalgal 

biomass. ................................................................................................................................ 204 

Figure 7-19. Cumulative distribution function of the number of N. californiensis burrow holes 

per 0.25 m2

 in the seven target estuaries............................................................................... 206 

Figure 7-20. Cumulative distribution function of the number of U. pugettensis burrow holes per 

0.25 m2 in the seven target estuaries..................................................................................... 207 

Figure 7-21. Percent of intertidal area occupied by bare habitat as defined by the absence of Z. 

marina, Z. japonica, benthic green macroalgae, N. californiensis, or U. pugettensis in the 2.5 

x 2.5 m quadrats.................................................................................................................... 209 

Figure 7-22. Non-metric multidimensional scaling of the seven target estuaries based on the 

percent of intertidal area occupied by Z. marina, Z. japonica, benthic macroalgae, N. 

californiensis, U. pugettensis, and bare habitat .................................................................... 210 

Figure 7-23. Relative distributions of the total area occupied by Z. marina between the oceanic 

and riverine segments. .......................................................................................................... 214 

Figure 7-24. Relative distributions of the total area occupied by Z. japonica between the oceanic 

and riverine segments. .......................................................................................................... 214 
 x

Figure 7-25. Relative distributions of the total area occupied by benthic macroalgae between the 

oceanic and riverine segments .............................................................................................. 216 

Figure 7-26. Relative distributions of the total area occupied by N. californiensis between the 

oceanic and riverine segments .............................................................................................. 217 

Figure 7-27. Relative distributions of the total area occupied by U. pugettensis between the 

oceanic and riverine segments. ............................................................................................. 217 

Figure 8-1. Schematic of underwater camera apparatus............................................................ 222 

Figure 8-2. Lower limit of Z. marina versus distance from the mouth of the Alsea Estuary.... 225 

Figure 8-3. Lower limit of Z. marina versus distance from the mouth of the Coos Estuary..... 226 

Figure 8-4. Lower limit of Z. marina versus distance from the mouth of the Nestucca 

Estuary. ................................................................................................................................. 226 

Figure 8-5. Lower limit of Z. marina versus distance from the mouth of the Salmon River

Estuary. ................................................................................................................................. 227 

Figure 8-6. Lower limit of Z. marina versus distance from the mouth of the Tillamook 

Estuary. ................................................................................................................................. 227 

Figure 8-7. Lower limit of Z. marina versus distance from the mouth of the Umpqua River

Estuary. ................................................................................................................................. 228 

Figure 8-8. Lower limit of Z. marina versus distance from the mouth of the Yaquina 

Estuary. ................................................................................................................................. 228 

Figure 8-9. Light attenuation coefficient versus distance from the mouth of the Yaquina Estuary 

generated using long-term cruise dataset.............................................................................. 229 

Figure 8-10. Light attenuation coefficient versus distance from the mouth of the Yaquina 

Estuary generated using continuous dataset. ........................................................................ 230 

Figure 8-11. Light attenuation coefficient versus distance from the mouth of the Yaquina 

Estuary for classification dataset. ......................................................................................... 230 

Figure 8-12. Light attenuation coefficient versus distance from the mouth of the Alsea 

Estuary. ................................................................................................................................. 231 

Figure 8-13. Light attenuation coefficient versus distance from the mouth of the Nestucca 

Estuary. ................................................................................................................................. 231 

Figure 8-14. Light attenuation coefficient versus distance from the mouth of the Salmon River 

Estuary. ................................................................................................................................. 232 

Figure 8-15. Light attenuation coefficient versus distance from the mouth of the Tillamook 

Estuary .................................................................................................................................. 232 

Figure 8-16. Light attenuation coefficient versus distance from the mouth of the Umpqua River 

Estuary. ................................................................................................................................. 233 

Figure 8-17. Relationship between Z. marina lower depth limit and light attenuation coefficient 

in the Yaquina Estuary.......................................................................................................... 234 

Figure 8-18. Temporal relationship of epiphytic biomass per unit leaf area on Z. marina external 

leaves in the Yaquina Estuary............................................................................................... 235 

Figure 8-19. Epiphyte biomass per unit leaf area on older and younger Z. marina leaves by 

season in the oceanic and riverine segments of the Yaquina Estuary................................... 235 

Figure 8-20. Linear regression relationship between the percent of light reduction to log(x+1) 

transformed epiphyte biomass per unit Z. marina leaf surface area. .................................... 236 

Figure 8-21. Relationship between Z. marina maximum depth limit and Kd.

........................... 239 

Figure A-1. Distribution of intertidal and shallow subtidal Z. marina in the Alsea Estuary..... 268 

Figure A-2. Distribution of intertidal and shallow subtidal Z. marina in the Coos Estuary...... 269 
 xi

Figure A-3. Distribution of intertidal and shallow subtidal Z. marina in the Nestucca 

Estuary. ................................................................................................................................. 270 

Figure A-4. Distribution of intertidal and shallow subtidal Z. marina in the Salmon River 

Estuary. ................................................................................................................................. 271 

Figure A-5. Distribution of intertidal and shallow subtidal Z. marina in the Tillamook 

Estuary. ................................................................................................................................. 272 

Figure A-6. Distribution of intertidal and shallow subtidal Z. marina in the Umpqua River 

Estuary. ................................................................................................................................. 273 

Figure A-7. Distribution of intertidal and shallow subtidal Z. marina in the Yaquina Estuary. 274 
 xii

List of Tables 

Table 1-1. Ecological interactions of five taxa evaluated in the field surveys. ............................. 7 

Table 2-1. Consolidation of National Wetland Inventory habitat codes found in PNW 

estuaries................................................................................................................................... 19 

Table 2-2. Area and geomorphology of PNW estuaries and watersheds. ................................... 24 

Table 2-3. Examples of ecological resources and services in PNW estuaries............................. 35 

Table 2-4. Freshwater sources into estuaries, normalized freshwater inflow, mouth width, and 

structure of mouth for drowned river mouth and bar-built estuaries in the PNW. ................. 47 

Table 2-5. Crosswalk of the cluster analyses by the area of NWI consolidated wetland classes 

and the area of the NOAA land cover classes at the 5% significance level. .......................... 70 

Table 2-6. Crosswalk of the cluster analyses by the area of NWI consolidated wetland classes 

and the area of the NOAA land cover classes at the 60% significance level. ........................ 71 

Table 2-7. Crosswalk of the cluster analyses by the relative proportion of NWI consolidated 

wetland classes and the relative proportion of land cover classes at the 5% significance 

level......................................................................................................................................... 72 

Table 2-8. Crosswalk of the cluster analyses by the relative proportion of NWI consolidated 

wetland classes and the relative proportion of land use classes at the 60% significance 

level......................................................................................................................................... 73 

Table 2-9. The mean, median, and maximum values for each of the metrics of watershed 

alteration for the 89 coastal watersheds.................................................................................. 75 

Table 2-10. The mean, median, and maximum values for each of the metrics of watershed 

alteration for the 31 coastal watersheds used in the multivariate analysis. ............................ 76 

Table 3-1. Current and historical classifications of the seven target estuaries. ........................... 84 

Table 3-2. Estuary and watershed size of the seven target estuaries. .......................................... 87 

Table 3-3. Watershed attributes of the seven target estuaries...................................................... 88 

Table 3-4. Population size and density in the seven target estuaries. .......................................... 89 

Table 3-5. Hydrographic characteristics of the seven target estuaries......................................... 93 

Table 3-6. Wet and dry season freshwater inflow for the seven target estuaries......................... 94 

Table 3-7. Residence time calculated using the modified tidal prism and fraction of freshwater 

approaches for the seven target estuaries................................................................................ 94 

Table 3-8. Estimates of nutrient loading to six of the seven target estuaries............................... 95 

Table 3-9. Comparison of nitrogen sources during wet and dry seasons for the Yaquina 

Estuary. ................................................................................................................................... 95 

Table 4-1. Dates of cruises and number of stations sampled for the seven target estuaries........ 97 

Table 4-2. Number of short-term datasondes deployed during the dry season in each estuary and 

year deployed.......................................................................................................................... 99 

Table 4-3. Median wet season dissolved inorganic nitrogen and phosphate calculated using 

stations with salinity < 2 psu................................................................................................... 99 

Table 4-4. Median dry season chlorophyll a and total suspended solids for each estuary and its 

ranking. ................................................................................................................................. 105 

Table 4-5. Median dry season NO3

-

+NO2

-

, NH4

+

, and PO4

3- for each estuary and its ranking. 105 

Table 4-6. West Coast criteria for water quality parameters (U.S. EPA, 2004b)...................... 113 

Table 5-1. Summary of median observed salinities for Yaquina Estuary during May - September 

of 2003 and 2004. ................................................................................................................. 121 
 xiii

Table 5-2. Data used in the mixing model and time interval of salinity data utilized for each 

month. ................................................................................................................................... 127 

Table 5-3. Comparison of percent contribution of DIN sources estimated using transport model 

and Isosource model. ............................................................................................................ 127 

Table 5-4. Summary of dry season salinities in Alsea Estuary.................................................. 132 

Table 5-5. Ocean and river end members for Alsea calculations. ............................................. 132 

Table 5-6. Observed and predicted isotope ratios in Alsea Estuary. ......................................... 133 

Table 5-7. Summary of dry season salinities in Salmon River estuary. .................................... 136 

Table 5-8. Observed and predicted isotope ratios in Salmon River estuary.............................. 136 

Table 5-9. Summary of dry season salinities in Umpqua River Estuary. .................................. 139 

Table 5-10. Observed and predicted isotope ratios in Umpqua River Estuary.......................... 139 

Table 5-11. Observed and predicted isotope ratios in Coos Estuary. ........................................ 141 

Table 5-12. Summary of dry season salinities in Coos Estuary. ............................................... 143 

Table 5-13. Summary of dry season salinities in Tillamook Estuary. ....................................... 147 

Table 5-14. Observed and predicted isotope ratios in Tillamook Estuary................................. 148 

Table 5-15. Summary of dry season salinities in Nestucca Estuary. ......................................... 153 

Table 5-16. Observed and predicted isotope ratios in Nestucca Estuary................................... 153 

Table 5-17. Summary of the total estuarine area for each target estuary and the area and 

percentage of total in the oceanic and riverine segments based on salinity and macroalgal 

isotope ratios. ........................................................................................................................ 157 

Table 6-1. Mapping method utilized and dates of mapping for intertidal and shallow subtidal Z. 

marina distribution in the target estuaries. ........................................................................... 162 

Table 6-2. Summary of error matrix results for detection of Z. marina in five of the target 

estuaries based on the ground truth surveys. ........................................................................ 164 

Table 6-3. Estimates of Z. marina area in the target estuaries from the aerial/on-surface 

surveys. ................................................................................................................................. 166 

Table 6-4. Cumulative percent area of Z. marina within four depth intervals relative to MLLW 

in three PNW estuaries.......................................................................................................... 169 

Table 6-5. Relative distribution of the total Z. marina habitat area in the oceanic and riverine 

segments of the estuaries based on the aerial surveys. ......................................................... 171 

Table 6-6. Percent coverage of Z. marina within the oceanic and riverine segments of the 

estuaries based on the aerial surveys. ................................................................................... 172 

Table 7-1. Sampling design for the probabilistic field surveys and areas of the probabilistic 

frames.................................................................................................................................... 184 

Table 7-2. Sediment characteristics of the target estuaries from the probabilistic surveys....... 190 

Table 7-3. Percent of intertidal area with Z. marina estimated using different methods. ......... 195 

Table 7-4. Percent of intertidal area with Z. japonica estimated using different methods........ 195 

Table 7-5. Percent of intertidal area with green benthic macroalgae estimated using different 

methods................................................................................................................................. 200 

Table 7-6. Biomass estimates for benthic macroalgae............................................................... 200 

Table 7-7. Relative cover of the five ecologically important benthic taxa and bare habitat in the 

oceanic and riverine segments in the seven target estuaries. ................................................ 213 

Table 8-1. Estuaries sampled for the lower depth limit of Z. marina and the datum adjustment 

factor used to convert lower depth limit from the mean lower low water datum to mean sea 

level datum............................................................................................................................ 221 
 xiv

Table 8-2. Linear regression coefficients of maximum Z. marina depth versus distance from the 

mouth of each of the seven target estuaries. ......................................................................... 224 

Table 8-3. Overall mean depth, mean of deepest 1/3 of depth values, and maximum depth that Z. 

marina was observed in each of the seven target estuaries. ................................................. 225 

Table 8-4. Comparison of percent of surface irradiance needed to maintain Z. marina at its 

maximum colonization depth from published data and from Yaquina Estuary data............ 239 

Table B-1. Measurement quality objectives for data collected by Western Ecology Division . 276 

Table B-2. Quality calibration and control checks for instruments and parameters.................. 278

Table B-3. Accuracy and precision for nutrient analyses. ......................................................... 282 

Table B-4. Precision and accuracy for YSI Multiparameter Sondes. ........................................ 283 

Table B-5. Precision and accuracy of δ

15N data. ....................................................................... 283 

Table B-6. Precision and accuracy of TOC/N sediment analysis.............................................. 284 

Table B-7. Precision and accuracy of sediment particle size analysis....................................... 285 

Table B-8. Data comparability between four field crew members for intertidal probabilistic 

sampling................................................................................................................................ 285 

Table B-9. Image classification accuracy assessment. .............................................................. 288 

Table B-10. Bounding coordinates for the eight 2001 MRLC zones containing Pacific Coast 

watersheds............................................................................................................................. 293 

Table B-11 Overall accuracy of NLCD 2001 land cover analysis . .......................................... 294 
 xv

EXECUTIVE SUMMARY 

Increased anthropogenic nutrient loading and the subsequent eutrophication of coastal 

ecosystems is a growing ecological and economic problem both in the United States and 

globally. Eutrophication can result in a range of ecological impacts including hypoxic 

conditions, fish kills, loss of submerged aquatic vegetation (SAV), degraded benthic conditions, 

harmful algal blooms, and detrimental increases in benthic macroalgae. The nature and severity 

of the impacts vary with the level of nutrient loading as well as with the estuary type and 

regional drivers. 

One tool to help address this problem is the development of classification schemes to allow 

researchers and managers to extrapolate results from a limited number of well-studied estuaries 

to the larger domain of estuaries within the same class. Several estuarine classification schemes 

have been developed based on different approaches and endpoints. However, the ecological 

reality of these classification schemes for the Pacific Northwest (PNW) is not clear, in part, 

because of the limited baseline information available to evaluate the schemes. Additionally, the 

available information gives “mixed messages” as to whether eutrophication is occurring in the 

coastal PNW estuaries. Dissolved oxygen levels are generally high and chlorophyll a is 

moderate to low, indicating a non-eutrophic condition. However, nutrient loading is high and 

within the range of eutrophic estuaries on the East Coast. Additionally, benthic macroalgal 

blooms, which have been used as a diagnostic indicator of eutrophication in other parts of the 

world, are a seasonal event in the PNW. 

To help determine the extent of eutrophication, the Pacific Coastal Ecology Branch (PCEB) of 

the Western Ecology Division (WED) initiated a classification study of PNW estuaries. The 

PNW was defined as including the coastal estuaries from Cape Mendocino in Northern 

California (40.440oN) to Cape Flattery in Washington (48.383oN). Puget Sound was not 

included in the present study. Two different approaches were used. The first component of the 

research focused on a landscape analysis using existing data for the entire PNW. An inventory 

of all of the PNW estuaries was generated based on the occurrence of estuarine habitat as defined 

by the National Wetland Inventory (NWI), and all of the associated watersheds were delineated. 

The PNW contains a surprisingly large number of estuaries, a total of 103 based on the present 

NWI analysis. However, most of these estuaries are small, <1 km2

, and only 13 of them are 

>10 km2. These PNW estuaries appear to break out into seven general types based on 

geomorphology, oceanic exchange, and riverine influence: coastal lagoons, blind estuaries, 

tidally restricted coastal creeks, tidal coastal creeks, marine harbors/coves, drowned river mouth 

estuaries, and bar-built estuaries. The first three of these have restricted connection with the 

ocean on at least a periodic basis, and are potentially vulnerable to anthropogenic nutrient 

additions and watershed alterations. Conversely, the marine harbors/coves have extensive 

flushing and are presumably less sensitive to either nutrient enrichment or watershed alterations. 

The drowned river mouth estuaries are sub-divided into tide-dominated, moderately riverdominated, and highly river-dominated systems based on the extent of estuarine area or volume 

weighted freshwater flow. Because of potentially higher exchange, the river-dominated estuaries 

may be less sensitive to nutrient enrichment. 
 xvi

Geomorphology by itself did not cleanly separate groups with similar vulnerabilities among the 

remaining 31 tide-dominated river mouth estuaries, river-dominated river mouth estuaries, and 

bar-built estuaries. One approach taken to identify groups of estuaries was to evaluate spatial 

and temporal salinity patterns based on historical data as well as a “normalized freshwater 

inflow” index we developed. This index ranks estuaries by the relative amount of freshwater 

entering the systems, and was used as a quantitative approach to separating tide- versus riverdominated estuaries. Another approach was to conduct multivariate analyses to examine the 

biotic and watershed similarities among the remaining 31 estuaries. The estuaries were clustered 

based on the patterns of wetland habitats from the National Wetland Inventory (NWI) and 

independently on land cover patterns in the associated watersheds. Several different 

classifications were produced as well as a “crosswalk” between the two classifications to 

determine which estuaries were grouped together in both analyses. Six pairs of estuaries were 

similarly classified based on both wetland and land cover patterns, and these estuary pairs 

potentially could form a framework for developing ecologically relevant nutrient criteria for the 

PNW. 

The second component of the research was to develop a classification scheme based on nitrogen 

loading and sources, particularly as it related to the relative importance of oceanic- versus 

riverine-derived nitrogen. Seasonal coastal upwelling from approximately April to October is 

the major source of nitrogen and phosphorous to the near-coastal region. Previous research by 

PCEB showed that high nutrient oceanic water advected into the Yaquina Estuary, Oregon was 

the major nutrient source in the lower estuary during the dry season (May to October) and 

riverine nutrients (i.e., terrestrially derived) were the dominant nutrient source only in the upper 

Yaquina Estuary. In contrast, riverine inputs dominated through most of the estuary during the 

wet season (November to April); however, there is little utilization of the wet season riverine 

inputs due to low solar irradiance and short residence times associated with high river inflow. 

Overlaying the distribution of Zostera marina L., the native seagrass species, over the spatial 

pattern of nitrogen sources identified oceanic inputs as the major source for the bulk of the SAV 

population in the Yaquina Estuary during the dry season. 

To determine the generality of this pattern observed in the Yaquina Estuary, field studies were 

initiated in seven target estuaries spanning a range of sizes (2.0 km2

 to 54.9 km2) - Alsea, Coos, 

Nestucca, Salmon River, Tillamook, Umpqua River, and Yaquina. We focused the field studies 

on the dry season (May to October) because this is the primary period of biological nutrient 

utilization. One objective was to use the nitrogen source model developed for the Yaquina 

Estuary and site-specific water quality and stable isotope patterns in benthic macroalgae to 

delineate the ocean- and river-dominated segments in a suite of estuary types. Results from this 

component demonstrated that advection of high nutrient ocean water was the major nitrogen 

source in the lower estuary for the seven target estuaries. 

The next objective was to determine if the SAV populations and populations of other key 

estuarine resources were primarily exposed to ocean- or river-derived nutrients. Five biotic 

endpoints were evaluated, three primary producers, and two secondary consumers. The major 

focus was on the perennial, rooted aquatic seagrass Z. marina, which was evaluated both by field 

surveys and by aerial photography. In addition to Z. marina, probabilistic field surveys 

evaluated the abundance and distributions of two additional benthic primary producers, the 
 xvii

nonindigenous seagrass Zostera japonica Aschers. & Graebn. and benthic green macroalgae. 

The secondary consumers evaluated were two burrowing shrimp, Neotrypaea californiensis and 

Upogebia pugettensis. One pattern that emerged from comparing across estuaries was that 

macroalgal blooms occurred in all the estuaries, suggesting that it is a natural phenomenon, while 

stable isotope data suggest the presence of these blooms is not an indication of eutrophication. 

Another pattern was that the intertidal occurrence of the non-native Z. japonica exceeded that of 

the native Z. marina in several of the target estuaries. 

Overlaying the spatial distributions of these species on the spatial patterns of dry season nutrient 

sources showed that the bulk of the Z. marina, benthic macroalgae and both burrowing shrimp 

populations occurred in the oceanic segments of all seven estuaries. Thus, for two of the primary 

producers, oceanic nitrogen is the dominant source during the principal growing season. 

Likewise for the burrowing shrimp, the bulk of the populations would primarily be exposed to 

oceanic nitrogen during the dry season. During the wet season, terrestrially derived nitrogen is 

the major nutrient source for these estuaries. However, this is a period of low water column 

chlorophyll a and Z. marina and macroalgal production suggesting that this terrestrially derived 

nitrogen is not primarily driving production by these species. 

The exception to the pattern of the primary producers occurring primarily in the lower estuary 

was the non-native seagrass, Z. japonica, which was relatively abundant in both the oceanic and 

riverine segments. Thus, this non-native seagrass would have a higher exposure to terrestrially 

derived nitrogen than the native primary producers during the summer growing season. One 

possibility that has yet to be explored is whether low levels of nutrient enrichment stimulate the 

growth and establishment of this non-native seagrass. 

The field surveys also evaluated the lower depth margin of Z. marina across the seven estuaries 

and along estuarine gradients. The lower depth limit to which Z. marina grows decreases in the 

upper estuary segments, which correlates with a general decrease in water clarity in the upper 

segment of the estuaries. With further development, the lower depth limit of Z. marina could 

potentially be used as an integrative indicator for assessing decreases in water clarity, as could 

occur from nutrient stimulation of phytoplankton. Even though our research indicates that the 

current levels of benthic macroalgae are natural, increases in macroalgae coverage or biomass in 

the riverine segments could also be used as an indicator of nutrient enrichment especially if such 

studies were coupled with measurements of stable isotopes (δ

15N) to identify the nitrogen 

sources associated with the blooms. 

Results from our studies and a growing body of literature suggest several key points for the 

management of nutrient enrichment in the PNW. First, environmental drivers such as coastal 

upwelling strongly indicate that regional approaches to classification will be necessary to 

generate ecologically relevant groupings. Second, PNW coastal estuaries are, in general, not 

showing indications of cultural eutrophication. Additionally, the majority of the populations of 

four of the five biotic resources we evaluated occurred in the oceanic segment of the estuaries, 

indicating a lower vulnerability to terrestrially derived nitrogen. However, anecdotal 

observations of phytoplankton blooms in the upper estuary segments of a few estuaries suggest 

that the riverine segments of estuaries may be experiencing localized nutrient enrichment. Third, 

the development of national estuarine nutrient criteria that do not take into account the naturally 
 xviii

high nutrient concentrations resulting from upwelling are likely to result in numerous false nonattainments of criteria in PNW coastal estuaries. Similarly, the development of Total Daily 

Maximum Loads (TMDLs) for nutrients also needs to consider the effects of oceanic nutrient 

loadings. 
 1

CHAPTER 1: INTRODUCTION AND FRAMEWORK FOR THE 

PACIFIC NORTHWEST REGIONAL CLASSIFICATION PROJECT

Henry Lee II and Walter Nelson 

1.0 Scope 

As part of the U.S. EPA’s Aquatic Stressors Framework (U.S. EPA, 2002), a variety of 

approaches to the classification of estuarine systems with respect to observed or predicted 

responses to nutrient enrichment have been examined. This study explores approaches to 

classification at a regional scale using distributions of submerged aquatic vegetation (SAV), 

wetland classes, and other estuarine biological resources as classifying variables for Pacific 

Northwest (PNW) estuaries. We also explore whether spatial and temporal patterns of salinity 

and nutrient dynamics among PNW estuaries allow us to group them with respect to their 

vulnerability to nutrient enrichment. This document is a revised version of a previous internal 

EPA report (Lee et al., 2006), and includes new analyses and interpretations based on updated 

wetland and salinity data. A companion study (Dettmann and Kurtz, 2006) explored the use of 

empirical nutrient-SAV load-response models for estuaries of the New England region as well as 

the response of phytoplankton biomass to nutrient concentrations in a suite of estuaries in the 

eastern U.S. Together, these two studies provide assessments of a series of approaches to 

estuarine classification that may be of value in setting national and regional nutrient criteria for 

estuaries. 

1.1 Problem Statement 

Increased anthropogenic nutrient loading and the subsequent eutrophication of coastal 

ecosystems is a growing ecological and economic problem both in the U.S. and globally (e.g., 

Nixon, 1995; NRC, 2000; Cloern, 2001; Bricker et al., 2003; Scavia and Bricker, 2006). 

Eutrophication can result in a range of ecological impacts including hypoxic conditions, fish 

kills, loss of SAV, degraded benthic conditions, harmful algal blooms, and detrimental increases 

in benthic macroalgae. The nature and severity of the impacts vary with the level of nutrient 

loading as well as the estuary type and regional drivers (e.g., Cloern, 2001; Bricker et al., 2003). 

Ideally, each estuary would be evaluated independently as to the nature and extent of these 

impacts and any mitigation/regulatory actions would be tailored to each estuary or watershed. 

The reality is, however, that there are insufficient resources to conduct detailed scientific studies 

on each estuary or to develop estuary-specific water quality criteria or management plans for 

every coastal water body. Hence, approaches to reduce this complexity are needed. 

 

One general approach to prediction across a range of water bodies is to use water quality models, 

such as Basins (http://www.epa.gov/waterscience/basins/basinsv3.htm) or Sparrow 

(http://water.usgs.gov/nawqa/sparrow/). While these models are a powerful approach to making 

predictions in the better studied water bodies, they require extensive data input as well as 

expertise, reducing their general utility. A different, and complementary, approach is to classify 

estuaries that have similar responses to nutrient enrichment. If successful, such classifications 

allow extrapolation from one estuary to others within the same class, thereby reducing the 

amount of site specific data needed to make environmental decisions. Although lacking the 

same level of site specific predictive ability as the complex water quality models, classification is 

potentially a more practical approach to reducing the complexity inherent in extrapolating across 
 2

a range of estuaries, especially for those regions with limited data. As pointed out by the 

National Research Council, “a widely accepted estuarine classification scheme is a prerequisite 

for a systematic approach to extending lessons learned and successful management options from 

one estuary to another” (NRC, 2000, page 163). 

A plethora of classification approaches have been applied to estuarine systems, with 26 different 

classification frameworks identified in the review by Kurtz et al. (2006). Two classical 

approaches are geomorphic classification, which classifies estuaries as drowned river valleys, bar 

built, fjords, and tectonically formed estuaries (e.g., Pritchard, 1955, 1967), and the 

hydrodynamic approach, which classifies estuaries by their circulation patterns and stratification 

(e.g., Hansen and Rattray, 1966). Several recent efforts focused on grouping systems based on 

physical attributes which may be relevant to expression of nutrient impacts, such as the EPA’s 

classification framework for coastal systems (U.S. EPA, 2004a) or the ASSESTS approach to 

ranking the eutrophication status of coastal waters (Bricker et al., 2003). To build upon these 

efforts, we initiated a classification research program focused on PNW estuaries. As described 

below, this program was designed around both the potential management uses of an estuarine 

classification scheme and the key environmental drivers in the PNW. 

1.2 Regulatory Framework for Estuarine Classifications 

Given the large number of approaches to estuarine classification, one of the early questions we 

asked was how an estuarine classification scheme could be used in a management context, 

specifically in relation to the development of water quality criteria or the management of excess 

nutrients. That is, we viewed the initial question not as “How to classify?”, but rather “Why 

classify?” We identified five major approaches relating to the regulation of excess nutrient, as 

summarized below. Using this framework and an assessment of the data needs and availability 

in the PNW, we focused our research on management issues #2, #4, and #5 listed below. 

1) Classification by Current Ecological Condition: A fundamental management need is 

assessing the current condition of coastal water bodies, which is a type of classification when 

multiple water bodies are compared. Such a comparison has been used to classify nearcoastal water bodies as having “good”, “fair”, or “poor” ecological condition at regional and 

national scales in the National Coastal Condition Report (U.S. EPA, 2004b, 2006). Estuaries 

can also be compared in terms of their attainment of designated-use criteria. Classifying 

estuaries by their existing ecological condition requires: 1) a suite of ecologically relevant 

indicator metrics that can be practically measured over a range of different water body types 

and 2) field surveys that measure the metrics at the appropriate spatial and temporal scales. 

2) Classification by Nutrient Loading and Sources: Estuaries can be classified by nutrient 

loadings from non-point, point, and natural sources. Comparison of estuaries by the relative 

magnitude of different sources is critical in developing effective management strategies to 

reduce nutrient loadings (Driscoll et al., 2003) or, more fundamentally, evaluating the role of 

anthropogenic versus natural loadings. Specifically, classification by loadings can help 

prioritize remediation/enforcement efforts or develop Total Daily Maximum Loads (TMDLs). 

The data needed to generate loading/source classifications include measurements of nonpoint, point, and natural loadings across a suite of water body types with loading models used 

to estimate missing values. These types of data are similar to those needed for a TMDL; the 
 3

difference is that data to classify estuaries need to be taken over a suite of estuaries though 

less extensive data collection per estuary can be used to classify estuaries compared to what 

would be needed for regulatory actions. In addition to data on estuary nutrient concentrations, 

classification by loading will usually require data on land cover in the associated watershed. 

Accordingly, “Landscape conditions (e.g., % cover of land uses)” is listed as one of the 

recommended core water quality indicators in EPA guidance for Sections 303(d), 305(b) and 

314 of the Clean Water Act (http://www.epa.gov/owow/tmdl/2006IRG). 

3) Classification to Derive/Validate Water Quality Criteria: Comparison of ecological 

condition in a suite of estuaries along a nutrient gradient is one approach to deriving stressorresponse relationships (e.g., Latimer and Kelly, 2003). While more of a “natural experiment” 

than a classification per se, the change in ecological condition in response to increased 

exposure can identify nutrient thresholds. Such cross-estuary comparisons can also help to 

identify the most sensitive endpoints and diagnostic indicators of nutrient stress. Perhaps the 

greatest limitation of such natural experiments is the potential, and often unknown, 

confounding factors when comparing across estuaries. Nonetheless, such cross-system 

comparisons are a powerful tool in generating or validating water quality criteria under 

realistic conditions. The necessary requirement for this approach is a suite of “reasonably” 

similar estuaries that primarily differ in their nutrient concentrations or loadings. Finding 

such a nutrient gradient can be challenging, as in some regions finding a true “reference” 

estuary may be difficult while in other regions finding highly impacted estuaries may prove 

challenging. 

4) Classification by the Resources at Risk: A fundamental but often overlooked type of 

classification is to categorize estuaries by the ecological resources at risk. The States 

establish designated uses for water bodies as part of state water quality standards, and nutrient 

or other protective criteria are determined to be able to meet these specific designated uses. 

Given the need to protect designated uses, an evaluation of the resources at risk is an 

important early step in the development of a water quality criterion, risk assessment, or 

mitigation action. An obvious example is the application of nutrient criterion for SAV to 

classes of estuaries devoid of SAV, which may be under- or overprotective of the actual 

resources within such systems. More subtlety, the distribution of the resource within the 

estuary may be a key factor in the exposure of the resource to anthropogenic nutrients or other 

stressors. Classifying estuaries by their resources at risk requires: 1) identification of the 

regionally high priority ecological resources; 2) identification of how these high priority 

ecological resources are distributed within and across estuaries; and 3) evaluation of the 

overlap of the resource(s) with anthropogenically derived nutrients or other stressors. 

5) Classification by Estuarine Vulnerability: An approach related to classification by 

resources at risk is to categorize estuaries by their inherent susceptibility to nutrient 

enrichment (e.g., Bricker et al., 2003; U.S. EPA, 2004a). Identification of groups of estuaries 

likely to display adverse impacts at low to moderate nutrient concentrations/loadings versus 

those that have a higher assimilative capacity can assist managers in prioritizing monitoring, 

remediation, or enforcement actions. To the extent that vulnerability is related to regional 

drivers, such as climate or ocean conditions, a classification based on vulnerability can serve 

as the framework for developing defensible regional water quality criteria. Predicting 
 4

vulnerability is a complex process requiring consideration of natural and anthropogenic 

nutrient concentrations/loadings, sensitivity of the specific endpoint(s), spatial/temporal 

overlap of the endpoint(s) with excess nutrient concentrations, and mitigating or enhancing 

factors. In many cases, it will be necessary to draw on data (e.g., dose-response relationships) 

from other estuaries or from similar species. 

1.3 Overview of Eutrophic Conditions and Nutrient Dynamics in the Pacific Northwest 

Compared to the East and Gulf Coasts, relatively little baseline information on nutrient 

enrichment is available for PNW estuaries. In the most comprehensive national review of 

eutrophication, Bricker et al. (2007; also see Bricker et al., 2003) evaluated 138 estuaries across 

the U.S., which included 17 coastal PNW estuaries exclusive of Puget Sound. Nine of these 17 

estuaries were considered to have insufficient data for classification. Of the remaining eight 

estuaries, two were classified as having low eutrophic conditions, six were considered to have 

moderate low eutrophic conditions, and none were classified as having high eutrophic 

conditions. Several Pacific estuaries were classified as having high eutrophic conditions in 

central and southern California and in Puget Sound, but these are all in different biogeographic 

ecoregions than the PNW estuaries (Spalding et al., 2007). 

 

Water quality data from U.S. EPA’s Coastal Environmental and Monitoring Assessment 

Program (EMAP) 1999 and 2000 surveys can also be used to evaluate water quality on the 

Pacific Coast. The 1999 survey sampled the “small” estuaries of California, Oregon, and 

Washington while the 2000 survey sampled Puget Sound, San Francisco Estuary, and main stem 

of the Columbia River Estuary. In 200 water quality samples from the 1999 survey, the lowest 

dissolved oxygen (DO) value was 3.8 mg l-1 and less than 4% of the area of the small coastal 

estuaries had DO concentrations less than 5 mg l-1 (Nelson et al., 2005b), the level indicative of 

biological stress (Bricker et al., 2003). Even when the more urbanized estuaries from the 2000 

survey were included, only two sites out of 371 stations had DO values <2 mg l-1 (U.S. EPA, 

2004b), the level indicative of hypoxia (Bricker et al., 2003). Both of these low values occurred 

in Hood Canal in southern Puget Sound, which is a deep fjord type estuary with limited 

recirculation (http://www.hoodcanal.washington.edu). Likewise, chlorophyll a concentration on 

the Pacific Coast was rated as good, and only two sites in Puget Sound and one in California 

exceeded the threshold of 20 μg l-1 for a “poor” rating (U.S. EPA, 2004b). The generally high 

DO values and low to moderate levels of chlorophyll a suggested that eutrophication was not a 

wide-spread problem on the Pacific Coast, in particular in the PNW coastal estuaries. 

Even though the available evidence did not suggest that PNW estuaries were displaying 

symptoms of eutrophication, there are indications of high nutrient conditions. Water quality on 

the Pacific Coast was rated as “fair” because of high dissolved inorganic phosphorous (DIP) and 

poor water clarity (U.S. EPA, 2004b). Nitrogen loading normalized to estuarine area for the 

Yaquina Estuary, Oregon is 25 mmole N m-2 d-1 (Brown and Ozretich, 2009), which is as great 

as or greater than loading in a number of eutrophic systems (Nixon et al., 2001). Though 

phytoplankton blooms do not appear to be a wide-spread phenomenon, we have observed 

phytoplankton blooms in the upper regions of a few estuaries as well as the import of high 

chlorophyll a concentrations (> 40 μg l-1) from the ocean (Brown and Ozretich, 2009). Finally, 

benthic macroalgae, an indicator of eutrophication (CENR, 2003; Bricker et al., 1999), is 

abundant in several PNW estuaries (e.g., Thom, 1984; Kentula and DeWitt, 2003). Thus, the 
 5

emerging pattern for the PNW is high DIP concentrations and high nitrogen loadings, high levels 

of benthic macroalgae yet low or moderate phytoplankton concentrations. Similar cases of high 

nutrient loading with low chlorophyll a levels have also been observed in other Pacific Coast 

estuaries (Cloern, 2001), and certain estuaries appear less sensitive to nutrient loading due to 

“modulating filters”, such as strong tidal forcing, high turbidity, and dominance of benthic 

suspension feeders. 

One key factor affecting PNW estuaries is the seasonal upwelling that occurs during the springsummer (Hickey and Banas, 2003). Coastal upwelling is such a strong regional driver that the 

PNW coastal estuaries can be considered “extensions of the coastal ocean during the growing 

season” (Hickey and Banas, 2003). Another key factor is the highly seasonal pattern in 

precipitation, with dramatically reduced rainfall during the summer (Emmett et al., 2000). This 

pattern of increased coastal nutrient concentrations and reduced runoff potentially increases the 

relative contribution of nutrients from oceanic sources during the summer. Field and modeling 

research (Brown et al., 2007; Brown and Ozretich, 2009) showed that nutrient-rich ocean water 

advected into the estuary was the major source of nutrients and phytoplankton for the lower 

portion of the Yaquina Estuary during the spring and summer. Overlaying the distribution of 

seagrasses on the pattern of nitrogen sources within the estuary identified oceanic-derived 

nitrogen as the major source for the major portion of the SAV population in the Yaquina during 

the primary growing season. During the low river flow conditions of summer, riverine nutrients 

(i.e., terrestrially derived) were the dominant nitrogen source only in the upper Yaquina, which 

contains relatively little SAV. Consequently, the SAV in this estuary does not appear to be 

highly vulnerable to anthropogenic nitrogen increases during the primary growing season 

because the bulk of the population only has minor exposure to watershed-derived nutrients. 

These observations highlight the major differences in the nutrient dynamics of PNW estuaries 

compared to East and Gulf Coast estuaries largely resulting from summertime upwelling and 

reduced summertime river flow in the PNW. These differences could have substantial effects on 

regulatory strategies. For example, what is the ecological relevance and defensibility of water 

quality criteria in systems dominated by natural nutrient sources that periodically exceed the 

criteria? Another issue is how to develop criteria or implement TMDLs in estuaries with 

seasonally, and even tidally, variable fluxes of nutrients. One difficulty in addressing these 

issues is that research on the effects of upwelling on nutrient and phytoplankton dynamics in 

coastal estuaries has been limited to a relatively few PNW estuaries, primarily the Columbia 

River Estuary (e.g., Hamilton, 1984), Coos Estuary in Oregon (Fry et al., 2001; Roegner and 

Shanks, 2001; Roegner et al., 2002), Willapa Estuary in Washington (e.g., Hickey et al., 2002; 

Hickey and Banas, 2003; Newton and Horner, 2003), and Yaquina Estuary in Oregon (Brown et 

al., 2007; Brown and Ozretich, 2009). In particular, the pattern of the SAV being primarily 

exposed to ocean-derived nutrients has only been studied in the Yaquina Estuary, and without 

studies in different types of systems it is not clear whether this is a general pattern across PNW 

estuaries. 

1.4 Biotic Response Measures in PNW Estuaries 

In addition to the water quality patterns mentioned above, PNW estuaries are characterized by 

several dominant benthic macrophytes and secondary consumers. The dominant intertidal 

macrophytes in the PNW are the perennial, rooted aquatic seagrasses Zostera marina L. and 
 6

Z. japonica Aschers. & Graebn. and the benthic green macroalgae guild, where a guild is a set of 

species with similar ecological function. Two dominant benthic species in many PNW estuaries 

are the burrowing shrimp Neotrypaea californiensis and Upogebia pugettensis. Each of these 

primary producers and secondary consumers strongly affect other components of estuarine 

ecosystems, and can be considered ecological engineering species (sensu Jones et al., 1997). 

Specific ecological functions and interactions of these species/guilds are summarized below and 

in Table 1-1. Because of their importance in nutrient dynamics and food webs, these five taxa 

were chosen as practical biotic response measures, or endpoints, in our surveys to relate 

distributions of estuarine resources to nutrient sources. 

Z. marina is the dominant seagrass species in PNW estuaries, forming dense beds in the 

intertidal and shallow subtidal zones (e.g., Kentula and McIntire, 1986; Thom, 1990; Thom et 

al., 2001). Seagrasses promote estuarine productivity and diversity by serving as nurseries and 

foraging areas for recreationally and commercially important fish and shellfish, providing bird 

habitat, increasing the density of benthic invertebrates, and contributing to estuarine primary 

production (e.g., Bayer, 1979; Hemminga and Duarte, 2000; Jackson et al., 2001; Ferraro and 

Cole, 2007). A second seagrass species, the nonindigenous Z. japonica, was targeted because it 

appears to be spreading in the PNW (e.g., Young et al., 2008). The ecological role of this nonnative species is not well known, but appears to have both positive and negative ecological 

impacts (Posey, 1988; Larned, 2003; Wonham, 2003; Ruesink et al., 2006; Kaldy, 2006), and 

thus can be considered both a stressor and an ecological resource. 

Another important primary producer assemblage in PNW estuaries is benthic macroalgae, which 

primarily consists of green ulvoid species. Benthic macroalgae were targeted in our field 

surveys because blooms of Ulva and Enteromorpha have been associated with eutrophication in 

other parts of the world (e.g., Valiela et al., 1997; Hauxwell et al., 2003) and have been used as 

an indicator of eutrophication (Bricker et al., 1999; CENR, 2003). However, benthic macroalgal 

blooms are an annual event in many PNW estuaries and are likely to be an important component 

of primary production (e.g., Phillips, 1984; Thom, 1984; Kentula and McIntire, 1986; Kentula 

and DeWitt, 2003; Thom et al., 2003; Bulthuis and Shull, 2006). Thus, as with Z. japonica, 

benthic macroalgae can be considered both a resource and a stressor. 

In addition to the primary producers, the ghost shrimp, N. californiensis, and the mud shrimp, 

U. pugettensis, were selected as biotic indicators. Both species are harvested commercially and 

recreationally though they also have a negative effect on oyster aquaculture (Feldman et al., 

2000). Both of these deep burrowing ecological engineers modify sediment properties by 

turning over large quantities of sediment and by irrigating large amounts of water through the 

sediment (DeWitt et al., 2004). In response to this intense bioturbation, sediment nitrogen fluxes 

are increased and shrimp-dominated tide flats are second only to the ocean as a source of 

dissolved inorganic nitrogen (DIN) during the summer in the Yaquina Estuary (DeWitt et al., 

2004). While potentially stimulating phytoplankton by adding nutrients to the water column, 

burrowing shrimp simultaneously reduce phytoplankton concentrations by filtering large 

quantities of overlying water, perhaps as much as the entire volume of the Yaquina Estuary daily 

(Griffen et al., 2004). The intense bioturbation by N. californiensis also reduces the ability of Z. 

japonica to spread into unvegetated areas (Dumbauld and Wyllie-Echeverria, 2003). 
7

Table 1-1. Ecological interactions of five taxa evaluated in the field surveys. The effects listed here are to illustr

of interspecific and trophic interactions expected in Pacific Northwest estuaries and are not a complete listing of 

TAXON 

EFFECTS ON 

WATER 

COLUMN 

NUTRIENTS 

EFFECTS ON 

PHYTOPLANKTON 

EFFECTS 

ON 

Z. MARINA

EFFECTS ON 

BENTHIC 

INFAUNA 

EFFECTS ON 

JUVENILE 

FISHES & 

CRABS 

EFFECTS 

HIGHE

TROPH

LEVEL

Z. marina 

Decrease by 

uptake and 

potential export 

from estuary via 

rafting 

Compete for water 

column nutrient. Minor 

effect in PNW estuaries? NA 

Structure enhances 

species richness and 

density over bare 

sediment. Provides 

total organic carbon 

(TOC) to sediment. 

Nursery 

Habitat ref

and foragin

fish and cr

Consumed 

brant gees

America

widgeo

Z. japonica 

Decrease by 

uptake and 

potential export 

from estuary via 

rafting 

Compete for water 

column nutrient. Minor 

effect in PNW estuaries? 

Competitor 

in some 

estuaries 

Structure enhances 

species richness and 

density over bare 

sediment. Provides 

TOC to sediment. 

Both to a lesser 

extent than Z. 

marina? 

Nursery, though 

to lesser extent 

than Z. marina? 

Potential “habitat 

sink” for juvenile 

crabs during 

spring tides? 

Habitat ref

and foragin

fish and cr

Consumed 

brant gees

America

widgeo

Benthic 

macroalgae 

Decrease by 

uptake (release 

during decay) 

Compete for water 

column nutrient. Minor 

effect in PNW estuaries? 

Impact at 

high biomass 

Provides TOC to 

sediment. 

Detrimental at high 

levels due to 

smothering, shading, 

and H2S formation 

Minimal? Consumed 

some amph

Neotrypaea Increase benthic flux Minimal Inhibits colonization Reduces certain benthic guilds Minimal Prey 

Upogebia Increase benthic flux Decrease by feeding Neutral or may facilitate colonization Enhanced relative to bare sediment Minimal Prey 
 8

1.5 Design and Objectives of the Pacific Northwest Regional Study 

Based on our understanding of the nutrient dynamics and distribution of estuarine resources 

within the PNW, we focused our research on three of the previously identified management 

needs: 1) classification by nutrient loading and sources; 2) classification by the resources at risk; 

and 3) classification by estuarine vulnerability. Research to address these needs was conducted 

at two spatial scales. The first was at the landscape scale of entire estuaries and associated 

watersheds within the PNW, which is discussed in Chapter 2. The six primary objectives at the 

landscape scale were: 

1) Identify and delineate all estuaries and associated watersheds within the PNW. 

2) Classify estuaries by geomorphology and extent of river versus ocean influence. 

3) Classify estuaries by spatial and temporal salinity patterns 

4) Classify estuaries by wetland patterns. 

5) Classify coastal watersheds by land cover patterns. 

6) Integrate wetland and watershed classifications to identify functionally similar estuaries. 

A total of 103 estuaries were identified within the PNW based on the most recent NWI data, with 

their sizes spanning more than five orders of magnitude. A variety of classification schema were 

generated from these landscape-scale attributes based on the concept that different types of 

classifications are useful for addressing different scientific or management issues. A 

“crosswalk” of the wetland and land use classifications was then generated to identify 

functionally similar estuaries. 

The second spatial scale was at the level of the individual estuary where we conducted both field 

and aerial surveys. We identified seven target estuaries in Oregon spanning a range of size (3.1 

to 54.9 km2

), geomorphology, and perceived ocean versus riverine influence. The seven target 

estuaries were the Alsea, Coos, Nestucca, Salmon River, Tillamook, Umpqua River, and 

Yaquina. One of the goals at the estuary scale was to determine patterns and drivers of water 

quality across a range of estuary types. The specific objectives of the water quality surveys and 

modeling were to: 

1) Determine the within-estuary and among-estuary spatial patterns of water quality based on 

nutrients, chlorophyll a, and dissolved oxygen (Chapter 4). 

2) Assess the among-estuary patterns of spatial and temporal variation in salinity (Chapter 4). 

3) Based on water quality patterns, models, and isotope ratios in macroalgae, divide each of 

the estuaries into segments that are primarily dominated by ocean-derived nutrients versus 

riverine-derived nutrients (Chapter 5). 
 9

Another goal at the estuary scale was to quantify the within- and among-estuary distributions and 

abundances of each of the five biotic endpoints. Specific objectives of this research were to: 

1) Determine Z. marina’s intertidal/shallow subtidal bathymetric distribution using aerial 

survey data (Chapter 6). 

2) Using the aerial surveys of Z. marina distributions, assess the potential exposure to 

terrestrially derived nutrients by quantifying what proportions of the population occurred in 

the ocean- and river-dominated segments of the target estuaries (Chapter 6). 

3) Based on field probabilistic surveys, assess the potential exposure of each of the five biotic 

endpoints to terrestrially derived nutrients by quantifying what proportions of the populations 

occurred in the ocean- and river-dominated segments of the target estuaries (Chapter 7). 

4) Classify the target estuaries based on similarities in the relative abundances of the five 

biotic endpoints (Chapter 7). 

5) Determine the lower depth limit of Z. marina to determine its relationship to ambient water 

clarity and estuary type (Chapter 8). 

The final objective was to summarize the key patterns and processes that differentiate PNW 

estuaries from those on the East and Gulf coasts as well as those that result in different 

vulnerabilities to nutrient enrichment among PNW estuaries (Chapter 9). We suggest that this 

summary forms the nucleus of an emerging “Pacific Northwest Paradigm”. This paradigm can, 

and should, be refined as additional data are collected and as we gain better insights into the 

oceanic, estuarine, hydrologic, climatic, watershed, and ecological processes affecting nutrient 

dynamics and biotic distributions. 
10

CHAPTER 2: 

REGIONAL CLASSIFICATION OF PACIFIC NORTHWEST ESTUARIES 

BY WETLAND AND LAND COVER PATTERNS 

Henry Lee II, Deborah A. Reusser (USGS), Patti Haggerty (Indus Corp.), 

Cheryl A. Brown, and Patrick J. Clinton 

2.0 Introduction 

The overall goal of the regional classification was to evaluate the similarities and differences 

among Pacific Coast estuaries based on estuarine wetland distributions, land cover patterns in the 

Key Findings 

• A census of the coastal waterbodies on the Pacific Coast identified 230 estuaries 

(exclusive of Puget Sound) of which 103 occurred in the Pacific Northwest 

(PNW). The majority of the PNW estuaries are small, with 73 of the 103 

estuaries less than 1 km2, and most have extensive intertidal habitat. 

• PNW estuaries provide a suite of estuarine services and functions that vary by 

estuary size, with small- and moderate-sized estuaries (< 10 km2) 

disproportionately important to Coho smolts. 

• PNW estuaries were classified based on geomorphology and extent of ocean 

exchange. Eight estuaries were classified as coastal lagoons, 6 as blind estuaries, 

53 as tidally restricted coastal creeks, 2 as tidal coastal creeks, 3 as marine 

harbors/coves, 3 as bar-built estuaries, and 28 as drowned river mouth 

estuaries. 

• The drowned river mouth estuaries were further divided into river- or tidaldominated based on an “area-normalized freshwater inflow” metric, with 15 

classified as “highly river-dominated”, 7 as “moderately river-dominated”, and 

6 as tide-dominated estuaries. River-dominated estuaries have greater flushing 

and, hence, are less vulnerable to nutrient enrichment. 

• The 31 major river mouth and bar-built estuaries were then classified using a 

clustering approach based on either wetland or watershed land cover patterns. 

• A “crosswalk” was conducted to group estuaries that clustered together in both 

the wetland and watershed analysis. This approach identified groups of 

estuaries that presumably have similar nutrient dynamics and vulnerabilities. 

• Most PNW estuarine watersheds display relatively low levels of alteration based 

on development, agriculture, population density, and impervious surfaces. 
11

associated coastal watersheds, and spatial and temporal patterns of salinity distributions. 

Wetland and watershed data were synthesized for all the coastal estuaries of the Pacific Coast 

from the Tijuana Estuary in Southern California (32.5574oN) north to Cape Flattery in 

Washington (48.383oN). The current effort focuses on the outer coastal estuaries of the Pacific 

Northwest (PNW), which is defined as the coastal segment from Cape Mendocino in Northern 

California (40.440oN) to Cape Flattery in Washington. The Strait of Juan de Fuca and Puget 

Sound were not included in this analysis. 

 

The first step in the analysis was a comprehensive delineation of all the coastal estuaries and 

watersheds to assure a one-to-one relationship between each waterbody and its associated 

watershed. Based on this inventory of PNW estuaries and watersheds, we addressed the 

following objectives: 

1) Identify the broad resource types and general ecological services provided by PNW 

estuaries. 

2) Group estuaries by their similarities in wetland patterns and estuarine landscape 

attributes, including development of estuarine-scale metrics of key physical/climatic 

drivers. 

3) Group watersheds by their similarities in land cover patterns and other watershed 

attributes. 

4) Conduct a matrix match (“crosswalk”) of the classifications by wetlands and watersheds 

to identify estuaries overlapping in the two analyses. Use these groups to identify 

functionally similar estuaries and to help develop a research framework to evaluate the 

proposed classification schema. 

5) Develop a baseline of estuarine and watershed landscape data for the PNW. 

2.1 Estuarine, Wetland, and Landscape Data Sources and Methods 

Achieving the five objectives of the regional classification required a synthesis of multiple types 

of estuarine and watershed data and GIS analyses. Figure 2-1 provides an overview of this 

synthesis to achieve objectives 1-4. In terms of the fifth objective, the summarized wetland and 

watershed data are provided in Tables 2-2, 2-3, and 2-4. 

2.1.1 Estuary Definition and Inventory 

A national standard, the National Wetland Inventory (NWI) (http://www.fws.gov/nwi/; U.S. Fish 

Wild. Ser., 2002), was used as the criterion for defining estuaries. NWI classifies aquatic habitat 

types using a hierarchical set of attributes based on Cowardin et al. (1979), and includes marine, 

estuarine, riverine, palustrine, and lacustrine areas with further subdivisions for tide height, 

substrate type, and the presence of broad classes of wetland plants (e.g., emergent vs. aquatic 

bed). These geospatial data were obtained from NWI digital databases and on-site digitization of 

paper maps to fill in data gaps not available in digital format. The NWI codes found in PNW 

estuaries are summarized in Table 2-1.
12

Figure 2-1. Overview of the generation of the PNW estuary inventory and the wetland and 

landscape data synthesis and analysis. 
13

We defined estuaries as coastal water bodies that had a NWI estuarine polygon and that 

discharged directly into the ocean. Rivers, creeks, tributaries, and other water bodies within an 

estuary that do not discharge directly into the ocean were considered subestuaries. While only 

the estuarine polygons were used to identify estuaries, the sum of the marine, estuarine, and tidal 

riverine polygons was used to calculate the total estuarine area so as to capture the entire habitat 

area likely to contain euryhaline flora and fauna as well as to minimize the effects of any 

misclassifications of the salinity classes. The list of estuaries in the PNW was updated from 

those in the earlier version (Lee et al., 2006) based on the 2007 NWI revisions in the Pacific 

Northwest. In addition, any coastal water body that had an estuarine polygon in the 2001 NOAA 

coverage (see Section 2.1.3) but did not have an estuarine polygon in NWI was identified and 

delineated. These additional systems are listed for completeness but are not included in the 

wetland or watershed analyses. Semi-enclosed harbors or bays with only marine polygons and 

coastal streams with only tidal riverine polygons were delineated but were not classified as 

estuaries and were not included in the analyses. 

Based on these criteria, 103 PNW estuaries were identified (Figures 2-2 to 2-5). Sixteen of these 

estuaries were in California, 24 in Washington, 62 in Oregon, and the Columbia River Estuary 

which is divided between Washington and Oregon. The geomorphological class of each of these 

estuaries is given in Table 2-2. The classification of estuaries as drowned river mouth, bar-built, 

and blind comes from various sources (e.g., Bottom et al., 1979; Seliskar and Gallagher, 1983; 

Cortright et al., 1987; Rumrill, 1998; Emmett et al., 2000) as well as our own analysis. The 

classification of estuaries as coastal lagoons, tidally restricted coastal creek, and marine 

harbor/coves is based on the arguments made in Section 2.4 as is our approach to quantitatively 

separating tide- versus river-dominated estuaries. 

2.1.2 Wetland Habitats Based on NWI 

The NWI provides a regional-scale dataset to classify estuaries by wetland type. An advantage 

of using wetlands to classify estuaries is that a biotic/habitat endpoint integrates a wide range of 

environmental conditions, such as salinity patterns, flushing, and nutrient loading. Thus, 

estuaries with similar NWI wetland patterns presumably will display similar responses to 

nutrient enrichment. 

While the NWI provides a dataset to evaluate estuarine habitats at both local and regional scales, 

there are limitations. The NWI classifies habitats at a coarse resolution, such as “aquatic bed” or 

“unconsolidated shore”, and is thus unable to differentiate between benthic assemblages such as 

burrowing shrimp beds versus sand flats without shrimp. A second limitation is that some of the 

NWI data were generated in the late-1970s and early-1980s, and thus are historical snapshots of 

estuarine conditions. However the NWI is continuing to update its habitat maps, including an 

update of the Oregon estuaries in 2007. The list of estuaries and their sizes are based on these 

updated data so that some estuarine areas will differ slightly from the values in the earlier version 

of this document (Lee et al., 2006). A third limitation is that there was minimal field validation 

and some of the classifications may be incorrect. For example, our observations in Beaver Creek 

(Oregon) suggest that one potential error is the classification of tidal riverine wetlands as 

estuarine emergent wetlands in smaller coastal systems. 
14

Figure 2-2. Estuaries along the Washington coast from Cape Flattery to the Columbia River. 
15

Figure 2-3. Estuaries in northern Oregon. The Alsea, Nestucca, Salmon River, Tillamook, and Yaquina estuaries seven target estuaries in the field surveys.
16

Figure 2-4. Estuaries in southern Oregon. The Umpqua River and Coos estuaries were two of the target estuaries 
17

Figure 2-5. Estuaries in Northern California, north of Cape Mendocino. 
18

One procedural challenge was that some of the older maps either had obsolete or incorrect NWI 

codes, though we were able to translate these into the current standards. Another challenge was 

the inconsistent resolution in the use of special habitat modifiers for salinity and sediment type 

among estuaries. For example, codes to indicate the specific salinity class (e.g., mesohaline) 

were included in a few estuaries but were lacking in many others. When conducting statistical 

clustering using the original NWI codes habitats with a higher level of resolution would be 

considered different from the exact same habitat that did not contain the special modifiers. 

We took a number of steps to reduce the effects of these limitations on the classifications. As 

mentioned, we updated obsolete or incorrect codes; the updated codes are referred to as 

“corrected NWI codes.” To minimize the effect of misclassification of riverine tidal wetlands as 

estuarine wetlands, we defined total estuarine area as the sum of marine, estuarine, and tidal 

riverine polygons. Thus, the total estuarine area would be the same regardless of the specific 

salinity designation within this range. Including the riverine tidal portion is ecologically 

justifiable as inclusion of the low salinity habitat captures the full habitat range for euryhaline 

and oligohaline species. To control for different levels of habitat resolution, we derived 

“consolidated NWI codes” that merged the original and corrected NWI codes into broader 

habitat classes (Table 2-1). For example, both “regularly flooded emergent wetland” and 

“regularly flooded rooted aquatic bed” were consolidated into a “regularly flooded rooted” class. 

The original 118 marine, estuarine, and tidal riverine NWI codes in the PNW estuaries were 

merged into 48 consolidated codes (Table 2-1). These consolidated NWI codes were used in the 

classification analysis to assure similar levels of resolution in the clustering. Additionally, 

merging into broader habitat classes helped to minimize the effects of certain wetland 

misclassifications (e.g., a site classified as emergent vegetation versus a rooted aquatic bed). 

Merged NWI codes have been used in previous efforts, such as in NOAA’s “Spatial Wetland 

Assessment for Management and Planning” program (SWAMP; Sutter, 2001) and in wetland site 

prioritizations (Brophy, 2003). Brophy (2003) suggested that merging NWI polygons better 

identifies the “high biological value of a large, hydrologically interconnected wetland.” 

With these steps to standardize the data, the NWI should provide sufficient accuracy and 

resolution to address regional scale wetland patterns. As stated in Oregon’s “Wetlands Inventory 

User’s Guide” (Oregon Division of State Lands, no year), “The NWI provides excellent 

information for a variety of planning purposes” which include the identification of “the general 

location, extent, and type of wetlands on a regional basis, such as watershed or on tribal lands.” 

Also, as pointed out in the Oregon Watershed Assessment Manual (Brophy, 2007), the revised 

NWI is a suitable base layer for estuarine assessments and is an alternative to hydrogeomorphic 

(HGM) maps. Nonetheless, the NWI does not have the resolution of detailed site-specific 

studies, such as conducted previously in Oregon (Cortright et al., 1987). It is beyond the scope 

of this regional assessment to conduct a detailed comparison of the NWI wetland classes with the 

previous habitat delineations in Oregon, but a cursory comparison suggests that there is 

reasonable agreement on broad habitat classifications (e.g., aquatic beds, subtidal). The Oregon 

study does, however, provide details as to specific habitat type, such as the importance of algae 

growing on cobble/gravel in the Chetco River, which provide insights not possible from the 

broader NWI classes.
19

Table 2-1. Consolidation of National Wetland Inventory (NWI) habitat codes found in PNW estuaries. The NW

classes (estuarine, marine, tidal riverine) and tidal heights (subtidal, irregularly exposed, regularly flooded, irregu

given at the top in capitals and the consolidated habitat types falling within that salinity-tide height are given in t

bold. The original NWI codes making up the consolidated class are listed beneath the consolidated habitat. Origare obsolete or incorrect are given in italics with our interpretation of the corrected code in parenthesis. The “roo

analysis are used to capture the NWI “EM” codes (emergent = “erect, rooted, herbaceous hydrophytes”) and any 

identified as rooted or persistent. The “aquatic bed” classes are used to capture all “AB” codes (aquatic bed = “hplants that grow principally on or below the surface of the water for most of the growing season in most years”) oidentified as rooted or persistent. In PNW estuaries, areas classified as aquatic beds may be covered with SAV, mcombination of both. Because of the uncertainty of the presence of vegetation with the “unconsolidated shore” c

classified as unvegetated though they may contain up to 30% vegetation. Discussion of NWI codes can be found (1979) and Smith (1991) while an online translator is available at http://www.fws.gov/wetlands/Data/webatx/atx

ESTUARINE - SUBTIDAL 

Unvegetated Unvegetated – 

Excavated 

Unvegetated – 

Diked/ 

Impounded 

Aquatic Bed Rooted 

E1UBL, E1UB2L, E1UB3L, E1OWL (E1UBL)

E1UBLx E1UBLh E1ABL E1AB3L 

ESTUARINE – IRREGULARLY EXPOSED 

Unvegetated Unvegetated – 

Cobble-Gravel 

Unvegetated - 

Streambed 

Aquatic Bed Rooted 

E2USM, E2US2M, E2US3M, E2FLM (E2USM)

E2UB1M (E2US1M)

E2SBM E2ABM,

E2AB/FLM (E2ABM), E2AB/USM 

E2AB3M, E2EM1M 
20

ESTUARINE – REGULARLY FLOODED 

Unnvegetated Unvegetated - 

Streambed 

Unvegetated - 

Spoils 

Unvegetated - 

Excavated

Aquatic Bed Rooted Rooted – 

Diked/ 

Impounded 

E2USN, E2BBN (E2USN), E2FLN 

(E2USN), E2US2N, E2US3N, E2FL3N (E2US3N), E2FL6N 

(E2USN)

E2SBN E2USNS E2USNx E2AB1N, 

E2EM/AB1N, E2AB1/US3N, E2US3/AB1N, E2ABN, E2AB/FLN (E2ABN), E2AB/USN, E2AB2/US3N, E2US/ABN 

E2EMN, E2EM/ABN, E2EM1N, E2EM1/FLN (E2EM1N), E2EM1/US3N, E2EM1/ABN, E2EM/USN, E2EM1/USN, E2EM5N 

E2EM5Nh 

ESTUARINE – REGULARLY FLOODED (CONT.)

Shrub-Scrub RockyE2SSN E2RSN 

ESTUARINE – IRREGULARLY FLOODED 

Unnvegetated Unvegetated – 

Cobble-Gravel 

Rocky Rooted Rooted – 

Diked/ 

Impounded 

Rooted - Spoils Shrub-Scr

E2US2P, E2BBP (E2USP), E2FLP 

(E2USP), E2USP 

E2US1P E2RSPR 

(E2RSPr)

E2EM1/FLP (E2EM1P), E2EM1P, E2EM5P, E2EMP, E2EM1R (E2EM1P)

E2EM5Ph (E2EMPh) 

E2EMPs E2SSP, 

E2US/SSP (E2SSP), E2SS/EM1P (E2SSP), E2SS1P 

MARINE – SUBTIDAL & REGULARLY FLOODED, & IRREGULARLY FLOODED 

Irregularly 

Flooded - 

Unvegetated 

Irregularly 

Flooded – Aquatic 

Bed 

Irregularly 

Flooded – Rocky 

Regularly 

Flooded - 

Unvegetated 

Regularly 

Flooded – 

Rocky – 

Vegetated & 

Unvegetated 

Subtidal - 

Rooted 

Subtidal 

Unvegatat

M2USP, M2US2P 

M2AB1/USN M2RS2P, 

M2RSNr, M2RSPR (M2RSPr)

M2USN, M2US2N 

M2RSN, M2RS/ABN, M2AB/RSN 

M1AB3 M1UBL 
21

RIVERINE – TIDAL – PERMANENTLY & REGULARLY & SEMIPERMANENTLY FLOODE

Permanently 

Flooded Tidal - 

Unvegetated - 

Subtidal 

Permanently 

Flooded Tidal – 

Unvegetated – 

Subtidal – 

Excavated 

Permanently 

Flooded Tidal -

Aquatic Bed 

Regularly 

Flooded – 

Unvegetated - 

Shore 

Semipermanently 

Flooded Tidal – 

Unvegetated - 

Subtidal 

Semipermanently 

Flooded Tidal – 

Unvegetated - 

Shore 

R1OWV R1UBV 

R1UBVx R1ABV R1USN, R1FLN 

(R1USN) 

R1UBT R1UST, 

R1FLT (R1UST)

RIVERINE – TIDAL – SEASONALLY & TEMPORARILY & IRREGULARLY FLOODED 

Seasonally 

Flooded Tidal -

Unvegetated - 

Shore 

Seasonally 

Flooded Tidal - 

Unvegetated – 

Shore - Spoil 

Seasonally 

Flooded Tidal - 

Rooted 

Seasonally 

Flooded Tidal - 

Streambed 

Temporarily 

Flooded Tidal – 

Unvegetated - 

Shore 

Irregularly 

Flooded – 

Unvegetated - 

Shore 

R1USR, R1FLR (R1USR), R1SS/FLR (R1USR) 

R1USRS (R1USRs)

R1EMR R1SS/USR 

(R1SB/USR) 

R1USS R1FLY 

(R1USP)
22

2.1.3 Watershed Delineation

The watershed associated with each coastal estuary on the Pacific Coast was delineated, so that 

there was a unique one-to-one relationship between the estuary and its watershed. The estuarine 

watershed geospatial layer was derived and augmented from the watershed layer originally 

generated for NOAA’s Coastal Assessment Framework 

(ftp://sposerver.nos.noaa.gov/datasets/CADS/GIS_Files/ShapeFiles/caf/). This layer was not 

sufficiently detailed to represent the smaller estuaries on the Pacific Coast, requiring further 

delineation of a number of coastal watersheds in California, Oregon, and Washington. 

Watershed boundaries subtending these estuary sites were determined from several sources. In 

Washington, Oregon, and northwest California, the sixth field hydrologic unit (HUC) subwatershed geospatial layer created by the U.S. Forest Service from 1:24,000 scale USGS maps, 

digital elevation models, and other data sources 

(http://www.reo.gov/gis/projects/watersheds/REOHUCv1_3.htm) were used as primary 

references. A watershed layer refined to the seventh field HUC boundary lines 

(http://www.fsl.orst.edu/clams/cfsl0233.html) was also used for most of coastal Oregon north of 

the Rogue River. The digital basin layer CALWATER was used as the primary source for major 

basin delineations in central and southern California. CALWATER is the “California 

Interagency Watershed Map” produced in 1999 and updated in 2004, and represents the State of 

California's working definition of watershed boundaries (http://gis.ca.gov/meta.epl?oid=22175). 

In all states, final refinements to the drainage boundaries were based on review of the hydrologic 

drainage patterns derived from digital elevation data (10-meter horizontal resolution in Oregon 

and 30-meter horizontal resolution in Washington and California) and from USGS 1:24,000 

scale quadrangle maps. In addition, photographs and digital imagery for coastal features were 

examined from the California Coastal Records Project (http://www.californiacoastline.org), State 

of Washington database of shoreline photos (http://apps.ecy.wa.gov/shorephotos/), Terraserver 

(http://terraserver-usa.com/), and other on-line imagery sources. Digital boundaries for 

extraordinary sites – the Columbia River Basin, the interior portion of the San Francisco 

watershed, and the perimeter of the Tijuana River watershed – were located from additional 

sources and incorporated as being the “best available data”. Boundary lines and water bodies 

were plotted and reviewed for accuracy of coding and fidelity to the original sources. In some 

cases, adjustments were made to the attribute coding of water bodies to reflect judgments that 

these were part of an estuarine system. 

The entire watershed was delineated for each estuary (see Table 2-2). By delineating the entire 

watershed, these drainage areas are equivalent to the sum of NOAA’s Estuarine Drainage Area 

(EDA, portion of watershed that empties directly into the estuary and is affected by tides) and 

Fluvial Drainage Area (FDA, component of an estuary's watershed upstream of the EDA 

boundary; http://www.csc.noaa.gov/crs/lca/gloss.html). Entire watershed areas were analyzed 

rather than the area represented by EDAs to capture the entire landscape contributing to the 

nutrient loading of the estuary. For the Columbia, the tidal portion of the Columbia River 

watershed was delineated up to the Bonneville Dam using the NOAA land cover data while the 

entire Columbia Basin, including into Canada, was delineated by the Interior Columbia Basin 

Ecosystem Project Management Project (ICBEMP, 1996; http://www.icbemp.gov). The NOAA 

land cover data truncated the northeastern end of the Klamath watershed which was filled in 

using the National Land Cover Data (NLCD; http://www.epa.gov/mrlc/nlcd-2001.html) data. 
23

Because of the difficulty in delineating watersheds for the smallest estuaries (<0.1 km2), the 

estimates for these systems may overestimate the actual area draining into these systems. We 

also identified the coastal areas not containing an estuarine polygon that drain directly into the 

ocean, which are referred to as Coastal Drainage Areas (CDAs) by NOAA. The watershed 

associated with each CDA was delineated, but these results are not reported here. A number of 

watershed areas calculated here differ from those previously reported by NOAA’s Coastal 

Assessment Framework because multiple estuaries contained within a single NOAA EDA were 

split out to create a one-to-one relationship between each estuary and its watershed. 

2.1.4 Land Cover Patterns, Impervious Surfaces, and Population Density 

The land cover pattern of each coastal watershed was determined using the 1992 National Land 

Cover Data (NLCD, http://www.mrlc.gov) and both the 1995 and 2001 land cover data from 

NOAA’s Coastal Change Analysis Program (C-CAP, 

http://www.csc.noaa.gov/crs/lca/ccap.html). These data were derived from Landsat satellite 

imagery and produced at a 30-meter spatial resolution with an overall target accuracy 

requirement of 85%. The present analysis primarily utilized the 2001 NOAA data since they are 

the most recent, though the earlier NLCD data were used to fill in gaps such as portions of the 

Klamath watershed. The 2001 NOAA data are based on 22 land cover classes 

(http://www.csc.noaa.gov/crs/lca/oldscheme.html), which are not exactly the same as used in the 

NLCD. The percent impervious surface was calculated for each of the estuarine watersheds 

from the 2001 NLCD land cover data (http://www.epa.gov/mrlc/nlcd-2001.html). This dataset 

estimated the percent impervious surfaces on a scale of 0 to 100% by 30 meter cells over the 

entire contiguous United States, the highest resolution of impervious surfaces available at a 

regional scale. The earlier version of this report (Lee et al., 2006) calculated impervious surfaces 

using the default impervious coefficients in Attila for different land cover classes (U.S. EPA, 

2004c). 

The population within each coastal watershed was calculated using both the 1990 and 2000 

census data. This analysis was conducted at the census block scale, the smallest unit used by the 

Census Bureau. Population was prorated by area for census blocks that were transected by a 

watershed boundary. Population density (# per km2

) was calculated for each watershed using the 

total delineated watershed area and the percent population change from 1990 to 2000 was 

calculated. 

2.1.5 Classification Strategy and Methods 

We used a “hybrid” classification approach that combined qualitative analysis with more formal 

statistical methods. The qualitative analysis was used to initially separate out major types of 

estuaries primarily based on nutrient forcing functions and geomorphology especially as it 

related to oceanic exchange. An advantage of this type of analysis is that it allows the 

identification of broad groups of estuaries based on factors that are recognized as critical drivers 

but for which there is either a lack of quantitative data and/or a lack of suitable metrics to 

incorporate into a statistical analysis. A qualitative analysis also allows us to incorporate 

temporally variable or intermittent attributes, such as whether an estuary periodically closes off 

at the mouth (“blind” or intermittent estuary). 
24

Table 2-2. Area and geomorphology of PNW estuaries and watersheds. This table lists all coastal water bodies wpolygon, with the areas (km2) for the marine, estuarine, and tidal riverine NWI habitats. “Total Estuary” is the su

three NWI habitat classes, and is the area used for estuaries in our analyses. The total watershed area (km2) from 

land cover analysis is given for the associated watersheds. The watershed area for the Columbia River is up to thThe four italicized water bodies are those that have an estuarine emergent wetland or estuarine aquatic bed polygNOAA land cover analysis (see Table 2-3) but do not have a NWI estuarine polygon. These additional “estuarie

completeness but are not included in the analyses. Alternative geomorphological classifications are given in pareestuary not included in the previous analysis (Lee et al., 2006).

AREA (km2) 

ESTUARY LATITUDE 

WATERSHED 

AREA 

(km2) MARINE ESTUARINE TIDAL RIVERINE TOTAL ESTUARY 

Waatch River 48.344 38.4 0.0 0.93 0.24 1.16 Tide-dom

Hobuck Creek 48.336 2.3 0.0 0.0 0.0 0 Tidally 

Sooes River 48.324 107.9 0.0 0.50 0.07 0.56 Highly riv

Ozette River 48.181 232.1 0.0 0.03 0.0 0.03 Tidall

Quillayute River 47.908 1,625.0 0.0 0.50 0.46 0.96 Highly riv

Goodman Creek 47.823 81.7 0.0 0.03 0.0 0.03 

Mosquito Creek 47.798 43.8 0.0 0.01 0.0 0.01 Tidall

Hoh River 47.749 773.4 0.0 0.11 0.73 0.84 Highly riv

Cedar Creek 47.711 26.9 0.0 0.02 0.0 0.02 Tidall

Kalaloch Creek 47.607 45.4 0.0 0.02 0.0 0.02 Tidall

Queets River 47.544 1,166.3 0.0 0.60 0.84 1.43 Highly riv

Whale Creek 47.490 32.1 0.0 0.01 0.0 0.01 Tidall

Raft River 47.463 204.0 0.0 0.17 0.07 0.24 Tidall

(Blind 

Camp Creek 47.398 22.7 0.0 0.03 0.0 0.03 Tidall

Duck Creek 47.387 18.4 0.0 0.01 0.0 0.01 Tidally 

Quinault River 47.349 1,133.7 0.0 0.42 0.24 0.66 Highly riv
25

AREA (km2) 

ESTUARY LATITUDE 

WATERSHED 

AREA 

(km2) MARINE ESTUARINE TIDAL RIVERINE TOTAL ESTUARY 

Wreck Creek 47.284 17.9 0.0 0.01 0.0 0.01 Tidall

Moclips River 47.248 84.2 0.0 0.08 0.02 0.10 Tidall

(

Joe Creek 47.206 60.8 0.0 0.05 0.0 0.05 Tidall

Boone Creek 47.159 20.3 0.0 0.01 0.01 0.01 Tidall

Copalis River 47.126 105.3 0.0 0.78 0.08 0.86 Moderate

Conner Creek 47.091 36.9 0.0 0.17 0.0 0.17 Tidally 

Grays Harbor 46.950 6,981.3 0.0 254.39 8.34 262.73 Tide-dom

Willapa 46.373 2,774.5 0.0 389.74 1.12 390.86 Tide-dom

Loomis Lake Creek 46.490 5.6 0.0 0.01 0.0 0.01 Tidall

Columbia River 46.263 

14,520.8 

(to Bonneville 

Dam) 

0.0 411.53 257.45 668.98 Highly riv

Clatsop Spit* 46.277 0.7 0.0 0.08 0.0 0.08 Tidally 

(T

Necanicum 46.011 216.8 0.0 1.57 0.06 1.63 

Moderate

Indian Creek 45.931 7.0 0.0 0.004 0.0 0.004 Tidall

(

Chapman Point 45.915 0.7 0.0 0.001 0.0 0.001 Tidally 

Ecola Creek 45.899 54.7 0.0 0.06 0.002 0.06 Tidall

Arch Cape Creek* 45.803 11.6 0.0 0.001 0.0 0.001 Tidally 

(T

Cove Beach* 45.794 0.4 0.0 0.005 0.0 0.005 Tidally 

Short Sand Creek* 45.760 14.9 0.0 0.002 0.001 0.003 Tidall

Nehalem 45.658 2,215.2 0.0 10.43 1.23 11.65 Highly riv

Lake Lytle 45.636 7.4 0.0 0.0004 0.0034 0.004 
26

AREA (km2) 

ESTUARY LATITUDE 

WATERSHED 

AREA 

(km2) MARINE ESTUARINE TIDAL RIVERINE TOTAL ESTUARY 

Rockaway Beach 

Creek* 45.613 2.2 0.0 0.0004 0.0 0.0004 Tidall

Rockaway Clear 

Lake* 45.605 1.6 0.0 0.0005 0.0 0.0005 Tidall

Smith Lake 45.596 9.2 0.0 0.009 0.005 0.015 

(Tidall

Tillamook 45.513 1,455.3 0.0 36.98 0.51 37.48 Tide-dom

Netarts Bay 45.402 46.4 0.0 10.43 0.001 10.43 

Chamberlain Lake 45.308 0.5 0.0 0.04 0.0 0.04 

Sand Lake 45.276 51.5 0.0 4.28 0.0 4.28 

Sears Lake 45.247 1.5 0.0 0.002 0.0 0.002 

Miles Creek 45.231 2.7 0.0 0.06 0.0 0.06 Tidall

Nestucca 45.182 826.3 0.0 4.65 0.35 5.00 Highly riv

Daley Lake* 45.124 5.1 0.0 0.001 0.0 0.001 Tidall

(T

Neskowin Creek 45.100 53.3 0.0 0.009 0.005 0.014 Tidall

Salmon 45.046 192.6 0.0 3.08 0.04 3.11 

Moderate

Siletz 44.903 954.8 0.0 7.48 1.38 8.86 Moderate

Schoolhouse Creek 44.873 2.9 0.0 0.01 0.0 0.01 

(Tidall

Depoe Bay 44.808 13.4 0.0 0.04 0.0 0.04 M

(D

Yaquina 44.620 650.5 0.0 18.97 0.99 19.96 Tide-dom

Beaver Creek 44.524 87.2 0.0 0.53 0.02 0.55 Tidall

Alsea 44.422 1,221.6 0.0 12.49 0.0 12.49 Moderate

Big Creek 44.370 21.8 0.0 0.09 0.0 0.09 Tidall

Yachats 44.309 112.8 0.0 0.11 0.0 0.11 Tidall

Tenmile Creek North 44.225 60.6 0.0 0.04 0.0 0.04 Tidall
27

AREA (km2) 

ESTUARY LATITUDE 

WATERSHED 

AREA 

(km2) MARINE ESTUARINE TIDAL RIVERINE TOTAL ESTUARY 

Berry Creek 44.094 9.4 0.0 0.02 0.0 0.02 Tidall

(

Sutton Creek 44.060 38.5 0.0 0.15 0.0 0.15 Tidall

Siuslaw River 44.017 2,008.7 0.0 15.59 0.0 15.59 Moderate

Siltcoos River 43.873 185.3 0.0 0.33 0.04 0.36 Tidall

Tahkenitch Creek 43.815 93.1 0.0 0.07 0.19 0.26 Tidall

Umpqua River 43.669 12,146.2 0.0 27.73 6.05 33.78 Highly riv

Tenmile Creek South 43.561 222.9 0.0 0.04 0.46 0.50 Tidall

Coos 43.429 1,575.5 0.0 54.20 0.70 54.90 Tide-dom

Sunset Bay 43.335 14.7 0.0 0.12 0.0 0.12 M

Twomile Creek North 43.236 8.9 0.0 0.0 0.0008 0.0008 

(no N

Coquille River 43.123 2,729.8 0.0 5.08 1.81 6.89 Highly riv

Twomile Creek South 43.044 40.7 0.0 0.11 0.0 0.11 Tidall

New River 43.001 329.0 0.0 1.63 0.04 1.67 Blind 

Sixes River 42.853 347.5 0.0 0.31 0.08 0.39 Blind 

Elk River 42.793 236.4 0.0 0.51 0.16 0.66 Blind 

Port Orford Head 42.746 0.3 0.0 0.01 0.0 0.01 Tidall

Hubbard Creek* 42.734 17.5 0.0 0.01 0.0 0.01 Tidall

Brush Creek 42.685 28.5 0.0 0.02 0.0004 0.02 Tidall

Mussel Creek 42.616 26.6 0.0 0.02 0.0 0.02 Tidall

Euchre Creek 42.564 96.6 0.0 0.11 0.001 0.12 Tidall

Gregg Creek* 42.546 6.7 0.0 0.02 0.001 0.02 Tidall

Rogue River 42.422 13,500.9 0.0 1.32 1.44 2.77 Highly riv

Hunter Creek 42.386 115.2 0.0 0.07 0.02 0.1 Tidall
28

AREA (km2) 

ESTUARY LATITUDE 

WATERSHED 

AREA 

(km2) MARINE ESTUARINE TIDAL RIVERINE TOTAL ESTUARY 

Myers Creek 42.307 14.7 0.0 0.02 0.0 0.02 Tidall

(

Pistol River 42.28 271.9 0.0 0.44 0.11 0.55 Blind 

Burnt Hill Creek* 42.232 7.0 0.0 0.004 0.0 0.004 T

Cove at Boardman 

Park* 42.216 0.4 0.0 0.005 0.0 0.005 Tidally 

Thomas Creek* 42.166 7.0 0.0 0.01 0.0 0.01 Tidall

Whaleshead Creek* 42.144 0.0 0.0 0.02 0.0 0.02 Tidally 

Chetco River 42.045 911.5 0.0 0.40 0.32 0.72 Highly riv

Winchuck River 42.005 184.6 0.0 0.09 0.03 0.12 Blind 

Smith River 41.945 1,942.6 0.0 2.04 0.34 2.38 Highly riv

Lake Earl 41.826 73.1 0.0 9.01 0.0 9.01 

Crescent City Harbor 41.744 29.0 1.61 0.01 0.0 1.63 M

Klamath River 41.547 40,580.9 0.0 1.09 1.18 2.27 Highly rivJohnson Creek 41.463 1.4 0.0 0.01 0.0 0.01 Tidall

Ossagon Creek 41.442 2.5 0.0 0.01 0.0 0.01 Tidall

Squashan Creek 41.389 0.9 0.0 0.01 0.0 0.01 TidallRedwood Creek 41.292 731.7 0.0 0.22 0.09 0.30 Blind 

Freshwater Lagoon 41.269 5.5 0.0 0.0 0.0 0.0 

(p

Stone Lagoon 41.244 19.8 0.0 2.30 0.0 2.30 

Dry Lagoon 41.224 3.2 0.0 0.0 0.0 0.0 Blind – Ti

Big Lagoon 41.174 136.4 0.0 5.03 0.05 5.09 

Little River 41.027 117.3 0.0 0.06 0.0 0.06 Tidall

Mad River 40.942 1,286.8 0.17 1.16 0.0 1.33 Highly riv
29

AREA (km2) 

ESTUARY LATITUDE 

WATERSHED 

AREA 

(km2) MARINE ESTUARINE TIDAL RIVERINE TOTAL ESTUARY 

Humboldt 40.75 472.1 0.07 71.42 0.0 71.49 

Eel River 40.641 9,535.9 0.0 10.18 5.43 15.61 Highly riv

Guthrie Creek 40.542 22.6 0.0 0.005 0.0 0.005 Tidall

Bear River* 40.476 214.5 0.004 0.12 0.05 0.18 Tidall

(Blind 
30

After the initial identification of major estuarine types, hierarchical clustering based on group 

averages (see Clarke and Warwick, 2001; McCune and Grace, 2002) was used to classify the 

estuaries or watersheds based on similarities in wetland habitat or watershed land cover patterns, 

respectively. The Bray-Curtis metric was used as the measure of similarity in these analyses. 

Clustering was conducted using both the absolute area of the NWI or land cover classes as well 

as the relative proportions of the classes using untransformed data. When clustering variables 

measured in different units (e.g., population, % impervious surfaces), the data were normalized 

by subtracting the mean from each value and dividing by the standard deviation to generate a 

standard score, with Euclidean distance used as the similarity metric. All clustering was 

conducted with Primer6 (Clarke and Gorley, 2006; http://www.primer-e.com/). 

Two criteria were used to define estuary groups in the clustering analysis. The primary approach 

was whether the cluster was significantly different from a random reordering of the data within 

the branch in the dendrogram using the SIMPROF test in Primer6 (Clarke and Gorley, 2006). 

By testing for differences within each branch independently, significant differences can occur at 

different similarity levels in separate branches of the dendrogram, so that it is not necessary to 

choose a single similarity level. Clusters of estuaries that were not significantly different were 

not further divided regardless of the level of similarity. A value of p=0.05 was used as the 

default significance level though there is no inherent reason that this value is the ecologically 

“correct” level to identify groups of estuaries with similar functional attributes. Accordingly, we 

evaluated a range of significance values (p = 0.10, 0.20, 0.40, 0.60) in the SIMPROF tests. As 

the significance level is relaxed (i.e., p increases), a greater number of significant clusters will be 

generated and within-cluster similarity will increase though there will be fewer estuaries or 

watersheds per cluster. This sequential approach provides flexibility in developing a 

management framework, allowing managers to balance the extent of variability within estuary 

groups versus the practical issues of increasing the number of groups. A secondary criterion to 

defining groups was to combine estuaries within the branch if similarity was high (>75% with 

Bray-Curtis or <25% of the maximum dissimilarity with Euclidean distance) even if the branch 

showed a significant difference with SIMPROF. While this introduces a degree of subjectivity, 

it eliminates the problem of generating numerous classes that would likely require similar 

management strategies. 

While it would be desirable to have a single classification that captures all aspects of current 

impacts and future vulnerability, the reality is that multiple classification systems and approaches 

are needed to address different scientific and management issues. For example, a classification 

based on resource availability (e.g., extent of wetlands) might group an estuary differently than 

one based on loading (e.g., land cover pattern). Our approach was to evaluate several 

classifications schema both in this chapter and in Chapter 7 based on different biotic and 

landscape attributes related to nutrient dynamics or estuarine vulnerability. It should be 

recognized, however, that any classification system should be considered a hypothesis until it is 

evaluated with independent datasets demonstrating similar estuarine responses, nutrient 

dynamics, or vulnerability to increased loadings. 

2.1.6 Ecological Resources in Pacific Northwest Estuaries

The ultimate purpose of this classification exercise is to derive the insights required for the 

efficient management of ecological resources in PNW estuaries. Different types of resources, for 
31

example salmonids and submerged aquatic vegetation, are likely to respond differently to 

nutrient enrichment, thus requiring different management strategies. Additionally, the type and 

extent of ecosystem services varies across estuaries, which may suggest different management 

prioritizations among estuaries. Thus, as part of this effort, a preliminary evaluation of the 

general types of ecological services provided by each of the estuaries was summarized in 

Table 2-3 and further discussed in Section 2.3. 

One class of ecological services relates to the occurrence of estuarine intertidal and subtidal 

wetlands, which was evaluated using both the emergent wetland and aquatic bed classes in NWI 

and the 2001 NOAA land cover analysis. The irregularly flooded estuarine forest and shrub 

classes were not included since these semi-terrestrial wetland types are less vulnerable to 

estuarine water quality. Three other important PNW resources are oyster aquaculture, native 

salmon runs, and native and migratory birds. Oyster aquaculture was evaluated from known 

facilities as well as from state permits. The presence of current or historic salmon runs was 

based on reports in the literature, while Lawson et al. (2004) provided the predicted number of 

Coho smolts historically present for Oregon estuaries based on a watershed model. Two 

measures of an estuary’s importance to birds were whether the estuary had been classified as an 

Important Bird Area (IBA) by the Audubon Society 

(http://www.audubon.org/bird/iba/index.html) and whether it is recognized as an important 

shorebird site in the U.S. Shorebird Conservation Plan for the Northern Pacific Coast (Drut and 

Buchanan, 2000; http://www.fws.gov/shorebirdplan/RegionalShorebird/RegionalPlans.htm). 

The final function considered, but not quantified, was recreational support. Recreational use of 

PNW estuaries is primarily limited to boating, fishing, crabbing, and clamming, with relatively 

little direct water contact. 

2.2 Inventory of Estuaries on the Pacific Coast

The starting point of our comprehensive classification of estuaries was to conduct an inventory 

of the estuaries on the Pacific Coast. We found that the Pacific Coast contains a surprisingly 

large number of estuaries – 230 based on the criterion that the waterbody contains an estuarine 

NWI polygon and discharges directly into the ocean. This number does not include subestuaries 

within larger water bodies nor does it include the Strait of Juan de Fuca or Puget Sound. In 

addition, there are several harbors and coastal streams that do not contain NWI estuarine 

polygons but which fall within the size range of the coastal estuaries. Although not counted as 

estuaries, these additional water bodies may provide some of the same ecological functions as 

similar sized estuaries. As a result of the 2007 NWI update, the number of PNW estuaries 

increased from 216 in the earlier report (Lee et al., 2006) to 230 in the present analysis. The 

number estuaries may continue to change as NWI adds or deletes estuarine polygons from the 

smallest coastal waterbodies. 

The naming convention used in this report is to refer to a waterbody as an “estuary” if it consists 

of multiple named embayments, rivers, or creeks that constitute major geographic features. This 

is to avoid confusion whether the name refers to the entire waterbody or a single component. For 

example, “San Francisco Estuary” is used to refer to the combination of San Francisco Bay and 

the Delta, including San Pablo Bay, Suisan Bay, and other components of the Delta. The use of 

“San Francisco Bay” is restricted to identifying the bay proper. Similarly, we refer to the 

“Yaquina Estuary” or “Yaquina Bay Estuary” rather than “Yaquina Bay” because the estuary is 
32

< 0.01 0.01 - 0.1 0.1 - 1.0 1 - 10 10 - 100 > 100

0

20

40

60

80

4

15

40

72

59

40

Estuary Size Class (km2)

Number of Estuaries

Figure 2-6. Size distribution of all 230 coastal estuaries in California, Oregon, and Washington 

(exclusive of Puget Sound) identified by the presence of a NWI estuarine polygon. 

< 0.01 0.01 - 0.1 0.1 - 1.0 1 - 10 10 - 100 > 100

0

5

10

15

20

25

30

35

40

3

10

17

33

23

17

Estuary Size Class (km2)

Number of Estuaries

Figure 2-7. Size distribution of the 103 PNW coastal estuaries identified by the presence of a 

NWI estuarine polygon. 
33

0

10

20

30

40

50

3

42

% of Estuarine Habitat

7

Subtidal Irregularly

Exposed

Regularly

Flooded

Irregularly

Flooded

48

Figure 2-8. Distribution of estuarine subtidal and intertidal habitats in the 103 PNW estuaries. 

Intertidal habitat is defined as the sum of the irregularly exposed, regularly flooded, and 

irregularly flooded NWI estuarine and marine classes. 

composed of both Yaquina Bay proper and lower portions of the Yaquina River. Names of 

waterbodies composed of a single named component, such as Beaver Creek, are not modified. 

Estuaries on the Pacific Coast vary greatly in size. The smallest is Liddell Creek in Southern 

California (0.00017 km2

) and the largest is the San Francisco Estuary (3346 km2). Most of the 

Pacific Coast estuaries are small, with 171 (74%) having an estuarine area <1 km2 (Figure 2-6). 

Although there are numerous small estuaries, the bulk of the estuarine habitat is concentrated 

within a few systems. The San Francisco Estuary contains almost two thirds of the total 

estuarine area on the Pacific Coast, exclusive of Puget Sound, with the four largest estuaries (San 

Francisco, Columbia River, Willapa Estuary, and Grays Harbor) accounting for 88% of the 

estuarine area. 

A similar analysis for the PNW identified 103 coastal estuaries (see Figures 2-2 to 2-5) of which 

Burnt Hill Creek and Rockaway Beach Creek are the smallest (0.0004 km2; Table 2-2). The 

largest is the Columbia River Estuary with an area of 669 km2, of which 257 km2 is tidal riverine 

habitat much of which is fresh. Most PNW estuaries are small, with 59 (66%) of the estuaries <1 

km2 (Figure 2-7). Only the Columbia River, Willapa, and the Grays Harbor estuaries are larger 

than 100 km2. On an area basis, these three largest estuaries account for 79% of the total PNW 

estuarine area, exclusive of Puget Sound. In addition to these estuaries defined by NWI, the 

NOAA 2001 land cover survey identified four small water bodies that they classified as 

containing estuarine habitat but which did not contain a NWI estuarine polygon. These water 

bodies are listed in Table 2-2 but were not included in the numerical tallies of estuaries or the 

statistical analyses. 
34

One prominent characteristic of PNW estuaries is their extensive intertidal area. The intertidal 

area for the 103 estuaries was calculated as the sum of the NWI estuarine polygons defined as 

“irregularly exposed”, “regularly flooded” and “irregularly flooded”, which approximates tidal 

heights from mean lower low water (MLLW) to mean higher high water (MHHW). Averaging 

the NWI estuarine polygons across all of the PNW estuaries, intertidal habitat constituted 52% of 

the estuarine area (Figure 2-8). There is no apparent pattern in the extent of intertidal habitat as a 

function of estuary size, with the percent intertidal in the NWI estuarine habitats ranging from 

about 39% to 57% across estuarine size classes. 

2.3 Ecological Services of Small Pacific Northwest Estuaries 

Estuaries smaller than about 1 km2 or even less than 10 km2 have generally been excluded from 

previous estuarine classification efforts. For example, the National Estuarine Eutrophication 

Assessment (Bricker et al., 1999) included all 13 estuaries in the PNW >10 km2 but only 4 of the 

17 estuaries between 1 and 10 km2

 and none <1 km2. However, the large number of small PNW 

estuaries (Figure 2-7) suggests that it was important to include them in the regional analysis for 

several reasons. These smaller estuaries are important for native salmon, a critical regional 

resource and management issue (Lackey, 2004; Lackey et al., 2006a, b). Forty-six of the 73 

estuaries <1 km2

 have reported salmon runs (Table 2-3). An analysis of the predicted historical 

number of Coho smolts produced per watershed along the Oregon coast (Lawson et al., 2004) 

indicated that the small estuaries produced a proportionally greater number of smolts than the 

larger estuaries when normalized to estuarine area (Figure 2-9). Estuaries <1 km2 were 

contributing about 8% to total Coho runs while estuaries <10 km2 were contributing about 24%. 

These smaller coastal watersheds and estuaries may also serve as a future refuge for wild salmon 

with the increasing development and alteration of the larger estuaries and watersheds. Lackey et 

al. (2006c) suggest that the coastal rivers in California, Oregon, Washington, and southern 

British Columbia offer greater potential for preserving wild salmon runs than do the SacramentoSan Joaquin, Columbia, and lower Fraser Rivers, places where the most expensive salmon 

recovery efforts are now focused. In addition to salmon, small estuaries in Northern California 

(e.g., Smith River and Stone Lagoon) provide critical habitat for the endangered tidewater goby 

(Eucyclogobius newberryi) (Federal Register, 2002; Ahnelt et al., 2004), and many of the smaller 

estuaries provide habitat for resident and migratory birds (Table 2-3), including bald eagles and 

brown pelicans. 

In terms of other ecosystem services, several of the coastal lagoons <10 km2

 contain extensive 

emergent wetlands (e.g., Big Lagoon, Lake Earl) while many of the drowned river estuaries and 

tidal creeks between about 0.5 km2 and 10 km2 also contain considerable wetlands (Table 2-3). 

The wetlands in these small estuaries are likely important for coastal and migratory birds and to a 

limited number of brackish water fishes such as the tidewater goby. Among the smallest 

estuaries (<0.1 km2) only 7 of the 50 contain any emergent wetlands, none contain any aquatic 

beds (Table 2-3), and these smallest estuaries are little utilized by recreational crab or clam 

species. Thus, the ecosystem services provided by these smallest estuaries appear to be related 

to supporting wild salmon runs, recreation, and perhaps as bird habitat. These smallest estuaries 

and their associated rivers and creeks may also provide economic benefits in terms of increased 

property value, though we made no attempt to estimate this.
35

Table 2-3. Examples of ecological resources and services in PNW estuaries. Estuarine emergent wetlands and aNWI are the sums of classes with “EM” or “AB” codes, respectively. The estuarine emergent wetland and aquatare areas for these two land cover classes. “Estuary Area” is the sum of the NWI estuarine, marine, and tidal rive

2-2). “Oyster culture” indicates whether there is commercial oyster aquaculture or whether there are permits to aaquaculture. “Salmon present” indicates whether salmon are currently or have historically been reported from th

streams and rivers flowing into the estuary. “N?” is used to indicate estuaries where we suspect salmon are abse

size or other landscape attributes while “?” indicates that we are unaware of any reports of salmon from that estu

is the historical potential number of Coho smolts predicted to have occurred within Oregon estuaries (Lawson et italicized water bodies are those that have an estuarine emergent wetland or estuarine aquatic bed polygon in the 

cover analysis (see Table 2-2) but do not have a NWI estuarine polygon. SCP = listed as important site in the ShPlan with sites marked with an asterisk (*) supporting ≥ 4000 birds. IBA = Important Bird Areas. NA = Estuary 

NOAA dataset. 

ESTUARY ESTUARY 

AREA 

(km2) 

NWI 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NWI 

ESTUARINE 

AQUATIC BED

(km2) 

NOAA 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NOAA 

ESTUARINE 

AQUATIC BED 

(km2) 

OYSTER 

CULTURE

Waatch River 1.16 0.93 0.0 0.0 0.0 N 

Hobuck Creek 0.0 0.0 0.0 0.003 0.0 N 

Sooes River 0.56 0.17 0.0 0.01 0.0 N 

Ozette River 0.03 0.0 0.0 0.0 0.002 N 

Quillayute River 0.96 0.05 0.0 0.03 0.002 N 

Goodman Creek 0.03 0.0 0.0 0.0 0.0 N 

Mosquito Creek 0.01 0.0 0.0 0.0 0.0 N 

Hoh River 0.84 0.0 0.0 0.004 0.001 N 

Cedar Creek 0.02 0.0 0.0 0.0 0.0 N 

Kalaloch Creek 0.02 0.0 0.0 0.002 0.0 N 

Queets River 1.43 0.15 0.0 0.09 0.01 N 

Whale Creek 0.01 0.0 0.0 0.0 0.0 N 

Raft River 0.24 0.08 0.0 0.002 0.002 N 

Camp Creek 0.03 0.0 0.0 0.002 0.0 N 

Duck Creek 0.01 0.0 0.0 0.0 0.0 N 
36

ESTUARY ESTUARY 

AREA 

(km2) 

NWI 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NWI 

ESTUARINE 

AQUATIC BED

(km2) 

NOAA 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NOAA 

ESTUARINE 

AQUATIC BED 

(km2) 

OYSTER 

CULTURE

Quinault River 0.66 0.01 0.0 0.003 0.01 N 

Wreck Creek 0.01 0.0 0.0 0.001 0.0 N 

Moclips River 0.10 0.003 0.0 0.0 0.0 N 

Joe Creek 0.05 0.0 0.0 0.002 0.0 N 

Boone Creek 0.01 0.0 0.0 0.004 0.0 N 

Copalis River 0.86 0.40 0.0 0.001 0.0 N 

Conner Creek 0.17 0.0 0.0 0.0 0.0 N 

Grays Harbor 262.73 15.24 147.59 11.98 1.11 Y 

Willapa 390.86 39.44 181.47 29.04 2.67 Y 

Loomis Lake Creek 0.01 0.0 0.0 0.06 0.003 N 

Columbia River 668.98 28.63 4.07 18.99 0.79 N 

Clatsop Spit 0.08 0.06 0.0 NA NA N 

Necanicum 1.63 0.42 0.0 0.01 0.06 N 

Indian Creek 0.004 0.0 0.0 NA NA N 

Chapman Point 0.001 0.0 0.0 NA NA N 

Ecola Creek 0.06 0.02 0.0 0.0 0.001 N 

Arch Cape Creek 0.001 0.0 0.0 NA NA N 

Cove Beach 0.005 0.002 0.0 NA NA N 

Short Sand Creek 0.003 0.0 0.0 NA NA N 

Nehalem 11.65 2.34 0.14 2.76 0.02 N 

Lake Lytle 0.004 0.0 0.0 NA NA N 

Rockaway Beach 

Creek 0.0004 0.0 0.0 NA NA N 

Rockaway Clear 

Lake 0.0005 0.0 0.0 NA NA N 

Smith Lake 0.015 0.0 0.0 NA NA N 
37

ESTUARY ESTUARY 

AREA 

(km2) 

NWI 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NWI 

ESTUARINE 

AQUATIC BED

(km2) 

NOAA 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NOAA 

ESTUARINE 

AQUATIC BED 

(km2) 

OYSTER 

CULTURE

Tillamook 37.48 4.29 0.02 4.51 0.50 Y 

Netarts Bay 10.43 1.07 2.60 1.16 0.89 Y 

Chamberlain Lake 0.04 0.0 0.0 NA NA N 

Sand Lake 4.28 2.37 0.07 3.19 0.02 N 

Sears Lake 0.002 0.0 0.0 NA NA N 

Miles Creek 0.06 0.0 0.0 NA NA N 

Nestucca 5.00 0.88 0.09 1.14 0.36 N 

Daley Lake 0.001 0.0 0.0 NA NA N 

Neskowin Creek 0.014 0.0 0.0 0.002 0.0 N 

Salmon River 3.11 2.13 0.004 2.54 0.0 N 

Siletz River 8.86 2.16 0.0 1.54 0.45 N 

Schoolhouse Creek 0.01 0.0 0.0 0.0 0.0018 N 

Depoe Bay 0.04 0.0 0.0 0.002 0.0 N 

Yaquina 19.96 3.03 3.65 1.11 0.72 Y 

Beaver Creek 0.55 0.42 0.0 0.03 0.01 N 

Alsea 12.49 2.49 3.25 2.43 0.99 

N 

(under 

consideration) 

Big Creek 0.09 0.08 0.0 NA NA N 

Yachats River 0.11 0.0 0.02 0.0 0.0 N 
38

ESTUARY ESTUARY 

AREA 

(km2) 

NWI 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NWI 

ESTUARINE 

AQUATIC BED

(km2) 

NOAA 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NOAA 

ESTUARINE 

AQUATIC BED 

(km2) 

OYSTER 

CULTURE

Tenmile Creek 

North 0.04 0.02 0.0 0.0 0.0 N 

Berry Creek 0.02 0.0 0.0 0.0 0.0 N 

Sutton Creek 0.15 0.0 0.03 0.0 0.01 N 

Siuslaw River 15.59 3.32 1.30 1.14 0.09 Y 

Siltcoos River 0.36 0.08 0.0 0.0 0.06 N 

Tahkenitch Creek 0.26 0.0 0.0 0.001 0.07 N 

Umpqua River 33.78 3.18 0.07 1.43 0.94 Y 

Tenmile Creek 

South 0.50 0.01 0.0 0.0 0.01 N 

Coos 54.90 7.28 5.98 4.91 4.20 Y 

Sunset Bay 0.12 0.0 0.0 NA NA N 

Twomile Creek 

North 0.0008 0.0 0.0 0.01 0.0 N 

Coquille River 6.89 1.07 0.06 1.38 0.17 N 

Twomile Creek 

South 0.11 0.01 0.0 0.02 0.0 N 

New River 1.67 0.48 0.0 0.27 0.02 N 

Sixes River 0.39 0.03 0.0 0.05 0.0 N 

Elk River 0.66 0.05 0.0 0.01 0.0 N 

Port Orford Head 0.01 0.0 0.0 NA NA N 

Hubbard Creek 0.01 0.0 0.0 NA NA N 

Brush Creek 0.02 0.0 0.0 0.0 0.0 N 
39

ESTUARY ESTUARY 

AREA 

(km2) 

NWI 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NWI 

ESTUARINE 

AQUATIC BED

(km2) 

NOAA 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NOAA 

ESTUARINE 

AQUATIC BED 

(km2) 

OYSTER 

CULTURE

Mussel Creek 0.02 0.0 0.0 0.02 0.0 N 

Euchre Creek 0.12 0.02 0.0 0.08 0.0 N 

Greggs Creek 0.02 0.002 0.0 NA NA N 

Rogue River 2.77 0.06 0.0 0.25 0.10 N 

Hunter Creek 0.10 0.0 0.0 0.01 0.0 N 

Myers Creek 0.02 0.0 0.0 0.0 0.0 N 

Pistol River 0.55 0.01 0.0 0.14 0.0 N 

Burnt Hill Creek 0.004 0.0 0.0 NA NA N 

Cove at Boardman 

Park 0.005 0.0 0.0 NA NA N 

Thomas Creek 0.01 0.0 0.0 NA NA N 

Whaleshead Creek 0.02 0.0 0.0 NA NA N 

Chetco River 0.72 0.01 0.0 0.03 0.01 N 

Winchuck River 0.12 0.004 0.0 0.05 0.0 N 

Smith River 2.38 0.07 0.0 0.56 0.0 N 

Lake Earl 9.01 0.0 9.01 4.31 0.0 N 

Crescent City 

Harbor 1.63 0.0 0.0 0.0 0.0 Y (Permits) 

Klamath River 2.27 0.03 0.0 0.01 0.005 N Johnson Creek 0.01 0.0 0.0 0.0 0.0 N 

Ossagon Creek 0.01 0.0 0.0 0.0 0.0 N 

Squashan Creek 0.01 0.0 0.0 NA NA N Redwood Creek 0.30 0.0 0.0 0.18 0.0 N 

Freshwater Lagoon 0.0 0.0 0.0 0.10 0.0 N 

Stone Lagoon 2.30 0.0 0.0 0.08 0.0 N 

Dry Lagoon 0.0 0.0 0.0 0.12 0.0 N 

Big Lagoon 5.09 0.0 5.03 0.57 0.0 N 

Little River 0.06 0.0 0.0 0.14 0.0 N 
40

ESTUARY ESTUARY 

AREA 

(km2) 

NWI 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NWI 

ESTUARINE 

AQUATIC BED

(km2) 

NOAA 

ESTUARINE 

EMERGENT 

WETLAND 

(km2) 

NOAA 

ESTUARINE 

AQUATIC BED 

(km2) 

OYSTER 

CULTURE

Mad River 1.33 0.02 0.0 0.33 0.0 N 

Humboldt 71.49 3.93 19.82 9.35 0.0 Y 

Eel River 15.61 1.68 0.06 4.95 0.002 N 

Guthrie Creek 0.005 0.0 0.0 0.0 0.0 N 

Bear Creek 0.18 0.0 0.0 0.003 0.0 N 

TOTAL 1677.44 128.22 384.34 111.15 14.31 10 Y 
41

< 1 1 - 10 > 10

0

20

40

60

80

100

68

86

24

13 8

 Estuary Size Class (km2)

Percent Area or Smolts

 % Estuarine Area

 % Coho Smolt

1

Figure 2-9. Distribution of the projected historical number of Coho smolts by estuary size 

classes in 29 Oregon estuaries. Coho smolt data from Lawson et al. (2004; see Table 2-3). 

The final reason for the inclusion of these small water bodies is that they are likely to require 

different management strategies. Factors such as their smaller volumes, differences in seasonal 

and tidal variability in exchange and salinity, and different biotic communities are all likely to 

result in different exposures and vulnerabilities. Research focused on these largely ignored small 

systems is required to better understand how they function, but it is possible to speculate that 

their small size makes them more vulnerable to certain types of stressors while at the same time 

offering greater opportunities for cost-effective protection and/or mitigation efforts 

2.4 Classification by Geomorphology and Oceanic Exchange 

The 103 PNW estuaries were initially separated into broad classes based on the potential extent 

of oceanic exchange, a fundamental feature affecting vulnerability to terrestrial nutrient loading. 

Since residence times are not available for most of these waterbodies, we qualitatively estimated 

relative oceanic exchange from estuarine geomorphology based on the literature, aerial 

photographs, and personal observations. Photographic sources included the aerial photographs 

taken by the Pacific Coastal Ecology Branch (PCEB; see Chapter 6) as well as online sources 

including the Oregon Coastal Atlas (http://www.coastalatlas.net), high resolution (1:12,000) 

panchromatic imagery commercially available through GlobeExplorer TM, Terraserver 

(http://www.terraserver.com), GoogleEarth (http://www.google.com), Washington Coastal Atlas 

(http://www.ecy.wa.gov/programs/sea/SMA/atlas_home.html), Washington’s Department of 

Ecology Washington Shoreline Aerial Photos (http://apps.ecy.wa.gov/shorephotos/index.html), 

and the California Coastal Records Project (http://www.californiacoastline.org/). Additionally, 

the NWI coverages were used to evaluate the overall waterbody shape and structure. 
42

It is important to recognize that many estuaries display characteristics of several 

geomorphological classes so assigning an estuary to a single class is somewhat artificial. In 

recognition of this gradation, alternate classifications are given when appropriate. To the extent 

practical, an estuary’s structure and dynamics should be considered as well as its classification 

when assessing vulnerability or developing a management strategy. 

2.4.1 Coastal Lagoons and Blind Estuaries

We identified two types of moderate to large estuaries with restricted exchange. The first are 

coastal lagoons, defined here as coastal water bodies located behind berms with NWI estuarine 

polygons that are only intermittently open to the ocean. For example, some Southern California 

lagoons remain open only about a third of the time (Elwany et al., 1998). Coastal lagoons can 

breach during storms, after periods of heavy rain, or long-shore movement of dune sand (Elwany 

et al., 1998). Coastal lagoons are also artificially breached, especially in California, to increase 

circulation, improve water quality, and reduce flooding (e.g., Williams et al., 1999; Merritt Smith 

Consulting, 1999). We classified eight PNW water bodies as coastal lagoons (Table 2-2). The 

largest of these is Lake Earl while the smallest is the Sears Lake. 

The second type of waterbody with restricted oceanic exchange is the blind or intermittent 

estuary. Mouths of blind estuaries periodically close due to the formation of an ephemeral berm. 

The mouth of a blind estuary may not close every year, but closure is frequent enough to be a 

regular characteristic of the system. Blind estuaries are more common in the southern portion of 

the Pacific coast, with many of the smaller southern California estuaries closing during the late 

summer and early fall before the rains. We separate blind estuaries from coastal lagoons based 

on these attributes: blind estuaries are open to the ocean more frequently than lagoons; there 

appears to be more among-year variation in blind estuaries whether the mouth closes; and blind 

estuaries tend to have a more riverine shape compared the more “pond” or “lake” shape of 

lagoons that often run parallel to the dunes. Such a distinction is also made with the South 

African “Temporarily Open/Closed Estuaries” (TOCE), which are divided into “Intermittently 

Closed Estuaries” (ICEs), which have a connection to the ocean more than 50% of the time, and 

“Intermittently Open Estuaries” (IOEs), which are closed to the ocean more than 50% of the time 

(Whitfield and Bate, 2007). 

Based on the aerial photographs, literature (e.g., Bottom et al., 1979; Cortright et al., 1987), and 

online sources (http://www.coastalatlas.net), six PNW waterbodies were classified as blind 

(Table 2-2). The smallest is the Winchuck River while the largest is the New River. Note that 

this count of blind estuaries does not include the tidally restricted coastal creeks discussed below 

that may also periodically close off at the mouth. 

With both lagoons and blind estuaries, closure of the mouth eliminates any direct flushing with 

the ocean though there may be subsurface flow between the estuary and the ocean. The absence 

or greatly reduced exchange during closure can result in low dissolved oxygen, extended periods 

of low salinity at least at the surface, increased temperatures, and stratification. Fish kills have 

been reported during periods of closure in blind estuaries in Oregon due to the ponding of 

freshwater (Clifton et al., 1973 in Bottom et al., 1979). Blooms of nuisance algae can occur 

during periods of closure (e.g., John Gilchrist & Associates and Fall Creek Engineering, Inc., 

2005). Five dogs died after swimming in Big Lagoon in 2001, which was attributed to exposure 
43

to toxic blue green algae resulting from warm weather and the heavy nutrient load caused by the 

lack of winter breaching (Humboldt County Dept. Health Human Ser., 2005). Bottom dissolved 

oxygen in the Mattole River in northern California periodically decreased below 6 mg l-1 after 

“permanent lagoon formation” and occasionally dropped to 4 mg l-1, though interestingly the 

lowest dissolved oxygen levels occurred two weeks before the estuary closed off (Zedonis et al., 

2008). Salinity in these restricted waterbodies can vary drastically depending upon the extent of 

breaching. When closed for extended periods, some of these waterbodies can transform into 

freshwater systems. The Mattole River became a freshwater lagoon after the closure of the 

mouth (Busby, 1991). Similarly, our measurements of salinity in the New River ranged from 0 

to 1.6 psu in the summer of 2003 (H. Lee II, unpub. data). 

As a class, both the lagoonal systems and blind estuaries are highly vulnerable to nutrient 

enrichment during periods of restricted oceanic exchange. However, both lagoons and blind 

estuaries experience dramatic among-year and seasonal variability in flushing and corresponding 

variation in water quality and salinity, confounding how to manage these systems. One strategy 

is to assume “worst case” scenarios of restricted or blocked ocean access. Since salmon are 

present in many of these systems, an appropriate beneficial use defined in Oregon for these 

estuaries is to support “Resident Fish and Aquatic Life, Salmonid Spawning & Rearing”. The 

dissolved oxygen threshold for this beneficial use in Oregon for estuaries is 6.5 mg l-1

(http://www.sos.state.or.us/archives/rules/OARs_300/OAR_340/340_041.html). In addition, the 

Oregon narrative criteria states that “where a less stringent natural condition of water of the State 

exceeds the numeric criteria ... the natural condition supersedes the numeric criteria and 

becomes the standard for that water body.” Dissolved oxygen levels of > 6.5 mg l-1 may be 

difficult to obtain during periods of restricted flushing especially in the lagoonal systems. One 

management option is to artificially breach the barriers, though that may result in other impacts, 

such as the premature flushing of salmon smolts. 

2.4.2 Tidal Coastal Creeks/Tidally Restricted Coastal Creeks

Tidal coastal creeks are creeks or streams discharging directly into the ocean and which 

experience input of ocean water at least during high tide. These systems are the smallest type of 

“estuary”, some of which are essentially freshwater streams with a limited area influenced by 

ocean waters. However, the hydrodynamics of these systems appears to be more complex than 

their small size suggests. Based on field observations on a few coastal creeks and analysis of 

aerial photographs, most have restricted connections with the ocean during a portion of the year. 

We refer to these as “tidally restricted coastal creeks” which are defined by having one or more 

of the following characteristics at least intermittently: 1) there is a narrowing of mouth from sand 

movement sufficient to restrict exchange with the ocean; 2) formation of a berm that closes the 

mouth, separating the creek from the ocean; or 3) there is a development of a sill near the mouth 

sufficient to restrict exchange with the ocean. 

We tentatively identified 55 waterbodies as tidal coastal creeks in the PNW, ranging from 

Chapman Point Creek (0.001 km2

) to Beaver Creek (0.55 km2) (Table 2-2). Analysis of aerial 

photographs suggests that most of these systems have limited exchange during portions of the 

year, and 53 are tentatively considered tidally restricted coastal creeks. There is no clear 

demarcation between the larger tidally restricted coastal creeks and the tidal rivers. For example, 

Bear River was classified as a tidally restricted coastal creek though it alternatively could be 
44

classified as a blind drowned river mouth estuary. Several of the smaller tidal rivers in 

Washington, including the Waatch, Hoh, Quinault, Sooes, and Quillayute, have partially 

restricted mouths and may represent the transition between the tidally restricted coastal creeks 

and blind drowned river mouth estuaries. Part of the difficulty in cleanly separating these 

systems is the paucity of studies on the estuarine segments of tidal creeks. Even a relatively 

small field effort evaluating salinity patterns and mouth constrictions across a suite of tidal 

creeks would greatly promote our understanding of their dynamics. 

Based on our present knowledge, flushing is presumed to be very high in estuarine portion of 

tidally restricted coastal creeks when they are open, and salinity near the mouth is likely to 

undergo extreme tidal and seasonal fluctuations. An indication of the extent of these salinity 

variations can be found in the larger drowned river mouth estuaries, where salinity at the mouth 

can vary by more than 20 psu over a tidal cycle (Figures 4-8 and 4-9), and median salinity near 

the mouth of highly river-dominated systems can vary by 20 psu seasonally (Figure 2-11). We 

suspect that salinity near the mouth of the tidal creeks can cycle from essentially marine to fresh 

over a tidal cycle during the periods when they are open. During such periods, this high tidal 

exchange reduces the accumulation of terrestrially derived nutrients in the “estuarine” segments 

of these waterbodies. However, these extreme salinity fluctuations also limit the ability of all but 

the most euryhaline species to survive. A possible reflection of this salinity stress is the paucity 

of estuarine emergent wetlands and aquatic beds in tidal coastal creeks <0.1 km2

 (Table 2-3). 

During other periods, restriction near the mouth limits exchange in these tidal creeks. If the 

restriction is of sufficient duration, the “estuarine” portion of tidal creeks may develop low 

dissolved oxygen conditions. Additionally, an ongoing study of the Yachats River indicates that 

the formation of a sill near the mouth restricts exchange with the ocean to the spring high tides, 

essentially acting as a micro-fjord (C. Brown, unpublished data). This oceanic water advected 

during spring tides can form localized pockets of deeper, saline waters below a freshwater 

surface layer. With no or minimal exchange or turnover, the dissolved oxygen in these saline 

pockets declines until the next spring tide or storm. Qualitative observations on other tidal 

creeks suggests that the Yachats River is not unique in its structure; thus a number of tidally 

restricted coastal creeks may have localized areas of bottom water with low dissolved oxygen 

during a portion of the month. 

While there are uncertainties about the dynamics and classification of these coastal creeks, we 

suggest that as with blind estuaries these systems could be managed on a “worst case” scenario. 

The most sensitive resource is likely to be the juvenile salmon that utilize many of these systems 

(Table 2-3). In these cases, the Oregon dissolved oxygen standard of 6.5 mg l-1

(http://www.sos.state.or.us/archives/rules/OARs_300/OAR_340/340_041.html) would be an 

appropriate criterion, except for when natural conditions prevent attainment of this standard. An 

ongoing study of Yachats indicates that dissolved oxygen conditions fall below 6.5 mg l-1 

periodically in pockets of bottom water “trapped” by natural processes. The lack of typical 

“estuarine” resources and salmon in many of the estuaries smaller than about 0.01 km2 suggests 

that the application of estuarine criteria may not be the best approach to their management. In 

fact, the smallest estuarine polygon in a tidal creek was only 35 m long, making application of 

estuarine criteria to this system dubious. A more practical strategy may be to manage the 
45

freshwater component of these systems and/or to apply human health end-points, such as water 

contract criteria for fecal coliforms, to the water advected onto the beach. 

2.4.3 Marine Harbors/Coves

At the opposite extreme of these restricted estuaries, there is a group of coastal waterbodies that 

has an unobstructed connection with the ocean with relatively small freshwater inflow. These 

systems are separated as marine harbors/coves, and as a class should have a relatively low 

vulnerability to terrestrial nutrient loadings. Based on analysis of photographs and preliminary 

field observations, two coastal waterbodies fall into this class - Sunset Bay and Crescent City 

Harbor. A third system, Depoe Bay, the “world’s smallest harbor”, is tentatively classified as a 

marine harbor/cove. With a mouth width of about 28 meters, Depoe Bay is less open to the 

ocean than either Sunset Bay or Crescent City, however Depoe Bay appears to be relatively well 

flushed. With an open connection to the ocean and limited freshwater input, salinities in these 

systems will tend to remain high and undergo smaller tidal and seasonal variations than the 

drowned river mouth estuaries. 

2.4.4 Drowned River Mouth and Bar-Built Estuaries 

The remaining 31 estuaries can be split into two general classes based on geomorphology. The 

first are bar-built estuaries, which are formed when ocean currents and wind form coastal dunes 

that trap estuarine water behind them. Bar-built estuaries resemble lagoonal systems in having 

low freshwater inputs but differ in having greater exchange with the ocean. The two generally 

recognized bar-built estuaries in the PNW are Netarts Bay and Sand Lake. We classify a third 

estuary, the Humboldt Estuary, as bar built. Though the Humboldt has been considered a 

drowned river mouth estuary (e.g., Rumrill, 1998) there is no major river discharging into the 

estuary. More appropriately, Emmett et al. (2000) classified the Humboldt as a lagoon, and these 

authors pointed out that many lagoons are closed to the ocean during a portion of the year. The 

difficulty with classifying Humboldt as a lagoon is that it has permanent jetties maintaining an 

open connection with the ocean. Therefore, we suggest that with its current configuration the 

Humboldt more closely resembles a bar-built estuary. 

The second class is the drowned river mouth estuaries, which include 28 PNW estuaries. 

Drowned river estuaries were created by flooding of river valleys as sea level rose during the 

Holocene marine transgression after the last ice ago about 10,000 years ago (Emmett et al., 

2000). Drowned river mouth estuaries constitute the largest estuaries in the PNW, including the 

Columbia River, Willapa Estuary, and Grays Harbor. Although formed by flooding of river 

valleys, the geomorphology and size of many of the drowned river mouth estuaries in the PNW 

are influenced by the formation of ocean-built bars. The drowned river mouth estuaries with 

prominent ocean bars share characteristics with bar-built estuaries, and some authors have 

classified them as bar built. For example, Willapa and Grays (Seliskar and Gallagher, 1983) as 

well as the Salmon River (http://www.coastalatlas.net) have been classified bar-built estuaries. 

While recognizing the importance of these ocean bars to their dynamics, we classify these 

systems as drowned river mouth estuaries based both on their historic formation and the presence 

of one or more moderate to large sized rivers discharging into the estuary. 
46

2.4.4.1 Tide- and River-Dominated Drowned River Mouth Estuaries 

Drowned river mouth estuaries vary in the extent that their geomorphology and flushing are 

driven by tidal versus fluvial processes. For example, the Columbia River and Rogue River are 

considered to be strongly influenced by river flow (Cortright et al., 1987; McCabe et al., 1988) 

while Willapa has relatively little freshwater input for its size. While categorization of systems 

like the Columbia and Willapa is relatively straightforward, the difficulty comes in classifying 

the full suite of PNW estuaries without rigorous criteria. The Australians separate the two types 

of systems depending upon whether river or tide energy “dominated the evolution” of the estuary 

(http://dbforms.ga.gov.au/www/npm.Ozcoast.glossary?pType=Audit). In a similar vein, Elliott 

and McLusky (2002) define river-dominated estuaries as microtidal systems where rivers 

provide the majority of the sediment and tide-dominated estuaries as macrotidal systems where 

“there is a steady infilling of sediment provided at differing times from the sea and from the 

river.” Such geologically based definitions help highlight the factors driving the formation of an 

estuary but they provide little insight into flushing, nutrient retention, or salinity patterns. 

A more relevant approach to assessing estuarine vulnerability to nutrient enrichment is to 

separate tide- versus river-dominated systems based on the relative extent of freshwater flow 

through the estuary. To capture this relative input, we propose “normalized freshwater inflow” 

metrics. The first step in generating these metrics is to calculate the total average annual volume 

of rainfall over the entire watershed using the precipitation data in PRISM 

(http://prism.oregonstate.edu/; Daly et al., 2007), which has records from 1971 to 2000. This 

volume represents the total amount of freshwater impinging on the watershed, which then 

evaporates, percolates into the soil, or runs off into the estuary. As a first-order simplification, 

we assume that evaporation and percolation are similar among PNW watersheds, so that a 

similar fraction of the rainfall ultimately flows into each of the estuaries. Thus, this total volume

of precipitation is a relative measure of the average annual freshwater flow into the estuary. 

Once in the estuary, this freshwater inflow mixes with ocean water transported into the estuary 

with the extent of mixing dependent, in part, upon estuary volume. Therefore, the next step is to 

normalize the total annual precipitation (m3

 of freshwater per year) by the total volume of the

estuary (m3). This metric has units of year-1 and is referred to as the annual “volume-normalized 

freshwater inflow”. Higher values of this metric indicate a more freshwater (river) dominated 

system. Among the 17 PNW estuaries where bathymetry is available, the volume-normalized 

freshwater inflow varies from 4 year-1 in the Humboldt Estuary to 2501 year-1 in the Rogue River 

(Table 2-4). Because bathymetry is not available for most PNW estuaries, we developed an 

alternative approach of normalizing the total annual precipitation by estuarine area. This annual 

“area-normalized freshwater inflow” metric has units of m3

 of freshwater per m2 of estuary per 

year and is directly related to the volume normalized values (Figure 2-10). Since it is available 

for all the estuaries, we use the area-normalized metric as our approach to ranking estuaries by 

the extent of riverine influence. 

Advantages of these watershed-scale metrics of freshwater input compared to flow data from 

gauged stations are that they integrate precipitation over the entire watershed rather than 

measuring flow in a single or limited number of tributaries, they can be calculated for estuaries 

that are not gauged, and they can be linked to climate change scenarios to predict altered 

freshwater inflow under different precipitation regimes. Advantages over simply using the ratio 

of the drainage area to the estuary size are that the normalized freshwater inflow metrics capture 
47

Table 2-4. Freshwater sources into estuaries, normalized freshwater inflow, mouth width, and structure of mouth mouth and bar-built estuaries in the PNW. Bar-built estuaries are italicized. Estuaries with area-normalized fres

than 175 m3 m-2 year-1 are classified as tide dominated and estuaries with greater values are classified as river do

dominated estuaries are further subdivided into “highly river-dominated systems” (dark gray) with area-normaliz

values >400 m3 m-2 year-1 and “moderately river-dominated systems” (light gray) with values >175 m3 m-2 year-1 

year-1. Estuarine volume obtained from http://ian.umces.edu/neea/ with the exception of Salmon River which wa

Gonor (1982). The estimates for the normalized freshwater inflows for the Columbia River are based on the annversus the total volume of precipitation in the watershed and are not directly comparable to the values in the othe

NORMALIZED FRESHWATER 

INFLOW ESTUARY MAJOR FRESHWATER SOURCES AREA 

NORMALIZED 

(m3 m-2 year-1) 

VOLUME 

NORMALIZED 

(year-1)

MOUTH 

WIDTH 

(m)

ESTUARY M

Columbia Columbia River and multiple creeks 370 86 5038 

Jetties on both sidFreshwater inflow and not precipitati

Klamath Klamath River 18,082 2435 350 

Mouth constrained and south. Dunes width. 

Rogue Rogue river 6,537 2501 280 Jetties on both sid

may have migrate

Quinault Quinault River 6,286 38 Bluff constrains m

may constrict mou

Quillayute Quillayute River and 2 subsidiary creeks 5,220 250 Jetties on both sid

direct estuary nort

Chetco Chetco River 3,771 78 Jetties on both sid

Hoh Hoh River 3,468 28 

Mouth constrained and south. Dunes width. 

Queets Queets River 2,796 70 

Dunes may constrdendritic channels estuary northward 

Smith (CA) Smith River and 1 subsidiary creek 2,159 110 Mouth constrained 

Dunes direct estua
48

NORMALIZED FRESHWATER 

INFLOW ESTUARY MAJOR FRESHWATER SOURCES AREA 

NORMALIZED 

(m

3 m-2 year-1) 

VOLUME 

NORMALIZED 

(year-1

)

MOUTH 

WIDTH 

(m)

ESTUARY M

Mad Mad River 1,779 137 

Dunes direct estuaand form a “lagoocurrent mouth. Mclose . 

Eel Eel River and several subsidiary creeks 997 524 150 Forms dendritic ch

Dunes direct estua

Coquille Coquille River 695 143 175 Jetties on both sid

Sooes Sooes River 539 50 

Dunes direct estuaMouth constrained south. 

Nehalem Nehalem River 499 293 180 Jetties on both sid

direct estuary nort

Umpqua Umpqua River and Smith River 484 219 425 Jetties on both sid

Nestucca Nestucca River and Little Nestucca River 420 110 Dunes direct estua

Mouth constrained 

Necanicum Necanicum River, Neawanna Creek, and several subsidiary creeks 387 60 Dunes direct estua

southward from m

Copalis Copalis River and Cedar Creek 318 20 Dunes direct estua

Siletz Siletz River, Schooner Creek, and 3 subsidiary creeks 284 137 100 Dunes direct estua

Dunes may constrconstrained by lan

Siuslaw Siuslaw River and North Fork Siuslaw River 227 125 225 Jetties on both sid

direct estuary sout

Alsea Alsea River and several creeks including Lint and Drift Creeks 211 141 140 Mouth constrained 

Salmon Salmon River and several subsidiary creeks 177 392 40 Mouth constrained 

Dunes direct estua

Tillamook Kilchis, Wilson, Tillamook, Trask, and Miami Rivers 116 62 360 

Jetties on both siddirect estuary soutDunes result in acflow from five riv
49

NORMALIZED FRESHWATER 

INFLOW ESTUARY MAJOR FRESHWATER SOURCES AREA 

NORMALIZED 

(m

3 m-2 year-1) 

VOLUME 

NORMALIZED 

(year-1

)

MOUTH 

WIDTH 

(m)

ESTUARY M

Waatch Waatch River 101 32 Mouth constrained 

Dunes may constr

Yaquina Yaquina River and several creeks draining into sloughs 63 42 290 Jetties on both sid

Grays 

Chehalis River and Elk, Johns, and 

Hoquiam Rivers and several 

subsidiary creeks 

58 32 2750 Jetties on both sid

Coos 

Coos River and several creeks 

discharging into sloughs including 

South and Isthmus Slough, and 

Catching and Palouse Creek 

53 14 620 

Jetties on both siddirect main stem omouth. 

Sand Lake Sand Creek and Jewell Creek and 2 subsidiary creeks. Groundwater? 28 135 Dunes form mouth

accumulation behioccasionally close 

Willapa 

Bear, Bone, Cedar, Naselle, Nemah, 

Niawiakum, North, Palix, and 

Willapa rivers 

17 6 9165 

Mouth constrained Dunes direct estuaDunes result in acflow from rivers. 

Humboldt 

Freshwater Creek and Elk River and 

to lesser extent Jacoby Creek and 

Salmon Creek 

12 4 842 

Jetties on both sidruns behind dunes southward. Dunefreshwater flow fr

Netarts 13 minor creeks 11 8 114 Dunes direct estua
50

101102103104

101

102

103

Freshwater Inflow Normalized 

by Estuarine Volume (year-1

)

Freshwater Inflow Normalized by Estuarine Area (m3 m

-2

 year

-1)

log FWvolume = 0.90 * log(FWArea) - 0.10

r

2

 = 0.91 

Figure 2-10. Relationship between freshwater inflow normalized by estuarine area and 

normalized by estuarine volume. 

regional differences in rainfall, monthly values can be generated to evaluate seasonal changes in 

potential flushing, and, again, the metric can be linked to climate change scenarios. 

The area-normalized freshwater inflow was calculated for all the drowned river mouth estuaries 

and bar-built estuaries (Table 2-4) except for the Columbia Estuary. Total precipitation was not 

available for the entire Columbia Basin, though it was possible to generate a rough estimate from 

the Columbia River’s total annual discharge of approximately 198,000,000 acre-feet of water 

(http://vulcan.wr.usgs.gov/Volcanoes/Washington/ColumbiaRiver/description_columbia_river.ht

ml). This annual river discharge is equivalent to a normalized freshwater inflow of 

approximately 370 m3 per m

2

 per year. This estimate is a substantial underestimate compared to 

the values for other estuaries since it does not include any water lost to evaporation or 

percolation, which would be substantial over the entire Columbia Basin. While this value is not 

directly comparable to the other values, it indicates that the Columbia Estuary has a high river 

input, which is consistent with the tidal riverine segment constituting over a third of the entire 

estuarine area (see Table 2-2). 

Area-normalized freshwater inflow values vary over a thousand-fold, from about 11 in Netarts 

Bay to 18,000 m3 m

-2

 year

-1 in the Klamath. The metric successfully separates estuaries 

generally considered river-dominated systems (e.g., Klamath and Rogue) from those considered 

tide-dominated systems (e.g., Yaquina) suggesting that it can be used to classify less well studied 

systems. Additionally, spatial and seasonal patterns in salinity variations in these estuaries 

appear to be related to normalized freshwater inflow. Figure 2-11 shows salinity versus distance 

from the mouth for eight estuaries where both dry and wet season salinities are available. These 

eight estuaries range in area-normalized freshwater inflow from 11 (Netarts) to 695 m3 m

-2

 year

-1

 
51

Figure 2-11. Salinity versus distance from the mouth for the wet and dry seasons for estuaries with area-normaliranging from about 11 to 695 m3 m-2 year-1. Salinity variations are provided for bar-built, tide-dominated, moder

dominated, and highly river-dominated systems. The symbols indicate the median values and the error bars repr

percentiles. Salinity data are from the Oregon Department of Environmental Quality (http://deq12.deq.state.or.u

panel e. Data for panel e are from short-term deployments of sondes described in Chapter 4 Note that the Salmolargest volume-normalized freshwater inflow, reflecting its shallowness.
52

(Coquille). Systems with low freshwater inflow have less salinity variation along the main axis 

of the estuary, salt penetrates further into these systems, and they have less seasonal variations in 

salinity than river-dominated systems. For the two estuaries with the highest values (Umpqua 

and Coquille), there is essentially a linear decline in dry season salinity with distance from the 

mouth. Finally, there is a positive relationship between area-normalized freshwater inflow 

values and variation in salinity over a tidal cycle at the mouth of the seven target estuaries 

(Figure 4-11). Thus, it appears that the area-normalized freshwater inflow metric captures key 

aspects of tide versus river domination as measured by salinity. 

The next question becomes what value to use to separate tidal from riverine systems. A 

reasonably distinct break in values occurs between the Salmon and Tillamook estuaries, with the 

Tillamook value about two-thirds of the Salmon value (Table 2-4). This break is also associated 

with a difference in wet and dry season salinity variations (Figure 2-11e). Based on this pattern, 

we propose a value of >175 m3 m

-2

 year

-1 as a preliminary threshold for defining riverdominated estuaries. There is also a moderate break in values between the Nestucca and 

Necanicum (Table 2-4), which is also reflected in the salinity patterns in the Salmon and Alsea 

versus the Umpqua and Coquille (Figure 2-11). This pattern in salinity variation suggests a 

subdivision of river-dominated systems into “highly river-dominated” systems with inflow 

values greater than approximately 400 m3 m

-2and “moderately river-dominated” systems with 

inflow values between 175 m3 m

-2 and 400 m3

 m

-2

 year

-1

. 

This approach classifies 22 of the 28 drowned river mouth estuaries as river-dominated and 6 as 

tide-dominated (Table 2-4). Of the river-dominated estuaries, 16 are classified as highly riverdominated and 6 as moderately river-dominated. In terms of geomorphology, the riverdominated systems, and the highly river-dominated systems in particular, tend to have fewer 

freshwater inputs compared to the tide-dominated systems which tend to have smaller freshwater 

inputs distributed across several tributaries. Another pattern is that all of the small (<5 km2) 

river mouth estuaries are classified as river dominated with the exception of the Waatch, while 

10 of the 14 estuaries <5 km2 are classified as highly river dominated. All three bar-built 

estuaries have very low normalized freshwater inflows, indicating that functionally they more 

resemble the tide-dominated systems. 

In terms of vulnerability, our initial hypothesis is that, all other factors being equal, estuaries 

with higher normalized freshwater inflow will be less susceptible to terrestrially derived nutrient 

enrichment because of higher flushing. The higher river flow in riverine systems may actually 

increase the total loading from the watershed, but the high flushing should reduce the ability of 

phytoplankton to maintain high populations. This suggestion is consistent with the trend towards 

a decrease in median dry season chlorophyll a with increasing volume-normalized freshwater 

inflow (Figure 4-6). The data from this set of estuaries suggest that summer chlorophyll a levels 

in the estuaries may be determined by flushing time. Evaluation of this proposed relationship 

between normalized river inflow and chlorophyll a concentrations will require additional water 

quality studies across a range of estuary types as well as developing more complete water quality 

models to explain differences among estuaries with similar riverine inputs. Tide-dominated 

systems have close coupling with the coastal ocean and as a result may experience high nutrient 

and chlorophyll a levels and low dissolved oxygen conditions as a result of intrusion of ocean 

water into the estuary. 
53

2.5 Classification of Estuaries by NWI Wetland Classes 

The next step in classifying these drowned river mouth and bar-built estuaries was to determine 

similarity among estuaries based on wetland distributions. Distribution of NWI wetland types 

serves as an integrated response to multiple drivers, including estuarine geomorphology, 

watershed size and type, freshwater inflow, oceanic exchange, salinity structure, and tidal 

exposure. Thus, patterns of NWI wetlands classes are potentially a better indicator of 

environmental similarity than a classification based on any of those environmental drivers 

individually. Classification by NWI classes also provides a mechanism to identify estuaries with 

similar wetland resources, which could be used in establishing management prioritizations or 

assist in designing specific mitigation actions. 

Utilizing the consolidated NWI codes (Table 2-1), the 31 estuaries were first clustered based on 

the actual areas of the wetland/habitat classes. This and the following cluster analyses are based 

on the NWI available in 2006 since there were only minor changes in these larger estuaries in the 

recent update. Since this analysis groups estuaries by habitat areas, classification by area will 

tend to group larger and smaller estuaries, and in terms of management is probably most useful 

in identifying groups of estuaries with similar extents of wetland resources. The 31 estuaries 

clustered into four significant classes using the default p value of 0.05 (Figure 2-12). As 

expected, the clusters broke out by estuarine size. One group (Cluster A) consisted of the three 

largest estuaries: Willapa, Grays Harbor, and the Columbia River. The next group (Cluster B) 

consisted of the next four largest estuaries: Humboldt, Coos, Umpqua, and Tillamook. Cluster C 

consisted of the 10 moderate-sized estuaries, ranging in size from 4.28 km2

 (Sand Lake) to 

19.96 km2 (Yaquina Estuary). The final group (Cluster D) consisted of the suite of smaller 

estuaries (0.56 km2

 to 3.1 km2). The effect of increasing the p value was to break out the 

Columbia as a separate group from Grays Harbor and Willapa (at p=0.10), but further increasing 

the significance values did not split out any additional groups (Figures 2-13a to 2-13d). 

A similar analysis was conducted using the relative proportion of the area of each of the 

consolidated NWI classes. Use of proportional areas removed the direct effect of estuary size 

and should better identify groups of estuaries that are functionally similar compared to the 

classifications by actual areas. Based on the relative proportions, the 31 estuaries grouped into 

three clusters at p=0.05 (Figure 2-14). One group (Cluster A) consisted of eight estuaries 

showing a wide range in size, from the Quinault (0.66 km2

) to the Columbia River (669 km2). 

Though varying greatly in size, members in this cluster consist of the eight estuaries with the 

largest normalized freshwater inflow (Table 2-4), assuming that the Columbia ranks among the 

top eight. This grouping indicates that there are characteristic wetland profiles in the most highly 

river-dominated systems. Another group (Cluster C) consisted of the Salmon and Waatch, which 

showed little similarity with the other groups. These two systems separated from the other 

estuaries largely due to their high proportion of upper marsh habitat (estuarine, irregularly 

flooded, rooted class of Table 2-1). 

The third group (Cluster B) consisted of 21 estuaries including both river- and tide-dominated 

river mouth estuaries as well as all three of the bar-built estuaries. Size varied widely in this 

group, from 0.56 km2

 (Sooes) to 390.9 km2 (Willapa). The estuaries making up this cluster are 

so diverse that it does not seem useful in grouping systems in terms of nutrient dynamics. 

However, increasing the significance levels helped to separate estuaries with similar attributes 
54

(Figures 2-15a to 2-15d). The 10% significance level separated Grays Harbor and Willapa Bay 

as a group (Figure 2-15a), two estuaries that share many similarities. At a significance level of 

40%, 12 clusters were identified (Figure 2-15c) while the 60% significance level identified 16 

clusters (Figure 2-15d). Using the criterion of combining estuaries with > 75% similarity results 

in 10 “functional” groups at the 40% significance level and 12 “functional” groups at the 60% 

significance level (Figure 2-15d). 

Choosing which of these classifications is the “best” is not a statistical question but rather 

depends upon the goals of the classification. Our interpretation is that the classification based on 

the 60% significance level along with combining estuaries with > 75% similarity best captures 

ecological similarity in terms of drivers related to wetland patterns while avoiding separation of 

very similar systems (Figure 2-15d). Compared to the 40% significance level, the classification 

based on the 60% significance level separated all the tide-dominated estuaries from the highly 

river-dominated systems, though there is still some mixing of the tide-dominated, moderately 

river-dominated, and bar-built estuaries. However, the practical limitation of this classification 

is that it results in 12 functional groups, which may constitute too fine a division for 

management. An alternative might be to use the classification based on the 10% significance 

level which consists of four groups (Figure 2-15a).
55

Figure 2-12. Cluster analysis of 31 PNW estuaries at the 5% significance level based on the area 

of the consolidated NWI habitats. Estuaries joined with red lines are not significantly different 

(p>0.05) based on SIMPROF analysis. Clusters with >75% similarity (horizontal line) are 

combined in the analysis. Columbia Grays Willapa Umpqua Tillamook Coos

Humboldt

Eel

Siuslaw

Yaquina

Alsea

Sand Lake

Netarts

Coquille

Nestucca

Nehalem

Siletz

Sooes

Copalis

Necanicum

Mad

Smith

Rogue

Klamath

Chetco

Quillayute River

Quinault

Hoh

Queets

Waatch

Salmon

Estuaries

100

80

60

40

20

0

Similarity

A 

B 

C 

D 
56

Figure 2-13a. Cluster analysis of 31 PNW estuaries at the 10% significance level based on the 

area of the consolidated NWI habitats. Estuaries joined with red lines are not significantly 

different (p>0.10) based on SIMPROF analysis. Clusters with >75% similarity (horizontal line) 

are combined in the analysis. 

Figure 2-13b. Cluster analysis of 31 PNW estuaries at the 20% significance level based on the 

area of the consolidated NWI habitats. Estuaries joined with red lines are not significantly 

different (p>0.20) based on SIMPROF analysis. Clusters with >75% similarity (horizontal line) 

are combined in the analysis. Columbia Grays Willapa

Umpqua

Tillamook

Coos

Humboldt

Eel

Siuslaw

Yaquina

Alsea

Sand Lake

Netarts

Coquille

Nestucca

Nehalem

Siletz

Sooes

Copalis

Necanicum

Mad

Smith

Rogue

Klamath

Chetco

Quillayute River

Quinault

Hoh

Queets

Waatch

Salmon

Estuaries

100

80

60

40

20

0

Similarity

A B C D

E

Columbia

Grays

Willapa

Umpqua

Tillamook

Coos

Humboldt

Eel

Siuslaw

Yaquina

Alsea

Sand Lake

Netarts

Coquille

Nestucca

Nehalem

Siletz

Sooes

Copalis

Necanicum

Mad

Smith

Rogue

Klamath

Chetco

Quillayute River

Quinault

Hoh

Queets

Waatch

Salmon

Estuaries

100

80

60

40

20

0

Similarity

A B C D

E
57

Figure 2-13c. Cluster analysis of 31 PNW estuaries at the 40% significance level based on the 

area of the consolidated NWI habitats. Estuaries joined with red lines are not significantly 

different (p>0.40) based on SIMPROF analysis. Clusters with >75% similarity (horizontal line) 

are combined in the analysis. 

Figure 2-13d. Cluster analysis of 31 PNW estuaries at the 60% significance level based on the 

area of the consolidated NWI habitats. Estuaries joined with red lines are not significantly 

different (p>0.60) based on SIMPROF analysis. Clusters with >75% similarity (horizontal line) 

are combined in the analysis. Columbia Grays Willapa

Umpqua

Tillamook

Coos

Humboldt

Eel

Siuslaw

Yaquina

Alsea

Sand Lake

Netarts

Coquille

Nestucca

Nehalem

Siletz

Sooes

Copalis

Necanicum

Mad

Smith

Rogue

Klamath

Chetco

Quillayute River

Quinault

Hoh

Queets

Waatch

Salmon

Estuaries

100

80

60

40

20

0

Similarity

A B C D

E

Columbia

Grays

Willapa

Umpqua

Tillamook

Coos

Humboldt

Eel

Siuslaw

Yaquina

Alsea

Sand Lake

Netarts

Coquille

Nestucca

Nehalem

Siletz

Sooes

Copalis

Necanicum

Mad

Smith

Rogue

Klamath

Chetco

Quillayute River

Quinault

Hoh

Queets

Waatch

Salmon

Estuaries

100

80

60

40

20

0

Similarity

A B C D

E
58

Figure 2-14. Cluster analysis of 31 PNW estuaries at the 5% significance level based on the 

relative proportion of the consolidated NWI habitats. Estuaries joined with red lines are not 

significantly different (p>0.05) based on SIMPROF analysis. Clusters with >75% similarity 

(horizontal line) are combined in the analysis. Columbia Quinault Rogue Quillayute River Klamath Chetco Hoh Queets Grays Willapa Coos Yaquina Alsea

Sooes

Umpqua

Nehalem

Coquille

Siuslaw

Smith

Mad

Copalis

Eel

Humboldt

Netarts

Tillamook

Nestucca

Necanicum

Siletz

Sand Lake

Waatch

Salmon

Estuaries

100

80

60

40

20

Similarity

A B 

C 
59

Figure 2-15a. Cluster analysis of 31 PNW estuaries at the 10% significance level based on the 

relative proportion of the consolidated NWI habitats. Estuaries joined with red lines are not 

significantly different (p>0.10) based on SIMPROF analysis. Clusters with >75% similarity 

(horizontal line) are combined in the analysis. 

Figure 2-15b. Cluster analysis of 31 PNW estuaries at the 20% significance level based on the 

relative proportion of the consolidated NWI habitats. Estuaries joined with red lines are not 

significantly different (p>0.20) based on SIMPROF analysis. Clusters with >75% similarity 

(horizontal line) are combined in the analysis. Columbia Quinault Rogue Quillayute River Klamath Chetco Hoh Queets Grays Willapa Coos Yaquina Alsea

Sooes

Umpqua

Nehalem

Coquille

Siuslaw

Smith

Mad

Copalis

Eel

Humboldt

Netarts

Tillamook

Nestucca

Necanicum

Siletz

Sand Lake

Waatch

Salmon

Estuaries

100

80

60

40

20

Similarity

A

B C

D

Columbia

Quinault

Rogue

Quillayute River

Klamath

Chetco

Hoh

Queets

Grays

Willapa

Coos

Yaquina

Alsea

Sooes

Umpqua

Nehalem

Coquille

Siuslaw

Smith

Mad

Copalis

Eel

Humboldt

Netarts

Tillamook

Nestucca

Necanicum

Siletz

Sand Lake

Waatch

Salmon

Estuaries

100

80

60

40

20

Similarity

A

B C

D
60

Figure 2-15c. Cluster analysis of 31 PNW estuaries at the 40% significance level based on the 

relative proportion of the consolidated NWI habitats. Estuaries joined with red lines are not 

significantly different (p>0.40) based on SIMPROF analysis. Clusters with >75% similarity 

(horizontal line) are combined in the analysis. 

Figure 2-15d. Cluster analysis of 31 PNW estuaries at the 60% significance level based on the 

relative proportion of the consolidated NWI habitats. Estuaries joined with red lines are not 

significantly different (p>0.60) based on SIMPROF analysis. Clusters with >75% similarity 

(horizontal line) are combined in the analysis. Columbia Quinault Rogue Quillayute River Klamath Chetco Hoh Queets Grays Willapa Coos Yaquina Alsea

Sooes

Umpqua

Nehalem

Coquille

Siuslaw

Smith

Mad

Copalis

Eel

Humboldt

Netarts

Tillamook

Nestucca

Necanicum

Siletz

Sand Lake

Waatch

Salmon

Estuaries

100

80

60

40

20

Similarity

A B C

D

E

F

G

H I J

K L

M

N O

P

Columbia

Quinault

Rogue

Quillayute River

Klamath

Chetco

Hoh

Queets

Grays

Willapa

Coos

Yaquina

Alsea

Sooes

Umpqua

Nehalem

Coquille

Siuslaw

Smith

Mad

Copalis

Eel

Humboldt

Netarts

Tillamook

Nestucca

Necanicum

Siletz

Sand Lake

Waatch

Salmon

Estuaries

100

80

60

40

20

Similarity

A

B C

E

F

G H

I

J K

L

D
61

This classification separated out the eight highly riverine systems, Willapa and Grays Harbor, 

and the Salmon and Waatch. The limitation is that the fourth group consists of 19 estuaries 

ranging from bar-built to highly riverine dominated which appear to have different nutrient 

dynamics. One of the joint scientific and management challenges then becomes determining 

“how similar is similar enough”. 

2.6 Watershed Classification

Grouping estuaries by NWI classes identified suites of estuaries with similar patterns of wetland 

habitats and presumably similar vulnerabilities to nutrient enrichment. A different question is 

how the watersheds of the 31 estuaries group together. Within the same climatic regime, 

estuaries with similar land cover patterns in the watersheds presumably have generally similar 

nutrient loadings. To evaluate similarity in land cover patterns, the 31 watersheds were clustered 

using the land cover classes from the 2001 NOAA dataset. The NOAA coverage for the 

Columbia extended to approximately the Bonneville Dam, which captures the watershed 

adjacent to the Lower Columbia, representing about 2.5% of the entire Columbian Basin. Thus, 

the results for the Columbia watershed need to be interpreted in light of the fact that only a small 

portion of the entire land mass draining into the Columbia River was captured. The NOAA 

coverage for the Klamath was truncated at the northeastern segment, but as mentioned this area 

was filled in by using the 2001 NLCD data. A complexity in the PNW in that the presence of 

nitrogen-fixing alder can be an important component of nitrogen dynamics in these watersheds 

(see Sections 3.7 and 4.2). NOAA’s “deciduous forest” and “mixed forest” classes capture alder 

but do not separate them out from other deciduous trees. This level of detail should be sufficient 

to capture general similarities among watersheds, though differences in alder coverage may 

result in somewhat different nitrogen loadings among otherwise similar watersheds. 

2.6.1 Watershed Classes

The analysis based on the areas of the land cover classes resulted in seven clusters at a 

significance level of 5% (Figure 2-16). As expected, there was a tendency for larger watersheds 

to cluster together. The five estuaries with the largest watersheds – the highly riverine 

dominated Columbia, Klamath, Rogue, Umpqua, and Eel – all formed one group (Cluster A). 

Similarly, the three estuaries with the smallest watersheds (<100 km2) grouped together in 

Cluster G while the estuaries with the next smallest watersheds (105 km2 to 216 km2) formed a 

related cluster (Cluster F). Most of the estuaries with moderate-sized watersheds grouped in 

Clusters B, C, and D, which had a relatively high similarity (>55%) among the three groups. 

The Humboldt formed a unique cluster (Cluster E) that had moderate similarity (<50%) to the 

other moderate-sized watersheds. Using different significance levels in the cluster analysis did 

not substantially change the classifications (Figure 2-17a to 2-17d), with the major changes being 

the separation of the Eel Estuary from the functional group consisting of the other estuaries with 

large watersheds (A-D in Figure 2-17d) and splitting the tide-dominated Waatch from the barbuilt Sand Lake and Netarts watersheds (Figure 2-17c).
62

Figure 2-16. Cluster analysis of 31 PNW estuaries at the 5% significance level based on the 

areas of the land cover classes in the NOAA 2001 watershed data. Estuaries joined with red 

lines are not significantly different (p>0.05) based on SIMPROF analysis. Clusters with >75% 

similarity (horizontal line) are combined in the analysis. Columbia Umpqua Rogue Klamath Eel Grays Willapa Quillayute Smith (CA) Coquille Nehalem Siuslaw Yaquina Nestucca Siletz Tillamook Alsea

Coos

Mad

Queets

Quinalt

Hoh

Chetco

Humboldt

Sooes

Copalis

Necanicum

Salmon

Sand Lake

Netarts

Waatch

Estuaries

100

80

60

40

20

0

Similarity

A 

B C 

D 

E 

F G 
63

Figure 2-17a. Cluster analysis of 31 PNW estuaries at the 10% significance level based on the 

areas of the land cover classes in the NOAA 2001 watershed data. Estuaries joined with red 

lines are not significantly different (p>0.10) based on SIMPROF analysis. Clusters with >75% 

similarity (horizontal line) are combined in the analysis. 

Figure 2-17b. Cluster analysis of 31 PNW estuaries at the 20% significance level based on the 

areas of the land cover classes in the NOAA 2001 watershed data. Estuaries joined with red 

lines are not significantly different (p>0.20) based on SIMPROF analysis. Clusters with >75% 

similarity (horizontal line) are combined in the analysis. Columbia Umpqua Rogue Klamath Eel Grays Willapa Quillayute Smith (CA) Coquille Nehalem Siuslaw Yaquina Nestucca Siletz Tillamook Alsea Coos Mad

Queets

Quinalt

Hoh

Chetco

Humboldt

Sooes

Copalis

Necanicum

Salmon

Sand Lake

Netarts

Waatch

Estuaries

100

80

60

40

20

0

Similarity

A

B C

D

E

F G

Columbia

Umpqua

Rogue

Klamath

Eel

Grays

Willapa

Quillayute

Smith (CA)

Coquille

Nehalem

Siuslaw

Yaquina

Nestucca

Siletz

Tillamook

Alsea

Coos

Mad

Queets

Quinalt

Hoh

Chetco

Humboldt

Sooes

Copalis

Necanicum

Salmon

Sand Lake

Netarts

Waatch

Estuaries

100

80

60

40

20

0

Similarity

A

B C

D

E

F G
64

Figure 2-17c. Cluster analysis of 31 PNW estuaries at the 40% significance level based on the 

areas of the land cover classes in the NOAA 2001 watershed data. Estuaries joined with red 

lines are not significantly different (p>0.40) based on SIMPROF analysis. Clusters with >75% 

similarity (horizontal line) are combined in the analysis. 

Figure 2-17d. Cluster analysis of 31 PNW estuaries at the 60% significance level based on the 

areas of the land cover classes in the NOAA 2001 watershed data. Estuaries joined with red 

lines are not significantly different (p>0.60) based on SIMPROF analysis. Clusters with >75% 

similarity (horizontal line) are combined in the analysis. Columbia Umpqua Rogue Klamath Eel Grays Willapa Quillayute Smith (CA) Coquille Nehalem Siuslaw Yaquina Nestucca Siletz Tillamook Alsea Coos Mad

Queets

Quinalt

Hoh

Chetco

Humboldt

Sooes

Copalis

Necanicum

Salmon

Sand Lake

Netarts

Waatch

Estuaries

100

80

60

40

20

0

Similarity

A

B C

D

E

F G

H

I

J

K L

Columbia

Umpqua

Rogue

Klamath

Eel

Grays

Willapa

Quillayute

Smith (CA)

Coquille

Nehalem

Siuslaw

Yaquina

Nestucca

Siletz

Tillamook

Alsea

Coos

Mad

Queets

Quinalt

Hoh

Chetco

Humboldt

Sooes

Copalis

Necanicum

Salmon

Sand Lake

Netarts

Waatch

Estuaries

100

80

60

40

20

0

Similarity

A

B C

D

E

F G

H

I

J

K L
65

The clustering based on relative proportion of the areas of the land cover classes will tend to 

group estuaries associated with structurally similar watersheds. For example, clustering based on 

relative proportions of land use classes could group highly urbanized watersheds. In the PNW, 

there was a high degree of similarity (>70%) among all the watersheds (Figure 2-18). To a large 

extent, this similarity reflects the high percentage of the evergreen land cover class, which 

ranged from 36% to 80% in the watersheds. The mixed forest and scrub/shrub classes were also 

common across the watersheds, contributing 7% to 44% of the area. The similarity also reflects 

the relatively low extent of agriculture and urbanization in these PNW watersheds. Clustering at 

the 5% significance level resulted in nine groups (Figure 2-18) though joining clusters with 

>75% similarity reduced this to four functional groups. The first functional group (Clusters AD) largely consisted of watersheds associated with river-dominated estuaries, with the notable 

exceptions of Grays Harbor and Willapa Estuary. Both watershed and estuary size within this 

group varied almost 700-fold. The second functional group (Clusters E and F) consisted of the 

Columbia, Eel, and Humboldt estuaries. The third functional group (Clusters G and H) was a 

mix of estuarine types with moderate to large watersheds. The last group consisted of Netarts 

Bay, a bar-built estuary, which formed a unique cluster (Cluster I). Netarts separated from the 

other estuaries largely due to the high percentage of unconsolidated shore, 13.8% compared to 

<1% in most other watersheds, but resembled other watersheds in the high proportions of 

evergreen and mixed forest land classes. 

Increasing the significance level further divides the watersheds, with 11 groups identified at the 

40% significance level (Figure 2-19c) and 13 at the 60% significance level (Figure 2-19d). 

These finer divisions appeared to better capture similar watersheds, such as splitting the Grays 

Harbor and Willapa watersheds from the Copalis. However, all of these divisions occurred at 

similarities >75% so they did not increase the number of functional groups from the four 

identified at the 5% significance level. Until future research shows the need to more finely 

divide watersheds based on the relative proportions of land cover classes, we suggest that the 

four functional groups identified at the 5% significance level (Figure 2-18) are sufficient for an 

initial analysis of estuarine vulnerability based on watershed structure. 

2.7 Crosswalk of Wetland and Watershed Classifications

To further identify functionally similar estuaries, we conducted a matrix match (“crosswalk”) of 

the classifications by wetlands and watersheds. Estuaries were identified that overlapped both by 

clustering on wetland and land cover areas (Tables 2-5 and 2-6) and by clustering on relative 

proportions of wetlands and land cover (Tables 2-7 and 2-8), with groups with >75% similarity 

joined into functional groups. The basic assumption of the crosswalks is that estuaries grouped 

by both wetland and watershed characteristics share similar environmental conditions. 

Specifically, estuaries grouped by area share similar extents of wetland resources and land cover 

classes. These groups are most relevant in developing management approaches that scale to 

area, such as calculating total watershed loadings or strategies to protect the greatest extent of a 

wetland class. In comparison, grouping by relative proportions more closely captures functional 

attributes, and is probably the better approach to developing management strategies related to 

nutrient vulnerability at a system level. Crosswalks were conducted based on the clustering 

using the 5% and the 60% significance levels. Crosswalks based on the 5% significance level 

are at a coarser resolution and will tend to have fewer but more variable co-clustered estuaries 

compared to crosswalks based on the 60% significance level. 
66

Figure 2-18. Cluster analysis of 31 PNW estuaries at the 5% significance level based on the 

relative proportions of the land cover classes in the NOAA 2001 watershed data. Estuaries 

joined with red lines are not significantly different (p>0.05) based on SIMPROF analysis. 

Clusters with >75% similarity (horizontal line) are combined in the analysis. Grays Willapa Copalis Umpqua Rogue Chetco Waatch Mad Smith (CA) Klamath Queets Sooes Quillayute Hoh Quinalt Columbia Eel Humboldt Coos Coquille Necanicum Sand Lake Alsea Siuslaw

Siletz

Nehalem

Salmon

Yaquina

Tillamook

Nestucca

Netarts

Estuaries

100

95

90

85

80

75

70

Similarity

A 

B 

C D 

E F 

G H 

I 
67

Figure 2-19a. Cluster analysis of 31 PNW estuaries at the 10% significance level based on the 

relative proportions of the land cover classes in the NOAA 2001 watershed data. Estuaries 

joined with red lines are not significantly different (p>0.10) based on SIMPROF analysis. 

Clusters with >75% similarity (horizontal line) are combined in the analysis. 

Figure 2-19b. Cluster analysis of 31 PNW estuaries at the 20% significance level based on the 

relative proportions of the land cover classes in the NOAA 2001 watershed data. Estuaries 

joined with red lines are not significantly different (p>0.20) based on SIMPROF analysis. 

Clusters with >75% similarity (horizontal line) are combined in the analysis. Grays Willapa Copalis Umpqua Rogue Waatch Chetco Mad Smith (CA) Klamath Queets Sooes Quillayute Hoh Quinalt Columbia Eel Humboldt Coos Coquille Necanicum Sand Lake Alsea Siuslaw Siletz Nehalem Salmon Yaquina Tillamook Nestucca

Netarts

Estuaries

100

95

90

85

80

75

70

Similarity

A

B

C D

E F

G H

I

Grays

WillapaCopalis

Umpqua

Rogue

Waatch

Chetco

Mad

Smith (CA)

Klamath

Queets

Sooes

Quillayute

Hoh

Quinalt

Columbia

Eel

Humboldt

Coos

Coquille

Necanicum

Sand Lake

Alsea

Siuslaw

Siletz

Nehalem

Salmon

Yaquina

Tillamook

Nestucca

Netarts

Estuaries

100

95

90

85

80

75

70

Similarity

A

B

C D

E F

G H

I
68

Figure 2-19c. Cluster analysis of 31 PNW estuaries at the 40% significance level based on the 

relative proportions of the land cover classes in the NOAA 2001 watershed data. Estuaries 

joined with red lines are not significantly different (p>0.40) based on SIMPROF analysis. 

Clusters with >75% similarity (horizontal line) are combined in the analysis. 

Figure 2-19d. Cluster analysis of 31 PNW estuaries at the 60% significance level based on the 

relative proportions of the land cover classes in the NOAA 2001 watershed data. Estuaries 

joined with red lines are not significantly different (p>0.60) based on SIMPROF analysis. 

Clusters with >75% similarity (horizontal line) are combined in the analysis. Grays Willapa Copalis Umpqua Rogue Waatch Chetco Mad Smith (CA) Klamath Queets Sooes Quillayute Hoh Quinalt Columbia Eel Humboldt Coos Coquille Necanicum Sand Lake Alsea Siuslaw Siletz Nehalem Salmon Yaquina Tillamook Nestucca Netarts

Estuaries

100

95

90

85

80

75

70

Similarity

A B

C

D E

F G

H

I J

K

Grays

WillapaCopalis

Umpqua

Rogue

Waatch

Chetco

Mad

Smith (CA)

Klamath

Queets

Sooes

Quillayute

Hoh

Quinalt

Columbia

Eel

Humboldt

Coos

Coquille

Necanicum

Sand Lake

Alsea

Siuslaw

Siletz

Nehalem

Salmon

Yaquina

Tillamook

Nestucca

Netarts

Estuaries

100

95

90

85

80

75

70

Similarity

A B

C

D E

F G

H

J K

I

L

M
69

The columns in Tables 2-5 to 2-8 are the clusters based on the watershed analysis, while the rows 

are the clusters based on wetland analysis. Estuaries listed within the same cell are systems that 

were grouped together in both the watershed and wetland analyses. For example, Coos and 

Tillamook were clustered together in both the wetland and land cover analyses on areas (Table 2-

5). Clusters are ordered in the tables by the mean size of the watersheds or estuaries within the 

cluster, though proximity in the table does not necessarily connote greater similarity and Figures 

2-12 to 2-19 should be consulted for the actual similarities. To avoid confusion, the clusters 

based on wetlands were designated as A’, B’, etc. 

In the crosswalk based on areas at the 5% significance level (Table 2-5), the five highly riverdominated estuaries with large watersheds (column A) were dispersed across the four wetland 

clusters (rows A’ to D’), indicating that there are large watersheds with large estuaries (A-A’) and 

large watersheds with small estuaries (A-D’). The opposite pattern was not observed, and there 

were no small watersheds with large estuaries (i.e., no estuaries falling into F-A’, G-A’, F-B’, or 

G-B’). Another pattern is that the remaining river-dominated estuaries fell into relatively few 

cells, and were separated from the tide-dominated estuaries with the exception of the Yaquina 

occurring in cell D-C’. Additionally, nine of the highly river-dominated estuaries fell into three 

cells representing moderate to small estuaries with moderate-sized watersheds (C-C’, C-D’, and 

D-D’). Four of the other tide-dominated estuaries formed two pairs (Grays Harbor & Willapa 

and Coos & Tillamook), while the Waatch was not linked with any of the other estuaries. The 

pattern based on areas at the 60% significance level (Table 2-6) was the same with the exception 

that the Eel River and Waatch were broken into separate watershed clusters (columns). 

One obvious pattern in the crosswalk by relative proportions of wetland and land cover classes 

at the 5% significance level (Table 2-7) is the grouping of seven of the highly river-dominated 

systems in a single cell (A-D – A’). Excluding the Columbia River, these seven estuaries had the 

highest normalized freshwater inflow values (Table 2-4). Thus, estuaries with the highest 

freshwater inflow appear to have wetland and watershed patterns that differ from other riverdominated or tide-dominated systems. With the exception of the Salmon River, the remaining 

moderate-sized river-dominated estuaries were grouped into two cells (A-D – B’ and G-H – B’), 

though they were mixed with tide-dominated estuaries. At the 60% significance level (Table 2-

8), the four estuaries with the highest normalized freshwater inflow values (exclusive of the 

Columbia) still grouped together (A-E – A’-C’) though the three estuaries with next highest 

freshwater inputs were separated into two cells (A-E – E’ and A-E – D’) with lower average 

estuary size. At this higher significance level, the tide-dominated Grays Harbor and Willapa 

estuaries were grouped together, as were the Coos and Yaquina estuaries. Assuming that 

grouping by relative proportions captures functional aspects of estuaries and watersheds, we 

predict that the estuaries grouped at the 60% significance level will have similar nutrient 

dynamics. The practical difficulty in using the crosswalk based on the 60% significance level is 

the large number of groups, which is reduced by more than half with the crosswalk based on the 

5% significance level. It is not clear whether these groups show sufficient similarity for them to 

be managed in a similar fashion, though research to help resolve this question is outlined in 

Section 2.9. 
70

Table 2-5. Crosswalk of the cluster analyses by the area of NWI consolidated wetland classes (rows) and the are

cover classes (columns) at the 5% significance level. Letters refer to the cluster groups in Figures 2-12 for wetla

watersheds. Mean watershed area and mean estuary area within the clusters are given in parentheses, with the ar

watershed for the Lower Columbia to the Bonneville Dam. The clusters are ordered by mean watershed or estua

in the table does not necessarily imply high similarity. The geomorphology is indicated by color with tide-domin

moderately river-dominated estuaries in green, highly river-dominated estuaries in red, and bar-built estuaries in 

WATERSHED LAND USE CLUSTERS 

Clusters on Area / (Mean Watershed Area For Group)

5% Significance A 

(15161 km

2) 

B 

(3197 km

2) 

C 

(2009 km

2) 

D 

(1087 km

2) 

E 

(571 km

2) 

(

A’ 

(441 km

2) Columbia Grays Willapa

B’ 

(49 km

2) Umpqua Coos Tillamook Humboldt 

C’ 

(11.1 km

2) Eel 

Coquille 

Nehalem 

Siuslaw 

Alsea 

Nestucca 

Siletz 

Yaquina 

NWI WETLAND CLUSTER 

(Mean Estuary Area for Group) D’ (1.5 km

2) 

Klamath 

Rogue 

Quillayute 

Smith

Chetco 

Hoh 

Mad 

Queets 

Quinault

Ne
71

Table 2-6. Crosswalk of the cluster analyses by the area of NWI consolidated wetland classes (rows) and the are

cover classes (columns) at the 60% significance level. Letters refer to the cluster groups in Figures 2-13d for wewatersheds. Mean watershed area and mean estuary area within the clusters are given in parentheses, with the ar

watershed for the Lower Columbia to the Bonneville Dam. The clusters are ordered by mean watershed or estua

in the table does not necessarily imply high similarity. The geomorphology is indicated by color with tide-domin

moderately river-dominated estuaries in green, highly river-dominated estuaries in red, and bar-built estuaries in 

WATERSHED LAND COVER CLUSTERS 

(Mean Watershed Area For Group) Clusters on Area / 

60% Significance A-D (16567 

km

2) 

E 

(9536 

km

2) 

F 

(3197 

km

2) 

G 

(2009 

km

2) 

H 

(1087 

km

2) 

I 

(571 km

2) 

J 

(156 km

2) 

A’ 

(669 km

2

) Columbia

B’ 

(327 km

2) 

Grays 

Willapa

C’ 

(49 km

2) Umpqua Coos Tillamook Humboldt

D’ 

(11.1 km

2) Eel

Coquille 

Nehalem 

Siuslaw 

Alsea 

Nestucca 

Siletz 

Yaquina

NWI WETLAND CLUSTERS 

(Mean Estuary Area for Group) E’ (1.5 km

2) 

Klamath 

Rogue

Quillayute

Smith

Chetco 

Hoh 

Mad 

Queets 

Quinault

Copalis 

Necanicum

Salmon 

Sooes 
72

Table 2-7. Crosswalk of the cluster analyses by the relative proportion of NWI consolidated wetland classes (rowproportion of land cover classes (columns) at the 5% significance level. Letters refer to the cluster groups in Fig

Clusters with high similarity (>75%) were combined for the analysis. Mean watershed area and mean estuary arare given in parentheses. The clusters are ordered by mean watershed or estuary size and proximity in the table dimply high similarity. The geomorphology is indicated by color with tide-dominated estuaries in blue, moderatel

estuaries in green, highly river-dominated estuaries in red, and bar-built estuaries in black. 

WATERSHED LAND COVER CLUSTERS 

Clusters on Relative Proportion / (Mean Watershed Area For Group) 

5% Significance E-F 

(8209 km

2) 

A-D 

(4482 km

2) 

G-H 

(1175 km

2) 

A’ 

(85 km

2) 

Columbia 

Chetco 

Hoh 

Klamath 

Queets 

Quillayute 

Quinault 

Rogue 

B’ 

(46 km

2

)

Eel 

Humboldt 

Copalis 

Grays 

Mad 

Smith 

Sooes 

Umpqua 

Willapa 

Alsea 

Coos 

Coquille 

Necanicum 

Nehalem 

Nestucca 

Sand Lake 

Siletz 

Siuslaw 

Tillamook 

Yaquina

NWI WETLAND CLUSTERS 

(Mean Estuary Area for Group) 

C’ 

(2.1 km

2

) Waatch Salmon 
73

Table 2-8. Crosswalk of the cluster analyses by the relative proportion of NWI consolidated wetland classes (rowproportion of land cover classes (columns) at the 60% significance level. Letters refer to the cluster groups in Fi19d. Clusters with high similarity (>75%) were combined for the analysis. Mean watershed and mean estuary ar

are given in parentheses. The clusters are ordered by mean watershed or estuary size and proximity in the table dimply high similarity. The geomorphology is indicated by color with tide-dominated estuaries in blue, moderate

estuaries in green, highly river-dominated estuaries in red, and bar-built estuaries in black. 

WATERSHED LAND COVER CLUSTERS 

Cluster on Relative Proportion / (Mean Watershed Area For Group)

60% Significance F-G 

(8209 km

2) 

A-E 

(4482 km

2) 

H-L 

(1175 km

2) 

F’ 

(327 km

2) Grays Willapa

A’-C’ 

(135 km

2

) Columbia 

Klamath 

Quinault 

Quillayute 

Rogue

G’ 

(29 km

2

)

Alsea 

Coos 

Yaquina

N’ 

(22 km

2

) Humboldt 

Necanicum 

Nestucca 

Siletz 

Tillamook 

K’ 

(16 km

2

) Siuslaw 

H’-J’ 

(13 km

2

)

Sooes 

Umpqua

Coquille 

Nehalem 

M’ 

(8.2 km

2

)

Eel Copalis 

O’ 

(4.3 km

2

) Sand Lake 

P’ 

(2.2 km

2

) Waatch Salmon 

L’ 

(1.8 km

2

)

Mad 

Smith 

E’ 

(1.1 km

2) 

Hoh 

Queets 

NWI WETLAND CLUSTERS 

(Mean Estuary Area for Group)

D’ 

(0.7 km

2

) Chetco 
74

2.8 Watershed Alterations and Population Patterns

The last type of landscape analysis was evaluation of the extent of watershed alteration using a 

suite of metrics indicative of anthropogenic “pressure” on the PNW estuaries. The detailed 

analysis is based on the 31 estuaries used in the multivariate analysis (Table 2-4), while the 

regional analysis is based on the 89 estuaries used in the original report (Lee et al., 2006) rather 

than the 103 estuaries from the NWI update. The 14 watersheds associated with the “new” 

estuaries (see Table 2-5) only constitute 0.2% of the total coastal watershed area so that their 

exclusion has only a minor effect on the mean values for the region. 

One set of indicators of anthropogenic pressure is the relative percentages of high development, 

low development, and cultivated classes from the NOAA land cover data. NOAA has a class for 

grasslands but does not separate out natural grasses from anthropogenic grasslands such as 

pasture. Not to bias the estimates, we combined the 1992 NLCD classes for pasture/hay and 

urban/recreational grasses as the fourth land cover metric of alteration. No attempt was made to 

evaluate the effects of logging, which can increase non-point runoff of sediments and nutrients 

(e.g., Likens et al., 1970). A potential indirect effect of logging may be an increase in red alder 

after disturbance which may increase nitrogen fluxes from watersheds (Sections 3.7 and 4.2). 

While this analysis does not capture the direct and indirect effects of logging, it does identify 

those watersheds experiencing the greatest anthropogenic impacts from development and 

agriculture. Another measure of alteration is population density from the 2000 census. Densities 

were calculated on a watershed basis, which often cut across city or county boundaries. One 

threshold relating population density to nutrient fluxes is the 386 people per km2 used in the EPA 

“Classification Framework for Coastal Systems” to identify watersheds with a high risk for 

nitrate inputs (U.S. EPA, 2004a). 

The last metric of alteration is the percent impervious surfaces. High percentages of impervious 

surfaces within a watershed can affect water quality both by increasing the volume and rate of 

surface runoff, which in turn can increase erosion as well as non-point loadings of sediment, 

contaminants, and nutrients (U.S. EPA, 1997). Adverse impacts are first observed in surface 

waters in watersheds with about 10% impervious surfaces and with major impacts occurring at 

25-30% (Arnold and Gibbons, 1996). In tidal creeks in South Carolina, physical/chemical 

alterations and changes in fecal coliforms were first detected at 10-20% impervious surfaces with 

impacts on the benthos, shrimp, and food webs detected at values of 20-30% (Holland et al., 

2004). NOAA’s “Spatial Wetland Assessment for Management and Planning” (SWAMP) model 

uses a threshold of impervious surfaces of <7.5% as the cutoff for “exceptional” environmental 

condition (Sutter, 2001). 

The values of these metrics for all 89 estuaries (Table 2-9) and for the 31 estuaries used in the 

multivariate analysis (Table 2-10) indicate relatively low levels of alterations at the watershed 

scale. The percent of high development land use only exceeded 2% in Crescent City Harbor 

while the percent of low development only exceeded 5% in three watersheds and 1% in twelve 

watersheds. Pasture and urban/recreational grasslands were more abundant than the other altered 

land use classes in several of the watersheds. Anthropogenic grasslands exceeded 10% in the 

Lake Earl and Humboldt watersheds and exceeded 5% in another five watersheds including the 
75

Table 2-9. The mean, median, and maximum values for each of the metrics of watershed alteration for the 89 co“% High Development”, “% Low Development”, and “% Cultivated” are the percentage of the watersheds in the

from the NOAA 2001 data. The “% Pasture & Urban Grasslands” is the combination of the pasture/hay and urba

grasses classes from the 1992 NLCD data. Population Density is from the 2000 census, while the “% Imperviou

calculated using the 30-m grids from NLCD (http://www.epa.gov/mrlc/nlcd-2001.html). The % impervious surf

estuary with the highest percent impervious surfaces generated from Attila (U.S. EPA, 2004c) are given in paren

% HIGH 

DEVELOPMENT

% LOW 

DEVELOPMENT

% 

CULTIVATED 

% PASTURE & 

URBAN 

GRASSLANDS

% 

IMPERVIOU

SURFACE

(values from 

Attila) 

Mean 0.13 0.91 0.12 1.19 1.16 (2.54) 

Median 0.01 0.18 0.00 0.04 0.59 (2.02) 

Maximum 4.98 15.76 1.82 11.1 12.72 (16.11) 

Watershed With 

Maximum Crescent City Loomis Lake Twomile Creek South Lake Earl Loomis Lak

(Loomis Lak
76

Table 2-10. The mean, median, and maximum values for each of the metrics of watershed alteration for the 31 c

used in the multivariate analysis. The “% High Development”, “% Low Development”, and “% Cultivated” are 

watersheds in these land cover classes from the NOAA 2001 data. The “% Pasture & Urban Grasslands” is the c

pasture/hay and urban/recreational grasses classes from the 1992 NLCD data. Population Density is from the 20“% Impervious Surfaces” was calculated using the 30-m grids from NLCD (http://www.epa.gov/mrlc/nlcd-2001

impervious surfaces value for the Columbia is based on the watershed up to the Bonneville Dam. The % imperv

and the estuary with the highest percent impervious surfaces generated from Attila (U.S. EPA, 2004c) are given i

reference. 

% HIGH 

DEVELOPMENT

% LOW 

DEVELOPMENT

% 

CULTIVATED 

% PASTURE & 

URBAN 

GRASSLANDS

% 

IMPERVIOU

SURFACES (values from 

Attila) 

Mean 0.13 0.56 0.17 1.96 0.83 (2.25) 

Median 0.03 0.28 0.00 1.34 0.57 (2.05) 

Maximum 1.19 3.71 1.29 10.43 3.61 (4.67) 

Watershed With 

Maximum Humboldt Humboldt Columbia Humboldt Columbia (Humboldt) 
77

Columbia. Population densities were low to moderate across all the watersheds, and no 

watershed exceeded the threshold of 386 people per km2. Only six estuaries had watersheds with 

a population density exceeding 100 people per km2

, with a maximum density of 289 people per 

km2 in the Crescent City Harbor watershed. 

The percent impervious surface integrates several different types of watershed alterations and the 

recently available 30-m grid data from the NLCD (http://www.epa.gov/mrlc/nlcd-2001.html) 

allows detailed analysis of this driver. In general, the percent impervious surface is low in the 

coastal PNW watersheds (Tables 2-9 and 2-10). With about 12% impervious surfaces, only the 

watersheds associated with Crescent City Harbor and Loomis Lake Creek exceeded the lower 

10% threshold value from Arnold and Gibbons (1996). As a marine harbor/cove, Crescent City 

Harbor is well flushed and this level of impervious surfaces is not likely to have a major effect 

on its water quality. We do not have information on extent of flushing in Loomis Lake Creek 

but Loomis Lake is classified as eutrophic based on total phosphorus concentrations (O’Neal et 

al., 2001). Thus, it is possible that Loomis Lake Creek may have relatively high nutrient 

concentrations during periods of restricted exchange with the ocean, though it is not clear what 

estuarine resources would be exposed to these elevated nutrient concentrations (see Table 2-3). 

No other watershed exceeded the NOAA SWAMP threshold of 7.5% for exceptional condition 

and only six other watersheds had impervious surfaces >3%. Among the 31 estuaries, the 

Columbia had the highest percent impervious surfaces at 3.6% but this is based on the 2001 

NLCD data that extends only up to the Bonneville Dam and includes the city of Portland. 

Presumably this percentage would decline if more of the Columbian Basin were included. 

To evaluate the similarity in alterations, the 31 watersheds were clustered using the six metrics of 

alteration. Because these variables are measured in different units, the values were normalized 

and Euclidean distance was used as the metric of similarity. Classification by watershed 

alterations resulted in four significant clusters (Figure 2-20). One group (Cluster A) that split off

at a high degree of dissimilarity consisted of the lower Columbia River and Humboldt Estuary. 

These two systems have the highest percentages of high and low development land classes, 

anthropogenic grassland land cover classes, the highest percent impervious surfaces, and the 

highest population densities (Table 2-10). The Necanicum (Cluster B) broke out at a moderate 

degree of dissimilarity from the other estuaries. Though not as altered as the Columbia and 

Humboldt, the Necanicum watershed has the third highest values for the percentages of high 

development and low development land cover classes, population density, and percent 

impervious surfaces. Another group (Cluster C) that split out at a moderate degree of 

dissimilarity consisted of four estuaries, the Klamath, Grays, Eel, and Rogue. These four 

estuaries appeared to break out based on their moderately high values of both cultivated and 

pasture/urban grassland land cover classes. The last group (Cluster D) consists of the remaining 

24 estuaries, which as a group had no discernable pattern of watershed alteration. Thus, the 

watersheds of the major PNW estuaries separate into four patterns of alteration, with the 

Columbia and the Humboldt the most altered due to higher proportions of development and 

agriculture.
78

Figure 2-20. Cluster analysis of the 31 PNW estuaries based on six metrics of watershed 

alteration. Similarity among clusters is measured as Euclidean distance. Estuaries joined with 

red lines are not significantly different (p>0.05) based on SIMPROF analysis. The horizontal 

lines indicate 25% dissimilarity of the maximum observed dissimilarity for reference. Columbia Humboldt Necanicum Grays Rogue Klamath Eel Nestucca Tillamook Umpqua Coquille Mad Netarts Salmon Sand Lake Yaquina Copalis Sooes Quinalt Hoh Queets Quillayute Chetco Smith (CA) Nehalem Siletz Willapa Alsea Siuslaw Waatch

Coos

Estuaries

0

1

2

3

4

5

6

7

8

9

10

Euclidean Distance

A 

B 

C D 
79

2.8.1 Flow-Path Analysis of Impervious Surfaces 

While the percent impervious surfaces for the entire watersheds are low, the spatial distribution 

of watershed alterations is likely to have an important effect on the extent of nutrient, pollutant, 

and sediment runoff. The 30-m grid cells from the NLCD allow a flow path analysis of the 

percent impervious surfaces as a function of the distance from the estuary or nearest river or 

stream flowing into the estuary. Because of its complexity, we have only conducted a flow path 

analysis for the Yaquina watershed which is presented here as a potential future direction. 

Figure 2-21 shows the percent impervious surfaces within six concentric zones around the 

estuary, rivers, and streams, increasing in distance from a 0-108 meter zone to the nearest water 

to a 0-31,130 meter zone that represents the entire watershed. The key observation is that the 

percent impervious surface is higher in the portion of the watershed closer to the water than in 

land more distant from the water’s edge. The maximum percent impervious surface of 3.78% 

occurs within a band 0-500 meters around the estuary, river, and streams, a value more than 4-

times higher than the watershed average of 0.89%. Similar analyses should be conducted in 

additional watersheds, but it is likely that this general pattern of higher impervious surfaces near 

the water occurs in many, if not most, PNW watersheds as a result of the concentration of tourist 

facilities, commercial and recreational fishing activities, and housing along bay and river fronts. 

0

0.5

1

1.5

2

2.5

3

3.5

4

0 -108 0 - 500 0 - 1000 0 - 5000 0 - 10000 0- 31130

Buffer from Nearest Water (m)

% Impervious Surfaces in Buffer

Figure 2-21. Percent impervious surfaces in the Yaquina watershed for six buffer zones of 

different distances from the shoreline of the Yaquina Bay or Yaquina River. The 0–31,130 m 

buffer represents the entire Yaquina watershed. 
80

2.9 Synthesis of Classification Approaches and Research Needs 

Five classification schemas are presented in this chapter – classifications based on 

geomorphology and ocean exchange (Section 2.4), wetland clustering (Section 2.5), watershed 

clustering (Section 2.6), crosswalks between the wetland and watershed clustering (Section 2.7), 

and groupings on watershed alterations (Section 2.8). Which is the “best” approach depends 

upon the goals of the user. Even with the specific goal of identifying estuaries with similar 

vulnerabilities to nutrient enrichment, it is not clear which schema is best, in part because no 

specific criteria have been developed as to what constitutes “similar enough” for management 

purposes. It is possible, however, to provide some general guidelines on how the various 

schemas could be used for management purposes as well as future research directions. 

Of the various schemas, geomorphology provides the most general approach to grouping 

estuaries with similar nutrient dynamics. Assuming the ideal case of “all other factors being 

equal”, potential differences in the extent of flushing based on ocean exchange and freshwater 

inflow suggest the following relative vulnerabilities (from most to least vulnerable) to equivalent 

increases from terrestrial runoff: 

lagoon ≥ blind estuary > tide-dominated river mouth > river-dominated river mouth ≥ coves 

This suggested ranking is for increased nutrient concentrations within the estuarine portion of the 

system and not for total loading, which may show a pattern more closely tied to the normalized 

freshwater inflow and presence of red alder in the watershed. Nor does this ranking account for 

differences in the types of resources potentially at risk across these different classes of estuaries. 

The tidally restricted coastal creeks are more complex than suggested by their small size and do 

not easily fit into this ranking because of the potential occurrence of localized pockets of low DO 

water resulting from periodic advection and trapping of ocean water (C. Brown, unpublished 

data). Nor do bar-built estuaries readily fit into this scheme. The three PNW bar-built estuaries 

do not share many similarities (Tables 2-7 and 2-8) and may function more like similar size tidedominated estuaries than as a separate class of estuaries per se. 

Individual clustering by wetland or land cover classes provides a more detailed breakout of 

estuaries than that provided by the geomorphologic classes. While this approach is useful in 

identifying similarities in the resources at risk (wetland clustering) or differences in potential 

loading (watershed clustering), we suggest that the crosswalks are more likely to identify 

functionally similar estuaries than clustering by either attribute alone. As discussed in 

Section 2.7, the crosswalks identified fine resolution separations using the 60% significance level 

(Tables 2-6 and 2-8) and coarser level separations using the 5% significance level (Tables 2-5 

and 2-7). One of the factors in resolving which of these schemas are sufficient for management 

is how similar estuaries are within the groups compared to among-group differences. If, for 

example, variation in a key attribute within a group exceeds the differences among groups, then 

the classification may not be sufficiently robust for certain management purposes. Therefore, we 

suggest one avenue of research should be to compare within- and among-group variations in 

nutrient concentrations and dynamics across sets of highly similar estuaries, using the water 

quality survey framework described in Chapter 4. Because nutrient dynamics are influenced by 

drivers related to both the areas of estuaries and watersheds and to the relative proportions of 
81

wetland or watershed classes, we propose that estuaries that co-clustered based on both the areas 

(Tables 2-5 and 2-6) and on relative proportions (Tables 2-7 and 2-8) should show the highest 

similarities. This is essentially a “crosswalk of the crosswalks” to identify the most similar 

estuaries. 

Based on the 60% significance levels, there are six pairs of estuaries that co-clustered on area 

and relative proportions: 1) Klamath and Rogue; 2) Grays and Willapa; 3) Coquille and 

Nehalem; 4) Alsea and Yaquina; 5) Hoh and Queets; and 6) Nestucca and Siletz. These pairs 

span a range of estuary types, including large to small estuaries as well as tide-dominated, 

moderately river-dominated, and highly river-dominated estuaries (Tables 2-2 and 2-4). 

Variation in nutrient dynamics within these pairs represents the minimum variation that can be 

expected from groupings based on this approach. Results from such comparisons should be 

assessed by environmental managers to determine whether the within-group variation relative to 

the among-group variation is acceptable in a regulatory context. If the levels of variation are not 

acceptable, then: 1) a different classification approach is required; 2) management expectations 

may need to be modified regarding the actual level of similarity among PNW estuaries or; 3) the 

desire to manage groups of “similar” estuaries in a common fashion may be unrealistic. 

If the relative variation within and among these six pairs of estuaries is acceptable, the next step 

would be to conduct additional comparisons among sets of grouped estuaries. In particular, the 

three large groups in the 5% significance crosswalk based on relative proportions (A-D – A’, AD – B’, and G-H – B’ in Table 2-7) should be evaluated. If the relative variation falls within 

acceptable bounds, this classification could be used as a framework for developing management 

strategies for the PNW, with the caveat that additional studies would be required for the estuaries 

falling outside of these groups. However, failure to find ecologically relevant differences among 

groups at this level of significance while finding them in the six paired estuaries suggests that a 

finer resolution classification will be needed. 
82

CHAPTER 3: 

ENVIRONMENTAL CHARACTERISTICS 

 OF THE SEVEN TARGET ESTUARIES 

Cheryl A. Brown and Henry Lee II 

3.0 Introduction 

The purpose of this chapter is to provide an overview of the environmental conditions within the 

Pacific Northwest (PNW) and of the seven target estuaries chosen for field surveys and aerial 

photography. This chapter is not meant to be a comprehensive review and the reader is referred 

to Emmett et al. (2000) for an overview of environmental conditions for Pacific Coast estuaries, 

Hickey and Banas (2003) for a review of the effects of oceanographic conditions on PNW 

estuaries, the Oregon Coastal Atlas (http://www.coastalatlas.net) for a description of Oregon 

estuaries, and the Coastal Landscape Analysis and Modelling Study (CLAMS; 

http://www.fsl.orst.edu/clams/) for detailed information on coastal watersheds in Oregon. 

3.1 Pacific Northwest Climate 

Though the PNW has a reputation for being “wet and rainy”, the PNW coast essentially has a 

wet and dry season (see Emmett et al., 2000). From approximately October through March, the 

PNW experiences frequent rainfall with a peak in December through January. From 

approximately April through September, rainfall substantially declines often with only marginal 

rainfall in July through September. In addition to this seasonality, there is a geographic pattern 

with greater rainfall in the more northern portions of the PNW. Average annual precipitation in 

northern coastal Washington can reach as much as 350 cm compared to 140 cm in the Humboldt 

watershed in northern California. In terms of temperature, the PNW has a Mediterranean 

climate, with mild summers and winters. The difference between winter and summer air 

temperatures is small, only about 5○

 C near the Humboldt Estuary (Emmett et al., 2000). 

Freezes and snowfall at the lower elevations are relatively rare, and with the exception of the 

Columbia and Klamath watersheds there is relatively little snow pack in most of the coastal 

watersheds. 

3.2 Coastal Upwelling in the Pacific Northwest 

Estuaries in the PNW are adjacent to the California Current System, which exhibits strong 

interannual, seasonal and event-scale variability (Hickey and Banas, 2003). In this region, 

seasonal wind-driven upwelling advects relatively cool, nutrient rich water to the surface. The 

upwelling season typically commences in April and continues through September (Kosro et al., 

2006). During this time period, upwelling-favorable winds from the north dominate. The 

upwelling conditions are interrupted by brief periods of downwelling-favorable conditions, 

which usually persist for several days. Previous studies have demonstrated that the oceanic 

inputs of nutrients and phytoplankton are important for estuaries adjacent to coastal upwelling 

regions, such as the west coast of the U.S. (e.g., de Angelis and Gordon, 1985; Roegner and 

Shanks, 2001; Roegner et al., 2002; Newton and Horner, 2003; Colbert and McManus, 2003; 

Brown and Ozretich, 2009). Hickey and Banas (2003) examined variations in temperature, 

salinity, and alongshore wind stress for three estuaries along the Oregon and Washington coast, 

spanning a distance of 400 km. They demonstrated that there was coherence in the fluctuations 

in temperature and salinity among these estuaries during the summer resulting from the large-
83

scale patterns in along-shelf wind forcing at large scales (100s of kms). As a result, we believe 

that coastal ocean conditions are relatively uniform over the geographic extent of the seven target 

estuaries, which span 250 km (Figures 2-3 and 2-4). 

3.3 Criteria for Choosing Target Estuaries and Previous Estuarine Classifications 

The seven target estuaries were chosen to cover a range of estuarine and watershed conditions in 

the PNW. While the goal was to choose a suite of PNW estuaries representative of the range of 

systems, we focused this initial effort on Oregon estuaries because of logistical constraints. 

When this work was initiated, we had not completed our regional analysis of PNW (Chapter 2), 

and so relied on previous classifications and our personal experiences with these estuaries for the 

selection. The primary classification systems of the PNW estuaries that we utilized in estuary 

selection are summarized below and in Table 3-1. 

3.3.1 Management Classification of Oregon Estuaries 

Oregon estuaries were classified under Oregon’s Statewide Planning Goal 16 (Estuarine 

Resources) as natural, conservation, shallow draft development, or deep draft development 

(Cortright et al., 1987; http://www.inforain.org/oregonestuary/oregonestuary_page5.html) with 

the following definitions: 

Natural: Estuaries lacking maintained jetties or channels, and which are usually little 

developed for residential, commercial or industrial uses. They may have altered 

shorelines, provided that these altered shorelines are not adjacent to an urban area. 

Shorelands around natural estuaries are generally used for agriculture, forestry, recreation 

and other rural uses. 

Conservation: Estuaries lacking maintained jetties or channels, but which are within or 

adjacent to urban areas which have altered shorelines adjacent to the estuary. 

Shallow Draft Development: Estuaries with maintained jetties and a main channel (not 

entrance channel) maintained by dredging at 22 feet or less. 

Deep Draft Development: Estuaries with maintained jetties and a main channel 

maintained by dredging to deeper than 22 feet. 

The target estuaries included one natural estuary (Salmon River Estuary), two conservation 

estuaries (Alsea and Nestucca estuaries), two shallow draft development estuaries (Tillamook 

and Umpqua River estuaries), and two deep draft estuaries (Coos and Yaquina estuaries). Thus, 

the target estuaries represent a good cross-section of the Oregon management types. 

3.3.2 Geomorphological/Hydrological Classifications 

Based on the geomophological/ocean exchange classification we developed after the field 

surveys (Section 2.4), the seven target estuaries included three tide-dominated drowned river 

mouth estuaries (Coos, Tillamook, and Yaquina), two moderately river-dominated river mouth 

estuaries (Alsea, Salmon) and two highly river-dominated river mouth estuaries (Nestucca and 

Umpqua estuaries) (Table 3-1). Within this suite of estuaries, the normalized 
84

Table 3-1. Current and historical classifications of the seven target estuaries. The current classification we deveSection 2.4. 

Estuary 

Current 

Classification 

Oregon 

Management 

Classification 

Bottom et al. 

(1979) Parrish et al. (2001) Rumrill (

Alsea 

Moderately riverdominated drowned 

river mouth 

Conservation Drowned river valley, partially 

mixed 

Well-flushed drowned rivers, 

predominantly freshwater 

input? 

or 

Well-flushed drowned rivers, 

predominantly oceanic input? 

NA 

Coos Tide-dominated drowned river mouth Deep draft development Drowned river valley, well mixed Well-flushed drowned rivers, predominantly oceanic input Tide-domi

drowned 

mouth 

Nestucca 

Highly riverdominated drowned 

river mouth 

Conservation 

Drowned river 

valley, partially 

mixed 

Well-flushed drowned rivers, 

predominantly oceanic input NA 

Salmon River 

Moderately riverdominated drowned 

river mouth 

Natural Drowned river valley, partially 

mixed 

Well-flushed drowned rivers, 

predominantly freshwater input NA 

Tillamook Tide-dominated drowned river mouth Shallow draft development Drowned river valley, partially mixed Well-flushed drowned rivers, predominantly oceanic input Tide-domi

drowned 

mouth 

Umpqua River 

Highly riverdominated drowned 

river mouth 

Shallow draft 

development 

Drowned river 

valley, partially 

mixed 

Well-flushed drowned rivers, 

predominantly freshwater input 

River-dom

drowned 

mouth 

Yaquina Tide-dominated drowned river mouth Deep draft development Drowned river valley, partially mixed Well-flushed drowned rivers, predominantly oceanic input Tide-domi

drowned 

mouth 
85

freshwater inflow metric varies about 9-fold from the Coos Estuary to the Umpqua River 

Estuary. However, none of the estuaries with highest normalized freshwater inflow values were 

included (see Table 2-4). Thus, our selection of estuaries represents a reasonably wide range of 

drowned river mouth estuaries, but does not capture the full range expected if, for example, the 

Rogue River or Klamath River had been included. Additionally, no blind estuaries, coastal 

lagoons, marine harbor/coves, or tidally restricted coastal creeks, as defined in Chapter 2, were 

included. 

The various geomorophological classifications of Oregon estuaries that we initially used to 

choose our target estuaries are summarized in Table 3-1 along with our classification schema. 

There is agreement among all the classifications that the seven target estuaries are drowned river 

mouth estuaries. In terms of riverine versus oceanic influences, there is also agreement on the 

three tide-dominated estuaries and two of the river-dominated systems, the Umpqua and Salmon 

estuaries. However, Parrish et al. (2001) classified the Nestucca Estuary as having 

“predominantly oceanic input” versus our classification as highly riverine dominated. It is 

possible that the Nestucca’s division into two separate arms may affect how the normalized 

freshwater inflow values should be interpreted. Parrish et al. (2001) were uncertain whether to 

classify the Alsea Estuary as predominantly oceanic or freshwater input while we classified it as 

moderately river-dominated. 

3.4 Estuary and Watershed Sizes 

As discussed in Chapter 2, estuary size was defined as the sum of the NWI marine, estuarine, and 

tidal riverine polygons (http://www.fws.gov/nwi/; U.S. Fish Wild. Ser., 2002). Using this 

definition, estuary size among the target estuaries varied almost 20-fold, from about 3 km2 for 

the Salmon River Estuary to 55 km2

 for the Coos Estuary (Tables 2-2 and 3-2). The Coos, 

Tillamook, Umpqua, and Yaquina are the four largest estuaries in Oregon other than the 

Columbia River. The only other large PNW estuaries are Grays Harbor and the Willapa Estuary 

in Washington and the Humboldt Estuary in northern California. The smaller target estuaries 

(Alsea, Salmon River, and Nestucca) were considered to capture much of the environmental 

range in the moderate-sized estuaries in the PNW. 

As discussed in Chapter 2, PNW estuaries have extensive intertidal zones. The average percent 

intertidal area across all 89 PNW estuaries was 52% (Figure 2-8). The seven target estuaries 

bracketed that average, ranging from 32% in the Umpqua River Estuary to 87% in the Salmon 

River Estuary. 

The sizes of the associated watersheds varied 63-fold (Table 3-2), from about 190 km2

 in the 

Salmon River Estuary to over 12,000 km2 in the Umpqua River Estuary. The ratio of estuarine 

size to watershed size also varied by more than an order of magnitude. The Coos, Tillamook, 

and Yaquina all had relatively large estuaries compared to the watershed (>2.5%) while the 

Alsea and Salmon River had moderate-sized estuaries compared to the watershed (ca. 1%). Even 

though they differed 7-fold in estuary size, the Nestucca and Umpqua River had the smallest 

estuaries in relation to their watersheds, with estuary-to-watershed percentages of 0.61% and 

0.28%, respectively. 
86

3.5 Watershed Characteristics and Land Cover 

The percent land cover values for selected classes of the watershed associated with each of the 

target estuaries are given in Table 3-3 based on the 2001 NOAA land cover data (see Chapter 2). 

Averaged across the entire watershed, none of these watersheds are highly developed. The 

maximum “high intensity development” land cover class was only 0.23% of the Coos watershed. 

The combined “high intensity development” and “low intensity development” classes exceeded 

0.5% only in the Coos and Yaquina watersheds. The extent of cultivated land was also very low, 

with a maximum of 0.22% in the Alsea watershed. While there is little cultivated land, pasture 

for cattle grazing occurred within some of the coastal watersheds, in particular the Tillamook 

watershed. The NOAA survey does not have a land cover class specifically for pasture, but does 

have a class for grasslands which includes both natural grassland and pastures. To better capture 

the extent of pasture land, we used the 1992 National Land Cover Data (NLCD) land cover data, 

which has a pasture land cover class. Based on the NLCD data, the Tillamook, Umpqua River, 

and Nestucca watersheds each had about 3% to 4.5% pasture. 

An integrative measure of land cover alteration is the percent impervious surfaces (see 

Chapter 2). The target estuaries showed a small range in this metric, with all the values about 

2% (Table 3-3). These values are well below the 7.5% cutoff for “exceptional” environmental 

conditions used in the NOAA’s “Spatial Wetland Assessment for Management and Planning” 

(SWAMP) model (Sutter, 2001). These low percentages of altered land use and percent 

impervious surfaces among the target estuaries are representative of PNW watersheds in general 

(Tables 2-6 and 2-7). 

3.5.1 Watershed Slopes 

The median degree slope and median percent slope of the target estuaries are given in Table 3-3. 

For details on methods and datasets used to calculate watershed slope see Section B.12.4. We 

prefer degrees slope, which is bounded between 0 and 90 degrees, in comparison to percent 

slope, which can vary from 0 to infinity. Though they are the more commonly reported values, 

percent slopes can obtain very high values in areas with cliffs, which can skew the distribution 

and the mean compared to using degrees slope. The EPA Watershed Academy states that “high 

relief watersheds may have increments of 5-20 percent.” 

(http://www.epa.gov/owow/watershed/wacademy/wam/erosion.html). Using this criterion, all 

the target watersheds have a high relief, with median percent slopes ranging from 24.9% in the 

Salmon River watershed to 40.8% in the Tillamook watershed. The range in degrees slope was 

from about 14 degrees in the Salmon River watershed to over 22 degrees in the Tillamook 

watershed. Regardless of how it is measured, high relief is characteristic of PNW coastal 

watersheds 

3.5.2 Population Characteristics 

The Umpqua River and Coos watersheds had the largest human populations, about 100,000 and 

39,000, respectively (Table 3-4). Although the Umpqua watershed has the largest population, 

the majority of the population (about 74,000) is located in the Roseburg area, which is 125 km 

from the coast. In contrast, the bulk of the population in the Coos watershed is located adjacent 

to the estuary. Normalized to watershed area, the population density in the Coos watershed was 

about 25 people per km2

, about 66% to 6-fold greater than the watersheds in the other target 

estuaries. To put the Coos watershed density in perspective, the Coos population density is one 
87

Table 3-2. Estuary and watershed size of the seven target estuaries. Estuary area includes the marine, estuarine, NWI classes. Estuarine intertidal and subtidal areas and the percent intertidal only include the estuarine NWI cla

of the intertidal and subtidal may not equal the total estuary area. The intertidal area includes “irregularly exposeflooded” and “irregularly flooded” habitats (approximately MLLW to MHHW). 

ESTUARY LATITUDE 

(deg N) 

ESTUARY 

AREA 

(km2) 

ESTUARINE 

INTERTIDAL 

AREA 

 (km2) 

ESTUARINE 

SUBTIDAL 

AREA 

 (km2) 

% INTERTIDAL 

OF ESTUARINE 

AREA 

(km2) 

WATERSHE

SIZE 

(km2) 

Alsea 44.4227 12.49 7.83 4.66 62.7 1222 Coos 43.4294 54.90 29.89 24.32 55.1 1575 Nestucca 45.1827 5.00 2.88 1.79 61.7 826 Salmon 45.0469 3.11 1.72 0.25 87.3 193 Tillamook 45.5130 37.48 25.61 11.23 69.6 1455 Umpqua 43.6694 33.78 8.85 18.88 31.9 12146 Yaquina 44.6205 19.96 9.05 9.77 48.1 650 
88

Table 3-3. Watershed attributes of the seven target estuaries. Slope is given in degrees and percent slope. Land 

selected classes from the NOAA 2001 dataset except for the percentage for “pasture” which is from the 1994 NL

Impervious Surfaces” was calculated using the 30-m NLCD data (http://www.epa.gov/mrlc/nlcd-2001.html). The 

surface values generated from the NOAA land cover classes using Attila (U.S. EPA, 2004c) are given in parenth

LAND COVER (% WATERSHED AREA) 

ESTUARY 

High 

Intensity 

Development 

 Low 

Intensity 

Development Cultivated 

Grasslands / 

Pasture Deciduous Evergreen 

 Mixed 

Forest Shrub 

Alsea 0.02 0.13 0.22 3.31 / 1.30 3.07 57.07 25.86 6.82 Coos 0.23 0.86 0.00 7.09 / 1.39 1.45 48.64 15.89 15.43 Nestucca 0.02 0.14 0.00 5.49 / 3.01 4.63 44.60 29.22 12.15 Salmon 0.01 0.14 0.00 4.65 / 0.84 3.43 45.60 24.97 13.27 Tillamook 0.12 0.36 0.00 6.69 / 4.48 5.85 46.02 28.37 6.19 Umpqua 0.08 0.40 0.12 11.19 / 4.53 0.49 62.79 6.94 14.70 Yaquina 0.10 0.40 0.00 6.24 / 0.36 5.19 36.18 27.24 16.73 
89

Table 3-4. Population size and density in the seven target estuaries. Population estimates based on 2000 census 

watershed. Population densities (# per km2) are normalized both to the area of the watershed and to the area of th

POPULATION DENSITY (# per km2) 

ESTUARY 

POPULATION 

IN 

WATERSHED 

NORMALIZED TO 

WATERSHED SIZE 

NORMALIZED TO 

ESTUARY SIZE 

% PO

CHA

19

Alsea 4825 3.9 386 Coos 38,950 24.7 709 Nestucca 3404 4.1 681 Salmon 2881 14.9 926 Tillamook 7600 9.7 203 Umpqua 99,401 8.2 2943 Yaquina 7970 12.3 399 
90

to two orders of magnitude lower than in many Central and Southern California coastal 

watersheds, including Elkhorn Slough (339 km-2), South San Francisco Bay (745 km-2), San 

Diego (780 km-2), and Anaheim (2841 km-2). Population density was also normalized to the 

estuarine area, with the densest normalized population occurring in the Umpqua River watershed 

with over 2900 persons per km2 of estuary. This high value for the Umpqua River Estuary, in 

part, reflects the small estuary size compared to the large size of the Umpqua River watershed 

(Table 3-2). However, this value is still at least an order of magnitude smaller than those the 

moderate and large sized estuaries in Central and Southern California. 

The percent population change from 1990 to 2000 varied among the watersheds. The largest 

percent growth was in the Salmon River watershed which increased by 20% within a decade. At 

the opposite extreme, the Yaquina watershed actually showed a decrease in population of about 

5%. For the Yaquina watershed it is important to recognize that other than the bay front, most of 

the City of Newport lies outside of the watershed. The population decrease in the Yaquina 

watershed over this period probably reflects a population decrease in City of Toledo, located in 

the upper portion of the Yaquina watershed. 

3.6 Estuarine Hydrology 

The seven target estuaries are relatively shallow with mean depths of 1.5-3 m (Table 3-5). The 

volumes of the target estuaries range from 1 x 106 m3 (Salmon) to 2 x 108 m3 (Coos), and are 

considered small estuaries compared to many in the U.S. (Hickey and Banas, 2003). These 

estuaries are classified as mesotidal with a mean tidal range of about 2 m (Table 3-5) and have 

mixed semidiurnal tides. There is a close coupling between the estuaries and the coastal ocean 

as a result of the large tidal prism relative to estuary volume (Table 3-5 and Hickey and Banas, 

2003). Additionally, there is considerable variability in the mouth width of the estuaries, with 

the Salmon River Estuary having the smallest mouth (~ 40 m) and Coos and Umpqua estuaries 

having the largest (400 - 600 m, Table 3-5). 

Reflecting the seasonal pattern in rainfall, freshwater flow is about 5-fold to almost 10-fold 

higher in the wet season compared to the dry season (Table 3-6). The wet season is defined as 

November through April while the dry season is defined as May through October. This 

seasonality in riverine inflow is several times greater for estuaries in the PNW compared to those 

along the Atlantic coast of the U.S. (Hickey and Banas, 2003). The Simmons Ratio, which is the 

ratio of riverflow per tidal cycle to tidal prism, is often used to classify estuaries by stratification 

(Simmons, 1955). When the Simmons Ratio is greater than 1, the estuary is highly stratified. 

When the ratio is 0.2–0.5, the estuary is considered partially mixed and when it is less than 0.1, it 

is considered well mixed. Using Simmons ratio (calculated using annual average inflow), Alsea 

and Salmon River estuaries are considered partially mixed, and the remainder of the target 

estuaries are considered well mixed. 

Residence (or flushing) time is a measure of the retention of water within a defined boundary 

(Monsen et al., 2002). Residence time is often considered a mediating factor in assessing 

estuaries susceptibility to nutrient loading (e.g., Quinn et al., 1991; National Research Council, 

2000). Estuaries with long residence times are considered more susceptible to nutrient 

enrichment than estuaries with short residence times. Estuarine residence times are influenced 

by freshwater inflow, tides, wind, mixing, stratification, and system topography. There are 
91

numerous terms and methods for calculating these time scales of transport, including flushing 

time, residence time, local residence time, turnover time, freshwater replacement time, and 

transit time (Monsen et al., 2002; Abdelrhman, 2005). Two commonly used methods for 

calculating residence time are the fraction of freshwater method and the modified tidal prism 

approach (Dyer and Taylor, 1973). The fraction of freshwater method calculates the amount of 

time for the freshwater inflow to replace the freshwater in the system. It can be interpreted as the 

average transit time for freshwater in the system and is an appropriate time scale for materials 

that are input to the estuary from the river (Sheldon and Alber, 2002). The modified tidal prism 

is more appropriate for the dry season estimates when tidal forcing dominates (riverflow small 

compared to tidal flow) and for estuaries such as those in the PNW where the volume is small 

compared to the tidal prism. The fraction of freshwater method is more appropriate during the 

wet season when freshwater inflows dominate. Presented in Table 3-7 are wet and dry season 

residence times for the seven estuaries calculated using the modified tidal prism and fraction of 

freshwater inflow approaches. The residence times of the seven target estuaries are short 

(typically less than 1 month). During the wet season, the residence time calculated using the 

fraction of freshwater method ranges from 2 to 10 days; while during the dry season, the 

residence time calculated using the modified tidal prism method varies from 1 to 48 days. 

3.7 Nutrient Loading 

Previously published estimates are available for nitrogen and phosphorous loading for six of the 

target estuaries. The nitrogen loading varies 26-fold for the seven target estuaries with the 

Salmon River Estuary having the lowest nitrogen loading and the Umpqua River Estuary having 

the highest (Table 3-8). This variability in nitrogen loading is related to differences in stream 

flow among the estuaries (Table 3-6). The loadings in Table 3-8 represent point sources as well 

as nonpoint and upstream sources. The Umpqua River estuary had the highest phosphorous 

loading, while the Yaquina had the lowest (Table 3-8). Point source inputs represented the 

largest phosphorous source in Coos and Yaquina estuaries, while in Tillamook, and Umpqua 

estuaries non-point inputs from forested land was the dominant source of phosphorous (Quinn et 

al., 1991). Nonpoint source inputs associated with forested land was the dominant nitrogen 

source in Coos, Tillamook, Umpqua, and Yaquina estuaries (Quinn et al., 1991). Upstream 

sources were the major source of nitrogen and phosphorous in the Alsea Estuary (Quinn et al., 

1991). Nutrient loading estimates are often normalized by estuary area and volume to examine 

sensitivity to nutrient loading. When the nitrogen loading estimates are normalized to area, 

Yaquina and Coos have the lowest loading, while Umpqua and Alsea have the highest. 

The loadings presented in Table 3-8 do not include the input of nutrients from the coastal ocean. 

Brown and Ozretich (2009) compared the major sources of dissolved inorganic nitrogen (DIN) to 

Yaquina Estuary during the wet and dry seasons (Table 3-9). There are strong seasonal 

differences in the nitrogen sources to the estuary. During the wet season, riverine sources 

dominate, while during the dry season oceanic nitrogen inputs associated with coastal upwelling 

dominate. In the dry season, benthic flux of DIN from the sediments into the water column is the 

second largest source of nutrients. Atmospheric deposition of nitrogen is a minor nitrogen 

source with direct deposition on the estuary only representing about 0.05% of the inorganic 

nitrogen input to the estuary. In addition, atmospheric deposition on the watershed is small (8%) 

compared to the watershed input associated with nitrogen fixing red alder in the watershed. 

Annual nitrogen input from wastewater treatment facility effluent is estimated to be 0.4% of the 
92

total input to the estuary. The ocean is also a large source of phosphorous to PNW estuaries; 

however, at this time the oceanic phosphorous loading has not been quantified. 

Oregon Coast Range streams have high nitrate concentrations relative to other forested PNW 

watersheds (Compton et al., 2003). Wigington et al. (1998) hypothesized that forest vegetation, 

in particular the presence of red alder (Alnus rubra), is the primary control of spatial variability 

in stream nitrate concentrations in the Oregon Coast Range (including the Salmon River, Siletz, 

and Alsea watersheds). Red alder is a native species in the PNW that colonizes areas disturbed 

by fires, logging and landslides. Red alder have symbiotic N2-fixing bacteria that can fix 50-200 

kg N ha-1 y

-1 in pure stands (Binkley et al, 1994). Compton et al. (2003) found a significant 

relationship between alder cover in the watershed and nitrate concentration in streams in the 

Salmon River watershed. Naymik et al. (2005) found a similar relationship between stream total 

nitrogen and broadleaf cover (which is primarily red alder in the Coast Range) in the Tillamook 

watershed. Brown and Ozretich (2009) estimated that > 80% of the riverine nitrogen loading to 

Yaquina Estuary is related to red alder cover. 
93

Table 3-5. Hydrographic characteristics of the seven target estuaries. Sources were (1) Shirzad et al. (1988), (2(1982), (3) Percy et al. (1974), (4) Coastal Inlets Research Program (http://cirp.wes.army.mil/databases/inletsdb/

(5) http://tidesandcurrents.noaa.gov/tides04/tab2wc1b.html, and (6) http://ian.umces.edu/neea/siteinformation.ph

from the Pacific Coast Ecosystem Information System (PCEIS; Lee and Reusser, 2006). 

ESTUARY 

TIDAL RANGE 

(m) 

TIDAL PRISM 

(m3) 

ESTUARINE 

VOLUME 

(m3) 

RATIO OF 

ESTUARINE 

VOLUME 

TO TIDAL 

PRISM 

MOUTH 

WIDTH 

(m) 

MEAN 

DEPTH 

(m) 

Alsea 1.8 (1,5) 1.15 x 107 (1) 1.9 x 107 (6) 1.6 140 2.0 (1) Coos 1.7 (5) 5.27 x 107 (3) 2.1 x 108 (6) 3.9 620 1.5 (3) Nestucca 1.8 (5) 110 Salmon 1.6 (2,3) 9.5 x 105 (2) 1.4 x 106 (2) 1.5 40 Tillamook 1.6-1.9 (5) 4.81 x 107 (1) 7.0 x 107 (6) 1.5 360 1.8 (1) Umpqua 1.6 (5) 6.23 x 107(4) 7.5 x 107 (6) 1.2 425 Yaquina 1.8-1.9 (5) 2.38 x 107 (4) 3.0 x 107 (6) 1.3 290 3.0 (1) 
94

Table 3-6. Wet and dry season freshwater inflow for the seven target estuaries. Sources: (1) Shirzad et al. (1988

(1991), and (3) Percy et al. (1974). 

ESTUARY

WET 

SEASON 

(m3 s-1) 

DRY 

SEASON 

(m3 s-1) 

ANNUAL 

AVERAGE 

(m3 s-1) 

SIMMONS RATIO = 

RIVERFLOW PER TIDAL 

CYCLE / TIDAL PRISM 

ESTUARY TYPE 

BASED ON 

SIMMONS RATIO 

Alsea 112 15 65 0.3 Partially Mixed Coos 82 0.1 Well Mixed Nestucca 51 8 30 

43 

 Nestu

Estim

Salmon 18 4 11 0.5 Partially Mixed DTillamook 189 31 110 0.1 Well Mixed Umpqua 342 71 209 

210 

19 

0.1 

Well Mixed 

Umpq

Um

S

Yaquina 48 5 27 0.1 Well Mixed 

Table 3-7. Residence time calculated using the modified tidal prism and fraction of freshwater approaches for th

estuaries. 

MODIFIED TIDAL PRISM FRACTION OF FRESHWATER INFLOW 

ESTUARY

WET 

SEASON 

(days) 

DRY 

SEASON 

(days) 

WET 

SEASON 

(days) 

DRY SEASON 

(days) SOURCE 

Alsea 1 4-9 NA 14 - 17 Choi (1975) Coos 7 - 13 

13-16 

11 - 16 

40-48 

2 - 10 

3 – 11 

34 

19 - 31 

Choi (1975) Arneson (1976) 

Nestucca NA NA NA NA Salmon NA 1-2 NA 1-2 Askren et al. (1976) Tillamook 2 – 3 3 – 4 3 

1 – 7 

9 

5 - 32 

Choi (1975) Colbert and McManu

Umpqua 4 - 5 5 – 10 3 5 – 8 Choi (1975) Yaquina 6 6 -9 9 20 - 106 Choi (1975) 
95

Table 3-8. Estimates of nutrient (nitrogen and phosphorous) loading to six of the seven target estuaries. Load esavailable for Nestucca Estuary, and phosphorous loads are not available for Salmon River estuary. Total load es

Coos, Tillamook, Umpqua and Yaquina are from Quinn et al. (1991), while nitrogen load estimate for Salmon R

from flow and DIN from Compton et al. (2003). 

NITROGEN LOADING PHOSPHOROUS 

ESTUARY 

TOTAL 

(tons y-1) 

NORMALIZED 

BY ESTUARY 

AREA 

(tons km2 y-1) 

NORMALIZED 

BY ESTUARY 

VOLUME 

(tons m3 y-1) 

TOTAL 

(tons y-1) 

NORMALIZED 

BY ESTUARY 

AREA 

(tons km2 y-1) 

NO

BY 

V

(t

Alsea 3875 310.3 2.0 x 10-4 61 4.9 Coos 3054 55.6 1.5 x 10-5 95 1.7 Salmon 337 108.4 2.4 x 10-4 NA NA Tillamook 4315 115.1 6.2 x 10-5 73 2.0 Umpqua 8870 262.6 1.2 x 10-4 163 4.8 Yaquina 984 49.3 3.3 x 10-5 48 2.4 

Table 3-9. Comparison of nitrogen sources during wet and dry seasons for the Yaquina Estuary (Brown and Ozr

NITROGEN INPUT (mol DIN d-1) 

SOURCE WET SEASON DRY SEASON 

ANNUAL 

AVERAGE 

River 2.6 x 105 2.3 x 104 1.6 x 105Ocean 8.8 x 104 3.8 – 5.1 x 105 2.3 – 3.0 x 105Wastewater 1.8 x 103 1.5 x 103 1.6 x 103Benthic Flux1 - 4.3 x 104 -Atmospheric Deposition  On Estuary  On Watershed 

2.2 x 102 

1.1 x 104 

1.2 x 102 6.0 x 103

1.7 x 102 8.5 x 103

Source: 1DeWitt et al. (2004) 
96

CHAPTER 4: 

WATER QUALITY SURVEYS IN SEVEN TARGET ESTUARIES 

Cheryl A. Brown and Christina L. Folger 

4.0 Introduction 

We collected water quality data in seven target estuaries to evaluate current water quality 

conditions and for use in dividing each estuary into ocean- and river-dominated segments with 

respect to nitrogen sources. These estuaries (Alsea, Nestucca, Yaquina, Salmon River, Coos, 

Umpqua River and Tillamook) vary in size from about 3 km2

 to 55 km2, and from river to ocean 

dominated (Table 3-1 and 3-2). Our sampling consisted of high tide and low tide water quality 

cruises and of short-term deployments of water quality datasondes during the months of June 

through September of 2004 and 2005. 

Most of this chapter focuses on the dry season (May to October), because this is the time period 

of biological nutrient utilization. During the wet season (November to April), there is little 

nutrient utilization due to short residence times (associated with high freshwater inflow) and low 

solar irradiance. Mixing diagrams from Yaquina (unpublished data) and Tillamook estuaries 

(Colbert and McManus, 2003) reveal conservative transport of nutrients during the wet season. 

In addition, water column chlorophyll a and macroalgae biomasses are minimal during the wet 

season. 

Key Findings 

• Water quality conditions in the target estuaries during the dry season were 

dependent upon ocean conditions at time of sampling and fresh water inflow. 

• The coastal ocean was the primary source of phosphate during the dry season. 

• Estuaries received nitrogen from coastal ocean and watershed. 

• Dry season chlorophyll a levels were relatively low in target estuaries, with 85% 

of the stations sampled having chlorophyll a levels < 5 μg l-1. 

• No incidence of low dissolved oxygen (< 5 mg l-1) occurred at any of the stations 

sampled. 

• There were some instances of poor water quality conditions (based on high 

dissolved inorganic nitrogen) in Tillamook Estuary.

• Wet season dissolved inorganic nitrogen levels appear to be related to the red 

alder cover within the watersheds. 
97

Previous research (e.g., de Angelis and Gordon, 1985; Roegner and Shanks, 2001; Roegner et 

al., 2002; Colbert and McManus, 2003) has demonstrated that PNW estuaries are strongly 

influenced by conditions occurring on the shelf, in particular wind-driven coastal upwelling 

during the spring and summer. The NO3

-

, PO4

3-, and temperature of water entering estuaries 

during flood tides respond rapidly to changes in along-shore wind stress. High levels of 

dissolved inorganic nitrogen (primarily in the form of NO3

-

) and PO4

3- entering the Yaquina 

Estuary lags upwelling-favorable winds by about 2 days (Brown and Ozretich, 2009). The input 

of phytoplankton lags upwelling favorable winds by approximately 6 days and typically occurs 

during downwelling conditions (Brown and Ozretich, 2009). Variations in water properties 

determined by ocean conditions propagate approximately 11-13 km into the Yaquina Estuary. 

Recently, there have been occurrences of severe hypoxia on the inner continental shelf of Oregon 

(Grantham et al., 2004; Chan et al., 2008). At times low oxygen water from the inner shelf is 

advected into the Yaquina Estuary (Brown et al., 2007). 

4.1 Methods 

4.1.1 Water Quality Cruises 

Water quality cruises were conducted in the seven target estuaries during the summers of 2004 

and 2005. During each cruise between 10 and 17 stations were sampled in each estuary, 

depending upon the size of the estuary, and high and low tide cruises were conducted. The time 

required to complete each cruise depended upon the size of the estuary, with small estuaries 

(Salmon River, Nestucca, and Alsea) taking 2-3 hours and larger estuaries (Coos and Tillamook) 

taking about 4-5 hours. Due to logistical constraints some of the riverine stations in Coos and 

Tillamook were sampled up to 7 hours after the beginning of the cruise. To minimize the time 

required to complete each cruise, two boats were used to sample the three largest estuaries 

(Coos, Tillamook and Umpqua River). Both the low and high tide cruises proceeded from the 

estuary mouth upriver to follow the propagation of the tide. For location of water quality cruise 

stations in each target estuary see Chapter 5. The cruises extended from the marine to the tidal 

fresh regions for all estuaries except Coos Estuary. For Coos Estuary, the lowest salinity 

sampled during the dry season was 14 psu. The dates of the cruises and the number of stations 

occupied in each estuary are presented in Table 4-1. We didn’t sample during May or October 

because these are transitional months that may experience high freshwater inflow events. 

Additional cruises were conducted to characterize winter conditions during the wet seasons of 

2006 and 2007. 

Table 4-1. Dates of cruises and number of stations sampled for the seven target estuaries. 

DATES OF DRY SEASON 

CRUISES 

DATES OF WET SEASON 

CRUISES 

ESTUARY HIGH TIDE LOW TIDE HIGH TIDE LOW TIDE 

NUMBER 

OF 

STATIONS 

Alsea 9/24/04 9/28/04 3/11/07 3/12/07 10 

Salmon 7/20/04 7/16/04 3/21/07 3/27/07 10 

Yaquina 6/15/04 6/21/04 2/9/06 10 

Coos 8/9/05 8/8/05 2/16/07 2/15/07 17 

Nestucca 8/11/04 8/20/04 1/25/07 1/27/07 15 

Tillamook 7/22/05 7/23/05 3/2/07 3/1/07 15 

Umpqua 6/24/05 6/25/05 2/11/07 2/12/07 13 
98

At each station profiles of conductivity, temperature and depth (CTD; SBE 19 SEACAT Profiler, 

Sea-Bird Electronics, Inc., Bellevue, Washington), turbidity (Seapoint Turbidity Sensor, 

Seapoint Sensors, Inc., Kingston, New Hampshire), photosynthetically active radiation (PAR, 

LI-193 spherical sensor, LI-COR Biosciences, Lincoln, Nebraska) and in situ fluorescence 

(WETStar Chlorophyll Fluorometer, WET Labs, Philomath, Oregon) were measured. All 

instruments on the Sea-Bird profiler were factory calibrated by the manufacturer. The profile 

measurements were taken at 0.5-sec intervals from the water surface to 0.5 m above the bottom, 

and during post-processing the data were binned into 0.25-m intervals. If the water depth at the 

station was shallow (typically less than 4 m), a YSI 6600 multiparameter sonde (YSI, Yellow 

Springs, Ohio) was used for water quality measurements (temperature, conductivity, 

fluorescence, turbidity, depth, and dissolved oxygen). Dissolved oxygen measurements for the 

profiles were obtained using a YSI 6600 attached to the Seabird frame. Dissolved oxygen 

measurements (surface, mid-depth, and bottom) were collected in Tillamook, Coos and Umpqua 

River estuaries during both the low and high tide cruises, and during the low tide cruise in 

Salmon River Estuary. No dissolved oxygen measurements were collected in the Alsea, Yaquina 

and Nestucca estuaries. The YSI datasondes were calibrated using the methods presented in 

Section B.4. 

Water samples were collected from mid-depth at each station using a hand-operated pump, 

filtered (0.45 μm filter), and frozen until analysis. The samples were analyzed for dissolved 

inorganic nutrients (nitrate+nitrite, ammonium, phosphate and silicate) by MSI Analytical 

Laboratory, University of California-Santa Barbara, CA. One-liter surface water samples were 

collected from each station and analyzed for chlorophyll a. These water samples were filtered 

within 2 to 4.5 hours of sample collection using 47-mm GF/F filters. The volume of water 

filtered varied between 250 and 500 ml. Chlorophyll a was extracted by sonicating the filters 

and soaking them overnight in 10 ml of 90% acetone. The next morning the samples were 

centrifuged and analyzed for chlorophyll a content using a fluorometer (10 AU Fluorometer, 

Turner Designs, Inc., Sunnyvale, CA). Four-liter water samples were collected from each cruise 

station and analyzed for total suspended solids (TSS). Each sample was agitated prior to 

filtration and filtered using ashed Whatman 47 mm GF/F filters. Filters from samples were dried 

overnight in an oven at 70°C and allowed to reach room temperature in a desiccator prior to 

weighing. More information on the water column sample analyses can be found in Section B.2.1 

and B.2.2 

4.1.2 Datasonde Deployment 

YSI 6600 multiparameter sondes (YSI, Yellow Springs, Ohio) and Mini-CTDs (Star-Oddi, 

Iceland) were deployed in the seven target estuaries along the salinity gradient (see Table 4-2 for 

number of instruments deployed in each estuary and Chapter 5 for maps illustrating deployment 

locations). The YSI datasondes measured conductivity, temperature, turbidity, chlorophyll a (in 

situ fluorescence), dissolved oxygen, pH, and depth. Mini-CTD units measured depth, salinity 

and temperature. Both instruments collected data at 15 minute intervals. During 2004, we lost 

two instruments deployed in the lower portion of the Nestucca Estuary. In order to fill this data 

gap, in 2006 an additional YSI 6600 CTD was deployed at the mouth and three additional MiniCTDs were deployed in the mesohaline region of the estuary. Calibration procedures for all 

instrumentation are presented in Sections B.2-B.4. In addition to the datasondes that we 

deployed, there were additional YSI datasondes deployed in Coos Estuary associated with the 
99

South Slough Estuarine Research Reserve (Charleston Bridge and Valino Island) and Oregon 

Institute of Marine Biology (OIMB). The locations of these instruments (labeled Y5-Y7) are 

presented in Figure 5-10. 

Table 4-2. Number of short-term datasondes deployed during the dry season in each estuary and 

year deployed. 

ESTUARY 

NUMBER OF YSI 

DATASONDES 

NUMBER OF MINICTDS 

Alsea 2 (2004) 3 (2004) 

Coos 7 (2005) 3 (2005) 

Nestucca 

2 (2004) 

1 (2006) 

3 (2004) 

3 (2006) 

Tillamook 4 (2005) 2 (2005) 

Salmon 2 (2004) 3 (2004) 

Umpqua 4 (2005) 3 (2005) 

Yaquina 5 (2004) 0 

4.2 Riverine Nutrient Inputs During the Wet Season

To examine differences in riverine nutrient levels for the target estuaries, we calculated median 

wet season dissolved inorganic nitrogen (DIN) and PO4

3- using stations with salinity < 2 psu 

(Table 4-3). During the wet season, the Yaquina estuary had the highest riverine DIN levels, 

while Umpqua had the lowest. In general, estuarine differences in wet season DIN were similar 

to variations in deciduous cover among the estuaries (Table 4-3). This suggests that variations in 

riverine DIN among the estuaries primarily result from variations in red alder cover, which is 

similar to the findings of Compton et al. (2003) and Wigington et al. (1998). The Nestucca 

estuary had the highest median wet season PO4

3- (Table 4-3). The highest wet season DIN levels 

observed in the target estuaries were measured at Stations C4 and C12 in the Tillamook Estuary 

(Figure 5-12). Highest median NH4

+

 levels were measured in the Tillamook Estuary and the 

highest NH4

+

 values measured occurred at Stations C4 and C12 in the Tillamook Estuary. 

Table 4-3. Median wet season dissolved inorganic nitrogen and phosphate calculated using 

stations with salinity < 2 psu. The table entries are listed from highest to lowest DIN levels. 

MEDIAN WET SEASON 

ESTUARY DIN (μM) NH4

+

 (μM) PO4

3- (μM) 

DECIDUOUS 

COVER (%) 

Yaquina 92.6 0.93 0.35 5.19 

Tillamook 69.8 2.66 0.40 5.85 

Nestucca 61.9 0.87 0.59 4.63 

Alsea 35.0 0.99 0.38 3.07 

Salmon 34.9 0.88 0.25 3.43 

Coos 33.3 1.04 0.24 1.45 

Umpqua 17.6 1.60 0.28 0.49 
100

4.3 Coastal Ocean and River Conditions During Dry Season Sampling 

The seven target estuaries span about 250 km along the Oregon Coast (Figures 2-3 and 2-4). 

Flood-tide water temperature can be used as an indicator of ocean conditions during our surveys 

and to assess the variability in upwelling along the Oregon Coast. Low flood-tide water 

temperatures (8 – 10°C) indicate the input of high-nutrient (NO3

-

 and PO4

3-) water to the 

estuaries associated with upwelling while warm temperatures indicate downwelling conditions 

and input of low-nutrient water from the ocean. Figure 4-1 shows the flood-tide water 

temperature near the mouth of Yaquina Estuary (Station Y1, which has been previously been 

demonstrated as an indicator of upwelling/downwelling; Nelson and Brown, 2008) and water 

temperature for the high tide cruises for the outermost estuarine stations sampled for the 2004 

classification effort. Figure 4-2 shows the flood-tide water temperature near the entrance of 

Yaquina and Coos estuaries (which are about 140 km apart) and the water temperature from the 

high tide cruises from the 2005 classification effort. The close agreement between Yaquina and 

Coos estuaries flood-tide water temperatures and water temperatures from the classification 

cruises demonstrates that the ocean conditions are relatively uniform over the geographic range 

of estuaries that we sampled during 2004 and 2005. This agrees with the analysis by Hickey and 

Banas (2003) that demonstrated that water temperatures at the entrance of three estuaries along 

the Oregon and Washington coasts (Coos, Grays Harbor and Willapa), which spanned 400 km, 

were highly correlated during the upwelling season. During 2004, the Salmon River and 

Nestucca estuaries were sampled during low oceanic nutrient conditions, with the high tide 

cruise outermost stations having NO3

-

+NO2

-

 levels of < 1.3 μM and PO4

3- levels of < 0.5 μM. 

There was a delayed onset of coastal upwelling along the Oregon coast during 2005 with weak 

upwelling conditions occurring from late May to mid July and strong upwelling conditions not 

commencing until mid July (Kosro et al., 2006). During 2005, all estuaries that we sampled 

during high tide coincided with upwelling conditions, which is indicated by the relatively high 

NO3

-

+NO2

-

 and PO4

3- conditions at the outermost stations. The upwelling conditions were 

stronger during the Tillamook and Coos estuaries cruises than the Umpqua cruises (Figure 4-2). 

This is evident in the NO3

-

+NO2

-

 and PO4

3- levels during the high tide cruise at the outermost 

stations of the Tillamook and Coos estuaries compared with the Umpqua River Estuary. 

Nitrate+nitrite concentrations at the outermost stations were 19.1 and 23.6 μM for the Coos and 

Tillamook estuaries, respectively, compared to 12.9 μM in the Umpqua River Estuary. 

We used water temperature versus NO3

-

+NO2

-

 and PO4

3- relationships generated using data from 

the inner shelf off of Newport, Oregon (Wetz et al., 2005), to determine whether the nutrient 

concentrations in the water entering the estuaries during flood tides were consistent with values 

associated with coastal upwelling (Figures 4-3 and 4-4). For all of the estuaries, except for 

Alsea, the high tide outermost stations fell along the relationships developed using offshore data, 

suggesting that the NO3

-

 and PO4

3- levels near the estuary mouths were consistent with coastal 

upwelling. 

The elevated NO3

-

+NO2

-

 concentrations at the mouth of the Alsea Estuary were associated with 

high freshwater input. There was a relatively high freshwater inflow event on September 19, 

2004, which was 5 days prior to the high tide Alsea cruise. On September 19, the Alsea River 

gauge at Tidewater (http://waterdata.usgs.gov/or/nwis/sw) had a discharge of 1120 cubic feet s-1

(cfs), which is almost an order of magnitude higher than the long-term mean daily discharge for 

September (130 cfs). We had additional cruises of the Alsea Estuary on September 21, 2004, 
101

which had an NO3

-

+NO2

-

 concentration at the most riverine station of 73.8 μM (with salinity = 

0 psu). The Oregon Department of Environmental Quality (http://deq12.deq.state.or.us/lasar2/) 

sampled the Alsea River at a station about 20 miles upriver from the mouth of the estuary on 

September 22, 2004 which had a NO3

-

+NO2

-

 concentration of 49.4 μM, which was the highest 

value measured at this station during September in the past 11 years. The median NO3

-

+NO2

-

concentration at this station for the month of September was 6.3 μM (data from 1994-2005, 

n = 9). The anomalous NO3

-

+NO2

-

 concentrations at the Alsea Estuary were a result of this first 

freshwater inflow event of the fall. Interestingly, the PO4

3- concentrations during the 2004 Alsea 

cruise were not anomalous. 

6/1/2004 7/1/2004 8/1/2004 9/1/2004

8

10

12

14

16

18

20

Alsea

Nestucca

Salmon

Yaquina

Water Temperature (deg C)

Date

 Yaquina

 Classification

Figure 4-1. Flood-tide water temperature at Yaquina Estuary (solid line) and outermost high tide 

station from 2004 dry season classification cruises (filled circle). Each datapoint from the 

classification cruises is labeled with its estuary name. 
102

6/1/2005 6/15/2005 6/29/2005 7/13/2005 7/27/2005 8/10/2005

8

10

12

14

16

18

Coos

Tillamook

Umpqua

Water Temperature (deg C)

 Yaquina

 Coos

 Classification

Date

Figure 4-2. Flood-tide water temperature at Yaquina (black line) and Coos (gray line) estuaries 

and outermost high tide stations from 2005 dry season classification cruises (filled symbols). 

4.4 Estuarine Water Quality 

4.4.1 Dry Season Chlorophyll a and TSS Patterns 

In Tables 4-4 and 4-5 we present the median dry season chlorophyll a, TSS, NO3

-

+NO2

-

, NH4

+

, 

and PO4

3- for the target estuaries and rank the estuaries by these median values. Dry season 

chlorophyll a within the seven target estuaries was low with a median value of 2 μg l-1 (all 

estuaries and dry season cruises). Chlorophyll a was in the low to medium category for 

eutrophication symptoms (Bricker et al., 2003). Yaquina Estuary had the highest median 

chlorophyll a (5.6 μg l-1), while the Umpqua River Estuary had the lowest (0.9 μg l-1). The 

highest chlorophyll a measured was 17.3 μg l-1, which occurred near the mouth of the Coos 

Estuary (at Station C2). The Coos Estuary had the highest median total suspended solids 

(13.1 mg l-1), while the Umpqua River Estuary had the lowest (1.6 mg l-1). Our data suggests 

that ocean-dominated estuaries have higher chlorophyll a than river-dominated estuaries. It is 

not possible to separate out the chlorophyll a associated with oceanic import from that growing 

in situ in the estuary from this dataset. 
103

6 8 10 12 14 16 18 20

0

10

20

30

40

Alsea

NO3 + NO2 ( 

μM)

Water Temperature (deg C)

 NH-5

 Classification

Figure 4-3. Water temperature versus NO3

-

+NO2

-

 relationship generated using data from a 

station on the shelf (NH-5, Wetz et al., 2005; gray circles) and classification high tide outermost 

stations (hollow circles). All data are from the dry season. Data point from Alsea is identified. 

6 8 10 12 14 16 18 20

0

1

2

3

PO4 (

μM)

Water Temperature (deg C)

 NH-5

 Classification

Figure 4-4. Water temperature versus PO4

3-

 relationship generated using data from a station on 

the shelf (NH-5, Wetz et al., 2005; gray circles) and classification high tide outermost stations 

(hollow circles). All data are from the dry season. 
104

Roegner and Shanks (2001) demonstrated that oceanic chlorophyll a was imported into South 

Slough in the Coos Estuary and that chlorophyll a concentrations were variable ranging from 0 

to 15 μg l-1. Previous research in the Yaquina Estuary (Brown and Ozretich, 2009) has 

demonstrated that water entering the estuary during flood tides can have peak chlorophyll a

concentrations of 40-50 μg l-1, and that the chlorophyll a levels in the ocean-dominated portion 

of the estuary are dependent upon coastal wind forcing and are quite variable. The median 

chlorophyll a imported into the Yaquina Estuary during May-September of 2002 and 2003 was 

4 μg l-1 (n = 298). 

There was no clear pattern in chlorophyll a (from water samples) versus salinity, except for 

during the high tide cruise in Coos Estuary (Figure 4-5). During the high tide cruise in the Coos 

Estuary there was evidence of elevated chlorophyll a levels at higher salinities. In situ

fluorescence data from two of YSI datasondes in the Coos Estuary (Stations Y1 and Y3) showed 

the import of chlorophyll a from the coastal ocean to the estuary (indicated by a significant 

relationship between in situ fluorescence and salinity) and a strong tidal signal. The low 

chlorophyll a values in Umpqua were consistent with the in situ fluorescence data. The median 

values of in situ fluorescence at Stations Y1 and Y2 in Umpqua were 1.5 μg l-1, and a tidal signal 

was not apparent. For comparison, the median in situ fluorescence values at Stations Y1 and Y3 

in Coos Estuary were 8.2 and 3.7 μg l-1, respectively. The median value of chlorophyll a

observed in the Yaquina Estuary during this study was consistent with historical dry season data 

from this estuary (median = 4.9 μg l-1, n = 1205; Brown et al., 2007). Additionally, the median 

value in the Alsea Estuary was similar to that from monthly sampling conducted from May to 

September 2004 (median of all sampling events at six stations = 1.6 μg l-1, n = 30). 

For PNW estuaries, we compiled all available chlorophyll a data, including other data collected 

by our laboratory, National Coastal Assessment, Oregon Department of Environmental Quality, 

and Washington Department of Ecology. Figure 4-6 shows the median dry season chlorophyll a

for eight PNW estuaries (including six of the target estuaries) and the freshwater inflow 

normalized by estuary volume (Table 2-4). The median chlorophyll a patterns obtained by 

compiling data from numerous sources is consistent with values we obtained in our surveys. 

Median dry season chlorophyll a in the target estuaries appears to be related to freshwater inflow 

normalized by estuary volume. Estuaries with the low freshwater inflow normalized to volume 

have relatively high chlorophyll a with median values ranging from 3.3 - 4.9 μg l-1 for these tidedominated estuaries (Yaquina, Coos, Netarts and Willapa). Estuaries with relatively high 

freshwater inflow normalized to volume (e.g., Alsea, Salmon and Umpqua) have low median 

chlorophyll a (1-2 μg l-1). The tide-dominated estuaries may have relatively high chlorophyll a

compared to the river-dominated system because they have less flushing due to low freshwater 

inflow and as a result phytoplankton growing inside the estuary reach higher levels. An alternate 

explanation may be that the higher chlorophyll a levels in tide-dominated estuaries are due to 

import of chlorophyll a from the coastal ocean. Newton and Horner (2003) demonstrated that 

high productivity phytoplankton blooms are imported into Willapa Estuary from the coastal 

ocean and have species that are of oceanic origin; however, moderate blooms also occur within 

the estuary which are combination of phytoplankton species of oceanic and estuarine origin. 

This suggests that the high chlorophyll a levels in the ocean-dominated estuaries maybe due to a 

combination of the two explanations. 
105

Table 4-4. Median dry season chlorophyll a and total suspended solids (TSS) for each estuary 

and its ranking. In the ranking, 1 represents the estuary with the highest median value and 7 

represents the estuary with the lowest. 

ESTUARY

MEDIAN CHL a

(μg l-1) 

RANKING BY 

CHL a

MEDIAN TSS 

(mg l-1) 

RANKING BY 

TSS 

Alsea 1.0 6 7.4 3 

Coos 2.4 3 13.8 1 

Nestucca 2.8 2 4.9 6 

Salmon 1.6 5 5.1 5 

Tillamook 1.7 4 3.7 4 

Umpqua 0.9 7 1.6 7 

Yaquina 5.6 1 9.7 2 

Table 4-5. Median dry season NO3

-

+NO2

-

, NH4

+

, and PO4

3- for each estuary and its ranking. 

ESTUARY 

MEDIAN 

NO3

-

+NO2

-

(μM) 

RANKING 

BY 

NO3

-

+NO2

-

MEDIAN 

NH4

+

(μM) 

RANKING 

BY 

NH4

+

MEDIAN 

PO4

3-

 

(μM) 

RANKING 

 BY 

 PO4

3-

Alsea 30.9 2 4.6 1 0.7 5 

Coos 6.1 5 3.2 2 0.9 1 

Nestucca 10.4 3 2.2 3 0.7 4 

Salmon 3.3 6 1.4 6 0.5 6 

Tillamook 34.0 1 2.0 4 0.8 3 

Umpqua 1.9 7 0.9 7 0.3 7 

Yaquina 6.1 4 1.7 5 0.8 2 
106

0 10 20 30

0

5

10

15

Chlorophyll 

a ( 

μg l-1

)

Salinity (psu)

 Tillamook - High

 Tillamook - Low

 Coos - High

 Coos - Low

 Umpqua - High

 Umpqua - Low

 Alsea - High

 Alsea - Low

 Yaquina - High

 Yaquina - Low

 Salmon - High

 Salmon - Low

 Nestucca - High

 Nestucca - Low

Figure 4-5. Dry season chlorophyll a versus salinity for all estuaries with filled symbols 

representing high tide cruises and hollow symbols representing low tide cruises. 

100 200 300 400 500

0

1

2

3

4

5

Median Chlorophyll 

a (

μg l-1

)

Freshwater Inflow Normalized by Volume (year-1)

Yaquina (n = 1205)

Coos (n = 150)

Tillamook (n = 134)

Salmon (n = 168) Alsea (n = 75)

Umpqua (n = 52)

Netarts (n = 21)

Willapa (n = 606)

Figure 4-6. Median dry season chlorophyll a versus freshwater inflow normalized by estuary 

volume for eight PNW estuaries. The chlorophyll a includes data from this study, NCA, 

Washington Department of Ecology, and Oregon DEQ. 
107

4.4.2 Dry Season Nutrient Patterns

The Tillamook Estuary had the highest median dry season concentration of NO3

-

+NO2

-

, while 

the Umpqua River Estuary had the lowest (average of all stations, low and high tide cruises; 

Table 4-5). The Alsea Estuary had similar median NO3

-

+ NO2

-

 (rank = 2) as Tillamook, but this 

was due to the high freshwater inflow event prior to the cruises. Additional sampling in the 

Alsea Estuary suggested that the NO3

-

+NO2

-

 and NH4

+

 levels were anomalously high during the 

dry season cruises. Monthly cruises conducted in the Alsea Estuary from May to September 

2004 had a median NO3

-

+NO2

-

 value of 7.5 μM and an NH4

+

 value of 1.7 μM (n = 30). The 

median values of the nutrients in the Yaquina Estuary were similar to medians calculated from 

weekly cruises (with 6 stations) conducted from May 5 - September 27, 2004 (median NO3

-

+NO2

-

 = 10.9 μM, NH4

+

 = 2.7 μM, and PO4

3- = 0.9 μM). In the Tillamook Estuary about 44% of 

the stations were located in low salinity regions of the estuary (with salinity < 5 psu), and these 

low salinity stations had high NO3

-

+NO2

-

 levels (median = 40.3 μM). Excluding low salinity 

stations (< 5 psu), the median concentration of NO3

-

+NO2

-

 in Tillamook was about 21 μM. We 

recalculated the median NO3

-

+NO2

-

 only using stations with salinity ≥ 5 psu and the ranking of 

the estuaries by median NO3

-

+NO2

-

 was Alsea, Tillamook, Nestucca, Coos, Yaquina, Umpqua 

River, and Salmon River (highest to lowest). 

The Salmon River Estuary had the lowest NO3

-

+NO2

-

 probably due to the low nutrient 

conditions in the coastal ocean during the sampling of this estuary. The relatively high NO3

-

+NO2

-

 levels (calculated from stations with salinities > 5 psu) in the Tillamook Estuary were 

probably associated with strong upwelling conditions prior to the cruises (see Section 4.3) 

combined with relatively high watershed inputs. Colbert (2004) found median NO3

-

+NO2

-

levels 

of ~ 8 μM in Tillamook Estuary (using data from May – September 1998 and 1999 with 

salinities > 5 psu, n = 46), suggesting there is substantial interannual variability. Historical data

in Umpqua Estuary from the Oregon Department of Environmental Quality confirmed the low 

median dry season nutrients in this estuary (median NO3

-

+NO2

-

 = 1.6 μM and PO4

3- = 0.3 μM , n 

= 15). 

To examine differences in riverine nitrogen inputs among the target estuaries during the dry 

season, we calculated the median dissolved organic nitrogen (DIN) using only stations with 

salinity < 5 psu. Unfortunately, for the Coos Estuary our sampling did not extend far enough 

upriver to examine riverine nitrogen input. Alsea, Nestucca, Tillamook and Yaquina estuaries 

had similar DIN levels at the low salinity stations (< 5 psu) with median values of about 40 μM, 

while the Umpqua River Estuary had the lowest (2.4 μM), and the Salmon River was 

intermediate with median values of about 19 μM. Dry season riverine DIN input appeared to 

follow the trends in deciduous cover in the watersheds (Table 3-3), with Alsea, Nestucca, 

Tillamook and Yaquina watersheds having the highest deciduous cover ranging from 3.4 to 

5.9%, and the Umpqua watershed having the lowest 0.49%. 

During the dry season, there was a significant relationship between PO4

3- and salinity (Figure 4-

7) which demonstrates that the ocean was the dominant PO4

3-source for the target estuaries. A 

substantial amount of the variability in the Figure 4-7 is due to temporal variations in ocean 

conditions. In the PO4

3- versus salinity plots for individual cruises, the variability was reduced 
108

0 5 10 15 20 25 30 35

0.0

0.5

1.0

1.5

2.0 r

2

 = 0.40, n = 166

p < 0.0001

PO4 ( 

μM)

Salinity (psu)

Figure 4-7. Dry season phosphate versus salinity for all classification water quality stations (all 

estuaries, both years, high and low tide cruises). 

0 5 10 15 20 25 30 35

0

20

40

60

80

100

r

2

 = 0.2, n = 172

p < 0.0001

NO3 + NO2 ( 

μM)

Salinity (psu)

Figure 4-8. Dry season nitrate+nitrite versus salinity for all classification water quality stations 

(all estuaries, both years, high and low tide cruises). 
109

and the r2 was as high as 0.97 for some cruises. There was evidence of a PO4

3- source at low 

salinities in all the target estuaries. Plotting NO3

-

+NO2

-

 versus salinity (Figure 4-8) demonstrates 

that the estuaries receive NO3

-

+NO2

-

 from both the river and the ocean and that the riverine 

concentrations are higher than those associated with the ocean. EMAP data for Oregon estuaries 

exhibited a significant correlation between PO4

3- and salinity, but no relationship between 

dissolved inorganic nitrogen and salinity (Nelson and Brown, 2008). There were two 

anomalously high (~90 μM) NO3

-

+NO2

-

 datapoints that occurred at low salinities, which 

correspond to two sloughs in Tillamook Estuary. These two datapoints will be discussed in 

Section 4.5. 

4.4.3 Dry Season Salinity Fluctuations 

Salinity conditions near the mouth of the seven target estuaries were compared to assess the 

importance of riverine versus marine dominance during the dry season. For this analysis, we 

examined the data from the short-term datasondes for the station nearest the mouth in each 

estuary. Since we lost the datasonde deployed near the mouth of the Nestucca Estuary during 

mid August 2004, we used data collected in late August 2006. To confirm that this was an 

adequate substitution, we compared the high and low tide salinity data from the cruises of the 

outermost station (Station C10) to the data collected in 2006. The high and low tide data from 

Station C10 were comparable to the high and low tide values from the datasondes deployed in 

2006 (Station Y2) suggesting that this is an appropriate substitution. The datasondes were 

located at varying distances from the estuary mouths. The datasonde deployed at Station Y2 in 

the Nestucca Estuary was located at the mouth. The ones deployed in the Umpqua River and 

Salmon River estuaries were about 1 km from the mouth. The datasonde deployed at Station Y1 

in Tillamook Estuary was about 2 km from the estuary mouth, while the ones deployed in 

Yaquina and Alsea estuaries were about 3 km from the mouth. In the Coos Estuary, we had a 

Mini-CTD deployed near the mouth; however, it malfunctioned, so our datasonde closest to the 

mouth was 6 km from the inlet. Additional data were provided from OIMB, which is about 1 km 

from the estuary mouth. 

In the Yaquina Estuary, the salinity varied about 5 psu over a tidal cycle, even though this station 

is located about 3 km from the estuary mouth (Figure 4-9). In the Alsea Estuary, the salinity 

varied about 24 psu over a tidal cycle and this datasonde was deployed at a similar distance from 

the estuary mouth as the one in the Yaquina. This difference between the salinity variations 

between Yaquina and Alsea was probably due to differences in freshwater inflow. The dry 

season freshwater inflow to the Alsea Estuary is three times that into the Yaquina Estuary 

(Table 3-6). The influence of the high freshwater inflow event on September 19, 2004 was 

evident in the decrease in low tide salinities near the estuary mouth. Salinity data from Station 

C1 near the mouth of the Alsea Estuary differed by 7 psu between the high and low tide cruises, 

which was less than that at Station Y1 due to proximity to the estuary mouth. Even though the 

Salmon River datasonde was located close to the estuary mouth the salinity varied about 23 psu 

over a tidal cycle. These salinity variations were comparable to those in Alsea; however, the 

Alsea datasonde was deployed about 2 km further upstream than the one in Salmon River 

estuary. The salinity variations near the mouth of the Nestucca Estuary varied about 13 psu over 

a tidal cycle (Station Y2). Salinities near the mouth of the Umpqua River Estuary varied about 

19 psu over a tidal cycle. Using the datasonde near the mouth of Tillamook (Station Y1), the 

salinity varied about 14.6 psu over a tidal cycle. When plotted on the same axis, the salinity 
110

variations at Tillamook were comparable to those at Nestucca (but the Nestucca datasonde was 

located closer to the mouth of the estuary). The salinity variations near the mouth of Coos 

Estuary are similar to (or slightly less than) those in the Yaquina Estuary. 

Due to variability in the distance of the short-term datasondes from estuary mouths, we 

examined the salinity differences between the high and low tide of the station closest to the 

estuary mouth to rank the estuaries in terms of riverine dominance. Salinity variations at the 

mouth appear to be related to normalized freshwater inflows presented in Table 2-4 (Figure 4-

11). Based on salinity variations and normalized freshwater inflow, the ranking of estuaries in 

terms of riverine dominance would be Umpqua (most riverine), Nestucca, Alsea, Salmon, 

Tillamook, Yaquina and Coos (most marine). 

4.5 Comparison of Water Quality Data to EMAP Criteria 

Dry season water quality data were compared to EPA’s Environmental Monitoring Assessment 

Program (EMAP) West Coast criteria (Table 4-6). Data collected in the seven target estuaries 

were consistent with the EMAP data discussed in Section 1.3; however, since our water quality 

stations were not probabilistically sampled, we cannot express our results as percent of estuarine 

area. There was no incidence of low dissolved oxygen (< 5 mg l-1) at any of the stations that we 

sampled. All stations (both low and high tide cruises of all estuaries) had dissolved inorganic 

phosphorous levels in the fair range. As discussed previously, this was primarily resulting from 

oceanic input of PO4

3- to the estuaries (Figure 4-7). The majority (85%) of the stations sampled 

(all estuaries) had chlorophyll a levels in the good range, with the remainder in the fair range. 

The chlorophyll a was in the good range (< 5 μg l-1) for all stations and both sampling dates in 

the Alsea, Salmon River, and Umpqua River estuaries. In the Coos, Nestucca, and Tillamook 

estuaries most of the stations had chlorophyll a in the good range, but there were a few instances 

of chlorophyll a in the fair range (Coos had 4 samples, Nestucca had 6 samples, and Tillamook 

had 1 sample in the fair range). In Yaquina Estuary, most of the chlorophyll a samples were in 

the fair range (14 out of 20). Interestingly, the occurrences of chlorophyll a in the fair category 

occurred near the mouth in the Coos and Nestucca estuaries suggesting import of high 

chlorophyll a water from the coastal ocean. In contrast, the relatively high chlorophyll a 

concentrations in the Yaquina Estuary extended from the mouth to the tidal fresh portion of the 

estuary. 

Most (85%) of the stations sampled had dissolved inorganic nitrogen (DIN) levels in the good 

category (with 14% in the fair category and 1% in the poor category). All samples in the Coos, 

Salmon River and Umpqua River estuaries had DIN concentrations in the good category. All of 

the occurrences of DIN in the fair category occurred in the riverine portions of the estuaries with 

salinities less than 6.5 psu. The only estuary that had DIN concentrations in the poor range was 

Tillamook (Stations C4 & C12 during low tide cruise which are located in Hathaway and 

Dougherty sloughs with salinities of 0.2 and 11.6 psu). Unlike the anomalously high NO3

-

conditions in the Alsea Estuary during the classification cruises, there was not high river inflow 

to the Tillamook Estuary prior to this cruise. There was additional nutrient sampling in the 

vicinity of C4 and C12 by the Oregon Department of Environmental Quality during 1997 and 

1998, which had similar high dry season DIN (peak values of DIN of about 100 μM compared to 
111

7/1/2004 7/22/2004 8/12/2004 9/2/2004 9/23/2004

0

10

20

30

Salinity (psu)

Date

 Yaquina (Y1)

 Salmon (Y1)

 Alsea (Y1)

 Nestucca (Y2)

 Nestucca (C10)

 Alsea (C1)

Figure 4-9. Dry season salinities near the mouths of estuaries sampled in 2004 and data 

collected in Nestucca (Y2) during 2006 (shifted by 2 years) to fill data gap from lost instrument. 

The dashed line indicates the high freshwater inflow event on September 19, 2004. 

6/15/2005 6/29/2005 7/13/2005 7/27/2005 8/10/2005

0

10

20

30

Salinity (psu)

Date

 Yaquina (Y1)

 Umpqua (Y1)

 Coos (Y1)

 Coos (Y7)

 Tillamook (Y1)

 Umpqua (C1)

 Tillamook (C1)

 Coos (C1)

Figure 4-10. Dry season salinities near the mouths of estuaries sampled in 2005. Lines indicate 

short-term datasonde deployments, while symbols represent data from high and low tide cruises 

for station nearest the mouth of the estuary with station name present in parentheses of legend. 
112

0 1x102

2x1023x1024x1025x102

0

2

4

6

8

10

Difference between high and low tide salinity 

at station closet to estuary mouth

(psu)

Normalized Freshwater Inflow (m3 m

-2

 year

-1)

Coos

Yaquina

Tillamook

Salmon Alsea

Nestucca Umpqua

Figure 4-11. Difference between high and low tide dry season cruise salinity at station closest to 

estuary mouth versus area-normalized freshwater inflow. 

 92-96 μM observed during our low tide cruise). These two stations (C4 & C12) also had the 

highest wet season DIN levels observed in the target estuaries (see Section 4.2). 

This suggests that Hathaway and Dougherty sloughs might be receiving high DIN inputs from 

upland sources. Agricultural activities occur in this watershed adjacent to the estuary, including 

confined animal feeding operations. The DIN levels in these sloughs were not consistent with 

DIN levels for other nearby Oregon streams during the month of July. The median DIN in the 

tidal fresh portion of the Yaquina River during July was 18 μM and the maximum concentration 

measured during July was 39 μM (unpublished data of C. Brown, collected 2-3 times per week 

during 2002 and 2003). The median DIN in the tidal fresh portion of the Alsea River during July 

of 2004 was 4 μM (with a maximum of ~ 6 μM, n = 5, unpublished data of C. Brown). 

The degree of stratification was examined by calculating the difference between bottom and 

surface salinity with the surface being 0.5 m from the surface and the bottom being 0.5 m above 

the bottom for most stations. Strong stratification was defined as > 2 psu difference between 

surface and bottom readings. Due to the shallow water depth during the low tide cruises in the 

Salmon River and Nestucca estuaries, only one data point at mid-depth was collected; therefore, 

stratification could not be determined for these cruises. 
113

Table 4-6. West Coast criteria for water quality parameters (U.S. EPA, 2004b). 

RANKING 

PARAMETER GOOD FAIR POOR 

Dissolved Oxygen > 5 mg l-1 2-5 mg l-1 < 2 mg l-1

Chlorophyll a < 5 μg l-1 5 -20 μg l-1 > 20 μg l-1

Dissolved inorganic 

nitrogen 

< 0.5 mg l-1 0.5-1.0 mg l-1 > 1 mg l-1

Dissolved inorganic 

phosphorous 

< 0.01 mg l-1 0.01- 0.1 mg l-1 > 0.1 mg l-1

In aggregate (all cruises, all estuaries, all stations) about 29% of the stations sampled exhibited 

strong stratification. The estuary with the highest number of stations exhibiting strong 

stratification was the Alsea Estuary (45% of stations). The estuaries with the lowest number of 

stations exhibiting strong stratification were the Tillamook and Coos estuaries (13 and 19%, 

respectively). The high degree of stratification in Alsea was probably related to the high 

freshwater inflow event discussed earlier in this chapter. For estuaries where we had 

stratification data for low and high tide cruises (all estuaries except for Salmon River and 

Nestucca), strong stratification occurred more during the high tide sampling compared to the low 

tide (19 occurrences during high tide versus 8 during low tide cruises). Of the stations that 

showed strong stratification, the majority were upriver in the low salinity region (<15 psu). This 

pattern was particularly evident in the Salmon River, Nestucca, and Alsea estuaries. The 

Yaquina and Coos estuaries exhibited strong stratification in the ocean-dominated regions. 

4.6 Synthesis 

During the wet season, DIN levels were highest in the Yaquina and lowest in the Umpqua. 

Variations in the wet season DIN appear to be related to deciduous cover. This suggests that 

variations in red alder are resulting in differences in riverine DIN input to the estuaries. Dry 

season water quality conditions in PNW estuaries were dependent upon the ocean conditions 

during the surveys. Flood-tide water temperature as well as nutrient versus salinity relationships 

generated using data from the inner Oregon shelf were useful for confirming that ocean 

conditions were relatively uniform over the geographic extent of the estuaries we sampled and 

for assessing the ocean conditions during the dry season water quality cruises. The results from 

the Alsea Estuary demonstrated that when assessing nutrient levels of the estuaries it is important 

to examine freshwater inflow immediately prior to sampling events (even during the dry season) 

to ensure that there was not a strong freshwater inflow event that might result in elevated DIN 

levels. Dry season nutrient data from the cruises demonstrated that the estuaries received PO4

3-

mainly from the coastal ocean, while both the ocean and rivers were DIN sources. In the seven 

target estuaries, there were no occurrences of low dissolved oxygen, and chlorophyll a levels 

were relatively low. The tide-dominated estuaries appear to have higher chlorophyll a levels 

than the river-dominated estuaries. The dry season PO4

3- conditions were in the EMAP fair 

category but this is due to the input of high PO4

3- water from the coastal ocean. Generally, most 

of the DIN concentrations observed were in the good category (85%). There were two 

occurrences of poor DIN conditions in two of the sloughs of Tillamook Estuary. Umpqua River 

Estuary had the lowest NO3

-

+NO2

-

 and PO4

3- of the seven target estuaries. There are several 

potential explanations for the low nutrients in this estuary. Of the seven target estuaries, the 
114

Umpqua has the least amount of red alder in the watershed as evident by the lowest % Deciduous 

and % Mixed and the highest % Evergreen (Table 3-3). Red alder is the primary deciduous tree 

in the Oregon Coast Range, and NO3

-

 in streams has been related to its presence (see 

Section 3.7). In addition, Umpqua has the highest freshwater inflow of the seven target estuaries 

(even during the dry season, Table 3-6) and as a result is river dominated, which may result in 

less oceanic nitrogen and phosphorous input to the estuary. 
115

CHAPTER 5: 

ZONATION OF OCEAN AND RIVER DOMINANCE 

IN SEVEN TARGET ESTUARIES 

Cheryl A. Brown and James E. Kaldy III 

5.0 Introduction 

To assess the susceptibility of estuarine resources to watershed-derived nutrient loading, we 

estimated the contribution of nitrogen sources to water column dissolved inorganic nitrogen 

(DIN). Previous research at Yaquina Estuary has demonstrated that during the dry season (MayOctober), the ocean is the dominant nitrogen source, while during the wet season (November – 

April) riverine inputs dominate (Brown and Ozretich, 2009; Table 3-9). In addition, most of the 

riverine nitrogen inputs are believed to be related to the presence of red alder in the watershed 

(see Section 3.7). Point source input associated with a wastewater treatment facility increases in 

importance in the up-estuary region, particularly during periods of minimal riverine inputs. The 

purpose of this chapter is to assess the importance of oceanic versus riverine nitrogen sources in 

the seven target estuaries. 

 

We divided each target estuary into two segments, ocean dominated and river dominated, based 

upon the dominant dry season DIN sources. We used multiple types of data and analyses to 

derive the zonation of the estuaries and then translated the zonation into median salinities for use 

in estuaries with limited data. For the Yaquina Estuary, we had the most extensive dataset and 

analysis techniques for development of zonation based upon dry season nitrogen sources. We 

used a transport model combined with natural abundance stable isotopes (δ

15N) of green 

macroalgae to identify the dominant nitrogen sources within the estuary as a function of time and 

location. The transport model was validated by comparing predicted isotope ratios (using the 

Key Findings 

• Estuaries were divided into oceanic and riverine segments in terms of nitrogen 

sources during the dry season. 

• Zonation was based upon natural abundance of nitrogen stable isotopes of green 

macroalgae and transport modeling. 

• Analyses suggest that oceanic and riverine inputs are the dominant nitrogen 

sources for the target estuaries. 

• Coos Estuary had the highest isotope ratios with multiple stations having isotope 

ratios elevated above the oceanic end member, suggesting that wastewater 

inputs are important in this estuary. 

• Five estuaries (Alsea, Coos, Nestucca, Tillamook, and Yaquina) had >50% of the 

total estuarine area classified as ocean dominated.
116

transport model to mix isotopic end members) to observed macroalgal isotope ratios at five 

locations. There were several reasons why attached green macroalgae were used as the 

bioindicator in this study. In estuaries with high flushing, macroalgae are often the dominant 

primary producers (Valiela et al., 1997), and their presence is often used as an indicator of 

eutrophication (Bricker et al., 2003). During the dry season, there are dense blooms of green 

macroalgae in many estuaries in the region. In addition, the attached nature of the green 

macroalgae is beneficial in that it integrates the nitrogen sources over time, which is 

advantageous when there is substantial temporal variability in nitrogen sources and loadings. 

Nitrogen stable isotope ratios of green macroalgae are a useful indicator because the macroalgae 

take up DIN from the water column and incorporate this nitrogen into their tissue. Using the 

natural abundance isotope ratio of nitrogen, macroalgae can be linked to nitrogen sources with 

distinct isotopic signatures (McClelland and Valiela, 1998; Costanzo et al., 2001). Fixation of 

atmospheric nitrogen (including that by N2-fixing root nodules associated with red alder, see

Section 3.7) has δ

15N values slightly less than 0‰. Industrially produced fertilizer has a similar 

isotope ratio due to the atmospheric source of nitrogen in the Haber Process. Stable isotope 

signatures are particularly useful when the various sources contributing to a system are 

isotopically distinct (Figure 5-1). As an example, nitrogen stable isotopes may be used to 

distinguish fertilizer inputs from animal or human waste, but they are not useful for 

distinguishing human wastes (septic or wastewater treatment facility) inputs from animal wastes 

due to the overlap of isotopic signatures. Distinguishing nitrogen sources with overlapping 

isotopic signature (e.g., human waste from animal wastes) is often accomplished by comparison 

of nitrogen sources and land use patterns. Previous research has shown that green macroalgae 

integrate water column conditions that have occurred over a two week interval (Aguiar et al., 

2003; Cohen and Fong, 2005). An indicator that integrates water column conditions is desirable 

due to the variability of coastal ocean nutrient conditions associated with variations in wind 

stress (see Section 4.3) and variability in water column DIN within estuaries (see Section 4.4.2). 

Since it was impractical to develop a transport model for each estuary due to data limitations, we 

used a conservative transport two end member mixing model to estimate the contribution of 

riverine and oceanic DIN sources, which is possible due to their different salinity and nitrogen 

stable isotope signatures. Similarity between observed and predicted isotope ratios suggests that 

the oceanic and riverine inputs are the primary nitrogen sources to the estuary. If the observed 

isotope ratio is greater than that predicted based upon conservative mixing of oceanic and 

riverine nitrogen sources, this suggests that there may be additional nitrogen sources or 

denitrification may be important. If there is a wastewater treatment facility (WWTF) source in a 

given estuary, then we inferred the contribution of WWTF sources using Isosource (Phillips and 

Gregg, 2003), which calculates all feasible combinations of the three sources that can produce 

the observed isotope ratio at a given location and sampling time. To constrain the solutions, we 

selected a subset of feasible solutions, which were consistent with the salinity variations at the 

site. This method was validated for Yaquina Estuary through comparison to the results from the 

transport model. Once validated, this technique was used to estimate the contribution of DIN 

sources for estuaries sampled in this classification effort. In addition, salinity data were analyzed

for Yaquina Estuary to determine if zonation predicted from the isotope and transport models 

could be approximated using salinity data. For estuaries where there were limited data available 
117

we used salinity data (collected as part of the classification effort as well as historic salinity data) 

to aid in the zonation of the estuaries. 

Figure 5-1. Natural abundance nitrogen isotope ratio (δ

15N expressed as ‰) for various 

nitrogen sources. Sources: ocean represents the δ

15N of green macroalgae from the Oregon 

coast (Fry et al., 2001, and Kaldy unpublished), animal waste and fertilizer is from Kendall and 

McDonnell (1998), septic is from Cole et al. (2004), and red alder is from Hobbie et al. (2000), 

Tjepkema et al. (2000), Cloern et al. (2002). 

5.1 Stable Isotope Sampling 

In the Yaquina and Alsea estuaries, 5 sites were sampled monthly for the isotope ratio of 

macroalgal tissue. Sites were located along the salinity gradient from the mouth of the estuary to 

the region where the system becomes mesohaline. As part of the classification effort, samples 

were collected for the isotope ratio of green macroalgae at two sites in the Alsea, Nestucca, 

Salmon River, and Yaquina estuaries during 2004. In 2005, green macroalgae were sampled at 5 

sites in the Umpqua River and Coos estuaries and 4 sites in the Tillamook Estuary. 

Each site was characterized by the presence of either rock or other hard substrate (e.g. pilings, 

tree stumps, docks) to support green macroalgae. No attempt was made to identify algae to 

species. The dominant macroalgae were species from the family Ulvaceae, which includes a 

variety of genera. At each site, five replicate samples were collected by hand from the top or 

sides of rocks and pilings. Only thallus material that did not contact mud sediments was 

-5 0 5 10 15 20 25

Alder

Fertilizer

Septic

WWTF

Animal waste

Ocean

δ 15N 
118

collected. Individual samples were rinsed in the field with milli-Q water, transferred to labeled 

plastic bags and stored on ice. In the laboratory, samples were rinsed with milli-Q water to 

remove adhering sediments, frozen and lyophilized. Dried samples were then pulverized with a 

mortar and pestle. Percent nitrogen and the nitrogen isotope ratio (δ

15N) of dried tissue were 

measured using a Finnigan Mat Delta Plus XP isotope ratio mass spectrometer. All isotope 

ratios are presented in standard δ notation. 

5.2 Yaquina Estuary 

5.2.1 Transport Model 

A two-dimensional, laterally averaged hydrodynamic and water quality model (Cole and Wells, 

2000) was used to simulate the transport of riverine, oceanic and WWTF effluent DIN sources. 

The transport model incorporates the temporal variability in nitrogen loading, the location of the 

nitrogen inputs, and the time that each nitrogen source spends in the estuary. In the model 

simulations, Yaquina Estuary was represented by 325 longitudinal segments spaced 

approximately 100 m apart with each longitudinal segment having 1-m vertical layers. The 

model domain extended about 37 km from the tidal fresh portion of the estuary at Elk City, 

Oregon to the mouth of the estuary (Figure 5-2). Model simulations were performed for 2003 

and 2004 and included tidal and wind forcing, as well as freshwater inflow. Parameters 

simulated included water surface elevation, salinity, water temperature, and dissolved inorganic 

nitrogen. Each nitrogen source, riverine (NR), oceanic (NO), and WWTF effluent (NW), was 

modeled as a separate component. The nitrogen sources were modeled as 

dN

dt

= − transport Nμ (5.1) 

where N is the DIN source and μ is a loss/uptake rate. The same value of μ was used for all three 

nitrogen sources and the value of μ was determined by fitting total modeled DIN (NO+NR+NW) to 

observations of DIN within the estuary. The best fit to observations was found with μ = 0.1 d-1. 

Simulations were also performed with no uptake (μ = 0) which is equivalent to conservative 

transport of the sources. 

The results from the transport model were used to mix the three nitrogen sources using the 

following equation 

M R R O O W W δ = f δ + f δ + f δ (5.2) 

+ + = 1 R W O

f f f (5.3) 

where fR, fW, and fO are the fractions of riverine, WWTF, and oceanic DIN, respectively, and δR, 

δW, and δO are the isotope ratio of end members for riverine, WWTF effluent, and oceanic 

sources, respectively. The mixture of the three end members (δM) is assumed to be that which 

macroalgae utilizes. Estimates of the oceanic and riverine end members were obtained by 

examination of the observed isotope ratios at Stations N1 and N5. The riverine end member (δR

= 0 to +2 ‰) was estimated from the wet season isotope ratios at Station N5, while the oceanic 

end member (δO = +8 to +9 ‰) was estimated from dry season isotope ratios at Station N1. 

The initial estimate for the WWTF end member (δW = +15 to +22 ‰) was determined from the 

literature (Jones et al., 2003). To arrive at the final end member isotope ratios, model 
119

Figure 5-2. Location map for Yaquina Estuary showing the locations of datasondes, water 

quality stations, isotope samples, and WWTF. The red dashed line shows boundary between 

oceanic and riverine segments. 

simulations were performed varying each end member over the range estimated from the data 

and literature. The final isotope ratio of end members for the three sources (δR=+2 ‰, 

δW=+20 ‰, and δO=+8.4 ‰) was determined from the best fit (minimum root mean square error, 

RMSE) between predicted and observed isotope ratio at the five stations N1-N5 during 2003 and 

2004. The final oceanic end member selected is consistent with marine end members for the 

west coast of the U.S. (Fry et al., 2001), while the riverine end member is consistent with the 

isotope ratio expected for nitrogen associated with red alder (leaf tissue ranges between -3 and -

0.5‰; Hobbie et al., 2000; Tjepkema et al., 2000; Cloern et al., 2002). 

The δ

15N of green macroalgae collected from Yaquina Estuary (locations shown in Figure 5-2) 

during 2003 and 2004 exhibited strong seasonal patterns (Figure 5-3) associated with shifting 

nitrogen sources. During winter conditions with large freshwater inputs, there is a large degree 

of isotopic separation (∆ = 4‰) between samples collected near the ocean (N1) and near the 

riverine end-member (Station N4 or N5). This isotopic difference suggests that algae from these 

sites are utilizing different nitrogen sources. During the dry season, as river flow decreases and 

ocean upwelling becomes more pronounced, the isotope ratio of green macroalgae collected 
120

from the end-member stations becomes isotopically indistinguishable, suggesting that 

macroalgae are utilizing one nitrogen source. In some months during the dry season, the data 

suggest that a third nitrogen source, the WWTF, can become locally important (Figure 5-3). 

Data from 2004 also exhibit this same general pattern. 

The model (Equations 5.1-5.3) reproduces much of the observed temporal and spatial variability 

observed in the macroalgae isotope dataset. Comparisons between simulated and observed 

isotope ratios at Stations N2 and N3 are presented in Figures 5-4 and 5-5. The RMSE between 

observed and predicted isotope ratios calculated for Stations N1 - N5 for each year ranges from 

0.8 ‰ to 2.0 ‰ (with an average value of 1.3 ‰). This agreement between simulated and 

observed isotope ratios suggests that the model was correctly simulating the transport and mixing 

of the three nitrogen sources. Figure 5-6 shows the monthly average contribution of each of the 

three nitrogen sources (fR, fW, and fO ) at Stations N1-N5 and adjacent to the discharge point of 

the WWTF (region of maximum contribution of WWTF source). 

Jan. 2003

Mar

May

July

Sept

Nov

Jan 04

Mar

May

Jul

Sep

Nov

Jan 05

0

2

4

6

8

10

δ 15

N

Month

 N1

 N2

 N3

 N4

 N5

Figure 5-3. Macroalgae isotope data at five stations (N1-N5) for Yaquina Estuary with red boxes 

indicating dry seasons. The dashed line indicates δ

15N = 5.2 ‰ which is the demarcation 

between riverine and oceanic dominance, assuming two end member mixing of oceanic and 

riverine sources. 
121

There was interannual variability in the boundary between the oceanic and riverine segments. 

The exact location of this boundary varies with ocean conditions (e.g., El Niño, La Niña 

conditions) as well as freshwater inflow. Using the modeled contributions integrated over May – 

September, we estimated the position of this boundary during 2003 and 2004. During 2003 the 

freshwater inflow was less than in 2004 and as a result the oceanic DIN reached further upstream 

with the boundary between the oceanic and riverine segments being located between Station N3 

(which received 57% of DIN from ocean) and N4 (which received 44% of DIN from ocean). 

During 2004, the boundary was located slightly upstream of Y3 (Y3 received 53% of DIN from 

the ocean, while N3 received 32% from the ocean). In addition, during the dry season this 

boundary progressed up estuary as the freshwater inflow declined. This can be seen in 

simulation results from 2004 (Figure 5-6) which show that Station N1 was ocean dominated 

(fraction ≥ 0.5) during the entire dry season (May – September), while Station N2 was river 

dominated during May and ocean dominated from June – September. At Stations N3, N4, and 

N5 ocean inputs increased in importance from May – August, but didn’t dominate. This was 

also evident in that the salinity at up estuary sites increased from May – September. The results 

of this analysis may appear to contradict the loading comparisons presented in Table 3-9, which 

shows that ocean inputs dominate during the dry season. This difference is because the analysis 

presented in Table 3-9 is comparing total loading to the estuary, while the analysis described in 

this section examined contributions to nitrogen concentrations at specific locations, which 

represent the nitrogen sources that would be available to primary producers. Based on 

comparison of salinities (modeled and observed, Table 5-1) and modeled contribution of sources, 

we concluded that Yaquina Estuary was ocean dominated in areas where the median salinity was 

≥ 25 psu and river dominated in areas where the median salinity < 25 psu (calculated using 

salinity data from May – September). We used these salinity criteria to aid in the zonation of 

other estuaries with limited data. 

Table 5-1. Summary of median observed salinities for Yaquina Estuary during May - September 

of 2003 and 2004. NA denotes data not available and * denotes extensive gaps in record. 

MEDIAN SALINITY (psu) 

STATION 2003 2004 

Y1 31.9 (surface) 

29.1 (bottom*) 32.9 (bottom) 

Y2 30.7 NA 

Y3 27.3 27.3 

Y4 25.5 NA 

Y5 18.0 19.3 
122

Figure 5-4. Comparison of modeled and observed isotope ratio at Station N2 in Yaquina Estuary 

during 2004. The grey line is model predicted isotope rate and the symbols and error bars 

represent mean and standard deviations of observed macroalgae isotope ratio. 

 

 

Figure 5-5. Comparison of modeled and observed isotope ratio at Station N3 in Yaquina Estuary 

during 2004. The grey line is model predicted isotope rate and the symbols and error bars 

represent mean and standard deviations of observed macroalgae isotope ratio. 

0 50 100 150 200 250 300 0

2

4 

6 

8 

10

12

Julian Day 

δ 15Ν (‰)

RMS Error = 0.87 ‰

River = 2 ‰

Ocean = 8.4 ‰

WWTF = 20 ‰

0 50 100 150 200 250 300 2

3 

4 

6 

7

8

9

10

Julian Day 

δ 15Ν ( ‰) 

RMS Error = 1.50 ‰

River = 2 ‰

Ocean = 8.4 ‰

WWTF = 20 ‰

5 
123

Figure 5-6. Modeled contribution of each source of dissolved inorganic nitrogen in Yaquina Estuary during 2004shows concentrations and the lower panels show fractions of each source with shading indicating riverine (green

WWTF effluent (blue) nitrogen sources. 

2 4 6 81012

0

20406080

100

Toledo

2 4 6 81012

0

20406080

100

Boone

2 4 6 8 1012

0

20406080

100

Riverbend

2 4 6 81012

0

20406080

100

DIN, 

μ

M

OSU Dock

2 4 6 81012

0

20406080

100

Cannon Quarry

2 4 6 8 1012

0

20406080

100

Butler Bridge

2 4 6 81012

0

0.20.40.60.8

1

2 4 6 81012

0

0.20.40.60.8

1

2 4 6 8 1012

0

0.20.40.60.8

1

2 4 6 81012

0

0.20.40.60.8

1

Fraction

2 4 6 81012

0

0.20.40.60.8

1

2 4 6 8 1012

0

0.20.40.60.8

1

N1 N2 N3 N4 WWTF N5 

Month 

Month 
124

5.2.2 Conservative Mixing Model 

Since transport models are not available for the other target estuaries, we used a two-end member 

conservative mixing model to estimate the dominant nitrogen sources for different portions of 

each estuary. To test this analysis technique, we performed this analysis for Yaquina Estuary 

and compared the results to estimates obtained from the transport model (described above). 

Previous research has suggested that it is difficult to identify nitrogen sources due to the 

temporal variability of salinity and water nutrients in estuaries (e.g., Cohen and Fong, 2005). In 

this analysis, we incorporated temporal variability in nitrogen sources by utilizing time series of 

salinity data at multiple stations within the estuary to mix nitrogen sources. If time-series data 

were not available in the vicinity of the macroalgae sample collection, then salinity data from the 

high and low tide cruises from water quality stations in the vicinity were utilized. Briefly, 

nitrogen concentration (CM) was modeled as the mixture of riverine and marine end-members 

using the following equations (Fry, 2002) 

C fC f C M R O = + ( ) 1− (5.4) 

where C denotes concentration of DIN, the subscripts R and O indicate river and ocean endmembers, respectively, and f represents the fraction of freshwater in each sample calculated from 

salinity (time series or high and low tide values) 

f

S S

S S

O M

O R

=

−

−

 (5.5) 

where SO and SR are the salinity (psu) at the river and ocean end-members, respectively and SM is 

the salinity measured at a specific station. The isotope ratio of mixed estuarine samples was 

calculated as 

δ

δ δ

M

R R O O

M

fC f C

C

=

+ ( ) 1−

 (5.6) 

The same end members were used as in Section 5.2.1. Observed isotope ratios were compared to 

isotope ratios predicted from conservative mixing (Equations 5.4-5.6) using salinity data 

collected in each region of the estuary. The mean of the predicted isotope ratio (δM) was 

compared to the observations. Similarity between observed and predicted isotope ratios suggests 

that the oceanic and riverine inputs are the primary nitrogen sources to the estuary. If the 

observed isotope ratio is greater than that predicted based upon mixing of ocean and riverine 

nitrogen sources, this suggests that there may be additional nitrogen sources or denitrification 

may be important. If there is a WWTF source in a given estuary, then we inferred the 

contribution of WWTF sources using Isosource (Phillips and Gregg, 2003) which calculates all 

feasible combinations of the three sources that could produce the observed isotope ratio at a 

given location and sampling time. To constrain the solutions, we selected the subset of feasible 

solutions which were consistent with salinity variations at the site. For example, if the estimate 

for the ocean contribution using the conservative mixing model was 50% averaged over the two 

weeks prior to the macroalgae sampling, then we selected solutions with ocean contributions of 

40-60%. Through this technique we can identify the segments that are dominated by oceanic 

nitrogen sources and identify regions where other nutrient sources (riverine and wastewater 

treatment effluent) dominate. 

To test this technique of interpreting isotope data using temporal variations in salinity data 

(Equations 5.4-5.6), we compared results from this analysis to the estimate of the contribution of 
125

sources from the transport model. For both estimates of the contribution of nitrogen sources we 

integrated the results (transport model and conservative mixing model) over the 2 weeks prior to 

the macroalgae sampling date (if salinity data were available). Due to gaps in the salinity timeseries or biofouling of the sensor, we were not able to compare all months at all sites. Presented 

in Table 5-2 is the interval of salinity data used in the conservative mixing model. To maximize 

the number of data points for comparison, if there were gaps in the salinity time series we used 

salinity data within about 4 weeks of the sampling date. Salinity data from Stations Y2, Y4 and 

Y5 were used to predict the isotope ratios at Stations N2, N3, and N4 (Figure 5-2). We assumed 

that the salinity of the river end member was zero (SR = 0), and used the maximum salinity at 

Station Y1 during the 2 weeks prior to the isotope sampling as the ocean salinity (SO). Riverine 

and oceanic end members were determined from samples collected at Y1 and Elk City (tidal 

fresh region of the estuary). Presented in Table 5-2 are the values of SO, CR and CO used for each 

month. 

There was good agreement between the results calculated using the transport model and those 

using the conservative mixing model and Isosource (Table 5-3). Using the nitrogen stable 

isotope ratio of macroalgae combined with short time series of salinity, we feel we are able to 

estimate the contribution of 3 DIN sources within ± 10%, which is acceptable for the needs of 

this study. 

We performed sensitivity analyses to determine the effect of integration time period of the 

macroalgae and uptake rate using the results of the transport model (calculated for each monthly 

sampling at Stations N2, N3, and N4). The maximum difference in percent contribution of 

nitrogen sources calculated using a 2 week versus a 3 week integration period is 6% and the 

average absolute difference is 1%. Cohen and Fong (2005) found that Enteromorpha intestinalis

carries its nutrient history for weeks, suggesting that a 2-3 week integration period is appropriate. 

The largest differences in percent contribution calculated using 2 and 3 week integration 

intervals occurred at Station N2, which was probably related to the greater temporal variability in 

predicted isotope ratio at this site (Figure 5-4). Results calculated with no uptake (μ =0) 

compared to those calculated with μ = 0.1 d-1 had minimal differences during the wet season 

months (January – April). This is because there is little utilization of water column DIN during 

the wet season due to short residence time (associated with high freshwater inflow). The 

maximum difference in the percent contribution of the sources calculated with the two uptake 

rates occurred during May and June. Additionally, the riverine contribution is underestimated in 

the lower portion of the estuary for calculations using μ = 0.1 d-1 compared to conservative 

transport. This is because the transit time for riverine DIN is maximal in the lower portion of the 

estuary and there is more time for loss of water column DIN. During May to September the 

maximal difference of riverine contribution was 14% and the average (of Stations N2, N3 and 

N4) underestimate of the riverine contribution was 6% (with using μ = 0.1 d-1 compared to 

conservative transport). Using an uptake rate of 0.1 d-1 tends to overestimate the contribution of 

WWTF in the upper estuary (maximal difference of 7%) and had little effect in the lower estuary 

(0-1%). The average absolute difference during May to September in the WWTF contribution 

was 2%. 

As a confirmation of the zonation based upon median salinities, we used the conservative mixing 

model (Equations 5.4-5.6) combined with mean dry season end members (CR and CO) to 
126

determine the salinity (SM) where the nitrogen sources would switch from ocean to river 

dominance. During 2003, the mean dry season ocean end member DIN is 13.5 μM, while the 

riverine end member is 26 μM. Using these values the boundary between the oceanic and 

riverine segments occurred at a salinity of about 21 psu, which is similar to the value of 25 psu in 

determined in Section 5.2.1. In addition, assuming two end member (ocean and river) mixing, 

isotope ratios above 5.2‰ indicate oceanic DIN dominance while isotope ratios below this value 

indicate riverine DIN dominance. Therefore, the salinity demarking the zonation would range 

from 21 – 25 psu. 

There are several assumptions used in this analysis that need to be valid for us to apply this 

technique to the seven target estuaries. This analysis assumes that macroalgae do not exhibit 

fractionation (preferentially take up and incorporate 14N relative to 15N), hence the isotope ratio 

of the macroalgae reflects the isotope ratio of the DIN in the water column. Cohen and Fong 

(2005) showed that E. intestinalis did not fractionate during DIN uptake and assimilation. In 

addition, it assumes that variability of the isotope ratio of the DIN results from a mixture of three 

nitrogen sources (ocean, river and WWTF) and not from fractionation associated with other 

biogeochemical transformations (e.g. denitrification). Another assumption is that the isotope end 

members used in the mixing model are applicable at a regional scale (i.e., isotope end members 

are the same for all seven estuaries). Stable isotope analysis of samples from across the region 

suggests that this is a reasonable first approximation. Also, this analysis assumes that there is 

minimal temporal variability in the isotope ratios of the end members. We also assume that the 

freshwater inflow is primarily associated with riverine inputs and WWTF inputs don’t represent 

a significant source of freshwater. Based on riverine and WWTF flow rates, this was a good 

assumption even during periods of minimal riverflow (Table 3-6). 
127

Table 5-2. Data used in the mixing model and time interval of salinity data utilized for each 

month. NA indicates data are not available. 

TIME INTERVAL OF SALINITY DATA 

UTILIZED IN MIXING MODEL 

END MEMBER 

VALUES 

SAMPLING 

DATE 

STATION 

N2 STATION N3 STATION N4 

SO

(psu)

CO

(μM) 

CR

(μM) 

April 29 April 15 - 29 April 15 - 29 April 15 - 29 31.3 3.5 

(n=33) 

74.4 

(n=3) 

May 29 May 1-15 & 

30-31 NA May 15 - 29 31.7 4.1 (n=30) 

49.5 

(n=5) 

June 26 June 12 - 26 June 20 - 28 June 12 - 26 32.3 9.4 

(n=30) 

27.8 

(n=6) 

July 31 July 17 - 31 NA July 17 - 24 34.0 18.8 

(n=31) 

9.0 

(n=4) 

Aug 14 NA July 31 - Aug 14 NA 34.0 14.5 

(n=29) 

11.1 

(n=6) 

Sept 26 Sept 1 - 16 Sept 4 - 16 Sept 23 - 30 33.6 19.1 

(n=31) 

15.6 

(n=2) 

Table 5-3. Comparison of percent contribution of DIN sources estimated using transport model 

and Isosource model. Value presented for the transport model represent averages, while 

Isosource results are ranges of feasible solutions. * denotes observed isotope ratio below river 

end member, therefore solution is not feasible. 

TRANSPORT MODEL ISOSOURCE 

MONTH OCEAN RIVER WWTF OCEAN RIVER WWTF 

Station N2 

April 24.4 74.3 1.3 8-28 71-85 0-8 

May 42.3 55.8 1.9 20-40 48-62 11-19 

June 80.6 16.2 3.2 63-83 5-19 11-19 

July 91.9 5.0 3.1 86-100 0-9 0-6 

September 93.5 3.7 2.8 87-98 0-7 2-7 

Station N3 

April 8.5 89.8 1.7 0-1 99-100 0 

June 47.2 43.7 9.1 32-52 35-49 12-20 

August 86.8 6.2 7.0 74-94 4-18 1-9 

September 75.1 14.0 10.8 76-83 0-4 17-21 

Station N4 

April 4.5 93.8 1.6 * * * 

May 6.5 90.4 3.1 0-15 79-90 5-12 

June 32.1 56.6 11.3 19-39 46-60 14-22 

July 48.2 31.0 20.8 65 35 0 

September 61.1 22.4 16.5 70-85 0-10 15-22 
128

5.3 Alsea Estuary 

In addition to the data collected as part of this study, we also had other data sources available for 

the Alsea Estuary. For Alsea Estuary, we had monthly δ

15N of green macroalgae at five 

locations during 2004 extending from the ocean-dominated to the river-dominated portions of the 

estuary (labeled N1-N5 in Figure 5-7). As part of this study, Stations N1 and N4 were sampled 

for δ

15N of macroalgae on September 15, 2004. In addition, we had YSI datasondes deployed at 

three locations in this estuary from June - October 2004. Water quality cruises with six stations 

(S1-S6) were performed weekly from May through the end of September 2004 (Table 5-4). 

Historic salinity data were also available at twelve stations from the Oregon Department of 

Environmental Quality (with sample size at each station varying between 22 and 47). 

Figure 5-7. Location map for Alsea Estuary showing the locations of datasondes, water quality 

stations, isotope samples, and WWTF. The red dashed line shows the boundary between the 

oceanic and riverine segments. 
129

From June to October, the green macroalgae δ

15N at Stations N1 and N2 showed similar 

temporal patterns (Figure 5-8) and an isotope ratio which suggested that oceanic input dominated 

at these two stations (average values of 7.9 and 7.5 ‰ at Stations N1 and N2, respectively). The 

isotope ratio at Station N3 was slightly elevated above N1 and N2, suggesting that there may be 

an additional nitrogen source. Station N4 had isotope ratios below 5 ‰ from January through 

July suggesting that riverine nitrogen sources dominated. At Station N4 during August the δ

15N 

of the macroalgae exceeded the ocean end member suggesting that there may be a third nitrogen 

source or denitrification may be important during the low riverflow period. At Station N5, the 

isotope ratio suggested that riverine nitrogen sources dominated from March through July and 

October, and there may have been an additional nitrogen source during August and September or 

denitrification had increased in importance. Riverine inflow averaged over the interval of one 

month prior to the isotope sampling was at its minimum during the months of August and 

September. During periods of minimal riverine inflow, minor DIN sources (e.g., nonpoint inputs 

associated with septic systems along the river) may increase in importance. In addition, 

denitrification may increase in importance during this period of longer residence times (although 

see Discussion, Section 5.9). There is a WWTF that discharges into the Alsea Estuary; however, 

it is located downstream of Stations N3 - N5 (Figure 5-7). Nonpoint septic inputs may have been 

the cause of increased macroalgal δ

15N since the Oregon Department of Environmental Quality 

(DEQ) recently initiated a program to repair failing septic systems along the Alsea River. 

Jan 04 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

-2

0

2

4

6

8

10

River

Ocean

 N1

 N2

 N3

 N4

 N5

 N1

δ 15

N

Month

 N4 

Figure 5-8. Monthly average observed isotope ratio at 5 locations in Alsea Estuary (dry season 

is indicated by red box). The dashed line indicates δ

15N = 5.2 ‰ which is the demarcation 

between riverine and oceanic dominance, assuming two end member mixing of oceanic and 

riverine sources. 
130

Alsea Estuary is located 20 km south of Yaquina Estuary. In the conservative mixing model, we 

used data from Yaquina Estuary for the ocean end member (CO) DIN concentrations (sampling 

from Station Y1). Coastal ocean boundary conditions respond to regional phenomena such as 

upwelling and downwelling which encompass the entire Oregon coast. Consequently, ocean 

boundary conditions from a well characterized system such as Yaquina are applicable over a 

broad region. In Section 4.3, we demonstrated that ocean input (as indicated by flood-tide water 

temperature) was relatively uniform over the geographic range of the estuaries that we sampled. 

Riverine end member DIN (CR) data were obtained from water samples collected at Station S6 

(Figure 5-7). End member DIN concentrations were averaged over the 2 weeks prior to 

macroalgal sample data collection (Table 5-5). During September there were no DIN samples 

from Station S6 during the 2 weeks prior to our sampling so we used data from an Oregon DEQ 

Station (located about 20 miles from the mouth of the estuary) during the month of September 

from 1994-2006. We used salinity from Station Y1 (bottom) in the Yaquina Estuary averaged 

over the 2 weeks prior to the sampling date as the ocean salinity (SO). Salinity data from the 

stations nearest to the macroalgae sampling locations were used in the analysis (Table 5-4) and 

in the calculations we salinity measured during the 2 weeks prior to the macroalgae sampling. 

The similarity between the observed nitrogen stable isotope ratios and those predicted from the 

conservative mixing model at Stations N1 and N2 suggested that oceanic and riverine sources 

were the dominant DIN sources (Table 5-6). There was more uncertainty in the results 

calculated using the cruise data (Station N1) than those using the salinity time series (Station 

N2). At Station N2, the analysis suggested that this station receives 76-92% of DIN from the 

ocean. Unfortunately, there were not time series salinity data in the vicinity of Station N3 and 

this station is in a region of salinity changing from marine to mesohaline (compare the median 

salinities at Stations Y1 and Y2, Table 5-4). The limited salinity data prevented a thorough 

analysis of the nitrogen stable isotope ratio at this location; however, the similarity in temporal 

patterns in the δ

15N during the dry season at Station N3 compared to Stations N1 and N2 (Figure 

5-8), suggests that the ocean was the dominant nutrient source at this location, but there may be 

an additional nitrogen source as evident by δ

15N of Station N3 > δ15N of Stations N1 and N2. 

The macroalgal δ

15N at Stations N4 and N5 were consistent with the two end member mixing 

model during the months of April through July. During August and September the observed δ

15N at Stations N4 and N5 were elevated above that predicted from the two end member mixing 

model. During April and May, the mixing model suggested that Station N4 was river dominated 

(88-97%). During June at Station N4, there was considerable variability in the observed isotope 

ratio with two of the samples reflecting isotope ratios (2.8 ‰) similar to that predicted using the 

two end member mixing model and three having elevated isotope ratios (5.6-7.4 ‰). Assuming 

that the mixing model was more representative than the observed isotope ratios, Station N4 was 

river dominated (80%) during June. During July, the two end member mixing model suggests 

that Station N4 received 58% of the DIN from oceanic sources and 42% from riverine sources. 

There was high variability in the isotope ratio predicted using the two end member mixing model 

and the mean value was about 1‰ above the observed isotope ratio. Using Isosource the 

maximum ocean contribution that could produce the observed isotope ratio during July at Station 

N4 is 46%. Based on these two analyses, this station was probably located in the transition 

region during July. Isosource suggested that during August Station N4 received 35-55% of DIN 

from the ocean, 21-35% from river, and 23-31% from WWTF/septic inputs. During September, 
131

Station N4 became ocean dominated, with it receiving 50-70% of DIN from ocean, 28-42% from 

river, and 1-10% from WWTF. This suggested that Station N4 is in a transition region and the 

dominant nutrient source depends upon the month sampled (even within the dry season). 

Similarity between the observed nitrogen stable isotope ratio and the two end member mixing 

model during the months of April through July at Station N5 suggested that the river and the 

ocean are the two major nitrogen sources. In addition, the observed isotope ratio suggests that 

this station is river dominated (70-98% of DIN) during these months (particularly during April – 

June). Station N5 is located between Stations Y2 and Y3, and there were considerable 

differences in isotope ratio predicted using these two different stations, particularly during the 

months of July - September. Results from Isosource suggested that during August Station N5 

receives 57-71% from the river, 9-29% from ocean, and 13-21% from WWTF/septic. During 

September, Station N5 received 51-65% of DIN from riverine sources, 16-36% from the ocean, 

and 12-20% from WWTF/septic. 

We used the conservative mixing model (Equations 5.4-5.6) combined with mean dry season end 

members (CR and CO) to determine the salinity (SM) where the nitrogen sources would switch 

from ocean to riverine dominance. During 2004, the mean dry season ocean end member (CO) 

was 11.7 μM (using data from Station Y1 in Yaquina with n = 214), while the riverine end 

member (CR) was 15.4 μM (using data from Station S6 in Alsea with n =19). The riverine end 

member was similar to dry season average of 15.7 μM for an Oregon DEQ Station which is 

located at rivermile 20. Using these values the boundary between the oceanic and riverine 

segments would occur at a salinity of about 20 psu, which is similar to the value of 21 psu 

calculated for Yaquina Estuary. In addition, assuming two end members (ocean and river), 

nitrogen isotope ratios above 5.2 ‰ indicate that the ocean is the dominant nitrogen source while 

δ

15N ratios below this indicate river is the dominant N source. 

From the stable isotope data and the conservative mixing model, it is clear that Stations N1 and 

N2 were ocean dominated and Station N5 was river dominated during the dry season and the 

boundary between ocean and riverine dominance in nitrogen sources was located between 

Stations N2 and N4. Using the salinity criterion developed in the above paragraph, median 

salinities from Stations Y2 and S3, suggested that Station N4 was river dominated during the dry 

season. We used historical salinity (median dry season) from Oregon DEQ at several stations 

located between S2 and N3 combined with the salinity criterion developed above to determine 

the boundary between the oceanic and riverine segments (Figure 5-7). We have a large degree of 

confidence in these zonation patterns because they are based on multiple lines of evidence. 
132

Table 5-4. Summary of dry season salinities in Alsea Estuary. 

STATION

MEDIAN 

SALINITY (psu)

5

TH PERCENTILE 

(psu) 

95TH PERCENTILE 

(psu) 

Datasondes (Deployed June 17 – October 12, 2004) 

Y1 31.1 16.6 33.7 

Y2 12.3 3.1 25.3 

Y3 4.6 0.1 11.0 

Mini-CTDs (Deployed September 14-October 6, 2004) 

M1 NA NA NA 

M2 7.5 0.0 13.1 

Water Quality Cruises (June – October 2004) 

S1 31.4 20.8 33.4 

S2 25.0 4.7 33.2 

S3 13.4 1.3 30.1 

S4 9.0 0.0 19.9 

S5 1.5 0.0 12.5 

S6 0 0.0 7.3 

Classification Low And High Tide Cruises 

STATION

LOW TIDE 

SALINITY (psu) 

HIGH TIDE 

SALINITY (psu) 

C1 25.4 32.7 

C2 20.0 32.5 

C3 12.4 32.2 

C4 8.8 29.1 

C5 6.6 17.5 

C6 4.3 6.4 

C7 2.9 5.1 

C8 2.5 2.8 

C9 1.5 0.4 

C10 0.2 0 

Table 5-5. Ocean and river end members for Alsea calculations. 

 

MONTH Co (μM) CR (μM) 

April 5.9 (n = 4) 27.5 (n = 2) 

May 7.7 (n = 11) 29.2 (n = 3) 

June 13.8 (n = 25) 9.5 (n = 3) 

July 10.1 (n = 23) 4.1 (n = 3) 

August 6.2 (n = 6) 6.9 (n = 2) 

September 14.5 (n = 20) 9.5 (n = 10) 
133

Table 5-6. Observed and predicted isotope ratios (from two end member model) in Alsea 

Estuary. 

MONTH 

OBSERVED 

ISOTOPE RATIO 

(‰) 

δMIX 

(‰) STATION USED 

Station N1 

April 5.4 ± 0.9 5.0 ± 1.6 S1 

May 6.2 ± 0.5 4.5 ± 1.3 S1 

June 7.5 ± 0.2 7.9 ± 0.2 S1 

July 8.3 ± 0.4 7.9 ± 0.4 S1 

August 7.9 ± 0.2 7.4 ± 0.8 S1 

Station N2 

June 6.8 ± 0.5 7.4 ± 1.5 Y1 

July 7.9 ± 0.2 7.9 ± 0.6 Y1 

August 7.9 ± 0.1 7.5 ± 0.9 Y1 

September 8.3 ± 0.2 7.7 ± 0.7 Y1 

Station N3 

September 8.6 ± 0.1 6.0 ± 2.5 C4 

Station N4 

April 3.2 ± 0.2 2.2 ± 0.3 S3 

May 2.6 ± 0.6 2.7 ± 1.3 S3 

2.5 ± 0.2 S3 June 5.0 ± 2.0 3.3 ± 0.8 Y2 

5.9 ± 1.1 S3 July 4.8 ± 0.6 5.7 ± 1.0 Y2 

6.1 ± 1.5 S3 August 9.8 ± 0.7 4.6 ± 1.0 Y2 

September 6.8 ± 1.3 5.8 ± 0.9 Y2 

Station N5 

April 2.3 ± 0.2 2.2 ± 0.2 S4 

May 2.1 ± 0.4 2.1 ± 0.2 S4 

2.3 ± 0.4 S4 

3.3 ± 0.8 Y2 

June 

2.8 ± 2.5 

2.1 ± 0.1 Y3 

4.9 ± 1.2 S4 

July 4.4 ± 0.8 5.9 ± 1.1 Y2 

3.9 ± 0.7 Y3 

4.5 ± 1.0 S4 

August 6.3 ± 0.2 4.6 ± 1.0 Y2 

3.2 ± 0.4 Y3 

3.1 ± 0.4 M2 

September 6.6 ± 0.4 5.8 ± 0.9 Y2 

3.7 ± 0.7 Y3 
134

5.4 Salmon River Estuary 

Data used to determine the zonation in Salmon River Estuary included salinity data from shortterm YSI and Mini-CTD deployments in 2004 (Table 5-7), macroalgae isotope data (Table 5-8) 

collected as part of the classification effort at two sites (N1 & N7) and previous data collected at 

five locations (N2 – N6) (DeWitt and Eldridge, PCEB, unpublished data). The locations of all 

sampling stations are presented in Figure 5-9. 

The macroalgae δ

15N did not show as strong an ocean signal in Salmon River Estuary as in the 

Yaquina and Alsea estuaries (maximum in δ

15N near mouth of about 6‰ at Salmon compared to 

8 ‰ in Alsea and Yaquina). This is because the estuary switches from ocean- to river-dominated 

over a tidal cycle with the salinities at the mouth changing from 33 to 10 psu (Figure 4-8). As a 

result, there was not a strong oceanic signal in the nitrogen stable isotope data even at the mouth 

(Table 5-8). Using the salinity data from the classification data collection effort and the 

conservative mixing model (Equations 5.4-5.6), we examined the importance of oceanic and 

riverine nitrogen sources within the estuary. The concentration of DIN in the riverine end 

member was determined from Station C10 on the low tide cruise (CR = 21.6 μM). This riverine 

end member was consistent with other data collected by U.S. EPA (Ozretich, PCEB, unpublished 

data), which had a mean DIN of 21.3 μM during July and August of 2001. The DIN 

concentration for the ocean end member was determined from flood-tide nutrient samples 

collected at Yaquina Estuary every flood tide during June-August 2004 (CO = 13.4 μM). 

Salmon River Estuary is located 45 km north of Yaquina Estuary, and in Section 4.3 we 

demonstrated that ocean conditions are relatively uniform over the geographic extent of the 

target estuaries. We used an average value for the ocean end member since we were using 

isotope data from different years (2000 and 2004). 

Based on the conservative mixing model, the isotope ratio predicted using the salinity data from 

Y1, M1 and M2 averaged between 6.5 ‰ and 7.0 ‰, which is similar to observed isotope ratios 

at Stations N1-N6. Using the conservative mixing model and salinity data from Y2, the 

predicted isotope ratio was 4.6 ‰, which is similar to the observed isotope ratio at N7. The 

agreement between predicted and observed isotope ratios suggested that oceanic and riverine 

inputs are the two main sources of nutrients to this estuary. There was better agreement between 

the predicted and observed isotope ratio when a short-term salinity time series was used rather 

than data from the high and low tide cruises. The nitrogen stable isotope ratio predicted using 

the average of the high and low tide cruises appeared to underpredict the macroalgal δ

15N. The 

isotope mixing model suggested that Stations N1-N6 received between 70-88% of the nitrogen 

from the ocean, while Station N7 received about 60% of the nitrogen from the river. Based on 

the median salinity of 25.9 psu at Station M2 and the macroalgal δ

15N indicating ocean 

dominance at Station N6, the boundary between the oceanic and riverine segments was located 

slightly above Station N6. This is consistent with the boundary being located approximately at a 

median dry season salinity of 25 psu. 

 
135

Figure 5-9. Location map for Salmon River estuary showing the locations of datasondes, water 

quality stations and isotope samples. The red dashed line shows the boundary between oceanic 

and riverine segments. 
136

Table 5-7. Summary of dry season salinities in Salmon River estuary. 

STATION

MEDIAN 

SALINITY 

(psu) 

5

TH

 

PERCENTILE 

(psu) 

95TH

PERCENTILE 

(psu) 

Datasondes (Deployed July 13-20, 2004) 

Y1 30.6 14 33.4 

Y2 16.5 2.6 29.7 

Mini-CTDs (Deployed July 9-29, 2004) 

M1 29.61 12.1 36.1 

M2 25.9 15.2 31.5 

M3 16.7 4.5 33.8 

1

Evidence of biofouling after 7/18/2004. Median calculated using data 

from July 9-18. 

Classification Low And High Tide Cruises 

STATION 

LOW TIDE 

SALINITY (psu) 

HIGH TIDE 

SALINITY (psu) 

C1 22.7 30.0 

C2 14.8 30.1 

C3 13.3 29.9 

C4 10.1 29.5 

C5 7.9 29.1 

C6 4.4 26.7 

C7 26.8 

C8 1.8 4.0 

C9 1.8 

C10 0.4 0.5 

Table 5-8. Observed and predicted isotope ratios (from two end member model) in Salmon 

River estuary.

STATION

SAMPLING 

DATE 

OBSERVED 

ISOTOPE RATIO 

mean ± standard 

deviation (‰) 

δMIX

mean ± 

standard 

deviation (‰)

STATION 

USED FOR 

MIXING 

MODEL 

N1 7/14/2004 5.9 ± 0.3 6.5 ± 1.3 C1 

5.8 ± 2.4 C2 N2 8/23/2000 6.6 ± 0.4 7.0 ± 1.5 Y1 

7.0 ± 1.5 Y1 N3 8/23/2000 5.7 ± 0.4 5.8 ± 2.4 C2 

6.8 ± 2.1 M1 N4 8/23/2000 6.7 ± 0.3 5.6 ± 2.5 C3 

N5 8/23/2000 6.9 ± 0.9 5.3 ± 2.8 C4 

6.5 ± 2.4 M2 N6 8/23/2000 6.5 ± 0.4 5.1 ± 2.9 C5 

N7 7/14/2004 3.6 ± 0.5 4.6 ± 1.8 Y2 
137

5.5 Umpqua River Estuary 

Data used to determine the zonation in Umpqua River Estuary included salinity (Table 5-9) and 

macroalgae isotope ratios (δ

15N) collected at five sites as part this study in 2005 (Table 5-10). 

Unfortunately, the GPS had an error during the sampling of N2 and as a result we do not know 

the exact sampling location, only that the sample was collected between Stations N1 and N3. 

For the salinity data, we had short-term datasondes and Mini-CTD data collected in 2005 and 

historic salinity available from Oregon DEQ. 

Using the salinity data from the classification data collection effort and a conservative mixing 

model (Equations 5.4-5.6), we examined the importance of oceanic and riverine nitrogen sources 

within the estuary. The DIN concentration of the riverine end member was determined from the 

Station C11 low tide cruise (CR = 15.8 μM). The DIN concentration for the ocean end member

was the mean of the high tide cruise at Stations C1 - C3 (CO = 13.8 μM), which was similar to 

dry season mean values of DIN from Yaquina Estuary for 2002-2004 (average = 14 μM). 

Comparison of predicted and observed isotope ratios suggested that Stations N1-N3 received a 

mixture of ocean and riverine nitrogen (particularly using Station Y1) with the ocean 

contributing between 80-90% of the nitrogen at these sites. It was not possible to distinguish the 

effect of the WWTF located near the entrance to Umpqua Estuary (Figure 5-10) due to the 

variability in predicted nitrogen stable isotope ratios associated with salinity variations at Station

Y1. The disagreement between observed and predicted macroalgal δ

15N at Station N5 suggested 

that there may be a third nitrogen source in this estuary. The City of Reedsport has a WWTF in 

the vicinity of Station N5 (distance from WWTF to Station N5 is about 2 km, Figure 5-10). 

Using Isosource, we calculated all of the possible combinations of the three nitrogen sources 

which could produce the observed isotope value of 5.4 ‰ at Station N5. Using the observed 

salinity at this site, we rejected the results from Isosource that had ocean contributions greater 

than 10% based on salinity data and the two end member mixing model. Based on this analysis, 

we estimated that the nitrogen sources at Station N5 were about 74-82% riverine and 15-20% 

associated with WWTF effluent. The observed macroalgal δ

15N at Station N4 was slightly 

elevated above the predicted isotope ratio from two end member model; however, the variability 

in the predicted ratio made it difficult to determine whether this elevation was associated with an 

additional nitrogen source or variability in the isotope ratios associated with the salinity varying 

from 2 to 32 psu over a tidal cycle. Based on the nitrogen isotope data, the transition from ocean 

to riverine dominance occurred between Stations N3 and N4. This was consistent with the data 

from the high tide cruise, which have high salinity (> 30 psu) water reaching Station C4 and a 

dramatic decrease in salinity above this station (decrease of about 17 psu between Stations C4 

and C5). In addition, the high tide cruise showed the input of oceanic nutrients (NO3

-

+NO2

-

 and 

PO4

3-) to Stations C1 - C4. This was evident in that NO3

-

+NO2

-

 decreases from 10 μM to 1.5 μM 

at between Stations C4 and C5. 

 

Using the oceanic and riverine DIN end members (CR = 15.8 μM and CO = 13.8 μM) and the two 

end member conservative mixing model, the boundary between the oceanic and riverine 

segments occurred at a salinity of about 18 psu. This agrees fairly well with the salinity 

(including historical salinity from Oregon DEQ) and macroalgae isotopes. The Oregon DEQ 

database has a salinity station by Station C3 that has a median dry season salinity of 19 psu, 

while a salinity station by Station C4 has a median dry season salinity of 16 psu. 
138

Figure 5-10. Location map of Umpqua River Estuary showing the locations of datasondes, water 

quality stations, isotope samples, and WWTFs. The red dashed line shows the boundary 

between the oceanic and riverine segments. 
139

Table 5-9. Summary of dry season salinities in Umpqua River Estuary. 

STATION 

MEDIAN SALINITY 

(psu) 

5

TH PERCENTILE 

(psu) 

95TH PERCENTILE 

(psu) 

Datasondes (Deployed June 21 – 26, 2005) 

Y1 30.7 19.8 33.7 

Y2 6.2 0.7 21.3 

Y3 1.1 0.2 4.9 

Y4 0.1 0.0 0.5 

Mini-CTDs (Deployed June 20 – 26, 2005) 

M1 14.8 3.7 28.2 

M2 0.0 0.0 4.7 

M3 0.0 0.0 0.0 

Classification Low And High Tide Cruises

STATION 

LOW TIDE 

SALINITY (psu) 

HIGH TIDE 

SALINITY (psu) 

C1 23.4 32.7 

C2 15.3 32.7 

C3 9.8 32.3 

C4 5.3 30.5 

C5 1.9 13.7 

C6 1.8 4.21 

C7 1.3 1.46 

C8 0.1 5.6 

C9 0.8 6.7 

C10 0.2 2.6 

C11 0.0 0.6 

C12 0.0 0.9 

C13 0.0 0.1 

Table 5-10. Observed and predicted isotope ratios (from two end member model) in Umpqua 

River Estuary. 

STATION SAMPLING 

DATE 

OBSERVED 

ISOTOPE RATIO 

mean ± standard 

deviation 

(‰) 

δMIX 

mean ± standard 

deviation 

(‰) 

STATION 

USED FOR 

MIXING 

MODEL 

7.6 ± 0.9 Y1 N1 6/24/2005 7.8 ± 0.5 7.3 ± 1.4 C1 

N2 6/23/2005 7.3 ± 0.2 6.5 ± 2.5 C2 

N3 6/22/2005 7.2 ± 0.2 5.9 ± 3.2 C3 

N4 6/22/2005 5.7 ± 0.1 4.9 ± 1.6 M1 

2.2 ± 0.3 M2 N5 6/26/2005 5.4 ± 0.2 2.5 ± 0.7 C8 
140

5.6 Coos Estuary 

Coos Estuary had the highest isotope ratios of the seven estuaries that we sampled with three 

stations (N3-N5) having δ

15N values of about 10 ‰ (Table 5-11). This elevation of the isotope 

ratio above the marine end member may be associated with the presence of multiple WWTFs in 

the watershed. There are 3 major WWTFs adjacent to the estuary (Figure 5-11), each of which is 

permitted to discharge between 2 to 5 million gallons per day of treated effluent into the estuary 

(http://www.deq.state.or.us/wq/sisdata/facilitycriteria.asp). This is the only estuary that has 

multiple major WWTFs adjacent to the estuary. 

Using the salinity data from the classification data collection effort (Table 5-12) and the 

conservative mixing model (Equations 5.4-5.6), we examined the importance of oceanic and 

riverine nitrogen sources within the estuary. Since our most riverine station (C19) had a salinity 

of 13 psu, we could not use our data for the river end member. We used mean dry season DIN 

(CR = 13.7 μM, n = 28) from the Oregon DEQ database for a station at the Anson Roger Bridge 

on the Coos River (5 km upriver of Station C19). This value is similar to the concentration 

during the low tide cruise at Station C19 (10.3 μM). For the ocean end member, we used the 

mean high tide DIN (CO = 21.3 μM) from Stations C1, C4 and C5. This is probably a good 

estimate of the ocean end member since there were strong upwelling conditions for about 3 

weeks prior to our data collection effort (based on water temperature, see Figure 4-2). 

Unfortunately, the Mini-CTD near the mouth of Coos Estuary (M1) malfunctioned. Using the 

datasondes closest to the mouth of the estuary (Y1 and Y7) and the two end member 

conservative mixing model using ocean and river end members, we predict that the isotope ratio 

near the estuary mouth was 8.2-8.3 ‰. Unfortunately, we did not have macroalgae isotope ratio 

data at the estuary mouth, but Stations N1 and N2 are fairly close to the mouth and had similar 

isotope ratios. Assuming two end member mixing of riverine and ocean sources, the observed 

isotope ratios at Stations N1 and N2 suggest that the ocean provides 78 to 87% of the nitrogen at 

these sites. Fry et al. (2001) found that the nitrogen stable isotope ratio of green macroalgae 

during the dry season outside the mouth of Coos Estuary was 8.3 – 9.0 ‰ (sampled during 

October 1998 and July 1999) and inside the mouth of Coos Estuary the isotope ratio was 7.7 – 

7.8 ‰ sampled during the same months, which is similar to our data. 

 

The observed isotope ratios at Stations N3-N5 exceed those expected from oceanic nitrogen 

sources suggesting there is an additional nitrogen source. Isosource was used to calculate all 

possible combinations of ocean, riverine, and WWTF nitrogen sources that could produce the 

observed isotope ratios at Stations N3-N5. Since the short-term salinity time series provided 

better estimates of the isotope mixing than the average of low and high tide cruises, we used the 

two end member mixing model with data from Y2, Y3, and M3 to estimate the contribution of 

the ocean. The two end member mixing model of ocean and river sources using Station Y2 and 

Y3 suggested that the ocean provided about 97% of the DIN at Station N3 and N4. Using 

Isosource and selecting only those solutions with an ocean contribution ≥ 87%, the contribution 

of nitrogen sources were estimated to be 87-91% oceanic, 9-13% WWTF and < 2% riverine. 

The two end member mixing model of ocean and river sources using Station M3 suggested that 

the ocean provided about 65% of the DIN at Station N5. Using Isosource and selecting only 

those solutions with ocean contribution within ± 10% of that predicted from the two end member 

mixing model, the contribution of nitrogen sources at Station N5 were estimated to be 55-75% 

oceanic, 17-26% WWTF and 7-21% riverine. This may be an underestimate the contribution of 
141

the ocean and an overestimate of the contribution of the WWTF since M3 is located slightly 

upriver of N5. Predicted isotope ratios calculated using Stations C10 and C16 were higher than 

those using M3, and yield oceanic contributions of about 90%. 

Based on this analysis, all of the macroalgal δ

15N sampling locations (N1-N5) were dominated 

by oceanic nitrogen sources, and the elevation of the isotope ratio above ocean end member at 

Stations N3 and N5 is due to the nitrogen sources being a mixture of oceanic and WWTF 

sources. We used a median salinity of 25 psu (as a conservative estimate) as the boundary 

between the oceanic and riverine segments. We used historical salinity from Oregon DEQ and 

South Slough Estuarine Reserve to aid in the zonation. 

Table 5-11. Observed and predicted isotope ratios (from two end member model) in Coos 

Estuary. 

STATION

SAMPLING 

DATE 

OBSERVED 

mean ± standard 

deviation 

(‰) 

δMIX

mean ± standard 

deviation 

STATION 

USED FOR 

MIXING 

MODEL 

8.4 ± 0.0 C2 

N1 8/7/2005 7.6 ± 1.8 8.2 ± 0.1 Y5 

8.1 ± 0.1 Y6 

8.3 ± 0.1 Y1 

N2 8/7/2005 7.0 ± 1.9 8.2 ± 0.1 Y7 

8.3 ± 0.1 C4 

8.2 ± 0.1 Y2 N3 8/5/2005 9.6 ± 0.3 8.2 ± 0.1 C7 

8.2 ± 0.1 Y3 N4 8/6/2005 10.0 ± 0.4 8.1 ± 0.2 C6 

6.1 ± 0.9 M3 

N5 8/4/2005 10.0 ± 0.1 7.9 ± 0.1 C16 

7.8 ± 0.1 C10 
142

Figure 5-11. Location map of stations in Coos Estuary showing the locations of datasondes, 

water quality stations, isotope samples, and WWTFs. The red dashed line shows the boundary 

between the oceanic and riverine segments. 
143

Table 5-12. Summary of dry season salinities in Coos Estuary. 

STATION 

MEDIAN 

(psu) 

5

TH PERCENTILE 

(psu) 

95TH PERCENTILE 

(psu) 

Datasondes (Y1-Y4 Deployed August 3 – 10, 2005; Y5 And Y6 Deployed July 26 – 

August 24, 2006; Y7 Deployed July 6 – 20, 2006). 

Y1 33.5 31.9 34.0 

Y2 32.5 31.7 33.5 

Y3 32.4 30.8 33.9 

Y4 28.9 27.0 30.0 

Y5 32.5 31.4 33.1 

Y6 32.1 29.4 33.0 

Y7 32.4 30.4 33.4 

Mini-CTDs (Deployed August 3 – 10, 2006) 

M1 NA NA NA 

M2 27.8 24.9 30.8 

M3 20.6 8.6 24.5 

Classification Low And High Tide Cruises

STATION LOW TIDE SALINITY (psu) HIGH TIDE SALINITY (psu) 

C1 33.1 33.8 

C2 NA 34.2 

C3 NA 33.6 

C4 32.5 33.9 

C5 31.7 33.8 

C6 30.4 33.1 

C7 32.0 33.1 

C9 29.8 32.3 

C10 29.0 30.6 

C11 27.9 29.9 

C12 26.3 28.3 

C14 31.9 33.9 

C15 31.4 NA 

C16 29.4 30.9 

C17 20.6 20.7 

C18 23.2 28.3 

C19 12.5 18.5 
144

5.7 Tillamook Estuary 

Data used to determine the zonation in Tillamook Estuary included salinity data from short-term 

YSI and Mini-CTD deployments in 2005 (Table 5-13) and macroalgae isotope data (Table 5-14) 

collected as part of this study at four sites (N1-N4, Figure 5-12). Historic salinity data were also 

available at 23 stations from the Oregon DEQ (see Figure 5-13; 

http://deq12.deq.state.or.us/lasar2/). Using the salinity data from the classification data 

collection effort (Table 5-13) and the conservative mixing model (Equations 5.4-5.6), we 

examined the importance of oceanic and riverine nitrogen sources within the estuary. For the 

ocean end member in the conservative mixing model, we used the high tide DIN (CO = 24.1 μM) 

from Station C1. This is probably a good estimate of the ocean end member since there were 

strong upwelling conditions (based on water temperature in Figure 4-2) for about 1 week prior to 

the macroalgae isotope sampling. Since there are five rivers discharging into Tillamook Estuary, 

we used different river end members depending upon the macroalgae sampling location. 

Stations N1 and N2 are located in the vicinity of the Miami River. For Stations N1 and N2, we 

used the mean DIN concentrations from Station C3 (CR = 52.4 μM), which is in the Miami 

River. Station C3 had similar high and low tide DIN concentrations and a salinity of <1 psu. 

Colbert (2004) sampled the riverine end members of Tillamook in 1998 and 1999; their mean 

DIN for the Miami River during May-August was 51.4 μM, which was comparable to our river 

end member from Station C3. The Tillamook, Trask and Wilson rivers discharge into the 

southern portion of Tillamook Estuary. For Station N4, we used the mean of the low tide cruise 

for Stations C13-C15 (CR = 39 μM), which had salinities ranging from 0.1-1.3 psu. Colbert 

(2004) sampled the Tillamook, Trask and Wilson rivers in 1998 and 1999 and found similar dry 

season end members (mean = 32.2 μM). Since Station N3 is located in the central portion of the 

estuary, we calculated the isotope ratios using the two different values for the river end member 

(CR = 39 and 52.4 μM) and present results of both in Table 5-14. Interestingly, Tillamook 

Estuary had the highest values of riverine end members of the seven target estuaries that we 

sampled. 

The observed isotope ratio at Station N1 was about 1‰ less than the isotope ratio predicted using 

the two end member mixing model of ocean and river sources (using salinity data). Based on 

this analysis, the contribution of oceanic and riverine nitrogen sources was 85% and 15%, 

respectively. Using Isosource to mix the three end members, we found that the maximum ocean 

contribution that could produce the observed isotope ratio was 67% (with the remainder of the 

nitrogen being riverine). Based on both of these analyses, Station N1 was ocean dominated. 

In the vicinity of Station N2 there was high variability in the salinity which is evident in the large

difference between salinities at Stations Y1 and Y2 and C2 and C3. Stations Y1 and C2 are 

marine, while Stations Y2 and C3 are fresh. Since we did not have salinity observations at 

Station N2, and considering the high variability in the nearby stations, the two end member 

mixing model did not produce useful results. Isosource was used to calculate all possible 

combinations of ocean, riverine, and WWTF nitrogen sources that could produce the observed 

isotope ratio. There were limited results that could produce the observed isotope ratio, so the 

salinity constraint was not needed. Results from Isosource suggested that the primary nitrogen 

source was riverine (80-94%) with oceanic and WWTF being minor components (10-20% and 3-
145

7%, respectively). Based on this analysis, riverine inputs were the dominant nitrogen source at 

Station N2. 

Unfortunately, we did not have acceptable salinity time series data in the vicinity of Station N3. 

For the two end member mixing model, we used low and high tide cruise data from Stations C5 

and C6. The isotope ratios predicted using the two end member mixing model were similar to 

those observed, particularly for Station C6. Based on the two end member mixing model for 

Station N3, the ocean contribution ranged from 65 to 82% and the riverine contribution ranged 

from 18 to 34%, depending upon which station and river end member was used in the analysis. 

Using Isosource and only selecting those results that had oceanic contributions >55%, the 

oceanic contribution ranged from 55 to 68%, riverine ranged from 32 to 42% and WWTF was 

less than 4%. Both of these analyses suggested that Station N3 was ocean dominated. 

At Station N4 the observed isotope ratio was 1.7-2.6 ‰ higher than the isotope ratio predicted 

using the two-end member mixing model (depending upon which station is used for the salinity) 

suggesting that there may have been an additional nitrogen source. Based on a two end member 

mixing model, the oceanic contribution ranged from 9 to 23%, while the riverine contribution 

ranged from 76 to 91% (depending upon which station was used). There are two WWTF 

facilities (one major and one minor) within 4 km of Station N4 (Figure 5-12). Using Isosource 

and selecting only those solutions with the ocean contribution within ±10% of that predicted 

from the two end member mixing model, we estimated that the riverine contribution ranged from 

60 to 83%, oceanic contributions were less than 30%, and WWTF contributed between 5 and 

18%. Therefore, based on these analyses Station N4 was river dominated. 

Due to the uncertainty in the analysis of the isotope data due to multiple riverine end members, 

multiple WWTF inputs, and non-ideal availability of salinity data for the mixing model relative 

to macroalgae sampling locations, we used recent and historic salinity data in the derivation of 

the zonation. Using the values of the riverine and oceanic end members from Tillamook Estuary 

(CR = 39 -52 μM and Co = 24.1 μM) and the conservative mixing model, the boundary between 

oceanic and riverine dominance occurred at a salinity of about 20-23 psu. The value of the ocean 

end member was relatively high compared to averaged dry season values for Yaquina Estuary. 

Using Co = 13.4 μM (which is the June to August average for 2004 at Yaquina Estuary), the 

boundary would be located at about 25 psu. To be conservative in our estimates, we used a 

salinity criterion of 23-25 psu to divide the estuary into oceanic and riverine segments (Figure 5-

13). The boundary between the oceanic and riverine segments in the northern portion of 

Tillamook Estuary was based upon the analysis of the isotope data at Station N3. 

 
146

Figure 5-12. Location map for Tillamook Estuary showing the location of datasondes, water 

quality stations, isotope samples, WWTFs. The red dashed line shows the boundary between the 

oceanic and riverine segments. 
147

Table 5-13. Summary of dry season salinities in Tillamook Estuary (NA denotes missing or bad 

data). 

STATION 

MEDIAN 

(psu) 

5

TH PERCENTILE 

(psu) 

95TH PERCENTILE 

(psu) 

Datasondes (Deployed July 19-25, 2005) 

Y1 33.9 25.0 34.9 

Y2 2.4 0.0 27.2 

Y3 29.6* 10.6* 34.1* 

Y4 7.8 0.5 23.7 

* Evidence of biofouling, summary statistics computed using 2 day record. 

Mini-CTDs (Deployed July 19-25, 2005) 

M1 2.4 2.2 12.7 

M2 11.9 2.6 26.3 

M3 17.2 2.5 25.1 

Classification Low And High Tide Cruises

STATION LOW TIDE SALINITY (psu) HIGH TIDE SALINITY (psu) 

C1 29.7 33.4 

C2 27.3 33.2 

C3 0.1 0.1 

C4 11.6 9.1 

C5 25.2 33.1 

C6 19.4 32.1 

C7 NA 16.5 

C8 NA 7.4 

C9 11.0 27.7 

C10 NA 21.8 

C11 0.0 0.0 

C12 0.2 0.7 

C13 0.1 1.6 

C14 1.3 4.0 

C15 0.1 0.1 
148

Table 5-14. Observed and predicted isotope ratios (from two end member model) in Tillamook 

Estuary. 

STATION

SAMPLING 

DATE 

OBSERVED 

mean ± standard 

deviation 

(‰) 

δMIX 

mean ± standard 

deviation 

(‰) 

STATION 

USED FOR 

MIXING 

MODEL 

7.5 ± 1.0 Y1 

N1 7/20/2005 6.1 ± 0.9 7.7 ± 1.0 C1 

7.3 ± 1.4 C2 

7.3 ± 1.4 C2 

2.0 C3 

7.5 ± 1.0 Y1 N2 7/21/2005 3.2 ± 0.5 

2.6 ± 1.4 Y2 

7.0-7.2 C5 N3 7/22/2005 6.2 ± 0.6 6.2-6.5 C6 

3.5 ± 1.3 Y4 N4 7/23/2005 5.2 ± 0.4 2.6 ± 0.5 M1 
149

Figure 5-13. Median dry season salinities in Tillamook Estuary (with number of samples 

indicated in parentheses). The red dashed line shows the boundary between the oceanic and 

riverine segments. 
150

5.8 Nestucca Estuary 

Data used to determine the zonation in the Nestucca Estuary included salinity data from shortterm YSI and Mini-CTD deployments in 2004 (Table 5-15) and macroalgae isotope data 

(Table 5-16) collected as part of the classification effort at two sites (N1 & N2). During our 

2004 field work, we lost two instruments in the lower portion of the estuary. During September 

2006, we collected additional data in this estuary for use in confirming zonation. A YSI 

datasonde was deployed near the mouth (Y2) and Mini-CTDs were deployed at 3 locations (M3-

M5). 

As discussed in Section 4.3, the Nestucca Estuary was sampled during low ocean nutrient 

conditions, which is reflected by DIN concentration of about 2 μM at Stations C8-C10 during the 

high tide cruise. Flood-tide water temperature from Yaquina during the 2 weeks prior to the 

isotope sampling indicated that the coastal ocean conditions were variable switching between 

upwelling and downwelling conditions (Figure 4-1). There were limited oceanic end member 

DIN data from Yaquina Estuary during the two weeks prior to the collection date (8/19/04) of the 

isotope sampling in Nestucca; the mean ocean end member DIN concentration from Yaquina 

during August 5th - 7th was 5.7 μM (n=6), and averaged over one month prior to the isotope 

sampling was 15.7 μM (n = 38). Due to this variability in ocean end member DIN 

concentrations and limited end member data available for the 2 weeks prior to sampling, we 

present results using Co values of both 5.7 and 15.7 μM (Table 5-16). Since the river end 

member at Station C1 was fairly constant between the low and high tide cruises, we used the 

mean of the low and high tide cruises as the riverine end member (CR = 38.4 μM). We did not 

have short-term time series salinity data in the vicinity of Station N1 during 2004 due to the loss 

of instruments. We used salinity data from Station C9 for the isotope mixing model and as an 

approximation, we also used time-series salinity data from Station Y2, which was collected in a 

similar time period (month) as the macroalgae samples from N1 but two years later. Using 

salinity data from Y2, the observed isotope ratio at N1 was similar to that predicted using the 

two-end member conservative transport mixing model. Based upon isotope ratio, Station N1 

received about 70-90% of nitrogen from oceanic sources. 

The observed isotope ratio at Station N2 was substantially higher than that predicted from the 

two-end member conservative mixing model (regardless of which value of Co was used in the 

calculation), suggesting that there was an additional nitrogen source in the system or that 

denitrification was important (although see discussion Section 5.9). There is a WWTF located in 

the vicinity of Station N2, which discharges into the Nestucca River about 1.5 miles upstream of 

its confluence with Nestucca Estuary. In addition, there are two other minor WWTFs that 

discharge into the Nestucca River about 7 - 10 miles upstream of the mouth of the estuary. The 

presence of these three facilities suggests that WWTF input may be responsible for the observed 

deviation from the two-end member mixing model. Using Isosource constrained by the salinity 

data, the ocean contribution ranged between 8-13%, the riverine contribution ranged between 70-

80%, and the WWTF contribution ranged between 10-20% (depending upon which end member 

is used). 

Due to limited data availability, the southern boundary between the oceanic and riverine 

segments was based upon the salinity criterion (using data from Station M2). The northern 
151

boundary was based upon data from the high tide classification water quality cruises (salinity and 

dissolved inorganic nutrient data). Data from the high tide cruises suggested that ocean water (as 

indicated by high salinity and nutrient levels comparable to Station C10) reached Station C5 and 

there was a dramatic decrease in salinity between Stations C5 and C4 (a difference of about 19 

psu). In addition, the high tide cruise NO3

-

+NO2

-

 concentrations were low (< 2 μM) between 

Stations C10 and C5, reflecting the low ocean nutrient conditions during this cruise. There was 

an increase in NO3

-

+NO2

-

 (of about 13 μM) between Stations C5 and C4 reflecting an increase in 

importance of riverine (or WWTF) nitrogen sources. 

Using the conservative mixing model and the ocean and river end members for DIN (CR = 38 μM 

and CO = 15.7 μM), the boundary between ocean and river dominance occurred at a salinity of 

24 psu. Based on median salinity, the estuary should be river dominated in the vicinity of 

Station M1 (median salinity of 12 psu). The salinity data collected during 2006 from Stations 

M3 and M4 confirmed that they were river dominated and that Station M5 was ocean dominated. 

There is some uncertainty in the zonation of this estuary (in particular the northern line) due to 

limited data. 
152

Figure 5-14. Location map of Nestucca Estuary showing the locations of datasondes, water 

quality stations, isotope samples, and WWTF. The red dashed line shows the boundary between 

the oceanic and riverine segments. 
153

Table 5-15. Summary of dry season salinities in Nestucca Estuary. 

STATION 

MEDIAN 

(psu) 

5

TH PERCENTILE 

(psu) 

95TH PERCENTILE 

(psu) 

Datasondes (Y1 Deployed July 28 – August 19, 2004, And Y2 Deployed From August 29 – 

September 1, 2006) 

Y1 6.4 1.0 18.1 

Y2 33.2 22.4 33.5 

Mini-CTDs (M1 And M2 Deployed July 29 – August 19, 2004; M3 Deployed August 29-

September 1, 2006, M4 Deployed September 1 – 6, 2006, M5 Deployed August 29 – 

September 6, 2006.) 

M1 11.6 0.0 32.6 

M2 23.0 14.6 31.8 

M3 8.1 3.1 28.3 

M4 19.1 1.0 31.9 

M5 28.5 17.6 32.3 

Classification Low And High Tide Cruises

STATION LOW TIDE SALINITY (psu) HIGH TIDE SALINITY (psu) 

C1 1.6 3.8 

C2 3.3 5.1 

C3 5.2 9.0 

C4 6.8 12.2 

C5 7.2 31.0 

C6 11.0 30.5 

C7 16.5 32.2 

C8 18.1 32.7 

C9 21.3 32.7 

C10 23.9 32.7 

C11 19.2 31.6 

C12 17.7 30.7 

C13 17.7 27.5 

C14 19.9 20.5 

C15 19.8 15.2 

Table 5-16. Observed and predicted isotope ratios (from two end member model) in Nestucca 

Estuary.

STATION

SAMPLING 

DATE 

OBSERVED 

mean ± standard 

deviation 

(‰) 

δMIX 

calculated using 

Co = 5.7 μM 

(‰) 

δMIX 

calculated using 

Co = 15.7 μM 

(‰) 

STATION 

USED FOR 

MIXING 

MODEL 

3.6 ± 0.4 5.1 ± 0.5 C9 N1 8/19/2004 6.6 ± 0.1 7.0 ± 1.7 7.6 ± 1.2 Y2 

2.3 ± 0.3 2.8 ± 0.7 Y1 

N2 8/19/2004 5.3 ± 1.4 2.1 ± 0.1 2.2 ± 0.1 C1 
154

5.9 Synthesis and Uncertainties in Zonation 

Utilizing δ

15N of green macroalgae combined with short-time series of salinity, we divided the 

target estuaries into oceanic and riverine segments. In addition, using Isosource constrained by 

the salinity data we estimated the contribution of WWTF inputs. Based on our analysis, oceanic 

and riverine inputs were the dominant nitrogen sources within the target estuaries. The 

maximum contribution of WWTF estimated from stable isotope data was 30%. We feel that our 

analysis is robust since it uses multiple lines of independently gathered evidence which lead to 

similar conclusions. The analysis utilizing nitrogen stable isotopes as a tracer of oceanic and 

riverine nitrogen inputs appears to be consistent with zonation based upon median salinities. 

Depending upon the DIN concentration in the riverine end member, the zonation occurred at 

median salinities ranging from 18 to 25 psu. Shirzad et al. (1988) divided Oregon estuaries into 

three zones (seawater (≥ 25 psu), mixing (0.5-25 psu), and tidal fresh (0-0.5 psu) using average 

annual and depth-averaged salinity data. They included zonations for Alsea, Yaquina and 

Tillamook estuaries. Our oceanic zone extends further upriver (1.6, 3.2, and 8 km for Alsea, 

Tillamook and Yaquina, respectively) than the seawater zones presented in Shirzad et al. (1988). 

This is probably a result of our zonation being based upon dry season conditions, whereas the 

ones in Shirzad et al. (1988) are based upon average annual conditions. 

Treated effluent associated with WWTF is discharged into six of the target estuaries. In 

Yaquina, Umpqua River and Nestucca estuaries, the WWTF effluent is discharged into the 

riverine segment, while in the Alsea and Coos estuaries the discharge is in the oceanic segment. 

Tillamook Estuary has multiple WWTFs discharging into it, one into the oceanic segment and 

two into the riverine segment. Effluent discharged into oceanic segments would experience 

strong tidal mixing, and the effluent would remain in the estuary for a shorter time period. One 

method that may aid in determining the susceptibility of estuaries to nutrient enrichment would 

be to overlay point source inputs on the ocean-river zonation. 

There is some uncertainty in the estuarine zonation as a result of data gaps as well as interannual 

variability. This analysis was based upon one-time sampling of natural abundance stable isotope 

of green macroalgae. When possible, we compared our stable isotope values to other data 

available for the target estuaries. Coastal systems are highly dynamic, responding to 

environmental forcing at scales ranging from minutes (e.g. changes in tidal elevation) to days 

(e.g. storm tracks) to decades (e.g. the Pacific Decadal Oscillation). The zonation described here 

is based primarily on single sampling events or a few days of sampling. Consequently, it is 

imperative to recognize that these zonation schemes are intended to provide guidance at a gross 

scale. We are most confident in the results for simpler estuaries (e.g. one riverine source) and 

systems where extensive datasets were available (e.g., Alsea, Salmon and Yaquina). When 

possible, we compared our value of riverine and oceanic end members to values from other 

sources. These comparisons revealed that the river end members we utilized were consistent 

with historical data. For estuaries where there are multiple rivers flowing into the estuary (e.g., 

Coos and Tillamook) there is more uncertainty in the results given the logistical and financial 

constraints of sampling multiple riverine inputs. 

One of the limitations of this analysis is that we cannot distinguish human (WWTF or septic) 

from animal waste because their nitrogen stable isotope ratios are similar. Other chemical tracer 

analyses can make this distinction; however, these methods were not used in this study. 
155

Additionally, we assume that macroalgae do not fractionate (preferentially take up 14N relative to 

15N) during nitrogen assimilation, which is supported by the recent experimental work of Cohen 

and Fong (2005). We are currently conducting laboratory experiments to critically evaluate this 

assumption for green macroalgae collected from Yaquina Estuary. In addition, our method 

assumes that the nitrogen stable isotope ratio is a result of a mixture of oceanic, riverine and 

WWTF sources, and that the end member values are the same for all of the target estuaries. We 

believe this is a good assumption due to the similarity in land cover within the watersheds of the 

seven estuaries (Table 3-3) and the similarity in ocean conditions over the geographic extent of 

our study (Section 4.3). Of the target estuaries, Coos and Umpqua had the least amount of alder 

in the watershed (See Section 4.2). Fry et al. (2001) sampled water column nitrate and green 

macroalgae in the Coos Estuary. The δ

15N of nitrate at a riverine site averaged +1.7‰ and of 

macroalgae averaged 1.8 ‰, which is consistent with our riverine end member. Umpqua had the 

lowest amount of red alder in the watershed (< 0.5%), and the most riverine macroalgae 

sampling sites there had a minimum isotope ratio of +5.4 ‰. If the riverine end member for 

Umpqua was not +2 ‰, we may have over-estimated the contribution from WWTF inputs. The 

watersheds of the seven estuaries are primarily forested and have low population densities. If 

there is an additional source that we have neglected or if denitrification is important, we may 

have incorrectly calculated the contribution of the sources. Kendall and McDonnell (1998) 

recommend that the contribution of nitrogen sources from natural abundance stable isotopes be 

confirmed through an independent non-isotopic method. For Yaquina Estuary, we were able to 

confirm the isotopic contribution of sources using the transport model. 

Denitrification can result in elevation of the δ

15N, which can be misinterpreted as WWTF input 

in our analysis. Kendall and McDonnell (1998) estimated that denitrification of fertilizer nitrate 

with a δ

15N of 1‰ can result in the residual nitrate having an isotope ratio of 15‰, which is 

similar to animal or human waste. The importance of denitrification in nitrogen removal in 

estuaries is a function of residence time (Dettmann, 2001). Denitrification represents a 

significant loss of nitrogen in estuaries with relatively long residence times. The residence times 

of the target estuaries are relatively short (less than 1 month) due to their small volume, strong 

tidal forcing, and high freshwater inflow (see Tables 3-6 and 3-7). We would expect that the 

importance of denitrification would increase as the riverflow declines. However, based on the 

analysis of Dettmann (2001) and residence times (Table 3-7), less than 10% of the nitrate in the 

target estuaries would be denitrified. Furthermore, the ability of the Yaquina transport model to 

reproduce the spatial and temporal patterns in the isotope data strongly suggests that the nitrogen 

stable isotope ratio of the macroalgae is being determined primarily by a mixture of the three 

nitrogen sources (oceanic, riverine, and WWTF) rather than by denitrification. Our analysis also 

assumes that the δ

15N of the sources is the same across the region. Comparisons of macroalgal 

nitrogen stable isotope ratios between Yaquina and Alsea are similar for both the ocean and river 

end members (Figures 5-3 and 5-8). In addition, macroalgae δ

15N from Coos Estuary by Fry et 

al. (2001) further support the validity of this assumption. This analysis also assumes that there is 

minimal temporal variability in the δ

15N of the end-members. The ability of the Yaquina 

transport model to predict the δ

15N reliably using fixed values for the end-members (Figures 5-4 

and 5-5) indicates that this is a reasonable assumption. Finally, we assume that the WWTFs 

represent a minor contribution of the total freshwater input to these systems. Riverine flow rates 

are many times larger than WWTF inputs even during periods of low river flow, suggesting that 

this is a good assumption. 
156

Since we sampled intertidal macroalgae, there is some potential that our analysis would be 

biased (over estimating oceanic contribution of nitrogen) due to elevation of the samples. If 

macroalgal samples are collected at a high elevation relative to mean lower low water, then they 

may only be exposed to water during higher water elevations, which would tend to occur during 

flood tides. Since we primarily are concerned with nitrogen sources that seagrass habitat is 

exposed to and most of the seagrass habitat is located in the intertidal, this is probably not a 

significant source of error. This error would be more significant for highly stratified estuaries, 

which is typically not the case for Oregon estuaries during the dry season (Tables 3-1 and 3-6). 

Model simulations also reveal that this error would be most important in the lower portions of 

the estuaries compared to the upper. 

In our study area, most of the nitrogen sources to the estuary are isotopically distinct (Figure 5-1) 

which leads to a relatively clean 2 or 3 end-member mixing solution. As discussed in 

Section 3.7, watershed nitrogen inputs in the area are generally related to red alder (Alnus rubra) 

a nitrogen fixing species characterized by δ

15N ranging between -3 and -0.5 ‰ (Hobbie et al., 

2000; Tjepkema et al., 2000; Cloern et al., 2002). Samples from the Yaquina River indicate that 

the δ

15N of the nitrate in the river water ranges from +0.4 to +2.4 ‰ (Kaldy, unpublished), 

which is consistent with macroalgal δ

15N values for our riverine sites. Literature values for the δ

15N of oceanic nitrate (the dominant nutrient associated with upwelling) along the PNW coast of 

the United States range between +6.6 and +7.7 ‰ (Kienast et al., 2002; Wankel et al., 2006). 

The δ

15N of the nitrate for water samples collected from 25 miles offshore of the Yaquina 

Estuary range from +6.7 to +7.6 ‰ (Kaldy, unpublished). The δ

15N values of green macroalgae 

utilizing recently upwelled water along the Oregon coast typically range between +7 and +9 ‰ 

(Fry et al., 2001; and Kaldy, unpublished). WWTF effluent generally has high δ

15N with values 

ranging between +7 and +25 ‰ (Heaton, 1986; Jones et al., 2003). Future work includes the 

measuring the isotope ratios of WWTF effluent from the local sources. Agricultural fertilizer 

inputs to coastal Oregon estuaries are minimal since there are no major agricultural crops 

cultivated along the coast. The amount of cultivated land in the watersheds of the seven target 

estuaries varied from 0 to 0.22% (Table 3-3). In some localized areas animal waste inputs may 

be substantial, notably Tillamook Estuary; however our isotope analyses cannot distinguish 

between animal and human waste. 

The use of macroalgal δ

15N to identify nitrogen sources in estuaries has received attention in the 

recent scientific literature (Cohen and Fong, 2005). Unfortunately, the use of δ

15N is not always 

clear cut. Nitrogen dynamics are extremely complex, mediated by a variety of microbes. 

Different microbial biochemical transformations have specific isotope fractionation factors such 

that the products have different δ

15N values than the initial reactants (Fry et al., 2003). 

Consequently, caution must be used in the interpretation of δ

15N data, in particular neglecting 

the ocean end member. For example, the direct application of the δ

15N regression equations 

from Cole et al. (2004) to the data presented here would erroneously suggest that WWTF have a 

much greater impact on estuarine nitrogen dynamics. The background marine signal associated 

with upwelling is a departure from the general nutrient loading paradigm (e.g. nutrients primarily 

from anthropogenic watershed sources). The potential error can result from not accounting for 

all nitrogen sources and is likely to occur in areas where ocean upwelling is a dominant feature 

of seasonal nutrient inputs as it is along the Pacific Coast of the U.S. (Fry et al., 2003; Cole et al., 
157

2004). We have used multiple lines of evidence to support our conclusions. Specifically, we 

have used two-end member mixing models of δ

15N and salinity as well as Isosource (Phillips 

and Gregg, 2003) and a transport model. This approach was validated for the Yaquina Estuary 

and we are encouraged that all three of these approaches provide very similar zonation patterns. 

Based on the zonation scheme described in this chapter, five of the target estuaries had >50% of 

the total estuarine area classified as ocean dominated. The Salmon and Umpqua River estuaries 

were classified as having 29 and 30%, respectively, of total estuarine area classified as ocean 

dominated. Salmon River Estuary is the smallest of all the systems examined encompassing 

only 3.1 km2 of total estuarine area. As discussed in Section 4.4.3, both of these estuaries switch 

from ocean- to river-dominated over a tidal cycle and based on this we classified them as having 

the strongest river influence (Figures 4-8 and 4-9). Most systems were ocean dominated, 

indicating that the influence of the coastal ocean and the dynamics of oceanographic phenomena 

cannot be ignored. The strong influence of the coastal ocean on PNW estuaries is a departure 

from the usual paradigm associated with nutrient loading. 

Table 5-17. Summary of the total estuarine area for each target estuary and the area and 

percentage of total in the oceanic and riverine segments based on salinity and macroalgal isotope 

ratios. 

AREA 

(KM2) 

YAQUINA ALSEA SALMON UMPQUA COOS TILLAMOOK NESTUCCA

Total 

Estuarine

19.9 12.5 3.11 33.8 54.9 37.5 5.00 

Oceanic 

Segment 

13.4 7.75 0.91 10.1 42.7 23.4 4.07 

Riverine 

Segment 

6.58 4.72 2.19 23.7 12.1 14.0 0.93 

% in 

Oceanic 

Segment 

67 62 29 30 78 63 82 

% in 

Riverine 

Segment 

33 38 71 70 22 37 18 
158

CHAPTER 6: 

AERIAL MEASURES OF ESTUARINE INTERTIDAL AND SHALLOW SUBTIDAL 

ZOSTERA MARINA COVERAGE 

David R. Young, Patrick J. Clinton, Henry Lee II, David T. Specht, and T Chris Mochon Collura 

6.0 Introduction 

Zostera marina is a flowering marine plant that can form thick meadows or beds of perennial 

plants in the intertidal and subtidal sections of estuaries, providing a critical habitat and food 

source for numerous taxa including commercially important fish and shellfish (Heck et al., 1989; 

Sogard and Able, 1991; Dennison et al., 1993; Bostrom and Bonsdorff, 1997). Z. marina is one 

of the many species of seagrasses that have been severely impacted by anthropogenic activities 

around the world (Hemminga and Duarte, 2000; Short et al., 2001). One cost-effective approach 

of assessing current seagrass distributions as well as changes in distributions is through the use 

of aerial surveys. However, the intertidal and subtidal distribution of Z. marina presents 

challenges in mapping its distribution. Therefore, an aerial survey method of mapping the 

intertidal and shallow subtidal portions of the Z. marina habitat in PNW estuaries has been 

developed by Clinton et al. (2007). A method of mapping the deeper subtidal portion of the 

distribution not visible from the surface is still under development and testing (Dr. Ted DeWitt, 

pers. comm.). 

The objectives of this aerial photomapping study were to 1) assess the extent and distribution of 

intertidal and shallow subtidal Z. marina in the seven target estuaries, and 2) determine the

relative distribution of Z. marina in the oceanic and riverine segments of each of the estuaries. 

6.1 Methods 

6.1.1 Aerial Photography Sampling Frame and Design

Owing to the extensive eelgrass distribution in PNW estuaries, this component of the project 

used a remote sensing technique. The method employed aerial photographs and false-color nearinfrared (color infrared, CIR) film, which provide better contrast than full-color film in 

distinguishing submersed aquatic vegetation (SAV) distributions (Young et al., 1999). However, 

Key Findings 

• Intertidal and shallow subtidal distributions of Zostera marina were mapped in 

the seven target estuaries via aerial and on-ground surveys. 

• The extent of Z. marina varied among the estuaries, ranging from nondetectable via aerial surveys to about 11% of the intertidal area. 

• The majority of the intertidal and shallow subtidal Z. marina is found in the 

oceanic segments of the estuaries. 

• Most of the Z. marina habitat is found at depths ranging from -3 to 3 feet (-0.9 m 

to 0.9 m) above MLLW. 
159

because CIR film cannot resolve images more than a few cm below the water surface, the 

photographs must be taken during exposed intervals (i.e., daylight low tides). In addition, the 

weather must be cloud-free (or uniform high overcast) to obtain uninterrupted photo survey 

coverage. In addition, the presence of benthic green macroalgae can confound the interpretation 

of the SAV signals; thus, it is important to acquire the photography of target estuaries during late 

spring or early summer, when there is enough Z. marina growth and sunlight for imaging, but 

before the summertime bloom of benthic green macroalgae (see Section 7.2.4). Although most 

of the photography was conducted when the tide level was between 0 and -2 ft (-0.6 m) relative 

to Mean Lower Low Water (MLLW), the upper portion of the immersed eelgrass plants in the 

shallow subtidal zone (down to about -6 ft or -1.8 m) was floating on the surface and could be 

detected by the CIR film. Thus, the range of the intertidal/shallow subtidal zone sampled by this 

technique was from the upper margin of the zone at about +6 ft (+1.8 m) to about -6 ft (-1.8 m), 

the approximate lower depth limit of detection. (English units are used for most available tide 

level and bathymetry data in the PNW, and thus are the primary units used here). The lateral 

extent of a study area generally ranged from about the ocean entrance of the estuary to the 

upriver termination of the reported distribution of intertidal Z. marina in that system. In the 

Yaquina Estuary, the aerial photography extended only about 11 km upriver from the ocean 

entrance; thus, this study area did not include the entire riverine segment. However, numerous 

boat surveys by the authors have shown that only a small proportion of the intertidal/shallow 

subtidal Z. marina occurs upriver of the study area. 

The photosurveys were conducted under contract by a commercial vendor. Maps of the required 

flightlines and photocenters, tables of daylight low tide windows, and specifications of the 

photography (e.g., large-format camera focal length, front and side overlap, maximum aircraft 

tilt, camera calibration, etc.) were provided by PCEB. The photoscale used in the 2004 

photography was 1:10,000, and in 2005 was 1:20,000. The resultant film (or diapositive copy) 

was digitally scanned, and the digital photographs then were converted to digital photomaps with 

pixels corresponding to 0.25 m x 0.25 m on the ground using ERMapper®

desktop 

orthorectification. These orthophotos were mosaiced for each estuary and then each pixel was 

classified into one of two classes, defined as: (1) eelgrass bed (>10% cover by Z. marina) or (2) 

bare substrate (< 10% cover by Z. marina). 

Image classifications were accomplished with a combination of digital image processing and 

manual techniques. Terrestrial portions of the image were masked in ERMapper®

 using vector 

polygons, and a vegetation index algorithm was applied to mask patently unvegetated areas in 

the remaining imagery. A seven class unsupervised classification using ERMapper® ISOCLASS 

algorithm was applied to the masked imagery and the results converted to seven ArcInfo® format 

binary grids representing each band of the isoclassification. The grids were manually edited in 

ArcMap®

by the photointerpreter using ArcScan® raster editing tools to remove false-positive 

pixels and recompiled to form the seagrass map. 

An example of the distribution of Z. marina coverage in the Yaquina Estuary obtained from the 

classification of the April 2004 digital photomaps is presented in Figure 6-1. The aerial 

photography did not yield acceptable images of Z. marina beds in the Nestucca and Salmon 

River estuaries. For these estuaries, a vessel equipped with a differential-corrected global 

positioning system (DGPS) was used to position the visible edges of intertidal and shallow 
160

Figure 6-1. Intertidal and shallow subtidal distribution of Z. marina from digital image 

classification of aerial photos of Yaquina Estuary taken in April 2004. The eastern (upriver) 

edge of the survey area was selected because little intertidal Z. marina occurs beyond the 

boundary shown. 
161

 

Figure 6-2. Track of lower margin of surface-visible distribution of Z. marina in the nearsubtidal zone (using DGPS in a small boat), and of upper margin (walking with DGPS), in the 

Yaquina Estuary. 
162

subtidal Z. marina beds within each estuary. In some cases, upper margins also were positioned 

by carrying a DGPS while walking this margin during exposed conditions. The root mean 

square error of the DGPS system used in this study was ± 0.6 m. An example of such 

positionings of the visible edges of an intertidal and shallow subtidal Z. marina meadow in 

Yaquina Estuary is presented in Figure 6-2. 

6.1.2 Estuaries Surveyed 

The sampling dates and mapping method utilized for the seven target estuaries are presented in 

Table 6-1. 

Table 6-1. Mapping method utilized and dates of mapping for intertidal and shallow subtidal 

Z. marina distributions in the target estuaries. 

ESTUARY DATE MAPPING METHOD 

Alsea April 9, 2004 

Summer 2005 

Aerial Orthophotography/ 

Surface Positioning 

Coos May 26, 2005 Aerial Orthophotography 

Nestucca Summer 2004 Surface Positioning 

Salmon Summer 2004 Surface Positioning 

Tillamook April 9, 2004 Aerial Orthophotography 

Umpqua July 24, 2005 Aerial Orthophotography 

Yaquina April 9, 2004 

July 23, 1997 (above river mile 7) Aerial Orthophotography 

6.1.3 Ground Surveys 

“Ground truth” surveys were conducted in the five estuaries surveyed by aerial photography 

usually in the same season that the photosurvey was conducted but sometimes a year or two 

later. The methodology was based upon the recommendations of Congalton and Green (1999). 

First, the most reliable map available of the target estuarine intertidal zone was used to separate 

expected Z. marina from bare substrate strata. One hundred stations then were positioned 

randomly within each stratum. Utilizing hovercraft transport and the high-resolution DGPS, 30-

70 stations in each class were located during low tide, positioned, and surveyed for percent cover 

of Z. marina. A 1.25 m x 1.25 m quadrat, equipped with two orthogonal sets of five taut strings, 

was placed successively in the four compass quadrats around the target position. The percent 

cover of native Z. marina, green macroalgae, non-native Z. japonica, or bare substrate then was 

quantified using the point-intercept method. Following the criterion established by the NOAA 

Coastal Change Analysis Program (Dobson et al., 1995), stations with Z. marina coverage 

greater than 10% were classified as Z. marina sites. Interference by benthic green macroalgae or 

Z. japonica in the separation of ground stations into Z. marina or bare substrate classes was 

negligible in all five estuaries surveyed. Approximately 10% of the stations surveyed in each 

class were randomly withdrawn and used for training the geographical information system (GIS) 

photointerpreter conducting the image classification. The data for these stations were not used in 

the accuracy assessments of the resultant classifications. 
163

6.1.4 Classification Accuracy Assessments 

Following the classification of the digital photomaps, positions of the non-training stations 

determined on-site by DGPS were provided to the GIS photointerpreter for the five estuaries in 

which ground surveys were conducted. An area equivalent to 2.5 m x 2.5 m on the ground 

around a station’s DGPS position was subsampled from the digital classification into a 

10 pixel x 10 pixel array. Each pixel had been classified either as “Z. marina” or “bare 

substrate.” If the number of pixels classified as Z. marina was greater than 10 (>10% of the 

total), that station was classified as Z. marina. Otherwise it was classified as a bare substrate 

station. The same criterion was applied to the ground survey data. The results from the digital 

classification and ground survey then were incorporated into a classical error matrix (Congalton 

and Green, 1999). 

6.1.5 Estuary Bathymetry 

In view of the importance of substrate elevation to estuarine ecology, a bathymetric model was 

developed for Yaquina Estuary. Several surveys of the main channel have been conducted by 

the U.S. Army Corps of Engineers (USACE) in recent years, but little information existed for the 

extensive tide flats of the estuary. Therefore, in 2002 WED/PCEB contracted with USACE to 

extend their surveys into the shallow sectors of the estuary. Soundings were conducted at 

extreme high tides near the end of the year along transect lines every 200 feet (67 m) over the 

general area surveyed by aerial photography. Measured water depths were related to those 

recorded by tide gauges within the estuary, and adjusted relative to Mean Lower Low Water. 

The total area covered by the several bathymetric surveys utilized in this study is illustrated in 

Figure 6-3. The data resulting from these surveys were used by PCEB to construct a bathymetric 

model of Yaquina Estuary. Survey easting, northing, and depth values were interpolated using 

the TOPOGRID method provided in ArcInfo Workstation. This model is discussed further in 

Section 6.2.3. 

Figure 6-3. Sectors of Yaquina Estuary covered by bathymetric surveys conducted by the U.S. 

Army Corps of Engineers between 1998 and 2002. 
164

6.2 Results 

6.2.1 Accuracy Assessments of Photomap Image Classifications 

Error matrices were prepared from the digital classification and ground survey results for the 

Alsea, Coos, Tillamook, Umpqua, and Yaquina estuaries. Errors of omission (failing to include 

a ground-truth station in the classification), commission (erroneously including a station in the 

classification), and overall error for the classifications of the five orthophotographs are listed in 

Table 6-2. These results for five of the seven target estuaries indicate that the Z. marina habitat 

and bare substrate classifications from the estuary orthophotographs have accuracies of 83% or 

greater, and the median overall accuracy was 89% (median overall error of 11%). For more 

details on the accuracy assessment of the mapping see Section B.9 in Appendix B. 

Table 6-2. Summary of error matrix results for detection of Z. marina in five of the target 

estuaries based on the ground truth surveys. 

ESTUARY 

CLASSIFIED 

ERRORS OF 

OMISSION 

ERRORS OF 

COMMISSION 

OVERALL 

ERROR 

NO. OF 

STATIONS 

Alsea 

 Z. marina 

 Bare Substrate 

0% 

0% 

0% 

0% 

0% 44 

60 

Coos 

 Z. marina

 Bare Substrate 

 

26 % 

12 % 

 

12 % 

26 % 

17 % 38 

77 

Tillamook 

 Z. marina

 Bare Substrate 

12% 

16% 

16% 

12% 

14% 100 

81

Umpqua 

 Z. marina

 Bare Substrate 

10 % 

11 % 

11 % 

10 % 

11 % 41 

91 

Yaquina 

 Z. marina

 Bare Substrate 

2 % 

4 % 

 4 % 

 2 % 

3 % 51 

28 

6.2.2 Among-Estuary Comparison of Zostera marina Coverage 

Interpretation of the distribution and abundance of Z. marina from the aerial surveys and from 

the probabilistic field surveys discussed in Chapter 7 requires an understanding of how areas 

were sampled in the two approaches. The aerial photography detects Z. marina in both the 

intertidal and shallow subtidal zones. As discussed above (Section 6.1.1), it appears that the 

aerial photography detects Z. marina to a depth of approximately 6 feet (1.8 meters) below Mean 

Lower Low Water (MLLW). In comparison, the probabilistic field surveys used an intertidal 

sampling frame, approximately from Mean Lower Water (MLW) to Mean Higher Water 

(MHW). Thus, the probabilistic surveys would not capture the subtidal portion of the SAV 

population that was included in the aerial surveys. To compare directly the areal extents of Z. 

marina between the two surveys, the sampling frames used in the probabilistic surveys were 

overlaid on the aerial photography, generating estimates of the areas of Z. marina from the aerial 
165

photography within the same areas as sampled by the field surveys. Additionally, to calculate 

the percent of the area covered by Z. marina (relative cover) it is necessary to have an accurate 

estimate of the area actually surveyed by the aerial photography. Such areas could be calculated 

assuming detection to 6 feet (1.8 m) if bathymetric data were available. However, bathymetric 

data are currently available for only three of the seven target estuaries. Therefore, the percent 

cover of Z. marina was calculated using the portion of the aerial surveys that fell within the 

probabilistic sampling frames, which have known areas. These percent cover values only apply 

to the intertidal zone; future efforts will be directed at obtaining bathymetry for all the target 

estuaries, allowing percent cover estimates that also include the shallow subtidal zone. 

To compare the extent of variation of Z. marina among estuaries (Objective 1), the absolute and 

relative areas of Z. marina in the five digitally classified estuaries, and the two surface-mapped 

estuaries were calculated (Table 6-3). Maps of the aerial extent of Z. marina for each of the 

target estuaries are presented in Appendix A. One comparison of interest is the percent coverage 

values for intertidal Z. marina within the probabilistic frame. Three of the estuaries – Coos, 

Tillamook, and Yaquina – show agreement within a factor of three (4.7 to 11.5% cover). For the 

Umpqua River Estuary, the percent coverage value (1.3%) is substantially lower. The Alsea, 

Nestucca, and Salmon River estuaries have very little intertidal Z. marina coverage within the 

probabilistic frame. At present we have no complete explanation for the near absence of 

intertidal Z. marina in the Nestucca and Salmon River estuaries. However, one possible 

contributing factor may be the high wave energy in these estuaries. Nestucca and Salmon River 

estuaries have the lowest values for median percent fines (1.9% and 7.9%, respectively; see 

Figure 7-10), suggesting that the intertidal zones of these estuaries are high energy environments, 

which may result in Z. marina being eroded or buried. Additionally, high tidal currents and 

shifting sands were commonly observed at the mouth of Nestucca during peak flood and ebb 

tides (see Section 8.2.5). Salinity may also be a contributing factor limiting Z. marina in these 

estuaries as discussed in Section 7.2.2. 

6.2.3 Bathymetric Distribution of Zostera marina

A comparison of elevations predicted by the bathymetric model for Yaquina estuary with those 

obtained from an independent total station survey of a large embayment there (Idaho Flat) is 

presented in Figure 6-4. This comparison indicates that there is agreement between the 

measured and modeled elevations to within about 0.3 ft. Specifically, for the 167 points within 

the depth interval -3.0 ft to +3.0 ft, the median value for the difference between the survey and 

model elevations is -0.32 ft, indicating that, on average, the actual substrate elevations in this 

interval may be approximately 0.3 ft (0.1 m) lower than the model elevations. 

Bathymetric data collected by Professor Chris Goldfinger at Oregon State University for Alsea 

Estuary (2002) and by the USACE for Tillamook Estuary (1995) similarly were used to obtain 

bathymetric models for these estuaries. The results presented in Figures 6-6 to 6-8 indicate 

similar bathymetric distributions for intertidal/shallow subtidal Z. marina in these three PNW 

estuaries. The aerial percentages of Z. marina occurring between specific depth intervals around 

MLLW are summarized in Table 6-4. Again, the population included in this study is Z. marina

that is visible from the surface at low tide (MLLW), falling within the approximate depth range: 

-6 ft (-1.8 m) to +6 ft (+1.8 m). 
166

Table 6-3. Estimates of Z. marina area in the target estuaries from the aerial/on-surface surveys. 

The “Total Area of Z. marina” is the total area of Z. marina detected in the aerial/on-surface 

surveys in the intertidal and shallow subtidal areas. The “Area of Z. marina within Probabilistic 

Frame” is the area of Z. marina found within the intertidal probabilistic frame, which does not 

include the shallow subtidal area sampled by the aerial photographs. The “% Coverage of Z. 

marina within Probabilistic Frame” is the percent coverage of Z. marina relative to the area of 

the probabilistic frame. A similar metric cannot be calculated for the entire aerial frame since 

the exact area sampled by the photographs is unknown. The areas of Z. marina in the Nestucca 

and Salmon River estuaries were determined by on-surface mapping rather than aerial 

photography. 

-6 -4 -2 0 2 4 6

-4

-2

0

2

4

6

Modeled Elevations (ft, MLLW)

Measured Elevations (ft, MLLW)

y = 0.75 x + 0.42

r

2

 = 0.94

Figure 6-4. Comparison of substrate elevations from a bathymetric model and independent total 

station survey measurements within Yaquina estuary.

ESTUARY 

TOTAL AREA OF 

Z. MARINA IN 

AERIAL/ ONSURFACE SURVEY 

(km2) 

AREA OF Z. MARINA

WITHIN 

PROBABILISTIC 

FRAME (km2) 

% COVERAGE OF 

Z. MARINA

WITHIN 

PROBABILISTIC 

FRAME 

Alsea 0.026 0.005 0.09 

Coos 2.141 1.238 4.66 

Nestucca 0.004 0.0 0.00 

Salmon 0.004 0.0003 0.07 

Tillamook 3.27 2.38 11.46 

Umpqua 0.338 0.095 1.33 

Yaquina 0.809 0.635 9.91 
167

Figure 6-5. Interpolated bathymetry and Z. marina distribution for a section of the lower 

Yaquina Estuary. 
168

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

0

10

20

30

40

0.92 0.76 0.61 0.45

2.59

19.93

45.94

10.65

6.93

4.58 2.62 1.68

 Depth Interval, ft (MLLW)

% of Total

0.88

Figure 6-6. The percentage distribution of intertidal and shallow subtidal Z. marina habitat area 

classified from aerial photography that occurs in one foot intervals predicted by the bathymetric 

model for Yaquina Estuary (-6 ft to +6 ft). 

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

0

10

20

30

40

1.19 0.80 2.17 3.27

8.08

13.29

45.38

6.86 7.14 5.35

1.37 2.00

 Depth Interval, ft (MLLW)

% of Total

1.02

Figure 6-7. The percentage distribution of intertidal and shallow subtidal Z. marina habitat area 

classified from aerial photography that occurs in one foot intervals predicted by the bathymetric 

model for Alsea Estuary (-6 ft to +6 ft). 
169

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

0

10

20

30

40

50

0.12 0.10 0.83

4.36

11.61

17.52

49.63

6.52

3.71 2.27 0.79 1.41

 Depth Interval, ft (MLLW)

% of Total

0.39

Figure 6-8. The percentage distribution of intertidal and shallow subtidal Z. marina habitat area 

classified from aerial photography that occurs in one foot intervals predicted by the bathymetric 

model for Tillamook Estuary (-6 ft to +6 ft). 

Table 6-4. Cumulative percent area of Z. marina within four depth intervals relative to MLLW 

in three PNW estuaries. 

DEPTH INTERVAL (ft) 

ESTUARY -1’ to +1’ -2’ to +2’ -3’ to +3’ -6’ to +6’ 

Yaquina 76.6 86.0 91.4 97.7 

Alsea 65.9 80.3 88.8 96.8 

Tillamook 73.6 88.9 95.5 99.0 

As shown in Table 6-4, approximately 90 percent of the intertidal and shallow subtidal Z. marina

classified from the orthophotography images occurred within the depth range -3.0 ft to +3.0 ft (-

0.9 m to +0.9 m) around the MLLW datum. In comparison, Borde et al. (2003) reported that the 

upper limit of the Z. marina meadows observed in Coos Bay during summer 1999 was 

approximately +0.8 m (MLLW), in good agreement with our results. Corresponding 

observations for the maximum depths range from -0.2 m to -1.5 m (median: -0.85 m; Thom et 

al., 2003), compared to our approximate lower limit value of -0.9 m. Further, these authors 

reported maximum depths ranging from -0.4 m to -1.2 m (median: -0.80 m) in Willapa Bay, WA 

(for location see Figure 2-2), again consistent with our findings. In a subsequent study, Thom et 

al. (2008) reported that Z. marina shoot densities decreased to zero below -1.5 m in Coos Bay 

and Willapa Bay. 
170

Orth and Moore (1988) concluded from their studies in lower Chesapeake Bay that both 

optimum and maximum depths for this species can vary considerably within a particular region, 

depending upon water clarity. The same observation was made for the maximum depth of Z. 

marina at numerous locations throughout the seven target estuaries surveyed in this study 

(Chapter 8). In general, deepest limits occurred near the mouths of the estuaries where the 

clearest water usually is found. The median of the maximum depth readings, relative to MLLW, 

for each estuary ranged from -1.82 m (for Alsea Estuary) to +0.21 m (for Coos Estuary), with an 

overall median of -0.76 m. (For the Tillamook, Yaquina, and Alsea estuaries alone, the overall 

median was -1.19 m). Again, these findings are in reasonable agreement with the lower limit 

value of -0.9 m we obtained for approximately 90 percent of the Z. marina in our bathymetric 

distributions for three of the seven target estuaries. 

 

This comparison presented in Table 6-4 suggests a method of estimating the suitability of 

intertidal and shallow subtidal habitat for Z. marina as a function of bottom depth in a given 

estuary. The estimate, for a specified depth interval, is calculated as the area in which Z. marina

occurs divided by the total area of the target estuary within that depth interval. These “frequency 

of occurrence” values obtained for one-foot (0.3 m) depth intervals around MLLW between -6 ft 

and +6 ft are illustrated in Figure 6-9. 

 

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

0

10

20

30

40 Yaquina

 Alsea

 Tillamook

 Depth Interval, ft (MLLW)

Z. marina Frequency of Occurrence, %

Figure 6-9. Frequency of occurrence values for Z. marina within one-foot depth intervals around 

MLLW in Yaquina, Alsea, and Tillamook estuaries (-6 ft to +6 ft). 

Because the interval -3 ft (-0.9 m) to +3 ft (+0.9 m) contains approximately 90% of the Z. marina

classified in the color-infrared aerial photomaps for each of these three estuaries, this zone is 

termed the primary depth interval for Z. marina. The average (± 1 standard error of the mean) 

frequency of occurrence values obtained for this interval in Yaquina, Alsea, and Tillamook 

estuaries are 13.9 ± 3.6%, 0.44 ± 0.12%, and 14.4 ± 3.3%, respectively. The averages for 
171

Yaquina and Tillamook estuaries are identical (14%), but are higher than that for the Alsea 

Estuary (0.44%) by about a factor of 30. These relative values are consistent with those obtained 

from the data presented in Table 6-3. Again, further studies are required to elucidate the causes 

of differences in percent coverage values for Z. marina among PNW estuaries. 

6.2.4 Within-Estuary Distribution of Zostera marina

As discussed in Chapter 5, the seven target estuaries were divided into riverine and oceanic 

segments based on their primary nutrient sources. One of the objectives of the aerial surveys as 

well as the probabilistic surveys (Section 7.3.1) was to assess the distribution of Z. marina

between these two estuarine segments. To address this question, the percent of total area of 

intertidal Z. marina habitat classified from the aerial surveys (or surface mapping in the case of 

the Nestucca and Salmon River estuaries) was apportioned within each segment, using both the 

Total Aerial Frame and the Probabilistic Frame (Table 6-5). 

Table 6-5. Relative distribution of the total Z. marina habitat area in the oceanic and riverine 

segments of the estuaries based on the aerial surveys. Estimates are based both on the Z. marina

detected within the entire area covered by the aerial surveys and the portion of the Z. marina

detected within the subset of the intertidal probabilistic frame. NA = not applicable because no 

Z. marina was detected within the probabilistic frame. 

% TOTAL Z. MARINA FROM AERIAL SURVEY 

CALCULATED USING TOTAL 

AERIAL FRAME 

CALCULATED USING 

PROBABILISTIC FRAME 

ESTUARY OCEANIC RIVERINE OCEANIC RIVERINE 

Alsea 79.6 20.4 98.8 1.2 

Coos 96.2 3.8 96.6 3.4 

Nestucca 100.0 0.0 NA NA 

Salmon 87.3 12.6 99.6 0.4 

Tillamook 99.8 0.2 99.7 0.3 

Umpqua 52.8 47.2 88.5 11.5 

Yaquina 98.5 1.5 99.7 0.3 

A major implication of these results is that for all of the target estuaries, the majority of the 

intertidal and shallow subtidal Z. marina classified from the aerial (or surface mapping) surveys 

is found in the oceanic segment. Generally, the distributions between the oceanic and riverine 

segments calculated using the aerial and probabilistic frames are similar, with the exception of 

the Umpqua River Estuary. In this estuarine system, Z. marina habitat using the Total Aerial 

Frame is evenly divided between the oceanic (52.8%) and riverine (47.2%) segments. However, 

the percent-of-total value for the oceanic segment of the Umpqua using the Probabilistic Frame 

(88.5%) is much higher. One possible explanation for the difference between the two 

approaches is that there may be mis-classification of Z. marina in the riverine segment. In 

particular, two other seagrasses, Z. japonica and Ruppia maritima (widgeon grass), that occur in 

lower salinity areas are difficult to differentiate from Z. marina using remote sensing. 

Nonetheless, the presence of at least 53% and up to 100% of the total Z. marina mapped from the 

aerial surveys occurring within the oceanic segments demonstrates the importance of this 

segment to this species in the PNW. 
172

The distribution of Z. marina between the oceanic and riverine segments was also evaluated 

using the relative percent cover of Z. marina as determined from the aerial photomapping 

(Table 6-6). As discussed in Section 7.3, relative cover is defined as the percent of the area of 

the oceanic segment or the riverine segment occupied by Z. marina. Calculation of relative 

cover is limited to the probabilistic frame portion of the aerial frame since the area sampled is 

needed to calculate the percent cover value. Higher relative cover values are assumed to indicate 

a better habitat for a species given that it occupies a greater percentage of the total habitat area. 

Based on this assumption, the oceanic segment constitutes a substantially better habitat for 

Z. marina, with relative cover 7-fold to almost 200-fold higher in the oceanic segments in the six 

estuaries with detectable Z. marina habitat. These results are consistent with the relative 

distributions of the total Z. marina population (Table 6-5) and support the view that the lower 

estuary is the prime habitat for Z. marina in PNW estuaries. 

Table 6-6. Percent coverage of Z. marina within the oceanic and riverine segments of the 

estuaries based on the aerial surveys. Estimates are based on the relative cover in the 

probabilistic portion of the aerial survey frame. NA = not applicable because no Z. marina was 

detected within the probabilistic frame. 

RELATIVE % COVER OF Z. MARINA

ESTUARY OCEANIC RIVERINE 

Alsea 0.10 0.01 

Coos 5.63 0.79 

Nestucca NA NA 

Salmon 0.08 0.002 

Tillamook 15.28 0.08 

Umpqua 2.86 0.26 

Yaquina 12.03 0.18 

6.3 Synthesis 

Color infrared aerial photosurveys of the target estuaries yielded orthophotographs that were 

digitally classified into Z. marina and bare substrate classes. The classifications were checked 

via ground surveys using a stratified random sampling design. Classical accuracy assessments 

yielded individual accuracies of 83% or greater, with an overall median accuracy of 89%. This 

provided intertidal and shallow subtidal Z. marina distributions within five target estuaries, while 

two other estuaries were mapped via ground surveys because of the limited areal extent of Z. 

marina habitat. Comparison with bathymetric models for three estuaries showed that the onefoot depth interval bracketing MLLW contained 66% to 77% of the intertidal Z. marina, and that 

89% to 96% of the measurable Z. marina occurred within the primary depth interval -3 ft to +3 ft 

(-0.9 m to +0.9 m). The ratio of the area occupied by Z. marina relative to the total estuarine 

area within this interval provided frequency of occurrence values representative of habitat 

suitability that showed Alsea Estuary contained a much lower percentage cover of Z. marina in 

the primary depth interval than in Yaquina and Tillamook estuaries. A similar result was 

obtained from probability-based ground surveys. Causes for such differences presently are 

unknown. However, the maps of Z. marina distribution clearly indicated that a large majority of 

intertidal and shallow subtidal Z. marina occurred in the oceanic segments of the target estuaries. 
173

CHAPTER 7: 

AMONG AND WITHIN ESTUARINE DISTRIBUTIONS OF SEAGRASSES 

 AND ECOLOGICALLY IMPORTANT BENTHIC SPECIES IN PACIFIC 

NORTHWEST ESTUARIES 

Henry Lee II, David R. Young, Christina L. Folger, Cheryl A. Brown, 

Janet O. Lamberson, Katharine M. Marko, and Faith A. Cole 

7.0 Introduction 

Probabilistic field surveys of the intertidal habitats were conducted in the seven target estuaries 

with the objectives of: 1) assessing the among-estuary patterns of distribution and abundance of 

Zostera marina, Z. japonica, benthic macroalgae, and two burrowing shrimp, Neotrypaea 

californiensis and Upogebia pugettensis and 2) determining the within-estuary distribution of 

each of these taxa especially in relationship to their distribution between the oceanic and riverine 

segments within each estuary. An additional objective was to develop practical methods to 

Key Findings 

• The areal extents of Z. marina, Z. japonica, benthic green macroalgae, two 

burrowing shrimp (N. californiensis and U. pugettensis), and “bare” habitat in 

the intertidal zone were estimated in the seven target estuaries using 

probabilistic field surveys. 

• All three tide-dominated estuaries had moderate to extensive coverage of 

Z. marina. In comparison, Z. marina was not found in three of the riverdominated estuaries and there was only limited coverage in the fourth. 

Differences in median salinity and/or salinity variability among estuary classes 

appear to be a major factor determining the extent of Z. marina though 

sediment movement/energy and high densities of N. californiensis may also be 

contributing factors. 

• The nonindigenous Z. japonica occurred in six of the estuaries and the area 

occupied exceeded that of Z. marina in four of them. 

• Blooms of green macroalgae in coastal PNW estuaries appear to be a natural 

process and not an indication of cultural eutrophication. 

• Burrowing shrimp were a major component of the intertidal in all the estuaries, 

though the species had different distributions among estuaries.

• A multivariate analysis on the benthic habitats grouped the three tidedominated estuaries together along with the river-dominated Alsea Estuary. 

The river-dominated Nestucca and Salmon formed a second group, while the 

river-dominated Umpqua separated out from the other estuaries. 
174

quantify seagrasses, other biotic resources, and key environmental factors in regional-scale 

surveys. 

The first objective addresses the classification of PNW estuaries based on biotic structure, 

providing information at finer spatial scale and with a higher level of biotic resolution than the 

wetland classifications conducted at a regional scale (Chapter 2). The second objective 

addresses one component of vulnerability, the potential exposure of the five taxa to 

anthropogenically derived nutrients. In particular, the objective is to test the hypothesis that the 

majority of the Z. marina occurs in the oceanic segment across a range of different types of PNW 

estuaries. Probabilistic survey designs are well suited to addressing this objective as they 

provide statistically unbiased estimates of the intertidal area occupied by each of the taxa. The 

distribution of Z. marina was also evaluated through aerial photography (Chapter 6), and each 

technique has its own strengths and limitations. It is beyond the scope of this document to 

contrast the two approaches, and in the context of assessing vulnerability, the key question is 

whether both approaches showed the same general patterns in Z. marina distributions. 

In addition to the two Zostera species, benthic macroalgae and the two burrowing shrimp are 

ecologically important benthic species in PNW estuaries, affecting both the physical and 

chemical structure of the benthic environment and the structure of estuarine food webs (see 

Section 1.5 and Table 1-1). Comparison of their coverage and/or abundance among estuaries 

quantifies how these food web components vary by estuary type. By determining their 

distributions among the oceanic and riverine segments, the probabilistic surveys identify which 

of these food web components are most likely to be exposed to terrestrially derived nutrients, and 

hence be more vulnerable to nutrient enrichment. 

7.1 Methods 

7.1.1 Probabilistic Sampling Frame and Design 

A probabilistic sample design, as utilized by the Environmental Monitoring and Assessment 

Program (EMAP), was used to generate statistically robust estimates of the abundance and 

distribution of seagrasses and other benthic resources. An overview of the probabilistic sampling 

approach used in previous coastal EMAP surveys in California, Oregon, and Washington can be 

found in Nelson et al. (2005a, b). Additional details on the probabilistic approach to describing 

the condition of ecological resources can be found in Diaz-Ramos et al. (1996), Stevens (1997), 

Stevens and Olsen (1999), and at http://www.epa.gov/nheerl/arm. 

The probabilistic field surveys used the same sampling frame as the coastal EMAP survey of 

intertidal wetlands conducted in 2002 (Nelson et al., 2007). The estuarine intertidal sampling 

frame was defined as all estuarine NWI polygons that were “regularly flooded”, which was 

interpreted as being between Mean Lower Low Water (MLLW) and Mean Higher High Water 

(MHHW). NWI polygons labeled “irregularly exposed” were interpreted as being below MLLW 

while polygons labeled “irregularly flooded” were interpreted as being above MHHW, neither of 

which was included in the sampling frame. Thus, the sampling frame did not include either the 

shallow subtidal or emergent marshes. Areas of the sampling frame and the post-sampling 

division between the oceanic and riverine segments are summarized in Table 7-1. 
175

The Alsea, Nestucca, Yaquina and Salmon River estuaries were sampled in 2004, with the Coos, 

Umpqua River, and Tillamook estuaries sampled in 2005 (Table 7-1). The field surveys were 

completed during the index period of June through mid-September, with the exception of 15 

samples in the Tillamook that were taken in December 2005 because of tidal constraints. These 

sites were resampled in August 2006, and the updated results are used in the analysis. Nine 

additional samples were taken in the Alsea Estuary in September 2006 to sample a portion of the 

original sample frame that was inadvertently omitted. Measures of water quality and δ

15N stable 

isotopes in macroalgae were collected synoptically with the probabilistic surveys as discussed in 

Chapters 4 and 5. 

A total of 100 random sites and a corresponding number of random replacement sites were 

allocated to each of the seven estuaries. In an attempt to have a sufficient number of samples in 

both the oceanic and riverine segments, the estuaries were a priori divided into sampling strata 

(Figures 7-1 to 7-7) with a designated number of samples allocated to each stratum (Table 7-1). 

The four estuaries sampled in 2004 were divided into two sampling strata based on an initial 

estimate of the demarcation between the oceanic and riverine segments of the estuaries. In 2005 

we modified our approach, and divided the Coos, Tillamook, and Umpqua estuaries into a 

greater number of sampling strata based on salinity distributions. Sites that were found to be 

located in inappropriate habitat (e.g., marsh, rocky areas) or were inaccessible were relocated 

within 30 m of the original location site using a protocol developed for the EMAP wetland 

survey (Lamberson and Nelson, 2002). If it was still not possible to obtain a sample, the site was 

abandoned and the first replacement site within the same sampling stratum was chosen. 

7.1.2 Data Analysis 

Data from the probabilistic surveys were primarily analyzed using cumulative distribution 

functions (CDFs). Coastal EMAP studies have used CDFs to evaluate local and regional 

patterns of coastal resources (e.g., Summers et al., 1993; Strobel et al., 1995; Hyland et al., 1996; 

Nelson et al., 2005a, b). Details on the methods for calculating CDFs and the 95% confidence 

intervals can be found in Diaz-Ramos et al. (1996). Briefly, CDFs describe the percentage of the 

area of the sampling frame across the full range of the indicator values (e.g., percent cover, 

burrow hole counts) based on the cumulation of the weighted samples. The sample weight of 

each sample was determined by the area of the stratum divided by the number of samples taken 

within stratum. The CDFs were generated for each target estuary over the entire sampling frame 

(i.e., intertidal area of the estuary). The CDFs were also generated independently for each 

oceanic and riverine segment within an estuary based on the areas determined to fall into these 

segments after the zonation of the estuaries (Chapter 5). Since the demarcation line for the 

oceanic and riverine segments transected some of the original sample strata, the sample weights 

were recalculated based on these new strata areas and sample numbers. 

7.1.3 Field Methods 

Seagrass, macroalgae, and burrowing shrimp presence and abundance were estimated using two 

quadrat sizes (Figure 7-8). Visual estimates were made in large quadrats (2.5 m on a side or 6.25 

m

2

). A length of braided cord ten meters long with markers every 2.5 m was used to lay out the 

2.5 m x 2.5 m sampling plot. Polyvinyl chloride (PVC) stakes were used to secure the four 

corners and to define the site boundaries. The presence/absence of Z. marina, Z. japonica, 
176

Figure 7-1. Locations of the probabilistic sampling strata and the Z. marina lower limit surveys 

(Chapter 8) in the Alsea Estuary. There were three sampling strata in the Alsea, with the third 

stratum sampled in 2006 indicated by the hatched area. Additional lower limit points sampled in 

2006 are not indicated in the figure. The locations where Z. marina was detected by aerial 

photography are indicated in green. Also see Figure A-1 in Appendix A for a map of the 

Z. marina distribution based on the aerial surveys.
177

Figure 7-2. Locations of the probabilistic sampling strata and the Z. marina lower limit surveys 

(Chapter 8) in the Coos Estuary. There were five sampling strata in the Coos. The locations 

where Z. marina was detected by aerial photography are indicated in green. Also see Figure A-2 

in Appendix A for a map of the Z. marina distribution based on the aerial surveys.
178

Figure 7-3. Locations of the probabilistic sampling strata and the Z. marina lower limit surveys 

(Chapter 8) in the Nestucca Estuary. There were two sampling strata in the Nestucca. The 

locations where Z. marina was detected by the on-ground survey are covered by the lower limit 

symbols and the reader is referred to Figure A-3 in Appendix A for a map of the Z. marina

distribution based on the aerial surveys.
179

Figure 7-4. Locations of the probabilistic sampling strata and the Z. marina lower limit surveys 

(Chapter 8) in the Salmon River Estuary. There were two sampling strata in the Salmon. The 

locations where Z. marina was detected by the on-ground survey are obscured by the lower limit 

symbols and the reader is referred to Figure A-4 in Appendix A for a map of the Z. marina

distribution based on the aerial surveys.
180

Figure 7-5. Locations of the probabilistic sampling strata and the Z. marina lower limit surveys 

(Chapter 8) in the Tillamook Estuary. There were five sampling strata in the Tillamook. The 

locations where Z. marina was detected by aerial photography are indicated in green. Also see 

Figure A-5 in Appendix A for a map of the Z. marina distribution based on the aerial surveys.
181

Figure 7-6. The locations of the probabilistic sampling strata and the Z. marina lower limit 

surveys (Chapter 8) in the Umpqua River Estuary. There were five sampling strata in the 

Umpqua. The locations where Z. marina was detected by aerial photography are indicated in 

green. Also see Figure A-6 in Appendix A for a map of the Z. marina distribution based on the 

aerial surveys.
182

Figure 7-7. The locations of the probabilistic sampling strata and the Z. marina lower limit 

surveys (Chapter 8) in the Yaquina Estuary. There were two sampling strata in the Yaquina. 

The locations where Z. marina was detected by the aerial photograph are indicated in green. 

Also see Figure A-7 in Appendix A for a map of the Z. marina distribution based on the aerial 

surveys.
183

benthic macroalgae, burrowing shrimp, and other indicators of biological activity were visually 

estimated in each of these large quadrats. In 2005 and 2006, the percent cover of both seagrass 

species in the large quadrats was assigned to one of five cover classes (0%, 1-10%, 10-40%, 40-

70%, and >70%). Two smaller 0.25 m2 quadrats were then placed within the larger 2.5 x 2.5 m 

plot site, with one quadrat designated for percent plant cover and the other for burrowing shrimp. 

The plant quadrat was placed in the center of the sampling plot to avoid placement bias. The 

following measurements were taken within the plant quadrat: 1) percent plant cover of both 

seagrasses and macroalgae; 2) number of vegetative, reproductive and seedling shoots; 3) leaf 

measurements on five vegetative shoots; 4) macroalgal volume. The shrimp quadrat was placed 

in an area within the 2.5 m x 2.5 m plot with less macroalgae or seagrass so that thick cover 

would not obscure burrow holes. 

We found that the design of the field sheets was critical to having the field crews accurately 

collect the pertinent habitat information from quadrats. To assist in future investigations, the 

current version of the field sheets is reproduced in Figure 7-9. Additional details on the field 

methods follow. 

Point-intercept estimate of plant cover – Point intercepts within the 0.25 m2 plant quadrat were 

used to estimate the cover of the two seagrasses and benthic macroalgae. The point-intercept 

estimate was based on the number of macroalgae and seagrass occurrences under 25 intercepts of 

two sets of equally spaced five strings on the plant quadrat. The percent cover of seagrass and 

macroalgae were measured independently in three dimensions (i.e., captured occurrence of plants 

overlaid by other plants), thus the sum could exceed 100%. 

Number of seagrass shoots – All Z. marina shoots were counted within the 0.25 m2 plant quadrat 

in 2004-2006. Due to the potentially high number of Z. japonica shoots present in a 0.25 m2

area, the number of Z. japonica shoots was only approximated in the plant quadrats in 2004. In 

2005 and 2006, the number of Z. japonica shoots was counted in a 0.01 m2 cell within the plant 

quadrat. For Z. marina, vegetative and reproductive shoots were counted independently and a 

determination was made as to whether it was an annual or perennial form by the presence or 

absence of rhizomes. 

Leaf measurements - Leaf measurements were taken on five haphazardly chosen shoots of both 

seagrass species within each plant quadrat when present. Leaf measurements consisted of the 

length of the longest blade to the nearest 1 cm, width of the same leaf at the widest point, and 

number of leaves per shoot. Where fewer than five vegetative shoots were present within a 

quadrat, the measurements were collected from all vegetative shoots present in the quadrat. 

Macroalgal volume - Macroalgal volume was measured by removing all the algae from the 

0.25 m2 plant quadrat, and measuring the volume of algae using a four-liter volumetric cylinder 

following the method of Robbins and Boese (2002). Macroalgal volume was measured to the 

nearest 100 ml after macroalgae were compressed using a plunger to a point where interstitial 

water had been removed. A volume of 50 ml (half the detection limit) was used when 

macroalgae was present but not in sufficient quantity to be measured in the four-liter cylinder. 

 
184

Table 7-1. Sampling design for the probabilistic field surveys and areas of the probabilistic frames. Estuarine insum of the NWI “regularly flooded” estuarine polygons, which is approximately from MLLW to MHHW. The Pthe actual area that was sampled. The areas of the oceanic and riverine frames are the areas within the oceanic vsegments (Chapter 5) that resulted from the post-survey division of the estuaries. The samples taken in 2006 in tTillamook are included in the analysis. 

PROBABILISTIC FRAME NUMBER OF SAMP

 ESTUARY

NWI 

ESTUARINE 

REGULARLY 

FLOODED 

AREA 

(km2) 

TOTAL 

AREA 

(km2) 

OCEANIC 

FRAME 

(km2) 

RIVERINE 

FRAME 

(km2) 

NUMBER 

OF 

SAMPLING 

STRATA TOTAL 

OCEANIC 

FRAME 

Alsea 6.97 5.20 4.77 0.44 3 109 50 Coos 26.96 26.56 21.25 5.31 5 101 86 Nestucca 2.59 2.01 1.95 0.06 2 101 99 Salmon 1.56 0.45 0.37 0.07 2 100 52 Tillamook 24.6 24.38 14.50 9.88 5 97 56 Umpqua 7.45 7.16 2.95 4.22 5 99 55 Yaquina 7.87 6.40 5.26 1.15 2 100 48 
185

Figure 7-8. Diagram of the intertidal sampling site layout with the 2.5 x 2.5 m quadrat and the 

0.25 m2 plant and shrimp quadrats used in the probabilistic surveys. PSA = Sample for particle 

size analysis. TOC = sample for total organic carbon. 

 

Shrimp 

Quad

 Plant 

Quad 

2.5 m 

 2.5 m 

0.25 m2

0.25 m2

PSA 

TOC Salinity 
186

Figure 7-9a. Front of the field sheet used to record estimates of habitat characteristics and 

burrowing shrimp hole counts within the 2.5 x 2.5 m quadrat. 
187

Figure 7-9b. Back of the field sheet used to record plant cover, point intercepts, shoot counts, 

and blade widths in the 0.25 m2

 plant quadrat and burrowing shrimp hole counts in the 0.25 m2

shrimp quadrat.
188

Burrowing shrimp abundance - The number of burrow holes of N. californiensis and U. 

pugettensis were each counted within the shrimp quadrat. Macroalgae cover was recorded 

within the shrimp quadrat as a possible correction factor for obscured burrow holes. At every 

tenth site, the number of shrimp burrow holes was counted by a second crew member as a 

quality assurance check. 

Habitat measures - Sediment samples for grain size and total organic carbon and nitrogen 

(TOC/N) analysis were collected within the large quadrat (Figure 7-8). An 8-cm x 20-cm PVC 

core was inserted into the sediment within the 2.5 x 2.5 m quadrat to obtain a sample for particle 

size analysis (PSA). The top 10-cm of the sediment in the core was collected into a clean 

resealable plastic bag. The sediment sample for TOC/N analysis was collected in a 4-cm x 15-

cm core inserted into the sediment adjacent to the PSA core and the top 5 cm were collected. A 

sediment sample was collected for water content by scraping the sediment surface into a 50-cc 

centrifuge tube. All sediment samples were stored on ice immediately after collection and frozen 

as soon as was practical. 

Sediment particle size was determined using a Beckman Coulter LS-100Q laser diffraction 

particle size analyzer. The data are presented as percent fines which is the sum of percent silt 

and clay. The TOC/N samples were analyzed using a Carlo Erba EA 1108 elemental analyzer 

according to manufacturer’s instructions as modified for sediment. The surface sediment sample 

collected in the centrifuge tube was centrifuged to extract interstitial water, and salinity of the 

supernatant water was measured with a bench-top salinity meter. 

Sand ripples and waves were measured by laying a meter stick on the sediment surface. If sand 

ripples were present, the number of wave crests along the 1-meter distance was counted. If the 

sand ripples were larger than a meter, the distance between crests was measured using a 100-

meter transect tape. Data recorded in number of crests per meter were converted to distance 

between crests. Sand waves were only recorded in the 2005 and 2006 field surveys. At each 

sampling location, photographs were taken of the quadrats, including the meter stick or transect 

tape to document surface structure. In addition, the type of shoreline development was visually 

assessed, recorded, and photographed. 

Integrated Measure of Presence/Absence (P/A) of SAV, macroalgae, and burrowing shrimp and 

bare habitat – Several different techniques were combined to obtain an “integrated” measure of 

the presence/absence of Z. marina, Z. japonica, benthic green macroalgae, burrowing shrimp, 

and other plant species over a 2.5 x 2.5 m area. It was first determined if the taxon was recorded 

as present in either the 0.25 m2 quadrat or the 2.5 x 2.5 m quadrat. If the taxon was not recorded 

in either quadrat, then the field notes and the site photographs were evaluated for indications of 

the presence of the taxon within the large quadrat. The taxon was recorded as present if it was 

observed in the site photograph. Use of the integrated P/A reduced any errors from 

misclassifications in the field, and is used as the best indicator of a species presence or absence. 

“Bare habitat” was defined as any 2.5 x 2.5 m quadrat that did not contain any Z. marina, 

Z. japonica, benthic green macroalgae, or burrowing shrimp. 
189

7.1.4 Comparison of 0.25 m2 versus 2.5 x 2.5 m Quadrats 

As part of this study, we evaluated the efficacy of using 0.25 m2 (small) versus 2.5 x 2.5 m 

(large) quadrats in estimating the distribution and abundance of seagrasses. The 0.25 m2

quadrats are small enough to allow point-intercept estimates or counts of seagrass shoots. The 

2.5 x 2.5 m quadrats have the advantages that they are same size as the minimum mapping unit 

in the aerial photography and are of sufficient size to “average out” small scale patchiness. Both 

methods are valuable, but the results need to be interpreted in light of the differences in scale. 

For all seven estuaries, estimates of the area occupied by Z. marina and Z. japonica from the 

point intercepts were lower than those from the large quadrats (Tables 7-3 and 7-4). The larger 

quadrats appear to generate a more comprehensive estimate of seagrass distribution because they 

have a higher “capture” probability for relatively sparse species. Given this higher capture 

probability, the large quadrats were judged the preferred approach to estimating the area 

occupied by the two seagrasses. While the quantitative estimates of the density class from the 

large quadrat are preferred over presence/absence, the percent cover classes were only collected 

in the large quadrats in 2005 and 2006. Accordingly, the integrated measure of presence/absence 

in the 2.5 x 2.5 m quadrats was used as the primary method of quantifying the intertidal area 

occupied by Z. marina and Z. japonica. The point-intercept and shoot-count methods were used 

in a supplemental fashion to estimate seagrass density. 

As discussed in Chapter 6, the aerial photography has a detection limit of approximately 10% 

seagrass cover within a 2.5 x 2.5 m minimum mapping unit. Therefore, the most direct 

comparison between the probabilistic and the aerial surveys are the estimates of seagrass area 

from the >10% density class from the large quadrats used in Tillamook, Coos, and the Umpqua. 

7.2 Across-Estuary Patterns 

7.2.1 Sediment Grain Size and TOC in the Target Estuaries 

Two key sediment characteristics quantified were the percent total organic carbon (TOC) and 

grain size, which was summarized as percent fines. The “muddiest” estuaries were the Yaquina 

and Coos with 50% of the area having percent fines >30% (Table 7-2, Figure 7-10). The 

sandiest estuary was the Nestucca with median percent fines of less than 2%. The Umpqua, 

Salmon, Alsea, and Tillamook estuaries were intermediate with median percent fines ranging 

from about 8% to 20%. 

The median values for the percent total organic carbon (TOC) generally mirrored the results for 

percent fines with Yaquina and Coos having the highest median values and Nestucca the lowest 

(Table 7-2, Figure 7-11). The National Coastal Condition Report II (U.S. EPA, 2004b) used 

TOC values >5% as an indication of “poor” sediment condition and values between 2% and 5% 

as an indication of “fair” condition. Only 2 out of almost 700 stations across all the estuaries 

exceeded 5% TOC, with a maximum value of 5.3% in the Coos Estuary. All seven estuaries had 

TOC values >2% and <5%, though most values were at lower end of the “fair” range (<3%). 

The highest coverages within this range occurred in the Yaquina and Coos, where 21% and 17% 

of the intertidal sites were classified as “fair”, respectively. Based on the minimal development 

in the associated watersheds (Chapter 2) and the lack of major point sources, these higher TOC 

values do not appear to be the result of anthropogenic loading. 
190

Table 7-2. Sediment characteristics of the target estuaries from the probabilistic surveys. Median values based othe parameter in the intertidal. The 75th percentile was used for distance between wave crests because all the meTotal organic carbon (TOC) values between 2% and 5% are classified as “fair” in the National Coastal Condition 2004b) while values >5% are classified as “poor”. 

ESTUARY 

MEDIAN 

% FINES 

MEDIAN 

% TOC 

% OF AREA 

WITH 

2%< TOC <5 % 

% OF AREA 

WITH 

TOC >5% 

MEDIAN 

% N 

75th 

PERCENTIL

DISTANC

BETWEE

WAVE CRE

(cm) 

Alsea 12.1 0.69 2.5 0 0.06 NA Coos 30.7 0.90 17.0 <1 0.07 <1 Nestucca 1.9 0.16 7.1 0 0.02 NA Salmon 7.9 0.34 7.3 <1 0.07 NA Tillamook 12.4 0.41 2.6 0 0.04 12 Umpqua 19.7 0.51 9.6 0 0.05 14 Yaquina 37.9 1.30 21.4 0 0.10 NA 
191

0 10 20 30 40 50 60 70 80 90 

0

102030405060708090

100

Percent of Intertidal Area

% Fines

 Alsea Coos Nestucc Salmon Tillamoo Umpqua Yaquina

Figure 7-10. Cumulative distribution function (CDF) of the percent fines in the intertidal zone of the seven targesamples taken in the Alsea in September, 2006 are not included.
192

0 1 2 3 4 5 

0

102030405060708090

100

Percent of Intertidal Area

% TOC

 Alsea Coos Nestucca Salmon Tillamook Umpqua Yaquina

Figure 7-11. Cumulative distribution function (CDF) of the percent total organic carbon (TOC) in the intertidal ztarget estuaries. The nine samples taken in the Alsea in September, 2006 are not included. The red dashed vertic2% and 5% TOC thresholds used in the National Coastal Condition Report II (U.S. EPA, 2004b) with “fair” sedidefined as between 2% and 5% and “poor” sediment conditions as >5%. 
193

7.2.2 Among-Estuary Distribution of Zostera marina

Tillamook had the greatest areal extent of Z. marina, occurring over 34% of the intertidal zone 

(Table 7-3), while the Coos and Yaquina had coverages of about 12-17%. In contrast, the 

Umpqua had only about 6% coverage and no Z. marina was found in the probability surveys in 

the Alsea, Salmon, or Nestucca estuaries. Though the probability surveys did not detect 

Z. marina in these estuaries, the aerial photography detected small amounts of Z. marina in the 

Alsea and on-ground mapping found small beds in the Salmon and Nestucca (see Table 6-3). As 

with any sampling method, the probabilistic survey has a detection limit and even a survey of 

100 sites per estuary can fail to detect seagrasses with high confidence at low densities (e.g., 

Bailey et al., 1998). While the probabilistic surveys failed to detect these small amounts of 

seagrass, the surveys were qualitatively correct in classifying these estuaries as having low 

coverage. 

The relatively wide expanses of seagrass found in Tillamook, Coos, and Yaquina as well as the 

large expanses reported in Willapa Bay and Grays Harbor (e.g., Wyllie-Echeverria and 

Ackerman, 2003) suggest that moderate to large tide-dominated estuaries contain proportionally 

large areas of suitable habitat for Z. marina. In comparison, river-dominated estuaries have 

substantially less Z. marina habitat. Only small beds occur in the Alsea, Nestucca, and Salmon 

estuaries, while the Umpqua only contains one-half to one-sixth the percent cover compared to 

the tide-dominated estuaries (Table 7-3). Estuarine size alone does not explain this pattern, as 

the Umpqua and Alsea are roughly comparable in size to the Coos and Yaquina, respectively. 

Additionally, the Netarts Estuary, which is smaller than either the Yaquina or Alsea, has 

extensive seagrass coverage (Kentula and McIntire, 1986; Wyllie-Echeverria and Ackerman, 

2003). 

The occurrence of this pattern is supported by the low percent cover of NWI aquatic beds in the 

river-dominated estuaries. The average percent cover of NWI “aquatic beds” classes in the 22 

river-dominated estuaries was 1.8% compared to 21.5% in the 6 tide-dominated estuaries (see 

Table 2-3). Limiting this comparison to estuaries >10 km2, the percentages were 6.1% versus 

25.7% in river- and tide-dominated estuaries, respectively. Though not as large a difference, the 

same pattern was observed in the mid-1970’s survey of Oregon estuaries (Cortright et al., 1987). 

In this study, the average percent cover for seagrass and seagrass/algae classes was 15.7% in the 

three tidal estuaries compared to 7.8% in eleven river-dominated estuaries. It appears, then, that 

our definition of tide- and river-dominated estuaries helps to separate estuaries that support more 

extensive Z. marina populations. 

We hypothesize that the most important drivers resulting in these among-estuary patterns are 

differences in average salinity values and, perhaps more importantly, differences in the temporal 

variation in salinity. In Puget Sound, Z. marina appears to grow best at 20-32 psu (Phillips, 

1984), and higher growth rates occurred at 30 psu compared to 10 psu in experiments (Thom et 

al., 2001, 2003). Additionally, diversions of fresh water into estuaries have been associated with 

declines in seagrass (Estevez, 2000), while Montague and Ley (1993) found that increasing 

salinity variation was associated with declines in seagrass biomass in Florida. Coastal PNW 

estuaries are subject to substantial seasonal salinity variations in response to the seasonal pattern 

in precipitation, with winter salinities dropping below 10 psu or even 5 psu over much of the area 
194

of many estuaries (e.g., OR Dept. State Lands, 1985; Brown, unpubl. data; Figure 2-11). In 

particular, river-dominated estuaries appear to experience greater seasonal variations than tidedominated systems (Figure 2-11; OR Dept. State Lands, 1985; Haertel and Osterberg, 1967). 

These extreme seasonal fluctuations are well illustrated in the Salmon River where differences in 

summer and winter median salinities approach 20 psu and where much of the estuary has a 

salinity <5 psu during the winter (Figure 2-11). In the Umpqua, much of the estuary has a winter 

salinity <10 psu (Figure 2-11). In addition to this seasonal variability, dry season salinity near 

the mouths of PNW estuaries can vary 10-25 psu over a tidal cycle (Figures 4-9 and 4-10). The 

extent of this tidal variation is related to the normalized river inflow (Figure 4-11), with the 

river-dominated systems demonstrating about a 2- to 10-fold greater variation in high and low 

tide salinities than the tide-dominated estuaries. The extensive tidal variation within the Alsea 

(Figure 4-10) may contribute to the paucity of seagrass in that system even though seasonal 

fluctuations are not as great as in the Salmon or Umpqua estuaries. At the opposite extreme, the 

occurrence of extensive seagrass beds in Netarts Bay (Kentula and McIntire, 1986), which is 

characterized by minimal seasonality in salinity (Figure 2-11), is consistent with the hypothesis 

that salinity variations limit seagrass abundance and distribution. 

A different and potentially complementary mechanism reducing Z. marina in river-dominated 

systems is the greater current energy and sediment transport in these high flow systems, as 

suggested in Section 6.2.2. Yet another possible contributing factor may be the presence of the 

burrowing shrimp N. californiensis. N. californiensis can inhibit Z. marina (Dumbauld and 

Wyllie-Echeverria, 2003), and its extensive coverage in both the Salmon and Nestucca estuaries 

(Figure 7-19) may make it more difficult for Z. marina to become established. While these 

suggestions need to be evaluated further, several independent data sets indicate that riverdominated systems within the PNW have reduced Z. marina cover compared to tide-dominated 

and bar-built estuaries. 

7.2.3 Among-Estuary Distribution of Zostera japonica

The regional distribution of the nonindigenous Z. japonica differed from the native seagrass in 

several respects. Z. japonica was found in six of the target estuaries in the probability surveys, 

including the Salmon and Nestucca (Table 7-4). The only target estuary where Z. japonica was 

not found was the Alsea, and we are unaware of any reports of this species in the Alsea. In 

contrast to Z. marina, Z. japonica occupied moderately large areas in both tide-dominated 

estuaries, such as the Coos, and the river-dominated Umpqua. Besides these six estuaries, 

Z. japonica has been reported in a wide range of PNW estuaries including the river-dominated 

Columbia, Siletz, Necanicum, and Coquille estuaries; the tide-dominated Willapa and Grays 

Harbor estuaries; and the bar-built Humboldt and Netarts (Lee and Reusser, 2006). In the 

probabilistic surveys, the intertidal extent of Z. japonica actually exceeded that of Z. marina in 

the Salmon, Nestucca, Coos, and Umpqua estuaries. Coverage of Z. japonica in these four 

estuaries ranged from 4% to 23% compared to 0% to 12% for Z. marina based on the integrated 

measure of presence/absence (Tables 7-3 and 7-4). This comparison is only for the intertidal 

populations, and inclusion of the shallow subtidal Z. marina could result in more similar 

percentages among the two species since Z. japonica is rare or absent in the shallow subtidal in 

these estuaries. 
195

Table 7-3. Percent of intertidal area with Z. marina estimated using different methods. Estimates for presence/a

several types of data over the 2.5 x 2.5 m quadrat (see Section 7.1.3). Estimates for “Large Quadrat” are the percarea with >1% cover or >10% cover within the 2.5 x 2.5 m quadrats. Estimates from the point intercepts are the at least one intercept with Z. marina in the 0.25 m2 plant quadrats. NA = not available. 

ESTUARY 

# SITES WITH 

Z. MARINA 

P/A 

% INTERTIDAL 

AREA 

P/A

% INTERTIDAL AREA 

LARGE QUADRAT 

COVER >1%

% INTERTIDAL AREA 

LARGE QUADRAT 

COVER >10%

Alsea 0 0 NA NA Coos 12 11.7 10.4 2.4 Nestucca 0 0 NA NA Salmon 0 0 NA NA Tillamook 28 34.2 34.2 15.6 Umpqua 8 5.5 5.5 0.5 Yaquina 11 17.4 NA NA Table 7-4. Percent of intertidal area with Z. japonica estimated using different methods. Estimates for presence/

integrate several types of data over the 2.5 x 2.5 m quadrat (see Section 7.1.3). Estimates for “Large Quadrat” arintertidal area with >1% cover or >10% cover measured within 2.5 x 2.5 m quadrats. Estimates from the point inpercent intertidal areas where there was at least one intercept with Z. japonica in the 0.25 m2 plant quadrats. NA 

ESTUARY 

# SITES WITH 

Z. JAPONICA 

FROM P/A 

% INTERTIDAL 

AREA 

P/A 

% INTERTIDAL AREA 

LARGE QUADRAT 

COVER >1% 

% INTERTIDAL AREA 

LARGE QUADRAT 

COVER >10% 

Alsea 0 0 NA NA Coos 17 19.4 19.4 10.0 Nestucca 19 23.4 NA NA Salmon 3 3.6 NA NA Tillamook 9 10.5 10.5 5.0 Umpqua 22 20.7 19.3 3.2 Yaquina 18 11.9 NA NA 
196

Zostera japonica has obtained these extensive within- and among-estuary distributions within the 

last 30 to 50 years since it was first recorded on the Pacific Coast in 1957 and in Yaquina in 1976 

(Harrison and Bigley, 1982; Bayer, 1996). Further, Z. japonica is continuing to rapidly expand 

its distribution within portions of the Yaquina (Young et al., 2008). While it is apparent that 

Z. japonica is expanding, the ecological consequences of this expansion are not clear. Both the 

native and nonindigenous seagrass species occurred within the same coastal PNW estuaries we 

surveyed, but they are not presently competing within these estuaries. Of the 138 occurrences of 

seagrasses in the probabilistic surveys, only 2 sites (1.5%) contained both species. The low 

overlap of the two species is due to their different intertidal distributions. Z. marina was 

primarily found in the shallow subtidal and lower intertidal zones, while Z. japonica tended to 

colonize a band higher in the intertidal and, based on our observations, around freshwater 

streamlets (also see Kaldy, 2006; Ruesink, 2006). However, as Z. japonica continues to expand, 

it is possible that the two species will overlap as reported for areas in Puget Sound and British 

Columbia (Nomme and Harrison, 1991) and in Netarts (Dudoit, 2006), and then potentially 

compete for space and light (Wonham, 2003; Bando, 2006). Additionally, as Z. japonica

expands it could eventually become sufficiently abundant to have an effect on primary 

production and other components of estuarine food webs (e.g., Posey, 1988; Wonham, 2003; 

Ruesink et al., 2006; Kaldy, 2006). 

7.2.4 Composition and Seasonality of Benthic Green Macroalgae 

Determining benthic green macroalgal distribution and abundance presents two additional 

challenges compared to the seagrasses. The first is that green macroalgae are composed of 

several species. In the Yaquina Estuary in 2001, benthic green macroalgae was found to be 

comprised on average of taxa most closely resembling Ulva linza: ~60%; U. fenestrata: ~30%; 

U. flexuosa: ~10%; U. intestinalis: < 5% (D. Young, unpubl. data). Given the difficulty in 

separating the species, all the green macroalgae are treated as a single taxon. 

Another issue is the seasonality of the macroalgal blooms, which could potentially confound 

differences among estuaries if they are sampled at different times. To address macroalgae 

seasonality, we utilized data from a 1999 survey in the Yaquina that quantified seasonal changes 

to derive a seasonal adjustment for biomass. The 1999 survey measured percent cover and 

biomass of Z. marina and benthic green macroalgae monthly from June through December as a 

function of distance from estuary mouth and elevation above MLLW at six sites within the lower 

Yaquina Estuary (D. Young, unpubl. data). For this analysis, we took the mean of the monthly 

averages of both percent cover and biomass (grams dry weight [gdw] m-2) for all six sites. These 

data illustrate the strong seasonality in macroalgae in both cover (Figure 7-12) and biomass 

(Figure 7-13), which increase from June through September and decline rapidly after October. 

At its peak in September and October, the average macroalgal dry weight exceeded 200 gdw m-2 

and average surficial cover exceeded 50% at these non-random stations. 

While aware of this seasonality, the extensive logistic effort required in the probabilistic surveys 

necessitated sampling from early June through mid September (Figure 7-14). The Yaquina 

Estuary was sampled earliest in the growing season, while the Alsea Estuary was sampled during 

September, the period of peak macroalgal biomass (Figure 7-14). To minimize underestimation 

of macroalgal occurrence during the initial phase of its growth spurt, the integrated 
197

June July Aug Sept Oct Nov Dec

0

20

40

60

80

100

Average Percent Cover

Month

Figure 7-12. Average percent cover values (± 1 std. error) of benthic green macroalgae at six 

sites in the Yaquina Estuary during 1999. 

June July Aug Sept Oct Nov Dec

0

50

100

150

200

250

300

Avg Macroalgal Biomass (gdw m-2

)

Month

Figure 7-13. Average biomass values (± 1 std. error) of benthic green macroalgae at six sites in 

the Yaquina Estuary during 1999. 
198

150 200 250 300 350

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Proportion of Macroalgal Biomass

Julian Day

Alsea

Coos

Nestucca

Salmon

Tillamook

Umpqua

Yaquina

Y = -0.000128 X2 + 0.0616 X - 6.41

r

2

 = 0.84

Figure 7-14. Average proportions of green benthic macroalgal biomass relative to the September 

peak measured in the Yaquina Estuary during 1999. Dates are given in Julian days. The solid 

line connects the monthly averages while the dashed line is the polynomial regression fit to the 

monthly data. The polynomial regression was used to derive the seasonally adjusted macroalgal 

biomass in all of the target estuaries based on sampling date. The horizontal gray bars indicate 

the sampling dates for each target estuary. 

presence/absence from the 2.5 x 2.5 m quadrats was used as the primary data source for 

distribution since its larger sample area is more likely to detect sparse populations. To correct 

for seasonality in biomass, we derived a polynomial regression from the 1999 Yaquina dataset 

relating the relative percentage of biomass to the monthly maximum value in September 

(Figure 7-14). This polynomial was used to derive seasonally adjusted biomass estimates based 

on actual sampling dates with the assumption that similar seasonal patterns occur in all Oregon 

estuaries. This approach generated estimates of standing stock within the range previously 

observed in Yaquina in six of the estuaries. However, the model predicted biomasses greater 

than 12,000 gdw m-2 at a few Yaquina sites, several-fold higher than have been observed in our 

previous studies or reported in other studies in Yaquina (Davis, 1981; Kentula and DeWitt, 2003; 

D. Young and B. Boese, unpubl. data). It is possible that the model over predicted biomass at 

these stations since these predictions utilized the steepest portion of the adjustment curve 

(Figure 7-14). In any case, the maximum seasonally adjusted values in the Yaquina need to be 

evaluated cautiously 
199

7.2.5 Among-Estuary Patterns of Benthic Macroalgae

Six of the target estuaries had areal coverages of macroalgae in the range of 41% to 64% of the 

intertidal zone while macroalgae occurred over 22% of the intertidal zone in the Umpqua 

(Table 7-5). Benthic macroalgae has been reported from a number of other coastal estuaries in 

the PNW as well as from Puget Sound (Phillips, 1984; Thom, 1984; Kentula and McIntire, 1986; 

Nelson and Lee, 2001; Thom et al., 2003), and we have observed macroalgal mats in a number 

of additional moderate-sized estuaries such as the Siletz. The presence of benthic macroalgae in 

systems ranging from Puget Sound to the Salmon Estuary indicate that macroalgae is a widespread feature of PNW estuaries greater than about 3 km2

 in size. Limited observations suggest 

that green benthic macroalgae is sparse or absent in estuaries smaller than about 0.5 km2, while 

additional observations are needed for estuaries between 0.5 and 3.0 km2 in size. 

At a sufficiently high density, macroalgae can result in anoxic sediment conditions, smother 

benthos and seagrasses, and reduce light availability for seagrasses (e.g., Hull, 1987). In lieu of 

an established threshold relating percent cover to these effects, we use >70% cover as the 

threshold for “high” macroalgal cover. At this percent cover, macroalgae “blanket” the sediment 

surface though we have no direct evidence it results in detrimental effects to seagrasses. Based 

on the point intercepts (Figure 7-15), the estuaries divided into three groups with the Umpqua 

having no high macroalgal cover, the Alsea, Coos, Nestucca, Salmon, and Tillamook estuaries 

having moderate extents (4-7%) of high cover, and the Yaquina Estuary having an extensive 

area (18%) of high macroalgal cover. This high cover occurred within the Yaquina Estuary 

even though it was sampled the earliest during the macroalgal growth season (Figure 7-14). 

Results from the 2.5 x 2.5 m quadrats show a similar pattern in the three estuaries where the data 

are available. High macroalgal cover occurred over 6-8% of the intertidal area in the Coos and 

Tillamook estuaries and was not detected in the Umpqua (Figure 7-16). 

Another indicator of potential macroalgal impact is standing stock, with a value of 100 gdw m-2

identified as a potential threshold for impacts on seagrasses in Chesapeake Bay (Bricker et al., 

2003). This threshold is in the general range of observed effects from an experimental removal 

of the green macroalgae Ulvaria obscura on shoot production in subtidal Z. marina in Puget 

Sound (Nelson and Lee, 2001). In this experiment, sequential removals of about 144 gdw m-2, 

40 gdw m-2, and 115 gdw m-2 over a month resulted in a slower decline in shoot production than 

in the controls. The percentages of the intertidal area exceeding this threshold using the 

unadjusted biomass (Figure 7-17) and seasonally adjusted biomass (Figure 7-18) were similar in 

six of the target estuaries (Table 7-6). In the Yaquina Estuary, there was about a two-fold 

difference between the estimates, with about 20% of the intertidal exceeding the threshold using 

the unadjusted biomass versus about 42% using the seasonally adjusted values. Based on either 

the unadjusted or adjusted values, the target estuaries break into three relatively distinct groups. 

The first is the Yaquina Estuary with a high percentage of the intertidal zone (20-42%) 

exceeding the biomass threshold, which is consistent with the pattern based on percent cover 

(Figure 7-15). The second group consists of the Alsea, Coos, Nestucca, and Tillamook estuaries 

with a moderate percentage (4-6%) of the intertidal exceeding the biomass threshold. The third 

group consists of the Salmon and Umpqua estuaries which had minor areas (<1%) exceeding the 

threshold. The Salmon differed from the Umpqua in having an extensive area of macroalgal 

cover though the biomass did not reach high standing stock. 
200

Table 7-5. Percent of intertidal area with green benthic macroalgae estimated using different methods. Estimatepresence/absence (P/A) integrate several types of data over the 2.5 x 2.5 m quadrat (see Section 7.1.3). Estimateare the percent of the intertidal area with >1% cover or >10% cover measured within 2.5 x 2.5 m quadrats. Estimintercept are intertidal areas where there was at least one intercept with macroalgae in the 0.25 m2 plant quadrats

ESTUARY 

# SITES WITH 

MACROALGAE 

FROM P/A 

% INTERTIDAL 

AREA 

P/A 

% INTERTIDAL AREA 

LARGE QUADRAT 

COVER >1% 

% INTERTIDAL AREA 

LARGE QUADRAT 

COVER >10% 

Alsea 44 54.4 NA NA Coos 60 54.9 53.7 10.4 Nestucca 44 41.4 NA NA Salmon 71 56.2 NA NA Tillamook 65 63.6 54.1 14.9 Umpqua 26 22.2 22.1 3.2 Yaquina 35 58.4 NA NA 

Table 7-6. Biomass (MB) estimates for benthic macroalgae. The percent of the intertidal area with MB >100 gdw 

from the benthic macroalgal cumulative distribution functions (CDFs, Figures 7-16 and 7-17). The number of siand seasonally adjusted MB >100 gdw m-2 indicates the number of sampling sites that increased above the thresh

adjustment. Note that the slightly different percent areas with the unadjusted and adjusted values, even though thnumber of samples, is due to interpolating values from different CDF curves. ESTUARY 

# SITES WITH MB

MEASUREMENTS 

(MB >0 gdw m-2) 

# UNADJUSTED 

SITES WITH 

MB >100 gdw m-2

# SEASONALLY 

ADJUSTED SITES WITH 

MB>100 gdw m-2

% INTERTIDAL 

AREA WITH 

UNADJUSTED 

MB>100 gdw m-2

Alsea 24 3 3 4.0 Coos 31 6 6 4.9 Nestucca 14 5 5 3.9 Salmon 50 0 0 0 Tillamook 38 11 12 5.8 Umpqua 12 1 2 <0.1 Yaquina 31 12 26 18.0 
201

0 10 20 30 40 50 60 70 80 90 

0

102030405060708090

100

Percent of Intertidal Area

% Cover

 Alsea Coos Nestucc Salmon Tillamo Umpqu Yaquin

Figure 7-15. Cumulative distribution function (CDF) of the % cover of benthic macroalgae estimated from the pusing the 0.25 m2 quadrats. The red dashed vertical line indicates 70% cover which is used as the threshold for “
202

0 1 - 10% 10 - 40% 40 - 70% >70%

0

10

20

30

40

50

60

70

80

0

6 8

0 < 1 3 1

7

3

19

49

43

78

36

Percent of Intertidal Area

Macroalgae Density Class

 Coos

 Tillamook

 Umpqua

46

Figure 7-16. Percent of the intertidal area covered by benthic macroalgae by density class. The 

data are from estimates in the 2.5 x 2.5 m quadrats in the 2005 and 2006 surveys. The threshold 

for “high” macroalgal cover is >70%. 

The overall pattern that emerges is that seasonal macroalgal blooms are a common feature across 

a range of different types and sizes of PNW estuaries. However, estuaries vary several fold in 

the extent of their intertidal areas with high percent cover (>70%) or standing stocks 

>100 gdw m-2, with the highest values occurring in the Yaquina. One possible reason for the 

high macroalgal biomass in the Yaquina Estuary is its high nutrient concentrations as indicated 

by having the highest wet season dissolved inorganic nitrogen (DIN) concentrations as well as 

the second highest cover of red alder in its watershed (Table 4-3). It is possible that the 

macroalgae respond to this high nitrogen concentration early in the growing season when light 

limitation is removed, rapidly building up a high biomass. The importance of DIN is further 

suggested by the system with the lowest cover of macroalgae (Umpqua) also having the lowest 

wet season DIN (Table 4-3). Regardless of the cause(s), macroalgae may reach biomasses 

sufficient to adversely impact seagrasses over a moderate to substantial portion of the intertidal 

in the Yaquina Estuary and over lesser areas in the Alsea, Coos, Nestucca, and Tillamook 

estuaries, assuming the Chesapeake Bay biomass threshold is appropriate for the PNW. As 

discussed in Section 7.4, these blooms appear to be a natural phenomenon and not an indication 

of cultural eutrophication. 
203

0 100 200 300 400 500 600 

0

102030405060708090

100

Percent of Intertidal Area

Biomass (gdw m-2)

 Alsea Coos Nestucc Salmon Tillamo Umpqu Yaquin

Figure 7-17. Cumulative distribution function (CDF) of the unadjusted benthic macroalgal biomass (gdw m-2). T

indicates the 100 gdw m-2 value which has been associated with adverse impacts on seagrasses in Chesapeake Ba
204

0 100 200 300 400 500 600 

0

102030405060708090

100

Percent of Intertidal Area

Biomass (gdw m-2)

 Alsea Coos Nestucc Salmon Tillamo Umpqu Yaquin

Figure 7-18. Cumulative distribution function (CDF) of the seasonally adjusted benthic macroalgal biomass (gdw 

vertical line indicates the 100 gdw m-2 value which has been associated with adverse impacts on seagrasses in Ch

Yaquina distribution extends to approximately 12,000 gdw m-2.
205

7.2.6 Among-Estuary Patterns of Burrowing Shrimp 

The spatial extent and density of burrowing shrimp were measured as burrow hole counts in the 

0.25 m2

 quadrats, visual estimates using five density classes in the 2.5 x 2.5 m quadrats, and 

from an integrated measure of presence/absence. Because of their high density, the burrow hole 

counts in the 0.25 m2 quadrats were an effective method to determine both the presence and 

density of both species. 

Neotrypaea californiensis beds were a major feature in all the target estuaries, occurring over 

21% to 74% of the intertidal area (Figure 7-19). The Salmon River had both the greatest area of 

the intertidal occupied by N. californiensis and the densest population, with burrow hole counts 

exceeding 100 per 0.25 m2 at several sites. Neotrypaea also occupied a large extent of the 

intertidal in the Nestucca though populations were not as dense as in the Salmon. The areal 

extent and abundance of this burrowing shrimp in the other five estuaries varied from the largest 

in the Alsea to the lowest in the Coos Estuary. The other burrowing shrimp, U. pugettensis, 

showed a different pattern among the estuaries (Figure 7-20). Maximum coverage of U. 

pugettensis occurred in the Alsea with 53% of the intertidal area occupied and a maximum 

density exceeding 50 burrow holes per 0.25 m2

. The Coos, Nestucca, Tillamook, and Yaquina 

estuaries had moderate coverages, ranging from 17% to 33% of the intertidal area. In 

comparison, U. pugettensis occurred over less than 4% of the area in the Salmon and none were 

found in the Umpqua. 

 

Our results and other studies (e.g., Feldman et al., 2000; DeWitt et al., 2004) demonstrate that 

burrowing shrimp are major components of the intertidal zones of estuaries larger than about 3 

km2. Of the 698 quadrats taken in the target estuaries, one or both of the burrowing shrimp 

occurred in 424 samples (61%). The absence of U. pugettensis in the Umpqua and its low 

coverage in both the Salmon and Nestucca (Figure 7-20) suggests that this species is less 

abundant in highly river-dominated estuaries, which is consistent with an earlier assessment that 

concluded that N. californiensis and U. pugettensis were probably absent from the Rogue 

Estuary (ODFW, 1979). The among-estuary pattern for N. californiensis does not appear to be 

as closely linked to extent of freshwater flushing. It has low coverage in the river-dominated 

Umpqua but high coverage in two other river-dominated estuaries, the Salmon and Nestucca, and 

its among-estuary distribution may be controlled by a number of factors. In terms of estuary 

size, limited observations suggest that both burrowing shrimp are sparse or absent in estuaries 

less than about 0.5 km2

, while additional observations are needed for estuaries between 0.5 and 

3.0 km2. 

Both species directly and indirectly affect estuarine food webs within the PNW. Various fishes, 

birds, and even whales prey on these species (e.g., Stenzel et al., 1976; Armstrong et al., 1995; 

Feldman et al., 2000; Dumbauld et al., 2008). Perhaps more important are the indirect effects 

these bioengineering species have on nutrient fluxes and phytoplankton. The intense 

bioturbation and irrigation activities of these species increase the benthic flux of nitrogen 

(D’Andrea and DeWitt, 2003), which contributes to the total estuarine nutrient loading (Brown 

and Ozretich, 2009). At the same time, these species filter large quantities of the overlying water 

(Griffen et al., 2004), potentially decreasing phytoplankton concentrations and turbidity. 
206

0 20 40 60 80 100 

0

102030405060708090

100

Percent of Intertidal Area

Density (# of holes per 0.25 m2)

 Alsea Coos Nestucc Salmon Tillamoo Umpqua Yaquina

Figure 7-19. Cumulative distribution function (CDF) of the number of N. californiensis burrows holes per 0.25 m

estuaries. 
207

0 10 20 30 40 50 60 70 

0

102030405060708090

100

Percent of Intertidal Area

Density (# of holes per 0.25 m2)

 Alsea Coos Nestucc Salmon Tillamoo Umpqua Yaquina

Figure 7-20. Cumulative distribution function (CDF) of the number of U. pugettensis burrows holes per 0.25 m2 

estuaries. 
208

The overall effect of these species on nutrient and phytoplankton concentrations will depend 

upon a number of factors, including the relative proportion of the two species and the amongestuary differences in the advection of oceanic nutrients and phytoplankton. It is important to 

note, however, that the role of U. pugettensis in the regional food webs may be diminished in 

response to a recent invasion by a parasitic bopyrid isopod (Orthione griffenis) (Markham, 

2004), which may substantially reduce the density of the host shrimp within PNW estuaries 

(Smith et al., 2008). 

7.2.7 Bare Habitat 

The last benthic habitat quantified was “bare habitat”, which was defined as habitat devoid of 

any seagrasses, macroalgae, or burrowing shrimp in the 2.5 x 2.5 m quadrats. As used here, it is 

meant to capture the area of the estuary that is unsuitable or at least poor habitat for these five 

ecologically important benthic taxa. It is not meant to imply that it is poor habitat for all species, 

and a number of benthic infauna characteristically inhabit “bare sediment” in the PNW (Ferraro 

and Cole, 2007). The coverage of bare habitat varied several fold, with only 1% of the intertidal 

zone of the Nestucca not having identifiable biotic structure compared to over 20% of the 

intertidal zone in both the Coos and Umpqua estuaries (Figure 7-21). These data indicate that at 

least one of the five target taxa occupied >70% to 99% of the intertidal area of these PNW 

estuaries. 

7.2.8 Multivariate Analysis of the Target Estuaries 

The differences in the areal extents of the benthic habitat types presented above allow managers 

to group the estuaries based on the extent of a single resource (e.g., Z. marina) or on a potential 

stressor (e.g., macroalgae). Another approach is to evaluate the overall similarity in the patterns 

of these benthic habitat types. Since there are a limited number of estuaries and variables 

(habitat types), ordination by non-metric multidimensional scaling (nMDS; Clarke and Warwick, 

2001; McCune and Grace, 2002) was used instead of clustering to evaluate relationships. The 

nMDS analysis was conducted with Primer6 (Clarke and Gorley, 2006; 

http://web.pml.ac.uk/primer/primer6.htm) on the percent area occupied by the five target taxa 

and the bare habitat in each of the estuaries using the Bray-Curtis similarity index. 

The nMDS ordination had a very low stress (<0.01) indicating that the relationships among the 

estuaries are well represented in two-dimensional space (Figure 7-22). The tide-dominated 

Coos, Tillamook, and Yaquina estuaries form a group with a very high degree of similarity 

(>80%), with the Alsea associated with this group at >75% similarity. The river-dominated 

Nestucca and Salmon estuaries form a second cluster with high similarity. These six estuaries 

form a single group at a moderately high (65%) similarity, with the Umpqua separate from the 

other estuaries. This pattern indicates that tide-dominated estuaries have very similar relative 

distributions of the six benthic habitats. The river-dominated Alsea is more closely aligned to 

this group than to the smaller river-dominated Nestucca and Salmon, suggesting that there are 

physical drivers related to estuary size or geomorphology that effect the biotic composition of 

river-dominated estuaries. The separation of the Umpqua suggests that larger highly riverdominated estuaries have different proportions of these intertidal benthic habitat types, in part 

because of the low areal extent of macroalgae and absence of U. pugettensis.
209

This analysis by benthic habitat type is based on a finer breakout of the intertidal habitat types 

than the classifications by NWI wetland classes (Chapter 2). In particular, differentiating the two 

burrowing shrimp habitats from unvegetated habitat and separating Z. marina and Z. japonica as 

discrete habitats offers a much finer resolution of the regularly flooded NWI wetland habitats. 

However, this analysis does not include the subtidal or emergent wetland classes that were 

included in the NWI classification. Even with these differences, there are a number of 

similarities. The Coos, Yaquina, and Alsea estuaries showed a high degree of similarity and the 

Umpqua was separated from the tide-dominated and moderately river-dominated estuaries in 

both analyses (Figures 2-14d and 7-22). The major difference is that the Salmon River showed 

little similarity with the other estuaries based on the NWI analysis in comparison to its relatively 

high similarity in the present analysis. The Salmon River separated from the other PNW 

estuaries in the NWI analysis largely based on its high percentage (61%) of irregularly exposed 

marshes, habitats not included in the present analysis. 

Alsea Coos Nestucca Salmon Tillamook Umpqua Yaquina

0

10

20

30

7.1

28.1

16.4

11.8

1.5

21.5

Percent of Intertidal Area

6.6

Figure 7-21. Percent of intertidal area occupied by bare habitat as defined by the absence of 

Z. marina, Z. japonica, benthic green macroalgae, N. californiensis, or U. pugettensis in the 2.5 x 

2.5 m quadrats.
210

Resemblance: S17 Bray Curtis similari

Alsea

Coos

Nestucca

Salmon

Tillamook

Umpqua

Yaquina

2D Stress: 0

Figure 7-22. Non-metric multidimensional scaling (nMDS) of the seven target estuaries based on the percent of occupied by Z. marina, Z. japonica, benthic macroalgae, N. californiensis, U. pugettensis, and bare habitat. Con

different levels of similarity as measured by the Bray-Curtis similarity index.
211

The comparison of the classifications based on benthic habitats versus NWI wetland classes 

highlights the fact that different schema can be generated depending on the suite of estuaries and 

variables used in the analysis. While identifying which is the “best” classification depends upon 

the goals of the user, a general characteristic of a successful classification is that it maximizes 

differences among groups while minimizing differences within groups. As discussed in 

Section 2.9, one approach to identifying similar estuaries for evaluation of within-group 

variability is to focus on estuaries that clustered together by more than one method. Of the seven 

target estuaries, the Coos and Yaquina estuaries show high similarity based on benthic habitats 

(Figure 7-22) and in the NWI and land cover crosswalk (Table 2-8). To the extent that these 

classifications are related to nutrient dynamics, these two estuaries should have similar nutrient 

and salinity patterns, which appear to be the case. Among the target estuaries, the Coos and 

Yaquina were ranked sequentially in the dry season concentrations of PO4

3-, NO3

-

+NO2

-

, and 

total suspended solids (TSS) and were within two ranks for chlorophyll a (Tables 4-4 and 4-5). 

Yaquina and Coos showed a very similar extent of tidal variation at the mouth of the estuaries 

when plotted against the area-normalized freshwater inflow (Figure 4-11), suggesting similar 

short-term salinity dynamics. Additionally, Coos and Yaquina showed similar wet and dry 

season salinity patterns along the estuarine gradient (Figure 2-11), indicating similar seasonal 

dynamics. In contrast, the Umpqua was the least similar of the target estuaries in the nMDS 

analysis and was separated from the other target estuaries in the NWI and land use crosswalk 

(Table 2-8). This highly river-dominated estuary had the lowest median values for all the water 

quality parameters among the target estuaries during the dry season (Tables 4-4 and 4-5) and 

lowest wet season DIN and red alder cover in the watershed (Table 4-3), supporting the 

suggestion that river-dominated estuaries have different nutrient dynamics and, presumably, 

vulnerabilities. 

7.3 Within-Estuary Distribution of Seagrasses and Other Benthic Resources 

One of the objectives of the probabilistic surveys was to test the hypothesis that the majority of 

the Z. marina and other target taxa occurred within the oceanic segments (see Chapter 5) of 

different types of estuaries. Two nonexclusive mechanisms could generate this pattern. The first 

is that the total intertidal area is greater in the oceanic segments, resulting in the majority of the

population occurring within the oceanic segment even if it did not constitute high quality habitat. 

To evaluate whether the population primarily occurred in the lower or upper estuary, we 

calculated the relative distributions of each taxon, which was calculated as the percent of the 

total area occupied by each taxon occurring in the oceanic versus riverine segments. This metric 

represents how the total population is split between the two estuarine segments, and sums to 

100%. The second mechanism is that either the lower or upper estuary represents better habitat 

independent of its area. Habitat quality was estimated from each taxon’s relative cover which 

was independently calculated as the percent of the area of the oceanic segment occupied by the 

target taxon and the percent the area of the riverine segment occupied by the target taxon. For 

example, a taxon could occupy 10% of the area of the oceanic segment versus 25% of the 

riverine segment, indicating that the riverine segment provided a better relative habitat. We 

attempted to define sample frames a priori within each of the estuaries so that there was an 

adequate number of samples in both the oceanic and riverine segments. This initial distribution 

was successful in six of the seven estuaries, with >15 samples in each segment (Table 7-1). 
212

However, only two samples were located in the riverine segment of the Nestucca, so any 

percentage coverages for this portion of the estuary are preliminary. 

7.3.1 Within-Estuary Distributions of Seagrasses 

Zostera marina was nearly exclusively found in the oceanic segments in the four estuaries where 

it was detected (Figure 7-23). The lowest proportion of the total area occupied by Z. marina in 

the oceanic segment was 79% in the Tillamook with 98-100% of the seagrass occurring in the 

oceanic segments in the Coos, Umpqua, and Yaquina. These results are qualitatively similar to 

those from the aerial surveys (Table 6-5). Thus, both approaches demonstrate that the majority 

of the native seagrass population occurs in the oceanic segments of both tide-dominated and 

river-dominated estuaries. 

While difference in sizes of the oceanic and riverine segments contributed to this pattern 

(Table 7-1), the distribution of Z. marina is not simply a consequence of the oceanic segments 

being larger as indicated by its relative cover. Z. marina occupied very little of the intertidal 

zone (0-1%) in the riverine segments of the Coos, Umpqua, and Yaquina estuaries compared to 

its relative cover in the oceanic segments (13-17%; Table 7-7). There was a smaller difference 

between the two segments in the Tillamook (45% vs. 18%) but even here the relative cover in the 

riverine segment was less than half that in the oceanic segment. The higher relative cover 

occupied in the oceanic segments of all four estuaries indicates that the oceanic segments provide 

substantially better habitat for Z. marina per unit of intertidal area. 

Compared to Z. marina, the non-native Z. japonica displayed both a wider within-estuary range 

and more variability among the estuaries. The majority of the area occupied by Z. japonica

occurred in the riverine segments of the Tillamook and Umpqua (Figure 7-24). In contrast, the 

majority of the Z. japonica population occurred in the oceanic segments in the Coos, Salmon, 

and Yaquina estuaries. Averaged across all seven estuaries, 53% of the area occupied by 

Z. japonica occurred in the oceanic segments versus 47% in the riverine segments. The relative 

cover of Z. japonica was about 2- to 10-fold higher within the riverine segment than the oceanic 

segment in the Coos, Tillamook, and Yaquina (Table 7-7). In the Umpqua and Salmon estuaries 

comparable relative covers occurred in both the oceanic and riverine segments. Thus, in contrast 

to Z. marina, the upper estuary constitutes a high quality environment for this non-native species, 

which presumably reflects Z. japonica’s ability to tolerate lower salinities. The lowest sediment 

salinity at which Z. japonica was found was 0.4 psu while several sites had salinities between 5 

and 10 psu. In comparison, the lowest sediment salinity for Z. marina was 14 psu. The greater 

among-estuary variation in the relative distributions of Z. japonica compared to Z. marina may 

be a result of the non-native species not yet reaching an “equilibrium” population within some or 

all of the estuaries. 

Because a substantial portion of the Z. japonica population is in the riverine segments, this 

species has more exposure to terrestrially derived nutrients and hence is more vulnerable to 

anthropogenic nutrient sources than Z. marina. One possible scenario is that a moderate level of

nutrient enrichment would stimulate Z. japonica’s growth in the riverine segment while having 

little effect on the lower estuary Z. marina population. Such a stimulation could potentially 

promote the establishment and spread of this invader either through enhanced vegetative growth 

or seed production. 
213

Table 7-7. Relative cover of the five ecologically important benthic taxa and bare habitat in the oceanic and riveseven target estuaries. Relative cover is the percent area of the oceanic segment or riverine segment occupied by ranges from 0 to 100% for each segment. 

RELATIVE COVER FOR EACH HABITAT 

Z. marina Z. japonica Macroalgae Neotrypaea Upogebia 

ESTUARY Oceanic Riverine Oceanic Riverine Oceanic Riverine Oceanic Riverine Oceanic RiveAlsea 0 0 0 0 52 80 36 20 54 1Coos 15 0 17 30 65 15 23 11 31 0 Nestucca 0 0 24 0 40 0 64 50 18 0 Salmon 0 0 4 2 49 94 78 58 4 2 Tillamook 45 18 2 22 61 67 34 34 42 1Umpqua 13 0 19 22 29 17 61 37 0 0 Yaquina 21 1 10 18 71 1 46 61 40 3 
214

Alsea Coos Nestucca Salmon Tillamook Umpqua Yaquina

0

20

40

60

80

100

1

99

0

100

79

21

0 0 0 0 0

100

0

% of Total Z. marina Area

 Oceanic

 Riverine

0

Figure 7-23. Relative distributions of the total area occupied by Z. marina between the oceanic 

and riverine segments. No Z. marina was found in the Alsea, Nestucca, or Salmon in the

probabilistic surveys. 

Alsea Coos Nestucca Salmon Tillamook Umpqua Yaquina

0

20

40

60

80

100

28

72

61

39

86

14

9

91

0

100

31

69

0

% of Total Z. japonica Area

 Oceanic

 Riverine

0

Figure 7-24. Relative distributions of the total area occupied by Z. japonica between the oceanic 

and riverine segments. No Z. japonica was found in the Alsea. Only two samples were taken in 

the Nestucca riverine segment. 
215

At high levels of nutrient enrichment, excess nutrients could result in eutrophic conditions in the 

riverine segment that could potentially reduce Z. japonica in the upper estuary. Such reductions 

would be considered detrimental to the extent that Z. japonica provides ecosystem services. 

Thus, development of management strategies to reduce nutrient impacts on seagrasses will 

depend, in part, upon the assessment of the ecosystem services provided by Z. japonica versus 

any potential detrimental effects such as the potential for competition with Z. marina (e.g., 

Bando, 2006). 

7.3.2 Within-Estuary Distributions of Benthic Macroalgae 

The majority of the intertidal area occupied by benthic macroalgae occurred in the oceanic 

segment in all seven target estuaries (Figure 7-25). At least 88% of the intertidal area occupied 

by macroalgae was within the oceanic segments of the Alsea, Coos, Nestucca and Yaquina. A 

smaller proportion of the macroalgae occurred in the oceanic segments of the Tillamook, 

Umpqua, and Salmon but still >55% of the total macroalgal cover occurred in the lower estuary 

in these systems. Thus, the bulk of the macroalgal populations are exposed to ocean-derived 

nutrients during the growing season. 

In terms of its relative cover, the extent of occurrence of macroalgae in the oceanic segments 

ranged from 29% in the Umpqua to 71% in the Yaquina (Table 7-7). In comparison to this 

relatively small range, the percentage of the riverine segment occupied by macroalgae varied 

widely across estuaries. Macroalgae occurred over approximately 94% of the riverine segment 

of the Salmon River versus only about 1% of the riverine segment in the Yaquina even though 

the Yaquina had the highest coverage within the oceanic segment. The reasons for these 

differences among estuaries are not clear though it is possible that differences in sampling dates 

may have contributed to some extent. However, eutrophication does not appear to be the cause 

based on the landscape analysis of these estuaries (Chapter 3). It is possible that differences in 

sampling dates may have contributed to some extent these differences in within-estuary spatial 

patterns. For whatever reason, under certain conditions the river-dominated segments provide 

suitable habitat for benthic macroalgae, and these upper estuary populations are more likely to be 

exposed to any increases in terrestrially derived nutrients. 

7.3.3 Within-Estuary Distributions of Burrowing Shrimp 

The populations of both burrowing shrimp species primarily occurred in the oceanic segments 

(Figures 7-26 and 7-27) though the two species differed in their relative concentration within the 

lower estuary. Seventy-nine to 100% of the estuarine area occupied by U. pugettensis occurred 

in the oceanic segments in all seven estuaries (Figure 7-27). The high percentage in the oceanic 

segment reflects that the riverine segments constitute a poor habitat for this species with <3% of 

the upper estuary being occupied in six of the seven estuaries (Table 7-7). Only the Tillamook 

had more than 10% of the intertidal area occupied by U. pugettensis in the riverine segment. 
216

Alsea Coos Nestucca Salmon Tillamook Umpqua Yaquina

0

20

40

60

80

100

0

100

45

55

43

57

27

73

0

100

5

95

12

% of Total Macroalgae Area

 Oceanic

88 Riverine

Figure 7-25. Relative distributions of the total area occupied by benthic macroalgae between the 

oceanic and riverine segments. Only two samples were taken in the Nestucca riverine segment. 

The majority of the intertidal area occupied by N. californiensis also occurred in the oceanic 

segments (Figure 7-26), though it exhibited a wider range in its distribution among estuaries. 

About 78% to 98% of the total area occupied by N. californiensis was located in the oceanic 

segment in the Alsea, Coos, Nestucca, Salmon, and Yaquina estuaries. In comparison, about 55-

60% of the occupied area occurred in the oceanic segments of the Tillamook and Umpqua. 

Another difference from U. pugettensis was that the riverine segments constituted relatively 

good habitat for N. californiensis. N. californiensis occupied more than 10% of the riverine 

segment in all seven estuaries and occupied at least 50% of the upper estuary in the Nestucca, 

Salmon, and Yaquina (Table 7-7). As a consequence of this greater riverine population, N. 

californiensis might be expected to have a greater vulnerability to terrestrially derived nutrient 

enrichments than U. pugettensis.
217

Alsea Coos Nestucca Salmon Tillamook Umpqua Yaquina

0

20

40

60

80

100

22

78

45

55

40

60

13

87

2

98

11

89

% of Total 

5

Neotrypaea Area

 Oceanic

 Riverine 95

Figure 7-26. Relative distributions of the total area occupied by N. californiensis between the 

oceanic and riverine segments. Only two samples were taken in the Nestucca riverine segment. 

Alsea Coos Nestucca Salmon Tillamook Umpqua Yaquina

0

20

40

60

80

100

2

98

0 0

21

79

9

91

0

100

0

100

0

% of Total Upogebia Area

 Oceanic

 Riverine

100

Figure 7-27. Relative distributions of the total area occupied by U. pugettensis between the 

oceanic and riverine segments. Only two samples were taken in the Nestucca riverine segment. 
218

7.4 Benthic Macroalgae as Diagnostic Indicator of Cultural Eutrophication 

High levels of macroalgae have been used in several areas of the world as a diagnostic of 

eutrophic conditions (e.g., Valiela et al., 1992; Bricker et al., 1999, 2003). However, applying 

this diagnostic to PNW coastal estuaries as an early indicator of cultural eutrophication is 

problematic as the seasonal macroalgal blooms appear to be a natural regional phenomenon. 

Summer blooms of macroalgae were observed in all seven target estuaries with macroalgae 

occurring over 40% of the estuarine intertidal area in six of the estuaries (Table 7-5; Figure 7-

15). Macroalgal blooms have also been reported from other PNW coastal estuaries and Puget 

Sound (e.g., Phillips, 1984; Thom, 1984; Kentula and McIntire, 1986; Nelson and Lee, 2001; 

Thom et al., 2003; Bulthuis and Shull, 2006). We suggest that such a widespread occurrence of 

blooms is more indicative of a regional response than of localized anthropogenic enrichment. 

Furthermore, it seems unlikely that all seven target estuaries are experiencing cultural 

eutrophication given the low level of urbanization, cultivation, and percent impervious surfaces 

as well as low population densities in the coastal watersheds (Section 2.8; Tables 3-3 and 3-4). 

The possibility that red alder may increase after logging somewhat complicates this conclusion 

as this nitrogen fixer contributes to the nitrogen loadings in the riverine segments (Section 3.7). 

However, as discussed in this chapter, and supported by the δ

15N ratios (Chapter 5), the 

macroalgae in the target estuaries were primarily exposed to oceanic- versus riverine-derived 

nitrogen. Thus, under present conditions, terrestrially derived nutrients could only stimulate a 

minority of the macroalgae in these PNW estuaries. 

Based on these complementary lines of evidence, we conclude that the present level of benthic 

macroalgae is not an indicator of cultural eutrophication in PNW estuaries. This conclusion is 

specifically for coastal PNW estuaries, and certain subestuaries within Puget Sound may be 

experiencing anthropogenically driven increases in macroalgae (e.g., Shaffer, 2001) as are some 

estuaries within Southern California (e.g., Cohen and Fong, 2006). While the present level of 

macroalgae is not an indicator of eutrophication, increases over the existing baselines could serve 

as a diagnostic indicator of increasing eutrophic conditions. Increases in macroalgae in the upper 

estuary, where riverine-derived nutrients are the dominant source, would be the better diagnostic 

than changes in the lower estuary, which are more likely to reflect among-year differences in 

oceanic inputs. It would be important to couple measurements of abundance and extent of 

macroalgae with measurements of δ

15N stable isotope ratios to differentiate oceanic versus 

terrestrial nutrient sources. Measurement of macroalgal tissue nitrogen concentrations could 

complement the abundance and δ

15N measurements, especially as an integrative measure of 

nutrient pulses (Fong et al., 1998; Cohen and Fong, 2001). The practical limitations, however, of 

utilizing changes in macroalgal abundance as an indicator include its highly seasonal pattern, 

among-year variations, and paucity of quantitative baselines. These limitations could be 

mitigated as we generate additional baselines and develop a better understanding of the relative 

importance of the factors driving the seasonal pattern and the differences among estuaries. Until 

that time, however, we caution against simply using macroalgae as an eutrophication indicator in 

the PNW.
219

CHAPTER 8: LOWER DEPTH LIMIT OF ZOSTERA MARINA

IN SEVEN TARGET ESTUARIES 

Bruce L. Boese, Walter G. Nelson, Cheryl A. Brown, Robert J. Ozretich, Henry Lee II, 

Patrick J. Clinton, Christina L. Folger, T Chris Mochon-Collura, and Theodore H. DeWitt 

8.0 Introduction 

Seagrass meadows are essential to the health and function of estuaries, providing critical habitat 

to economically and ecologically important fish, invertebrates, and birds (den Hartog, 1977; 

Thayer et al., 1975; Thayer and Phillips, 1977; Dennison et al., 1993; Batiuk et al., 2000). 

Seagrass depth distribution is dependent upon light penetration, with coastal seagrasses 

extending to depths receiving about 11% of the irradiance at the water’s surface (Duarte, 1991). 

If the maximum depth that seagrasses grow in an estuary is a result of water clarity alone, then 

the maximum colonization depth may be a useful integrative water quality assessment measure 

(Dennison et al., 1993). Some have suggested its use as a monitoring tool (Sewell et al., 2001; 

Virnstein et al., 2002). Additionally, understanding the minimum light requirements for 

seagrasses is necessary for protection of existing seagrass meadows as well as restoration (Batiuk 

et al., 2000; Dennison et al., 1993; Fonseca et al., 1998). 

In the U.S., the vast majority of seagrass research has been conducted on the Atlantic and Gulf 

coasts with a goal to establish water quality criteria to assure the survival and restoration of 

seagrass meadows (Batiuk et al., 2000). In contrast, there have been few studies on the light 

requirements of U.S. Pacific Coast seagrasses, predominately Zostera marina, with the exception 

Key Findings 

• Lower depth limit for colonization of Z. marina and light attenuation coefficients 

(Kd) were determined at multiple locations within the seven target estuaries. 

• Leaf epiphyte biomass was determined seasonally at multiple locations in 

Yaquina Estuary. 

• Z. marina generally tended to colonize deeper toward the mouth of each estuary. 

• Lower depth limit for Z. marina generally followed trends in light attenuation 

(Kd), which tended to be greater in upriver estuarine reaches. 

• Minimum light levels needed to maintain Z. marina in these estuaries was 

estimated to be ~13% of ambient surface irradiance.

• Epiphyte loads were greatest in summer and fall in the oceanic segment of the 

Yaquina Estuary resulting in light reductions at the leaf surface by as much as 

60%. 
220

of the work of Zimmerman and his colleagues in San Francisco Bay (Zimmerman et al., 1991, 

1995) and Thom et al. (1998) in Puget Sound. 

Light criteria have been proposed as part of the guidelines for restoring and maintaining 

Z. marina habitat in Chesapeake Bay (Batiuk et al., 2000). However, applying these values to 

the PNW Z. marina populations is problematic due to differences in tidal amplitude which tend 

to narrow the depth range of seagrasses (Koch and Beer, 1996) and other factors including 

temperature and high estuarine flushing rates. Criteria for Chesapeake Bay Z. marina were 

derived for the growing season (spring through fall) (Batiuk et al., 2000), where carbohydrates 

are accumulated and used to maintain plants during the winter when plants cannot sustain a 

positive carbon balance (Zimmerman et al., 1989). In contrast, for Z. marina in PNW estuaries, 

winter irradiance appears to be sufficient for the maintenance of a positive carbon balance and as 

a result plants continue to grow through the winter, albeit at a slower rate (Boese et al., 2005). 

The present study measured the lower depth limit of Z. marina at multiple locations within seven 

target estuaries. These depth measurements were then compared to determine if there were 

spatial differences within and across estuaries. Light profiles were measured within these 

estuaries to determine the water clarity, expressed as a light attenuation coefficient. These 

measurements were then used to determine if the lower depth limits were correlated with water 

clarity differences within and across estuaries. Finally these depth and water clarity values were 

compared to literature values obtained for Z. marina in other estuaries. 

8.1 Methods 

8.1.1 Selection of Sampling Points 

During the summers of 2004 and 2005, the seven target estuaries (Table 8-1) were sampled to 

determine the maximum depth of Z. marina. In each estuary, 25-45 sampling points were 

determined a priori using digital habitat maps from the Oregon Estuarine Plan Book (Cortright et 

al., 1987). Sampling locations were determined by randomly selecting points on a line running 

on the channel side of each seagrass-containing polygon using the ArcView v3.3 extensions 

Random Point Generator v1.2 and Mila Utilities. Randomly selected sampling points were never 

closer than 10 m to each other. If no seagrass was present at one of these pre-selected sampling 

points, while on site, a replacement sampling point was selected from the nearest seagrass bed or 

patch that was closest to the original a priori sampling point. The Yaquina Estuary was sampled 

in both 2004 and 2005 with the additional points randomly selected to increase spatial coverage. 

The Alsea Estuary was sampled in 2004 and again in 2006 to increase spatial coverage. In 

practice, the number of sampling points varied with the estuary and conditions found on the 

sampling days. The actual number of points sampled is presented in Table 8-1 with their 

approximate locations within each of the seven estuaries shown on Figures 7-1 through 7-7.
221

Table 8-1. Estuaries sampled for the lower depth limit of Z. marina and the datum adjustment 

factor used to convert lower depth limit from the mean lower low water (MLLW) datum to mean 

sea level (MSL) datum. *Value is mean of Oregon estuaries as specific correction was not 

available for this estuary. 

ESTUARY 

ESTUARINE 

AREA 

(km2) 

# SAMPLE 

POINTS YEAR SAMPLED

MLLW TO MSL 

DATUM 

ADJUSTMENT 

(m) 

Alsea 12.49 39 2004, 2006 +0.90 

Coos 54.90 34 2005 +1.33 

Nestucca 5.00 30 2004 +1.25* 

Salmon 3.11 30 2004 +1.25* 

Tillamook 37.48 24 2005 +1.36 

Umpqua 33.78 27 2005 +1.25* 

Yaquina 19.96 64 2004, 2005 +1.39 

8.1.2 Sampling 

The lower margin of seagrass at a sampling point was determined using a color underwater video 

camera (Sea-Drop 650 series, SeaViewer Cameras, Tampa, FL). The camera was secured to a 

fin stabilizer that was on the end of a 5-m aluminum pole which was attached to a davit mount 

on the research vessel (Figure 8-1). For each sampling, the boat with attached camera was 

initially positioned at the predetermined point and the boat was then moved approximately 50 m 

offshore from the visible edge of a seagrass bed. The boat would then slowly proceed shoreward 

on the transect line until seagrass became visible on the monitor. In 2004, when seagrass was 

encountered a pole was used to anchor the boat while data were collected. In 2005 and 2006 

reverse thrust would be applied to the boat while the depth and GPS data were collected as 

quickly as possible. 

In areas where the lower margin of a seagrass bed was found in water <1 m in depth, this margin 

was most easily found by visual inspection. As the boat approached the lower edge of a seagrass 

bed, an individual would exit the boat and hold it at that position while data were collected. 

Occasionally sites were sampled in shallow water by wading from shore to the lower bed margin. 

Data recorded included the site location, water depth, and the date and time of observations. 

Water depth was determined using a lead line and/or hand-held depth sounder (deeper water) or 

using a meter stick if the depth was <0.8 m. Depth sounder accuracy was checked using a lead 

line during ideal conditions (i.e., no current, wind or waves). Depth values were adjusted to 

actual tidal heights determined from the nearest NOAA tide station. This was done by entering 

the estuary specific time and location adjustment factors (see: 

http://hmsc.oregonstate.edu/weather/tides/tideadj.html) for each measurement into the tide 

prediction software WXTIDE32 v4.4 and subtracting the resulting predicted tide height relative 

to Mean Lower Low Water (MLLW) from the depth measurement. To be consistent with 

seagrass lower limit values in the literature, lower limits relative to the MLLW datum were 
222

converted to lower limits relative to mean sea level (MSL) using the datum adjustment factors 

for each estuary if available (Table 8-1). If no correction values were available for a given 

estuary, the average correction value for Oregon estuaries (+1.25 m) was used. For more details 

on the tidal corrections see Section B.10.1. 

Figure 8-1. Schematic of underwater camera apparatus. 

8.1.3 Light Attenuation Coefficients 

Photosynthetically active radiation (PAR) is defined as irradiance in the 400- to 700-nm 

waveband, which encompasses the portion of the light spectrum that plant pigments use in 

photosynthesis. PAR profile measurements were made using LI-COR®

 spherical sensors. These 

measurements were made during single day cruises both at high and low tides at multiple 

locations in each estuary. At each of these locations PAR was determined at 0.25 m depth 

intervals from the surface to the bottom on both the down and up casts. Estimates of the overall 

water column light attenuation coefficient (Kd) values were determined from these profile data 

based on the slope of the linear regression of ln PAR vs. depth. 

The Kd values were also determined in the Yaquina Estuary from long-term datasets. These 

values were determined in two ways. A series of nearly monthly cruises measured PAR vertical 

depth profiles at multiple sites from June 1998 though September 2004. These 0.25-m interval 

profiles were taken usually during flood tides. The Kd values for these profiles were determined 

by the slope of the regression of ln PAR vs. depth for the 0.50 m to 1.25 m intervals and also for 

the 1.00 m to 3.75 m intervals, representing near surface and near bottom light attenuations. 
223

The second set of long-term Yaquina Estuary PAR measurements was taken at five fixed sites 

located at 3.9, 4.5, 11.9, 16.3, and 18.9 km from the estuary mouth1. Measurements at these sites 

were taken nearly continuously from 1999 though 2001 using two PAR sensors placed 0.75 m 

apart in depth. Kd values for this continuous measurement were determined from the difference 

between PAR measurements for the two sensors. 

Once Kd was determined, the fraction of surface irradiance reaching a given depth was calculated 

as 

K z

o

d e

I

I −

= (8.1) 

where I is the irradiance at depth (m below MSL), Io is the irradiance at the surface, Kd is the 

light attenuation coefficient (m-1), and z is depth (m below MSL). 

8.1.4 Epiphyte Biomass 

A study of epiphytes growing on Z. marina leaves was conducted within the Yaquina Estuary 

from 2000 though 2004. Data were collected at six stations distributed between 3.5 and 17 km 

upriver from the estuary mouth. Leaves from collected plants were subdivided into outer (older) 

and inner (younger) leaves. Epiphytes were scraped from these leaf groups, and dry weights (24-

36 hours at 60-70 °C) of the removed material determined for each individual plant. The effect 

of epiphyte cover on light (PAR) availability to Z. marina was estimated in the laboratory using 

a LI-COR®

 LI-190SA quantum sensor. Freshly removed epiphytes from a single leaf were 

washed into a Plexiglas cylinder with distilled water (60 mL). A light source was placed above 

this cylinder with the PAR sensor below the chamber and the amount of irradiance was 

determined. This value was then compared to a similarly measured irradiance value obtained 

using the same cylinder containing 60 mL of distilled water without epiphytes. 

For analysis, dry weight data were partitioned by collection date into wet season (NovemberApril) and dry season (May – October). Stations were also combined into two groups (oceanic 

and riverine segments) each with three stations based on the zonation presented in Chapter 5. 

8.2 Results 

8.2.1 Sampling Points/Sampling Errors 

Inclement weather, tidal constraints, and channel navigation difficulties allowed for the 

completion of only 24 sampling stations in the Tillamook Estuary. Seven of these points were 

missing corresponding GPS data and therefore the sampling times for correcting depth were 

estimated from field notes. Two points in the Coos Estuary also had no corresponding GPS 

values. These latter locations were estimated from field notes using their relative positions to 

other sampling points where GPS values were obtained. 

Electronic depth sounders were tested in the laboratory against a lead line to determine accuracy. 

Depth sounder readings were consistently within 0.1 and 0.2 meters of the lead line reading. The 

 

1

 The distance from the mouth of an estuary is here defined as the distance from the apparent ocean shore line, not 

from the end of the projecting jetties. 
224

sonar scattering effect of benthic macroalgae and seagrass was a possible source of error in depth 

data. To help minimize this effect depth soundings were taken from within 50 cm of visible 

Z. marina shoots and not from directly over them. This precaution could not be taken when 

shoots were not clearly visible to the naked eye; roughly 40% of transect depth soundings were 

too deep (i.e. ≥ about 1.5 meters, depending on turbidity) to consider this precaution reliable. In 

many cases seagrass occurred on or just above benthic slopes estimated to exceed 45 degrees. 

When this occurred, given the difficulty in stopping the boat it was unlikely that depth was 

measured exactly above the edge of the seagrass bed. In these worse case conditions, we 

estimate that the accuracy of the depth measurement was ± 60 cm. 

8.2.2 Depth Distribution 

Figures 8-2 through 8-8 show the lower depth limits of Z. marina as a function of distance from 

the mouth within the seven target estuaries. The lower depth limit of Z. marina varied 

considerably within a given estuary, but tended to be greater toward the mouth of each estuary, 

with the exception of the Nestucca Estuary (Table 8-2). 

The lower depth limit was also significantly different among estuaries (Table 8-3). The overall 

mean lower depth limits for Z. marina in the Coos and Nestucca estuaries were not statistically 

different from each other, but were different from the other five estuaries. The lower limit of 

Z. marina at any particular site might depend upon several factors other than depth such as 

sediment type, current velocity, salinity and bathymetry profiles, and the increased amongestuary variance resulting from these factors makes it more difficult to detect the effects of light 

availability. We attempted to reduce this variance added by other factors by reanalyzing only the 

deepest 1/3 of the depth limit data. Although this procedure changes mean depth, standard error, 

and the rank order of the estuaries, the re-analysis did not alter the results of the pairwise 

comparisons (Table 8-3). 

Table 8-2. Linear regression coefficients of maximum Z. marina depth (meters below MSL) 

versus distance (km) from the mouth (apparent shore line) of each of the seven target estuaries. 

Significant constants and slopes are indicated by bold text and probability value. Note that 

Nestucca’s significant regression is opposite of the general trend in that the lowest depths 

observed are farthest from the estuary mouth. 

ESTUARY CONSTANT SLOPE r2 

Alsea 4.33 (p<0.001) -0.297 (p<0.001) 0.50 

Coos 1.36 (p<0.001) -0.0056 (p=0.77) 0.003 

Nestucca 1.11 (p<0.001) 0.134 (p<0.001) 0.58 

Salmon 3.46 (p<0.001) -0.889 (p<0.001) 0.57 

Tillamook 3.21 (p<0.001) -0.137 (p=0.049) 0.16 

Umpqua 2.62 (p=0.001) -0.0251 (p=0.505) 0.0018 

Yaquina 3.70 (p<0.001) -0.118 (p<0.001) 0.32 
225

Table 8-3. Overall mean depth (meters below MSL), mean of deepest 1/3 of depth values, and 

maximum depth that Z. marina was observed in each of the seven target estuaries. Mean values 

are mean ± standard error (n). Bold indicates that two of the seven estuaries were statistically 

different from the other five estuaries but not significantly different from each other (HomeSidak method of pairwise comparisons, p ≤ 0.05). 

ESTUARY 

OVERALL MEAN 

DEPTH (n)* 

DEEPEST 1/3 OF 

DEPTH VALUES (n)* 

MAX. DEPTH 

(m) 

Alsea 2.95 ± 0.19 (39) 4.26 ± 0.26 (13) 6.55 

Coos 1.28 ± 0.12 (34) 2.09 ± 0.15 (11) 3.03 

Nestucca 1.49 ± 0.07 (30) 1.85 ± 0.02 (10) 1.94 

Salmon 2.25 ± 0.16 (30) 3.37 ± 0.06 (10) 3.70 

Tillamook 2.46 ± 0.23 (24) 3.72 ± 0.39 (8) 5.17 

Umpqua 2.45 ± 0.16 (27) 3.36 ± 0.28 (9) 4.71 

Yaquina 2.44 ± 0.13 (64) 3.62 ± 0.11 (24) 4.53 

*statistical difference in mean values (ANOVA, p<0.001). 

Distance from Mouth (km)

0 2 4 6 8 10

Depth Limit (m)

0

2

4

6

Figure 8-2. Lower limit of Z. marina (m below MSL) versus distance (km) from the mouth of 

the Alsea Estuary. 
226

Distance from Mouth (km)

0 5 10 15 20 25 30

Depth Limit (m)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Figure 8-3. Lower limit of Z. marina (m below MSL) versus distance (km) from the mouth of 

the Coos Estuary. 

Distance from Mouth (km)

0 1 2 3 4 5 6 7

Depth Limit (m)

0.0

0.5

1.0

1.5

2.0

2.5

Figure 8-4. Lower limit of Z. marina (m below MSL) versus distance (km) from the mouth of 

the Nestucca Estuary. 
227

Distance from Mouth (km)

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Depth Limit (m)

0

1

2

3

4

Figure 8-5. Lower limit of Z. marina (m below MSL) versus distance (km) from the mouth of 

the Salmon River Estuary. 

Distance from Mouth (km)

0 2 4 6 8 10 12 14

Depth Limit (m)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

 

Figure 8-6. Lower limit of Z. marina (m below MSL) versus distance (km) from the mouth of 

the Tillamook Estuary. 
228

Distance from Mouth (km)

0 2 4 6 8 10 12 14 16 18

Depth Limit (m)

0.0

1.0

2.0

3.0

4.0

5.0

Figure 8-7. Lower limit of Z. marina (m below MSL) versus distance (km) from the mouth of 

the Umpqua River Estuary. 

Distance from Mouth (km)

0 2 4 6 8 10 12 14 16 18 20

Depth Limit (m)

0

1

2

3

4

5

Figure 8-8. Lower limit of Z. marina (m below MSL) versus distance (km) from the mouth of 

the Yaquina Estuary. 

 
229

8.2.3 Kd vs. Distance from Estuarine Mouth 

The long-term irradiance datasets from the Yaquina Estuary showed strong linear relationships 

between Kd and distance from the mouth (Figures 8-9 and 8-10). These results indicate that 

water clarity generally decreases from the ocean-dominated mouth into river-dominated reaches 

of the estuary. These annualized results are similar to data obtained on high and low tide cruises 

conducted in the Yaquina Estuary on June 15 and June 21, 2004 (Figure 8-11). 

Unfortunately, the same trend in Kd observed during the one day cruises within the Yaquina 

Estuary were generally not seen or were not statistically significant in the other estuaries 

examined in the same manner (Figures 8-12 through 8-16)). In the Alsea Estuary, there was a 

large tidal difference in Kd values (Figure 8-12). While the Kd values tended to increase in the 

upriver portions of the Nestucca and Salmon River estuaries (Figures 8-13 and 8-14), these 

trends were not significant and low tide Kd values were not determined in the Nestucca due the

shallowness of this estuary during the low tide cruise. In the Tillamook Estuary, low Kd values 

on both the high and low tide cruises were observed at the station farthest up the estuary 

resulting in a non-significant regression (Figure 8-15). Tillamook Estuary Kd values were further 

complicated by highly variable weather conditions which resulted in an extremely variable 

ambient light field during many of the PAR sensor casts. Few Kd values were determined for the 

Coos Estuary due to instrument malfunction. Only in the Umpqua River Estuary was a 

significant relationship found between distance from the estuary mouth and Kd (Figure 8-16). 

Distance from Mouth (km)

0 5 10 15 20 25 30

K

d

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Kd = (0.0355*Distance) + 0.74, r2 = 0.74, p<0.001

Figure 8-9. Light attenuation coefficient (Kd, m-1) versus distance from the mouth (km) of the 

Yaquina Estuary generated using long-term cruise dataset. Kd values are means determined from 

PAR depth profile measurements taken approximately monthly from June 1998 though August 

2004. 
230

Distance from Mouth (km)

2 4 6 8 10 12 14 16 18 20

K

d

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Kd = (0.0603*Distance) + 0.48, r2 = 0.81, p =0.037 

Figure 8-10. Light attenuation coefficient (Kd, m

-1) versus distance from the mouth (km) of the 

Yaquina Estuary generated using continuous dataset Kd values are means determined from PAR 

measurements that were taken at five sites which were monitored continuously from 1999 

though 2001. 

Distance from Mouth (km)

0 5 10 15 20 25 30

K

d

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Kd = (0.0512*Distance)+0.390, r2 = 0.86, p<0.001

Figure 8-11. Light attenuation coefficient (Kd, m

-1) versus distance from the mouth (km) of the 

Yaquina Estuary for classification dataset. Kd values determined from light vs. depth profiles 

measured in the Yaquina Estuary during a single high (●) and low (o) tide in June 2004. 
231

 

Distance from Mouth (km)

0 1 2 3 4 5 6 7

Kd

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Kd = (0.012*Distance) +0.89, r2 = 0.003, p = 0.892

Figure 8-12. Light attenuation coefficient (Kd, m

-1) versus distance (km) from the mouth of the 

Alsea Estuary. Values were determined during high (●) and low (o) tides. 

Distance from Mouth (km)

0 1 2 3 4 5 6 7

K

d

0.0

0.5

1.0

1.5

2.0

2.5

Kd = (0.27*Distance)-0.0314, r2 = 0.64, p=0.104

Figure 8-13. Light attenuation coefficient (Kd, m

-1) versus distance (km) from the mouth of the 

Nestucca Estuary. Values were determined during high tide. 
232

Distance from Mouth (km)

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Kd

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Kd = (0.115*Distance)+0.485, r2 =0.324, p = 0.182

Figure 8-14. Light attenuation coefficient (Kd, m

-1) versus distance (km) from the mouth of the 

Salmon River Estuary. Values were determined during both high (●) and low (o) tides. 

Distance from Mouth (km)

0 2 4 6 8 10 12 14

K

d

0.0

0.5

1.0

1.5

2.0

2.5

Kd = (0.0013*Distance)+1.102, r2 = 0.000, p = 0.986

Figure 8-15. Light attenuation coefficient (Kd, m

-1) versus distance (km) from the mouth of the 

Tillamook Estuary. Values were determined during high (●) and low (o) tides. 
233

Distance from Mouth (km)

0 5 10 15 20 25 30

K

d

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Kd = (0.0252*Distance)+0.427, r2 =0.293, p = 0.030

Figure 8-16. Light attenuation coefficient (Kd, m

-1) versus distance (km) from the mouth of the 

Umpqua River Estuary. Values were determined during high (●) and low (o) tides. 

8.2.4 Maximum Zostera marina Depth, Kd and Light Relationship 

As there was a significant relationship between Kd and distance from the mouth in the Yaquina 

and Umpqua River estuaries, it was possible to attempt to derive a relationship between the 

lower limit for Z. marina and the estimated Kd values (Figure 8-9 and 8-16). Although there was 

a significant relationship between Kd and lower depth limit of Z. marina for the Yaquina Estuary 

(Figure 8-17), the relationship was not significant within the Umpqua Estuary (r2 = 0.034, 

p=0.34). Using Equation 8.1, the amount of surface irradiance reaching the observed lower 

depth limits for Z. marina within the Yaquina Estuary was estimated. These values ranged from 

2 to 85% with a mean ± standard error of 13.4 ± 0.2 % (n = 64). Similar values for the other 

estuaries sampled in this study were not determined due to the lack of reliable Kd estimates. 

 
234

Kd

0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

Depth (m)

0

1

2

3

4

5

Depth = 5.7 - (2.98 * Kd), r2 = 0.32, p<0.001

Figure 8-17. Relationship between Z. marina lower depth limit (m below MSL) and light 

attenuation coefficient (Kd, m

-1) in the Yaquina Estuary. 

8.2.5 Epiphyte Patterns and Impact on Light 

In the Yaquina Estuary, there was a general annual pattern in 2000 though 2003 in which 

epiphyte biomass increased in the spring to maximal amounts in the summer and fall. This 

statistically significant parabolic relationship was most clearly seen on the older, external 

seagrass blades (Figure 8-18). In the Yaquina Estuary, epiphyte biomass per unit surface area of 

seagrass leaves was higher in the oceanic segment than in the riverine segment in both wet and 

dry seasons (Figure 8-19), although only the dry season differences were statistically significant. 

Epiphyte biomass per unit leaf surface area was higher in the dry season than the wet season 

within both segments. There is a significant positive linear relationship between percent light 

reduction and log+1 transformed epiphyte biomass (Figure 8-20). 
235

y = -0.0002 x2 + 0.0757 x - 1.1484

r

2

 = 0.60

0

2

4

6

8

10

12

0 50 100 150 200 250 300 350 400

Julian Day

Biomass (mg cm-2

)

Figure 8-18. Temporal relationship of epiphytic biomass per unit leaf area on Z. marina external 

leaves in the Yaquina Estuary. Values are for 2000 though 2003. 

Wet Season Dry Season Wet Season Dry Season

0

2

4

6

8

10

12

Biomass (mg cm-2

)

 Oceanic Segment

 Riverine Segment

External Internal

Figure 8-19. Epiphyte biomass per unit leaf area on older (external) and younger (internal) 

Z. marina leaves by season (wet or dry) in the oceanic and riverine segments of the Yaquina 

Estuary. Wet season is from November through April. Dry season is from May through 

October. 
236

Figure 8-20. Linear regression relationship between the percent of light reduction to log(x+1) 

transformed epiphyte biomass per unit Z. marina leaf surface area. 

8.3 Discussion 

8.3.1 Sampling Method 

The video camera sampling technique was difficult to conduct in typical field conditions. High 

winds and strong currents made maintaining position or tracking a straight line difficult. 

Stopping the vessel in time to mark the location of a Z. marina patch was frequently challenging, 

as the vessel would drift off location during the time required to set anchor or stabilize position 

before recording GPS position. Water clarity was also a factor which introduced variability in 

estimating the lower limit of Z. marina because visibility was often as low as 0.2 to 1 m. 

Measuring the lower depth margin of Z. marina using manual sampling methods appeared to be 

more precise than the video collection technique. While this method could only be employed 

during low tides and in areas where the Z. marina lower margin was shallow (< 1.5 m), when the 

researcher exited the vessel quickly, the position and depth of the edge of the seagrass bed could 

be determined with a very high degree of accuracy and precision. 

8.3.2 Depth Distributions 

In general, the maximum colonization depth (Zc) limit of Z. marina is ultimately determined by 

water clarity (Duarte, 1991). However, we observed a high variability (Figure 8-2) in these 

y = 79.044x + 4.8381

r

2

 = 0.80

0

20

40

60

80

100

0 0.2 0.4 0.6 0.8 1 1.2 1.4

log (biomass (mg cm-2) +1) 

Light Reduction (%)
237

maximum depth values within the seven target estuaries which cannot be explained by water 

clarity relationships alone. Factors, such as current velocity, sediment characteristics, and other 

non-optical water properties, play a role in determining where and how deep seagrasses grow 

(Koch, 2001; Virnstein et al., 2002), possibly restricting the plants to shallower waters. 

Although some variability in the depth distribution data were reduced by only analyzing data for 

the deepest one-third of all the patches found within in each estuary (Table 8-3), this method 

introduces a degree of subjectivity. However, given this caveat, there was a general trend (i.e., in 

six of the seven estuaries) for the maximum depth of Z. marina colonization to be deepest 

nearest the mouth (Figures 8-2 through 8-8) and a statistically significant linear model could be 

fit to four of these estuaries (Table 8-2). The exception to this trend was within the Nestucca 

Estuary where the opposite trend appeared to occur. This latter result may be due to the overall 

shallowness of this estuary when compared to the others, which may have contributed to its 

general lack of extensive Z. marina beds. Relative to the other estuaries in the present study, 

Z. marina was not found near the mouth of the Nestucca Estuary where presumably mean water 

clarity may be greater. This lack of Z. marina was most likely due to high tidal current velocities 

and shifting sands, which we commonly observed at the mouth of this estuary during peak flood 

and ebb tides. In addition, the loss of multiple instruments near the mouth of the Nestucca 

Estuary supports this assessment that this is a high-energy environment. Upriver Z. marina was 

limited to fringing patches next to deeper water channels, where it is also possible that high 

seasonal current velocities may preclude deeper colonization depths. 

Comparing these maximum colonization depths to other PNW estuaries is difficult. Although 

several studies have measured these values (Thom et al., 2001; Thom et al., 2003; Selleck et al., 

2005), the data are presented in graphical form making it difficult to compare mean and 

maximum colonization depths directly. Data from Thom et al. (2003) indicate that the deepest 

Z. marina observed in the Coos and Willapa estuaries was approximately 1.8 m and 2.8 m below 

MSL, respectively. Although these depths are shallower than those observed in the present 

study, there is no indication that Thom et al. (2003) made a concerted effort to find the maximum 

colonization depth. Their study was instead focused on determining the depth of maximum 

shoot density and relating this to salinity and irradiance. 

Nonetheless, based on the present study and those of Thom and his colleagues (Thom et al., 

2001; Thom et al., 2003), maximum colonization depths for Z. marina within coastal PNW 

estuaries are shallower than those observed in Puget Sound (Thom et al., 1998; Dowtry et al., 

2005; Selleck et al., 2005), where Z. marina is commonly found 5 m below MSL, with some 

areas having shoots present to three times that depth (Selleck et al., 2005). 

8.3.3 Water Clarity Relationships 

A priori we assumed that overall water clarity would decrease in the more riverine portions of 

estuaries and this trend in water clarity would be apparent in light attenuation measurements 

taken over an interval of only a few days. This assumption appeared to be true for the Yaquina 

Estuary, as long-term water clarity measures averaged over a number of years were similar to 

those measured during two days in 2004. However, this trend was not consistently observed in 

the other estuaries examined. It is likely that our short-term measurements of irradiance at depth 

are not adequate to define the spatial variability within PNW estuaries. For example, in the 

Alsea Estuary, Kd values at a given location were almost 4-fold greater during low tide than high 
238

tide. This variability is probably a natural occurrence in many PNW estuaries. At the four 

locations within the Yaquina Estuary where Kd values were determined nearly continuously from 

1999 to 2001, the coefficient of variation ranged from 33 to 66%, indicating considerable tidal 

and seasonal variability. 

Only within the Yaquina Estuary were there sufficient long-term measurements of Kd to derive a 

relationship with Z. marina colonization depth. This linear relationship (Figure 8-17) was 

consistent with the idea that some portion of the reduction in colonization depth with increasing 

distance from the mouth of the Yaquina Estuary is related to a reduction in water clarity. This 

upriver trend is consistent with that reported by Thom et al., (2003) who noted an upriver 

reduction in Z. marina abundance in the Coos and Willapa estuaries, which they attributed to 

increased turbidity and reduced salinity. 

Differences in Z. marina colonization depth between PNW outer coast estuaries and Puget Sound 

bays are also likely due to water clarity, as Kd values for Puget Sound are generally lower (Thom 

et al., 2001, 2003). The trend for greater colonization depth with increased water clarity is 

evident for all species of seagrasses, where the maximum colonization depth corresponded to 

approximately 11% of surface irradiance (Duarte, 1991). However, the data for Z. marina

presented by Duarte (1991) suggest that the amount of light needed to sustain this species at 

depth is almost double that for seagrasses in general (Table 8-4). Duarte (1991) also suggested 

that the world-wide relationship between Kd and the maximum seagrass colonization depth (all 

species) was linear, and that it could be simply calculated as 

d

c

K

Z

86.1

= (8.2) 

where Zc is the maximum colonization depth (m). 

Duarte (1991) also noted that this result was similar to the results obtained for Z. marina (Zc = 

1.53/Kd and Zc = 1.62/Kd) on the Atlantic Coast of the U.S. (Dennison, 1987) and within Danish 

estuaries (Nielsen et al., 1989). The trends in Zc and Kd for Z. marina in the Yaquina Estuary are 

consistent with and appear to extend Duarte’s (1991) relationship into more turbid waters of the 

Yaquina Estuary (Figure 8-21). These Kd values were converted (Equation 8.1) to the percent of 

surface irradiance at Zc which are presented in Table 8-4. Also included in Table 8-4 are the 

minimum light requirements recommended for the growth and survival of submerged aquatic 

vegetation (SAV) in Chesapeake Bay (Batiuk et al., 2000). 

Although the mean percent of surface irradiance needed to maintain Z. marina in the Yaquina 

Estuary appears to be lower than mean literature values, individual measurements within these 

literature values are highly variable with mean values of the present study within the range of 

those previously reported (Table 8-4). 

 
239

Table 8-4. Comparison of percent of surface irradiance needed to maintain Z. marina at 

its maximum colonization depth from published data and from Yaquina Estuary data. 

SOURCE MEAN 

STANDARD 

ERROR OF MEAN N MAX MIN 

Duarte, 1991 20.5 2.1 29 43.9 4.7 

Current Study 13.4 1.9 64 85.8 1.5 

Batiuk et al., 2000 15a  

a

 Value is not a mean but is based on an analysis of literature and on an evaluation of 

monitoring and modeling research. 

Kd

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Depth Limit (m) 

0

2

4

6

8

10

Figure 8-21. Relationship between Z. marina maximum depth limit (m) and Kd.(●) Data from 

Duarte (1991) and the Yaquina Estuary (o) from the present study. 

8.3.4 Zostera marina Light Criteria 

Minimum light requirements for maintaining and restoring seagrass have been proposed for 

Chesapeake Bay (Batiuk et al., 2000) and Puget Sound (Thom et al., 1998). Chesapeake Bay 

light criteria values were empirically estimated by measuring the maximum depth of seagrass 

annually and associated Kd values monthly (Dennison et al., 1993). For Chesapeake Bay, 

proposed water column light requirements vary by estuarine salinity classification with higher 

light requirements suggested for polyhaline and mesohaline (>22% of surface irradiance) regions 

than for fresh and oligohaline regions (>15% of surface irradiance). These proposed criteria 

were modified (Batiuk et al., 2000) by the amount of light absorbed by epiphytes encrusting 

seagrass leaf surfaces, which reduced these criteria values to 9 and 15 %, respectively. These 
240

proposed criteria are also applicable only to the seagrass growing season (typically spring though 

fall). 

In contrast, light requirements for Z. marina in Puget Sound, Sequim Bay, Willapa Bay, and 

Coos Bay were reported as integrated light intensity levels (Thom et al., 1998; Thom et al., 

2008). These were estimated using maximum seagrass depth measures, Kd values and 

production-irradiance (P vs. I) relationships. Based on this methodology, Thom et al. (2008) 

suggested that the minimum light requirements to maintain Z. marina is ~300 μmoles photons 

m

-2

 s

-1 for at least three hours daily or ~3 moles photons m-2 d-1 during May through September. 

However, growth would be light limited during the spring and summer at light levels < 7 moles 

photons m-2 d-1. Thom et al. (1998) went on to suggest that for Z. marina to minimally persist 

would require mid-day minimum irradiance values at the maximum depth limit to be 

approximately 150 μmoles photons m-2

 s

-1 during the year. Assuming that mid-day surface 

irradiance is in the range of 1000-2000 μmoles photons m

-2

 s

-1, this corresponds to 

approximately 15-30% of surface irradiance which is consistent with other published criteria 

values and the present study (Table 8-4). At a lower margin Z. marina site in the Yaquina 

Estuary, the mean daily irradiance value was approximately 3.8 moles photons m-2 d-1 (Kaldy 

and Lee, 2007). Although this value exceeds the Thom et al. (1998) criterion, irradiance values 

were highly variable, ranging from 0.5 to 7 moles photons m-2 d-1, with extended periods of 

apparently inadequate lighting at depth from October to December (Kaldy and Lee, 2007). 

However, even during these periods of apparently inadequate irradiance, Yaquina Estuary 

Z. marina continues to grow (Boese et al., 2005; Kaldy and Lee, 2007). This continued growth 

and survival under conditions of negative carbon balance is most likely maintained by the 

utilization of rhizome carbohydrate reserves (Cabello-Pasini et al., 2002). 

8.3.5 Yaquina Estuary Zostera marina Light Criteria 

While it is tempting to directly apply the existing light criteria values to PNW estuaries, there are 

several additional factors that need to be considered. Chesapeake Bay, and the estuaries from 

which Duarte (1991) and Thom et al. (1998) derived their relationships are generally less turbid 

(mean Kd ~ 0.5 m-1) than the Yaquina (mean Kd ~ 1.1 m-1) and the other estuaries surveyed in the 

present study. Zostera marina has been shown to adapt to lower winter irradiance by increasing 

chlorophyll content (Zimmerman et al., 1995). Although we are not aware of any study that 

documents an analogous response to turbidity, a similar response to chronically more turbid 

water might allow for deeper colonization. 

Temperature is a possible confounding factor. The range of near-surface temperatures within 

Chesapeake Bay, Puget Sound, and in the estuaries used in Duarte’s (1991) review are likely 

greater than those observed within the Yaquina (Boese et al., 2005) due to the latter’s twice daily 

flushing with cold ocean water. Increased respiration rates due to higher summer temperatures 

in these other estuaries would potentially need to be offset by increased irradiance for plants not 

only to maintain themselves but to store carbohydrates in rhizomes which could then be used to 

maintain the plant during the winter when irradiances may be less than optimal (Zimmerman et 

al., 1995; Burke et al., 1996; Zimmerman and Alberte, 1996). Therefore, it is possible that 

Z. marina in PNW coastal estuaries may require less spring and summer irradiance to perform 

the same function because of the generally cooler water temperatures in these systems. 
241

Our study of epiphytes growing on Z. marina leaves in the Yaquina Estuary revealed a reduction

in the amount of epiphyte biomass in the riverine segment (lower salinity region). With the 

exception of 2004 there appeared to be a seasonal pattern in epiphyte biomass such that the 

greatest biomass occurred in the summer and fall. This accumulation of epiphytes reduced the 

amount of light reaching leaf surfaces by about 60% in the oceanic segment of the Yaquina 

Estuary where the greatest densities of Z. marina occur. At present we are not sure how the 

epiphyte load and its impact on light availability compares to that found in other estuaries, but 

such variation will need to be considered in future efforts to derive water column light criteria for 

Z. marina. 
242

CHAPTER 9: 

EMERGING PACIFIC NORTHWEST PARADIGM 

Henry Lee II and Cheryl Brown 

9.0 Overview of the Pacific Northwest Environment and Regulatory Implications 

In an effort to see the “forest” from the “trees” presented in the previous eight chapters, we 

offer a synopsis of the environmental conditions and nutrients dynamics in PNW coastal 

estuaries. This overview is based on our long-term research in the Yaquina Estuary, the 

classification study presented here, and a growing body of literature on the dynamics of 

PNW estuaries. Some of the environmental conditions presented here will not apply to 

every PNW estuary, in particular the estuaries with restricted tidal flushing. Nor does this 

synopsis apply to Puget Sound, which would require a separate analysis. Additionally, in 

most cases we had to extrapolate from a limited number of estuaries. Nonetheless, we 

believe these regional elements form the basis of an emerging “Pacific Northwest 

Paradigm.” We then evaluate how these regional characteristics affect the classification of 

PNW estuaries as well as their potential regulatory implications. 

9.1 Environmental Conditions 

Climate and River Flow

The PNW has a mild Mediterranean climate with a wet winter and dry summer. River 

flows reflect this seasonal precipitation pattern. 

Average annual rainfall is on the order of 140 to 350 cm y-1, with higher rainfall in the 

northern portion of the PNW. 

Watersheds and Land Cover

Watersheds are primarily forested, with low population densities and percent impervious 

surfaces. 

Logging is common in many coastal watersheds but this alteration is not well captured in 

the standard land cover data. 

Estuarine Inventory and Characteristics

There are 103 estuaries in the PNW. Most of these are small, with 73 estuaries less than 

1 km2 in size. Thirteen estuaries are larger than 10 km2, of which Grays Harbor and the 

Willapa Estuary in Washington and the Columbia River Estuary are larger than 100 km2. 

PNW estuaries are characterized by large intertidal zones, which on average equal 

approximately 50% of the estuarine area. 

PNW estuaries are classified as mesotidal, with tidal ranges of about 2 meters. 
243

In general, PNW estuaries have high flushing with short residence times. Flushing is higher 

in the winter due to increased river flow. In the summer, flushing is driven by tidal 

exchange. 

The PNW estuaries can be classified into seven classes (see Section 9.2), with the drowned 

river mouth estuaries sub-divided into tide-dominated, moderately river-dominated, and 

highly river-dominated systems. 

Nutrient Concentrations and Loadings

Total nitrogen loading into PNW estuaries is high, often equivalent to loadings in eutrophic 

systems. 

Dissolved inorganic phosphorous (DIP) concentrations are high, and on a national scale are 

rated as “fair”. 

Differences in the extent of flushing and freshwater inflow suggest that estuary classes vary 

in their relative vulnerability to terrestrial nutrient loading in the following order (from 

most to least vulnerable): 

lagoon≥blind estuary>tide-dominated river mouth>river-dominated river mouth≥coves/harbors 

Nutrient Sources

Seasonal coastal upwelling from approximately April to October is the major nutrient 

source to the near-coastal region. The intensity of upwelling is highly variable over scales 

of days to decades. 

Advection of oceanic water into the lower estuary is the major nutrient (nitrate and 

phosphate) source for the lower estuarine segments of PNW estuaries during the summer 

growing season. This portion of the estuary is referred to as the “ocean-dominated” 

segment of the estuary. 

Terrestrially derived nutrients are the dominant nitrogen source for the upper estuary 

portions of PNW estuaries during the summer growing season and for most of the estuary 

during the winter. This portion of the estuary is referred to as the “river-dominated” 

segment of the estuary. 

Nitrogen fixation associated with red alder, a native tree, may be an important terrestrial 

nitrogen source. Red alder populations may increase after fire, logging, and other 

disturbances but for the purpose of this analysis they are considered a “natural” nutrient 

source. 

Point sources, such as sewage discharges may be locally important nitrogen sources but do 

not appear to be major sources for PNW estuaries in general. 
244

Phytoplankton Concentrations and Sources 

Chlorophyll a concentrations in PNW estuaries are generally low to moderate, and were 

rated as “good” on a national scale. 

Advected oceanic phytoplankton is a major source of phytoplankton in the lower portion of 

the estuaries. 

Ocean-dominated estuaries appear to have higher chlorophyll a levels than river-dominated 

estuaries. 

Grazing by the benthic communities and by burrowing shrimp in particular, may 

substantially reduce phytoplankton concentrations and help to ameliorate enrichment 

effects. 

Submerged Aquatic Vegetation

The native Zostera marina is the dominant species of submerged aquatic vegetation (SAV) 

in PNW estuaries in the lower intertidal and shallow subtidal. 

The areal extent of Z. marina varies substantially among estuaries. In general, the largest 

SAV beds are in the tide-dominated and bar-built estuaries. 

The lower depth limit to which Z. marina grows decreases in the upper estuary segments, 

which correlates with a general decrease in up estuary water clarity. There are differences 

among estuaries in the lower limit to which Z. marina grows, which are also likely related 

to differences in water clarity but additional factors, especially current velocities may 

contribute to these differences. 

The non-native Zostera japonica has become abundant in a number of Pacific Coast 

estuaries over the last 20 to 50 years, and its populations appear to be expanding. In several 

estuaries, it covers a greater percentage of the intertidal zone than the native Z. marina. 

The research to date suggests that Z. japonica may have both beneficial and detrimental 

impacts on Z. marina, estuarine food webs, nutrient dynamics, and maintenance of 

ecological services. 

Benthic Macroalgae

Seasonal blooms of benthic macroalgae are a natural phenomenon in many of the PNW 

estuaries and are important primary producers in the estuaries larger than about 3.0 km2

. 
245

Benthic macroalgae cover substantial portions of the intertidal in many estuaries, and reach 

standing stocks associated with adverse impacts on seagrasses in Chesapeake Bay. It is not 

known to what extent, if any, benthic macroalgae impacts SAV in the PNW. 

The primary source of nitrogen for the intertidal benthic macroalgae blooms is natural, 

primarily consisting of oceanic water transported into the estuary. 

Benthic Communities and Burrowing Shrimp

The burrowing shrimp, Neotrypaea californiensis and Upogebia pugettensis, are two 

frequently occurring ecological engineering species in moderate- to large-sized PNW 

estuaries. These species affect sediment structure, benthic community composition, and 

benthic nutrient fluxes through intense bioturbation and irrigation of the sediment. 

Neotrypaea californiensis extends further up estuary than U. pugettensis. Additionally, 

U. pugettensis is either less abundant or absent in river-dominated estuaries. 

Exposure of SAV and Other Resources to Oceanic vs. Riverine Nutrients

A fundamental question in evaluating vulnerability to anthropogenic nutrient loading is 

whether a species is primarily exposed to oceanic or riverine nutrients during the summer 

growing season. Populations that are primarily exposed to oceanic nutrients would have a 

relatively low vulnerability to terrestrially derived nutrient enrichments. Conversely, 

species that have a large proportion of its population in the riverine segment would be 

exposed to any increases in terrestrially derived nutrients, and hence have a higher 

vulnerability to nutrient loading from watersheds. 

The majority of the Z. marina, benthic macroalgae, N. californiensis, and U. pugettensis

populations occurred in the oceanic segments. While there were some differences among 

estuaries, this basic pattern held for both river- and tide-dominated estuaries and for large 

and small estuaries. Thus, the bulk of the populations of these species are primarily 

exposed to a natural nutrient source during the summer growing season. 

The exception to this pattern was the nonindigenous Z. japonica which was abundant in 

both the oceanic and riverine segments. As a result of this distribution, Z. japonica

populations are more likely to be exposed to anthropogenic nutrient enrichments. This 

increases their vulnerability to high anthropogenic nutrient loading though moderate 

nutrient enrichment could potentially stimulate their growth and establishment. 

Expressions of Eutrophic Conditions

Dissolved oxygen concentrations in PNW estuaries are generally above levels indicative of 

eutrophic conditions. However, at times hypoxic shelf water can be advected into the lower 

portion of PNW estuaries (Brown et al., 2007). 
246

The current levels of benthic macroalgae appear to be natural and not an indication of 

eutrophication. 

The high nutrient loadings of PNW estuaries appear to be due to advection of high nutrient 

ocean water into the lower estuary and the effects of the nitrogen-fixing red alder in the 

watershed. 

Based on these observations, there is little evidence of cultural eutrophication at the 

estuarine scale in PNW estuaries. 

There are some observations suggesting that eutrophication may be occurring in localized 

areas of some estuaries. In particular, phytoplankton blooms and reduced dissolved oxygen 

may be occurring in sloughs with reduced flushing in the riverine segments of some 

estuaries. 

While not presently showing eutrophic conditions over large areas, increases in nutrient 

loading from anthropogenic sources could potentially result in harmful algal blooms and 

depressed oxygen in the upper estuary segments of these systems. However, more detailed 

studies will be required to accurately assess the vulnerability of the upper estuary segment 

and to predict the nutrient concentrations or loadings at which such deleterious impacts 

would be expected. 

Diagnostics of Eutrophication

With further development, the lower depth limit of Z. marina could potentially be used as 

an indicator for assessing estuarine condition in the PNW. 

Increases in macroalgae especially in the riverine segment could be used as an indicator of 

nutrient enrichment, though there are a number of sampling issues. 

Analysis of stable isotopes (δ

15N) in the benthic macroalgae would help determine whether 

ocean-derived or terrestrially-derived nitrogen was the major nutrient source associated 

with macroalgal blooms. 

9.2 Classification of Pacific Northwest Estuaries 

Based on the importance of upwelling, large tidal ranges and flushing, wet/dry seasons, and 

other environmental characteristics of the PNW, we suggest that a regional classification 

system that captures these drivers is required to group the estuaries in an ecologically 

meaningful manner. The classification schemes we presented are specifically for the PNW, 

and as with any classification scheme, should be considered hypotheses until it is 

demonstrated that they adequately predict types of biotic assemblages, nutrient 

concentrations/loadings, impacts, or vulnerability.

Pacific Northwest estuaries break out into seven types based on geomorphology, ocean 

exchange, and river influence: coastal lagoons, blind estuaries, tidal coastal creeks, tidally 

restricted coastal creeks, marine harbors/coves, bar-built estuaries, and drowned river 
247

mouth estuaries. Using metrics normalizing total precipitation in the watershed to either 

estuarine volume or area, the drowned river mouth estuaries are further divided into tidedominated river mouth estuaries and river-dominated river mouth estuaries. 

Small PNW estuaries <1 km2 are an important resource for salmon. Many of these systems 

were tentatively classified as “tidally restricted coastal creeks” but additional information is 

needed to evaluate the accuracy of this grouping. 

Classification of estuaries by the absolute or relative areas of wetland classes from the 

National Wetland Inventory (NWI) offers the advantage of grouping estuaries with similar 

biotic habitats, which presumably integrates a wide range of environmental conditions. In 

the same way, classification of estuaries by similarity in land cover in the associated 

watersheds generates insights into potential anthropogenic loadings. 

Six pairs of estuaries showed a high degree of similarity based on the intersection of the 

clustering by NWI wetland habitat type and the clustering by land cover patterns. These six 

estuary pairs - Grays Harbor and Willapa, Yaquina and Alsea, Coquille River and Siuslaw 

River, Rogue River and Klamath River, Quillayute River and Smith River, and Hoh River 

and Queets River – could potentially form the basis for developing management 

frameworks in the PNW. 

9.3 Regulatory Implications 

The primary regulatory implication of our results is that the development of national 

estuarine nutrient criteria that do not take into account the naturally high nutrient 

concentrations resulting from upwelling are likely to result in numerous false non 

attainments of nutrient criteria in PNW coastal estuaries (Brown et al., 2007). These false 

non attainments are most likely to occur in the lower estuary during the summer (Brown et 

al., 2007). 

Another implication is that the development of total daily maximum loads (TMDLs) for 

nutrients need to take into account the intrusions of high nutrient ocean water into estuaries 

during a portion of the year. The influence of nitrogen fixation by red alder also needs to be 

evaluated in the development of TMDLs. 

Our results strongly suggest that a regional approach is required both for the development 

of nutrient criteria and in the formulation of TMDL strategies. 

9.4 Future Directions 

This report describes a pilot effort at classifying PNW estuaries with regards to landscape 

attributes and their susceptibility to nutrient enrichment. In addition to the seven target 

estuaries described in this report, we have continued the sampling effort and to-date have 

sampled an additional 8 estuaries (Coquille, Humboldt, Klamath, Necanicum, Netarts, 

Siletz, Sixes, Yachats) using a modification of the methods described in this report. As 

these and any new data are analyzed, the classifications and models presented here will be 

tested and refined as needed. 
248

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267

APPENDIX A: MAPS OF THE DISTRIBUTION OF ZOSTERA MARINA 

BASED ON AERIAL SURVEYS 

The maps in this appendix show the areal extent of the intertidal and shallow subtidal Zostera 

marina in seven target estuaries. Distributions were mapped using aerial photography combined 

with ground survey results (see Chapter 6). The superimposed boxes show the extent of 

orthophotography used in mapping. 
268

Figure A-1. Distribution of intertidal and shallow subtidal Z. marina in the Alsea Estuary. 
269

Figure A-2. Distribution of intertidal and shallow subtidal Z. marina in the Coos Estuary. 
270

Figure A-3. Distribution of intertidal and shallow subtidal Z. marina in the Nestucca Estuary.
271

 

Figure A-4. Distribution of intertidal and shallow subtidal Z. marina in the Salmon River 

Estuary. 
272

Figure A-5. Distribution of intertidal and shallow subtidal Z. marina in the Tillamook Estuary. 
273

Figure A-6. Distribution of intertidal and shallow subtidal Z. marina in the Umpqua River 

Estuary. 
274

Figure A-7. Distribution of intertidal and shallow subtidal Z. marina in the Yaquina Estuary. 
275

APPENDIX B: DETAILED DESCRIPTION OF QUALITY ASSURANCE 

PROCEDURES 

B.1 Introduction 

The quality assurance/quality control (QA/QC) program for this study is defined by the “Western 

Ecology Division Data Quality Management Plan (QMP) (US EPA, 2006). Measurement 

Quality Objectives (MQO’s) establish the data user’s requirements for precision and accuracy. 

The Measurement Quality Objectives for each parameter in this study are presented in Table B1. Quality control measures were incorporated to assure data reliability and comparability and 

are described in the QMP plan. All contributing research was performed in compliance with an 

approved Quality Assurance Project Plan (QAPP). In addition, Standard Operating Procedures 

(SOP’s) were followed to standardize routine data collection, processing and analysis for specific 

parameters. All procedural documents and QA/QC plans are approved by the WED Quality 

Assurance Manager. 

Standard QMP protocols include routine instrument calibrations, measures of analytical accuracy 

and precision (e.g., analysis of standard reference materials, spiked samples, and field and 

laboratory replicates), overall data, range checks on the various types of data, cross-checks 

between original data sheets (field or lab) and the various computer-entered datasets, and 

participation in intercalibration exercises. Additionally, QA/QC included ensuring field and 

laboratory personnel were properly trained and experienced. Specific QA procedures are 

detailed in the following sections relative to each data parameter. 

Accuracy and precision are indicators of MQO’s and were established from considerations of 

instrument manufacturer’s specifications, scientific experience, and/or historical data. Measures 

of systematic error (accuracy) and of random error (precision) are used to evaluate the quality of 

the results. Accuracy is a measure of how close measured values are to true values. In this 

appendix, accuracy is calculated using the following equation: 

Accuracy (%) = (1.0 - ((∑( |Vt – Vm |)/ n )/ Vt ) * 100 

where Vt

 is the true or standard value, Vm is the measured values, and n is the number of 

measured values. Precision is an indication of the similarity of repeated analyses or sampling. 

Precision is calculated with the following equation: 

Precision (%) = (1.0-(SD / X )) * 100 

where SD is the standard deviation, and X is the mean. 
276

Table B-1. Measurement quality objectives for data collected by Western Ecology Division. 

Parameter units Expected range Accuracy Precision 

Seabird SBE-19 instrumentationConductivity mS cm-1 0-100 ± 0.5% of reading 0.01 mS cm-1 Salinity PSU 0 – 35 PSU ± 0.5% of reading or 0.1 PSU 0.01 PSU Temperature °C 0 - 25 °C ± 0.15°C 0.01°C Sea Point Turbidity NTU 0-125 NTU ± 2% of reading or 2 NTU 0.1 NTU Depth meters 0-15 m ± 0.018 m 0.001 m YSI 6600 Multiparameter Datasonde Conductivity mS cm-1 0-100 mS cm-1 ± 0.5% of reading or ± 0.001 mS cm-1 0.01 mS cm-1 Salinity PSU 0 – 35 PSU ± 1 % or 0.1 PSU 0.1 PSU Temperature °C 0 to 25 °C ± 0.15°C 0.01°C Turbidity NTU 0 to 1,000 NTU ±2% of reading or 0.3 NTU 0.1 NTU Depth meters 0-15 m ± 0.018 m 0.001 m Dissolved Oxygen mg l-1 or % Saturation 0-20 mg l-1 or 0-200% saturation ± 0.2 mg l-1 or ±2% of reading 0.01 mg l-1 Star ODDI Mini-CTD Salinity PSU 0-40 PSU ± 0.75 PSU 0.02 PSU Temperature °C -1°C to +40°C ±-0.1°C 0.032°C Depth meters 100 m ±0.4% of range 0.03% of full scale Discrete Water SamplesDissolved NO3-+NO2- μM 0 - 100 ±5% 5% Dissolved NH4+ μM 0 - 5 ±5% 5% Dissolved PO43- μM 0 - 3 ±5% 5% Dissolved Si(OH)4 μM 1 - 100 ±10% 10% 
277

Table B-1. Measurement quality objectives for data collected by Western Ecology Division. 

Parameter units Expected range Accuracy Precision 

Total Suspended Solids mg l-1 1 - 150 15% 15% Water Column chlorophyll a μg l-1 0-200 ±15% ±15% Zostera marina and Macroalgae Data Seagrass shoot density shoots m-2 1-1500 m-2 ± 2 shoots 0.25 m-2 1 shoot per 0.25 m2 quad Z. marina shoot length mm 5-1200 mm ± 2 mm ± 1 mm Z. marina shoot width mm 1-7 mm ± 1 mm ± 1 mm Z. marina shoot dry wt. mg 0-1500 mg ± 1 % ± 0.1 mg Z. marina lower limit depth cm 0-10 m MLLW ≤60 cm N/A Epiphyte biomass dry wt. mg 0-1500 mg ± 1 % ± 0.1 mg % Plant cover (Visual) % area 0-100% 85% ±15% Point intercept (Frequency of Occurrence) SAV # of intercepts 0-25 10% 11% Algal volume (wet) ml 0-20,000 100 ml 50 ml Macroalgae nitrogen isotope ratio (δ15N) ‰ -2 to +25 ‰ 0.5 ‰ 0.5 ‰ Burrowing Shrimp Shrimp Hole Density holes m-2 0-1500 m-2 ± 10% 30% Physical DataEstuary bathymetry ft -40 to +10 MLLW ± 0.5 N/A Sediment waves crests m 0.1 – 5 m ± 0.1 ± 2 % Sediment Properties Sediment total organic carbon and nitrogen content Percent of dry wt 0-12% ± 10% of standard ± 10% Sediment grain size distribution Percent of dry wt 0-100% (0.4 μm – 948μm) ± 5% of dry wt 10% 
278

Table B-2. Quality calibration and control checks for instruments and parameters. Instrument Calibration procedure Quality Control Check Frequency Acceptance criteria Seabird CTD Conductivity 

SeaBird factory calibration Solution of known conductivity If drift is 

suspected 

MQO 

Seabird CTD Temperature SeaBird factory calibration Place in water bath with NIST traceable thermometer If drift is suspected MQO Seabird CTD Depth (pressure) 

SeaBird factory calibration In air reading compared to Fortin type 

Hg barometer (Nat. Wea. Serv. type) 

If drift is suspected 

MQO 

Seapoint turbidity Seapoint factory calibration 1. Laboratory 3-levels of spherical 

particle solutions 2. Field total suspended solids vs. reading 

1. annually 2. quarterly to monthly 

Linear R2>0

slope ±25% initial 

YSI Multiparameter Sondes (6600 models) 

Factory calibrated for depth; calibrations performed to manufacturer’s specifications for remaining parameters 

All parameters checked against factory standards upon retrieval from deployment 

Immediately prior to deployment and upon retrieval 

Performed to factory specificationmethods and standards 

YSI Hand-held meters 

Force to temperature corrected standard 

All parameters checked against factory standards 

Monthly Performed to 

factory specification 

CMT-DGPS Self-calibrates with satellites with 

every use 

Visit type 1 USGS reference site with multiple readings 

Annually MQO 

LiCor LI-193SA sensor 

Li-Cor factory calibration performed every 2 years 

Solar noon clear sky exposure Bi-annually MQO 

Balance (5-place) 

Factory representative adjusts on annual basis 

Check with standard weights-Class ‘S’ Before each 

session 

Within speciclass toleran

Turner 10-AU Fluorometer 2-point calibration using solid secondary standard Check solid secondary standard reading (low and high settings) for instrument drift. At start, middle and end of every run If high settin

secondary streading diffe1% from true 
279

Table B-2. Quality calibration and control checks for instruments and parameters. Microbalance Check calibration with internal weights Readings of 5 class ‘S’ masses that cover expected range Before each session ± 2 m

Background and reference gas 

stability Zero enrichment test Before each session ± 0.07‰ pre

< 0.05‰ o

from ze

Isotope Ratio Mass Spectrometer Working standards calibrated to 

NIST or equivalent certified 

standards 

Nitrogen and carbon isotope ratios of 

working standards 

24 QC 

working 

standards per 

run of 72 

samples 

± 0.2 ‰ pre

within a 

Mettler AT250 balance 

Annual calibration by contractor (Quality Control Services, Inc.) 

Check accuracy with ‘S’ class weights With each use Contractor determinatio

Coulter LS 100 Q Beckman-Coulter factory calibrated Analyze grains of known size. Instrument compares background with factory established reference 1-2 times a year Every sample t-test compa

results; Visuevaluation odifferences 

Carlo Erba LS 100 Q 1108 elemental analyzer 

Annual calibration by factory technician Run standards of known TOC With each run MQO 
280

B.2 Water Quality CTD Profiles and Grab Samples

Water quality cruises were conducted in each of the target estuaries at both high and low tides. 

Depending on the depth, either a Seabird SBE 19 CTD or YSI sonde 6600 was used to measure 

water quality parameters. For deep stations (>1.5 m) water quality parameters were measured 

continuously through the water column and processed into 0.25-m depth intervals. At shallower 

stations, readings were recorded at the surface, mid, and bottom. Variables measured by the 

CTD and associated instruments include depth, temperature, conductivity, turbidity, 

photosynthetically active radiation (PAR), and in situ fluorescence, and calculated variables 

include salinity and density. In addition to the PAR sensor attached to the Seabird unit, PAR 

readings were also taken in the air with a flat sensor and at 15 cm below the surface for profile 

interpretation. Dissolved oxygen was measured at discrete depths with a YSI sonde or a DO 

meter attached to the frame of the Seabird CTD. 

 

For each water quality profile, the CTD was lowered to the bottom and a water sample collected 

from mid-depth during the up-cast. This water sample was analyzed for chlorophyll a, total 

suspended solids (TSS), and dissolved inorganic nutrients. Near-bottom conditions were 

measured at 0.5 m above the bottom. Data were collected every second and binned with 

‘Seasoft’ software into 0.25 m discrete intervals. The YSI sonde was calibrated prior to use 

following the manufacturer’s specifications. Light attenuation coefficients (k) were calculated 

for the water column on the downcast. Prior to analysis, the data were reviewed to eliminate any 

false reading caused by reflection from the aluminum boat. Care was taken so that the PAR 

sensor on the CTD was not in the shadow of the boat. 

The water column was sampled at each site for dissolved inorganic nutrients (Si(OH)4, NO3

-

+NO2

-

, NH4

+

, and PO4

3-), chlorophyll a concentration, and total suspended solids (TSS). Water 

column samples were collected and prepared per MES SOP09.rev 0 (2003). Water quality 

samples were filtered and processed on board the boat or upon return to the laboratory or hotel 

within several hours of collection. Water samples were stored in coolers on ice prior to filtration. 

A performance-based approach was used for evaluating the quality of the chemical analysis. 

Depending upon the compound, laboratory practices included 1) continuous laboratory 

evaluation through the use of Certified Reference Materials (CRMs) and/or Laboratory Control 

Materials (LCMs), 2) laboratory spiked sample matrices, 3) laboratory reagent blanks, 4) 

calibration standards, and 5) laboratory and field replicates. 

B.2.1 Chlorophyll a and Total Suspended Solids 

Water samples for chlorophyll a and total suspended solids (TSS) analyses were collected in 

duplicate and filtered on board (if possible) or upon return to the hotel or laboratory. Typically, 

the samples were filtered within 1-2 hours of sample collection. For TSS analysis, 1-liter of 

unfiltered seawater was collected at mid-depth as described above, filtered on board the boat and 

further processed according to SOP WRS 14B.2. The complete procedure for sample processing 

and analysis of chlorophyll a samples is detailed in WED SOP06.rev 0. The samples were stored 

in the freezer until analyses. Samples were extracted in 90% acetone solution. The fluorometer 

was calibrated with a 10-AU solid secondary standard and “blanked” with freshly prepared 90% 

acetone solution prior to each sample set analyzed. The high setting of the solid secondary 

standard was used in calibration and the low setting was used as a quality control check after 
281

calibration. During analyses, the solid secondary standard and 90% acetone blank were checked 

midway through and at the end of a sample set to verify that the fluorometer performance had 

not changed. If the solid secondary standard high setting differed from true values by ±1%, the 

instrument was re-calibrated and the previous half-set of samples were reanalyzed. 

The solid secondary standard was calibrated to chlorophyll a concentrations using fresh 

chlorophyll a standards provided by the manufacturer (Turner Designs). The chlorophyll a

standards supplied by the manufacturer (Turner Designs) consisted of a high concentration 

(181 μg l-1) and a low concentration (18.2 μg l-1). The solid secondary standard was calibrated 

with newly purchased standards in 2002. During 2006, the solid secondary standard was 

checked using additional set of chlorophyll a standards. This quality control check revealed that 

the accuracy was 99.2%. To assess the accuracy of the chlorophyll a measurements, we 

compared the known value of the solid chlorophyll a standard (low setting) to the actual 

measured values of the solid standard. Accuracy analysis was performed for all chlorophyll a

data used in this report. The accuracy and precision for the chlorophyll a data reported in this 

study are estimated to be 99.7 %. 

B.2.2 Nutrient Data 

Nutrient analysis for nitrate+nitrite, ammonium, phosphate and silicate was performed by the 

Marine Science Institute Analytical Laboratory (MSIAL) of University of California at Santa 

Barbara. Nutrient analysis was carried out with a Lachat Instruments Model QuickChem 8000 

Flow Injection Analyzer. Data quality indicators include representative calibration data, reagent 

blanks, replicate analyses and percent recovery analyses of spiked and control samples. 

Acceptable levels for these parameters are detailed in the MSIAL QA guidelines and provide 

means of monitoring data quality (MSIAL Quality Assurance Manual 2005). In addition to these 

internal QA checks, samples obtained from the National Institute of Sampling and Technology 

and other producers of certified reference material were analyzed periodically to audit 

performance. Deionized (DI) water blanks and sea water blanks (low-nutrient natural sea water, 

aged to allow nutrient values to drop to near-zero levels) were also run. An independently 

prepared “control” solution containing an intermediate concentration of each of the nutrients was 

also run. The chemistries used in determining the various nutrient species on this instrument 

have been developed by the manufacturer to have little or no salt effect, so the analytical 

response is the same for fresh, DI water samples, and standards as for salt water samples and 

standards. Saltwater samples, however, exhibit a refractive index-related response in the flowthrough detector, so the sea water blank was used to adjust the measurement timing parameters 

to compensate for the refractive index effect. Instrument calibration was checked at the 

beginning of a sample-batch run, at the end of the run, and periodically during the run. Each 

calibration sample was analyzed in duplicate, and the resulting data were used to establish 

calibration curves for each nutrient species. If the mean of the two replicates of any standard 

differed from the known concentration of that standard by more than ten percent or more than 

one-half the concentration of the lowest standard, whichever, was greater, the calibration for that 

species was considered invalid and the calibration run was repeated. (See MSIAL Quality 

Assurance Manual 2005 for more details). The precision of the replicates and the accuracy of 

the standards for the data used in this report are presented in Table B-3. 
282

Table B-3. Accuracy and precision for nutrient analyses. 

PO4

3- (%) NO3

-

+NO2

-

 (%) NH4

+

 (%) 

Precision 98.0 99.5 96.8 

Accuracy 99.1 98.5 98.2 

B.3 Handheld YSI Meters 

The handheld multiparameter Yellow Springs Instruments (YSI) meters were checked on a 

monthly basis and prior to use as needed with the manufacturer’s (YSI) conductivity standard. 

To check the dissolved oxygen (DO) reading, the DO probe was placed in a 100% saturated 

environment for several minutes. If the percent saturation value differed by more than 2% from 

100% saturation, the electrodes were cleaned, a new KCl solution was applied and the membrane 

was replaced. A second DO reading was taken after the DO probe was serviced to ensure that it 

was operating properly. All QA/QC checks and calibrations were recorded in a database 

(Microsoft® Office Access 2003). 

B.4 YSI Multiparameter Sonde 

The data presented in this report were collected with YSI 6600 Multiparameter Sondes. The 

sondes were calibrated prior to deployment following the manufacturer’s recommendations. 

Conductivity was calibrated with a one-point calibration using standards with conductivity 

values closest to the expected salinity range (50 mS cm-1 for high salinity stations, 10 mS cm-1

for mesohaline and 1 mS cm-1 for low salinity stations). Turbidity was calibrated with a twopoint calibration; using reverse osmosis water (RO) followed by a 123 NTU YSI standard 

solution. The DO sensor was calibrated for DO in air at sea level using the saturated air in water 

method. The DO anode was cleaned and fresh KCl solution added prior to applying a new 

membrane film. The probes were set in a calibration cup with a small amount of water for 

maximum water vapor saturation for 15 minutes before the calibration reading was taken. The 

barometric pressure was determined from either a mercury barometer or from a YSI 650 or 556 

hand-held meter. Temperature cannot be calibrated but its performance was checked. The 

datasondes were set in a flow through seawater bath in the laboratory for multiple readings 

immediately before deployment and upon return to the lab. The temperature and salinity of the 

water bath were cross-checked using an independent YSI handheld unit (YSI 650 or 556). All 

QA/QC calibration data and ancillary metadata are recorded in an Access database. The 

calibration accuracy for conductivity and turbidity was defined as the accuracy of the probe in a 

standard solution. Post deployment accuracy was defined as the accuracy of the probes after 

they are retrieved from the field and were tested against the known standard solutions. A 

database was used to calculate the pre- and post-deployment accuracy of the readings when 

compared to the known standards (Table B-4). 
283

Table B-4. Precision and accuracy for YSI multiparameter sondes. *The post-calibration 

data were not recorded for conductivity. 

 Temperature 

(%) 

Salinity

(%) 

Conductivity 

(%) 

Turbidity 

(%) 

Dissolved Oxygen 

(%) 

Calibration Accuracy 

Pre-deployment 98.1 98.6 99.5 100.0 99.7 

Post Deployment 

Accuracy (includes the 

effect of biofouling) 

92.8 96.5 N/A* 91.9 98.5 

B.5 Stable Isotope Data 

Five replicate macroalgae samples were collected from each sampling location in each of the 

estuaries. Algal material was collected from hard substrates to eliminate contamination from any 

additional nutrient sources other than the water column. Samplers wore sterile lab gloves while 

collecting to prevent contamination. Each algae sample was washed thoroughly in Milli-Q 

water, frozen and lyophilized. The dried material was ground into a fine powder for isotope 

analysis. Grinding mortar and pestles were thoroughly rinsed with acetone and allowed to 

completely dry between samples (QAPP 02.01, 2002). 

The EPA Integrated Stable Isotope Research Facility (ISIRF) analyzed the macroalgae samples 

for δ

15N according to SOP CL-6 (1999). Samples were combusted using a Carla Erba elemental 

analyzer (model # 1108) equipped with a 4 meter poraplot Q gas chromatograph column directly 

coupled to an isotope ratio mass spectrometer operating in a continuous flow mode (Delta S, 

Finnigan MAT, San Jose, CA, USA). This continuous flow mode also provides a direct 

measurement of nitrogen content. Protocol and methods for operation of the mass spectrometer 

are all based on published approaches that have been verified through multiple approach 

analyses and inter-lab comparisons. A concentration, calibration and reference standard were 

run at the beginning, mid and end of each run. Additionally, a spike, concentration standard, and 

blank were run every ten samples. Standard material included NBS tomato leaves (1573) for 

concentration standard; NBS spinach (1570) for reference standard; NIST Corn Stalk for 

calibration standard and spike recovery. Results for precision and accuracy for stable isotope 

data used in this study are presented in Table B-5.

Table B-5. Precision and accuracy of δ

15N data.

δ

15N (%) 

Precision 99.1 (n=23) 

Accuracy 96.6 (n=63) 
284

B.6 Total Organic Carbon and Nitrogen of Sediment Sample 

At all intertidal stations, a small sediment core was collected for total organic carbon and 

nitrogen (TOC/N) analysis. Sediment cores were stored on ice until they could be frozen upon 

return to the lab. One ml of 10% Reagent Grade HCl in Milli-Q water was added to the sediment 

vial prior to analysis. The acid was mixed thoroughly into the sample using a glass rod. The 

sediment samples were then left at room temperature overnight and checked in the morning to 

confirm that pH was below 2. Sediment samples were then dried overnight in a 70o C oven with 

the last hour at 105o C. After drying the samples were homogenized on the roller mill for 24 

hours, sub-sampled and pelletized. Samples were analyzed using a Carlo Erba EA 1108 

elemental analyzer. Work was performed by Dynamac Corporation on-site at the WED 

laboratory in Corvallis. Blanks, unknowns and three different standards were analyzed at the 

beginning of the analytical run and after every tenth sample. Every 10th experimental sample 

was also analyzed in duplicate and a 3rd aliquot was spiked with the EuroVector Soil 3 SRM. 

Results are reported as weight %. 

The empirically determined limit of quantitation (LOQs) for carbon and nitrogen are 0.38 and 

0.08 wt. %, respectively. The empirically determined limit of detection (LODs) for carbon and 

nitrogen are 0.08 and 0.02 wt. %, respectively. These LOQs and LODs values are based on 

sediment samples weighing approximately 30 mg. If the C or N concentration of an 

experimental sample is below the detection limit, the value is flagged but reported. If this 

condition is true for duplicate samples, the DQOs for precision are ignored. The actual 

calculated value for precision is reported. Acceptable precision and accuracy values for both C 

and N are > 90% and < 110% for the BCR 101 spruce needle, EuroVector Soil 3, and MESS-2 

marine sediment SRMs. Acceptable precision and accuracy values for both C and N are > 90% 

and < 110% for acetanilide. All samples met the DQOs and the r2

 values for the acetanilide 

standard curves ranged from 0.9991 to 0.9998 for N and from 0.9994 and 0.9998 for C. Results 

for precision and accuracy for TOC/N data used in this study are presented in Table B-6. 

Table B-6. Precision and accuracy of TOC/N sediment analysis. 

 Nitrogen Carbon 

Precision (duplicates, %) 97.5 95.2 

Spike Recovery (Accuracy, %) 98.4 101.7 

B.7 Particle Size Analysis (PSA) 

Sediment aliquots were analyzed for particle size by laser diffraction on a Beckman Coulter LS 

100 Q series. Aliquots of sediment samples introduced into the LS100Q sample chamber were 

well-mixed in water by a high speed, reversing mixer to assure representativeness. For each 

sample, two aliquots were analyzed, with a minimum of two runs of each. The final value was 

an average of all replicates. The precision calculations were run on the sum of all size fractions 

in each sediment size class; clay: 0.38 – 3.86 μm and silt: 3.86 – 63.41 μm. The data were 

reported as percent fines which were calculated as the sum of the silt and clay size fractions. 

Control standards of nominally 15 μm garnet and 500 μm glass particles were purchased from 

the manufacturer. They are provided with a certification of their average size and the standard 

deviations of the size distributions. A certified Coulter technician evaluated the accuracy of the 

instrument using these standard materials on seven service calls between 2001 and 2005 
285

(Table B-7). During operation, the instrument performs a number of self-checks. One of these is 

a measure of the background reading prior to introduction of every sample, and this background 

measurement is compared to a reference background reading file established at the factory. The 

Coulter LS100Q software is programmed to automatically check and align its laser beam and to 

measure the sensor offsets at startup and every 60 minutes thereafter. 

Table B-7. Precision and accuracy of sediment particle size analysis. 

 Silt Clay 

Precision (duplicates) % 96.2 95.0 

Accuracy (control recovery) % 98.6 

B.8 Intertidal Probabilistic Sampling 

QA/QC information for the probabilistic intertidal sampling was collected by comparing repeat 

measurements taken by four individuals from the same quadrat. Either seven or fourteen 2.5 m2

quadrats were laid out in intertidal habitats spanning the natural range of seagrass and 

macroalgae densities (low to high densities). Two smaller 0.25 m2 quadrats were laid out within 

the larger 2.5 m2 quadrats in the same arrangement as in the actual field sampling and additional 

measurements were collected. Four individuals independently recorded data from the same plots 

and quadrats without discussing their estimates. After all quadrats were examined the data were 

analyzed for precision. The results of the independent analysis are presented in Table B-8. 

Table B-8. Data comparability between four field crew members for intertidal probabilistic 

sampling. 

Measure 

(units) 

Precision 

(%) 

Avg. STDEV 

between reported 

values 

Avg. Max 

Difference 

between 

reported 

readings 

# of 

Quads 

(n) 

Number of Seagrass Shoots 

(Number shoots per quadrat) 82.3 5.8 5.5 14 

Point Intercept Seagrass 

(Number of intercepts) 78.6 7.6 2.7 14 

Point Intercept Macroalgae 

(Number of intercepts) 98.1 0.1 0.1 12 

# Leaves Z. marina per shoot 

(Number per shoot) 88.4 0.7 1.3 7 

Longest Z. marina Leaf Length 

(cm) 94.2 1.5 3.0 7 

Z. marina Leaf Width 

(mm) 92.2 0.4 0.7 7 

Sediment Wave Crests 

(cm) 91.6 0.9 1.7 7 
286

Percent cover data were collected for five class categories (i.e., 0 [absent], 1-10%, 11-40%, 41-

70%, 71-100%), therefore, precision could not be calculated as with other numeric data. 

However, in comparing the individual data assessments from four field crew participating in an 

exercise to evaluate consistency between individuals the following statements can be made about 

the categorical data. Percent Cover Green Macroalgae: Out of the 14 plots evaluated there was 

no more than one class difference between the four independent assessments in ten of the plots 

with four plots having 100% agreement. Percent Cover Seagrass: Out of 14 plots evaluated there 

was no more than one class discrepancy between the four evaluations at eight of the plots with 

six plots having 100% agreement. Percent Cover Other Algae: Out of seven plots examined there 

was 100% agreement in six plots with only one of the four data collectors reporting one size 

class difference in the eighth plot. 

In assessing sediment color, several individuals combined the categories to encompass color 

variations in the surface sediment so calculating a precision value was not straightforward; 

however, for five of the seven plots all four crew included one or more of the same colors in their 

assessment. In the other two plots, three of the four individuals agreed 75% of the time. 

Sediment firmness was more subjective. In three of the seven plots all four crew agreed 100% of 

the time, in two plots they agreed 75% of the time and in two other plots the four crew was split 

between two different firmness values. 

Macroalgae biomass was determined by the volumetric cylinder method (Robbins and Boese 

2002). At each site all macroalgae were removed from each quadrat and measured 

volumetrically in-situ using a 4000-ml plastic cylinder. The algae were pressed with a plunger to 

remove excess water before recording the algal level in ml. This quick field method was 

determined to be an accurate surrogate for biomass dry weight determination with a linear 

relationship yielding an r2 value of 0.78 to 0.88 for macroalgae species (Robbins and Boese, 

2002). 

B.9 Aerial Mapping of Z. marina

The remote sensing procedure used in this study to map the intertidal and shallow subtidal 

distribution of Z. marina utilizes aerial photography with false-color near-infrared (color 

infrared, CIR) film. This allows an aerial survey to be conducted during daylight low tides (and 

clear or uniform overcast weather) when a large majority of the Z. marina distribution in PNW 

estuaries is exposed or visible. The CIR film provides substantially better spectral resolution of 

exposed intertidal vegetation than true color film (Young et al., 1999). To map perennial Z. 

marina habitat, the surveys were conducted in late spring or early summer before the summer 

bloom of benthic green macroalgae that can interfere with the classification of Z. marina. 

Photoscales utilized ranged from about 1:6,000 to 1:20,000. The aerial photographs were 

digitally scanned and georectified while correcting for terrain and camera distortions to produced 

digital orthophotos. The spatial accuracy of the photomap for this estuary (photoscale: 1:10,000) 

was assessed by comparing 14 Root Mean Square Error (RMSE) offset values for positions of 

photovisible objects obtained from the photomap, referenced to published National Geodetic 

Survey (NGS) positions. The mean offset was 0.72 m ± 0.27 m (95% CI; Young et al., in press). 

The digital orthophotos were classified into Z. marina (defined as >10% cover) and bare 

substrate habitats (defined as ≤ 10% cover). On-the-ground resolution of 0.25 m was obtained in 

this process. A hybrid technique using both unsupervised and supervised classification steps has 

been developed for this habitat mapping project (Clinton et al., 2007). The technique requires 
287

training data from ground truth surveys, with station positioning accomplished by a differentialcorrected global positioning system (GPS). The RMSE of GPS positions obtained at an NGS 

first-order monument in Yaquina Estuary was 0.62 m. 

The ground survey technique provides percent cover measurements or estimates from quadrat 

placement or 35 mm snapshots within each stratum being classified, preferably augmented by 

GPS traces of sections of the intertidal meadow upper margins, as training data used in the image 

classification process. Another part of the ground survey employed a detailed procedure based 

upon the recommendations of Congalton and Green (1999) to provide accuracy assessment data 

from randomly positioned stations within each stratum (Young et al., in press). The results were 

analyzed with the aid of an error matrix (also know as “confusion matrix”). This is a square 

array of numbers set out in columns and rows that expresses the number of sample units assigned 

to a particular category in one classification relative to the number of sample units assigned to a 

particular category in another classification (Congalton and Green, 1999). Here, the number of 

stations (units) classified as Z. marina or bare substrate habitat based on the ground survey 

measurements (listed in the table’s “column total” cells) are considered to be correct (i.e. 

reference data), while the values listed in the individual table cells are the corresponding number 

of stations in each class generated from the remotely sensed data. The quotient (as a percentage), 

obtained by dividing the total number of correctly classified sample units in a given category 

(e.g. Z. marina stratum) by the total number as indicated by the reference data, is termed 

“producer’s accuracy”. Similarly, the quotient obtained by dividing the total number of correct 

units in the given category by the total number of units classified as belonging to that category 

(listed in the table’s “row total” cells) is termed “user’s accuracy”. A third accuracy value, 

obtained by dividing the sum of the “table diagonal” units by the total number of sample units in 

the matrix, is termed “overall accuracy” (Congalton and Green, 1999). To assess the 

effectiveness of this classification method, values for the Kappa index KHAT (and their 

estimated 95% confidence intervals) for these matrices also were calculated as a measure that 

assesses improvement over chance (Fielding and Bell, 1997). Results were obtained in the 

spring of 2004 from 51 randomly positioned stations within intertidal Z. marina meadows and 28 

randomly positioned stations within bare substrate strata of Yaquina Estuary. 

Based upon a comparison of results from the image classification with those from the ground 

survey (taken as the reference), application of the classical error matrix analysis yielded an 

overall accuracy for Yaquina Estuary of 97%, with a Kappa Index value of 0.94 ± 0.002 

(Table B-9). The high index value attained indicates excellent agreement (Landis and Koch, 

1977). The investigators attribute this very high accuracy level to the extensive training data 

provided via GPS mapping of the intertidal Z. marina meadow margins (Young et al., in press). 

For corresponding classifications of intertidal Z. marina and bare substrate strata in Tillamook, 

Umpqua and Coos estuaries, obtained without meadow margin mapping for training of the image 

analyst, overall accuracy values were 86%, 89% and 83%, respectively. In contrast, in Alsea 

Bay where extensive Z. marina meadow margin mapping was conducted, an overall accuracy of 

100% was obtained (Table B-9). Due to the subtidal habitat of seagrass in Nestucca and Salmon 

River estuaries, seagrass patches were outlined using either a boat or by foot with a GPS unit. A 

“best fit” line was created from three replicate traces. 
288

Table B-9. Image classification accuracy assessment. 

 

Z. marina Bare 

Substrate 

row 

total 

Tillamook Estuary 

Image Class (Predicted) Z. marina 88 13 101 

 Bare Substrate 12 68 80 

 column total 100 81 181 

Producer’s Accuracy 88 % 84 % 

User’s Accuracy 87 % 85 % 

Overall Accuracy: 86 % 

Kappa Index KHAT*: 0.72 ± 0.003 

Yaquina Estuary 

Image Class (Predicted) Z. marina 50 1 51 

 Bare Substrate 1 27 28 

 column total 51 28 79 

Producer’s Accuracy 98 % 96 % 

User’s Accuracy 98 % 96 % 

Overall Accuracy: 97 % 

Kappa Index KHAT*: 0.94 ± 0.002 

Alsea Estuary 

Image Class (Predicted) Z. marina 44 0 44 

 Bare Substrate 0 60 60 

 column total 44 60 104 

Producer’s Accuracy 100 % 100 % 

User’s Accuracy 100 % 100 % 

Overall Accuracy: 100 % 

Kappa Index KHAT*: 1.0 

Umpqua Estuary 

Image Class (Predicted) Z. marina 37 10 47 

 Bare Substrate 4 81 85 

 column total 41 91 132 

Producer’s Accuracy 90 % 89 % 

User’s Accuracy 79 % 95 % 

Overall Accuracy: 89 % 

Kappa Index KHAT*: 0.76 ± 0.004 

Coos Estuary 

Image Class (Predicted) Z. marina 28 9 37 

 Bare Substrate 10 68 78 

 column total 38 77 115 

Producer’s Accuracy 74 % 88 % 

User’s Accuracy 76 % 87 % 

Overall Accuracy: 83 % 

Kappa Index KHAT*: 0.62 ± 0.005 

* 95% confidence interval assuming KHAT is normally distributed 
289

B.10 Zostera marina Lower Depth Limit 

Lower depth margin of Zostera marina was determined by georeferencing the position where the 

deepest seagrass was encountered. Transects were randomly selected in distinct Z. marina beds 

(identified from aerial photography) and were approached either by boat or foot depending on 

the water depth. Sampling was conducted on the lowest tides possible to increase the accuracy 

of locating plants growing at the lowest depth limit. In the deeper systems, an underwater video 

camera was mounted on a long PVC pole linked to a video monitor on deck. When the first 

Z. marina patch was seen on the monitor the pole was quickly thrust into the sediment to stop the 

momentum of the boat. A GPS reading was taken and a lead line was used to record the depth to 

the closest centimeter. In shallower waters, seagrass blades were clearly apparent on the water 

surface and could easily be approached by foot. Personnel walked along the transect from shore 

to the deepest Z. marina patch, recorded a GPS location, measured the depth with a lead line. 

B.10.1 Tidal Corrections for Lower Depth Limits 

Tidal corrections were applied to account for variations in tide elevation at time of lower limit 

depth observations, with corrected lower depth limits expressed as depth below mean lower low 

water (MLLW). Tidal predictions were used to make these tidal corrections. Tidal predictions 

are least accurate during storms and extreme low and high tides. Review of weather and tidal 

conditions during time periods when the lower depth limit was measured, indicated that 

conditions were relatively calm during the sampling and not collected during extreme tides. 

Tidal heights relative to MLLW were calculated using WXTIDE 32 for each depth site using the 

time of data collection and the nearest tide prediction location. The largest source of error in the 

tidal corrections resulted from not having predicted tides available for all locations along the 

longitudinal axis of the estuary and having to use the closest tide prediction station. To estimate 

the error associated with the tidal correction, differences in tidal corrections between the two 

stations that are located upstream and downstream of the observations were calculated

B.10.1.1 Yaquina 

The difference between predicted and observed tidal heights at the South Beach tide gauge was 

less than 0.15 m on all data collection days. Tidal predictions that take into account variations in 

tidal amplitude and phase lags were available for four locations (South Beach, Yaquina, Winant, 

and Toledo) in the Yaquina Estuary (http://co-ops.nos.noaa.gov/tides05/tab2wc1b.html#132). 

The maximum distance along the river at any given depth station from a tide station was 4.5 km. 

The error associated with the tidal correction is estimated to be 0.1-0.2 m. The error associated 

with the tidal correction increases with distance from the mouth of the estuary, being a minimum 

of 0.1 m in the lower estuary and as high as 0.2 m near Toledo. 

B.10.1.2 Tillamook 

The maximum difference between predicted and observed tidal heights at the Garibaldi tide 

gauge was 0.3 m on data collection days. Tidal predictions that take into account variations in 

tidal amplitude and phase lags were available at five locations (Barview, Garibaldi, Miami Cove, 

Bay City and Hoquarten Slough) in the Tillamook Estuary 

(http://co-ops.nos.noaa.gov/tides05/tab2wc1b.html#132). The maximum distance along the river 

any given depth station from a tide station was ~ 3.6 km. The error associated with the tidal 

correction is estimated to be 0.1- 0.3 m. The error associated with the tidal correction increases 
290

with distance from the mouth of the estuary, being a minimum of 0.1 m in the lower estuary and 

as high as 0.3 m between Bay City and Hoquarten Slough. 

B.10.1.3 Nestucca 

The difference between predicted and observed tidal heights at the Nestucca tide gauge on data 

collection days is unknown. The tidal predictions for Nestucca used parameters for variations in 

tidal amplitude and phase lags from a tidal reference station in Crescent City, CA for a single 

station at the bay entrance available at: http://co-ops.nos.noaa.gov/tides04/tab2wc1b.html#132. 

The maximum distance along the river at any given depth station from the tide station was ~ 4.7 

km. The error associated with the tidal correction increases with distance from the mouth of the 

estuary. To estimate the error associated with the tidal correction, we compared the maximum 

distance above to the maximum distances from tide prediction stations in Yaquina and Tillamook 

Bays and estimate the error to be between 0.1 m and 0.3 m. 

B.10.1.4 Salmon 

The difference between predicted and observed tidal heights at the Salmon River on data 

collection days is unknown. No tidal prediction stations are available in the Salmon estuary. The 

nearest tidal prediction station is at Taft in the Siletz Estuary which is 11.1 km to 12.5 km from 

the sample locations in the Salmon Estuary. The tidal predictions for Taft use parameters for 

variations in amplitude and phase lags of tides from a tidal reference station (Crescent City, CA) 

for a single station at the bay entrance available at: http://coops.nos.noaa.gov/tides04/tab2wc1b.html#132 . The error associated with the tidal correction is 

unknown. 

B.10.1.5 Alsea 

The difference between predicted and observed tidal heights at Alsea Bay on data collection days 

is unknown. The tidal predictions for Alsea used parameters for variations in tidal amplitude and 

phase lags from a reference station at Crescent City, CA for two single stations available at: 

http://co-ops.nos.noaa.gov/tides04/tab2wc1b.html#132. The maximum distance along the river 

at any given depth station from the tide station was ~ 1.2 km. To estimate the error associated 

with the tidal correction, we compared the predicted tide lag between the Waldport, Alsea Bay 

station and the Drift Creek, Alsea River station (which are ~7.6 km apart) during hours of 

upstream data collection. The error associated with time lag in the predicted tides for the data 

collected is less than 0.1 m. 

B.10.1.6 Umpqua 

The difference between predicted and observed tidal heights at Umpqua River on data collection 

days is unknown. The tidal predictions for Umpqua used parameters for variations in tidal 

amplitude and phase lags from the tidal reference station in Humboldt Bay, North Spit, CA for 

three stations available at: http://co-ops.nos.noaa.gov/tides04/tab2wc1b.html#132. The 

maximum distance along the river at any given depth station from the tide station was ~5.5 km. 

To estimate the error associated with the tidal correction, we compared the predicted tide lag 

between the Umpqua River, Entrance station, the Umpqua River, Gardiner station and the 

Umpqua River, Reedsport station (which are ~10.5 km and ~ 4.0 km apart) during hours of data 

collection. The error associated with time lag in the predicted tides for the data collected is less 

than 0.37 m. 
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B.10.1.7 Coos 

The maximum difference between predicted and observed tidal heights at the Charleston, Coos 

Bay tide station was 0.1 m. Historic tide data for the Charleston, OR station is available at: 

(http://tidesandcurrents.noaa.gov/geo.shtml?location=9432780). The tidal predictions for Coos 

use parameters for variations in amplitude and phase lags of tides from a tidal reference station 

within the estuary (Charleston, OR) and for two more stations, Empire and Coos Bay available 

at: http://co-ops.nos.noaa.gov/tides04/tab2wc1b.html#132. The maximum distance along the 

river at any given depth station from the tide station was ~10 km. The error associated with time 

lag in the predicted tides for the data collected is unknown because ~70% of the depth stations 

are upstream of the tide stations. 

B.11 Field Locations and Distance Upriver Calculations 

The geoposition of each station was collected in the field with a Global Positioning System 

(GPS) and differentially corrected in post processing with data from the nearest National 

Geodetic Survey (NGS) Continuously Operating Reference Station (CORS). The GPS data 

collection device (CMT March II) published post-processed differentially corrected two 

dimension root mean square error (spatial accuracy) ranges from 1 m to 5 m. 

The distance from the mouth of each estuary to each station was calculated using GIS mapping 

software ArcMap. For each estuary, National Hydrologic Dataset (NHD) center line features 

extending the length of the rivers from the ocean shore (disregarding jetties) were converted into 

route files. The ArcToolBox linear referencing tool, “LocateFeaturesAlongRoutes”, was used to 

calculate the distances in meters for each station from the ocean shore to the nearest point along 

the centerline route feature. The distance from each station to the nearest point along the 

centerline route was calculated simultaneously by the geoprocessing tool. 

B.12 Estuarine and Watershed Landscape Analysis 

To capture the diversity of estuary types on the Pacific Coast, a national standard, the National 

Wetland Inventory (NWI) developed by the U. S. Fish & Wildlife Service (U.S. Fish Wild. Ser., 

2002; http://www.fws.gov/nwi) was used as the criterion for defining estuaries. The NWI 

classifies aquatic habitat types using a hierarchical set of attributes based on Cowardin et al. 

(1979), which includes marine, estuarine, riverine, palustrine, and lacustrine areas with further 

subdivisions for tide height, substrate type and the presence of broad classes of wetland plants 

(e.g., emergent vs. aquatic bed). In an effort to inventory all estuaries, an estuary was defined as 

any coastal water body that contained an NWI estuarine polygon. 

B.12.1 Estuary Inventory 

The estuary watershed geospatial layer generated for NOAA’s Coastal Assessment Framework 

(ftp://sposerver.nos.noaa.gov/datasets/CADS/GIS_Files/ShapeFiles/caf/) was used as the initial 

source to identify the larger Pacific Coast estuaries. However, this layer was not sufficiently 

detailed to represent the moderate to small estuaries. To develop a complete inventory of coastal 

estuaries, estuary water bodies were identified from previously mapped estuarine polygons from 

the NWI. Additionally, visual inspection of coastal sites either in the field or from aerial 

photographs was used to identify any additional coastal water body that appeared to have 

“estuarine” characteristics but that did not have a NWI estuarine polygon. Geospatial data from 

the NWI were obtained from digital databases and from on-site digitization of paper maps not 
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otherwise available in digital formats. Sites classified by NWI as estuarine, and in some cases, 

marine, palustrine or lacustrine, were inserted into the estuarine/watershed GIS layer, retaining 

the NWI classification system attributes. In some cases, adjustments were made to the attribute 

coding of water bodies to reflect judgments that these were part of an estuarine system. 

B.12.2 Watershed Delineation 

The Sixth Field hydrologic unit code (HUC) subwatershed geospatial layer created by the Forest 

Service from 1:24,000 scale USGS maps, digital elevation models and other data sources 

(http://www.reo.gov/gis/projects/watersheds/REOHUCv1_3.htm) was used as a primary 

reference. In Oregon, the Forest Service and Oregon State University have produced a 

watershed layer refined to the 7th field HUC boundary lines for most of coastal Oregon 

(http://www.fsl.orst.edu/clams/cfsl0233.html) north of the Rogue River. 

In all states, final refinements to the drainage boundaries between coastal and estuarine basins 

were often based on review of the hydrologic drainage patterns derived from digital elevation 

data (10-meter resolution in Oregon) and from USGS 1:24,000 scale quadrangle maps. 

The watersheds delineated in this project capture the entire drainage area for each of the 

estuaries. By delineating the entire watershed, these watershed areas are equivalent to the sum of 

NOAA’s Estuarine Drainage Area (EDA, portion of watershed that empties directly into the 

estuary and is affected by tides) and Fluvial Drainage Area (FDA, portion of an estuary's 

watershed upstream of the EDA boundary; see http://coastalgeospatial.noaa.gov). 

B.12.3 Land Cover Sources 

The estuary watersheds and CDAs were used as clipping boundaries for several land use/land 

cover datasets that are available for the Pacific coastal region at this time. The National Land 

Cover Data (NLCD, http://www.mrlc.gov) represents land cover circa 1992 and its extent is 

nationwide. This dataset was clipped to the watershed boundary for each estuary or CDA and is 

complete for all watersheds except for the interior Columbia River basin in Canada, and the 

Tijuana River estuary in Mexico. The 1992 NLCD data contains 21 classes of land cover 

(http://erg.usgs.gov/isb/pubs/factsheets/fs10800.pdf) with some modification in the 2001 NLCD 

data (http://www.epa.gov/mrlc/definitions.html#1992). The area of each land cover class in 

square kilometers and as a percentage of the watershed was calculated and entered into an 

Access database. 

Two additional land cover datasets have been created by the NOAA’s Coastal Services Center 

(C-CAP, http://www.csc.noaa.gov/crs/lca/ccap.html) program. The more recent data were 

derived from late 2000 and 2001 Landsat TM (thematic mapper) imagery. NOAA also produced 

a layer from imagery collected circa 1995-1996 and the earlier dataset was used to generate a 

land cover change layer. As the NOAA data were generated primarily to investigate land cover 

effects on coastal resources, the Pacific coast datasets did not completely cover the entire 

drainage areas of the largest three estuary watersheds. The interior portion of the San Francisco 

Bay estuary and the eastern portion of the Klamath River drainage were not included in the 

NOAA datasets. The lower Columbia River basin downstream of Bonneville Dam is included, 

but much of the interior Columbia River basin is not within the extent of the NOAA datasets. 

The 2001 NOAA data are based on 22 land cover classes 
293

(http://www.csc.noaa.gov/crs/lca/oldscheme.html), which are not exactly the same as those used 

in the NLCD. 

In January 2007, the Multi-Resolution Land Characteristics Consortium (MRLC, 

http://www.mrlc.gov) released a new national land cover data, NLCD 2001. The areas classified 

by the NOAA C-Cap program were incorporated into the 2001 release. Procedures used in the 

development of the 2001 land cover data layer are presented in Homer et al. (2004). The land 

cover in NLCD 2001 is based on 30-meter resolution data derived from Landsat imagery and 

uses 21 classes that are a modified version of the land classes used in the 1992 NCLD analysis 

(see http://www.epa.gov/mrlc/definitions.html#1992). The 2001 data were analyzed by zone, 

with the analysis of the Pacific Coast watersheds utilizing data from eight zones. The bounding 

coordinates of these eight zones are given in Table B-10. 

Table B-10. Bounding coordinates for the eight 2001 MRLC zones containing Pacific Coast 

watersheds. 

MRLC Longitude, degrees Latitude, degrees 

Zone West East North South 

1 -125.274529 -118.634037 49.66092 45.14402 

2 -126.118507 -121.051106 46.59432 41.63202 

3 -125.110557 -121.036959 42.36827 37.2476 

4 -122.777682 -115.735672 39.03779 31.62669 

5 -123.051273 -118.480769 41.04824 34.4985 

6 -122.184297 -117.601669 41.35061 34.52835 

7 -124.458076 -119.43724 45.86137 40.46927 

12 -121.009858 -113.825176 42.42003 36.26734 

B.12.4 Land Cover Patterns and Watershed Characteristics 

Land cover data from the 1992 and 2001 MRLC NLCD data and from the NOAA 1995 and 2001 

data were used to calculate the area and percentage of the watershed for each of the 21 (NLCD) 

or 22 (NOAA) land cover classes. Accuracy of the 1992 NLCD data by EPA region is presented 

at http://landcover.usgs.gov/accuracy/index.php. Based on this analysis, users were cautioned 

about applying the data to highly localized studies, such as over a small watershed. Accuracy of 

the 1992 NLCD can be found at http://www.epa.gov/mrlc/accuracy.html while discussion of the 

2001 NLCD data accuracy can be found at http://www.epa.gov/mrlc/accuracy-2001.html. The 

overall accuracy by MRLC zone ranged from about 86% to 98% (Table B-11) 

These datasets were used to generate estimates of the area of impervious surfaces in each 

watershed using default coefficients from the Analytical Tools Interface for Landscape 

Assessments (ATtILA) software (U.S. EPA, 2004c). The MRLC 2001 impervious surface layer, 

which represents an estimate of developed impervious surface per pixel by percent 

imperviousness, was clipped and summarized for each watershed. Overall accuracies for the 

impervious surfaces from the 2001 MRLC data range from 83 to 91 percent (Homer et al., 2004; 

Yang et al., 2003), and represent a higher resolution estimate of impervious surfaces than 

available from ATtILA. Estimates of nitrogen and phosphorus loadings from land cover were 

calculated from the watershed land cover data using coefficients from the ATtILA program. 
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Estimates of slope were calculated for each watershed from slope surfaces generated from 10-

meter (Oregon) digital elevation models (DEMS). Mean slope by percent and by degrees for 

land surfaces above the mean high water level were calculated and all slope values were 

exported to an Access database. The 30 meter DEMS were obtained from the National Elevation 

Dataset (NED, http://ned.usgs.gov), a seamless mosaic of the best elevation data. The 10-meter 

elevation data for Oregon was obtained by the USDA Forest Service for the Coastal Landscape 

Analysis and Modeling Study project (CLAMS, http://www.fsl.orst.edu/clams) from USGS 

drainage enforced digital elevation models. 

Table B-11. Overall accuracy of NLCD 2001 land cover analysis. Zones refer to the 

MRLC zones containing Pacific Coast estuarine watersheds. 

MRLC Zone Overall Accuracy (%) 

1 86.1 

2 86.1 

3 88.0 

4 88.0 

5 85.2 

6 88.3 

7 98.9 

12 84.2 

B.12.5 Population Density 

Human population estimates from the 1990 and 2000 censuses (http://www.census.gov/) were 

generated for each drainage unit. Area weighted estimates of total population by census block 

were summed for each drainage and population density (individuals km-2) was calculated from 

the total drainage population estimate. 

B.12.6 Climate 

Estimates of air temperature and precipitation were made for each watershed from several 

sources of long-term climate data. Annual mean temperature and mean precipitation were 

obtained from the 1 kilometer resolution climate summaries from 1980 to 1997 from the Daily 

Surface Weather and Climatological Summaries (DAYMET; http://www.daymet.org). Annual 

mean, annual mean minimum, annual mean maximum temperature and mean annual 

precipitation were also obtained from the 800 meter resolution PRISM climate model for the 

period 1971-2000 (http://www.climatesource.com). Comparisons were made between the two 

climate datasets and diverging values were examined. 

B.12.7 National Wetland Inventory 

As part of the development of the sampling frame for the Western Coastal EMAP 2002 survey, 

the available National Wetlands Inventory digital layers for coastal estuaries in Oregon, 

Washington and northern California were collected by EPA’s onsite GIS staff in 2001. Some 

significant areas of Oregon estuaries were not included in the existing NWI digital dataset and 

paper maps were obtained for these areas and scanned. The scanned maps were registered and 

were used for “heads up” digitizing of the missing features. 
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In July 2005, all of the available NWI datasets for the Pacific coast were retrieved from the 

National Wetlands Inventory website. Digital maps were downloaded from the NWI website, 

mostly individual ESRI shapefiles of 1:24,000 scale quadrangle maps. The zipped versions of 

the shapefiles were created by NWI in the spring of 2003, according to the file creation dates. In 

some cases where shapefiles for specific quadrangles were missing, older ArcInfo coverages 

were available and retrieved from the NWI download website. All of these datasets are what the 

current NWI mapping team calls the “static” data. Where the circa “2005” data differed from the 

final version of the EPA NWI aggregation, the most recent data from the NWI website 

superseded the previous data for inclusion in the estuary geospatial layer. The NWI targeted 

mapping unit for the scale of photography most often used in the Pacific estuary classification 

varied from 1 to 3 acres. 

Currently the NWI Mapping project is transitioning to the ESRI geodatabase format with 

wetlands data being available in larger seamless blocks (1:100,000 scale) or as selected views 

from an online mapping tool. The latest data for the Pacific Northwest (as of mid August 2006) 

shows some alterations to the previous wetlands in portions of the Oregon coast. These changes 

were captured in a revision of the Oregon estuary watershed geospatial layer in December 2007. 

Descriptions for parsing the NWI attribute field codes to a habitat type were obtained from the 

NWI documentation and from queries to the online Map Codes Search form 

(http://www.fws.gov/wetlands/Data/webatx/atx.html). Some of the polygons in the NWI data 

selected for inclusion in the estuary classification spatial layer have codes that are considered 

obsolete under the current system. These will be kept in the database until revised geospatial 

data are available from the NWI. The coding used in the wetlands classification system used is 

based upon the Cowardin et al. (1979). 

B.12.8 Estuarine Characteristics 

Strahler stream order, which represents a hierarchical view of stream complexity based on the 

number of tributary junctions in a stream system from headwaters to mouth, was recorded for the 

largest order stream entering an estuary. A summarization of all the stream orders entering each 

estuary has been completed for the Pacific Northwest and will be completed for all the Pacific 

Coast estuaries. Order was recorded for the streams included in the National Hydrological 

Dataset (NHD; http://nhd.usgs.gov/), which corresponds to streams on the 1:100,000K map 

series and often is complete with streams mapped at the 1:24,000 level. Mouth width for each 

estuary was estimated using aerial photography from Terraserver (http://www.terraserver.com) 

and other imagery sources. 

B.13 Other Data Sources 

Historical salinity and nutrient data were obtained from the Oregon Department of 

Environmental Quality web-based database. Data from this site were previously reviewed 

internally by DEQ and assigned a data quality grade. Only data with grades of A and A+ were 

used as part of this report. 
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B.14 Hydrodynamic and Nutrient Source Model 

All field and laboratory data used in the development of hydrodynamic and nutrient source 

model were collected in accordance with WED’s SOPs and QAPPs. A two-dimensional, 

laterally averaged hydrodynamic and water quality model (Cole and Wells, 2000) was used to 

simulate the transport of riverine, oceanic and wastewater treatment facility (WWTF) effluent 

dissolved inorganic nitrogen (DIN) sources. This model is well suited for long-narrow estuaries, 

such as Yaquina Bay, where there are minimal lateral variations in water column properties. 

U.S. EPA (2001) suggested that this model may be useful in the estuarine nutrient criteria 

development and has been used in developing estuarine Total Maximum Daily Loads (TMDLS). 

In the model simulations presented in this study, Yaquina Estuary was represented by 325 

longitudinal segments spaced approximately 100-m apart with each longitudinal segment having 

1-m vertical layers. The model domain extended about 37 km from the tidal fresh portion of the 

estuary at Elk City, Oregon to the mouth of the estuary. Model simulations were performed for 

the interval January 1 to October 1 of 2003 and 2004 and included tidal and wind forcing as well 

as freshwater inflow. Parameters simulated included water surface elevation, salinity, water 

temperature, and DIN. 

Model calibration is the process of determining model parameters that are appropriate for the 

specific study location and time interval being simulated. The model used in this study was 

calibrated through adjustment of friction coefficient, eddy viscosity, and eddy diffusivity. To 

assess the model performance at simulating the hydrodynamics, we compared simulated and 

observed water level variations at two locations in the estuary and salinity and water temperature 

at four locations utilizing data from the YSI datasondes. Since the datasondes used at these 

stations were not leveled in we could only compare relative water level fluctuations, not absolute 

water level (referenced to MLLW). In addition, temperature and salinity from the CTD cruises 

were compared to simulated values. The model was assessed by calculating the root mean 

square error between observed and predicted variables. 

Each nitrogen source, riverine (Nriver), oceanic (Nocean), and WWTF effluent (Nwwtf), was modeled 

as a separate component. The nitrogen sources were modeled as 

dN

dt

= − transport Nμ

where N is the DIN source and μ is a loss/uptake rate. The same value of μ was used for all three 

nitrogen sources and the value of μ was determined by fitting total modeled DIN 

(Nocean+Nriver+Nwwtf) to observations of DIN within the estuary. The best fit to observations was 

found with μ = 0.1 d-1. Simulations were also performed with no uptake (μ = 0) which is 

equivalent to conservative transport of the sources. The results from the transport model were 

used to mix the three nitrogen sources using the following equation 

M R R O O W W δ = f δ + f δ + f δ

+ + = 1 R W Of f f

where fR, fW, and fO are the fractions of riverine, wastewater treatment facility, and oceanic DIN, 

respectively, and δR, δW, and δR are the isotopic end members for riverine, wastewater treatment 

facility effluent, and oceanic sources, respectively. Estimates of the oceanic and riverine end 

members were obtained by examination of the observed isotope ratios at the stations located near 
297

the mouth of the estuary and in the riverine portion of the estuary and comparison to the 

literature. The initial estimate for the WWTF end member (δW = 15-22‰) was determined from 

the literature. To arrive at the final end member isotope ratios, model simulations were 

performed varying each end member over the range estimated from the data and literature. The 

final isotope ratio of end members for the three sources (δR=2‰, δW=20‰, and δO=8.4‰) was 

determined from the best fit (minimum root mean square error, RMSE) between predicted and 

observed isotope ratio at the five isotope sampling stations during 2003 and 2004. The final 

oceanic end member selected is consistent with marine end members for the west coast of the 

United States (Fry et al., 2001). While the riverine end member is consistent with the isotope 

ratio expected for nitrogen associated with red alder (leaf tissue ranges between -3 and -0.5‰; 

Hobbie et al., 2000; Tjepkema et al., 2000; Cloern et al., 2002). 

B.15 Quality Assurance Project and Quality Management Plans (QAPPs) Used in This 

Study 

QAPP 2000.01. Changes in the Abundance and Distribution of Estuarine Keystone Species in 

Response to Multiple Abiotic Stressors. T.H. DeWitt, EPA, 2000. 

QAPP 01.02. Modeling of Landscape Change Effects on Estuarine Trophodynamics: an 

Optimization Approach Using Inverse and Forward Modeling. P. Eldridge, EPA, 2001. 

QAPP 01.04. Assessment of the Spatial and Temporal Distribution of Submersed Aquatic 

Vegetation and Benthic Amphipods within the Intertidal Zone of Yaquina Bay Estuary, 

Oregon via Color Infrared Aerial Photography. D.R. Young, EPA, 2001. 

QAPP 01.06. Upper Margin Expansion: Influences on Seagrass, Zostera marina L. B.L. Boese, 

EPA, 2001. 

QAPP 02.01. Autecological studies of marine macrophytes including the sea grasses Zostera 

marina and Z. japonica in Yaquina Bay, Oregon. J. Kaldy, EPA, 2002. 

QAPP 04.01. Seagrass Research - Epigrowth Light Attenuation Task: Estimation of spatial and 

temporal variation in light attenuation due to epigrowth on Zostera marina in Yaquina Bay. 

W. Nelson, EPA, 2004. 

QAPP-06.01. Development of the Pacific Coast Ecosystem Information System (PCEIS). H. 

Lee II and D. Reusser, 2006. 

Marine Science Institute Analytical Laboratory University of California, Santa Barbara. Quality 

Assurance Manual-Draft. 2005. 

U.S. Environmental Protection Agency. 2006. Quality Management Plan. NHEERL. Western 

Ecology Division. 

B.16 Standard Operating Procedures (SOPs) Used in This Study 

CL -6. V.2. Standard Operating Procedures for Stable Isotope Ratio Mass Spectrometer Analysis 

of Organic Material. EPA. 1999. 

GPEP SOP 3.01 – TERA SOP. Carbon/Nitrogen Elemental Analysis. Rick King, Dynamac 

Corporation. 1989. 

MES EP01.rev 0. Draft. Standard Operating Procedure for Collecting and Processing Zostera 

marina and Associated Epiphytes for Light Attenuation Measurements. Dynamac 

Corporation. 2004. 

MES SOP09.rev 0. Standard Operating Procedure for Preparing Water Samples for Nutrient 

Analysis. Dynamac Corporation. 2003. 
298

MES SOP02.rev 0. Standard Operating Procedures for Weighing Food Web Samples and 

Submitting them to ISIRF for Stable Isotope Analysis. K. Rodecap, Dynamac Corporation. 

2002. 

WED SOP06.rev 0. Standard Operating Procedure for Preparation and Analysis of Estuarine 

Water Samples for Determination of Chlorophyll-a Content. Dynamac Corporation. 2004. 

SOPCMT.02, Sampling and Preparation for TOC Analysis, Sercy, Kathleen, et al, US EPA, 

Western Ecology Division. 1991. 

SOPIOP.09. Operating Procedure For YSI Series 6 Multiparameter Water Quality Meters, Model 

#s 6000UPG and 6600, 6600EDS. D.T. Specht. EPA. 2004. 

SOPFSP.01. Use of The Seabird Seacat (SBE-19) CTD Package. R. J. Ozretich. EPA. 1999. 

WRS 14B.2. Standard Operating Procedure for the Determination of Total Suspended Solids 

(Non- Filterable Residue). Dynamac Corporation. 2005. 

SOPPMP.04. Measurement of sediment grain size distribution using a laser diffraction particle 

size analyzer. James H. Power. EPA. 2007. 

SOP-NHEERL/WED/PCEB/PC/2006-01-r1. Standard Operating Procedures for producing 

digital aerial photomaps of estuarine intertidal ecosystems using color infrared film, 

classifying eelgrass and non-vegetated habitats, and assessing the accuracy of the 

classifications. Clinton, P.J. and D.R. Young. EPA. 2006.