EPA 600/R-07/046 

An Approach to Developing Nutrient Criteria for Pacific Northwest Estuaries: 

A Case Study of Yaquina Estuary, Oregon

Principal Authors 

Cheryl A. Brown, Walter G. Nelson, Bruce L. Boese, Theodore H. DeWitt, 

Peter M. Eldridge, James E. Kaldy, Henry Lee II, James H. Power, and David R. Young 

US EPA Office of Research and Development 

National Health and Environmental Effects Research Laboratory 

Western Ecology Division 

June 2007 

Additional Contributors 

Robert J. Ozretich, Anne C. Sigleo, Christina Folger, Katharine M. Marko, 

Patrick J. Clinton, Faith A. Cole, TChris Mochon-Collura, David T. Specht, 

and Anthony D’Andrea 
ii

Preface 

Disclaimer 

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

Agency. It has been subjected to review by the National Health and Environmental Effects 

Research Laboratory and approved for publication. Approval does not signify that the contents 

reflect the views of the agency, nor does mention of trade names or commercial products 

constitute endorsement or recommendation for use. 

Citation 

Brown, C.A., W.G. Nelson, B.L. Boese, T.H. DeWitt, P.M. Eldridge, J.E. Kaldy, H. Lee II, J.H. 

Power, and D.R. Young. 2007. An Approach to Developing Nutrient Criteria for Pacific 

Northwest Estuaries: A Case Study of Yaquina Estuary, Oregon. USEPA Office of 

Research and Development, National Health and Environmental Effects Laboratory, 

Western Ecology Division. EPA/600/R-07/046.

Acknowledgements 

 We greatly appreciate the assistance of numerous employees of Dynamac Inc. who 

provided technical support to Western Ecology Division (WED) for many aspects of the work 

described in this document, including Adam DeMarzo, Lucas Nipp, Stacy Strickland, and Una 

Monaghan. We appreciate the administrative support provided by Janet Lamberson (WED). 

Gary Arnold with the Oregon Department of Environmental Quality (ODEQ) provided helpful 

review comments on this document as well as information on water quality standards and 

historical information. Tim McFetridge (ODEQ) and Lee Ritzman (Public Works Department, 

City of Newport) provided information on point source inputs. Deneb Karentz (University of 

San Francisco) kindly provided some unpublished historical data. Anne Fairbrother, Marilyn ten 

Brink, Robert Ozretich, and James Hagy provided comments on the manuscript which improved 

the quality of the report. Numerous WED staff contributed unpublished data to this report, 

including Bruce Boese (Z. marina lower margin and macroalgae), Cheryl Brown (water quality), 

Pat Clinton (maps of Z. marina and macroalgae coverage), Ted DeWitt (macroinvertebrate and 

benthic flux), Peter Eldridge (water quality), James Kaldy (Z. marina and stable isotope), Henry 

Lee (watershed landscape), Walt Nelson (epiphyte data), Robert Ozretich (cruise and irradiance), 

David Specht (datasonde), and David Young (macroalgae and Z. marina data).
ii

Executive Summary

 A proposed approach that could be used by the State to develop nutrient criteria for the 

Yaquina Estuary, Oregon is presented. The approach is based on a synthesis of research results 

derived from field sampling at multiple temporal and spatial scales, assembling data to construct 

historical trends in water quality parameters, and a variety of modeling approaches. 

 Yaquina Estuary is a small, drowned, river valley estuary located along the central 

Oregon coast. Approximately 48% of the estuarine area is intertidal. The designated uses within 

the Yaquina Estuary and River include aquatic life harvesting (shellfish growing and fishing), 

agricultural (livestock watering), municipal (public water supply), recreation (water contact 

recreation), ecological (resident fish and aquatic life, salmonid spawning and rearing, 

anadromous fish passage) and aesthetics. 

 Spatial and temporal variability in water quality indicators were assessed for multiple 

water quality parameters, including nitrogen, phosphorus, chlorophyll a, dissolved oxygen, total 

suspended solids, and water column light attenuation. Spatial scales examined included variation 

within the Yaquina Estuary, as well as comparison of some parameters to short term studies of 

six additional Oregon estuaries, and comparison to a single sampling of 14 additional Oregon 

estuaries conducted by the US EPA National Coastal Assessment program. Green macroalgal 

occurrence was evaluated to determine whether this was an appropriate indicator for nutrient 

responses within the Yaquina Estuary. Lower depth limits for the seagrass (Zostera marina) 

were determined in order to estimate the minimum light requirements for sustaining seagrass. 

Field results were used to confirm output from a Seagrass Stressor-Response Model. 

 Because there were limited data for applying the reference condition approach for the 

class of estuaries similar to the Yaquina Estuary, we used in situ observations within Yaquina 

Estuary as a basis for determining an Estuarine Reference Condition. Cumulative distribution 

functions (CDFs) were produced for water quality variables for the Yaquina Estuary and 

compared to CDFs for other Oregon estuaries using two independent data sets. Key percentiles 

(25th, 50th, 75th) for water quality parameters were used as inputs to a Seagrass Stressor-Response 

Model to determine whether particular percentile values would be adequately protective of 

seagrass within the Yaquina Estuary. 
iii

 The Yaquina Estuary has strong seasonal variation in the magnitude of nutrient loading 

and in the dominant nutrient sources. Response variables (particularly, chlorophyll a and 

dissolved oxygen) exhibited similar patterns of seasonal variation. During the wet season 

(November-April), riverine nitrogen inputs dominate, whereas during the dry season (May – 

October) oceanic nitrogen sources dominate. Riverine inputs are primarily related to the 

presence of nitrogen-fixing red alder (Alnus rubra) trees in the watershed. There are also strong 

zonal differences in nutrient levels, response variables, and dominant nutrient sources within the 

Yaquina Estuary. In the lower estuary (Zone 1), water quality conditions are strongly influenced 

by ocean conditions, while in the upper portions of the estuary (Zone 2), watershed and point 

source inputs increase in importance. 

 We suggest that criteria be developed for wet and dry seasons to address the strong 

seasonal variation in nutrient loads and sources. Dry season criteria (May-October) are most 

important, since during the wet season, there appears to be little utilization of nutrients within the 

estuary, and chlorophyll a levels are low and the dissolved oxygen concentrations are high. 

Thus, it is not clear that wet season criteria are needed within the Yaquina Estuary. We suggest 

that separate criteria be developed for Zones 1 and 2, with dry season criteria for Zone 2 a first 

priority. The high degree of ocean-estuary coupling found for Zone 1 within the Yaquina 

Estuary with associated short-term variability in water quality parameters suggests that 

monitoring for compliance with nutrient criteria in this region may be problematic. During the 

dry season, phosphate, nitrate, chlorophyll a, and dissolved oxygen levels in Zone 1 are primarily 

determined by ocean conditions and separation of oceanic from anthropogenic inputs would 

require, at the least, continuous monitoring capability, and may require additional techniques. 

 Use of Total Suspended Solids (TSS) as a water quality criterion may not be practical due 

to inconsistent spatial and temporal patterns relative to that of adjacent Oregon estuaries. 

Macroalgal biomass response within Yaquina Estuary appears to be primarily driven by oceanic 

nitrogen input, and thus does not appear to be useful as an indicator of cultural eutrophication. 

 Based on weight of scientific evidence, we conclude that Yaquina Estuary is not 

exhibiting symptoms of cultural eutrophication. Thus, following the recommendations in U.S. 

EPA (2001), median values could be used as criteria for most water quality parameters. The 

Seagrass Stress-Response Model confirmed that the median percentile for water clarity (kd) 

would be protective of the existing eelgrass (Zostera marina) habitat in the Yaquina Estuary. 
iv

Modeling results were consistent with analysis of seagrass depth limits which indicated that the 

median kd provided for persistence of seagrass at depths within the two estuarine zones that were 

comparable to current depth distributions. 

 The current Oregon DO criterion of 6.5 mg l-1 should be adequately protective of 

estuarine resources, but is closer to the 25th percentile value rather than the median value for DO 

data in Zone 2. Recent DO measurements demonstrate that hypoxic water is imported into the 

estuary from the coastal shelf during the dry season. As a result, exceedances of the DO 

criterion should be expected particularly in Zone 1. The current Oregon chlorophyll a criterion 

of 15 μg l-1 is approximately 3 times greater than the median value for Zone 2. The chlorophyll a

criterion is determined as a 3-month average, and if chlorophyll a levels were to approach the 

present criterion for such a time period, significant trophic shifts in the estuary would be likely. 

Thus, the current chlorophyll a criterion may not prevent some impacts on designated use. 

Potential dry season criteria for the Yaquina Estuary based on median values for all parameters 

except for DO. 

Parameter (units) Zone 1 Zone 2 

DIN (μM) 14 14 

Phosphate (μM) 1.3 0.6 

Chlorophyll a (μg l-1) 3 5 

Water Clarity (m-1) 0.8 1.5 

Dissolved Oxygen (mg l-1) 6.5 

 
v

Table of Contents 

Preface ............................................................................................................................................. ii 

Executive Summary........................................................................................................................ ii 

List of Figures.............................................................................................................................. viii 

List of Tables ................................................................................................................................. xi 

1. Introduction..........................................................................................................................1 

1.1 Purpose of This Case Study ............................................................................................ 1 

1.2 Nutrient Criteria Objective ............................................................................................. 1 

1.3 Designated Uses of Yaquina River and Estuary, Impairments and Assessments........... 2 

1.4 Oregon Estuarine Water Quality Criteria ....................................................................... 4 

1.5 Summary of Yaquina Case Study Approach .................................................................. 4 

2. Description of Study Area ...................................................................................................6 

2.1 Physical Characteristics of the Estuary........................................................................... 6 

2.2 Biotic Characteristics...................................................................................................... 9 

2.3 Land Use and History of Anthropogenic Modifications............................................... 13 

2.4 Classification of the Yaquina Estuary........................................................................... 17 

2.5 Conceptual Model for Yaquina Estuary ....................................................................... 18 

3. Description of Sources/Sinks of Nutrients.........................................................................20 

3.1 Background................................................................................................................... 20 

3.2 Nitrogen Loading to Yaquina Estuary .......................................................................... 20 

3.2.1 Watershed ............................................................................................................. 21 

3.2.2 Ocean Input........................................................................................................... 22 

3.2.3 Benthic Processes.................................................................................................. 23 

3.3 Zonation Based Upon Nitrogen Sources....................................................................... 24 

4. Data Sources and Methods.................................................................................................26 

4.1 Yaquina Estuary Data ................................................................................................... 26 

4.1.1 Recent Data........................................................................................................... 26 

4.1.2 Additional Data Sources ....................................................................................... 27 

4.2 Oregon Estuarine Classification Study ......................................................................... 30 

4.3 National Coastal Assessment (NCA)............................................................................ 30 

4.4 Percentile Approach...................................................................................................... 31
vi

4.5 Statistical Analysis........................................................................................................ 32 

5. Spatial and temporal patterns in water quality parameters in the Yaquina Estuary ..........34 

5.1 Salinity .......................................................................................................................... 34 

5.2 Nutrients........................................................................................................................ 35 

5.3 Chlorophyll a ................................................................................................................ 37 

5.4 Nutrient Limitation and Primary Productivity.............................................................. 38 

5.5 Relationships between TN and TP and Chlorophyll a.................................................. 40 

6. Nitrogen and Phosphorus as Water Quality Criteria..........................................................43 

6.1 Seasonal, Zonal, and Long-term Trends in N and P..................................................... 43 

6.2 Percentile Approach for Nitrogen and Phosphorous .................................................... 48 

7. Chlorophyll a as a Water Quality Response Measure .......................................................50 

7.1 Seasonal, Zonal, and Long-Term Trends in Chlorophyll a .......................................... 50 

7.2 Percentile Approach for Chlorophyll a......................................................................... 52 

8. Dissolved Oxygen as a Water Quality Response Measure................................................53 

8.1 Seasonal, Zonal and Long-term Trends in Dissolved Oxygen ..................................... 53 

8.2 Percentile Approach for Dissolved Oxygen ................................................................. 58 

9. Water Clarity (kd) and Turbidity as Water Quality Response Measures ...........................60 

9.1 Seasonal and Zonal Patterns in Water Clarity and Turbidity ....................................... 60 

9.2 Percentile Approach for Water Clarity and TSS........................................................... 62 

10. Macroalgal Biomass as a Water Quality Response Measure.............................................65 

10.1 Introduction................................................................................................................... 65 

10.2 Approach....................................................................................................................... 65 

10.3 Results and Discussion ................................................................................................. 65 

10.3.1 Annual Variation: 1997 - 1998 ............................................................................ 65 

10.3.2 Seasonal Variation: 1999-2000............................................................................ 66 

10.4 Percentile Approach...................................................................................................... 68 

10.5 Comparisons with Findings from Other Regions ......................................................... 69 

11. Submerged Aquatic Vegetation (SAV) as a Management Objective (Designated Use)...71 

11.1 Background................................................................................................................... 71 

11.2 Spatial Seagrass Patterns............................................................................................... 73

11.3 Temporal Seagrass Patterns .......................................................................................... 75 
vii

11.4 Water Clarity and Seagrass Lower Depth Limit........................................................... 77 

11.4.1 Background........................................................................................................... 77 

11.4.2 Methods................................................................................................................. 78 

11.4.3 Relationship between Lower Margin and Water Clarity ...................................... 78 

11.5 Epiphyte Patterns and Impact on Z. marina.................................................................. 82 

11.5.1 Spatial and Temporal Patterns in Epiphytes ......................................................... 82 

11.6 Zostera marina Light Requirements............................................................................. 84 

12. Stress-Response Approach for Protection of SAV ............................................................88 

12.1 Introduction................................................................................................................... 88 

12.2 Description of Model .................................................................................................... 88

12.3 Model Simulations and Input Data ............................................................................... 90 

12.4 Results........................................................................................................................... 91 

12.5 Discussion..................................................................................................................... 94 

13. Conclusions and Recommendations ..................................................................................97 

13.1 Recommendations......................................................................................................... 99

References....................................................................................................................................102 

Appendix A: Benthic Processes in Yaquina Estuary...................................................................122

Appendix B: Description of Methods and Quality Assurance Procedures.................................128 

Appendix C: Classification of Oregon Estuaries........................................................................156 

Appendix D: Survey of Effects of Macroalgae on Biota............................................................159 

Appendix E: Stress-Response Model Calibration, Input Data, and Results...............................162 
viii

List of Figures 

Figure 2.1. Location map of Yaquina Estuary............................................................................... 6 

Figure 2.2 Map of watershed of the Yaquina Estuary, showing the two primary tributaries........ 7 

Figure 2.3 Monthly discharge statistics (Yaquina River + Big Elk Creek)................................... 8 

Figure 2.4 a) False-color, infrared aerial photography mosaic of Yaquina Estuary and 

b) map of intertidal seagrass and macroalgae in Yaquina Estuary. .................................. 12 

Figure 2.5 Populations in the cities of Toledo and Newport, Oregon from 1890 to 2005. ......... 14 

Figure 2.6 Number of logs floated or rafted on the Yaquina River from 1918 to 1978.............. 16 

Figure 2.7 Conceptual model of factors influencing nutrient and response variables in the 

Yaquina Estuary................................................................................................................ 19 

Figure 3.1 Modeled contribution of WWTF effluent, riverine, and oceanic sources to DIN at 

5 locations in the estuary during January – September of 2004. ...................................... 25 

Figure 5.1 Salinity versus distance from mouth of the estuary during the a) dry and 

b) wet seasons.. ................................................................................................................. 34 

Figure 5.2 Spatial variation in dry season NO3

-

+ NO2

-

.. ............................................................. 36 

Figure 5.3 Spatial variation in dry season PO4

3-

...........................................................................36 

Figure 5.4 Dry season chlorophyll a versus salinity (all stations from 1974-2006)..................... 38 

Figure 5.5 Mean dry season (2006) total nitrogen (TN) and total phosphorous (TP) versus 

distance from mouth of estuary......................................................................................... 41 

Figure 5.6 Mean dry season (2006) chlorophyll a versus distance from mouth of estuary......... 41 

Figure 5.7 Total nitrogen (TN) versus chlorophyll a for the dry season (2006) with data 

divided by zones. .............................................................................................................. 42 

Figure 5.8 Total phosphorous (TP) versus chlorophyll a during the dry season (2006) with 

data divided by zones........................................................................................................ 42

Figure 6.1 Comparison of historic and recent NO3

-

+NO2

-

 during the dry season in a) Zone 1 

and b) Zone 2. ................................................................................................................... 46 

Figure 6.2 Comparison of historic and recent NO3

-

+NO2

-

 during the wet season in a) Zone 1 

and b) Zone 2. ................................................................................................................... 46 
ix

Figure 6.3 Comparison of historic and recent PO4

3-

 during the dry season in a) Zone 1 and 

b) Zone 2........................................................................................................................... 47 

Figure 6.4 Comparison of historic and recent PO4

3-

 during the wet season in a) Zone 1 and 

b) Zone 2. ........................................................................................................................ 47 

Figure 7.1. Box plot of monthly chlorophyll a data from the Yaquina Estuary (all stations from 

1973-2006)........................................................................................................................ 51 

Figure 7.2 Comparison of historic and recent dry season chlorophyll a for a) Zone 1 and 

b) Zone 2 in the Yaquina Estuary. .................................................................................... 51 

Figure 8.1 Seasonal pattern in dissolved oxygen at all locations and all years in the Yaquina 

Estuary and River with squares and triangles representing samples from Zones 1 and 2, 

respectively. ...................................................................................................................... 54 

Figure 8.2 Interannual trend in residual dissolved oxygen values during 1960 to 1986 for a) 

Zone 1 and b) Zone 2. ......................................................................................................55

Figure 8.3 a) Time-series of dissolved oxygen and salinity and b) salinity versus dissolved 

oxygen showing import of hypoxic ocean water at a station 3.7 km from mouth of 

estuary............................................................................................................................... 57 

Figure 9.1. Spatial variation in turbidity during wet and dry seasons (1998-2006). ................... 60 

Figure 9.2 Light attenuation coefficients (kd) versus distance from the mouth of the estuary from 

cruise data (years 1998 to 2006), with filled and open symbols representing dry and wet 

seasons, respectively. ....................................................................................................... 61 

Figure 9.3. Median monthly light attenuation coefficients from the continuous data set at 

5 locations in Yaquina Estuary (1999-2003). ................................................................... 62 

Figure 10.1 Photomap of intertidal vegetation in Yaquina Estuary from aerial surveys of 

July 23, 1997 and August 10, 1998 .................................................................................. 67 

Figure 10.2 Average percent cover and biomass values (+ 1 std. err.) of benthic green 

macroalgae in the Yaquina Estuary between June 1999 and May 2000........................... 68 

Figure 11.1 Spatial distribution of Yaquina Estuary eelgrass...................................................... 72 

Figure 11.2 Z. marina depth distribution in the marine dominated portion (Zone 1) of 

Yaquina Estuary................................................................................................................ 74 
x

Figure 11.3 Z. marina depth distribution in the river dominated portion (Zone 2) of 

Yaquina Estuary................................................................................................................ 74 

Figure 11.4 Comparison of the spatial distribution of Z. marina in a portion of the 

Yaquina Estuary (see inset on Figure 11.1) from 1997, 2000, and 2004. ........................ 76 

Figure 11.5 Historical distribution (mid 1970's) of Z. marina from the Oregon Estuary Plan 

Book (Cortright et al., 1987)............................................................................................. 77 

Figure 11.6 Relationship between distance from mouth of the estuary and the lower depth 

limit (below Mean Sea Level) for Z. marina. ................................................................... 79 

Figure 11.7 Relationship between Z. marina maximum depth limit (m below MSL) and kd.. .. 81 

Figure 11.8 Temporal relationship of epiphytic biomass per unit leaf area on Z. marina external 

leaves in the Yaquina Estuary, 2000-2003. ...................................................................... 83 

Figure 11.9 Epiphyte biomass per unit leaf area on old (external) and young (internal) Z. marina

leaves by season (wet or dry) and salinity zone in the Yaquina Estuary.......................... 83 

Figure 11.10 Linear regression relationship between the percent of light reduction to log(x+1) 

transformed epiphyte biomass per unit Z. marina surface area........................................ 84 

Figure 12.1 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) 

depth distribution based on the median case..................................................................... 92 

Figure 12.2 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) 

depth distribution based on the 25th percentile case.. ....................................................... 93 

Figure 12.3 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) 

depth distribution based on the 75th percentile case.. ....................................................... 94 
xi

List of Tables 

Table 1.1 Designated uses and water quality attainments for Yaquina Estuary/River.................. 3 

Table 1.2 Selected water quality criteria for Oregon estuaries....................................................... 4 

Table 3.1 Comparison of the magnitude of nitrogen sources during wet and dry seasons for 

Yaquina Estuary, Oregon. ............................................................................................... 23 

Table 4.1 Sampling frequency for cruise data collected by U.S. EPA from 1998-2006. ............ 28 

Table 4.2 Summary of historic data compiled include source of the data, temporal and spatial 

sampling frequency of the data set and parameters measured.......................................... 29 

Table 6.1 Comparison of historic and recent NO3

-

+NO2

-

 (μM) concentrations in the Yaquina 

Estuary. ............................................................................................................................. 45 

Table 6.2 Comparison of historic and recent PO4

3-

 (μM) concentrations in the Yaquina 

Estuary. ............................................................................................................................ 45 

Table 6.3 Percentiles for DIN (μM) calculated using Yaquina (1998-2006), Classification (2004-

2005), and NCA Oregon estuaries (1999-2000) data sets.. .............................................. 49 

Table 6.4 Percentiles for PO4

3-

 (μM) using Yaquina (1998-2006), Classification (2004-2005), 

and NCA Oregon estuaries (1999-2000) data sets. NCA and Classification values are for 

dry season only, while Yaquina include dry and wet season values. ............................... 49 

Table 7.1 Percentiles for chlorophyll a (μg l-1) calculated using Yaquina (1998-2006), 

Classification (2004-2005), and NCA Oregon estuaries (1999-2000) data sets. ............. 52 

Table 8.1 Percentiles for dissolved oxygen (mg l-1) calculated Yaquina (1998-2006), 

Classification (2004-2005), and NCA Oregon Estuaries (1999-2000) data sets.............. 59 

Table 9.1 Percentiles for light attenuation coefficient kd (m-1) calculated using continuous (1999-

2003) and cruise (1998-2006) data sets from the Yaquina Estuary for dry and wet 

seasons. ............................................................................................................................. 64 

Table 9.2 Percentiles for TSS (mg l-1) calculated using Yaquina (1998-2004), Classification 

(2004-2005), and NCA (1999-2000) data sets. ................................................................ 64 

Table 10.1 Percentiles for benthic green macroalgae biomass (gdw m-2) for Yaquina Estuary 

(1998 – 2004, Zone 1 only) and the Classification data set (2004 - 2005)....................... 69 

Table 11.1 Comparison of mean and range of percent of water column surface irradiance needed 

to maintain Z. marina at its colonization depth from published data and from Yaquina 

Estuary data....................................................................................................................... 81 
xii

Table 12.1 Input data from percentile approach for different SRM simulations......................... 90 

Table 12.2 Summary of the protective capacity of different potential criteria derived from 

Yaquina Estuary percentile data. .................................................................................... 92 

Table 12.3 Dry season median light attenuation and DIN values for Yaquina Estuary as 

compared to water quality management targets for other estuaries that are protective of 

eelgrass habitat.................................................................................................................. 96 

Table 13.1 Potential dry season criteria for the Yaquina Estuary based on median values for all 

parameters except for DO. .............................................................................................. 101 
1

1. Introduction 

1.1 Purpose of This Case Study 

 The Office of Science and Technology (OST), Office of Water, U.S. EPA provides 

guidance to the States and tribes for developing nutrient criteria for estuarine and coastal waters. 

The Office of Research and Development, National Health and Environmental Effects 

Laboratory (NHEERL) has been conducting research to support improvements to the scientific 

basis for estuarine nutrient criteria for over 5 years under the NHEERL Aquatic Stressors 

Research Program. Parallel research efforts have been on going at the Western (WED), Gulf 

(GED) and Atlantic Ecology Divisions (AED). To support the OST criteria effort, NHEERL 

scientists have synthesized the research results of field sampling, trend analyses, and modeling 

approaches to produce nutrient criteria case studies for Yaquina Estuary, OR and Pensacola Bay, 

FL. Each case study describes one or more approaches that may be used for establishing nutrient 

criteria and offers specific recommendations for the particular system. Here we describe a 

recommended approach for developing nutrient criteria values for the Yaquina Estuary. 

1.2 Nutrient Criteria Objective 

 The Nutrient Criteria Technical Guidance Manual: Estuarine and Coastal Marine Waters 

(U.S. EPA, 2001) provides a detailed summary description of the nutrient criteria development 

process in Section 1.4 of the manual. This guidance defines two objectives for establishment of 

numeric nutrient criteria: 

To reduce the anthropogenic component of nutrient overenrichment to levels that restore 

beneficial uses (i.e. described as designated uses by the CWA), or to prevent nutrient 

pollution in the first place. 

Quantitative, long term data on the status of eutrophication in most Oregon estuarine systems is 

limited (Bricker et al., 1999). The EPA National Coastal Assessment (U.S. EPA, 2004a) 

sampled Oregon estuaries for a variety of water quality indicators in 1999-2000, and concluded 

that there was little evidence of eutrophication effects in Oregon estuaries. Additional qualitative 

and quantitative assessments of Oregon estuaries by WED generally support the conclusions of 

the NCA report, but also suggest that in limited regions under certain circumstances, water 

quality problems may arise. Thus, the principle objective in developing nutrient criteria for the 
2

Yaquina Estuary is to prevent future degradation of estuarine water quality and accompanying 

loss of beneficial uses from the system. 

1.3 Designated Uses of Yaquina River and Estuary, Impairments and Assessments 

 The Yaquina River and Estuary have many designated uses, including aquatic life 

harvesting (shellfish growing and fishing), agricultural (livestock watering), municipal (public 

water supply), recreation (water contact recreation), ecological (resident fish and aquatic life, 

salmonid spawning and rearing, anadromous fish passage) and aesthetics (Table 1.1). Causes for 

impairment listings in the Yaquina Estuary and River include pathogens, thermal modifications, 

diminished biologic integrity, and organic enrichment/low dissolved oxygen (Table 1.1). Most 

of the impairments occur in the Yaquina River, with the exception of fecal coliform impairment 

which occurs in the lower portion of the estuary. 

 The Oregon Department of Environmental Quality (ODEQ) assessed the water quality in 

the Oregon Mid Coast Basin, which includes the Yaquina River during 1986-1995 (Cude, 1995). 

The following is an excerpt from this assessment. 

Nitrate nitrogen is the primary limiting factor on water quality throughout the Mid Coast 

basin. High levels of nitrates accompanied by increases in total phosphates, total solids, 

and biochemical oxygen demand, appear during periods of heavy precipitation. 

Nutrient-rich erosion products deposited during storm events place a high demand on 

available dissolved oxygen in the water. These products may be naturally occurring, but 

are more likely the result of non-point source pollution. 

 As part of this ODEQ study, an Oregon Water Quality Index (OWQI) that incorporates 

temperature, dissolved oxygen, biochemical oxygen demand, pH, total solids, ammonia and 

nitrate nitrogen, total phosphorous and fecal coliforms was developed. Based on this index, 

water quality for the Yaquina River (at Rivermile 24.9) was categorized as poor during the fall, 

winter, and spring, and good during the summer. The water quality in the Yaquina estuary was 

reassessed in 2006 using data from water years 1996-2005 (Mrazik, 2006). In this more recent 

assessment, the Yaquina River was assessed as having good condition throughout the year. 

 In the National Estuarine Eutrophication Assessment (Bricker et al., 1999), the Yaquina 

Estuary was placed in the low category of eutrophication status based on a qualitative assessment 

that it exhibited few symptoms of eutrophication. However, conditions were expected to worsen 
3

by 2020, primarily as a result of increasing population pressures. The confidence levels for the 

assessment of the eutrophic conditions in the Oregon region were low due to paucity of data 

(Bricker et al., 1999). 

Table 1.1 Designated uses and water quality attainments for Yaquina Estuary/River. (Source: 

U.S. EPA National Assessment Database, 305(b) Lists/Assessment Unit Information Year 

2002; http://www.epa.gov/waters/305b/index.html) 

Rivermile State Designated 

Use 

Attainment 

Status Threatened Basis of Impairment Classification 

0 – 6.3 Shellfish Growing Not Supporting No Fecal Coliform 

5.1-15.4 Shellfish Growing Not Supporting No Fecal Coliform 

6.3-14.2 Shellfish Growing Fully Supporting No NA 

Resident Fish and 

Aquatic Life 

Salmonid Fish 

Spawning 

Water Contact 

Recreation 

Fully Supporting No NA 

Salmonid Fish 

Rearing 

15.4 – 27.6 

Anadromous Fish 

Passage 

Not Supporting No Thermal Modifications 

27.6 – 42 Resident Fish and 

Aquatic Life 

Partial 

Supporting No Biologic Integrity1

Aesthetics 

Fishing 

Livestock Watering 

Resident Fish and 

Aquatic Life 

Water Contact 

Recreation 

Water Supply 

Fully Supporting No NA 

Salmonid Fish 

Rearing 

Salmonid Fish 

Spawning 

27.6–57.5 

Anadromous Fish 

Passage 

Not Supporting No Organic 

Enrichment/Low 

Dissolved Oxygen 

1

Aquatic communities (primarily macroinvertebrates) which are ≤60% of the expected 

reference community for multimetric and multivariate model scores are considered impaired. 
4

1.4 Oregon Estuarine Water Quality Criteria 

 Water quality criteria standards are developed to protect beneficial uses. The State of 

Oregon presently has numeric water quality criteria for chlorophyll a and dissolved oxygen for 

estuarine waters (Table 1.2). The dissolved oxygen (DO) criterion for estuarine waters is 

primarily based on the freshwater literature and on salmon and trout requirements (ODEQ, 

1995). This DO criterion is relatively high compared to DO criterion for other estuaries, such as 

the Chesapeake Bay (U.S. EPA, 2003). In addition to the criteria in Table 1.2, the state wide 

narrative criteria states that: 

“where a less stringent natural condition of a water of the State exceeds the numeric 

criteria” ... “the natural condition supersedes the numeric criteria and becomes the 

standard for that water body.” 

In the narrative criteria, the natural condition refers to non-anthropogenic conditions. A review 

of the DO criterion (ODEQ, 1995) found that the 6.5 mg l-1 may be difficult to achieve in Oregon 

estuaries during the summer due to natural background conditions. If it is not achievable due to 

natural background conditions, then the background conditions become the criteria. 

Table 1.2 Selected water quality criteria for Oregon estuaries. 

Parameter Estuarine Criterion Water Quality Limited Determination 

Chlorophyll a 15 μg l-1 Average based on minimum of 3 samples collected 

over any 3 consecutive months at a minimum of one 

representative location exceeds criterion1 

Dissolved Oxygen 6.5 mg l-1 Greater than 10% of samples exceed the criterion and 

a minimum of at least 2 exceedances of the criterion 

for the time period of interest. A minimum of 5 

representative data points per site collected on 

separate days per applicable time period. Daily 

means of continuous data represents 1 data point.2 

Note: 1Criterion applies to river and estuaries; 2Estuarine waters defined as those with 

conductivity > 200 μS cm-1 for dissolved oxygen criterion. Other dissolved oxygen 

criterion applies to freshwater region. 

1.5 Summary of Yaquina Case Study Approach 

 In Chapter 2, we provide a description of the watershed and estuary, including a 

description of landuse in the watershed, and a brief history of anthropogenic activities in the 

estuary and watershed. Chapter 3 presents a summary of the nitrogen inputs to Yaquina Estuary 

with discussion of the seasonality and magnitude of natural and anthropogenic sources. A 
5

summary of data used in this report and analysis techniques are presented in Chapter 4. 

Chapter 5 provides information on spatial and temporal patterns in water quality data, including 

important factors influencing water quality distributions. Seasonal, zonal, and long-term trends 

in causal (nitrogen and phosphorous) and response (chlorophyll a, dissolved oxygen, and water 

clarity) variables are presented in Chapters 6-9. These chapters also include cumulative 

distribution functions for causal and response variables and comparison of water quality 

conditions in Yaquina Estuary to those in other Oregon estuaries. In addition, comparisons of 

observations from Yaquina Estuary to existing State of Oregon chlorophyll a and dissolved 

oxygen criteria are summarized in Chapters 7 and 8. Chapter 10 examines the usefulness of 

macroalgal biomass as a response variable for the Yaquina Estuary, including descriptions of 

seasonal, interannual, and zonal patterns in macroalgal biomass and factors which influence its 

distribution. Chapter 11 provides a description of the distribution, variability, and factors 

influencing Zostera marina habitat in the estuary as well as light requirements for this species. 

Chapter 12 provides a demonstration of using a mechanistic stress-response model for Z. marina

to assess whether specific water clarity percentiles are protective of existing habitat. A summary 

of the results of this study and recommendations are provided in Chapter 13. 
6

2. Description of Study Area 

2.1 Physical Characteristics of the Estuary 

Yaquina Estuary is a small, drowned, river valley estuary located along the central 

Oregon coast (latitude = 44.62°N, longitude = 124.02° W) of the United States (Figure 2.1) with 

an estuarine surface area of 19 km2

 and a watershed area of 650 km2 (Figure 2.2; Lee et al., 

2006). Approximately 48% of the estuarine area is intertidal. This estuary experiences mixed 

semidiurnal tides and is mesotidal with a mean tidal range of approximately 1.9 m and a tidal 

prism volume of 2.4 x 107

 m3 (Shirzad et al., 1988). Yaquina Estuary has jetties that extend into 

the Pacific to the 10-m depth contour. Due to the small volume of the estuary (25 x 106

 m3 at 

Mean Lower Low Water (MLLW)) and the strong tidal forcing, there is close coupling between 

the estuary and the coastal ocean. Approximately 70% of the volume of the estuary is exchanged 

with the coastal ocean during each tidal cycle (Karentz and McIntire, 1977). 

Figure 2.1. Location map of Yaquina Estuary. The estuary is divided into “marine dominated” 

(Zone 1) and “riverine dominated” (Zone 2) segments (Lee et al., 2006) based on the 

relative proportion of oceanic-derived nutrients versus terrestrially-derived nutrients. 
7

Yaquina Estuary receives freshwater inflow primarily from two tributaries, the Yaquina 

River and Big Elk Creek, which have similarly sized drainage areas and contribute 

approximately equally to freshwater inflow (Figure 2.2; State Water Resources Board, 1965). 

The long-term median freshwater input to Yaquina Estuary is 7.5 m3

 s

-1. There is a strong 

seasonal pattern in freshwater input to the Yaquina Estuary (Figure 2.3). During the months of 

November through April, the Oregon coast receives high precipitation and the estuary is river 

dominated. Beginning in May and continuing through October, there is a decline in the riverine 

freshwater inflow and the estuary switches from riverine to marine dominance. For this 

document, we defined the wet season (November – April) as months when the median monthly 

discharge exceeds the long-term median annual discharge and the dry season (May- October) as 

months when the median monthly discharge is less than the long-term median. The estuary is 

well mixed under low flow conditions, and partially- to well- mixed during winter high inflow 

conditions (Burt and McAlister, 1959; Kulm and Byrne, 1966). The flushing time of the estuary 

during the dry season varies from 1 day near the mouth to 9 days in the upstream portions (Choi, 

1975). 

Figure 2.2 Map of watershed of the Yaquina Estuary, showing the two primary tributaries 

(Yaquina River and Big Elk Creek). 
8

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

30

60

90

Discharge, m3 s-1

Month

Figure 2.3 Monthly discharge statistics (Yaquina River + Big Elk Creek) calculated using data 

from 1972-2002 Chitwood gauge (corrected for Big Elk Creek using relationship from 

Brown and Ozretich, in review). In the plot, the boxes represent the 25th and 75th

percentiles, the whiskers represent the 5th and 95th percentiles, and the horizontal line is 

the median. The dashed line indicates 30-year median discharge. 

Estuaries in the Pacific Northwest (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 (NO3

-

 and 

PO4

3-

) water to the surface. The upwelling season typically commences in April and continues 

through September, approximately coinciding with the dry season. 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 
9

important for estuaries adjacent to coastal upwelling regions, such as the west coast of the United 

States (e.g., de Angelis and Gordon, 1985; Roegner and Shanks, 2001; Roegner et al., 2002; 

Colbert and McManus, 2003; Brown and Ozretich, in review). 

2.2 Biotic Characteristics 

 PNW estuaries, including Yaquina Estuary, are highly productive ecosystems, supporting 

several hundred species of macrophytes, macroinvertebrates, fish, birds, marine and terrestrial 

mammals. The Yaquina estuarine ecosystem contains six major habitats, three of which are 

defined by the presence of an ecosystem-engineering species: tidal channels (including water 

column and subtidal unvegetated sediments), eelgrass beds (lower intertidal and shallow subtidal 

sediments dominated by Zostera marina), mud shrimp beds (mid- to lower intertidal muddy 

sediments dominated by Upogebia pugettensis), ghost-shrimp beds (lower- to upper intertidal 

sediments dominated by Neotrypaea californiensis), unvegetated intertidal sediments, and tidal 

marshes. Each habitat supports different floral and faunal communities (Seliskar and Gallagher, 

1983; Simenstad 1983; Phillips, 1984; Ferraro and Cole, 2006). 

 One hundred twenty-eight species of macroalgae (Kjeldsen, 1967) and three species of 

seagrass have been recorded in Yaquina Estuary. The species diversity and biomass of 

macroalgae is greatest near the mouth of the estuary and decreases up river (Kjeldsen, 1967). 

From late spring to early fall, green macroalgae (principally Enteromorpha spp. [6 spp.], Ulva

spp. [6 spp.], and Cheaetomorpha spp. [2 spp.]) form extensive intertidal and shallow subtidal 

mats in the lower portion of the estuary, but are largely absent upstream of Poole Slough (about 

11 km from mouth; Figure 2.4). Above Toledo, macroalgae diversity declines to <5 spp. and 

biomass is negligible (Kjeldsen, 1967; WED unpublished data). Large meadows and long 

patches of the native seagrass, Zostera marina (eelgrass), occur on the intertidal flats and along 

channel edges in the lower estuary. From Poole Slough upriver to Toledo, eelgrass occurs 

sporadically in the shallow subtidal, with the largest patches occurring near the Toledo public 

boat launch, which is about 18 km from the mouth of the estuary (Figure 2.4). The introduced 

seagrass, Z. japonica, occurs in the upper-to-mid intertidal zone from the lower estuary to 

Toledo, occasionally forming large beds; its abundance is increasing and it may eventually 

compete for space with the native eelgrass. Widgeon grass (Ruppia maritima), the third seagrass 
10

species, occurs in small, isolated patches, but is uncommon relative to the other seagrasses 

(Bayer, 1996). 

 Recent surveys identified over 168 species of macroinvertebrates in Yaquina Estuary, 

with diversity and biomass highest in the lower estuary and lowest in the upper estuary (WED 

unpublished data). Polychaetes are the most numerous macroinvertebrate taxa, but ghost and 

mud shrimp (Neotrypaea californiensis and Upogebia pugettensis) dominate the infaunal 

biomass (WED unpublished data). Bioturbation, bioirrigation, and feeding activities of these 

shrimps accelerate carbon and nutrient cycling within the estuary, and enhance the flux of 

dissolved nitrogen from sediments to the water column (DeWitt et al., 2004). Deposit and filter 

feeders are the most abundant benthic consumers, with filter feeders, primarily mud shrimp, 

dominating in the lower estuary. As mud shrimp abundance declines up-estuary, deposit feeders 

become more abundant. 

 Five species of bivalves (cockle [Clinocardium nuttali], soft-shell clam [Mya arenaria], 

littleneck clam [Venerupis staminea], gaper clam [Tresus capax], and butter clam [Saxidomus 

giganteus]) are harvested recreationally, primarily in the lower portions of Yaquina Estuary. 

Although commercial harvest of these species is currently allowed, there have been no 

significant landings since the mid-1990's. Prior to that time, commercial landings, varied 

between 1,000 and 8,000 lbs. per year (P.M. Vance, Oregon Department of Fish and Wildlife 

(ODFW), personal communication). Non-native Pacific oysters (Crassostrea gigas) are grown 

commercially on 519 acres of leased tidelands in the middle reach of Yaquina Estuary, near 

McCaffrey and Poole Sloughs, with an annual production of 15,028 bushels valued at $594,000 

(Oregon Department of Agriculture 2000-2005). This equates to 45% of the Oregon commercial 

oyster production on state-owned tidelands. 

 At least 62 species of finfish and epibenthic crustaceans occur in Yaquina Estuary, with 

the highest diversity and abundance found in the lower estuary, and reduced diversity and 

abundance upriver (DeBen et al., 1990). Fish and crustacean abundance and diversity is highest 

during summer and lowest in winter. Estuary-wide, English sole [Parophrys vetulus], Pacific 

snake blenny [Lumpenus sagitta], and shiner sea perch [Cymatogaster aggregata] are the three 

most abundant fishes, and sand shrimp [Crangon spp.], dungeness crabs [Cancer magister], and 

mysids [Neomysis mercedis]) are the three most abundant epibenthic crustaceans (DeBen et al., 

1990). Of these, dungeness crabs have the greatest economic value, supplying recruits to the 
11

offshore commercial crab fishery (Armstrong et al., 2003) and adults to the within-estuary 

recreational fishery. Annually, approximately 75,000 dungeness crabs are harvested in the 

recreational fishery in Yaquina Estuary (P.M. Vance, ODFW, personal communication). Fifteen 

species of fish account for >90% of the fish caught recreationally for food or bait from Yaquina 

Estuary (PSMFC, 2006). Statewide, lower-estuary recreational finfish fishing contributes $18.8 

million to Oregon’s economy (The Research Group 2005). Estuary-specific estimates for the 

recreational fishery’s value are not available, but recreational salmon fishing in Newport-area 

estuaries (i.e., predominantly Yaquina Estuary) contributes $4.05 million to the State economy 

(The Research Group, 2005). The Yaquina watershed and estuary support breeding populations 

of five salmonid species (chinook salmon [Oncorhynchus tshawytscha], coho salmon [O. 

kisutch], chum salmon [O. keta], steelhead [O. mykiss], and cutthroat trout [O. clarki clarki]), 

including the southern-most population of chum salmon in North America (Bob Buckman, 

ODFW, personal communication). Coho salmon are being considered for special conservation 

status because of reduced population size in Yaquina and other Oregon mid-coast estuaries 

(ODFW, 2006). The only commercial finfish fishery within the estuary is for Pacific herring 

(Clupea pallasii pallasii), whose ovaries and roe are marketed to Asia, with an annual average 

(1979-2006) landing of 153,300 lbs, valued at $76,300 (Keith Matteson, ODFW, personal 

communication). 

 Thousands of birds live in or migrate through Yaquina Estuary, which is designated as a 

Continental Important Bird Area (IBA) by the American Bird Conservancy and as a State IBA 

by the National Audubon Society. Two hundred-sixteen species of birds have been observed 

during 1994-2006 Christmas Bird Count surveys (National Audubon Society, 2002). Sixtyseven species of waterbirds were censused during 1993-1994 in the estuary, of which 41 were 

year-round or seasonal residents; maximum diversity and abundance occurred in December, and 

was at minimum in June (Merrifield, 1998). 

 Small populations of Pacific harbor seals (Phoca vitulina richardsi) and California 

sealions (Zalophus californianus) are present year-round in Yaquina Estuary, feeding on fish and 

crabs in the lower estuary (Orr et al., 2004; Brown et al., 2005). Killer whales (Orcinus orca), 

gray whales (Eschrichtius robustus), and harbor porpoises (Phocoena phocoena) occasionally 

and briefly enter the lower estuary. Other common mammals in the tidal portions of Yaquina 

Estuary include river otter (Lutra Canadensis), raccoons (Procyon lotor), muskrat (Ondatra 
12

Figure 2.4 a) False-color, infrared aerial photography mosaic of Yaquina Estuary (taken in 

1997) and b) map of intertidal seagrass and macroalgae in Yaquina Estuary classified 

from image analysis (WED unpublished imagery). 
13

zibethicus), and nutria (Myocastor coypus), particularly in low-salinity tidal marshes (USFWS, 

1968). River otter hunt for fish in tidal channels, raccoons forage for molluscs and crustaceans 

on tide flats, whereas muskrats and nutria feed on marsh plants (Howerton, 1984). Over 70 other 

mammals are reported from Lincoln County watersheds, many of which use wetland and 

terrestrial habitats bordering the estuary (National Wildlife Federation eNature ZipGuides 

website: http://www.enature.com/zipguides/). 

2.3 Land Use and History of Anthropogenic Modifications 

 The Yaquina watershed covers an area of 650 km2

, tapering towards the mouth of the 

Yaquina Estuary but extending about 35 km inland. The watershed contains the city of Toledo, 

however, most of the city of Newport with the exception of the “Bay Front” lies outside of the 

watershed boundaries (Figure 2.2). The total population in the Yaquina watershed in 2000 was 

approximately 7970 or 12.3 persons per km2

 (source: Lee et al., 2006). The population density 

in the Yaquina watershed is similar to other PNW estuarine watersheds (mean = 15 persons per 

km2

, Lee et al., 2006), and is much lower than the national average for the coastal region of the 

United States (mean = 116 persons per km2

; Crossett, et al., 2004). The population trend in the 

Yaquina watershed differs from many coastal watersheds in the United States in that the 

population in the Yaquina watershed declined by 4.8% from 1990 to 2000. Population changes 

during the interval of 1980 to 2000 in PNW coastal watersheds (excluding Puget Sound and 

Columbia River) are among some of the lowest in the United States (Crossett, et al., 2004). 

Utilization of the Yaquina Bay Front increases substantially with the influx of tourists during the 

summer. 

 Historical population data on a watershed basis are not available before 1990 because the 

census block data are not available in a GIS format to allow proration of the population by 

watershed boundaries. However, it is possible to track the historical population changes in the 

cities of Newport and Toledo (Figure 2.5). The major population center in the Yaquina 

watershed is the city of Toledo, which accounted for 44% of the population in the watershed in 

2000. Toledo has experienced low growth, increasing by 12% from 1960 to 2005. In contrast, 

Newport has grown steadily, increasing by 84% from 1960 to 2005. While most of the city of 

Newport lies outside of the Yaquina watershed, the increase in population, as well as an increase 
14

in tourism over this period, reflects an increase in utilization of the Yaquina Estuary through 

recreational boating, recreational fishing, and utilization of bay-side restaurants and facilities. 

 The Yaquina watershed is heavily forested with deciduous, evergreen and shrub land use 

classes constituting 85% of the watershed (Lee et al., 2006 based on NOAA 2001 C-CAP data 

(www.csc.noaa.gov/crs/lca/ccap.html)). Grasslands constitute 6% of the watershed while high 

and low intensity development combined only constitute 0.5% of the watershed. While 

developed areas constitute a small percentage of the watershed, they are increasing with the high 

residential and low residential land use classes increasing by 4.5% and 6.8%, respectively, from 

1995 to 2001. Reflecting the low extent of development, the percent impervious surface is only 

2.4% (Lee et al., 2006). As is typical of coastal watersheds in the PNW, the Yaquina watershed 

is “rugged” with a median slope of 29.7 percent (16.5 degrees). 

1880 1900 1920 1940 1960 1980 2000

0

2000

4000

6000

8000

10000

Population

Date

 Newport

 Toledo

Figure 2.5 Populations in the cities of Toledo and Newport, Oregon from 1890 to 2005. 
15

Although primarily forested and showing little “urban footprint”, the Yaquina watershed 

has been impacted by a variety of disturbances during the last century, in particular fires and 

logging. The largest fire occurred in 1853 when the “Yaquina Burn” consumed 1942 km2

 of 

coastal forest from near Corvallis to Yaquina Estuary. Logging of the coast range began in the 

mid-1800’s and extensive logging of Sitka spruce occurred along the coast during World War I, 

much of it centered near Toledo 

(www.ohs.org/education/oregonhistory/narratives/subtopic.cfm?subtopic_ID=76). Logging has 

continued within the Yaquina watershed to the present, and forest lands are the dominant land 

use within the watershed, accounting for 566 km2 (90%) of the land zoning area in the Yaquina 

Basin (area = 639 km2; Garono and Brophy, 2001). While the intensity of logging varies with 

economic trends and the age and marketability of the standing timber, it is not uncommon to see 

patches of clear cut forest within the Yaquina watershed. 

 In addition to the direct effects of logging on erosion and water quality, rafting of logs 

can potentially affect freshwater and estuarine habitats by physical disturbance, altering flow 

regimes, and accumulation of wood and bark debris which in turn can smother the benthos and 

result in low dissolved oxygen and/or elevated H2S (Sedell et al., 1991). During the early 1900s 

until the 1980s, the estuaries and streams of the PNW were used for the transport and storage of 

logs (Sedell and Duval, 1985). Logs have been rafted in the Yaquina since at least 1920, with a 

substantial increase after the construction of the Georgia Pacific West mill in 1957 in Toledo 

(Figure 2.6). Peak abundance of rafted logs occurred in 1962, and log rafts declined through the 

early 1980s with the increase in environmental regulation and changes in markets (Figure 2.6); 

Sedell and Duval, 1985). In addition to the bark debris, accumulation of sawdust has also been 

observed in the estuary (Kulm and Byrne, 1966). 

 In addition to log rafting, three other sources of biological oxygen demand (BOD) in the 

Yaquina Estuary are sewage from municipal discharges, industrial discharges, and non-point 

inputs, in particular from septic systems. As was common for the period, untreated sewage and 

industrial waste from Toledo and the Newport bay front were discharged directly into the 

Yaquina Estuary in the 19th century and the first half of the 20th century. Sufficient untreated 

sewage and other wastes were discharged that they represented a potential health hazard for the 

oysters grown in the bay in the first quarter of the 20th century (Fasten, 1931). 
16

Figure 2.6 Number of logs floated or rafted on the Yaquina River from 1918 to 1978 

(reproduced from Sedell and Duval, 1985). 

A combined sewage discharge with a pump station was constructed for Newport in the mid1950s, which eliminated the direct discharge of sewage from Newport into Yaquina Estuary (Lee 

Ritzman, City of Newport, personal communication). A municipal sewage system with primary 

treatment and an offshore discharge was constructed in Newport in 1964, which has since been 

upgraded to secondary treatment. A combined stormwater/sewage system that discharged raw 

sewage into the Yaquina River was constructed in Toledo in 1926, and then upgraded in 1954 to 

a primary treatment facility to handle the municipal waste from the city of Toledo (T. 

McFetridge, ODEQ, personal communication). This facility, which discharges into the Yaquina 

Estuary (about 22 km from the mouth of the estuary), was upgraded to secondary treatment in 

1981. In the late 1980’s and early 1990’s, the City of Toledo made improvements to their 

stormwater collection system, reducing the bypassing of the treatment plant during high flow 

periods. In 1996, the Toledo plant had a discharge of 0.979 million gallons per day (MGD) with 

a design capacity of 3.5 MGD (www.epa.gov/OW-OWM.html/mtb/cwns/1996report2/or.htm). 

 In addition to the Toledo municipal discharge, a number of houses along the Yaquina 

Estuary and River have on-site septic systems. The primary environmental impact of these 

septic systems appears to be microbial contamination which primarily affects the oyster industry 

in Yaquina Estuary. The lower portion of the Yaquina Estuary is impaired for shellfish growing 
17

due to fecal coliform (Table 1.1). Due to concern for microbial contamination associated with 

human and animal waste, a survey of residential septic systems was conducted during 1985-

1986. Septic systems for 160 residences adjacent to the Yaquina Estuary were surveyed and it 

was found that approximately 17% of the residences surveyed had marginal septic systems and 

16% had failing systems. The failing systems identified have since been corrected (Bill Zekan, 

Lincoln County Oregon, Planning and Development, personal communication). 

 There are three types of industrial discharges into Yaquina Bay/River. Six seafood 

processing plants discharge waste into Yaquina Bay 

(http://www.deq.state.or.us/wq/sisdata/sisdata.asp), all of which are classified as “minor” by the 

ODEQ. Though relatively small discharges, two of the companies have been fined by ODEQ for 

violating their permits. The Yaquina Bay Fruit Processors also discharges brine waste into 

Yaquina Estuary. The third type of discharge is waste from the Georgia Pacific West kraft pulp 

and linerboard mill. The mill went into production in 1957 with the primary discharge through 

an ocean outfall offshore of Newport. There is an emergency overflow outfall (located about 21 

km from the mouth of the estuary) that discharges directly into the Yaquina Estuary; however, 

this outfall has discharged only ten times from 1999 to 2004, with a maximum discharge of 0.24 

MGD. The discharges typically occur during heavy rain events for short time periods (less than 

24 hours). 

2.4 Classification of the Yaquina Estuary 

 Classification has been proposed as an important tool for developing nutrient criteria for 

estuarine systems (e.g., U.S. EPA 2001). Classification of estuaries in terms of their 

susceptibility to nutrient enrichment is theoretically highly desirable because of the large number 

of estuaries in the United States and limited resources, which make it unfeasible to develop 

nutrient criteria on a case by case basis for each individual system. Numerous types of estuarine 

classifications have been developed or proposed, including ones based on geomorphology, 

physical and hydrodynamic factors, and susceptibility to nutrient enrichment (Kurtz et al., 2006). 

A key aspect of the use of any classification system for setting nutrient criteria is that estuaries 

within the same class respond similarly to nutrients, which is a step that must be validated and 

has not yet been accomplished for national scale estuarine classifications in the U.S. 
18

 Several estuarine classifications have included Yaquina Estuary. Bottom et al. (1979) 

classified Yaquina Estuary as a “Drowned River Valley” and partially mixed estuary. NOAA 

classified Yaquina Estuary as “River Dominated” with “Straits and Terminal Bay.” Quinn et al. 

(1991) classified estuaries along the west coast of the United States based on their susceptibility 

to nutrient pollution. In this study, they classified the Yaquina Estuary as in the high category 

for dissolved concentration potential (DCP) and in the low category for particle retention 

efficiency. They estimated that the nutrient concentration for nitrogen and phosphorous would 

be in the medium class based on DCP and estimates of nutrient loadings. Additionally, Quinn et 

al. (1991) estimated that Yaquina Estuary would require > 20% increase in nutrient loading to 

change the concentration from medium to high class. Burgess et al. (2004) classified estuaries in 

the U.S. based on a statistical cluster analysis of physical and hydrologic factors. They classified 

Yaquina Estuary as a “Medium Area, Low Volume, Shallow and Mixed Salinity” estuary. 

2.5 Conceptual Model for Yaquina Estuary 

 Figure 2.7 illustrates some of the major drivers influencing causal (nutrients) and 

response (chlorophyll a, water clarity and dissolved oxygen) variables within the Yaquina 

Estuary, which will be presented in this case study. Nutrient, chlorophyll a, and dissolved 

oxygen conditions in the lower portion of the estuary are strongly influenced by ocean conditions 

due to close coupling between the shelf and the estuary resulting from strong tidal forcing. The 

watershed is primarily forested, and riverine inputs are related to the presence of nitrogen-fixing 

red alder (Alnus rubra) trees in the watershed. Seagrasses occur at shallower depths in the upper 

portions of the estuary than they do in the lower estuary, which we believe is related to increased 

turbidity upriver and the resulting light limitation. Dense macroalgal blooms occur in the lower 

portion of the estuary, but they appear to be fueled by oceanic nitrogen inputs rather than being a 

response to anthropogenic nutrient enrichment. 
19

Figure 2.7 Conceptual model of factors influencing nutrient and response variables in the Yaquina Estuary. Icon

University of Maryland Center for Environmental Science Integration and Application Network, http://ian
20

3. Description of Sources/Sinks of Nutrients 

3.1 Background 

In most estuaries, the major sources of nitrogen are atmospheric deposition, agricultural 

nitrogen fixation, fertilizer runoff, animal feeding operations runoff, and in heavily populated 

areas point source inputs associated with wastewater treatment facilities (WWTF) (Driscoll et al., 

2003; Howarth et al., 2002; Boyer et al., 2002). For many PNW estuaries (with the exception of 

Puget Sound), there is relatively low population density in the watersheds and low atmospheric 

nitrogen deposition. The watersheds are predominantly forested, resulting in low nitrogen inputs 

associated with fertilizer and agriculture nitrogen fixation. Upwelling provides a major source of 

nutrients to estuaries adjacent to coastal upwelling regions, such as the PNW (e.g., Hickey and 

Banas, 2003 and Brown and Ozretich, in review). Low intensity landuse and coastal upwelling 

result in a significant difference in dominant sources of nutrients to PNW estuaries compared to 

estuaries elsewhere in the U.S. 

In a recent review, Tappin (2002) found that the input of nitrogen to temperate and tropical 

estuaries from the ocean is poorly quantified. It is important to quantify the contribution of 

oceanic input to nutrient loading in order to determine background conditions for estuaries that 

are adjacent to upwelling regions and to distinguish natural variability from anthropogenic 

inputs. We also do not know how susceptible estuaries subjected to large oceanic inputs of 

nutrients (dissolved inorganic nitrogen and phosphorous) are to future changes in anthropogenic 

inputs of nutrients. Addressing issues associated with ocean input of nutrients is critical in the 

process of developing nutrient criteria for estuaries in the PNW region. 

3.2 Nitrogen Loading to Yaquina Estuary 

Brown and Ozretich (in review) compared the sources of nutrients to Yaquina Estuary 

during the wet and dry seasons (Table 3.1). There are large seasonal differences in the sources 

of nitrogen 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 dissolved inorganic nitrogen (DIN= NO2

-

+ NO3

-

+NH4

+

) from the 

sediments into the water column is the second largest source of DIN. Atmospheric deposition of 

inorganic nitrogen along the central Oregon coast is among the lowest in the United States. 
21

Average annual deposition during 1980-2002 was 0.6 kg N ha-1

 y

-1 (NADP, 2003). Atmospheric 

deposition of nitrogen is a minor component of nutrient input to Yaquina Estuary with direct 

deposition on the estuary only representing 0.05% of the nitrogen input to the estuary. 

Atmospheric deposition on the watershed is a small source (8%) compared to the watershed 

input associated with nitrogen-fixing red alder trees in the watershed (Brown and Ozretich, in 

review). Annual input of nitrogen from WWTF effluent is estimated to be 0.4% of the total 

nitrogen input to the estuary. A NOAA study of estuarine susceptibility to nutrients (Quinn et 

al., 1991) estimated point source loading to Yaquina Estuary as about an order of magnitude 

higher than our estimates. 

3.2.1 Watershed 

There is approximately an order of magnitude difference in the 30-year average daily 

riverine nitrogen input to Yaquina Estuary between the wet and dry seasons. In addition, there 

are considerable interannual differences in riverine nitrogen input, with wet season riverine 

nitrogen input varying from 6.5 x 104

 mol N d-1 to 5.2 x 105 mol N d-1, and dry season riverine 

nitrogen input ranging from 1.1 x 104

 mol N d-1 to 6.3 x 104 mol N d-1 (Brown and Ozretich, in 

review). During the wet season, riverine input is the largest source of DIN to the estuary, 

contributing approximately 78% of the input, while 91% of the annual riverine nitrogen input is 

delivered during the wet season. Our estimates of riverine nitrogen loading (Table 3.1) are 

similar to Quinn et al. (1991) whose estimate of non-point loadings are 7% higher than our 

estimate of annual riverine loading. Sigleo and Frick (2007) estimated that the annual riverine 

nitrate (NO3

-

) input to Yaquina varied from 2.4 x 105 mol N d-1 to 5.2 x 104 mol N d-1 during a 

drought year. 

 Oregon Coast Range streams have high NO3

-

 concentrations relative to other forested 

watersheds in the PNW (Compton et al., 2003; Wigington et al., 1998). Wigington et al. (1998) 

hypothesized that forest vegetation, in particular the presence of red alder, is the primary factor 

determining stream NO3

-

 levels in the Oregon Coast Range. Red alder is a native tree 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 and stream 

NO3

-

 concentration in the Salmon River watershed, which is about 45 km north of Yaquina 
22

Estuary. 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. In 

the Yaquina Estuary watershed, 23% of the watershed is vegetated with red alder (Brown and 

Ozretich, in review). Brown and Ozretich (in review) estimated that > 80% of the riverine 

nitrogen loading to Yaquina Estuary is related to red alder cover. Thus, riverine nutrient loading 

in the PNW is influenced by forest species composition. 

3.2.2 Ocean Input 

 Brown and Ozretich (in review) estimated oceanic input of DIN to the Yaquina Estuary 

during the dry season of 2002 and 2003. The oceanic input of DIN was calculated using the 

time-series of flood tide input of DIN multiplied by the volume of water entering the inlet during 

each tidal cycle. The volume of water entering the inlet was calculated using a two-dimensional, 

laterally averaged hydrodynamic and water quality model (described in Brown and Ozretich, in 

review). Daily water samples were collected during flood tide approximately 0.5 m below the 

surface at a station about 3.7 km from the mouth of the estuary. These samples were analyzed 

for dissolved inorganic nutrients (NO3

-

+ NO2

-

, NH4

+

, PO4

3-

 and Si(OH)4). During the dry season 

of 2002, the amount of DIN entering the estuary from the ocean during each flood tide varied 

from 8.8 x 103 mol N to 6.7 x 105 mol N with a mean value of 2.4 x 105 mol N, and the mean 

daily flood tide input of DIN was 4.7 x 105 mol N d-1. During the 2003 dry season, the mean 

oceanic input of DIN is 3.7 x 105 mol N d-1 or 21% less than 2002 dry season. Sigleo et al. 

(2005) calculated the flood tide input of NO3

-

 to Yaquina Estuary during August of 2000 to be 13 

x 105 mol N d-1, which is about triple our estimate. However, these ocean input numbers were 

calculated using a constant flood tide NO3

-

 of 30 μM. 
23

Table 3.1 Comparison of the magnitude of nitrogen sources during wet and dry seasons for 

Yaquina Estuary, Oregon. Benthic flux measurements not available (NA) for wet season. 

Nitrogen Input (mol N d-1) Source 

Wet Season Dry Season Annual Average 

River 2.7 x 105 2.5 x 104 1.4 x 105

Ocean 3.0 x 104 3.7-4.7 x 105 2.3 x 105

Wastewater 1.7 x 103 1.5 x 103 1.6 x 103

Benthic Flux1 NA 4.3 x 104 NA

Atmospheric Deposition2 

 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); 2NADP (2003) 

3.2.3 Benthic Processes 

 Intertidal and subtidal sediments can be sources and sinks for nutrients and organic 

matter, with the direction and magnitude of fluxes determined by infaunal invertebrates, benthic 

primary producers, and microbial communities living on or in the estuarine benthos. (See 

Appendix A for additional details on benthic processes). 

 Five studies of benthic nutrient flux have been conducted in Pacific estuaries north of San 

Francisco, however the reported benthic flux data in four of the studies (i.e., Dollar et al. 1991; 

Garber et al. 1992; Thom et al., 1994; Larned, 2003) may not accurately estimate estuary-scale 

nutrient fluxes in Yaquina Estuary because they do not account for the presence of thalassinid 

burrowing shrimp. The presence of burrowing shrimp can result in the water inside of the 

benthic flux chamber being exchanged with water outside of the chamber via shrimp burrows 

(e.g., Hughes et al., 2000), which violates the requirement that benthic chambers be closed 

microcosms (Forja and Gomez-Parra, 1998). 

To avoid this problem, DeWitt et al. (2004) inserted 1-m deep core barrels into sediments 

at their study sites, and fit benthic chambers to the tops of the core barrels to isolate water, 

sediments, shrimp and burrows inside the chamber from the outside world. DeWitt et al. (2004) 

demonstrated that DIN efflux was strongly affected by both burrowing shrimp species and 

population density (Appendix A). Integrated over the whole estuary, net DIN efflux for 

intertidal habitats in Yaquina Estuary was 4.3 x 104

 mol N d-1 from the benthos to the water 

column (DeWitt et al., 2004). (Additional details on composition of estimated DIN efflux 

provided in Appendix A) 
24

3.3 Zonation Based Upon Nitrogen Sources 

 We divided the estuary into two zones, one of which is dominated by ocean input 

(Zone 1) and the other which is more influenced by watershed and point source inputs (Zone 2, 

Figure 2.1). We used a transport model combined with natural abundance stable isotopes (δ15N) 

of green macroalgae to identify the dominant nitrogen sources within the estuary as a function of 

time and location for two years (2003 and 2004). The transport model was validated by 

comparing predicted isotope ratios (using the transport model to mix isotopic end members) to 

observed macroalgal isotope ratios at five locations. For more details on this analysis, see 

Chapter 5 of Lee et al. (2006). 

 Model simulations combined with δ15N of green macroalgae suggest that during the wet 

season, riverine nitrogen sources dominate throughout the estuary, which is consistent with our 

comparison of nutrient loadings presented in Section 3.2. During the dry season, ocean nitrogen 

sources dominate in Zone 1, comprising between 53 – 87% of DIN (depending upon location 

within the zone), whereas riverine and WWTF inputs contribute 12-40% and 2-8%, respectively. 

In Zone 2, riverine nitrogen sources dominate contributing between 56-92% of DIN (depending 

upon location). WWTF contribution to water column DIN is maximal during the month of 

August. 

 During the dry season, oceanic input of nitrogen propagates up estuary as the freshwater 

inflow declines. This can be seen in simulation results from 2004 (Figure 3.1) which show that 

Station N1 is ocean dominated (fraction $ 0.5) during the entire dry season (May – September), 

while Station N2 is river dominated during May and ocean dominated from June – September. 

At Stations N3, N4, and N5 ocean inputs increase in importance from May – August, but never 

dominate. There is interannual variability in the position of the line demarking the oceanic and 

riverine dominated zones. The exact location of this line varies with ocean conditions (e.g., El 

Niño, La Niña conditions) as well as freshwater inflow. To be conservative, we placed the line 

demarking the two zones at the most seaward location found in our analysis (see Figure 2.1 for 

location). Analysis of salinity data reveals that the demarcation of the two zones corresponds to 

a dry season median salinity of 26. 
25

1 2 3 4 5 6 7 8 9 0

4080

120

N4

1 2 3 4 5 6 7 8 9 0

4080

120

N3

1 2 3 4 5 6 7 8 9 0

4080

120

N2

1 2 3 4 5 6 7 8 9 0

4080

120

DIN, 

μ

M

N1

1 2 3 4 

0 

40 80 

120

1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9

Month

1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 0

0.20.40.60.8

1

Fraction

1 2 3 4 

WWTFRiverOcean

Figure 3.1 Modeled contribution of WWTF effluent, riverine, and oceanic sources to DIN at 5 locations in the e

– September of 2004. Stations N1 and N2 are located in Zone 1 about 3.7 and 10.1 km from the mouth orespectively. Stations N3, N4, and N5 are located in Zone 2 about 15.6, 18.4, and 26.0 km from the mout
26

4. Data Sources and Methods 

 We assembled causal (nutrient) and response variables (chlorophyll a, dissolved oxygen, water 

clarity, total suspended solids, macroalgae biomass, and submerged aquatic vegetation distribution) 

and physical data (temperature and salinity) at three spatial scales. Water quality data from Yaquina 

Estuary was compared to water quality data collected during the dry season for a set of seven Oregon 

estuaries for the purpose of estuarine classification (Section 4.2), and from a random sampling of all 

Oregon estuaries conducted as part of the EPA National Coastal Assessment (NCA) (Section 4.3). 

Historical data were assembled to assess whether there have been any long term trends in causal or 

response variables. For the trend analyses, we parsed the data into zones and seasons to minimize bias 

associated with differences in sampling (temporal or spatial). The zones are presented in Figure 2.1 

and discussed in Section 3.3, while the seasons are defined in Section 2.1. For details on the methods 

used and the quality assurance/quality control (QA/QC) of data used in this study see Appendix B. 

4.1 Yaquina Estuary Data 

4.1.1 Recent Data 

Data assembled for the Yaquina Estuary included recent (1998-2006) water quality cruises 

conducted by the Western Ecology Division, U.S. EPA. The sampling frequency and number of 

stations depended upon the year and month (Table 4.1). 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), 

in situ fluorescence (WETStar Chlorophyll Fluorometer, WET Labs, Philomath, Oregon), and 

photosynthetically active radiation (PAR; PAR LI-193 underwater irradiance sensor, Lincoln, 

Nebraska) were measured. 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. For the cruises conducted in 2006, dissolved oxygen was measured at surface, mid-depth, 

and bottom using a YSI multiparameter sonde (YSI 6600 EDS, YSI Inc., Yellow Springs, OH). At 

each station, water samples were collected, which were analyzed for dissolved inorganic nutrients 

(NO3

-

+ NO2

-

, NH4

+

, PO4

3-

 and Si(OH)4). During the 2006 cruises, additional water samples were 

collected and analyzed for total nitrogen (TN) and total phosphorous (TP). Water samples were 

collected for chlorophyll a analysis at each cruise location quarterly during 2002 and 2003 (surface 
27

samples), and monthly during 2006 (mid-depth samples). Additional water samples were collected for 

total suspended solids (TSS) analysis. 

Light attenuation coefficients (kd) were determined for each station as the slope of the regression 

of ln (PAR) vs. depth for the 1.00 m to 3.75 m depth intervals. Many of the light profiles measured 

during the cruises were conducted during flood tides; therefore, the light attenuation coefficients may 

be biased toward clearer flood tide conditions. In addition to the cruise data, PAR was monitored 

continuously with 15 minute averages recorded at five locations in the estuary (WED unpublished 

data). Three of these sites were in Zone 1 (located 3.7, 3.9, and 9.0 km from the mouth of the estuary) 

and two were in Zone 2 (located 18.4 and 16 km from the mouth of the estuary). Measurements at 

these sites were taken nearly continuously from 1999 through 2003 using two PAR sensors placed 0.75 

m apart in depth, which were used to calculate light attenuation coefficients. The sensors were cleaned 

at one to two week intervals. For the analyses presented in this document, we used attenuation 

coefficients measured at local noon time and within 4.5 days of cleaning for the continuous data set. 

Additional high temporal resolution data were collected at the riverine and oceanic boundaries to 

quantify the oceanic and riverine inputs of dissolved inorganic nutrients and chlorophyll a to the 

estuary. Continuous data (including water temperature, salinity, dissolved oxygen, and in situ

fluorescence at 15-min intervals) from YSI multiparameter sondes (YSI 6600 EDS, YSI Inc., Yellow 

Springs, OH) were available at approximately six locations in the estuaries (with the exact number of 

locations depending upon the year and month). 

4.1.2 Additional Data Sources 

A summary of historic data compiled for the Yaquina Estuary is provided in Table 4.2. In 

addition to the sources listed in Table 4.2, data were obtained from the Oregon Department of 

Environmental Quality Laboratory Analytical Storage and Retrieval (LASAR) database 

(http://deq12.deq.state.or.us/lasar2/), which included data for 27 sampling locations and spanned the 

time interval of 1960-2005. 

 There was a gap in the data for causal and response variables during the interval of 1984-1997. 

The majority of the nutrient data was in the form of dissolved inorganic nutrients rather than total 

nitrogen or phosphorous. Most of the data compiled was collected at fixed sampling locations, rather 

than through probabilistic sampling. All the data were collected along the main channel of the estuary, 

and did not extend into the sloughs. The locations of stations sampled extended from the mouth to the 
28

tidal fresh portion of the estuary. The estuary narrows upstream of about 25 km from the mouth and 

there was limited sampling upstream of this region. There were limited historic data for water clarity 

(secchi depth) and as a result we were unable to assess trends in water clarity. All of the chlorophyll a

data compiled were obtained using spectrophotometric or fluorometric methods. 

 

Table 4.1 Sampling frequency for cruise data collected by U.S. EPA from 1998-2006. 

Year Month 

Sampling 

Frequency 

# of Sampling 

Locations 

Distance from mouth 

of Estuary (km) 

1998 Jun, Jul, Sept, Nov Once a Month 35 2-21 

1999 Jan-Dec Once a Month Varied 5-35 2-21 

2000 Jan-Dec Once a Month Varied 5-36 2-21 

2001 Mar, Apr, Aug-Oct 

May-Jul 

Once a Month 

Twice a Month 

Varied 5-36 

Varied 12-34 

2-21 

2-21 

2002 

Apr-Sept 

Jan, Feb, Mar, Oct 

Nov, Dec 

Weekly 

Twice a Month 

Once a Month 

12 

12 

12 

2-21 

2-21 

2-21 

2003 

Apr-Sept 

Jan, Feb, Dec 

Mar, Oct 

Weekly 

Once a Month 

Twice a Month 

12 

12 

12 

2-26 

2-26 

2-26 

2004 April-September Twice a Month 12 3-26 

2006 February-December Once a Month 12 3-35 
29

Table 4.2 Summary of historic data compiled include source of the data, temporal and spatial 

sampling frequency of the data set and parameters measured. 

Source Time 

Interval 

Number 

of 

Sampling 

Events 

# of 

Stations

Dist. 

from 

mouth 

(km) 

Parameter 

Matson (1964) 11/62 – 1/64 30 4 2-81 Salinity, Water temp, DO, PO4

3-

, Si(OH)4

Gibson and 

Snow (1967) 6/66 – 11/67 30 6 7-16 Salinity, Water temp, DO 

De Ben et al. 

(1990) 3/67 – 11/68 42 10 2-26 Salinity, Water temp, DO 

Gibson (1974) 4/68 – 3/70 

8/71 – 1/72 

23 

11 

4 

2 

4-15 

15-16 

Salinity, Water temp, DO 

PO4

3-

, Si(OH)4, NO3

-

+ NO2

-

Johnson (1980) 6/73 – 10/74 80 5 9-16 Chlorophyll a

Amspoker 

(1977) 12/73 – 8/74 4 6 3-36 NO3

-

+ NO2

-

, Total Phosphates, 

Si(OH)4

Karentz (1975) 7/74 – 4/75 21 4 3-19 Chlorophyll a 

Karentz and 

McIntire (1977) 5/74 – 5/75 12 4 3-19 

Salinity, Water temp, NO3

-

+ 

NO2

-

, PO4

3-

, Si(OH)4, 

Chlorophyll a 

WED 

unpublished 

7/76- 12/77 9 16 3-42 Salinity, Water temp, PO4

3-

, 

NH3, NO2

-

, NO3

-

, TN, TP, TSS 

Frey (1977) 2/77 – 6/77 8 3 3-35 

Salinity, Water temp, PO4

3-

, 

Si(OH)4, NO3

-

+ NO2

-

, 

Chlorophyll a

Butler (1986) 6/83 – 8/85 12 7-16 2-35 Salinity, Water temp, DO, NO2

-

, NO3

-

, NH4

+

, PO4

3-

, Si(OH)4

Arnold et al. 

(1992) 4/86- 3/87 149 1 11 Salinity, Water temp, TSS 
30

4.2 Oregon Estuarine Classification Study 

As part of an effort to classify estuaries by the susceptibility of their submerged aquatic vegetation 

and food webs to nutrients, WED surveyed seven Oregon estuaries during the dry seasons of 2004 and 

2005 (Lee et al., 2006). The estuaries sampled have regional drivers and landuse characteristics 

similar to the Yaquina Estuary. Their watersheds were primarily forested (66-86%) with low land 

development (high and low intensity development ≤ 1%), and low human population densities (4 – 25 

individuals km-2; Lee et al, 2006). The estuaries sampled (Alsea, Nestucca, Yaquina, Salmon River, 

Coos, Umpqua River and Tillamook) vary in size from 2 to 55 km2, and from river dominated to ocean 

dominated. As is typical of many PNW estuaries, they have extensive intertidal zones with the 

percentage of intertidal area ranging from 32 to 87% of total estuarine area. 

Water quality data together with measurements of the natural abundance stable isotope ratio for 

nitrogen (δ15N) of green macroalgae data were collected to evaluate current water quality conditions. 

These data were also used to divide each estuary into oceanic and riverine dominated zones (in terms 

of nitrogen sources). The sampling consisted of high tide and low tide cruises and of short-term 

deployments of water quality datasondes. During each cruise between 10 and 17 stations were 

sampled in each estuary, depending upon the size of the estuary, and the stations extended from the 

mouth of the estuary to the fresh water portions of the estuary for all systems except Coos Estuary 

(lowest salinity in Coos was 14 psu). For more details on the methods used and the data collected, see 

Lee et al. (2006). 

4.3 National Coastal Assessment (NCA) 

 As a part of the NCA, the Environmental Monitoring and Assessment Program (EMAP) 

assessed the condition of estuarine resources of Oregon based on a range of indicators of 

environmental quality, including water quality indicators (chlorophyll a, nutrients, and dissolved 

oxygen). The study utilized a stratified random sampling design and sampled over two years (1999-

2000). The NCA Oregon estuary data set was obtained during the summer, and thus corresponds to 

the Yaquina Estuary “dry season.” The NCA data set allows comparison of Yaquina Estuary values 

for water quality parameters (e.g. median DO) to values for the same parameter across the set of all 

Oregon estuaries. 

 Details of the sampling program and results of the Oregon NCA assessment are provided in 

Nelson et al. (2004). Briefly, the Oregon 1999 sampling design consisted of 50 sites distributed among 
31

14 estuaries of the State. An additional 30 sites were sampled in Tillamook Bay to assess condition of 

this National Estuary Program system, and Tillamook Bay was thus not included in the sample 

selection for the other 50 sites. Tributary estuaries of the Columbia River that are located within 

Oregon were included in the 1999 sampling effort, while the main channel area was not sampled until 

2000. In 1999, estuaries were divided into four strata based on size, and approximately equal sampling 

effort was placed in each stratum, to insure sampling across the entire estuarine size spectrum. The 

Oregon 2000 study included only the main channel area of the Columbia River, and was split into two 

strata, the lower, saline portion and the upper, freshwater portion, with 20 and 30 sites sampled, 

respectively. Additional samples were obtained in the WA tributary estuaries of the Columbia River in 

1999, but were not included in the data presented in this section. A total of 128 out of the 130 target 

stations were successfully sampled for water quality indicators. 

4.4 Percentile Approach 

 Previous assessments of water quality conditions in PNW estuaries were hindered by the 

limited availability of water quality data for estuaries in the region, particularly in Oregon (Bricker et 

al., 1999). Since there were limited data for applying the reference condition approach for the class of 

estuaries similar to the Yaquina Estuary, we used in situ observations within Yaquina Estuary as a 

basis for the Estuarine Reference Condition (as recommended by U.S. EPA, 2001). To accomplish 

this, we produced cumulative distribution functions (CDFs) for the Yaquina Estuary and compared 

those to CDFs of other Oregon estuaries using two independent data sets (Classification Study and 

NCA for Oregon estuaries). The NCA and Classification data sets were sampled at different temporal 

and spatial scales. The Classification data set used in our analyses included samples from six estuaries, 

with 10-17 stations sampled per estuary during both flood and ebb tidal conditions. Data from the 

Yaquina Estuary collected as part of the Classification Study were not included in the computation of 

percentiles for this data set. The NCA data set sampled 14 Oregon estuaries. The number of stations 

in each estuary was randomly determined within an estuarine size stratum. Timing of sample 

collection with respect to tidal stage was random. The number of sampling locations per estuarine 

system in the NCA data set ranged from 1 (Alsea and Yachats) to 67 (Columbia). 

 In Appendix C, we present various classifications of the estuaries in the Classification Study 

and NCA data sets based upon geomorphology, susceptibility to nutrient pollution, and statistical 

clustering of physical and hydrologic variables. The number of classes of estuaries (or types) depends 
32

upon the scale of the classification system as well as the classification system utilized (see Table C.1). 

The estuaries sampled in the Classification Study and NCA data set fell into a limited number of 

estuary classes (2-4); however, there was not a consistent pattern in the grouping of estuaries within a 

class among the different classifications. One exception was the Columbia River Estuary, which 

consistently was placed in a separate class for classifications based on geomorphology, susceptibility 

to nutrient pollution, and statistical clustering. 

 Cumulative distribution functions (CDFs) were produced for each of the three data sets 

(Yaquina, Classification, and NCA). For this analysis, the data from the three data sets were divided 

into marine and riverine dominated regions (Zones 1 and 2, respectively). In addition, the Yaquina 

data set was further divided into wet and dry seasons. Only recent data (1998-2006) were used in 

creating the CDFs for Yaquina Estuary. The CDFs produced for the Yaquina and Classification data 

sets represent percentiles associated with the number of samples (i.e., not weighted by percentage of 

estuarine area). The NCA program typically computes CDFs using the appropriate sampling area 

weightings, which are based on areas of sampling strata determined from GIS (US EPA, 2004a). This 

allows estimation of the areal extent of Oregon’s estuaries associated with any value of an indicator 

variable. However, for the present study, estimates of percentiles for NCA data sorted by salinity zone 

were produced without use of area weightings and represent percentiles associated with the number of 

samples. This was done for consistency among data sets, and because area estimates of salinity zones 

were not available for all Oregon estuaries. An additional set of CDFs were produced for the NCA 

data set excluding the Columbia Estuary, which differs from the other Oregon estuaries in size, 

geomorphology, and other factors. 

4.5 Statistical Analysis 

 Due to the non-normal distribution of the data, non-parametric statistical tests were used for all 

analyses. The Mann-Whitney Rank Sum test was used to determine whether there were significant 

differences in median values between zones or seasons. The Kruskal-Wallis one way analysis of 

variance on ranks was used to test whether there were significant differences in the median values 

between the Yaquina (dry season only), Classification Study, and NCA data sets. If there were 

significant differences (p<0.05), then Dunn’s test was used for pairwise multiple comparisons. For all 

tests, p values less than 0.05 were considered significant. 
33

 To assess whether there were temporal trends in water quality measures, the data were divided 

into zones and seasons to minimize biases associated with differences in sampling (spatial and 

temporal). For chlorophyll a, there was insufficient wet season data available, so trend analysis was 

only performed for the dry season. A Mann Kendall trend test was used to test whether there were 

significant trends within a zone and a season. If there were significant seasonal patterns within a zone, 

then the Seasonal Kendall test was used to determine if there was a significant increasing or decreasing 

trend. The Seasonal Kendall test performs the Mann Kendall test for each season and then combines 

the results of these into one overall test for whether there is a consistent monotonic trend over time 

(Helsel et al., 2006). For the Seasonal Kendall test all of the data (within a zone and season) was used. 

For the Mann Kendall trend test, there can only be one observation for each date, so multiple 

observations (either multiple stations or sampling events) on a single day were averaged. For all trend 

tests, p values less than 0.05 were considered significant. In addition to the trend analysis, we divided 

the data into historical and recent groups and tested whether there were significant differences in 

median values using the Mann-Whitney Rank Sum test. 

 Mann-Whitney Rank Sum test and Kruskal-Wallis one way ANOVA were performed using 

SigmaStat software package (version 3.5, Systat Software, Inc., San Jose, CA), while trend analysis 

(Mann Kendall and Seasonal Kendall) were performed using a Windows Program written by the U.S. 

Geological Survey (Helsel et al., 2006). 
34

5. Spatial and temporal patterns in water quality parameters in the Yaquina Estuary 

 For the analyses in this report, we divided the year into two seasons (wet and dry) and divided 

the estuary into two zones. There are significant seasonal and spatial patterns in the water quality data 

resulting from differences in sources, transport, and losses. 

5.1 Salinity 

 There are strong seasonal differences in salinity within the Yaquina Estuary driven by 

differences in freshwater inflow (Figure 5.1 and Figure 2.3). During the dry season, Zone 1 is marine 

dominated with mean salinity of 26 psu at the boundary demarking Zones 1 and 2. Salt penetrates 

about 35 km into the estuary during periods of minimal freshwater inflow. 

0

10

20

30

0 10 20 30

0

10

20

30

Salinity, psu

a) Dry

Zone 1 Zone 2

 Distance from Mouth of Estuary, km

b) Wet

Figure 5.1 Salinity versus distance from mouth of the estuary during the a) dry and b) wet seasons. 

The gray lines demark the two zones 
35

5.2 Nutrients 

 During the wet season, NO3

-

is the primary form of DIN in the estuary (median of 88% of DIN, 

n = 873). There is little utilization of dissolved inorganic nutrients by phytoplankton within the estuary 

during the wet season due to short residence time (high freshwater inflow) and low solar irradiance. 

The average incident photosynthetically active radiation (PAR) varies from 15 mol quanta m-2 d-1

during the wet season to 38 mol quanta m-2 d-1 during the dry season. Mixing diagrams (property 

salinity plots) are often used to infer biogeochemical cycling occurring within estuaries (e.g., internal 

sources and sinks). Mixing diagrams of DIN for wet season cruises exhibit conservative mixing 

behavior, indicating river inputs are the primary nitrogen source and that there is little utilization 

within the estuary during this time. Minimal utilization of nutrients is also evident in the low 

chlorophyll a levels observed during the wet season (see Chapter 7). 

 The dry season coincides with the growth season and with upwelling on the shelf. As 

discussed in Chapter 3, nutrient rich water associated with coastal upwelling is advected into Yaquina 

Estuary during flood tides. During the dry season, high levels of DIN and PO4

3-

 enter the estuary about 

two days after upwelling conditions (Brown and Ozretich, in review). Median concentrations of 

oceanic NO3

-

 and PO4

3-

 entering the estuary during the dry season are 8.6 μM and 1.3 μM, respectively 

(n = 830). The maximal nutrient concentrations (NO3

-

 = 31.5 μM and PO4

3-

 = 2.9 μM) entering the 

Yaquina Estuary during upwelling periods are similar to those found in other upwelling regions 

(Dugdale, 1985) and elsewhere on the Oregon shelf (Corwith and Wheeler, 2002). 

During the dry season, NO3

-

 is the primary form of DIN (median of 75%, n =2028), while NO2

-

is a minor component only composing 2% of DIN. There is a mid-estuary minimum in mean dry 

season NO3

-

+ NO2

-

 (with a mean of 7 μM, Figure 5.2) suggesting that the estuary receives NO3

-

 from 

both the ocean and the river. For mixing diagrams to be useful in identifying the importance of 

internal processes (e.g., biological uptake) steady state conditions need to apply. Due to the temporal 

variability of the ocean end member, it is not appropriate to use mixing diagrams to determine the role 

of internal estuarine processes (i.e., biological uptake) in the formation of this mid estuary minimum. 

The primary source of PO4

3-

 to the system is the ocean and there is a steady decline in PO4

3-

 with 

distance into the estuary (Figure 5.3). The oceanic signal in NO3

-

and PO4

3-

 propagates approximately 

13 km up the estuary (Brown and Ozretich, in review). 
36

0 5 10 15 20 25 30

0

10

20

30

40

50

60

70

NO3+NO2, μ

M

Distance from mouth of Estuary, km

Figure 5.2 Spatial variation in dry season NO3

-

+ NO2

-

. The line indicates a 2nd order polynomial fit to 

the data (NO3

-

+ NO2

-

 = 20.4 – 1.9 * distance + 0.079 * distance2, r

2

 = 0.15, p < 0.001) 

0 5 10 15 20 25 30

0

1

2

3

PO3-

4 , μ

M

Distance from Mouth of Estuary, km

Figure 5.3 Spatial variation in dry season PO4

3-

. The line indicates linear regression (PO4

3-

 = 1.55 – 

0.041 * distance, r2

 = 0.24, p < 0.0001). 
37

5.3 Chlorophyll a 

 Previous studies have demonstrated that chlorophyll a is advected into estuaries along the 

Oregon and Washington coasts from the coastal ocean during the dry season (Roegner and Shanks, 

2001; Roegner et al., 2002). Brown and Ozretich (in review) found similar results for Yaquina 

Estuary. In Yaquina Estuary, peak chlorophyll a concentrations imported from the coastal ocean 

during the dry season reach 50 μg l-1 with a median value of 4 μg l-1 (n=181). The input of 

phytoplankton to the estuary lags upwelling favorable winds by approximately 6 days, suggesting that 

it takes this amount of time for phytoplankton to utilize the recently upwelled nitrogen and be 

transported across the shelf into the estuary (Brown and Ozretich, in review). 

Figure 5.4 shows the import of chlorophyll a from the ocean, as indicated by the fact that high 

chlorophyll a occurs at high salinities. The oceanic signal attenuates more rapidly for chlorophyll a

compared to NO3

-

 and PO4

3-

. The statistically significant relationship between oceanic chlorophyll a

concentrations and within estuary chlorophyll a is only evident up to about 11 km into the estuary 

(Brown and Ozretich, in review). The more rapid decline in the ocean signal in chlorophyll a is 

probably the result of benthic grazing on oceanic phytoplankton. Oyster aquaculture is present in 

Yaquina Estuary in the region 10-15 km from the mouth (Figure 2.1) and in the lower estuary there are 

tidal flats that have high densities of filter-feeding burrowing shrimp (DeWitt et al., 2004, see Section 

3.2.3). Data from an in situ fluorometer (located 3.7 km from the mouth of the estuary) indicate that 

there is an import of oceanic chlorophyll a to the estuary and that a 60% reduction in chlorophyll a

occurs between successive flood and ebb tides. Flood tide chlorophyll a values (median = 14 μg l-1) 

were significantly higher than ebb tide values (median = 9 μg l-1; Mann Whitney Rank Sum, p<0.001, 

n = 53). The import of chlorophyll a to Zone 1 is consistent with the findings of Karentz and McIntire 

(1977) that during the spring through fall seasons marine diatom genera dominated in the lower estuary 

(stations 3.4 and 6.7 km from the mouth of the estuary), while freshwater and brackish taxa dominated 

in the upper estuary (stations located 12.3 and 18.8 km from the mouth). Phytoplankton blooms occur 

in the tidal fresh portion of the estuary as indicated by the high chlorophyll a values at low salinities 

(Figure 5.4). 
38

0 5 10 15 20 25 30 35

0

20

40

60

80

Chlorophyll 

a, μg l-1

Salinity, psu

Figure 5.4 Dry season chlorophyll a versus salinity (all stations from 1974-2006) showing high 

chlorophyll a at high salinities, demonstrating the oceanic import of chlorophyll a from the 

coastal ocean into the Yaquina Estuary. Plot also shows the high chlorophyll a in the tidal 

fresh portion of the estuary. 

5.4 Nutrient Limitation and Primary Productivity 

Potential for nutrient limitation of phytoplankton is often estimated by examining the ratio of 

dissolved inorganic nutrients relative to the Redfield ratio (16 mol N: 1 mol P) and comparing the 

ambient dissolved inorganic nutrient concentrations to phytoplankton half saturation constants for 

nutrient uptake (e.g., Eyre, 2000). Typically, if the N:P ratio of the water column falls below 10:1 then 

phytoplankton may be limited by nitrogen, and if the ratio is greater than 20:1 there is the potential for 

phosphorous limitation (Boynton et al., 1982). In addition, if the ambient water column concentrations 

are less than the half saturation constants for nutrient uptake then we assume that the phytoplankton 

may be nutrient limited. Typical half saturation constants for DIN and DIP are 1.0 - 2.0 μM and 0.1 - 

0.5 μM, respectively. 

The median N:P ratio during the dry season is approximately 12:1, suggesting that nitrogen will 

be depleted prior to phosphorous for the majority of the estuary. There is evidence of phosphorous 

limitation in the upper portions of the estuary (17- 27 km from mouth) with the N:P ratio reaching as 

high as 260:1. In only 12% of the estuarine sampling events was the N:P ratio greater than 20 and DIP 
39

less than 0.5 μM, suggesting the potential for phosphorous limitation. During the dry season, the 

median DIN concentration is 14 μM (n=2028), and 95% of the time the DIN concentration is greater 

than 2 μM (typical half saturation constant for phytoplankton). In only 5% of the estuarine sampling 

events was the N:P ratio less than 10 and DIN less than 2 μM. This suggests that although the N:P 

ratio often falls below 16:1, the estuary is not usually limited by either nitrogen or phosphorous. This 

is supported by assimilation ratio data (primary production : chlorophyll a) of Johnson (1980) that was 

collected during the dry season at a station about 16 km from the mouth of the estuary (Figure 2.1). 

Johnson’s data showed 77% of the time there were sufficient nutrients for planktonic primary 

production, while 15% of the time there was borderline nutrient deficiency and 8% of the time there 

was evidence of nutrient depletion. 

Specht (1975) conducted algal bioassays at six locations in Yaquina Estuary during 1972-1975 

to examine the potential for nitrogen and phosphorous limitation. These experiments suggested that 

the upper portion of the estuary (26 km from mouth to tidal fresh) was predominantly phosphorous 

limited, while in the lower estuary, the system is nitrogen limited during the dry season and 

phosphorous limited during the wet season. 

There is limited water column primary productivity data for Yaquina Estuary. Water column 

primary production (at a station 14 km from the mouth of the estuary) during the dry season ranged 

from 0.25-2.8 g C m-2 d-1 with mean of 0.9 g C m-2 d-1 (Johnson, 1980). For comparison, primary 

productivity associated with benthic microalgae in the lower portion of the estuary (Zone 1) ranged 

from 125-325 g C m-2 y

-1 (depending upon the location and elevation; Riznyk and Phinney, 1972). 

Davis (1981) measured net primary production during the dry season in the lower portion of the 

estuary of 46 g C m-2 d-1 and 0.26 g C m-2 d-1 for green macroalgae and benthic microalgae, 

respectively. Net primary production for Zostera marina and Z. japonica in the lower portion of the 

estuary was 181 and 130 g C m-2

 y

-1, respectively (Kaldy, 2006ab). 

Based on the existing primary productivity data, Yaquina Estuary can be characterized as 

mesotrophic. Water column planktonic primary production is a minor component of the total primary 

productivity, which is dominated by benthic primary producers (macroalgae, microalgae and 

seagrasses). This is consistent with the findings of Valiela et al. (2000b) that for systems with 

moderate and high nitrogen loading, macroalgae is the dominant primary producer in short residence 

time estuaries (≤3 days), while phytoplankton dominate in systems with relatively long residence times 

(≥ 45 days). 
40

5.5 Relationships between TN and TP and Chlorophyll a 

 Relationships between causal and response variables are useful for demonstrating the 

relationship between nutrient loading and biological effects. Several studies have found relationships 

between nutrients (nitrogen and phosphorous) and chlorophyll a in estuaries (e.g., Monbet, 1992; 

Smith, 2006; Dettmann and Kurtz, 2006). During 2006, we conducted monthly cruises of the Yaquina 

Estuary to examine if similar relationships were present. The cruises included 12 sampling stations 

extending from the mouth of the estuary to the tidal fresh region. During the dry season, the Yaquina 

Estuary receives nitrogen from both the riverine and oceanic sources, resulting in a curvilinear 

relationship in total nitrogen (TN) versus distance, while the ocean is the main source of phosphorous 

(TP) to the estuary (Figure 5.5). There is also a curvilinear pattern in the chlorophyll a versus distance 

resulting from oceanic input of chlorophyll a from the ocean (Figure 5.6). Relationships between dry 

season nutrients (TN and TP) and chlorophyll a are driven by ocean input (rather than a response to 

watershed nutrient sources) as evident by the significant trends of increasing chlorophyll a with 

increasing nutrients (TN and TP) in Zone 1 but not in Zone 2 (Figure 5.7 and Figure 5.8). Based on 

these findings, we feel that these types of relationships would not be useful for developing nutrient 

criteria for the Yaquina Estuary. 
41

0

20

40

60

0 5 10 15 20 25 30 35 40

0

1

2

3

 TN = 61.9 - 6.42 * x + 0.37 * x2 - 0.0056 * x3

r

2

 = 0.98, p < 0.001

TN, 

μ

M

a)

 Distance from Mouth of Estuary (x), km

b)

TP, 

μ

M

TP = 3.0 - 0.05 * x

r

2

 = 0.84, p < 0.001

Figure 5.5 Mean 2006 dry season a) total nitrogen (TN) and b) total phosphorous (TP) versus distance 

from mouth of estuary with error bars representing standard errors (n=12). Solid and dashed 

lines represent a 3rd order polynomial and linear fit to TN and TP data, respectively. 

0 5 10 15 20 25 30 35 40

0

6

12

18

24

Chlorophyll a, μg l-1

Distance from Mouth of Estuary, km

Chl a = 20.1 - 1.38 * Distance + 0.029 * (Distance)2

r

2

 = 0.87, p < 0.001

Figure 5.6 Mean dry season (2006) chlorophyll a versus distance from mouth of estuary with error 

bars representing standard error (n = 12) and line representing 2nd order polynomial fit to the data. 
42

0 20 40 60 80

0

10

20

30

40

50

Chl a = -17.0 + 0.85 * TN

r

2

 = 0.76, p < 0.001

Chlorophyll 

a, μg l-1

TN, μM

 Zone 1

 Zone 2

 Zone 1: Linear Regression

Figure 5.7 Total nitrogen (TN) versus chlorophyll a for the dry season (2006) with data divided by 

zones and solid line showing significant regression for Zone 1. There is not a significant 

relationship for Zone 2. 

01234567

0

10

20

30

40

50

Chlorophyll 

a, μg l-1

TP, μM

 Zone 1

 Zone 2

 Zone 1: Linear Regression

Chl a = -13.7 + 9.48 * TP

r

2

 = 0.65, p < 0.001

Figure 5.8 Total phosphorous (TP) versus chlorophyll a during the dry season (2006) with data 

divided by zones and solid line showing significant regression for Zone 1. There is not a 

significant relationship for Zone 2. 
43

6. Nitrogen and Phosphorus as Water Quality Criteria 

6.1 Seasonal, Zonal, and Long-term Trends in N and P 

 There are seasonal differences in water column nutrients within the estuary. DIN levels are 

significantly higher during the wet season (median = 21.1 μM, n = 874) than during the dry season (dry 

season median = 13.9 μM, n = 2028; calculated using data from 1998-2006 combining Zones 1 and 2; 

Mann-Whitney Rank Sum Test, p< 0.05). In contrast, PO4

3-

 levels during the dry season (median = 

0.97 μM, n = 2029) are almost twice as high as those during the wet season (median = 0.52 μM, n = 

873; calculated using data from 1998-2006 combining Zones 1 and 2; Mann-Whitney Rank Sum Test, 

p<0.05). During both the dry and wet seasons, Zone 1 has significantly higher PO4

3-

 concentrations 

than Zone 2, reflecting the ocean input of phosphorous (Mann-Whitney Rank Sum Test, p<0.05). The 

ocean input of PO4

3-

 dominates during the dry season with Zone 1 PO4

3-

 levels (median = 1.25 μM, n = 

1114) twice that of those in Zone 2 (median = 0.64 μM, n = 915). During the dry season, there are no 

significant differences in DIN levels between Zones 1 and 2 (Mann-Whitney Rank Sum Test, p>0.05). 

In contrast, during the wet season, DIN levels in Zone 2 (median = 55.2 μM, n = 354) are significantly 

higher than those in Zone 1 (median = 11.6 μM, n = 520), reflecting the dominance of riverine inputs 

(Mann-Whitney Rank Sum Test, p< 0.05). 

 Because there are limited historic NH4

+

 data, we were unable to assess whether there are any 

long-term trends in DIN; however, we do have sufficient historical data to examine trends in NO3

-

+ 

NO2

-

 (Figures 6.1-6.4), the major component of DIN, and PO4

3-

 (Figures 6.5-6.8). Recent (1998-2006) 

dry season NO3

-

+ NO2

-

 and PO4

3-

 in Zone 1 are significantly higher than historical data (Table 6.1 and 

Table 6.2; Mann-Whitney Rank Sum, p<0.001). In contrast, historical dry season NO3

-

+ NO2

-

 and 

PO4

3-

in Zone 2 are significantly higher than recent data (Table 6.1 and Table 6.2). Peak wet season 

NO3

-

+ NO2

-

 concentrations in Yaquina Estuary are similar to wet season NO3

-

 observed in Oregon 

Coast Range streams (peak NO3

-

of 172 μM; Wigington et al., 1998). The relatively high NO3

-

+ NO2

-

concentrations that occur in the historic data from Zone 1 dry season (Figure 6.1) are related to an 

anomalous freshwater inflow event in June 1984 (peak flow of 634 cfs compared to long-term mean 

for June of 81 cfs; calculated using data from Chitwood gauge on Yaquina River). Historical wet 

season NO3

-

+ NO2

-

 levels are significantly higher than recent observations in Zones 1 and 2 (Table 6.1; 
44

Mann-Whitney Rank Sum, p<0.001). Wet season PO4

3-

 in Zone 1 was significantly higher in the 

historical data set compared to recent, while in Zone 2 there was no difference between recent and 

historic PO4

3-

levels (Table 6.2; Mann-Whitney Rank Sum, p>0.05). 

 There were no significant trends in NO3

-

+ NO2

-

 within Zones 1 or 2 during either season 

(Figures 6.1 and 6.2; determined using the Seasonal Kendall and Mann Kendall tests). In Zone 1, there 

was a significant increasing trend in PO4

3-

 during the dry season (Figure 6.3a) and a significant 

decreasing trend in PO4

3-

 during the wet season (Figure 6.4a), while in Zone 2 there were not 

significant trends during either the wet or dry season (Figures 6.3b and 6.4b). Due to the opposing 

seasonal trends in Zone 1, there was not a significant trend in PO4

3-

 using the Seasonal Kendall test. 

 Caution needs to be used in interpreting the trends and the differences in historic and recent 

median NO3

-

+ NO2

-

 and PO4

3-

 levels (Tables 6.1 and 6.2) due to differences in sampling frequencies. 

There are considerably more recent data (more stations and higher sampling frequency) than historic 

data, particularly in Zone 1 during the dry season (Table 6.1 and Table 6.2). Nutrient (NO3

-

+ NO2

-

 and 

PO4

3-

) inputs associated with oceanic sources are highly variable depending upon the wind forcing and 

respond rapidly to changes in wind forcing. In addition, there is considerable interannual variability in 

oceanic input to estuaries (Brown and Ozretich, in review) associated with variability in upwelling 

(Corwith and Wheeler, 2002; Wheeler et al., 2003). During the dry season, the recent nutrient data 

(NO3

-

+ NO2

-

 and PO4

3-

) are consistently higher than the historic data in Zone 1 (Tables 6.1 and 6.2), 

possibly reflecting either differences in ocean conditions or better characterization of ocean input due 

to increased sampling frequency in recent data. In contrast, the historic nutrient (NO3

-

+ NO2

-

 and 

PO4

3-

) median levels are higher than recent data in Zone 2 (Tables 6.1 and 6.2). Since the pattern in 

Zone 2 is opposite to that in Zone 1, this suggests that differences in ocean input are not driving this 

difference in Zone 2. Caution is needed in interpreting these differences since the trend analysis 

revealed that there were no significant trends in nutrients (NO3

-

+ NO2

-

 and PO4

3-

) in Zone 2. The 

differences in water column nutrients during the last 30-40 years, although some are statistically 

significant, do not indicate a major change in nutrient loading (as inferred by nutrient concentrations) 

as experienced in other estuarine and coastal systems (e.g., Cloern 2001; Soetaert et al., 2006). 
45

Table 6.1 Comparison of historic and recent NO3

-

+ NO2

-

 (μM) concentrations in the Yaquina 

Estuary. There are statistically significant differences in median concentrations between 

historic and recent data for all zones and both seasons (Mann-Whitney Rank Sum, p<0.001). 

Historic Recent 

Median Time Interval 

(Sample Size) 

Median Time Interval 

(Sample Size) 

Zone 1 

 Dry 6.5 1974-1984 (157) 10.0 1998-2006 (1127) 

 Wet 19.8 1974-1984 (65) 8.5 1998-2004 (520) 

Zone 2 

 Dry 14.0 1971-1984 (247) 9.8 1998-2004 (919) 

 Wet 69.6 1971-1984 (148) 52.6 1998-2004 (354) 

Table 6.2 Comparison of historic and recent PO4

3-

 (μM) concentrations in the Yaquina 

Estuary. There are statistically significant differences in median concentrations between 

historic and recent data for all zones and both seasons with the exception of Zone 2 wet season 

(Mann-Whitney Rank Sum, p<0.05) 

Historic Recent 

Median Time Interval 

(Sample Size) 

Median Time Interval 

(Sample Size) 

Zone 1 

 Dry 1.01 1963-1984 (223) 1.25 1998-2006 (1126) 

 Wet 0.83 1962 – 1984 (129) 0.59 1998-2004 (519) 

Zone 2 

 Dry 0.74 1963-1984 (308) 0.65 1998-2004 (919) 

 Wet 0.54 1962-1984 (212) 0.49 1998-2004 (354) 
46

0

30

60

90

1970 1975 1980 1985 1990 1995 2000 2005

0

30

60

90

NO3+NO2, μ

M NO3+NO2, μ

M

a)

 Year

b)

Figure 6.1 Comparison of historic and recent NO3

-

+ NO2

-

 during the dry season in a) Zone 1 and b) 

Zone 2. 

0

60

120

180

1970 1975 1980 1985 1990 1995 2000 2005

0

60

120

180

NO3+NO2, μ

M NO3+NO2, μ

M

a)

 Year

b)

Figure 6.2 Comparison of historic and recent NO3

-

+ NO2

-

 during the wet season in a) Zone 1 and b) 

Zone 2. 
47

0.0

1.5

3.0

1960 1970 1980 1990 2000

0.0

1.5

3.0

PO4, μ

M PO4, μ

M

PO4 = -16.2 + 0.0087 * Year

p = 0.01

τ = 0.13

a)

 Year

b)

Figure 6.3 Comparison of historic and recent PO4

3-

 during the dry season in a) Zone 1 and b) Zone 2. 

The line in the upper panel shows a significant increasing trend in Zone 1 (Mann Kendall, p = 

0.01). 

0.0

1.5

3.0

1960 1970 1980 1990 2000

0.0

1.5

3.0

PO4 = 22.1 - 0.011 * Year

p < 0.01

τ = −0.38

PO4, μ

M PO4, μ

M

a)

 Year

b)

Figure 6.4 Comparison of historic and recent PO4

3-

 during the wet season in a) Zone 1 and b) Zone 2. 

The line in the upper panel shows a significant decreasing trend in Zone 1 (Mann Kendall, p< 

0.01). 
48

6.2 Percentile Approach for Nitrogen and Phosphorous 

 There were significant differences in median DIN and PO4

3-

 values between the Yaquina (dry 

season), Classification Study, and NCA datasets for both Zones 1 and 2 (Kruskal-Wallis one way 

ANOVA on ranks, p<0.001). The dry season DIN concentrations observed for Yaquina Estuary 

(Zones 1 and 2) were significantly higher than those observed in the NCA data set both with and 

without the Columbia included (Table 6.3, Dunn’s method for pairwise comparison, p<0.05); however, 

there was not a significant difference between dry season DIN levels in the Yaquina Estuary (Zones 1 

and 2) and those observed in the Classification data set (Table 6.3). The PO4

3-

 levels were 

significantly higher in the Yaquina Estuary (Zones 1 and 2) than those observed in the Classification 

and NCA data sets (Table 6.4, Dunn’s method for pairwise comparisons, p<0.05). The higher DIN and 

PO4

3-

 levels in Zone 1 in the Yaquina Estuary as compared to the NCA data set is probably an artifact 

of sampling (both time of sampling and differences in the number of samples). In Zone 1, water 

column nutrients are dependent upon ocean conditions at the time of sampling. Inspection of the 

sampling dates during the 1999 NCA field effort, reveals that 82% of the estuaries were sampled 

during a time period of low nutrient conditions in the coastal ocean (determined using flood tide water 

temperature at Yaquina Estuary and a relationship generated between flood tide water temperature and 

NO3

-

+ NO2

-

, for details see Lee et al., 2006). 

 Zone 2 dry season DIN levels in the Yaquina Estuary are comparable to values for streams 

measured in Level III Ecoregion No. 1 - Coast Range (summer median NO3

-

+ NO2

-

 = 12 μM; U.S. 

EPA, 2000). The PO4

3-

 levels in Yaquina Estuary, particularly during the dry season and in Zone 1, 

are higher than Level III Ecoregion No. 1 - Coast Range values for streams (median = 0.28- 0.60 μM; 

U.S. EPA, 2000), due to the input of PO4

3-

 from oceanic sources. Wet season DIN concentrations in 

Zone 2 of Yaquina Estuary are similar to wet season NO3

-

 observed in Oregon Coast Range streams 

(median NO3

-

= 56 μM; Wigington et al., 1998) and streams data for Level III Ecoregion No. 1 - Coast 

Range (winter median NO3

-

+ NO2

-

 of 37 μM; U.S. EPA, 2000). Higher PO4

3-

 levels in Zone 1 

compared to Zone 2 are present in the NCA and classification data sets, demonstrating that oceanic 

input of PO4

3-

 occurs at a regional scale. The DIN and PO4

3-

 levels in Oregon estuaries would be 

considered to be medium levels using criteria from Bricker et al. (2003); however, based on analysis of 

sources (see Section 3.2) we believe that the high DIN and PO4

3-

 levels are associated with natural 
49

sources (i.e., red alder in the watershed and oceanic input) rather than anthropogenic sources. Systems 

in the PNW appear to have relatively high background levels of DIN and DIP compared to other 

estuaries in the U.S. (U.S. EPA, 2004a). 

Table 6.3 Percentiles for DIN (μM) calculated using Yaquina (1998-2006), Classification 

(2004-2005), and NCA Oregon estuaries (1999-2000) data sets. NCA and Classification 

values are for dry season only, while Yaquina data include values for dry and wet seasons. 

Percentiles for DIN (μM) 

Data set 25th 50th 75th Sample Size 

Zone 1 

Yaquina 

 Dry 

 Wet 

8.3 

6.8 

14.1 

11.6 

20.4 

19.1 

1113 

520 

Classification 5.7 11.1 18.6 68 

NCA 6.0 8.6 11.8 36 

NCA excluding Columbia 5.8 8.4 11.8 33 

Zone 2 

Yaquina 

 Dry 

 Wet 

7.3 

30.7 

13.7 

55.2 

23.1 

73.5 

915 

354 

Classification 8.3 14.0 36.9 88 

NCA 7.0 9.4 13.2 89 

NCA excluding Columbia 4.8 7.2 11.9 27 

Table 6.4 Percentiles for PO4

3-

 (μM) using Yaquina (1998-2006), Classification (2004-2005), 

and NCA Oregon estuaries (1999-2000) data sets. NCA and Classification values are for dry 

season only, while Yaquina include dry and wet season values. 

Percentiles for PO4

3-

 (μM) Data set 

25th 50th 75th Sample Size

Zone 1 

Yaquina 

 Dry 

 Wet 

0.88 

0.39 

1.25 

0.59 

1.69 

0.77 

1114 

519 

Classification 0.62 0.89 1.20 68 

NCA 0.76 0.95 1.15 36 

NCA excluding Columbia 0.76 1.00 1.16 33 

Zone 2 

Yaquina 

 Dry 

 Wet 

0.43 

0.41 

0.64 

0.49 

0.99 

0.62 

915 

354 

Classification 0.33 0.45 0.75 88 

NCA 0.35 0.52 0.71 89 

NCA excluding Columbia 0.18 0.33 0.73 27 
50

7. Chlorophyll a as a Water Quality Response Measure 

7.1 Seasonal, Zonal, and Long-Term Trends in Chlorophyll a 

Chlorophyll a is often used as a surrogate for phytoplankton biomass and as an indicator of 

trophic status in eutrophication assessments (Bricker et al., 1999). There were seasonal differences in 

water column chlorophyll a in the estuary. Peak chlorophyll a levels occurred during the months of 

June to August (Figure 7.1). Seasonal chlorophyll a patterns were likely related to light limitation and 

flushing (as discussed in Section 5.2). The median wet season chlorophyll a was 1.6 μg l-1 (n = 293), 

while during the dry season the median increased to 4.9 μg l-1 (n = 1205). Dry season chlorophyll a

levels in Zone 2 (Median = 5.3 μg l-1, n = 229) were significantly higher than those in Zone 1 (Median 

= 3.3 μg l-1, n = 347; Mann Whitney Rank Sum, p<0.001). During the late spring, there have been 

recurrent non-toxic red tide blooms of Myrionecta rubra in the vicinity of Toledo. During the dry 

season, chlorophyll a concentrations occasionally reached 15 μg l-1 in the vicinity of Toledo (8% of the 

recent observations). In the tidal fresh portion of the estuary, there were recurrent algal blooms during 

June and July, with chlorophyll a concentrations reaching 80 μg l-1. 

There are limited historical data to assess long-term trends in chlorophyll a. Comparison of 

historic (1973-1983) and recent (2000-2006) chlorophyll a levels during the dry season reveal that 

there has been a decline in median chlorophyll a levels in both zones (Figure 7.2); although these 

declines are statistically significant (Mann Whitney Rank Sum, p≤ 0.001) they do not indicate a shift in 

trophic status of the estuary. There was a statistically significant decreasing trend in dry season 

chlorophyll a in Zone 1 (Mann Kendall, p<0.001), while in Zone 2 there was no significant trend. The 

changes in chlorophyll a that occurred in the Yaquina Estuary are small in magnitude (1 μg l-1 ) 

compared to changes that have occurred in other estuaries (Cloern, 2001; Harding and Perry, 1997). 

For example, in Chesapeake Bay chlorophyll a levels increased 5- to 10-fold in the lower portion of 

the estuary during the interval of 1950-1994 (Harding and Perry, 1997). A statistically significant 

zonal difference in chlorophyll a levels (Zone 2 higher than Zone 1) is present in the historic data as 

well as the recent data (Mann Whitney Rank Sum, p ≤ 0.001). Peak chlorophyll a levels in Zone 1 

appear to be higher in the recent data compared to the historic; however, this is probably an artifact of 

sampling frequency. Blooms imported into Zone 1 from the coastal ocean are episodic in nature, 

reflecting the variability in wind forcing. Peak chlorophyll a levels in Zone 2 are similar for the 

historic and recent data. 
51

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

10

20

60

80

Chlorophyll a, μg l-1

Month

Mean

Min and Max

× 1st and 99th

Figure 7.1. Box plot of monthly chlorophyll a data from the Yaquina Estuary (all stations from 1973-

2006). The dashed line indicates the Oregon estuarine chlorophyll a criterion. The boxes 

represent the 25th and 75th percentiles, the whiskers represent the 5th and 95th percentiles, and 

the horizontal line is the median. 

0

25

50

1/1/1973 1/1/1978 1/1/1983 1/1/1988 1/1/1993 1/1/1998 1/1/2003

0

25

50

Chlorophyll a, 

μg l-1

Median = 4.6

n = 137 

Median = 3.3

n= 347

a) Zone 1

Median = 5.3

n = 229

Date

b) Zone 2

Median = 6.1

n = 377

Figure 7.2 Comparison of historic and recent dry season chlorophyll a for a) Zone 1 and b) Zone 2 in 

the Yaquina Estuary. The boxes indicate the time interval for the historic and recent median 

calculations. 
52

7.2 Percentile Approach for Chlorophyll a 

 The chlorophyll a levels in Oregon estuaries, including Yaquina Estuary, are relatively low 

with median values of 2 – 5 μg l-1 (Table 7.1). These chlorophyll a levels would be in the ‘low’ 

category when used as an indicator of eutrophication (Bricker et al., 1999) and in the ‘good’ category 

using the West Coast criteria for water quality parameters from the National Coastal Condition Report 

(US EPA, 2004a). The Oregon chlorophyll a criterion of 15 μg l-1 is exceeded 4% of the time during 

the dry season in Zones 1 and 2. At Elk City (tidal fresh part of the estuary) the 15 μg l-1 criterion is 

exceeded 28% of the time during the dry season (WED unpublished data; collected during 2002 and 

2003). There was a significant difference in median chlorophyll a between the Yaquina (dry season), 

Classification Study, and NCA datasets for both Zones 1 and 2 (Kruskal-Wallis one way ANOVA on 

ranks, p<0.001; Table 7.1). Dry season chlorophyll a levels in both zones of the Yaquina Estuary are 

significantly higher than those found in the other Oregon estuaries sampled in the NCA and 

Classification datasets (Dunn’s method for pairwise comparison, p<0.05). Although chlorophyll a

levels in the Yaquina Estuary are significantly higher than for the other Oregon estuaries sampled, they 

are ‘low’ compared to many other U.S. estuaries (US EPA, 2004a). 

Table 7.1 Percentiles for chlorophyll a (μg l-1) calculated using Yaquina (1998-2006), 

Classification (2004-2005), and NCA Oregon estuaries (1999-2000) data sets. NCA and 

Classification values are for dry season only, while Yaquina include dry and wet season 

values. 

Percentiles for Chlorophyll a (μg l-1) 

Data set 

25th 50th 75th Sample Size 

Zone 1 

Yaquina 

 Dry 

 Wet 

2.2 

0.6 

3.3 

1.1 

5.7 

1.7 

347 

95 

Classification 1.0 2.0 3.8 68 

NCA 1.5 2.1 3.8 36 

NCA excluding Columbia 1.5 2.0 3.2 33 

Zone 2 

Yaquina 

 Dry 

 Wet 

3.8 

0.4 

5.3 

0.9 

7.9 

2.5 

229 

46 

Classification 0.8 1.6 2.5 78 

NCA 2.0 3.3 4.9 89 

NCA excluding Columbia 1.4 1.8 2.6 27 
53

8. Dissolved Oxygen as a Water Quality Response Measure 

 Dissolved oxygen (DO) is an important water quality metric because of its effects on the wellbeing of estuarine resident and transitory organisms. Salmon and trout are particularly esteemed fishes 

in the PNW and traverse the estuaries during upstream and downstream migrations. The dissolved 

oxygen criterion for Oregon’s estuaries and streams focuses on the oxygen concentration needed for 

these fish because of their socioeconomic importance and their requirement for comparatively high 

oxygen levels. As a result, “salmon and trout rearing and migration” is a common designated use for 

Oregon coastal estuaries, including the Yaquina Estuary and River. 

 Two species of salmon that are of particular importance in the Yaquina are the coho salmon 

(Onchorhynchus kisutch) and steelhead trout (O. mykiss). The reduced size of Oregon coastal coho 

populations have been a cause of particular concern. Chinook salmon (O. tshawytscha) spawn and 

occur as juveniles in brackish waters, and are also present in the Yaquina River. The cutthroat trout 

(O. clarki) is a fourth important salmonid species found in the Yaquina system, and a portion of this 

population also follows the salmon life history of migrating to the sea, where it grows to adulthood 

before returning to the natal stream to spawn. The timing of salmonid migrations through the estuaries 

varies by species, and is influenced by local conditions and hydrology. However, adults generally 

enter the estuary in the fall and progress upstream to freshwater spawning streams. The juvenile 

outmigrants, termed “smolts,” typically move downstream during the months of March to June. 

8.1 Seasonal, Zonal and Long-term Trends in Dissolved Oxygen 

 There are strong seasonal patterns in dissolved oxygen within the Yaquina Estuary (Figure 8.1). 

Oxygen levels (expressed as both mg l-1 and % saturation) in the estuary are comparatively stable 

during the wet season, but show a decline during the dry season. The wet season dissolved oxygen 

have an overall mean value of 9.7 mg l-1 (n = 869) dissolved oxygen. The dry season data were fitted 

using a nonlinear least squares procedure to have a descending cosine curve that begins at the wet 

season value of 9.7 mg l-1, declines to a value of 5.8 mg l-1 on August 2, and then returns to the wet 

season value. Zones 1 and 2 appear to follow the same pattern. Subsequent data analyses used the 

deviations from this modeled seasonal pattern (the solid line in Figure 8.1), so that the overall seasonal 

changes in oxygen concentration and differences in sampling would not confound more detailed 

analyses. All dissolved oxygen values used in the analyses were collected during daylight hours. 

Therefore, diel cycling of oxygen values due to plant photosynthesis and respiration are not 
54

represented in the data. Nighttime respiration can significantly reduce water column oxygen levels 

below daytime levels. Dissolved oxygen, mg l-1

0

2

4

6

8

10

12

14

Date

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN

Zone 1 2

Wet season Dry season Wet season

Figure 8.1 Seasonal pattern in dissolved oxygen at all locations and all years in the Yaquina Estuary 

and River with squares and triangles representing samples from Zones 1 and 2, respectively. 

Solid line is nonlinear least-squares fit to data, which was modeled as a constant during wet 

season and a cosine function of date during the dry season. 

 During the interval of 1960-1984, there was a significant trend of increasing DO in Zone 2 

during both the dry and wet seasons (Figure 8.2b; Mann Kendall, p<0.05). In addition, there was a 

significant seasonal trend in Zone 2 (Seasonal Kendall, p<0.05). Similar significant trends were found 

regardless of whether dissolved oxygen was expressed as non-transformed, residual, or percent 

saturation. A report by the Federal Water Pollution Control Administration (1966) stated that the 

water quality in the lower portion of the Yaquina basin was “adversely affected by existing and manmade conditions,” including “inadequately treated wastes from municipalities and industries” that 

placed “an excessive demand on oxygen resources of Yaquina Bay during annual periods of low 

streamflow.” In 1956, the City of Toledo upgraded their wastewater treatment facility to primary 
55

treatment (prior to this raw sewage was discharged into the estuary), and in 1981 it was upgraded to 

secondary treatment. 

 

-5.0

-2.5

0.0

2.5

5.0

1960 1970 1980 2000 2005

-5.0

-2.5

0.0

2.5

5.0

Dissolved Oxygen residual, mg l-1

 Dry

 Wet

a)

 Year

b)

Figure 8.2 Interannual trend in residual dissolved oxygen values during 1960 to 1986 for a) Zone 1 

and b) Zone 2. Zone 2 regressions are significant at the p< 0.05 level, while Zone 1 regressions 

are not. The solid and dashed lines represent the significant dry and wet season trends, 

respectively (Mann Kendall, p<0.05). Data from recent years are also shown for comparison, 

but were not included in regression computations. 
56

 There was also a decline of log rafting in the Yaquina Estuary from 1962 through the 1980s 

(Seddell and Duval, 1985). One effect on the water column of bark debris associated with log rafts is 

increased biochemical oxygen demand (Seddell and Duval, 1985). Due to the multiple stressors on the 

Yaquina Estuary during this time period, there is no way to determine the cause of the observed trend 

in DO levels in Zone 2. Recent (2002-2006) DO levels in Zone 2 are similar to DO levels during the 

mid 1980’s, suggesting that there has been no recent changes in DO levels. In contrast, there were no 

significant trends in dry or wet season DO in Zone 1 (Figure 8.2a), suggesting that the trend in historic 

DO levels in Zone 2 was not a result of differences in ocean conditions. 

 Since 2002 there has been an increase in the incidence of hypoxic events on the Oregon shelf 

(Grantham et al., 2004), which have the potential to influence DO levels within the estuary 

(particularly Zone 1). DO data collected 3.7 km from mouth of the estuary (using a YSI datasonde 

deployed at a mean depth of 1 m below the surface; WED, unpublished data) demonstrate that there is 

import of hypoxic shelf water into Yaquina Estuary during flood tides. A time series of DO and 

salinity measured during July 9-19, 2002, coinciding with a documented hypoxic event on the Oregon 

shelf off of Newport, Oregon (Grantham et al., 2004), clearly shows import of hypoxic shelf water to 

the estuary (Figure 8.3a). Minimum DO levels occurred during maximum salinities, demonstrating 

that the hypoxic water was imported into the estuary during flood tides. In addition, minimum DO 

levels occur during minimum water temperatures (~ 9 deg C), which is indicative of recently upwelled 

water. This trend of increasing DO with increasing temperature is opposite the trends of solubility, 

suggesting that differences in solubility are not causing the observed variability in DO levels. During 

this 10-day interval, minimum DO levels were 0.42 mg l-1. The intervals of low DO conditions were 

relatively short, with DO levels increasing to 6-8 mg l-1 during ebb tides. The DO versus salinity plot 

(Figure 8.3b) shows that low DO levels occurred at high salinities (> 33 psu). A plot of dissolved 

oxygen expressed as percentage of saturation versus salinity had a similar pattern to that presented in 

Figure 8.3b, demonstrating that differences in solubility of dissolved oxygen are not the cause of the 

variability. 

 The import of hypoxic shelf water into Oregon estuaries is not a recent phenomena. Gibson 

(1974) found low dissolved oxygen (5 mg l-1) in the lower Yaquina Estuary during July 1968, which he 

attributed to coastal upwelling. Callaway observed the intrusion of low dissolved oxygen (< 2 mg l-1) 

into the Umpqua Estuary (as cited in Percy et al., 1974). The NCA data set is also suggestive of 

import of low dissolved oxygen at a regional scale as indicated by lower DO values in Zone 1 
57

compared to Zone 2. In a review of dissolved oxygen conditions in Oregon estuaries (ODEQ, 1995), 

the opposite spatial pattern was found, with minimum DO levels occurring near the upper end of salt 

water intrusion and higher concentrations associated with inflow of ocean water. They also stated that 

greater frequency of low DO would be expected if sampling occurred near the upper extent of salt 

water intrusion. Our results demonstrate that this may not be the case. 

7/9/2002 7/11/2002 7/13/2002 7/15/2002 7/17/2002 7/19/2002

0

2

4

6

8

10

30

32

34

29 30 31 32 33 34

0

2

4

6

8

10

Dissolved Oxygen, mg l-1

Date

 Dissolved Oxygen a) Salinity

 Salinity, psu

 Dissolved Oxygen, mg l-1

 Salinity, psu

b)

Figure 8.3 a) Time-series of dissolved oxygen and salinity and b) salinity versus dissolved oxygen 

showing import of hypoxic ocean water at a station 3.7 km from mouth of estuary. 
58

8.2 Percentile Approach for Dissolved Oxygen 

 Dry season DO levels in the Yaquina Estuary are comparable to those found in other Oregon 

estuaries (Table 8.1) and are relatively high compared to other estuaries in the U.S. (U.S. EPA, 2004a). 

There was not a significant difference in dissolved oxygen levels in Zone 1 of the Yaquina Estuary 

(dry season using discrete samples) and Zone 1 of the other estuaries sampled in the Classification and 

NCA data sets (Kruskal-Wallis one way ANOVA on ranks, p> 0.05). The dissolved oxygen levels in 

Zone 2 of the Yaquina Estuary (dry season using discrete samples) are significantly lower than those in 

Zone 2 of the estuaries sampled in the Classification and NCA (including Columbia) datasets (Dunn’s 

Method for pairwise comparison, p< 0.05). In the Yaquina Estuary during the dry season, the DO 

levels do not meet the Oregon criterion of 6.5 mg l-1 for 25% and 19% of the time in Zones 1 and 2, 

respectively (using discrete samples). There was not a statistically significant difference in dry season 

median DO levels between Zones 1 and 2 (calculated using recent discrete data; Mann-Whitney Rank 

Sum, p>0.05). During the wet season, DO conditions do not appear to be a cause for concern. 

 There is considerable temporal variability in DO levels, which is not well captured in discrete 

point measurements. Continuous data are valuable in that they provide insight into the processes 

influencing observations such as the import of hypoxic water (Section 8.1), and they can allow 

evaluation of DO levels during both day and night conditions. Continuous data were available from 

datasondes deployed at two locations (Zone 1 - 3.7 km from the estuary mouth, Zone 2 - 18 km from 

the estuary mouth) in the estuary. The data were inspected to ensure that biofouling was not 

influencing observations, and only data from the first 7 days of each deployment was included in the 

analysis. In Table 8.1 we present the percentiles of the discrete and continuous data for the dry season 

for comparison; however, we did not perform formal statistical analyses due to the large difference in 

sample size. Median DO levels are lower for the continuous data compared to the discrete data, 

particularly in Zone 1 

 Using the continuous data, we examined how often the State of Oregon DO criterion was not 

met during May-October of 2006. Dissolved oxygen levels fell below the 6.5 mg l-1 criterion 37% and 

28% of the time in Zones 1 and 2, respectively. The frequencies that observations fall below the 

criterion in the two zones are comparable but slightly higher than those calculated from the discrete 

samples (Table 8.1). A plot of salinity versus DO for the 2006 datasonde data has a pattern of low DO 

at high salinities (similar to that presented in Figure 8.3b) for the station in Zone 1; however, this 

pattern is absent in the datasonde data from the Zone 2 station. Based on these patterns, the below 
59

criterion observations in Zone 1 are probably related to the oceanic import of hypoxic water, but 

possibly not in Zone 2. In the continuous data, there is not significant difference in dry season DO 

levels (median =7.0 mg l-1) between the two locations (Mann Whitney Rank Sum, p>0.05). However, 

median DO levels expressed as percentage of saturation are significantly lower in Zone 1 than in Zone 

2 (Mann Whitney Rank Sum, p<0.001). The ODEQ (1995) review of the state DO criterion notes that 

in some bays, the 6.5 mg l-1 criterion may not always be achievable due to natural background 

conditions. This conclusion is consistent with the analyses in this section. 

Table 8.1 Percentiles for dissolved oxygen (mg l-1) calculated Yaquina (1998-2006), 

Classification (2004-2005), and NCA Oregon Estuaries (1999-2000) data sets. NCA and 

Classification values are for dry season only, while Yaquina include dry and wet season 

values. 

Percentiles for Dissolved Oxygen (mg l-1) 

Data set 

25th 50th 75th Sample Size 

Zone 1 

Yaquina 

 Dry 

 Discrete 

 Continuous 

 Wet 

6.5 

5.9 

9.0 

7.8 

7.0 

9.4 

8.5 

8.0 

9.5 

46 

2856 

36 

Classification 6.7 7.3 8.3 37 

NCA 7.2 7.8 8.4 35 

NCA excluding Columbia 7.3 8.0 8.5 32 

Zone 2 

Yaquina 

 Dry 

 Discrete 

 Continuous 

 Wet 

6.7 

6.4 

10.2 

7.2 

6.9 

11.0 

7.6 

7.5 

11.6 

259 

2862 

184 

Classification 6.9 8.1 9.3 53 

NCA 7.6 8.5 9.0 88 

NCA excluding Columbia 6.7 7.3 8.3 26 
60

9. Water Clarity (kd) and Turbidity as Water Quality Response Measures 

9.1 Seasonal and Zonal Patterns in Water Clarity and Turbidity 

 A turbidity maximum occurs about 15 km from the mouth of the Yaquina Estuary (Figure 9.1). 

The water is relatively clear throughout the year in the lower estuary due to the input of ocean water, 

which is evident in the low turbidities and light attenuations near the mouth (0 – 5 km; Figures 9.1 and 

9.2). Turbidity tends to decrease upriver of the turbidity maximum (Figure 9.1). There is a significant 

increase in light attenuation with distance from the mouth of the estuary during both the wet and dry 

seasons (Figure 9.2). In situ light attenuation measured at fixed stations up estuary from the turbidity 

maximum at 16 and 19 km from the mouth was generally greater than for fixed sites at 4 and 9 km 

from the mouth (Figure 9.3). This difference is not as clearly seen in the turbidity data set (Figure 9.1). 

 There was not a significant difference in dry and wet season total suspended solids (TSS) with 

median values of 7.8 and 8.9 mg l-1, respectively (Zones 1 and 2 combined; Mann Whitney Rank Sum, 

p> 0.05). TSS levels in Zone 2 (Median = 11.7 mg l-1, n = 119) were significantly higher than those in 

Zone 1 (Median = 6.5 mg l-1, n = 158; Mann Whitney Rank Sum, p<0.001). Light attenuation is 

positively correlated with turbidity (r2

 = 0.70, n = 1400), but not with chlorophyll a. 

0 5 10 15 20 25 30 35 40

0

30

60

90

120

150

Turbidity, ftu

Distance from Mouth of Estuary, km

 Dry

 Wet

Figure 9.1. Spatial variation in turbidity during wet and dry seasons (1998-2006). 
61

0 2 4 6 8 10 12 14 16 18 20

0

1

2

3

4

5

kd, m-1

Distance from Mouth, km

 Dry

 Wet

Ocean Dominated River Dominated

Zone 1 Zone 2

Figure 9.2 Light attenuation coefficients (kd) versus distance from the mouth of the estuary from 

cruise data (years 1998 to 2006), with filled and open symbols representing dry and wet 

seasons, respectively. Solid line - dry season regression (kd = 0.34 + [0.069 * Distance], r2

 = 

0.33, p<0.001). Dashed line - wet season regression (kd = 0.36 + [0.057 * Distance], r2 = 0.30, 

p<0.001). While dry and wet season light attenuation are significantly correlated with distance, 

their regression coefficients are not different. Combining both seasonal data sets yields (kd = 

0.34 + [0.066 * Distance], r2

 = 0.32, p<0.001). 
62

0 1 2 3 4 5 6 7 8 9 10 11 12

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

kd, m-1

Month

 OSU Dock (3.7 km)

 Marker 12 (3.9 km)

 Marker 20 (9 km)

 Marker 38 (16 km)

 Marker 47 (19 km)

Figure 9.3. Median monthly light attenuation coefficients from the continuous data set at 5 locations in 

Yaquina Estuary (1999-2003). 

9.2 Percentile Approach for Water Clarity and TSS 

 Due to methodological differences (NCA data set) and missing data (Classification data set), 

we calculated CDFs for light attenuation for only the Yaquina Estuary data sets. During the wet 

season, there is not a significant difference between light attenuation coefficients (kd) computed from 

the continuous and cruise data within each zone (Table 9.1; Mann Whitney Rank Sum, p>0.05). 

During the dry season, the light attenuation coefficients computed using the continuous data are 

significantly higher than those computed from the cruise data (Mann Whitney Rank Sum, p<0.001), 

and this difference is greater in Zone 2 (median about 16% higher for continuous) than in Zone 1 

(median about 8% higher). Median light attenuation coefficients within Zone 1 during the dry season 

were significantly higher (5-8% for continuous and cruise data, respectively) than those from the wet 

season (Mann Whitney Rank Sum, p<0.05). Within Zone 2, median light attenuation coefficients were 

significantly higher (10-27% for cruise and continuous data, respectively) in the dry season than in the 

wet season (Mann Whitney Rank Sum, p<0.001). 
63

 For the Yaquina Estuary, median values of TSS were similar within Zones between wet and dry 

seasons, but Zone 1 median values were approximately half that of Zone 2 values (Table 9.2). This 

pattern is consistent with the presence of a turbidity maximum at 14 km up the estuary, within Zone 2. 

There was a significant difference in median TSS between the Yaquina (dry season), Classification 

Study, and NCA datasets for both Zones 1 and 2 (Kruskal-Wallis one way ANOVA on ranks, p<0.001; 

Table 9.2). Comparison of the Yaquina Estuary data to that from both the Classification and NCA data 

sets showed some differences in zonal patterns. In Zone 1, there was not a significant difference 

between median TSS levels between the Classification and Yaquina data sets, but the Zone 2 median 

value in the Classification data set was significantly lower (66%) than the value for the Yaquina 

(Dunn’s method for pairwise comparison, p<0.05). NCA data for TSS showed still a different pattern 

across the region, where the median value was significantly higher (23%) than for the Yaquina in Zone 

1, while the median value was significantly lower (50%) than that for the Yaquina in Zone 2 (Table 

9.2; Dunn’s method for pairwise comparison, p<0.05). This pattern from the NCA data was present 

regardless of whether samples from the Columbia River Estuary were included. 

 The NCA study used a probability based sampling within the dry season that was generally 

random with respect to tidal stage. Much of the sampling from the Yaquina Estuary for TSS was from 

cruises during flooding tides. It is not clear whether the zonal pattern differences observed were the 

result of methodology differences or that the Yaquina Estuary is somehow different in its spatial 

pattern for TSS. 
64

Table 9.1 Percentiles for light attenuation coefficient kd (m-1) calculated using continuous 

(1999-2003) and cruise (1998-2006) data sets from the Yaquina Estuary for dry and wet 

seasons. 

Percentiles for Light Attenuation Coefficient kd (m-1) 

Data set 

25th 50th 75th Sample Size 

Zone 1 

Yaquina 

 Dry 

 Continuous 

 Cruise 

 Wet 

 Continuous 

 Cruise 

0.62 

0.56 

0.55 

0.53 

0.78 

0.72 

0.74 

0.66 

1.00 

0.94 

0.89 

0.87 

678 

541 

505 

248 

Zone 2 

Yaquina 

 Dry 

 Continuous 

 Cruise 

 Wet 

 Continuous 

 Cruise 

1.14 

1.09 

0.97 

0.95 

1.53 

1.32 

1.20 

1.20 

2.26 

1.72 

1.54 

1.54 

376 

439 

247 

178 

Table 9.2 Percentiles for TSS (mg l-1) calculated using Yaquina (1998-2004), Classification 

(2004-2005), and NCA (1999-2000) data sets. NCA and Classification values are for dry 

season only, while both dry and wet season values are provided for the Yaquina Estuary. 

Percentiles for TSS (mg l-1) Data set 

25th 50th 75th Sample Size 

Zone 1 

Yaquina 

 Dry 

 Wet 

3.0 

3.7 

6.3 

6.6 

9.6 

12.2 

102 

56 

Classification 4.6 6.7 10.8 66 

NCA 10.4 14.0 16.0 36 

NCA excluding Columbia 11.0 14.0 16.1 33 

Zone 2 

Yaquina 

 Dry 

 Wet 

6.9 

6.6 

11.2 

12.3 

19.6 

34.7 

83 

35 

Classification 1.4 3.8 8.2 83 

NCA 4.0 6.0 10.4 91 

NCA excluding Columbia 5.0 9.0 11.8 27 
65

10. Macroalgal Biomass as a Water Quality Response Measure 

10.1 Introduction 

 Excessive algal growth is one of the major symptoms of eutrophication in coastal estuaries 

(Bricker et al., 1999). Three of the principal classes of algae are phytoplankton, epiphytic algae, and 

macroalgae. In PNW coastal estuaries, epiphytic algae (attached to other organisms) and macroalgae 

(seaweed) generally are considered to be of greater concern than is excessive growth of phytoplankton, 

which is rapidly transported out of the estuaries by tidal exchange. In this section, we report on the 

macroalgae issue as it relates to the question of eutrophication in Yaquina Estuary. 

10.2 Approach 

 Beginning in 1997, numerous studies involving macroalgae have been conducted in Yaquina 

Estuary by WED. These include aerial photomapping surveys in 1997 and 1998, and intensive ground 

surveys of percent cover and biomass during 1998-2004. A listing of the individual studies conducted, 

and the analytical approaches utilized here, are presented in Appendix B. 

10.3 Results and Discussion 

10.3.1 Annual Variation: 1997 - 1998 

 The aerial distributions of benthic green macroalgae documented in the aerial photography of 

July 23, 1997 and August 10, 1998 indicate a substantial increase in coverage in 1998 (Figure 10.1). 

Part of the increase very probably is due to the fact that the 1998 aerial photographs were taken two 

and one half weeks later than were those in 1997. However, based on seasonal percent cover 

distributions obtained in 1999-2000 (Figure 10.2), an increase in cover of only about 15% would be 

expected. In contrast, the benthic macroalgal cover of bare substrate on August 10, 1998 was 

approximately 250 % that on July 23, 1997 (Fig. 10.1). 

 An empirical model has been developed that uses flood tide water temperatures to predict NO3

-

+ NO2

-

 concentrations in coastal ocean water entering Yaquina Estuary during flood tides (Brown and 

Ozretich, in review). The average concentrations predicted by this model for two-month intervals 

preceding the aerial surveys of 1997 and 1998 are 2.1 and 6.0 μM, respectively. This difference in 

average NO3

-

+ NO2

-

 concentration is assumed to be the result of the 1997 El Niño condition that 
66

suppressed normal upwelling of nutrient-rich subsurface water that year (Corwith and Wheeler, 2002). 

The ~ 300 % increase in the modeled nutrient concentration agrees well with the ~ 200 % net increase 

in intertidal macroalgal cover between 1997 and 1998 surveys. 

10.3.2 Seasonal Variation: 1999-2000 

 Monthly averages (+ 1 std. error) for percent cover and biomass of benthic green macroalgae 

were measured for six sites (Appendix B) in Zone 1 during 1999-2000 (Figure 10.2). Maximum 

values occurred in September - October for both percent cover (~ 50 %) and biomass (~ 200 gdw m-2), 

with rapid declines in November. Between December 1999 and May 2000 the respective averages 

were below 5 % and 5 gdw m-2. In Zone 1 more than 95% of the intertidal cover and biomass 

accumulation for benthic green macroalgae occurred during the dry season. 

 Macroalgal composition was assessed in 2001, and consisted of taxa most closely resembling 

Ulva linza: ~60%; U. fenestrata: ~30%; U. flexuosa: ~10%; U. intestinalis: <5%; (WED unpublished 

data). The seasonality and peak biomasses are consistent with historical data sets from the lower 

portion of Yaquina Estuary. Davis (1981) observed mean biomass of 400-500 gdw m-2 for green 

macroalgae during June to September, 1980 and Garber et al. (1992) observed green macroalgae 

biomass of 185-370 gdw m-2 during June to October of 1984 and 1985. These comparisons suggest 

that there was no increase in the frequency or intensity of macroalgal blooms within Yaquina Estuary 

over this 20 year period. 
67

Figure 10.1 Photomap of intertidal vegetation in Yaquina Estuary from aerial surveys of July 23, 1997 

and August 10, 1998 illustrating interannual differences of benthic green macroalgae cover. 

 
68

June

July

August

September

October 

November

December

January

February

March

April

May

0

50

100

150

200

250

300

0

10

20

30

40

50

60

70

Average Biomass, gdw m-2

Month

 Biomass

 Average Surficial Cover, %

 Cover

Figure 10.2 Average percent cover and biomass values (+ 1 std. err.) of benthic green macroalgae in 

the Yaquina Estuary between June 1999 and May 2000. The values are averages for six sites 

within Zone 1. 

10.4 Percentile Approach 

 CDFs for both dry and wet season were calculated for benthic green macroalgal biomass data 

from 1998-2004 within Zone 1 of Yaquina Estuary. In addition, CDFs were calculated for data from 

the Classification surveys conducted during the dry season in Zones 1 and 2 of six other Oregon 

estuaries (Table 10.1). 
69

Table 10.1 Percentiles for benthic green macroalgae biomass (gdw m-2) for Yaquina Estuary 

(1998 – 2004, Zone 1 only) and the Classification data set (2004 - 2005). 

Percentiles for Macroalgae Biomass 

Data set 

25th 50th 75th

Percentile for 

100 gdw m-2 Sample Size 

Zone 1 

Yaquina 

 Dry 

 Wet 

Classification 

0 

0 

0 

34.9 

0 

0 

189.8 

2.9 

11.6 

62.7 % 

95.1 % 

92.9 % 

4432 

2142 

351 

Zone 2 

Classification 0 0 0 99.6 231 

 Median Zone 1 dry season macroalgal biomass between 1999-2004 in Yaquina Estuary (Table 

10.1) was less than the mean value (83 gdw m-2) measured from six band transects in 1999-2000 

(Figure 10.2), and considerably higher than the median value from the Classification study. In the 

Yaquina Estuary, biomass exceeded 100 gdw m-2 for 20% of the intertidal area, compared with only 1-

6% of intertidal area for the six estuaries of the Classification Study. The Classification Study found 

that >98% of benthic green macroalgae occurred in the ocean dominated Zone 1 of the Yaquina 

Estuary (Lee et al., 2006). The reasons for the higher algal biomass found in the Yaquina Estuary 

compared to other Oregon systems studied is not clear, and makes extrapolation of information to the 

rest of the Oregon coast difficult. 

10.5 Comparisons with Findings from Other Regions 

 Literature review demonstrates that there is a wide range of macroalgal densities that cause, or 

are correlated with, negative effects on estuarine organisms (Appendix D, Table D.2). Water 

temperatures reported in the reviewed literature ranged from 9 to 20 °C versus 8-18 °C for Zone 1 of 

Yaquina Estuary, and were thus reasonably similar. Approximately one-third of the studies reported 

negative ecological effects from macroalgae for percent cover values of ≥50% and biomass densities of 

≥200 gdw m-2. In the Yaquina Estuary, ~27% of the intertidal zone exceeded 50% cover, and ~10% 

had macroalgal biomass exceeding 200 gdw m-2 (Lee et al., 2006). We also note that this density (200 

gdw m-2) is twice the threshold accepted for damage by macroalgae to seagrass in Chesapeake Bay 

(Bricker et al., 2003). 
70

 Literature values for macroalgal impacts suggest that during the dry season, the accumulation 

of benthic green macroalgae could have a negative effect on the abundance of some infaunal 

invertebrates (while possibly enhancing epifauna), and on certain other fauna (e.g., juvenile flatfish, 

shorebirds). However, the preponderance of green macroalgae occurs in the marine dominated Zone 1 

of Yaquina Estuary during the dry season. Results from stable isotope studies (Section 3.3 and Lee et 

al., 2006) provide strong support for the conclusion that benthic green macroalgae in Zone 1 of the 

Yaquina Estuary derive most of their nutrients from tidal influx of near shore marine waters. Summer 

green macroalgal blooms thus appear to be a natural response of the estuarine system. Thus, at 

present, the occurrence of benthic green macroalgae does not appear to be a useful indicator of 

eutrophication in Yaquina Estuary. 
71

11. Submerged Aquatic Vegetation (SAV) as a Management Objective (Designated Use) 

11.1 Background 

 The NHEERL Aquatic Stressors Framework (U.S. EPA, 2002) defines loss of submerged 

aquatic vegetation (SAV) as a major assessment endpoint for nutrient effects research. Seagrass, the 

dominant marine SAV, provides a critical three-dimensional structure often used by commercially and 

ecologically important species as a refuge from predation, and simulates estuarine biogeochemical 

cycling through trapping and recycling of seston and leaf material in sediments. Seagrasses also 

influence water quality and clarity by attenuating current velocity, promoting sediment deposition, and 

removing nutrients (N and P) from the water column. Thus, seagrass habitats function in a way that 

improves the quality of coastal and estuarine ecosystems. Sustaining seagrasses has become an 

important priority for federal agencies, the States, and tribes. 

 Eelgrass (Zostera marina), the principal seagrass in PNW estuaries (Phillips, 1984), is a rooted, 

flowering plant, which is present in many temperate estuaries world wide (den Hartog, 1970). Eelgrass 

meadows serve as a nursery ground for juveniles of commercially important species such as Pacific 

herring (Clupea pallassi) and as a refuge for juvenile salmonids (Griffin, 1997; Simenstad and 

Wissmar, 1985; Levings, 1990; den Hartog, 1977). Eelgrass meadows are significant sites of primary 

production and eelgrass shoots can be utilized directly for food by some waterfowl such as the western 

black brant (Branta bernicula) (Griffin, 1997; Kentula and McIntire, 1986), and indirectly by many 

species via consumption of detritus (Thayer et al., 1975). Eelgrass roots stabilize the sediment (Thayer 

et al., 1975) and the presence of eelgrass dampens wave energy which may serve to reduce erosion and 

to enhance larval settlement (Orth, 1992). Because of these characteristics, species abundances in 

eelgrass patches are usually greater than in other estuarine habitats (Everett et al., 1995). In 

recognition of the importance of seagrass beds, EPA Region III has proposed a “Shallow-water Bay 

Grass Designated Use” for Chesapeake Bay to insure adequate protection of living resources. 

 Anthropogenic nutrient additions have been suggested by many authors as a cause for the 

dramatic decline in seagrasses world wide and for Z. marina in particular on Atlantic Coasts (Short et 

al., 1995: Valiela et al 2000a; Hauxwell et al., 2003). The principal effect of excess nutrients is to 

reduce light available at leaf surfaces via enhanced macroalgal and leaf epiphyte production and by 

increasing the water column light attenuation coefficient (kd) through the stimulation of the production 

of phytoplankton (Hauxwell et al, 2001; Madden and Kemp, 1996). 
72

 The Yaquina Estuary contains ~98.5 hectares of eelgrass which covers approximately 5% of 

the total area of the estuary (Figure 11.1). This eelgrass is in three zones consisting of: 1) a permanent 

bed of perennials in the lower intertidal and subtidal1

 (below Mean Lower Low Water, MLLW), 2) an 

intertidal transition zone (0.0 m to 0.5 m above MLLW) consisting of perennial patches and annual 

shoots; and 3) an upper intertidal zone (0.5 m to 1.5 m above MLLW) consisting of only annual shoots 

(Bayer, 1979). 

Figure 11.1 Spatial distribution of Yaquina Estuary eelgrass. 

 

1

 Below -1.0 m MLLW 
73

11.2 Spatial Seagrass Patterns 

 The spatial distribution of Z. marina was determined within the Yaquina Estuary from 1997 to 

the present utilizing aerial photographs and false-color near-infrared (color infrared, CIR) film (Young 

et al., 1999). Details on how this analysis was accomplished are presented in Lee et al. (2006) and 

Appendix B. 

 Permanent bed perennial shoots make up the vast majority (90%) of the eelgrass population 

(Boese and Robbins, in prep.), and almost all of this eelgrass is in the intertidal zone in both the ocean 

(Figure 11.2) and river dominated (Figure 11.3) estuarine portions. Details on the methods used to 

generate Figures 11.2 and 11.3 are presented in Appendix B. The portions of the graphs corresponding 

to depths deeper than -1.5 m (MLLW) may have errors due to limitations in mapping methods and 

bathymetric modeling. Most (97%) of the Z. marina in the Yaquina Estuary is located in ocean 

dominated estuarine portions (Figure 11.1), which is illustrated by the differences in y-axis scales in 

Figure 11.2 and Figure 11.3. Although the distribution suggests an effect of salinity on eelgrass 

distribution, Z. marina appears to be able to tolerate a wide range of salinities (Nelson, 2005). 

Z. marina also appears able to survive short-term exposures to fresh water, however, net leaf 

photosynthesis decreases in waters with salinities below 5 and totally ceases in completely fresh water 

(Hellblom and Björk, 1999; Biebl and McRoy, 1971). Within the Yaquina Estuary, Kentula and 

DeWitt (2003) found that salinity appeared to be a statistically significant factor in controlling the 

within estuary distribution of Z. marina, even though the reported mean summer and winter salinity 

ranged from 25 to 33, which are well within published tolerance limits for Z. marina (Nelson, 2005). 

The results of the Kentula and DeWitt (2003) study were complicated by changes in light attenuation 

and temperature that tended to co-vary with salinity. Results may have been further complicated since 

the bathymetry of the Yaquina Estuary changes with distance from the estuary’s mouth such that the 

amount of suitable area in the optimal depth range for seagrass growth becomes limited in upriver 

estuarine segments (Lee et al., 2006). 
74

0

2

4

6

8

10

12

14

16

-3.6-3.4-3.2-3.0-2.8-2.6-2.4-2.2-2.0-1.8-1.6-1.4-1.2-1.0-0.8-0.6-0.4-0.2

0.00.20.40.60.81.01.21.41.61.82.02.22.42.6

Depth Below MLLW (m)

Hectares

Mean Sea Level

Figure 11.2 Z. marina depth distribution in the marine dominated portion (Zone 1) of Yaquina 

Estuary. 

0.0

0.1

0.2

0.3

0.4

-3.6-3.4-3.2-3.0-2.8-2.6-2.4-2.2-2.0-1.8-1.6-1.4-1.2-1.0-0.8-0.6-0.4-0.2

0.00.20.40.60.81.01.21.41.61.82.02.22.42.6

Depth Below MLLW (m)

Hectares

Mean Sea Level

Figure 11.3 Z. marina depth distribution in the river dominated portion (Zone 2) of Yaquina Estuary. 
75

11.3 Temporal Seagrass Patterns 

 Aerial photos of the Yaquina Estuary suggest that there is little year to year variability in Z.. 

marina coverage from 1997 to the present. For example, Figure 11.4 shows details of the spatial 

distribution of a large Z. marina meadow and a narrow fringing bed, both of which are in a portion of 

the ocean dominated area of the estuary. Although there are apparent differences in these seagrass 

coverages across years, most of these differences are likely within classification error limits resulting 

from differences in ambient lighting conditions, the presence/absence of small amounts macroalgae, 

and subtle differences in how photos were interpreted. For the fringing seagrass bed in Figure 11.4, 

there is a possible relationship between seagrass temporal variability and the formation of intertidal 

drainage channels. Fringing seagrass beds which grow on steeply sloped sites are often less 

aggregated and tend to form into elongated and complicated shapes (Fonseca et al., 1983; Fonseca and 

Kenworthy, 1987; Frederiksen et al., 2004) thus providing more bed edges where erosion may be more 

effective in dislodging shoots. Erosion and strong physical disturbance events have often been 

observed in these marginal seagrass areas of the Yaquina Estuary where tidal drainage channel changes 

and storm events have either eroded Z. marina bed margins or deposited large woody debris on top of 

them (Boese and Robbins, in prep.). Episodic events such as these have been implicated in other 

studies (e.g. Krause-Jensen et al., 2003) as factors which alter shallow water seagrass populations. 

Thus, it is likely that the Z. marina losses observed at this marginal seagrass habitat area of the 

Yaquina Estuary were due to natural rather than anthropogenic stressors. Overall the result of our 

aerial surveys, when coupled with ancillary published (Boese et al., 2003; Young et al., 1999) and 

WED unpublished data, indicate that over the past decade the spatial distribution of Z. marina within 

the Yaquina Estuary has been stable. 

 The oldest known spatial coverage data for Z. marina in the Yaquina Estuary were published in 

the Oregon Estuary Plan Book (Cortright et al., 1987). This coverage was based on aerial photographs 

that were taken in the mid 1970’s. A comparison of this historical coverage (Figure 11.5) to the 

present Z. marina distribution (Figure 11.1 and Figure 11.4) suggests an overall loss of seagrass in the 

Yaquina Estuary. However, there is no indication of what was meant by “seagrass bed” in terms of 

percent cover criteria that were used to delineate areas where Z. marina was present or absent 

(Cortright et al., 1987). In general, where seagrass habitat is shown on the Oregon Plan Book map, 

some seagrass is found in that general location either in recent photographs or has been observed as 

less than 10% cover during recent ground truthing surveys. Considering the differences in 
76

methodologies, there appears to be no gross differences in the spatial distributions of Z. marina within 

the Yaquina Estuary over the last thirty years. 

Figure 11.4 Comparison of the spatial distribution of Z. marina in a portion of the Yaquina Estuary 

(see inset on Figure 11.1) from 1997, 2000, and 2004. Figure shows a large contiguous 

meadow (north of channel) and a narrow fringing bed (south of channel). 
77

Figure 11.5 Historical distribution (mid 1970's) of Z. marina from the Oregon Estuary Plan Book 

(Cortright et al., 1987). 

11.4 Water Clarity and Seagrass Lower Depth Limit 

11.4.1 Background 

 The depth distribution of seagrasses has been shown to be dependent upon light penetration, 

with coastal seagrasses in general extending to depths receiving, on average, ~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 depth to which seagrass grows might be used as an 

integrative water quality assessment measure (Dennison et al., 1993), and has been suggested for use 

as a monitoring tool (Sewell et al., 2001; Virnstein et al., 2002). Additionally, understanding the 

minimal light requirements for seagrasses is necessary for preservation of existing seagrass meadows 

and for restoration purposes (Batiuk et al., 2000; Dennison et al., 1993; Fonseca et al., 1998). 

 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 U.S. 

Pacific Coast is problematic due to differences in tidal amplitude that tend to narrow the depth range of 
78

seagrasses (Koch and Beer, 1996) and due to other differences including lower temperature ranges and 

faster estuarine flushing rates. Criteria for Chesapeake Bay Z. marina were derived for a spring 

through fall growing season (Batiuk et al., 2000), when carbohydrates are accumulated and used to 

maintain plants during the winter when they cannot maintain a positive carbon balance (Zimmerman et 

al., 1989). In contrast, for Z. marina in the Yaquina Estuary, 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). 

11.4.2 Methods 

 During the summers of 2004 and 2005 the lower depth limit of Z. marina was determined at 64 

randomly selected locations in Yaquina Estuary by underwater video and direct visual observation 

techniques (for methods see Appendix B). These data were then compared to calculated light 

attenuation coefficient (kd) values that were measured at or near the same locations during a series of 

sampling cruises (See Section 4.1.1). 

11.4.3 Relationship between Lower Margin and Water Clarity 

 Figure 11.6 shows the relationship between the lower depth limits of Z. marina and the distance 

from the mouth of the Yaquina Estuary. Although this relationship shows a great deal of variability, 

the lower depth limit appears to be greater toward the estuary’s mouth and in the ocean dominated 

estuarine areas. There also appears to be a difference in this depth-distance relationship depending 

upon whether it is determined in the ocean or river dominated sections of the estuary, as illustrated by 

the lack of a significant linear relationship when the data from the ocean dominated section of the 

estuary are excluded (Figure 11.6). 

 The reduction in the Z. marina lower depth limit is consistent with a reduction in mean water 

clarity, which was also linearly related to distance from the mouth (Figure 9.2). We derived an 

additional relationship between the lower limits for Z. marina and the estimated kd values as follows. 

 Depth = 4.4 - (1.79*kd), r2 = 0.32. (11.1) 

where Depth = m below Mean Sea Level (MSL)2

 and kd is the light attenuation coefficient (m-1) 

computed from Figure 9.2 (for wet and dry seasons combined) at the location of the lower limit 

 

2

 For the Yaquina Estuary MSL = MLLW + 1.39 m 
79

observation. 

Distance (km)

2 4 6 8 10 12 14 16 18 20 22

m below MSL

0

1

2

3

4

5

Ocean Dominated River Dominated

Depth = 2.8 - (0.05*Distance), r2=0.00, p=0.465

Depth = 4.6 - (0.23*Distance), r2

=0.36, p<0.001

Depth = 3.8 - (0.12*Distance), r2

=0.31, p<0.001

Figure 11.6 Relationship between distance from mouth of the estuary and the lower depth limit (below 

Mean Sea Level) for Z. marina. The three regression lines are for Zone 1, Zone 2, and the 

entire estuary. 

 The maximum depth to which a seagrass grows is dependant upon water clarity which is often 

presented in the literature as the fraction of surface irradiance found at the maximum seagrass 

colonization depth (Duarte, 1991). This value is calculated from kd as 

k zd e

I

I − =

0

 (11.2) 

where I is the irradiance at depth, I0 is the surface irradiance, kd is light attenuation coefficient, and z is 

depth (m below MSL). Using this equation, the amount of surface irradiance reaching the observed 

lower depth limits for Z. marina within the Yaquina Estuary was estimated. These values ranged from 

7 to 68% with a mean ± standard deviation of 12.6 ± 1.9 % (n = 64). Standard deviation was 

determined using the propagation of error associated with estimating by linear regression from 

multiple kd values at a given distance from the mouth of the estuary (see ANOVA with regression in 

Sokal and Rohlf, 1981). 
80

 The trend for deeper depth limits 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) suggested that 

the amount of light needed to sustain this species at depth is almost double that for seagrasses in 

general (Table 11.1). Duarte (1991) went on to note 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 1.86 = (11.3) 

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 = 

1.62/kd and Zc = 1.53/kd) on the Atlantic Coast of the U.S. (Dennison, 1987) and within Danish 

estuaries (Nielsen et al., 1989), respectively. The trends in Zc and kd for Z. marina in the Yaquina 

Estuary are consistent with Duarte’s (1991) relationship (Figure 11.7) even though the waters in the 

Yaquina Estuary were more turbid than those reported by Duarte (1991). These kd values from Duarte 

(1991) were converted (Equation 11.2) to the percent of surface irradiance at Zc to generate the values 

which are presented in Table 11.1. Also included in Table 11.1 are the minimum light requirements 

recommended for the growth and survival of SAV in Chesapeake Bay (Batiuk et al., 2000). 

 Although the mean percent of surface irradiance needed to maintain Z. marina in the Yaquina 

Estuary is lower than literature values, they are within the range of published values. Literature values 

were either determined in waters which were considerably less turbid than those of the Yaquina 

Estuary (Duarte, 1991) or derived from a synthesis of literature values and area specific research 

(Batiuk et al., 2000). The Z. marina values published by Duarte (1991) rely heavily on Danish studies, 

especially Nielsen et al. (1989), which account for 20 of the 29 literature values shown in Figure 11.7. 
81

Kd

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

Depth (m below MSL)

0

2

4

6

8

10

Figure 11.7 Relationship between Z. marina maximum depth limit (m below MSL) and kd. Filled 

circles represent data from Duarte (1991) and hollow circles represent data from the Yaquina 

Estuary. 

Table 11.1 Comparison of mean and range of percent of water column surface irradiance needed 

to maintain Z. marina at its colonization depth from published data and from Yaquina Estuary 

data. SE = standard error. 

Range 

Source Mean N Max Min 

Duarte (1991) 20.5 29 43.9 4.7 

Current Study 12.6 64 68.3 7.2 

Batiuk et al. (2000) 22a a

Value is not a mean but according to the authors is based on an analysis of literature and on an 

evaluation of monitoring and modeling research. 
82

11.5 Epiphyte Patterns and Impact on Z. marina 

 The Chesapeake Bay water quality criteria for shallow water bay grass includes values for 

percent of ambient light reaching a plant through the water column, and a value for percent of light at 

the leaf, after attenuation by epiphytes. A study of epiphytes growing on Z. marina leaves was 

conducted within the Yaquina Estuary from 2000 though 2004 at six stations distributed between 3.5 

and 17 km upriver from the mouth of the Yaquina Estuary. Methodological details are presented in 

Appendix B. 

11.5.1 Spatial and Temporal Patterns in Epiphytes 

 In the Yaquina Estuary, there was a general annual pattern in 2000 though 2003 in which 

epiphyte biomass increased in the spring to a maximum in the summer and fall. This statistically 

significant parabolic relationship was most clearly seen on the older, external seagrass blades within a 

shoot (Figure 11.8). For unknown reasons, this yearly pattern was not observed in the 2004 samples. 

 In the Yaquina Estuary, epiphyte biomass per unit surface area of seagrass leaves was higher in 

Zone 1 (ocean dominated) than in Zone 2 (river dominated) in both wet and dry seasons (Figure 11.9). 

However, 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 zones (Lee et al., 2006). 
83

0 50 100 150 200 250 300 350 400

0

2

4

6

8

10

12

Y = -0.0002 X2+0.0757 X - 1.1484

r

2

 = 0.60

Biomass, mg cm2

Julian Day

Figure 11.8 Temporal relationship of epiphytic biomass per unit leaf area on Z. marina external leaves 

in the Yaquina Estuary, 2000-2003. 

Wet Season - External Dry Season - External Wet Season - Internal Dry Season - Internal

Biomass, mg cm-2

0

2

4

6

8

10

12

Zone 1 

Zone 2 

Figure 11.9 Epiphyte biomass per unit leaf area on old (external) and young (internal) Z. marina

leaves by season (wet or dry) and salinity zone in the Yaquina Estuary. 
84

 There was a significant positive linear relationship (Figure 11.10) between percent light 

reduction and log+1 transformed biomass data, for external and internal blades combined. The linear 

regression relationship overestimates light reduction for the low epiphyte biomass samples. 

y = 79.044x + 4.8381

R2

 = 0.7954

0.00

20.00

40.00

60.00

80.00

100.00

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40

log (biomass (mg/cm^2)+1)

Light Reduction (%)

Figure 11.10 Linear regression relationship between the percent of light reduction to log(x+1) 

transformed epiphyte biomass per unit Z. marina surface area. 

 Epiphytes reduced the amount of light reaching the surface of Z. marina leaves. The monthly 

range of variation in light reduction was high, ranging from 4 - 91% for external leaves, and 2 – 62 % 

for internal leaves. The range in mean light reduction for a plant was estimated as 3 – 76 %, with an 

overall mean estimated light reduction of 53% (n=18, SE=4.6). As a result of the spatial differences in 

epiphyte biomass within the two salinity zones in the estuary, average light reduction for a plant was 

higher in Zone 1 (61%, n=72, SE=3.4) than in Zone 2 (37%, n=44, SE=4.5) as a result of the more 

heavily fouled external blades. 

11.6 Zostera marina Light Requirements 

 Minimum light requirements for maintaining and restoring SAV 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 SAV annually and 
85

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 zones (>22% of surface irradiance) than for tidal fresh and oligohaline 

zones (>13% of surface irradiance). These zonal differences are in part due to the different species of 

SAV which are typically found in the different salinity zones (Batiuk, 1992). It is also important to 

recognize that the Chesapeake Bay values are designed to be protective of multiple SAV species, and 

not just Z. marina. The zonal irradiance values were based upon previously published kd values of 2.0 

m-1 for tidal fresh and oligohaline sections and 1.5 m-1 for polyhaline and mesohaline sections (Batiuk 

et al., 1992). The proposed irradiance criteria were adjusted for the amount of light absorbed by 

epiphytes encrusting SAV leaf surfaces and reported as the Percent of Light at the Leaf surface or PPL. 

These minimum PPL values were 9 and 15 % respectively for the two salinity groupings (Batiuk et al. 

2000). These proposed criteria were also applicable only to the SAV growing season (typically spring 

though fall). 

 In contrast, light requirements for Z. marina in Puget Sound were reported as integrated light 

intensity levels (Thom et al., 1998). These were estimated using maximum seagrass depth measures, 

kd values and production-irradiance (P vs. I) relationships. Based on this methodology Thom et al., 

(1998) suggested that to maintain the greatest densities of Z. marina, ~300 μmoles m-2 s

-1 (3 moles m-2

d-1) were required for at least three hours daily during the growing season. 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 m-2 s-1 during the year. These same values 

are also suggested as minimum requirements for outer coast PNW estuaries like Willapa Bay and Coos 

Bay (R. Thom, Pacific Northwest Environmental Laboratory, pers. comm.). Assuming that mid-day 

surface irradiance is in the range of 1000-2000 μmoles m-2 sec-1, the minimum light requirement 

corresponds to approximately 15-30% of surface irradiance, which is consistent with other published 

criteria values and with the present study (Table 11.1). Additional verification of these minimum light 

requirements within the Yaquina Estuary is currently in progress (WED unpublished data). The mean 

daily irradiance value was approximately 3.8 moles m-2 d-1 at a single lower margin site for Z. marina

in the Yaquina Estuary (WED unpublished data). Although this value exceeds the Thom et al. (1998) 

criteria, irradiance values were highly variable, ranging from 0.5 to 7 moles m-2 d-1, with extended 

periods of apparently inadequate lighting at depth from October to December (WED unpublished 
86

data). However, even during these periods of apparently inadequate irradiance, Z. marina in the 

Yaquina Estuary continued to grow (Boese et al., 2005). 

 While it is tempting to directly apply the existing light criteria values to PNW estuaries like the 

Yaquina, there are several additional factors that need to be considered. 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 (see Table 9.1). Z. 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 Estuary (Boese et al., 2005) due to the latter’s twice daily 

flushing with cold ocean water. Increased respiration rates due to higher summer temperatures 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 eelgrass in the Yaquina Estuary may require less spring 

and summer irradiance to perform the same function because of the generally cooler waters of these 

systems. 

 Additionally, the Yaquina Estuary is mesotidal. Koch and Beer (1996) found that greater tidal 

amplitude reduced the range of water depths that Z. marina colonized in Long Island Sound. Due to 

increased tidal amplitudes and turbidity, Z. marina growing in western Long Island Sound was limited 

to a 1 m depth range compared to the 4 m range observed in eastern Long Island Sound (Koch and 

Beer, 1996). Plants that are forced into a narrower depth range by these factors are likely to be more 

vulnerable to stressors such as storm events which may have contributed to the historic losses of Z. 

marina meadows. Thus, to assure seagrass survival in mesotidal and macrotidal estuaries, it may be 

prudent to establish more restrictive water clarity requirements in those estuaries. 

 Our study of epiphytes growing on Z. marina leaves in the Yaquina Estuary revealed a 

reduction in the amount of epiphyte biomass in upriver, lower salinity areas. 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, when ambient light levels are highest. The accumulation of epiphytes 
87

was estimated to reduce the amount of light reaching leaf surfaces by an average of about 60% in the 

ocean dominated portion of the Yaquina Estuary (Zone 1). At present we are not sure how epiphyte 

load and its impact on light availability to eelgrass leaves 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. Epiphyte light reduction will be incorporated in future versions of the seagrass stress-response 

model described in Chapter 12. 

Although the effects of tides, temperature and epiphytes constitute current uncertainties in 

estimating minimum light requirements for seagrass in Yaquina Estuary, general conclusions can be 

made. Maximum depth of colonization of eelgrass in Yaquina Estuary suggests that a mean of 12.7% 

of surface illumination is required for persistence of seagrass at the deepest edge of the bed. Applying 

the median light extinction coefficient (kd) for Zone 1 (0.8 m-1, Chapter 9) to Equation 11.2 yields an 

estimate of percent of surface illumination at depths of 1, 2, and 3 m of 36, 20 and 9 %, respectively. 

This suggests that the use of the median kd as a criterion in Zone 1 would allow persistence of eelgrass 

to a depth between 2-3 m. Use of the median kd for Zone 2 (1.5 m-1) in Equation 11.2, yields estimates 

of percent surface illumination at depths of 1 and 2 m of 22 and 5%, respectively. This suggests that 

the use of the median kd as a criterion in Zone 2 would allow persistence of eelgrass to a depth between 

1-2 m. These results are generally consistent both with empirical data on bathymetric distribution of 

eelgrass within the Yaquina Estuary and with the conclusions generated by use of the StressorResponse Model (see Chapter 12). 
88

12. Stress-Response Approach for Protection of SAV 

12.1 Introduction 

 A previous report summarizes EPA research to develop mechanistic modeling approaches for 

examining the sensitivity of seagrasses to nutrient stressors (Kaldy and Eldridge, 2006). Here we use 

the mechanistic Seagrass Stressor-Response Model (SRM) developed by Kaldy and Eldridge (2006) in 

a heuristic fashion to assess the protective capacity of the Percentile approach (see Section 4.4). 

 The SRM used the 25th, median, and 75th percentile results from the cumulative distribution 

function (CDF’s) developed in Sections 6.2 and 9.2 to determine if these potential criteria are 

protective of seagrass distribution and biomass in Yaquina Estuary. Our approach was to use the 

quartile values from the CDF’s as inputs to the seagrass SRM with the objective of testing which 

values maintained seagrass at present depth distributions and which values resulted in decline of 

seagrass. These evaluations will provide guidance to aid in the selection of water clarity criteria that 

are protective of seagrass habitat in PNW estuaries. The response variables of the SRM model were 

seagrass biomass and carbohydrate content. 

 The SRM is composed of a set of mechanistic models that can be run in a variety of 

configurations depending on the study or management goals. The advantage of this approach is that, 

unlike the regression model approach, we can examine the direct and indirect effects of particular 

environmental conditions. Full model details and validation description are provided by Kaldy and 

Eldridge (2006). 

12.2 Description of Model 

 The seagrass SRM was developed through an integrative effort that used a variety of data 

sources such as field studies and manipulative experiments, published literature, and existing and new 

models. A detailed description of the SRM development, calibration and validation is provided by 

Kaldy and Eldridge (2006). Briefly, the SRM is composed of an Allocation Model, a Plant 

Productivity Model and a Sediment Diagenetic Model. The Allocation Model integrates field data and 

provides estimates of carbon, nitrogen and phosphorus fluxes between plant components and the 

environment. These flux rates are then used to parameterize the Plant Productivity Model. The 

seagrass Plant Productivity model predicts above-ground biomass, carbohydrate reserves and plant 

growth in response to nutrients (both water-column and sediment porewater), salinity and underwater 
89

light, and it provides boundary conditions for the Sediment Diagenetic Model. The Sediment 

Diagenetic Model provides estimates of the inorganic chemical environment in the root zone of the 

plant. The build-up or depletion of particular compounds in the sediments may have positive or 

negative effects on seagrass health and production. These models can be run independently or can be 

coupled together and run as the full SRM. Only the plant model configuration was used in the current 

analysis as there were no sediment geochemical data for the upper Yaquina Estuary (Zone 2). 

Calibration data for the plant model are shown in Appendix Figure E.1. Field and mesocosm 

experiments were used to validate model predictions (Kaldy and Eldridge, 2006). 

The SRM has been used to examine seagrass response to a number of environmental variables 

including nutrients, canopy level irradiance, water turbidity, and organic matter input to sediments. 

The SRM can be used to assess the effectiveness of proposed nutrient loading criteria designed to be 

protective of seagrass. Assessment of the protective capacity of a particular water quality criterion was 

based on evaluation of trends in modeled seagrass biomass and carbohydrate for each depth interval. 

A downward trajectory in simulated biomass indicates that the water quality criterion was not 

protective at that depth. We also looked at the clustering of model outputs to assess breakpoints 

among the depth contours. Large differences in biomass or carbohydrate concentration between 

contours provides an approximation of the depth where conditions become inhospitable. 

 Models are simplifications of observed processes; as such they are subject to a number of 

simplifying assumptions and caveats. Further these models are being revised to include new types of 

calibration data that presumably will produce more accurate predictions. For example, the SRM does 

not include the effects of irradiance attenuation due to epiphytes, algae, self-shading or surface 

reflectance. As a result, the current simulations represent the “best case scenario” for underwater light. 

The model does include the effects of turbidity, nutrients, and salinity (Appendix Figure E.2). 

Furthermore, seagrass physiology was assumed to be similar between Zones 1 and 2. For presentation 

purposes, an upper margin for seagrass distribution of 0.2 m above mean lower low water (MLLW) 

was used; however, there are areas throughout the bay where the upper limit can not easily be defined 

by a single bathymetric level as a result of differential effects of desiccation, erosion, and sediment 

deposition. 
90

12.3 Model Simulations and Input Data 

 The SRM was used in a heuristic fashion with idealized input data. Composite temperature and 

salinity time series were generated using YSI datasonde data from two stations, one located in each 

zone (distances from mouth of the estuary of 3.7 and 17.9 km). The composite time series represented 

average conditions from 1999-2003. For the solar irradiance, a composite incident photosynthetically 

active radiation (PAR) time series was generated using data collected by WED during 1999-2003 

(Appendix Figure E.2). This PAR time series represents average incident light conditions (Io) in the 

study area at 15 minute intervals for the year. The underwater light environment used in the model 

includes daily variations in surface irradiance with the addition of tidal variations in water surface 

elevation, and zonal and seasonal differences in water clarity (k). The underwater light environment 

was simulated for each zone as 

( )

1,..., ( , , ) ( ) k z hi n o jS i I z j t I t e

− + = =

where j represents the zone, zirepresents the depth (relative to MLLW), h is tidal variation in water 

surface elevation, and kjS is the diffuse light attenuation coefficient for the specific zone (j) and season 

(S), which were obtained from the percentile analysis calculated using the continuous Yaquina Estuary 

data set (Table 9.1). Tidal variations in water surface elevation (h) were incorporated using hourly 

water level data from a tide gauge in Zone 1 (http://tidesandcurrents.noaa.gov/, Station 9435380 South 

Beach). 

Table 12.1 Input data from percentile approach for different SRM simulations. 

Dry Season Wet Season 

Case Zone kd, m-1 DIN, μM kd, m-1 DIN, μM 

1 1 0.78 14.1 0.74 11.6 

(Median) 2 1.53 13.7 1.20 55.0 

2 1 0.62 8.3 0.55 6.8 

(25th %) 2 1.14 7.3 0.97 30.7 

3 1 1.00 20.4 0.89 19.1 

(75th %) 2 2.26 23.1 1.54 73.5 

 Simulations were run for 3 cases, the median, 25th, and the 75th percentiles as representative of 

different levels of potential protective criteria for Yaquina Estuary. For these analyses, the estuary was 

divided spatially into a lower (Zone 1) and upper (Zone 2) region and temporally into a wet and dry 

season (Table 12.1). The transition from wet to dry season conditions for both kd and DIN 
91

concentrations was done as a step-function (Table 12.1). In these model simulations, water column 

DIN was used in nutrient uptake kinetics for the seagrass plant, but does not include indirect nutrient 

effects such as epiphyte, macroalgal or phytoplankton blooms. Additionally, simulations were 

conducted for parameters at a series of depths ranging from 0 m relative to mean lower low water 

(MLLW) down to a maximum depth of 5 m below MLLW in Zone 1 and 0 to 2.5 m below MLLW in 

Zone 2. The expanded depth range in Zone 1 was used because of lower light attenuation in this 

region. Additionally, this range encompasses the known depth distribution of Z. marina in Yaquina 

Estuary and allows for the expansion of the seagrass into deeper waters. 

12.4 Results 

As described above, the individual model simulation runs differed by depth, irradiance 

attenuation and DIN (Table 12.1), and temperature and salinity (Appendix Figure E.2). Temperature 

and salinity were also different between Zones 1 and 2, with a greater range in each variable occurring 

in Zone 2 as a result of seasonal heating and cooling. The model incorporated functions that increased 

photosynthesis and metabolism with temperature. The larger range in water temperature in Zone 2 

affected seagrass physiology, while the relatively stable water temperatures in Zone 1 had a minimal 

impact (Appendix Figure E.2). Salinity had no influence on seagrass biomass or production in Zone 1 

but affected production in Zone 2 during winter months. 

 Model results indicated that the median values would maintain the existing distribution of 

seagrass within Yaquina Estuary (Figure 12.1). Current maps indicate that seagrass covers 

approximately 0.97 km-2 in Zone 1 and 0.013 km-2 in Zone 2. In Zone 1 (lower estuary), seagrass 

would be protected to a depth of about 2 m below MLLW. The median values are representative of 

present conditions. In Zone 2 (upper estuary), the median criteria would protect seagrass to a depth of 

about 0.5 m below MLLW. Model simulations indicated that criteria based on the 25th percentile were 

the most protective (Table 12.2), permitting seagrass survival to depth of 3 m below MLLW in Zone 1 

and 1 m below MLLW in Zone 2 (Figure 12.2). In contrast, model simulations using criteria generated 

from the 75th percentile were the least protective of seagrass (Table 12.2). The 75th percentile was 

protective of seagrass to about 2 m below MLLW in Zone 1 but, only maintained seagrass at a depth of 

0 m MLLW in Zone 2 (Figure 12.3). Model simulations for each case and zone are provided in 

Appendix Figures E.3-E.8. 
92

Table 12.2 Summary of the protective capacity of different potential criteria derived from 

Yaquina Estuary percentile data. Depth (below MLLW) to which these potential criteria 

permit long-term seagrass persistence and a narrative description for each case and zone. 

Percent change is relative to the median case. 

Case Zone Protective Capacity 

Depth, m % change 

1 1 >2 0 

(Median) 2 >0.5 0 Present Condition 

2 1 >3 +38 

(25th %) 2 >1 +41 Most Protective 

3 1 >2 0 

(75th %) 2 0 -48 Least Protective 

Figure 12.1 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) depth 

distribution based on the median case. Brown regions are unsuitable for eelgrass survival. 

Inset boxes show that the median case should maintain current eelgrass distribution in both 

Zones 1 and 2. 
93

Figure 12.2 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) depth 

distribution based on the 25th percentile case. Brown regions are unsuitable for eelgrass 

survival. Inset boxes show that the 25th percentile case should permit expansion at the lower 

margin of the current eelgrass distribution in both Zones 1 and 2. 
94

Figure 12.3 Observed eelgrass distribution (yellow cross-hatch) and simulated eelgrass (green) depth 

distribution based on the 75th percentile case. Brown regions are unsuitable for eelgrass 

survival. Inset boxes show that the 75th percentile case would eliminate much of the current 

eelgrass distribution in Zone 2. 

12.5 Discussion 

Many seagrass monitoring and assessment programs rely on presence/absence data or periodic 

evaluations of biomass and distribution (Pulich and White, 1997; Berry et al., 2003). However, these 

parameters are not very sensitive indicators of seagrass decline since they require very large sample 

sizes to detect modest changes (Heidelbaugh and Nelson, 1996). For example, biomass is a classic 

response variable; however, by the time monitoring programs can detect changes in biomass the 

perturbation may have caused seagrass decline. Better indicators of stress and decline are required to 

adequately assess seagrass condition. Non-structural carbohydrates may provide a more sensitive and 

integrative response variable since carbohydrate is the energy “currency” of the plant. Our assessment 

of the protective capacity of potential criteria was based on an evaluation of modeled carbohydrate 

content and modeled biomass. 
95

Our analysis suggests that the 25th percentile and median criteria are protective of seagrass in both 

Zones 1 and 2 in the Yaquina Estuary (Table 12.2). Simulations using median kd and DIN values 

project that eelgrass maintains its current depth distribution. The present depth distribution of eelgrass 

in Zone 1 (ocean dominated lower bay) is deeper than in Zone 2 (upper bay) due to greater light 

penetration. The 25th quartile simulations show that the seagrass permanent bed in Zone 1 might be 

extended from the present depth of 2 m to 3 m (below MLLW), but the depth limit in the upper bay 

would not change. Adoption of the 25th percentile criteria would potentially expand seagrass habitat 

by 38% and 41% in Zones 1 and 2, respectively, relative to the median case. The 75th percentile 

simulations predict a loss of 48% of habitat in Zone 2 relative to the median case. Most of the change 

occurs at the lower margin since the upper margin was fixed at 0.2 m above MLLW. While these 

changes in seagrass habitat are large in Yaquina Estuary, larger changes might be expected in 

shallower bays, while smaller changes might be expected in systems with steep bathymetric gradients. 

 Dry season median kd values in the Yaquina Estuary (Table 12.1) are comparable to criteria that 

are currently being used in several other systems including Peconic Bay, NY, Long Island Sound, CT 

and Chesapeake Bay (Table 12.3). The criteria from the other systems are based on the requirements 

for restoration of Z. marina; therefore they may be more restrictive than those required for maintaining 

an existing eelgrass bed (EEA Inc., 1999). The Zone 2 median kd values were similar to the criteria 

values for the restoration of seagrass to depths of 1 m below mean low water (MLW) in Chesapeake 

Bay (Table 12.3). The DIN criteria used by other studies are generally lower than the median values 

observed in the Yaquina Estuary (Table 12.1). DIN values from the 25th percentile in our study were 

comparable to the concentrations for Chesapeake Bay (Table 12.3). The proposed DIN criteria for 

Long Island Sound and Peconic Bay are much lower than observed DIN levels in the Yaquina Estuary, 

illustrating the importance of regional nutrient criteria. As discussed in Section 3.2, nutrient loading to 

Oregon estuaries is highly dynamic and naturally large as a result of coastal upwelling and alder 

dominated forests. 
96

Table 12.3 Dry season median light attenuation and DIN values for Yaquina Estuary as 

compared to water quality management targets for other estuaries that are protective of 

eelgrass habitat. 

Location kd (m-1) DIN (μM) Citation 

Yaquina Estuary, OR1 <0.78 (Zone 1) 

<1.5 (Zone 2) 14 This study 

Peconic Bay, NY2 <0.75 1.4 EEA, Inc. 1999 

Long Island Sound, CT <0.7 2.1 Holst et al., 2003 

Chesapeake Bay, MD*3 <1.5 10.7 Batiuk et al., 1992, 2000; 

Wazniak and Hall, 2005 

Chesapeake Bay, MD*4 <0.8 10.7 Batiuk et al. 1992, 2000; 

Wazniak and Hall, 2005 

1

 Based on dry season median values; 2 Based on mean summer values; *Meso and Polyhaline 

portions of the bay during the growth season (April-October); 3

 Requirements for restoration 

of seagrass to 1 m MLW depth; 4

 Requirements for restoration of seagrass to 2 m MLW depth. 
97

13. Conclusions and Recommendations 

 The Yaquina Estuary is characterized by strong seasonal variation in the magnitude of natural 

nutrient loading and in the dominant nutrient sources. Response variables (particularly, chlorophyll a

and dissolved oxygen) show similar patterns of seasonal variation. During the wet season, riverine 

nitrogen inputs dominate, while during the dry season oceanic nitrogen sources dominate. There are 

also strong zonal differences in nutrient levels, response variables, and dominant nutrient sources 

within the Yaquina Estuary. In the lower estuary (Zone 1), water quality conditions are strongly 

influenced by ocean conditions, while in the upper portions of the estuary (Zone 2), watershed and 

point source inputs increase in importance. 

 The DIN and PO4

3-

 levels in the Yaquina Estuary would represent medium levels using the 

criteria developed by Bricker et al. (2003) for eutrophication assessment. During the wet season, water 

column DIN levels within the estuary are relatively high. These high nitrogen levels are believed to be 

a naturally high background condition associated with the presence of red alder in the watershed. 

Some portion of the red alder related nitrogen inputs may be related to anthropogenic activities, since 

there may have been changes in red alder distribution related to logging activities in the watershed. 

However, we are presently unable to quantify the relative importance of natural and anthropogenic 

factors influencing watershed forest composition. During the dry season, PO4

3-

, NO3

-

, chlorophyll a, 

and dissolved oxygen levels in Zone 1 are primarily determined by ocean conditions. There is 

considerable interannual variability in ocean conditions that results from Pacific – scale processes, 

such as El Niño/La Niña and the Pacific Decadal Oscillation. The high degree of ocean-estuary 

coupling found for Zone 1 within the Yaquina Estuary suggests that monitoring for compliance with 

nutrient criteria in this region may be problematic. For example, hypoxic water and dense 

phytoplankton blooms at times are advected into Zone 1 from the coastal ocean during the dry season. 

Nutrient criteria developed for Zone 1, and any proposed monitoring process to determine compliance, 

would need to take into account this variability in ocean conditions. Distinguishing responses to 

anthropogenic nutrient inputs from those due to natural background variability for such indicators as 

chlorophyll a and dissolved oxygen may be difficult. At a minimum it may require acquisition of 

continuous monitoring data from multiparameter datasondes, an approach which is currently both 

expensive and labor intensive. WED is examining several rapid assessment approaches to allow 
98

determination of whether conditions in Zone 1 are ocean derived, but these techniques have not yet 

been validated. 

 In addition to patterns associated with nutrients, there are strong zonal patterns in turbidity, 

TSS and water clarity within Yaquina Estuary, with increasing turbidity and light attenuation with 

distance from the mouth of the estuary. The lower depth limit of Z. marina habitat becomes shallower 

with distance from the mouth of the estuary, suggesting that light conditions may be influencing the 

distribution of Z. marina particularly within Zone 2 of the estuary. Median values of TSS are not 

considered at this time for inclusion in the list of potential water quality indicators. Comparison of 

spatial and temporal patterns of TSS with other estuaries in Oregon showed inconsistencies for reasons 

that are not clear at present. 

 Chlorophyll a levels within the Yaquina Estuary are typically low (median of 2-5 μg l-1), and 

would be considered in the ‘low’ category when used as an indicator within the NOAA eutrophication 

framework (Bricker et al., 1999) and in the ‘good’ category using the West Coast criteria for water 

quality parameters from the National Coastal Condition Report (US EPA, 2004a). The present Oregon 

criterion (15 μg l-1) is rarely exceeded except in the tidal fresh region (upper Zone 2) where the 

criterion is exceeded frequently during May-August. 

 There do not appear to have been major long-term changes in either water column nutrients or 

chlorophyll a within the Yaquina Estuary. Although the Yaquina Estuary experiences dense 

macroalgal blooms during the dry season (particularly in the lower estuary), we do not believe that 

these blooms have increased in frequency, duration or intensity, nor are they likely to be a product of 

cultural eutrophication. Modeling combined with determination of natural abundance, stable isotope 

patterns demonstrated that these macroalgal blooms are primarily fueled by oceanic nitrogen. We 

therefore conclude that green macroalgae biomass is not a useful indicator of cultural eutrophication in 

Yaquina Estuary. 

 Comparison of recent and historic Z. marina distributions suggests that there have not been any 

major changes in the last 30 years. The trend analyses did reveal that there was a significant increasing 

trend in DO levels in Zone 2 during the interval of 1960-1984. Review of watershed history suggests 

that current anthropogenic impacts are probably less than they were historically (particularly during 

1960’s-1980’s). 

 Assessment of lower depth limits for eelgrass within the Yaquina Estuary allowed estimation of 

the minimum light requirements for sustaining seagrass. The mean light requirement was compared to 
99

the estimated percentage of surface light available at various depths using the median zonal light 

extinction coefficients (kd). Results indicated that the median kd provided for persistence of seagrass at 

depths within the two estuarine zones that were comparable to current depth distributions, and that 

were consistent with results from the seagrass modeling effort. 

 The State of Oregon dissolved oxygen criterion (6.5 mg l-1) is relatively high compared to other 

estuarine DO criteria (see values in U.S. EPA, 2003). The historical record of DO in Zone 2 

demonstrates that this portion of the estuary may be susceptible to DO degradation. Our analyses 

showed that DO levels fall below of the present State of Oregon DO criterion in both Zones 1 and 2, 

but more frequently in Zone 1. We believe these periods of low DO in Zone 1 are related to the import 

of hypoxic water from the coastal ocean into the estuary; however, the causes of the low DO in Zone 2 

are unknown. There are several potential causes of low DO in Zone 2 including import of hypoxic 

ocean water, in situ processes occurring within the estuary, as well as possible effects of WWTF 

effluent discharge. The current Oregon DO criterion should be adequately protective of estuarine 

resources, but is closer to the 25th percentile value rather than the median value for DO data in Zone 2. 

Using the present numeric Oregon DO criterion, we estimate that between 20 and 30% of 

measurements would not meet the criterion in Zone 2. 

 There are still uncertainties with respect to the development of nutrient criteria and the testing 

of potential criteria using the Seagrass Stress-Response Model (SRM). Uncertainties in defining the 

minimum light requirement of eelgrass include the effects of tidal action and temperature. The 

primary input used for the SRM was water clarity, which is affected by multiple factors, including 

turbidity that may be independent of nutrient loads. The SRM did incorporate water column DIN as a 

limiting nutrient, but it did not incorporate indirect nutrient effects, such as relationships between 

nutrient loading and water column chlorophyll a. The SRM does not yet include a term for epiphyte 

effects on light attenuation to seagrass, and thus there is still some uncertainty about precise levels of 

water column light required for maintenance of healthy seagrass. This uncertainty will be resolved in 

future versions of the SRM. 

13.1 Recommendations 

 Based on the analyses presented in this report, we suggest that criteria be developed for the wet 

and dry seasons to address the extremely strong seasonal variation in nutrient loads and sources in the 

Yaquina Estuary. Establishment of dry season criteria (May-October) is of first priority since during 
100

the wet season there appears to be little utilization of nutrients within the estuary, chlorophyll a levels 

are low, and the dissolved oxygen concentrations are high. Because of the high degree of ocean 

influence on water quality parameters that occur in Zone 1 during the dry season and strong tidal 

flushing, a first priority would be the establishment of criteria for Zone 2. An additional justification 

for this prioritization is the potential difficulties in sustaining the data collection needed to differentiate 

natural from anthropogenic nutrient inputs in Zone 1. We suggest that priority for monitoring for 

compliance with any proposed nutrient criterion in the Yaquina Estuary be for Zone 2 in the dry 

season. This is the most likely region and time period where anthropogenic nutrient effects would be 

expressed. 

 EPA (2001) summarized that a “Recognized Unique Excellent Condition” estuary would have 

a watershed that is unimpacted with “very little human development, is distant from the influence of 

local population centers, adjacent land uses are undisturbed, and is outside of major atmospheric 

deposition of nitrogen.” With the exception of “adjacent land uses are undisturbed” the Yaquina 

watershed meets all of these conditions. Additionally, from a nutrient (and land use) standpoint we 

believe that the Yaquina watershed is undisturbed compared to many other systems in the U.S., even 

though extensive silviculture occurs in the watershed. 

 Following the recommendations in U.S. EPA (2001), median values are the suggested criteria 

for estuaries in “Recognized Unique Excellent Condition” (Table 13.1). The assessment of seagrass 

light requirements in Chapter 8 and the Seagrass Stress-Response Model demonstration in Chapter 12 

indicate that the median percentiles for the water clarity criterion in both Zones would be protective of 

the existing Z. marina habitat in the Yaquina Estuary. The dry season median light attenuation values 

(Table 13.1) for Yaquina Estuary are comparable to water clarity criteria for the protection of 

Z. marina habitat in other estuaries, including Chesapeake Bay, Long Island Sound, and Peconic Bay. 

Since the existing State of Oregon dissolved oxygen criterion is based on a review of physiological 

requirements of biota and appears to be adequately protective of the designated uses, we would 

recommend that the DO criterion remain at this level. 

 The present Oregon chlorophyll a criterion is determined as a 3-month average. If the 3-month 

average chlorophyll a levels within the Yaquina Estuary were to approach the present criterion, 

significant trophic shifts in the estuary would be likely. Thus, the current chlorophyll a criterion may 

not prevent some impacts on designated use. A more conservative criterion would be the adoption of 

the medians for chlorophyll a within both Zones 1 and 2 (Table 13.1). 
101

Table 13.1 Potential dry season criteria for the Yaquina Estuary based on median values for all 

parameters except for DO. 

Parameter (units) Zone 1 Zone 2 

DIN (μM) 14 14 

Phosphate (μM) 1.3 0.6 

Chlorophyll a (μg l-1) 3 5 

Water Clarity (m-1) 0.8 1.5 

Dissolved Oxygen (mg l-1) 6.5 
102

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122

Appendix A: Benthic Processes in Yaquina Estuary 

 Benthic communities harboring actively burrowing, tube- or burrow-dwelling infaunal are 

often associated with elevated rates of DIN advection from sediments (e.g., Aller, 1988; Kristensen et 

al., 1991; Marinelli and Williams, 2003). Bioturbation and bioirrigation by infauna oxygenates 

sediments and mixes labile organic matter into sediments, stimulating the activity of microbial 

communities responsible for recycling of nutrients (Kristensen, 1988; Welsh, 2003). Benthic fauna 

consume organic matter from the water column (i.e., filter feeders), at the sediment surface (i.e., 

herbivores, surface deposit-feeders, carnivores), or below the sediment surface (i.e., sub-surface 

deposit feeders, carnivores). The presence of seagrasses (i.e., Zostera spp.), green macroalgae (i.e., 

Enteromorpha spp. and Ulva spp.), or microphytobenthic algae usually results in a net benthic uptake 

of DIN during daylight hours (Underwood and Kromkamp, 1999; Hansen et al., 2000; Sundbäck et al., 

2000; Sundbäck et al., 2003). Benthic primary producers are recycled into the benthos (i.e., consumed 

by benthic herbivores or surface deposit feeders, buried by bioturbators, decay at the sediment surface) 

or are transported out of the estuary by currents. 

Five studies of benthic nutrient flux have been conducted in Pacific estuaries north of San 

Francisco, but the reported benthic flux-chamber data in four (i.e., Dollar et al. 1991; Garber et al. 

1992; Thom et al. 1994; Larned 2003) may not be appropriate for the purpose of estimating estuaryscale nutrient fluxes in Yaquina Estuary because they did not adequately take into account the presence 

of thalassinid burrowing shrimp. As described in Section 3.2.3 Benthic Processes, previous studies of 

benthic nutrient flux in PNW estuaries may have underestimated the nutrient fluxes from sediments by 

failing to account for the presence of burrowing shrimp. Both ghost shrimp and mud shrimp were 

present, or were likely to have been present, at field sites in all of the studies. Burrowing shrimp are 

very common, and frequently very abundant, in NE Pacific estuaries (DeWitt et al., 2004 and 

references therein), and they construct deep (>50 cm), branching burrows with openings 10’s of cm 

apart. These shrimp are prodigious bioturbators, actively irrigate their burrows, and thus greatly 

elevate nutrient flux from sediments (Waslenchuk et al., 1982; DeWitt et al., 2004; Webb and Eyre, 

2004; Papaspyrou et al., 2004). 

Failure to take the presence of burrowing shrimp into account can result in water inside the 

chamber being exchanged with water outside of the chamber via shrimp burrows (e.g., Hughes et al. 

2000), which violates the requirement that benthic chambers be closed microcosms (Hofman and de 

Jong, 1993, Forja and Gomez-Parra 1998). In the case of one study, benthic chamber treatments 
123

deliberately excluded shrimp (Thom et al., 1994), and while these measurements would represent tide 

flat habitats that have no shrimp, such areas are only 16% of the total Yaquina tide flat area (DeWitt et 

al., 2004). Thus, because of these uncertainties, benthic flux measurements from those four studies 

(Dollar et al. 1991; Garber et al. 1992; Thom et al. 1994; Larned 2003) were not used for estimating 

the estuary-scale nutrient fluxes presented in Table 3.1 for the Yaquina Estuary. 

Bioturbation

Deposit Feeding

Filter Feeding

Remineralization

Particulate → Dissolved

Organic → Inorganic

DIN, PO4,

SiO2, DOC

Detritus

Production

Nutrients

Seston

Predators

Sediment

Advection

Tide

Current

O2, DOM,

Seston

Bioirrigation

Neotrypaea californiensis burrows

Upogebia pugettensis burrows

Light

Figure A.1 Conceptual model of the dominant processes (boxes) driving carbon and nutrient flux 

(arrows) between the benthos and water column in Pacific Northwest estuaries. 

 To avoid this problem, DeWitt et al. (2004) inserted 1-m deep core barrels into sediments at 

their study sites, and fit benthic chambers to the tops of the core barrels to isolate water, sediments, 

shrimp and burrows inside the chamber from the outside world. Core barrels were inserted >10d 

before the chamber tops were attached so that shrimp enclosed in the barrel could construct new 

burrow openings within the chamber. DeWitt et al. (2004) produced estuary-scale maps of benthicpelagic fluxes of NO3

-

+ NO2

-

, NH4

+

, and DIN for Yaquina Estuary (Figures A.2 and A.3) by linking 

estuarine-scale maps of burrowing shrimp populations to density-dependent flux measurements. DIN 

fluxes had great spatial variability owing to the differences in shrimp species and abundance across the 
124

estuarine landscape. DIN efflux (release) from sediments was estimated to be much greater than DIN 

uptake in the presence of burrowing shrimp, but in the absence of the shrimp DeWitt et al. (2004) 

estimated a net uptake of DIN by sediments. Most of the DIN efflux was projected to occur in U. 

pugettensis - dominated habitat, mostly due to enhanced NH4

+

 efflux. Tide flats lacking burrowing 

shrimp or having low densities of N. californiensis were shown to have a net uptake of DIN, most of 

which is expected to be NO3

-

+ NO2

-

. DIN efflux was estimated to be much greater than DIN uptake in 

the lower portion of the estuary, mostly due to the presence of dense populations of U. pugettensis. 

Using this shrimp species- and density-dependent nutrient flux model, the benthos in the upper 

(mesohaline) regions of the Yaquina Estuary was estimated to have a large net uptake of DIN because 

of the spatial dominance of N. californiensis and scarcity of U. pugettensis. 

 Much of the organic matter that is produced within, or advected into, the estuary is available for 

consumption by benthic herbivores, filter-feeders, or deposit feeders. The dominant benthic herbivores 

in Yaquina Estuary are ampithoid amphipods, isopods, and nereid polychaetes. Herbivores are not 

abundant among benthic infauna in Yaquina Estuary, with biomass 1-10% of filter-feeders and deposit 

feeders (Figure A.4). Deposit feeders are abundant throughout the estuary, and dominate the upperestuary infauna. In lower and upper reaches, the most abundant deposit-feeder by biomass is N. 

californiensis. Organic matter consumption rates have not been calculated for the infaunal depositfeeder guild in Yaquina Estuary. Filter-feeders are more abundant in the lower than in the upper 

estuary, primarily because that is the distribution pattern of the dominant species of this guild, U. 

pugettensis. Griffen et al. (2004) estimated that populations of the mud shrimp in the lower Yaquina 

Estuary pump the entire volume of water covering the tide flats through their burrows every day. 

Combining per-capita grazing rates for mud shrimp (Griffen et al., 2004), patterns of mud shrimp 

population distribution in the estuary (Figure A.2a), bathymetry, chlorophyll a data and a 

hydrodynamic model, it has been estimated that mud shrimp populations graze approximately 60% of 

the phytoplankton that enters the lower estuary. This estimate is similar to measured differences in 

flood- and ebb-tide chlorophyll a concentrations, suggesting that filter feeding is an important sink for 

phytoplankton in the lower estuary (see Section 7.1). 
125

Neotrypaea Burrow hole density (m-2)

N –Flux, mmol m-2

d-1

0 100 200 300 400 500 600

-15

-10

-5

0

5

10

15

NH4

+ NO3

A

N –Flux, mmol m-2

d-1

Upogebia Burrow hole density, m-2

0 100 200 300 400 500 600

0

10

20

30 NH4

+ NO3 B

Figure A.2 Density-dependent flux of NH4

+

 (black triangles and dotted line) and NO3

-

 (gray circles 

and solid line) associated with Neotrypaea californiensis (a) and Upogebia pugettensis (b). 

Positive values = efflux from sediments, negative values = uptake by sediments. Dashed and 

dotted curves represent best-fit regression models for flux = f(shrimp burrow density). Solid 

black line represents 0 nitrogen flux. 
126

Upogebia Neotrypaea Mixed Spp.

1-30 m-2

31-80

81-150

151-200

>200

0 m-2

1-50 m-2

51-150

151-280

281-400

>400

1-40 m-2

41-115

115-220

221-300

>300

Burrowing Shrimp

Population Density (# m-2)

A

mmol DIN m-2 d-1

B

Figure A.3 a) Distribution and abundance of two species of burrowing shrimp (red = Neotrypaea 

californiensis, blue = Upogebia pugettensis, green = mixed species; darker color = higher 

density). b) Distribution and magnitude of benthic-pelagic flux of DIN in Yaquina Estuary 

(positive values = efflux from sediments, negative values = uptake by sediments). 
127

0.1

1.0

10.0

100.0

1,000.0

Herbivore Surface

Deposit Feeder

SubSurface

Deposit Feeder

Filter Feeder Facultative

Filter/Deposit

Feeder

Carnivore

Trophic Groups

Biomass , g dry wt m-2

Lower

Mid

Upper

Figure A.4 Mean biomass distribution among trophic guilds in the lower, mid, and upper reaches of 

Yaquina Estuary. The error bars indicate minimum and maximum observed biomass values. 
128

Appendix B: Description of Methods and Quality Assurance Procedures 

 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, 2001). Measurements 

Data Quality Objectives (MQOs) establish the data user’s requirements for precision and accuracy. 

The Measurement Quality Objectives for each parameter in this study are presented in Table B.1. 

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 data sets, 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 MQOs and were established from considerations of 

instrument manufacturer’s specifications, scientific experience, and/or historical data. A measure of 

systematic error (measured vs. true or expected): accuracy and the random error (precision) is 

presented. Accuracy is a measure of how close measured values are to true values. In this appendix, 

accuracy is calculated using the following equation: 

(%) (1.0 ( ( )/ / ) *100 t n Vt Accuracy = − ∑ V −V n

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. 
129

Table B.1 Measurement Quality Objectives for data collected by Western Ecology Division. 

Parameter units Expected range Accuracy Precision SO

Sea-Bird CTD and YSI Multiparameter SondesConductivity  Sea-Bird CTD mS cm-1 0-100 ± 0.5% of reading 0.01 mS cm-1 SOP FS

manual 

Salinity  Sea-Bird CTD psu 0 – 35 psu 

± 0.5% of 

reading or 0.1 

psu 

0.01 psu 

Calculaconducttempera

Temperature  Sea-Bird CTD °C 0 - 25 °C ± 0.15°C 0.01°C SOP FS

manual 

Turbidity  Sea-Bird CTD ntu 0-125 ntu ± 2% of reading or 2 ntu 0.1 ntu SOP FS

manual 

Depth  Sea-Bird CTD meters 0-15 m ± 0.018 m 0.001 m SOP FS

manual 

Conductivity  YSI Sonde mS cm-1 0-100 mS cm-1

± 0.5% of 

reading or 

± 0.001 mS cm-1

0.01 mS cm-1 SOP IO

Salinity  YSI Sonde 

psu 0 – 35 psu ± 1 % or 0.1 psu 0.1 psu 

Calculaconducttempera

Temperature  YSI Sonde 

°C 0 to 25 °C ± 0.15°C 0.01°C SOP IO

Dissolved Oxygen  YSI Sonde 

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 SOP IO

Depth  on YSI Sonde meters 0-15 m ± 0.018 m 0.001 m SOP IO
130

Table B.1 Measurement Quality Objectives for data collected by Western Ecology Division. 

Parameter units Expected range Accuracy Precision SO

Water Column NutrientsDissolved NO3

-

μM 0 - 100 ±5% 5% MSIAL 

Dissolved NO2

-

μM 0 - 1 ±5% 5% MSIAL 

Dissolved NH4

+

μM 0 - 5 ±5% 5% MSIAL 

Dissolved PO4

3- 

μM 0 - 3 ±5% 5% MSIAL 

Dissolved Si(OH)4 μM 1 - 100 ±10% 10% MSIAL Total Nitrogen μM 0-100 ±5% ±5% WRS 3Total Phosphorous μM 0-3 ±5% ±5% WRS 3Total Suspended Solids mg l-1 1 - 150 15% 15% WRS 1Water Column chlorophyll a μg l-1 0-200 ±15% ±15% MES SOZostera marina Data Biomass gdw m-2 0 -300 gdw m-2 0.1 g-1 sample 0.1 g -1 sample QAPP0Shoot Density shoots m-2 1-15,000 m-2 ± 2 m-2 1 shoot per m2 quad QAPP0Growth rate of leaf blades gdw m-2 d-1 0 to 5 gdw m-2 d-1 95 % 95 % QAPP0Tissue CHN g C/g tissue as % 0.1 - 50 % ± 10% of standard CV # 10% QAPP0
131

Table B.1 Measurement Quality Objectives for data collected by Western Ecology Division. 

Parameter units Expected range Accuracy Precision SO

Tissue P g P/g tissue 0.1 - 10 % ± 10% of standard CV # 10% QAPP0Shoot Length mm 5 - 1200 mm ± 2 mm ± 1 mm QAPP 0Shoot Width mm 1 - 7 mm ± 1 mm ± 1 mm QAPP 0Shoot Dry wt. mg 0-1500 mg ± 1 % ± 0.1 mg QAPP 0Z. marina lower limit depth cm 0-10 MLLW ≤60 cm n/a Epiphyte Biomass dry wt. mg 0-1500 mg ± 1 % ± 0.1 mg QAPP 0%Plant Cover (Visual) % area 0-100% 10% 10% QAPP 9Macroalgae% cover of algae, or Bare Substrate (visual estimate) 

percent m-2 0-100% ± 15% 0.70 QAPP 2

Biomass gdw m-2 0 -300 gdw m-2 0.1 g-1 sample 0.1g -1 sample QAPP0Macroalgae nitrogen isotope ratio (δ15N) ‰ -2 to +25 0.5 ‰ 0.5 ‰ QAPP0

QAPP 0

Burrowing Shrimp Parameters
132

Table B.1 Measurement Quality Objectives for data collected by Western Ecology Division. 

Parameter units Expected range Accuracy Precision SO

Burrowing Shrimp Hole Density holes m-2 0-1500 m-2 ± 10% 0.70 QAPP 2Burrowing Shrimp Density individuals m-2 0-600 m-2 ± 10% 0.70 QAPP 2Benthic Invertebrate Abundance individuals m-2 0-5000 m-2 ± 10% ± 10% QAPP 2Burrow Surface Area cm2 5-1600 cm2 ± 5 cm2 ± 1 cm2 QAPP 2Burrow Volume cm3 1-800 cm3 ± 5cm3 ± 1 cm3 QAPP 2General Lab Mass mg 0.5-150 ± 5% ± 5% SOP IOPhysical featuresEstuary bathymetry ft -40 to +10 MLLW ± 0.5 NA survey 

D.Youn

Ground position CMT GPS system, HPGPS-L4 

UTM: meters 

a) Easting 

b) Northing 

414480 - 428210 

4933695-4942656

± 0.5-2.5 m ± 1.5 m CMT G

GPS-L4 
133

Table B.2 Quality Calibration and Control Checks for Instruments and Parameters. Instrument Calibration procedure Quality Control Check Frequency Acceptanc

criteria 

SBE-19 package: Conductivity SeaBird factory 

calibration 

Solution of known conductivity If drift is 

suspected 

MQO 

Temperature SeaBird factory calibration Place in water bath with NIST traceable thermometer If drift is suspected MQO Depth (pressure) SeaBird factory 

calibration 

In air reading compared to Fortin type Hg barometer (Nat. Wea. Serv. type) 

If drift is suspected 

MQO 

PAR sensors LiCor factory 

calibration- every 2 years 

Solar noon clear sky exposure Bi-annually 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>

slope ±25% initial 

YSI Multiparameter Sondes (6000 and 6600 models) 

Factory calibrated for depth; calibrations performed to manufacturer’s specifications 

All parameters checked against factory standards upon retrieval from deployment 

Immediately prior to deployment and upon retrieval 

Performed factory specificatimethods anstandards 

YSI Hand-held meters 

 All parameters checked against 

factory standards 

 

CMT-DGPS Self-calibrates with 

satellites ( speed of light as the calibrant)-every use 

Visit type 1 USGS reference site with multiple readings 

Annually MQO 
134

LiCor LI-193SA sensor 

Li-Cor factory calibration – every 2 years 

Solar noon clear sky exposure Bi-annually MQO 

Balance (5-place) 

Factory representative adjusts on annual basis 

 Before each 

session 

Within thaclass tolera

Drying oven Check reading of thermometer Before each use MQO Fluorometer Turner 10-AU 

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 settof secondastandard rediffers by from true v

Microbalance Check calibration 

with internal weights 

Readings of 5 class ‘S’ masses that cover expected range 

Before each session 

± 2 mg 

Mass Spectrometer 

 Peak centering Before each 

analysis 

Center for of the masbeing mea

Mass Spectrometer 

 Nitrogen isotope ratio of working 

organic standard 

Once every 10 samples 

± 0.2‰ of 

term measvalue 

Mettler AT250 balance 

Annual calibration by contractor (Quality Control Services, Inc.) 

Check accuracy with ‘S’ class weights 

With each use Contractor 

determinat
135

Water Quality CTD Profiles and Grab Samples

 Profiles of water quality parameters were measured using the Seabird SBE 19 CTD with 

data logging capability. Variables measured by the CTD and other associated instrumentation 

are depth, temperature, conductivity, turbidity, photosynthetically active radiation (PAR), and in 

situ fluorescence, and calculated variables include salinity and density. During 2006 cruises, 

dissolved oxygen was measured at discrete depths. Discrete water samples were collected at 

representative depths (surface, mid-depth, and bottom) for analysis of chlorophyll a, total 

suspended solids, and dissolved inorganic nutrients. Since this report includes data collected for 

various projects and principal investigators, the number and location of discrete samples varied 

with sampling interval. 

 For each water quality profile, the CTD was lowered to the bottom, and discrete water 

samples were collected during the upcast at bottom, mid-depth and surface depths. 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. During 2006 cruises, dissolved 

oxygen was collected using a YSI 6600 Sonde attached to the CTD cage. 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-

), total nitrogen and phosphorous (2006 cruises only), 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 within several hours of collection. 

 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. 
136

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 laboratory. Typically, the 

samples were filtered within 1-2 hours of sample collection. For TSS analysis, 1-liter of 

unfiltered seawater was collected at relative depths 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. Standard 

Operating Procedure for Preparation and Analysis of Estuarine Water Samples for Determination 

of Chlorophyll-a content. The samples were stored in the freezer until analyses. Chlorophyll a

was extracted by sonicating the filters in 90% acetone and quantified using a fluorometer (10 AU 

Fluorometer, Turner Designs, Inc, Sunnyvale, CA). 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 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 recalibrated 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 fluorometric 

chlorophyll a standards supplied by the manufacturer (Turner Designs) consisted of a high 

concentration (181μg 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 presented in this report. The 

accuracy and precision for the chlorophyll a data reported in this study are estimated to be 

98.7%. 
137

Nutrient Data 

 Nutrient analysis for nitrate+nitrite, 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 analysis 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 are analyzed 

periodically to audit performance. Deionized water blanks and sea water blank (low-nutrient 

natural sea water, aged to allow nutrient values to drop to near-zero levels) are also run. An 

independently-prepared “control” solution containing an intermediate concentration of each of 

the nutrients is also prepared. 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 flow-through detector, so the sea water blank is used to adjust the measurement timing 

parameters to compensate for the refractive index effect. Instrument calibration is checked at the 

beginning of a sample-batch run, at the end of the run, and periodically during the run. Each 

calibration sample is analyzed in duplicate, and the resulting data is used to establish calibration 

curves for each nutrient species. If the mean of the two replicates of any standard differs from 

the known concentration of that standard by more than ten percent or more than one-half the 

concentration of the lowest standard, whichever, is greater, the calibration for that species is 

considered invalid and the calibration run is repeated. (See MSIAL Quality Assurance Manual 

2005 for more details) 
138

Table B.3 Precision and accuracy (expressed as %) of phosphate, silicate, nitrate+nitrite, 

ammonium, and nitrite Data. 

Measure PO4

3- Si(OH)4 NO3-

+NO2

-

 NH4

+

 NO2

-

Precision, % 

97.6 

(n=265) 

98.3 

(n=258) 

98.2 

(n=266) 

98.0 

(n=265) 

94.6 

(n=7) 

Accuracy, % 

98.2 

(n=82) 

98.2 

(n=253) 

96.5 

(n=195) 

96.8 

(n=140) 

97.9 

(n=4) 

Total Phosphorous and Total Nitrogen 

 Total nitrogen and total phosphorous analysis was performed by the Willamette Research 

Station (WRS), an EPA (WED) research facility in Corvallis Oregon. A Lachat Quikchem® 

8000 Two-Channel FIA was used for analysis. WRS adheres to strict EPA QA/QC procedures 

and the following procedures are detailed in the standard operating procedure document WRS 

34A.3 (2005). 

 A second source check standard (SSCS) (NIST traceable) was included in each 

automated analyses run. Instrument calibration and stability was validated after every tenth 

samples. A blank (Reagent water) was run after each SSCS to ensure negligible carryover. 

Calibration verification was monitored throughout the run by checking SSCS recovery. If 

measurement exceeded ±10% of the theoretical value, the instrument was recalibrated. A control 

check standard (QCCS) is a bulk sample digested at least once each digest batch set to show 

inter-run consistency. The bulk sample is collected as needed from a local stream or river and 

prepared in the same manner as samples. An analytical duplicate was run as a separate analysis 

(digestion and analysis) no less than once every 10 samples. Inter-run consistency and column 

performance was monitored with the QCCS (bulk sample) that is analyzed once each analytical 

run. 

 Three of each of the TN and TP quality control check standards (nicotinic acid and 

ascorbic acid) were digested with each batch to verify TN and TP recovery. Three reagent water 

blanks were digested to determine the nitrogen and orthophosphate blank present in the mixed 

persulfate digestion reagent. Digested standards and blanks were used to monitor digestion 

efficiency and background contribution of the persulfate. A method detection limit is the 

minimum concentration of an analyte that can be measured and reported with 99% confidence. 
139

A MDL was established for each analysis based on at least seven repeated measurements (within 

a run) of a low level standard. 

Table B.4 Precision, accuracy, and recovery for total nitrogen and phosphorous analyses. 

Measure Total Nitrogen Total Phosphorous 

Precision, % 

98.7 

(n=39) 

96.7 

(n= 36) 

Accuracy, % 101.8 97.7 

Digestion Recovery, % 96.3 98.5 

Handheld YSI Meters 

 The handheld multiparameter Yellow Springs Instruments (YSI) meters were checked 

prior to use with manufacturers (YSI) conductivity standard. To check the dissolved oxygen 

reading the DO probe was placed in a 100% saturated environment for several minutes. If the 

percent saturation value was several points away from 100% the electrodes were cleaned, a new 

KCL solution was reapplied and the membrane was replaced. Another percent saturation reading 

is taken after the DO probe has been serviced. All QA/QC checks and calibrations recorded in a 

database (Microsoft® Office Access 2003). The frequency of checks and calibrations was 

intermittent and depends on how often the units are being used. 

YSI Multiparameter Sonde 

 YSI 6600 Multiparameter 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 two-point calibration; using reverse osmosis water (RO) 

followed by a 123 NTU YSI standard solution. The dissolved oxygen (DO) sensor was 

calibrated for dissolved oxygen 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. 
140

Temperature can not be calibrated but its performance was checked. The data sondes 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 is defined as the accuracy of the probe in a standard 

solution. Post deployment accuracy is defined as the accuracy of the probes after they are 

retrieved from the field and are tested against the known standard solutions. 

Table B.5 Precision and Accuracy (expressed as percentage) of YSI Multiparameter Sondes. 

 Temperature Salinity Conductivity Turbidity DO 

Calibration Accuracy 

Pre-deployment 

99.0 98.2 100.0 100.0 99.6 

Post Deployment 

Accuracy (includes the 

effect of biofouling) 

98.7 96.5 97.8 97.1 94.4 

Burrowing Shrimp Densities 

 Shrimp densities were assigned classes (0 holes, 1-10 holes, 11-50 holes, 51-100 holes, 

and 101-175) by direct counts of burrow holes within a 0.25 m2

 quadrat. These were designated 

as classes 0-4, respectively. Shrimp burrow identity and density-class, date, time, and 

geographic location data were collected along a survey track using a dynamic line setting 

recording the data onto a March II GPS-data logger (Corvallis Microtechnology Inc., Corvallis, 

OR) at 1-s intervals. As a change in species or density was observed, the line feature was ended, 

and a new dynamic line was started with the new species and density attribute. Burrow identity 

and density classification was verified approximately every 30 min during the survey by 

qualitatively sampling burrows using a bait pump and counting burrow hole densities using a 

0.25 m2

 quadrat. The same procedure was also used whenever the survey team was uncertain of 

burrow attributes. Each ‘QA’ quadrant was also photographed with a digital camera mounted on 

a PVC frame to provide a consistent reference. As a check on burrow hole counting , the burrow 

holes were later re-counted from the digital pictures. 
141

 To quantify the relationships between burrow density class, burrow opening density, and 

shrimp density, spatially coincident “baseline” samples were collected at 90 random sites 

throughout Yaquina estuary. Shrimp density was determined by hydraulically excavating a 

megainfaunal core barrel (40 cm diameter x 100 cm depth) using a suction-dredge, and washing 

the core barrel contents through a 3 mm mesh to retain burrowing shrimp and other large 

infauna. 

Stable Isotope Data 

 Macroalgae samples were collected monthly during 2003 and 2004 from five locations 

along the salinity gradient ranging from polyhaline to oligiohaline conditions. Algal material 

was collected from hard substrates to eliminate contamination from any additional nutrient 

sources other than the water column. Five replicates of healthy macroalgae were collected from 

each sampling site. Samplers wore sterile lab gloves while collecting to prevent contamination. 

Each algae sample was washed thoroughly in RO 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 samples for 

δ15N according to SOP CL-6 (1999). Nitrogen isotope ratios were measured on macroalgae 

samples combusted in 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. 
142

Table B.6 Precision and Accuracy of δ15N data. 

δ15N 

Precision, % 99.1 (n=23) 

Accuracy, % 96.6 (n=63) 

 

Aerial Mapping of Seagrass and Macroalgae 

 The remote sensing procedure used in this study to map the intertidal distribution of 

eelgrass and benthic green macroalgae (conducted from 1997 – present) utilizes aerial 

photography with false-color near-infrared (color infrared, CIR) film. This allows an aerial 

survey to be conducted during daylight low tide (typical tide level about -0.5 m MLLW) when 

the majority of the eelgrass habitat in the Yaquina Estuary is exposed. CIR film has been found 

to provide substantially better spectral resolution of exposed intertidal vegetation than has true 

color film (Young et al., 1999). The mapping method is able to detect inundated and submerged 

Z. marina to a depth of about 1 m below water level at the time of the aerial photograph. To map 

perennial eelgrass habitat, the surveys are conducted in late spring or early summer before the 

summer bloom of benthic green macroalgae that can interfere with the classification of eelgrass 

habitat. Mid-summer surveys are used to map macroalgal distributions upslope of the eelgrass 

meadows. Photoscales utilized range from about 1:6,000 to 1:20,000. The aerial photographs 

are 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; Clinton et 

al., in review). The digital orthophotos are classified into eelgrass and bare substrate habitats, 

defined as > 10% cover or < 10% cover, respectively. On-the-ground resolution of 0.25 m is 

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., in 

review). The technique requires training data from ground truth surveys, with station positioning 

accomplished by a differential-corrected global positioning system (GPS). The RMSE of GPS 

positions obtained at an NGS first-order monument in Yaquina Estuary was 0.62 m. 
143

 Another part of the ground survey employs 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 review). Results were 

obtained in spring 2004 from 51 randomly positioned stations within intertidal eelgrass 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 of 

97%, with a Kappa Index value of 0.9447 + 0.0024, indicating 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 eelgrass meadow margins (Young et al., in 

review). 

Depth Distribution of Z. marina 

 The amount of Z. marina at a specific tidal height (Figures 11.2 and 11.3) was 

determined by overlaying the Z. marina maps with the results of an extensive bathymetric depth 

survey conducted by the U.S. Army Corps of Engineers in 2002. The bathymetric model and the 

seagrass classification are both ArcInfo format grids. The bathymetric depth data was 

interpolated onto a grid using TopoGrid in ArcInfo. A 2.0 m floating point bathymetric model 

grid was integerized and resampled to 0.25 cell size to match the binary seagrass classification 

grid. The bathymetric and seagrass grids were then overlaid using the map algebra function 

COMBINE, which produces a grid value attribute table with counts of cells for each unique 

combination of cell values from each grid. 

 The aerial mapping method utilized is capable of classifying some submerged seagrass 

beyond the depth at which near-infrared radiation is absorbed by water; however, some of the 

data returned by the COMBINE function is undoubtedly a result of spatial misregistration 

between the aerial photo classification and the bathymetric model. A few outliers were trimmed 

from both tails of the distribution curve. It is also quite possible that a physical survey could 

return some higher percentages of overall seagrass distributions below -1 m MLLW. The result 

of this analysis is presented as distribution curves of total area of intertidal eelgrass for ocean and 

river dominated estuarine areas. 
144

Macroalgal Biomass and Cover 

 Benthic green macroalgae coverage and biomass measurements were conducted as part 

of several studies in Yaquina Estuary between 1998 and 2004 (Table B.7). Most of the 

measurements were made during the dry season (May – October) in the marine-dominated sector 

(Zone 1). Although both non-random and random sampling designs were employed in different 

survey efforts, all the macroalgal data within Yaquina Estuary (WED unpublished data; Kentula 

and DeWitt, 2004) were combined for analysis. To obtain a regional perspective, the data from 

the Classification Study (Section 4.2) also were summarized for comparison (Lee et al., 2006). 

Table B.7. Sources of ground survey macroalgae data used in this study. All data were 

collected by WED. Random and non-random sampling designs are denoted by R and 

NR, respectively.

Period Zone No. of Samples 

Zone 1 Zone 2 

Sampling 

1998 1 69 - NR 

1998-99 1 65 R 

1999-2003 1 4159 - R 

2001-2002 1 2094 - NR 

2002-2004 1 140 - NR 

2004 1 & 2 47 53 R 

 To examine seasonal variability in benthic green macroalgae cover and biomass, six band 

transects were established within Zone 1 of Yaquina Estuary during 1999. These sites (I –VI, 

respectively) were situated 3.9, 5.0, 6.3, 7.8, 8.6, and 10.6 km from the ocean end of the jetties at 

the mouth of the estuary (Figure 2.1). The transects were 30 m wide, and extended 100 m 

upslope from the MLLW tide line perpendicular to the channel. Along each transect, sampling 

stations were regularly spaced at 10 m intervals. At Sites II and III, the bathymetric slope was 

lower than for other sites, and in August 1999 the transects were extended to about 350 m from 

MLLW, with the additional sample stations situated at ~ 40m intervals. Stations along the 

transects were generated with a random number generator prior to field sampling. For each 

station, three randomly-selected distances between 1 and 30 m perpendicular to the sampling 

transect were sampled. Between June and December 1999, most of the sites were sampled every 

other week during daylight low tides, while less frequent sampling of these sites continued 

through May 2000. 
145

At each plot, a percent cover value within a 0.25 m2

 quadrant was visually estimated for SAV 

and macroalgae. For much of the data, a frequency-of-occurrence value was also measured by 

recording which class of cover occurs directly beneath each of 25 point intercepts formed by two 

orthogonal sets of string intercepts. The purpose of such frequency-of-occurrence measurements 

was to provide a quality check on the estimated percent cover values. Pilot studies comparing 

the linear regression of the estimated percent cover values against the measured percentage 

frequency of occurrence values have yielded r2

 values of 0.91 - 0.97. For this current study only 

the green macroalgae data was used for analysis. 

 Samples of the alga taxa was collected from a 0.05 m2

 area of the 0.25 m2 quadrant at two 

of the three replicates sites along the station line. After cleaning, the alga was identified by 

qualified individuals. Alga keys and identification aides were available for cross-referencing. 

The alga biomass was determined by drying the alga at 80°C until dry. A sample was considered 

dry when there was no further weight change due to loss of water after additional oven drying. 

Returning a subset of sample to the oven after recording the initial dry weight was also a 

measure of repeatability. The final dry weighs for the 0.05 m2

 sample was converted into gdw 

m-2.

 An alternative method was used to determine macroalgae biomass in the 2002 field 

season. A volumetric biomass estimate was collected according to the graduated cylinder 

method as described in Robbins and Boese 2002. The macroalgae was collected from a 0.25 m2

quadrant and placed into the 2000 ml cylinder. The algae were pressed with a plunger to remove 

excess water before recording the algal volume in ml. This quick field method was determined 

to be an accurate surrogate for biomass dry weight determination with a linear relationship 

yielding a r2 value of 0.78 to 0.88 for macroalgae species (Robbins and Boese 2002). 

Zostera Marina Lower Depth Limit 

 The lower depth margin of Z. 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 by 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. 
146

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 waters surface and could easily be approached by foot. Personnel walked from 

shore along the transect to the deepest Z. marina patch, recorded a GPS location, measured the 

depth with a lead line and collected other water quality parameters. 

Tidal corrections for Lower Depth Limits of Z. marina 

 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 of Z. marina was measured, 

suggests that conditions were relatively calm during the sampling and not collected during 

extreme tides. In addition, 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 amplitude and phase lags of tides are available for four locations 

(South Beach, Yaquina, Winant, and Toledo) in the Yaquina Estuary (http://coops.nos.noaa.gov/tides05/tab2wc1b.html#132). 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 maximum distance along the river any given depth station from a tide 

station was 4.5 km. The largest source of error in the tidal corrections results 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, 

we compared the differences in tidal corrections between the two stations that are located 

upstream and down stream of the observations. 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. 
147

Epiphyte Methods 

 Epiphytes growing on Z. marina leaves were collected within the Yaquina Estuary from 

2000 though 2004. Data were collected at six stations distributed between 3.5 and 17 km upriver 

from the mouth of the Yaquina Estuary. Leaves from collected plants were subdivided into outer 

(older) and inner (younger) leaves. Epiphytes were scraped from these leaf groups with dry 

weights (24-36 hours at 60-70 °C) of the removed material determined for each individual plant. 

The effect of epiphyte cover in terms of reducing light (PAR) availability to eelgrass 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. 

Station Location 

 The geoposition of each station was collected in the field with a Global Positioning 

System (GPS) and differentially corrected in post processing with data form 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 1-5 m. 

Distance Upriver Calculations 

 The distance from the mouth of the Yaquina to each station was calculated using GIS 

mapping software Arcview. After a center line shape file extending the length of the river was 

converted into a route file, the orthogonal distance from each station to the nearest point along 

that route file was calculated. The ArcToolBox linear referencing tool, 

LocateFeaturesAlongRoutes, was used to calculate the distances in meters for each station from 

the mouth of the estuary to the nearest point along the centerline route feature. 
148

Landscape Data 

The Yaquina landscape analysis was done as part of a larger Pacific west coast estuary 

survey. Watershed boundaries subtending the Yaquina estuary basin were primarily determined 

from the Sixth Field hydrologic unit code (HUC) 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. 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. Boundary lines and water bodies were plotted 

and reviewed for accuracy of coding and fidelity to the original sources. The Yaquina watershed 

delineated in this project captures the entire drainage area (EDA). By delineating the entire 

watershed, the watershed area is 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://spo.nos.noaa.gov/projects/cads/description.html#caf). 

Land Cover Sources 

 The estuary watershed was 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 Yaquina watershed boundary. The 1992 NLCD data 

contains 21 classes of land cover (see http://erg.usgs.gov/isb/pubs/factsheets/fs10800.pdf). 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 use datasets have been created by the NOAA’s Coastal Services Center (CCAP, 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 
149

change layer. The 2001 NOAA data are based on 22 land use classes 

(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/classification.htm for a crosswalk of the two schemes). 

Land Use 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 use 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 a watershed. Accuracy of the 

NLCD data of the MRLC zone that contains the Yaquina watershed is estimated to be 86.1%. 

These data sets 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, 2004b). The MRLC 2001 impervious surface layer, 

which represents an estimate of developed impervious surface per pixel by percent 

imperviousness, was clipped and summarized for the 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 use were 

calculated from the watershed land cover data using coefficients from the ATtILA program. 

 Estimates of slope were calculated for each watershed from slope surfaces generated 

from 10 meter (Oregon) DEMS (digital elevation models). 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 
150

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. 

Population Density 

 Human population estimates from the 1990 and 2000 censuses (http://www.census.gov/) 

were generated for the drainage unit. Area weighted estimates of total population by census 

block were summed for each drainage and population density (individuals/sq. kilometer) was 

calculated from the total drainage population estimate. 

Other Data Sources 

 Historical sources (data collected in the 1960, 70 and 80’s and presented in technical 

reports, thesis dissertations or journal publications) were carefully reviewed for information on 

quality procedures implemented in their data collection. Methodological approaches for 

measuring DO, chlorophyll-a, and nutrients were reviewed for analytical method utilized. Data 

obtained from the Oregon Department of Environmental Quality database were previously QAed 

and only data with grades of A and A+ were used as part of this report. Often historical data 

collected before the use of GPS systems were given as locations on map, common station names, 

or as kilometers upriver. From these descriptions, the northing and easting UTM locations were 

estimated using Topozone or Google Earth. Locations given in Latitude and Longitude were 

converted into UTM units using a batch converter located at 

http://www.uwgb.edu/dutchs/UsefulData/HowUseExcel.HTM or in Topozone. Units were 

converted to metric units (i.e. feet to meters, Fahrenheit to Celsius, etc.). Most of the data was 

hand entered from tables to electronic format in Excel. Data was entered electronically by one 

person and checked for errors by another independent person. Data points presented only in 

graphical format were digitized into electronic format and translated into tables. Errors and any 

resulting changes were documented and traced to the source. All data were entered into an 

Access database and data were reviewed to ensure that there were no duplicate entries. 
151

Models 

Stress-Response Model 

 All field and laboratory data used in the development of the Stress-Response model 

(SRM) were collected in accordance with WED SOP’s and QAPP’s. (Tables B.1 and B.2). A 

complete list of these parameters is found in Kaldy and Eldridge (2006) Volume II-Tables 1, 2, 

3, 4, and 5. All the measurements of physical and biological quantities used in our modeling are 

subject to uncertainties. Further these measurements were often combined to produce new 

derived quantities, each of which has its own uncertainties. We calculated these uncertainties 

using means and standard deviations of the data and error propagation algorithms from 

http://teacher.nsrl.rochester.edu/phy_labs/AppendixB/AppendixB.html. 

 Once the SRM was calibrated to the biomass data (see Kaldy and Eldridge 2006, 

Figure F.1), we conducted a series of tests to examine the models sensitivity to parameters. A 

model that is overly sensitive to parameters is considered unstable. Kaldy and Eldridge (2006) 

Volume II-Table 8 provide a complete sensitivity analysis of the model used herein and a 

discussion of the sensitive results is presented on page 55 of Kaldy and Eldridge (2006). 

 Another aspect of the model quality assurance is the development of validation 

experiments. We developed the model using local seagrass and environmental data, but plan to 

use the model to address regional or national level questions. Z. marina physiology and genetic 

alleles (minor variations of the same gene) diverge significantly in different regions of the 

continental United States. The regional differences in Z. marina require that we run our 

validation experiments at multiple scales. At the local scale we have developed plant level-tracer 

experiments to evaluate the allocation of carbon within a plant (Kaldy and Eldridge 2006). At 

the regional scale we are planning running validation experiment in Puget Sound, Washington 

during 2007. At the national scale we have conducted Z. marina mesocosm experiment in 

Narragansett, Rhode Island in collaboration with AED and the University of Rhode Island. 

These data will be used to validate or recalibrate the SRM for regional or national level 

implementation of the SRM. The combination of the data uncertainty analysis, the model 

calibration and sensitivity analyses, and the local, regional, and national scale SRM validations 

constitute our QC/QA program. 
152

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 SOP’s and QAPP’s. (Tables B.1 and B.2). 

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 
153

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

f R + fW + fO =1

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 

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). 

List of Quality Assurance Project Plans (QAPPs) Used in This Study

QAPP98.04. Evaluation of the Susceptibility of Eelgrass Beds in Oregon Estuaries to Changes in 

Watershed Uses. R. J. Ozretich, EPA, 1998. 

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. 
154

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. 

Marine Science Institute Analytical Laboratory University of California, Santa Barbara. Quality 

Assurance Manual-Draft. 2005. 

List of 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. 

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. 

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. 

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. 
155

WRS 14B.2. Standard Operating Procedure for the Determination of Total Suspended Solids 

(Non- Filterable Residue). Dynamac Corporation 2005. 

WRS 34A.3.Standard Operating Procedure for the Digestion and Analysis of Fresh Water 

Samples for Total Nitrogen and Total Phosphorus. 2005. 

Clinton, P.J., Young, D.R., and Specht, D.T. In Review. 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. SOP-NHEERL/WED/PCEB/PJC/06-01-000 09/15/06, U.S. 

Environmental Protection Agency, Pacific Coastal Ecology Branch, Newport, OR. 
156

Appendix C: Classification of Oregon Estuaries 

 Of the estuaries that were sampled as part of the Classification and NCA data sets, the 

number of classes of estuaries (or types) depends upon the scale of the classification system as 

well as the classification system utilized (see Table C.1). Of the seven estuaries sampled by 

WED in 2004-2005 which form the Classification data set, five were also included in a NOAA 

classification scheme, with 4 estuaries classified as “river dominated with straits and terminal 

bay”, and 1 estuary classified as “coastal embayment – v-shaped and semienclosed.” Lee et al. 

(2006) classified all seven of the estuaries sampled in the Classification effort as “drowned river 

valley” and Bottom et al. (1979) classified 6 of them as “partially mixed” and the 7th (Coos) as 

“well mixed.” For the Oregon estuaries sampled as part of the NCA effort, the NOAA 

classification would define eight as “river dominated with straits and terminal bay”; one as “river 

dominated with salt wedge” (Columbia), one as “coastal embayment” (Coos), and one as 

“lagoon” (Netarts). 

 Quinn et al. (1991) classified West Coast estuaries based on their relative susceptibility to 

nutrient pollution, defined as an estuary’s capability to concentrate dissolved and particulate 

pollutants. In their 1991 study, Quinn et al. classified the estuaries by dissolved concentration 

potential (DCP), which is the ability of the estuary to concentrate dissolved substances, and 

particle retention efficiency (PRE), which is a measure of the ability to retain suspended 

particulates within the estuary. In this classification system, 8 Oregon estuaries (including Alsea, 

Coos, Nehalem, Netarts, and Siletz, Siuslaw, Tillamook, and Yaquina) classified as having 

“high” DCP and “low” PRE. Umpqua Estuary classified as “medium” (near border of high) 

DCP and “low” PRE, while the Columbia River Estuary had “low” DCP and “low” PRE. All of 

the Oregon estuaries classified as being in the medium category of nitrogen concentrations 

(estimated using the loadings and DCP). 

 Burgess et al. (2004), Engle et al. (2007), and Bricker et al. (in prep.) classified estuaries 

of the United States using statistical cluster analysis of physical and hydrologic variables to 

determine the response of estuaries to nutrient loading. The Burgess et al. (2004) classification 

included physical and hydrologic parameters including estuarine area, estuary drainage area, area 

of mixing, seawater and tidal fresh portions of the estuary, tide, riverflow, estuary volume, tidal 

prism volume, salinity, depth, DCP and PRE. The primary variables contributing to the 

separation of 11 clusters (or classes of estuaries) in the Burgess et al. (2004) classification were 
157

size of the estuarine drainage area, estuary area and volume, riverflow, depth and salinity. In a 

more recent estuarine classification, Engle et al. (2007) updated the Burgess et al. (2004) 

classification scheme to incorporate average air and water temperature and surface and bottom 

water temperature and found that the estuaries clustered into 9 classes. Bricker et al. (in prep.) 

also used a cluster analysis to classify the same estuaries and they found that the estuaries were 

best clustered by estuary depth, tide, ratio of freshwater input to estuary area, temperature, and 

mouth openness, resulting in a classification with 10 classes of estuaries. 

 Using the Engle et al. (2007) classification, the estuaries sampled in the Classification 

data set fall into two classes (Alsea and Umpqua in one; and Coos, Tillamook, and Yaquina in a 

second class). In the Bricker et al. classification, the estuaries sampled in the Classification data 

set fall into two classes; however, Alsea, Tillamook, Umpqua and Yaquina are in one class and 

Coos is in another. The Oregon estuaries sampled in the NCA data set fall into 3 classes in the 

Engle et al. (2007) classification with 8 of the 11 classified as within one class, while in the 

Bricker et al. classification these same estuaries fell into 2 classes with 9 of the 11 being in the 

same class. 
158

Table C.1. Classifications of 14 Oregon estuaries sampled in WED Classification Study and NCA data sets (data set denoted as column). Lee et al. (2006) Classification based on Oregon Coastal Atlas (http://www.coastalatlas.net) and Bottom et al. (1979). 

NOAA Qu

Estuary Class Type Geomorphology Lee et al (2006) Stratification Bottom et al. (1979) Burgess et al. (2004) Engle et al. (2007) Bricker et al. (in prep.) Dissolved Concentration Potential 

Alsea (C,N) 

River Dominated 

Straits w/ Term. Bay 

Tidal dominated drowned river 

Partially 

Mixed 9 1 4 H 

Columbia (N) 

River Dominated Salt Wedge River dominated drowned river NA 1 5 4 L 

Coos (C,N) 

Coastal Embayment 

V-shaped & semi-encl. 

Tidal dominated drowned river Well Mixed 6 8 1 H 

Nehalem (N) 

River Dominated 

Straits w/ Term. Bay 

Tidal dominated drowned river 

Partially 

Mixed 9 8 4 H 

Nestucca (C,N) NA NA Tidal dominated drowned river Partially Mixed NA NA NA NA Netarts (N) Lagoons Limited FW Inflow Bar built Well Mixed 11 8 1 H Rogue (N) 

River Dominated 

Straits w/ Term. Bay 

River dominated drowned river 

Partially 

Mixed 9 8 4 M 

Salmon R. (C,N) NA NA Bar built or drowned river Partially Mixed NA NA NA NA Siletz (N) 

River Dominated 

Straits w/ Term. Bay 

Tidal dominated drowned river 

Partially 

Mixed 9 8 4 H 

Siuslaw (N) 

River Dominated 

Straits w/ Term. Bay 

Tidal dominated drowned river 

Partially 

Mixed 9 8 4 H 

Tillamook (C,N) 

River Dominated 

Straits w/ Term. Bay 

Tidal dominated drowned river 

Partially 

Mixed 9 8 4 H 

Umpqua (C,N) 

River Dominated 

Straits w/ Term. Bay 

River dominated drowned river 

Partially 

Mixed 9 1 4 M 

Yachats (N) NA NA Tidally Restricted coastal Creek NA NA NA NA NA Yaquina (C,N) 

River Dominated 

Straits w/ Term. Bay 

Tidal dominated drowned river 

Partially 

Mixed 6 8 4 H 

Legend: NA denotes classification not available; H, M, and L represent high, medium, and low classes (respectively). 
159

Appendix D: Survey of Effects of Macroalgae on Biota 

Publications concerning the ecological effects of macroalgae were reviewed, and reported 

threshold values of percent cover and/or biomass for effects on infauna, epifauna, fishes, and 

shorebirds are summarized in Table D.2 
160

Table D.2. Summary of literature regarding the effects of macroalgae (biomass or percent cover) on 

estuarine infauna, epifauna, fishes, and shorebirds.

Taxa Type Location T 

(

o

 C) 

Sediment 

H2S or Low 

Eh 

Effect* Macroalgal 

Density 

Citation 

Shorebirds 

S England 

(S. coast) Y ↓ abundance 

Cover: 75% in 

20% of 

intertidal 

Tubbs 

(1977) 

Shorebirds 

S England 

(S. coast) Y 

Neg. correl.: areas 

densest algal mats vs. 

abundance 

Cover: >25% in 

40% of 

intertidal 

Tubbs & 

Tubbs 

(1980) 

Infauna 

Epi.fauna 

Shorebirds 

S England 

(S. coast) Y 

↓ infauna 

↑ epibenth.fauna 

↓ shorebirds 

Cover: 42 % Nicholls et al. 

(1981) 

Infauna 

Shorebirds S England 

(S. coast) Little effect on 

zoobenthos or shorebirds ~ 300 gdw m-2 Soulsby et al. 

(1982) 

Shorebirds 

S England 

(S. coast) 

↓ abundance 

(refutes Soulsby et al., 

1982) 

Cover: >25% in 

40% of 

intertidal 

Tubbs & 

Tubbs 

(1983) 

Infauna/ 

Epi.fauna F Ireland 

(S.W. coast) Y ↓ infauna 

↑ epi.fauna 

Thrush 

(1986) 

Infauna F Scotland 

(E. coast) Y ↓ amphipods 

↑ polych. & bivalves 60 gdw m-2 Hull 

(1987) 

Infauna F Sweden 

(S. coast) ~20 ↓ larval settlement 143 gdw m-2 Olafsson 

(1988) 

Infauna S Scotland 

(E. coast) Altered infaunal 

composition 440 gdw m-2 Raffaelli et al. 

(1989) 

Infauna F Central 

California Y ↓ bivalves 

↓ phoronids 

800 gdw m-2

Cover: 100 % 

Everett 

(1991) 

Infauna F Scotland 

(E. coast) Y ↓ amphipods 

by ~90 % 200 gdw m-2 Raffaelli et al. 

(1991) 

Infauna S North Baltic 

Sea ~16 ↓ bivalves by 73 % 832 gdw m-2 Bonsdorff 

(1992) 

Epibenthic 

fauna S Sweden 

(W. coast) 14 - 20 ↓ epi.fauna by > 50 % 

Epiphyte cover 

on eelgrass: 

80% 

Isaksson & 

Pihl (1992) 

Juvenile 

flatfish S Sweden (W. 

coast) 4 - 20 ↓ abundance Cover: 50% Pihl & van der 

Veer (1992) 

Infauna F Central 

California ↓ bivalves by ~ 70 % 220 gdw m-2 

Cover: 100 % 

Everett 

(1994) 

Decapods 

Pred. Fish F Sweden 

(W. coast) 10-20 ↑ decapods 

↓ foraging 

Cover: 35 % 

Cover: 75 % 

Isaksson et al. 

(1994) 

Fishes S Sweden 

(W. coast) 14-20 ↓ fish biomass 7 – 225 

gdw m-2

Pihl et al. 

(1994) 

Juvenile 

Flatfish L 

S 

Sweden 

(W. coast) 9–12 ↓ settling 

↓ abundance 

Cover: 80 % 

179 gdw m-2

Wennhage & 

Pihl 

(1994) 

Epibenthic 

fauna 

Juvenile 

Flatfish 

S Sweden 

(W. coast) 14-20 

↑ epi.fauna 

↓ epi.fauna 

↓ fish sp. & foraging 

Cover:30-50% 

Cover: 90% 

Cover:30-40% 

Pihl et al. 

(1995) 

Shorebirds S Portugal No effect on feeding 30-60 gdw m-2 Múrias 

et al. (1996) 
161

Zoobenthos 

F North Baltic 

Sea 11-16 Y ↓ abundance 

& biomass by 87-94 % 440 gdw m-2

Norkko & 

Bonsdorff 

(1996a) 

Gastropod 

& bivalve 

Mollusks 

F North Baltic 

Sea 11-16 Y ↓ abundance & biomass 440 gdw m-2

Norkko & 

Bonsdorff 

(1996b) 

Infaunal 

bivalve 

&shrimp 

L North Baltic 

Sea 6 - 20 Y ↓ survival by 80% 

↓ survival by 83% 

200 gdw m-2 

440 gdw m-2

Norkko & 

Bonsdorff 

(1996c) 

Zoobenthos S North Baltic 

Sea Y Alters community 300 gdw m-2 Bonsdorff 

et al. (1997) 

Zoobenthos F Scotland 

(E. coast) Y ↓ abundance of invert. 

prey of fishes/birds 

< 600 

gdw m-2

Raffaelli et al. 

(1998) 

Infauna L North Baltic 

Sea 20 ↓ juv. bivalve moll. Cover: 50 % Norkko 

(1998) 

Infauna 

S Maine, 

U.S.A. Y Shifted community structure to detritivores 

50 – 200 

gdw m-2

Thiel & 

Watling 

(1998) 

Infauna F Scotland 

(E. coast) Y Altered infauna 200 gdw m-2 Bolam et al. 

(2000) 

Infauna F Portugal ↑ infaunal 

detritivores 158 gdw m-2 Lopes et al. 

(2000) 

Zoobenthos S 

L. 

North Baltic 

Sea 19 Y Altered structure. 

↓ bivalve & amphipod 181 gdw m-2 Norkko et al. 

(2000) 

Amphipods S Portugal Y Population 

zeroed by algae crash 413 gd w m-2** Pardal 

et al. (2000) 

Shorebird 

Black-tailed 

Godwit 

S Ireland 

(S. coast) ↓ abundance Cover: 40-70% 

Lewis & 

Kelly (2001) 

Infauna F Sweden 

(W. coast) 17-18 Y ↓ suspension feeding 

bivalves by ~90 % 

300 gdw m-2 Österling & 

Pihl (2001) 

Epibenthic 

Mudsnail S Portugal 

(W. coast) ↑ abundance 250 gdw m-2** Cardoso 

et al. (2002) 

Copepods 

F New York, 

U.S.A. Y ↓ copepods by ~85% 100 gdw m-2

Franz & 

Friedman 

(2002) 

Infauna 

Pred. birds F Ireland 

(S. coast) 

↓ amphipod abundance; 

Black-headed gulls 

avoid algal cover 

206 – 277 

gdw m-2

Lewis 

et al. (2003) 

Zoobenthos F Portugal 

(W. coast) Y ↑ abundance mudsnail, 

polych. worm 600 gdw m-2 Cardoso 

et al. (2004) 

Infauna F Australia 

(E. coast) Y ↓ polychaete, molluscan 

abundance 450 gdw m-2 Cummins et 

al. (2004) 

Infauna 

S England (S. 

coast) Y 

↓ infaunal abundance 

~70% 

↓ infaunal biomass 70% 

Cover: 90 % Jones & Pinn 

(2006) 

Zoobenthos S NE Baltic 

Sea Minor alterations to 

community structure 

347 gdw m-2 

Cover ~25% 

Lauringson& 

Kotta (2006) 

Infauna 

F Netherlands No major changes in 

infauna 

80 gdw m-2 

buried in 

sediment 

Rossi (2006) 

Type of Study: S is survey, F is field manipulation, L is lab experiment; Effects: ↓ decreased; ↑ increased; 

* For surveys, negative correlation suggesting a possible effect; ** Ash free dry weight 

 
162

Appendix E: Stressor-Response Model Calibration, Input Data, and Results

.

0

50

100

150

200

250

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

100

200

300

400

500

Biomass (gdw m-2

)

 Model

 Data

Carbohydrate (gNSC m-2

)

Month

Figure E.1 Stressor-Response Model Calibration for biomass and carbohydrate during 2002 in 

Zostera marina. Error bars represent standard errors. 
163

4

6

8

10

12

14

16

18

20

22

24

0

5

10

15

20

25

30

35

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

500

1000

1500

2000 Temperature (0C)

 Zone 1

 Zone 2

Salinity (psu)

Photon Flux Density 

(μmol m-2 s-1

)

Month

Figure E.2 Environmental input data (temperature, salinity and photon flux density) used in the 

SRM simulations for Zone 1 (lower estuary) and Zone 2 (upper estuary). Data presented 

are a composite created by averaging data from 1999-2003. 
164

0

50

100

150

200

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

0

100

200

300

400

Year 2 Year 3 Year 4

Biomass (gdw m-2

)

Year 1

Carbohydrates 

(g NSCC m-2

)

 0 m

 -1 m

 -2 m

 -3 m

 -4 m

 -5 m

Figure E.3 Case 1, Zone 1; simulations using median light attenuation and DIN values. Zostera 

marina biomass and carbohydrate trajectories for depths from 0 to 2 m below MLLW are 

stable indicating that median values are protective in Zone 1. At depths greater than 2 m 

below MLLW, the trajectories indicate that the median values are not protective. 
165

0

50

100

150

200

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

0

100

200

300

400

Year 2 Year 3 Year 4

Biomass (gdw m-2

)

Year 1

Carbohydrates 

(g NSCC m-2

)

 0 m

 -0.5 m

 -1.0 m

 -1.5 m

 -2.0 m

 -2.5 m

Figure E.4 Case 1, Zone 2; simulations using median light attenuation and DIN values. Stable 

Zostera marina biomass and carbohydrate trajectories indicate that the median values are 

protective in Zone 2 for depths between 0 and 0.5 m below MLLW. Simulations at the 1 

m depth contour appear to be more closely associated with the deeper depths (1.5 to 2.5 

m below MLLW) suggesting that this is a break-point that would be susceptible to 

decline from minor perturbation. At depths below 1 m MLLW, the trajectories indicate 

that the median values are not protective. 
166

0

50

100

150

200

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

0

100

200

300

400

Year 2 Year 3 Year 4

Biomass (gdw m-2

)

Year 1

Carbohydrates 

(g NSCC m-2

)

 0 m

 -1 m

 -2 m

 -3 m

 -4 m

 -5 m

Figure E.5 Case 2, Zone 1; simulations using 25th percentile light attenuation and DIN values. 

Stable Zostera marina biomass and carbohydrate trajectories indicate that the 25th

percentile values are protective in Zone 1 for depths between 0 and 3 m below MLLW. 

Simulations for depths below 3 m (MLLW) exhibited trajectories which indicate that the 

25th percentile values were not protective. 
167

0

50

100

150

200

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

0

100

200

300

400

Year 2 Year 3 Year 4

Biomass (gdw m-2

)

Year 1

Carbohydrates 

(g NSCC m-2

)

 0 m

 -0.5 m

 -1.0 m

 -1.5 m

 -2.0 m

 -2.5 m

Figure E.6 Case 2, Zone 2; simulations using 25th percentile light attenuation and DIN values. 

Stable Zostera marina biomass and carbohydrate trajectories indicate that the 25th

percentile values are protective in Zone 2 for depths between 0 and 1 m below MLLW. 

Simulations for depths below 1 m (MLLW) exhibited trajectories which indicate that the 

25th percentile values were not protective. 
168

0

50

100

150

200

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

0

100

200

300

400

Year 2 Year 3 Year 4

Biomass (gdw m-2

)

Year 1

Carbohydrates 

(g NSCC m-2

)

 0 m

 -1 m

 -2 m

 -3 m

 -4 m

 -5 m

Figure E.7 Case 3, Zone 1; simulations using 75th percentile light attenuation and DIN values. 

Stable Zostera marina biomass and carbohydrate trajectories indicate that the 75th

percentile values are protective in Zone 1 for depths between 0 and 2 m below MLLW. 

Simulations for depths below 2 m (MLLW) exhibited downward trajectories which 

indicate that the 75th percentile values were not protective. 
169

0

50

100

150

200

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

Jan

Apr

Jul

Oct

0

100

200

300

400

Year 2 Year 3 Year 4

Biomass (gdw m-2

)

Year 1

Carbohydrates 

(g NSCC m-2

)

 0 m

 -0.5 m

 -1.0 m

 -1.5 m

 -2.0 m

 -2.5 m

Figure E.8 Case 3, Zone 2; simulations using 75th percentile light attenuation and DIN values. 

Zostera marina biomass and carbohydrate trajectories for depths greater than 0 m MLLW 

exhibited downward trajectories which indicate that the 75th percentile values were not 

protective at these depths in Zone 2.