EPA 60Q/R-Q7/Q4G | June 2007 [ www.epa.govford
United States
Environmental Protection
Agency
An Approach to Developing
Nutrient Criteria for
Pacific Northwest Estuaries:
A CASE STUDY OF YAQUINA ESTUARY, OREGON
Office of Research and Development
National Health and Environmental Effects Research Laboratory, Western Ecology Division
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EPA600/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
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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, I.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
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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.
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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 rubrd) 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 (kj)
would be protective of the existing eelgrass (Zostera marina) habitat in the Yaquina Estuary.
in
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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 I"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 jig I"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(uM)
14
14
Phosphate (uM)
1.3
0.6
Chlorophyll a (ug I'1)
Water Clarity (m )
0.8
1.5
Dissolved Oxygen (mg 1' )
6.5
IV
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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
Description of Sources/Sinks of Nutrients 20
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
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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 Obj ective (Designated Use) ...71
11.1 Background 71
11.2 Spatial Seagrass Patterns 73
11.3 Temporal Seagrass Patterns 75
VI
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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 onZ 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 ofMacroalgae on Biota 159
Appendix E: Stress-Response Model Calibration, Input Data, and Results 162
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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 NOs + NC>2 36
Figure 5.3 Spatial variation in dry season PC>4 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 NOs +NC>2 during the dry season in a) Zone 1
and b) Zone 2 46
Figure 6.2 Comparison of historic and recent NOs +NC>2 during the wet season in a) Zone 1
and b) Zone 2 46
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Figure 6.3 Comparison of historic and recent PO4 during the dry season in a) Zone 1 and
b) Zone 2 47
Figure 6.4 Comparison of historic and recent PC>4 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 andb) 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
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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+l)
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
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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 (uM) concentrations in the Yaquina
Estuary 45
Table 6.2 Comparison of historic and recent PO4 (uM) concentrations in the Yaquina
Estuary 45
Table 6.3 Percentiles for DIN (uM) calculated using Yaquina (1998-2006), Classification (2004-
2005), andNCA Oregon estuaries (1999-2000) data sets 49
Table 6.4 Percentiles for PO43 (uM) 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 (ug I"1) calculated using Yaquina (1998-2006),
Classification (2004-2005), andNCA Oregon estuaries (1999-2000) data sets 52
Table 8.1 Percentiles for dissolved oxygen (mg I"1) calculated Yaquina (1998-2006),
Classification (2004-2005), andNCA 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 I"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
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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
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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 over enrichment 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
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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 eutrophi cation. However, conditions were expected to worsen
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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
0-6.3
5.1-15.4
6.3-14.2
15.4-27.6
27.6-42
97 f\ ^7 ^
Z / .O J / . J
State Designated
Use
Shellfish Growing
Shellfish Growing
Shellfish Growing
Resident Fish and
Aquatic Life
Salmonid Fish
Spawning
Water Contact
Recreation
Salmonid Fish
Rearing
Anadromous Fish
Passage
Resident Fish and
Aquatic Life
Aesthetics
Fishing
Livestock Watering
Resident Fish and
Aquatic Life
Water Contact
Recreation
Water Supply
Salmonid Fish
Rearing
Salmonid Fish
Spawning
Anadromous Fish
Passage
Attainment
Status
Not Supporting
Not Supporting
Fully Supporting
Fully Supporting
Not Supporting
Partial
Supporting
Fully Supporting
Not Supporting
Threatened
No
No
No
No
No
No
No
No
Basis of Impairment
Classification
Fecal Coliform
Fecal Coliform
NA
NA
Thermal Modifications
Biologic Integrity1
NA
Organic
Enrichment/Low
Dissolved Oxygen
Aquatic communities (primarily macroinvertebrates) which are <60% of the expected
reference community for multimetric and multivariate model scores are considered impaired.
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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 I"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
Chlorophyll a
Dissolved Oxygen
Estuarine Criterion
ISugr1
6.5 mgl'1
Water Quality Limited Determination
Average based on minimum of 3 samples collected
over any 3 consecutive months at a minimum of one
representative location exceeds criterion1
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: Criterion applies to river and estuaries; 2Estuarine waters defined as those with
conductivity > 200 uS 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
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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.
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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 Mclntire, 1977).
Toledo
Wastewater
Treatment
Plant
Effluent
Zone 1:
Marine
Dominated
Oyster Growing Area
Marsh
Intertidal
Subtidal
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.
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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).
7
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90-
w 60-
"E
CD
CD
O
CO
b so H
o-
i i i i i i i i i i i r
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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 (NOs and
PO4 ) 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
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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 Upogebiapugettensis), 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
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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 californiemis 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 Ibs. 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 fmfish 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
10
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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 offish account for >90% of the fish caught recreationally for food or bait from Yaquina
Estuary (PSMFC, 2006). Statewide, lower-estuary recreational fmfish 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 fmfish fishery within the estuary is for Pacific herring
(Clupeapallasiipallasii), whose ovaries and roe are marketed to Asia, with an annual average
(1979-2006) landing of 153,300 Ibs, 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 (IB A) by the American Bird Conservancy and as a State IB A
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). Sixty-
seven 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 richardsf) 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 (Phocoenaphocoend) 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
11
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Newport
Okm/
^
^-
cm+*i
ifefcv
Toledo
v i •mm -
>^%.
• &•?*>*. ^ap-
Sallys
Bend
20km
/
/I-
/ ->
X-
10 km v
// +
15km
0 0.5 1 2 3
j^H Seagrass
Macroalgae
HI Intertidal
I I Subtidal
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).
12
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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
13
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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).
10000-
8000-
O 6000 •
IS
Newport
Toledo
Q.
O
4000-
2000-
1880 1900 1920 1940 1960 1980 2000
Date
Figure 2.5 Populations in the cities of Toledo and Newport, Oregon from 1890 to 2005.
14
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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/oregonhistorv/narratives/subtopic.cfm7subtopic 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).
15
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1890 1900 1910 IS 20
1930 lt4O 1950
YEAR
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 mid-
1950s, 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 (MOD) 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
16
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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
MOD. 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.
17
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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 rubrd) 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.
18
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Ocean in put of nutrients, phytoplankton,and
hypoxic water associated with coastal upwelling
Riverine input of freshwater (blue) & nutrients (red)
Watershed primarily forested,
nutrient inputs associated with Red Alder.
Pulp mill adjacent to estuary;discharge offshore
Shellfishaquaculture
Recreational uses, in eluding fishing,
clamm in g,crabbingand boating.
'NO; i 'p
Logging occurs in watershed n
Nutrient input from municipal wastewater
Benthic input of nutrients & grazing control
Intermittent high nutrients
[ '^, Intermittent phytoplankton blooms
V Tf
f- ~*
Intermittent bw dissolved oxygen water
Figure 2.7 Conceptual model of factors influencing nutrient and response variables in the Yaquina Estuary. Iconography from the
University of Maryland Center for Environmental Science Integration and Application Network, http://ian.umces.edu.
Development along estuary shoreline
Sea grass primarily in lower estuary;
occur at shallower depth in upper estuary.
Macroakjal blooms primarily in lower estuary;
fueled by oceanic nutrients.
Water clearer in lower estuary; attenuation
of light greater in upper estuary.
Strong tidal forcing
19
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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= NC>2 + NOs +NFLt ) 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.
20
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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.
3.2.7 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 (NOs ) input to Yaquina varied from 2.4 x 105 mol N d"1 to 5.2 x 104mol N d"1 during a
drought year.
Oregon Coast Range streams have high NOs 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 NOs 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
concentration in the Salmon River watershed, which is about 45 km north of Yaquina
21
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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
+ 3-
for dissolved inorganic nutrients (NO3 + NO2 , NH4 , PO4 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 NOs 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 NOs of 30 uM.
22
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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 xlO5 2.5 x 104 1.4 xlO5
Ocean S.OxlO4 3.7-4.7xl05 2.3 x 105
Wastewater 1.7xl03 l.SxlO3 1.6xl03
Benthic Flux1 NA 4.3 x 104 NA
Atmospheric Deposition2
On Estuary 2.2 xlO2 1.2 xlO2 1.7xl02
On Watershed l.lxlO4 6.0 xlO3 8.5 x 103
Source: DeWitt 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; Thorn 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)
23
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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 (515N)
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 515N 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 Nl 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
Nino, La Nina 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.
24
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120
80
40
N1
• WWTF
H River
H Ocean
N2
N3
N4
N5
120
80
40
120
80
40
120
80
40
120
80
40
n
123456789
123456789
123456789
123456789
123456789
o
13
0.4
0.2
0
123456789 123456789 123456789 123456789 123456789
Month
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. Stations Nl and N2 are located in Zone 1 about 3.7 and 10.1 km from the mouth of the estuary,
respectively. Stations N3, N4, and N5 are located in Zone 2 about 15.6, 18.4, and 26.0 km from the mouth of the estuary.
25
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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; SEE 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
+ 3-
(NOs + NO2 , NH4 , PO4 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
26
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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 In (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://deql2.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
27
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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
1998
1999
2000
2001
2002
2003
2004
2006
Month
Jun, Jul, Sept, Nov
Jan-Dec
Jan-Dec
Mar, Apr, Aug-Oct
May-Jul
Apr- Sept
Jan, Feb, Mar, Oct
Nov, Dec
Apr- Sept
Jan, Feb, Dec
Mar, Oct
April-September
February -December
Sampling
Frequency
Once a Month
Once a Month
Once a Month
Once a Month
Twice a Month
Weekly
Twice a Month
Once a Month
Weekly
Once a Month
Twice a Month
Twice a Month
Once a Month
# of Sampling
Locations
35
Varied 5-35
Varied 5-36
Varied 5-36
Varied 12-34
12
12
12
12
12
12
12
12
Distance from mouth
of Estuary (km)
2-21
2-21
2-21
2-21
2-21
2-21
2-21
2-21
2-26
2-26
2-26
3-26
3-35
28
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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
Matson (1964)
Gibson and
Snow (1967)
De Ben et al.
(1990)
Gibson (1974)
Johnson (1980)
Amspoker
(1977)
Karentz (1975)
Karentz and
Mclntire (1977)
WED
unpublished
Frey (1977)
Butler (1986)
Arnold et al.
(1992)
Time
Interval
1 1/62 - 1/64
6/66 - 1 1/67
3/67 - 1 1/68
4/68 - 3/70
8/71 - 1/72
6/73 - 10/74
12/73 - 8/74
7/74 - 4/75
5/74 - 5/75
7/76- 12/77
2/77 - 6/77
6/83 - 8/85
4/86- 3/87
Number
of
Sampling
Events
30
30
42
23
11
80
4
21
12
9
8
12
149
#of
Stations
4
6
10
4
2
5
6
4
4
16
3
7-16
1
Dist.
from
mouth
(km)
2-81
7-16
2-26
4-15
15-16
9-16
3-36
3-19
3-19
3-42
3-35
2-35
11
Parameter
Salinity, Water temp, DO, PO4
, Si(OH)4
Salinity, Water temp, DO
Salinity, Water temp, DO
Salinity, Water temp, DO
PO43", Si(OH)4, NO3"+ NO2"
Chlorophyll a
NO3"+ NO2", Total Phosphates,
Si(OH)4
Chlorophyll a
Salinity, Water temp, NO3 +
NO2", PO43", Si(OH)4,
Chlorophyll a
3-
Salinity, Water temp, PO4 ,
NH3, NO2", NO3", TN, TP, TSS
3-
Salinity, Water temp, PO4 ,
Si(OH)4, NO3"+ NO2",
Chlorophyll a
Salinity, Water temp, DO, NO2
, N03", NH4+, P043", Si(OH)4
Salinity, Water temp, TSS
29
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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 (515N) 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
30
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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
11
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upon the scale of the classification system as well as the classification system utilized (see Table C.I).
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.
32
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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).
33
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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.
Zone 1 Zone 2
30-
20-
10-
ro
:;! " * ?'^: $!;'>• r^
• • • *• • =i •• • !-• ;
30-
20-
10-
10 20 30
Distance from Mouth of Estuary, km
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
34
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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 PC>4 enter the estuary about
two days after upwelling conditions (Brown and Ozretich, in review). Median concentrations of
oceanic NO3 and PC>4 entering the estuary during the dry season are 8.6 uM and 1.3 uM, respectively
(n = 830). The maximal nutrient concentrations (NO3 =31.5 uM and PC>4 = 2.9 uM) 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 uM, 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 PC>4 to the system is the ocean and there is a steady decline in PC>4 with
distance into the estuary (Figure 5.3). The oceanic signal in NO3 and PC>4 propagates approximately
13 km up the estuary (Brown and Ozretich, in review).
35
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1 U •
60-
50-
! 40-
z 30-
b"
Z 20-
10-
0-
•
• J •
I • *
• •
. " . ! 1
• T . ' i
•: i '!
i
!;-i
\gn
3{
t«3;
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. '.- -/ ' ; /
.-« »i . : , : * \ l /
,.• 1 •» i .'• : •!••/
• •
1 •*.
;»;.
•
I . '. .- 2 If
* • •: • j • /\
t •:. .h A'^ \
1 M!^*%^
|.n%..I:
i .3! HI •: •: X. I :
i .1 1 n Xt
' 1 ' 1 ' 1 ' 1 ' 1 '
0 5 10 15 20 25 30
Distance from mouth of Estuary, km
Figure 5.2 Spatial variation in dry season NOs + NO2 . The line indicates a 2nd order polynomial fit to
the data (NO3"+NO2" = 20.4 - 1.9 * distance + 0.079 * distance2, r2 = 0.15, p < 0.001)
3-
2-
^
co ^r
O
CL
1-
D-
•
.;i
H" • • ••
,i|
• f
^1
!S
M
1-
'<:
!H
• *JT 1
• *• 1
v .1
• • i
f
j! "."."•• !
• 'i •
lll::'Gi i:
*i f« : . fr. 1
"i I^^-L ! i
Iffltrf
i* ••. ":' • •
20
5 10 15
Distance from Mouth of Estuary, km
25
30
Figure 5.3 Spatial variation in dry season PC>4 . The line indicates linear regression (PC>4 =1.55-
0.041 * distance, r2 = 0.24, p < 0.0001).
36
-------
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 ug I"1 with a median value of 4 ug I"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 NOs and PO4 . 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 ug I"1)
were significantly higher than ebb tide values (median = 9 ug I"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 Mclntire
(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).
37
-------
80-'
60-
O)
co
40-
.
Q.
.
O
15 20
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 uM and 0.1-
0.5 uM, 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
38
-------
less than 0.5 uM, suggesting the potential for phosphorous limitation. During the dry season, the
median DIN concentration is 14 |jM (n=2028), and 95% of the time the DIN concentration is greater
than 2 |jM (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 uM. 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).
39
-------
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.
40
-------
60
40-
20-
1-
0
a) TN = 61.9 - 6.42 * x + 0.37 * x2 - 0.0056 * x3
r2 = 0.98, p < 0.001
---a...
b) TP= 3.0-0.05 *x
r2 = 0.84, p < 0.001
0
40
5 10 15 20 25 30 35
Distance from Mouth of Estuary (x), km
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.
24'
18-
= 12.
>,
.n
Q.
o
6-\
Chi a = 20.1 -1.38 * Distance + 0.029 * (Distance)
r2 = 0.87, p< 0.001
0
40
5 10 15 20 25 30 35
Distance from Mouth of Estuary, km
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.
41
-------
50-
40-
O)
O
10-
0
• Zone 1
o Zone 2
Zone 1: Linear Regression
Chi a = -17.0 + 0.85 * TN
r2 = 0.76, p < 0.001
0
20
60
80
40
TN, uM
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.
50-
40-
Zone 1
Zone 2
Zonel: Linear Regression
Chi a = -13.7 + 9.48 *TP
r2 = 0.65, p < 0.001
O)
">
Q.
_0
6
20-
10-
TP, |iM
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.
42
-------
6. Nitrogen and Phosphorus as Water Quality Criteria
6.1 Seasonal, Zonal, and Long-term Trends in N andP
There are seasonal differences in water column nutrients within the estuary. DIN levels are
significantly higher during the wet season (median = 21.1 uM, n = 874) than during the dry season (dry
season median = 13.9 uM, n = 2028; calculated using data from 1998-2006 combining Zones 1 and 2;
Mann-Whitney Rank Sum Test, p< 0.05). In contrast, PO4 levels during the dry season (median =
0.97 uM, n = 2029) are almost twice as high as those during the wet season (median = 0.52 uM, 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 concentrations
than Zone 2, reflecting the ocean input of phosphorous (Mann-Whitney Rank Sum Test, p<0.05). The
ocean input of PO4 dominates during the dry season with Zone 1 PO4 levels (median = 1.25 uM, n =
1114) twice that of those in Zone 2 (median = 0.64 uM, 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 uM, n = 354) are significantly
higher than those in Zone 1 (median = 11.6 uM, 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 NOs +
NO2 (Figures 6.1-6.4), the major component of DIN, and PO4 (Figures 6.5-6.8). Recent (1998-2006)
dry season NO3 + NO2 and PO4 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 NOs + NO2 and
PO4 in Zone 2 are significantly higher than recent data (Table 6.1 and Table 6.2). Peak wet season
NOs + NO2 concentrations in Yaquina Estuary are similar to wet season N(V observed in Oregon
Coast Range streams (peak NO3" of 172 uM; 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;
43
-------
Mann-Whitney Rank Sum, p<0.001). Wet season PC>4 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 "levels (Table 6.2; Mann-Whitney Rank Sum, p>0.05).
There were no significant trends in NOs + NC>2 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 PC>4 during the dry season (Figure 6.3a) and a significant
decreasing trend in PC>4 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
3-
seasonal trends in Zone 1, there was not a significant trend in PC>4 using the Seasonal Kendall test.
Caution needs to be used in interpreting the trends and the differences in historic and recent
median NOs + NC>2 and PC>4 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 (NOs + NC>2 and
PC>4 ) 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
(NOs + NC>2 and PC>4 ) 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 (NOs + NC>2 and
PC>4 ) 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 (NOs + NC>2 and PC>4 ) 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).
44
-------
Table 6. 1 Comparison of historic and recent NOs + NC>2 (uM) 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).
Zone 1
Dry
Wet
Zone 2
Dry
Wet
Historic
Median
6.5
19.8
14.0
69.6
Time Interval
(Sample Size)
1974-1984(157)
1974-1984 (65)
1971-1984 (247)
1971-1984(148)
Recent
Median
10.0
8.5
9.8
52.6
Time Interval
(Sample Size)
1998-2006(1127)
1998-2004 (520)
1998-2004 (919)
1998-2004 (354)
3-
Table 6.2 Comparison of historic and recent PC>4 (uM) 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)
Zone 1
Dry
Wet
Zone 2
Dry
Wet
Historic
Median
1.01
0.83
0.74
0.54
Time Interval
(Sample Size)
1963-1984 (223)
1962-1984(129)
1963-1984(308)
1962-1984 (212)
Recent
Median
1.25
0.59
0.65
0.49
Time Interval
(Sample Size)
1998-2006(1126)
1998-2004(519)
1998-2004 (919)
1998-2004 (354)
45
-------
90-
="- 60-
Cf .
z
90-
CM
O 60-
CO
z 30-
n
19
Figure 6. 1 Comparison of h
Zone 2.
180-
=*- 120-
i
z
0-
180-
^
0" 120-
z
+co
§ 60-
n.
a)
i
i 1 i 1 i 1 i 1 i I i 1 i I
b> !
• i -j.
.; ' (< :-:;j
70 1975 1980 1985 1990 1995 2000 2005
Year
istoric and recent NOs + NC>2 during the dry season in a) Zone 1 and b)
a)
: «• •" ij. t
,i is £ nnh
b) « :
•I * I 8 Ij
I : J: ' 4;iH
1970 1975 1980 1985 1990 1995 2000 2005
Year
Figure 6.2 Comparison of historic and recent NOs + NC>2 during the wet season in a) Zone 1 and b)
Zone 2.
46
-------
3.0-
0*1-5-1
CL
0.0
3.0-1
CL 1.5-1
0.0
P0 =-16.2+ 0.0087* Year
b)
. I
1960 1970 1980 1990
Year
2000
Figure 6.3 Comparison of historic and recent PC>4 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).
3.0-
0*1-5-1
CL
0.0
3.0-1
a)
PO4 = 22.1 -0.011 *Year
p<0.01
= -0.38
b)
. \
1960 1970 1980 1990
Year
2000
Figure 6.4 Comparison of historic and recent PC>4 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).
47
-------
6.2 Percentile Approach for Nitrogen and Phosphorous
There were significant differences in median DIN and PC>4 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 PC>4 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 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 uM; U.S.
EPA, 2000). The PC>4 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 uM;
3-
U.S. EPA, 2000), due to the input of PC>4 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 uM; Wigington et al., 1998) and streams data for Level III Ecoregion No. 1 - Coast
Range (winter median NO3"+ NO2" of 37 uM; U.S. EPA, 2000). Higher PO43 levels in Zone 1
compared to Zone 2 are present in the NCA and classification data sets, demonstrating that oceanic
input of PC>4 occurs at a regional scale. The DIN and PC>4 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 levels are associated with natural
48
-------
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 (uM) 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.
Data set
Zone 1
Yaquina
Dry
Wet
Classification
NCA
NCA excluding Columbia
Zone 2
Yaquina
Dry
Wet
Classification
NCA
NCA excluding Columbia
Percentiles for DIN (uM)
25th
8.3
6.8
5.7
6.0
5.8
7.3
30.7
8.3
7.0
4.8
50th
14.1
11.6
11.1
8.6
8.4
13.7
55.2
14.0
9.4
7.2
75th
20.4
19.1
18.6
11.8
11.8
23.1
73.5
36.9
13.2
11.9
Sample Size
1113
520
68
36
33
915
354
88
89
27
Table 6.4 Percentiles for PO43" (uM) 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.
Data set
Zone 1
Yaquina
Dry
Wet
Classification
NCA
NCA excluding Columbia
Zone 2
Yaquina
Dry
Wet
Classification
NCA
NCA excluding Columbia
Percentiles for PO4 (uM)
25th
0.88
0.39
0.62
0.76
0.76
0.43
0.41
0.33
0.35
0.18
50th
1.25
0.59
0.89
0.95
1.00
0.64
0.49
0.45
0.52
0.33
75th
1.69
0.77
1.20
1.15
1.16
0.99
0.62
0.75
0.71
0.73
Sample Size
1114
519
68
36
33
915
354
88
89
27
49
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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 ug I"1 (n = 293),
while during the dry season the median increased to 4.9 ug I"1 (n = 1205). Dry season chlorophyll a
levels in Zone 2 (Median = 5.3 ug I"1, n = 229) were significantly higher than those in Zone 1 (Median
= 3.3 ug I"1, n = 347; Mann Whitney Rank Sum, p<0.001). During the late spring, there have been
recurrent non-toxic red tide blooms ofMyrionecta rubra in the vicinity of Toledo. During the dry
season, chlorophyll a concentrations occasionally reached 15 ug I"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 ug I"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 ug I"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.
50
-------
80-
60-
n Mean
Min and Max
x 1st and 99th
= 20-
Q.
O
O
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
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.
50-
25-
O)
to"
=^ 0.
.c
o
o
O
25-
a) Zone 1
1
Median = 4.6
n = 137
Median = 3.3
n=347
b) Zone 2
ill! " •.
Median = 6.1
n = 377
•• i
..H! i
Median = 5.3
n = 229
o-
1/1/1973 1/1/1978 1/1/1983 1/1/1988 1/1/1993 1/1/1998 1/1/2003
Date
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.
51
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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 ug I"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 ug I"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 ug I"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 (ug I"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.
Data set
Zone 1
Yaquina
Dry
Wet
Classification
NCA
NCA excluding Columbia
Zone 2
Yaquina
Dry
Wet
Classification
NCA
NCA excluding Columbia
Percentiles for Chlorophyll a (ug I"1)
25th
2.2
0.6
1.0
1.5
1.5
3.8
0.4
0.8
2.0
1.4
50th
3.3
1.1
2.0
2.1
2.0
5.3
0.9
1.6
3.3
1.8
75th
5.7
1.7
3.8
3.8
3.2
7.9
2.5
2.5
4.9
2.6
Sample Size
347
95
68
36
33
229
46
78
89
27
52
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8. Dissolved Oxygen as a Water Quality Response Measure
Dissolved oxygen (DO) is an important water quality metric because of its effects on the well-
being 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. clarkf) 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 I"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 I"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 I"1, declines to a value of 5.8 mg I"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
53
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represented in the data. Nighttime respiration can significantly reduce water column oxygen levels
below daytime levels.
O)
E
cf
o>
O)
>,
x
o
T3
CD
"5
CO
co
b
0
JAN FEE MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN
Date
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 man-
made 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
54
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treatment (prior to this raw sewage was discharged into the estuary), and in 1981 it was upgraded to
secondary treatment.
D)
"(0
0)
0)
D)
>,
6
0)
5.0-
2.5-
0.0
-2.5
-5.0
5.0
2.5
0.0-
-2.5-
-5.0-
a)
o
o
o
b)
Q
I
• Dry
o Wet
J /_
1960
1970
2000 2005
1980
Year
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.
55
-------
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 I"1. The intervals of low DO conditions were
relatively short, with DO levels increasing to 6-8 mg I"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 I"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 I"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
56
-------
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.
D)
cf
8
E
6-
-------
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 I"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 I"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
58
-------
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 I"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 I"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 I"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.
Data set
Zone 1
Yaquina
Dry
Discrete
Continuous
Wet
Classification
NCA
NCA excluding Columbia
Zone 2
Yaquina
Dry
Discrete
Continuous
Wet
Classification
NCA
NCA excluding Columbia
Percentiles for Dissolved Oxygen (mg I"1)
25th
6.5
5.9
9.0
6.7
7.2
7.3
6.7
6.4
10.2
6.9
7.6
6.7
50th
7.8
7.0
9.4
7.3
7.8
8.0
7.2
6.9
11.0
8.1
8.5
7.3
75th
8.5
8.0
9.5
8.3
8.4
8.5
7.6
7.5
11.6
9.3
9.0
8.3
Sample Size
46
2856
36
37
35
32
259
2862
184
53
88
26
59
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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 I"1, respectively (Zones 1 and 2 combined; Mann Whitney Rank Sum,
p> 0.05). TSS levels in Zone 2 (Median = 11.7 mg I"1, n = 119) were significantly higher than those in
Zone 1 (Median = 6.5 mg I"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.
150.
5 10 15 20 25 30 35 40
Distance from Mouth of Estuary, km
Figure 9.1. Spatial variation in turbidity during wet and dry seasons (1998-2006).
60
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Zone 1
Ocean Dominated
Zone 2
River Dominated
o
T
16
18 20
8 10 12 14
Distance from Mouth, km
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).
61
-------
2.4 •
2.1 -
1.8-
1.5-
1.2-
0.9-
0.6-
0.3-
o.o.
OSU Dock (3.7 km)
Marker 12 (3.9 km)
Marker 20 (9 km)
Marker 38 (16 km)
Marker 47 (19 km)
01 23456789 10 11 12
Month
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 (&j) 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).
62
-------
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.
63
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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.
Data set
Zone 1
Yaquina
Dry
Continuous
Cruise
Wet
Continuous
Cruise
Zone 2
Yaquina
Dry
Continuous
Cruise
Wet
Continuous
Cruise
Percentiles for Light Attenuation Coefficient kd (m"1)
25th
0.62
0.56
0.55
0.53
1.14
1.09
0.97
0.95
50th
0.78
0.72
0.74
0.66
1.53
1.32
1.20
1.20
75th
1.00
0.94
0.89
0.87
2.26
1.72
1.54
1.54
Sample Size
678
541
505
248
376
439
247
178
Table 9.2 Percentiles for TSS (mg I"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.
Data set
Zone 1
Yaquina
Dry
Wet
Classification
NCA
NCA excluding Columbia
Zone 2
Yaquina
Dry
Wet
Classification
NCA
NCA excluding Columbia
Percentiles for TSS (mg I"1)
25th
3.0
3.7
4.6
10.4
11.0
6.9
6.6
1.4
4.0
5.0
50th
6.3
6.6
6.7
14.0
14.0
11.2
12.3
3.8
6.0
9.0
75th
9.6
12.2
10.8
16.0
16.1
19.6
34.7
8.2
10.4
11.8
Sample Size
102
56
66
36
33
83
35
83
91
27
64
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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 NOs
+ NC>2 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 uM, respectively. This difference in
average NOs + NC>2 concentration is assumed to be the result of the 1997 El Nino condition that
65
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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
Ulvalinza: -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.
66
-------
:.j a ji?:jy^r •% Sfe
•-'•* - #*'
-/Ji&, Ip
...^jHB-t.'jf^
Eelgrass dominated
1997
1998 macroitlgac
1997 and 1998 macroalgae
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.
67
-------
300
Biomass
Cover
70
60
50
40
CD
U
(Q
-------
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).
Data set
Zone 1
Yaquina
Dry
Wet
Classification
Zone 2
Classification
Percentiles for Macroalgae Biomass
25th
0
0
0
0
50th
34.9
0
0
0
75th
189.8
2.9
11.6
0
Percentile for
100 gdw m"2
62.7 %
95.1%
92.9 %
99.6
Sample Size
4432
2142
351
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).
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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.
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11. Submerged Aquatic Vegetation (SAV) as a Management Objective (Designated Use)
77.7 Background
TheNHEERL 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 berniculd) (Griffin, 1997; Kentula and Mclntire, 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).
71
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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).
Zone 2:
.Riverine
Dominated
Figure 11.1 Spatial distribution of Yaquina Estuary eelgrass.
Below-1.0m MLLW
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77.2 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 eel grass 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 Bjork, 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).
73
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I I I
I I I
Depth Below MLLW (m)
Figure 1 1.2 Z. marina depth distribution in the marine dominated portion (Zone 1) of Yaquina
Estuary.
u.t
0.3
CO
CD
£ 0.2
•* CM C
DC
rn
CD •<*
rfTTTTTTTTH
CM O CO CD -si- C\
o
DO
-
.0
_
st
~
-
rv
_
CD
CD
—
CO
0)1
W,
i!
i
i ITh-m-i-rr|Th n , ,
Depth Below MLLW (m)
Figure 11.3 Z marina depth distribution in the river dominated portion (Zone 2) of Yaquina Estuary.
74
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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
75
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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).
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Zone 2:
Riverine
Dominated L
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
77
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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 - (I.19*kd\ r2= 0.32. (11.1)
where Depth = m below Mean Sea Level (MSL)2 and feis 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
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observation.
4 -
W 3 -
£
u
2 -
1 -
Ocean Dominated
Depth = 4.6 - (0.23*Distance), r-0.
River Dominated
:*Distance), ^=0.31 , p<0.001
Depth = 2.8 - (0.05*Distance), r
<0.001
=0.00, p=0.465
6
8
16
18
20
22
10 12 14
Distance (km)
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
^ = e-k«z (11.2)
where /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).
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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 1 1% 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:
Zc= (11.3)
d
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 =
\.62lkd 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 1 1.7) 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 1 1.2) to the percent of surface irradiance at Zcto 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 1 1.7.
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GO
10
8 -
6-
.
4 -
CD
Q
0 -
oo 8
o
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8
Figure 11.7 Relationship between Z. marina maximum depth limit (m below MSL) and kj. 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.
Source
Duarte (1991)
Current Study
Batiuk et al. (2000)
Mean
20.5
12.6
22a
N
29
64
Range
Max
43.9
68.3
Min
4.7
7.2
aValue 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.
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77.5 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).
82
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12.
10-
o
D)
E
6-
co
I 4.
in
2-
Y = -0.0002 XN-0.0757 X - 1.1484
r2 = 0.60
0
50
100
300
350
400
150 200 250
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.
12
E
o
D)
E
ro
o
in
0
Wet Season - External Dry Season - External Wet Season - Internal Dry Season - Internal
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.
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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.
100.00
g.
o
80.00
60.00
40.00
20.00
0.00
y = 79.044X + 4.8381
R2 = 0.7954
0.00 0.20 0.40 0.60 0.80 1.00
log (biomass (mg/cmA2)+1)
1.20
1.40
Figure 11.10 Linear regression relationship between the percent of light reduction to log(x+l)
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 Zoster a marina Light Requirements
Minimum light requirements for maintaining and restoring SAV have been proposed for
Chesapeake Bay (Batiuk et al., 2000) and Puget Sound (Thorn et al., 1998). Chesapeake Bay light
criteria values were empirically estimated by measuring the maximum depth of SAV annually and
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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 (Thorn et al., 1998). These were estimated using maximum seagrass depth measures,
kd values and production-irradiance (P vs. I) relationships. Based on this methodology Thorn et al.,
(1998) suggested that to maintain the greatest densities of Z. marina, -300 jimoles m"2 s"1 (3 moles m"2
d"1) were required for at least three hours daily during the growing season. Thorn 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 jimoles 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. Thorn, Pacific Northwest Environmental Laboratory, pers. comm.). Assuming that mid-day
surface irradiance is in the range of 1000-2000 (imoles 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 Thorn 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
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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 Thorn 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-surf ace 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
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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 Stressor-
Response Model (see Chapter 12).
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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).
72.2 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
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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.
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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 (I0) 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
T(Z. , it} -I (t}e~js
1 \^i =!,...,« M .M/ ~~ 1o\l)K
where y represents the zone, z, represents 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 (/') 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 : //ti desandcurrents . noaa . govA Station 9435380 South
Beach).
Table 12.1 Input data from percentile approach for different SRM simulations.
Case
1
(Median)
2
(25th %)
3
(75th %)
Zone
1
2
1
2
1
2
Dry Season
kd, m"1
0.78
1.53
0.62
1.14
1.00
2.26
DIN, uM
14.1
13.7
8.3
7.3
20.4
23.1
Wet Season
kd, m"1
0.74
1.20
0.55
0.97
0.89
1.54
DIN, uM
11.6
55.0
6.8
30.7
19.1
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
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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.
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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
1
(Median)
2
(25th %)
3
(75th %)
Zone
1
2
1
2
1
2
Protective Capacity
Depth, m
>2
>0.5
>3
>1
>2
0
% change
0
0
+38
+41
0
-48
Present Condition
Most Protective
Least Protective
0.2 - -2.0m 0.2 - -0.5m
< -2.0m < -0.5m
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.
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Newport
Toledo
+ 5 km
I IZ. marina
Zone 1
0.2- -3.0m 0.2--1.0m
<-3.0m <-1.0m
+ 15 km
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.
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Zone 2:
Riverine
Dominated
0.2 - -2.0m 0.2 - 0.0m
< -2.0m < 0.0m
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.
72.5 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.
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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.
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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
Yaquina Estuary, OR
Peconic Bay, NY2
Long Island Sound, CT
Chesapeake Bay, MD*3
Chesapeake Bay, MD*4
^(m-1)
<0.78 (Zone 1)
<1.5 (Zone 2)
<0.75
<0.7
<1.5
<0.8
DIN (|iM)
14
1.4
2.1
10.7
10.7
Citation
This study
EEA, Inc. 1999
Hoist et al., 2003
Batiuketal., 1992,2000;
Wazniak and Hall, 2005
Batiuketal. 1992,2000;
Wazniak and Hall, 2005
Based on dry season median values; 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.
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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 PC>4 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, PC>4 , NOs, 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 Nino/La Nina 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
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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 ug I"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 jig I"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 eutrophi cation. 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 eutrophi cation 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
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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 I"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
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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).
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Table 13.1 Potential dry season criteria for the Yaquina Estuary based on median values for all
parameters except for DO.
Parameter (units)
DIN (uM)
Phosphate (uM)
Chlorophyll a (ug I'1)
Water Clarity (m"1)
Dissolved Oxygen (mg 1" )
Zone 1
14
1.3
3
0.8
Zone 2
14
0.6
5
1.5
6.5
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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; Sundback et al.,
2000; Sundback 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; Thorn et al. 1994; Larned 2003) may not be appropriate for the purpose of estimating estuary-
scale 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
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deliberately excluded shrimp (Thorn 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; Thorn et al. 1994; Larned 2003) were not used for estimating
the estuary-scale nutrient fluxes presented in Table 3.1 for the Yaquina Estuary.
Remmerahzation
Participate —» Dissolved
Organic —> Inorganic
Neotrypaea californiensis burrows
Upogebiapugettensis burrows
Nutrients
Seston
Predators
Tide
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 benthic-
+
pelagic fluxes of NOs + NC>2 , 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
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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 TV. californiemis were shown to have a net uptake of DIN, most of
which is expected to be NOs + NC>2. 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 TV. 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 upper-
estuary infauna. In lower and upper reaches, the most abundant deposit-feeder by biomass is TV.
californiensis. Organic matter consumption rates have not been calculated for the infaunal deposit-
feeder 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. Griff en 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).
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15 '
T3 10 '
evi
E 5 1
o
I °
•t
I -5
LJ_
I -10
NH4+-
A
.A-""
.••••""* A
0 100 200 300 400 500 600
Neotrypaea Burrow hole density (rrr2)
I
*r 30 '
es
E
± 20 '
O
E
E 1Q.
3 \
v- 4
1 n t
z {
Nn4 •••••
?
a—uf^l ^
U
INU3
i
O ^ ^
^o^
O QU
A!
o
/^
-•s0"
o
o
o
0 100 200 300 400 500 600
Upogebia Burrow hole density, m2
+
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 Upogebiapugettensis (b).
Positive values = efflux from sediments, negative values = uptake by sediments. Dashed and
dotted curves represent best-fit regression models for flux =y(shrimp burrow density). Solid
black line represents 0 nitrogen flux.
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Burrowing Shrimp
Population Density (# nr2)
1-30 m-2 1-50 nr2 1-40 nr'
• 31-80 0& 51-150 • 41-115
• 81-150 • 151-280 >200 ^^ >400 <* >300
Upogebia Neotrypaea Mixed Spp.
0 0.5 1 1.5 2 2.5 3 35 A 45 5 Wkjmeters
mmol DINm-2d-1
-10 to -5
-5 to -0.1
DtoS
5 to 10
10 to 15
15 to 25
0 0.5 1 2.5 3 3.5 Kilon >ts
B
Figure A.3 a) Distribution and abundance of two species of burrowing shrimp (red = Neotrypaea
californiensis, blue = Upogebiapugettensis, 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).
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1,000.0 -,
Herbivore Surface Subsurface Filter Feeder Facultative
Deposit Feeder Deposit Feeder Filter/Deposit
Feeder
Trophic Groups
Carnivore
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.
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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.I.
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:
Accuracy(%} = (1.0-(£(|F, -Vn\)lnlVt)* 100
where Vt is the true or standard value, Vm is the measured values, and n is the number of measured
values. Precision is an indication of the similarity of repeated analyses or sampling. Precision is
calculated with the following equation:
Precision (%) = (1.0 - SDIX) * 100
where SD is the standard deviation, and X is the mean.
128
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Table B.I Measurement Quality Objectives for data collected by Western Ecology Division.
Parameter
units
Expected range
Accuracy
Precision
SOP(s) or other
Sea-Bird CTD and YSI Multiparameter Sondes
Conductivity
Sea-Bird CTD
Salinity
Sea-Bird CTD
Temperature
Sea-Bird CTD
Turbidity
Sea-Bird CTD
Depth
Sea-Bird CTD
Conductivity
YSI Sonde
Salinity
YSI Sonde
Temperature
YSI Sonde
Dissolved Oxygen
YSI Sonde
Depth
on YSI Sonde
mS cm"1
psu
°C
ntu
meters
mS cm"1
psu
°C
mg I"1 or
% Saturation
meters
0-100
0-35 psu
0 - 25 °C
0-125 ntu
0-15 m
0-100 mS cm"1
0-35 psu
0 to 25 °C
0-20 mg I"1 or
0-200% saturation
0-15 m
± 0.5% of
reading
± 0.5% of
reading or 0.1
psu
±0.15°C
± 2% of reading
or 2 ntu
± 0.018m
± 0.5% of
reading or
± 0.001 mS cm"1
± 1 % or 0.1 psu
±0.15°C
iO^mgl"1 or
±2% of reading
± 0.018m
0.01 mS cm"1
0.01 psu
O.OFC
0.1 ntu
0.001 m
0.01 mS cm"1
0.1 psu
O.OFC
0.01 mg I"1
0.001 m
SOP FSP.03, SeaCat
manual
Calculated from
conductivity and
temperature
SOP FSP.03, SeaCat
manual
SOP FSP.03, SeaCat
manual
SOP FSP.03, SeaCat
manual
SOP IOP.09, YSI manual
Calculated from
conductivity and
temperature
SOP IOP.09, YSI manual
SOP IOP.09, YSI manual
SOP IOP.09, YSI manual
129
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Table B.I Measurement Quality Objectives for data collected by Western Ecology Division.
Parameter
units
Expected range
Accuracy
Precision
SOP(s) or other
Water Column Nutrients
Dissolved NOs
Dissolved NC>2
+
Dissolved NH4
Dissolved PC>4
Dissolved Si(OH)4
Total Nitrogen
Total Phosphorous
Total Suspended Solids
Water Column
chlorophyll a
uM
uM
uM
uM
uM
uM
uM
mgr1
Mgl'1
0- 100
0- 1
0-5
0-3
1 - 100
0-100
0-3
1 - 150
0-200
±5%
±5%
±5%
±5%
±10%
±5%
±5%
15%
±15%
5%
5%
5%
5%
10%
±5%
±5%
15%
±15%
MSIAL UCSB, 2005
MSIAL UCSB, 2005
MSIAL UCSB, 2005
MSIAL UCSB, 2005
MSIAL UCSB, 2005
WRS 34A.3, 2005
WRS 34A.3, 2005
WRS 14B.2
MES SOP06.revO
Zostera marina Data
Biomass
Shoot Density
Growth rate of leaf
blades
Tissue CHN
gdw m"2
shoots m"2
gdw m"2 d"1
g C/g tissue as
%
0 -300 gdw m"2
1-15,000 m'2
0 to 5 gdw m"2 d"1
0.1 -50%
0.1 g"1 sample
±2m'2
95%
± 10% of
standard
0.1 g-1
sample
1 shoot per
m2 quad
95%
CV < 10%
QAPP02.01
QAPP02.01
QAPP02.01
QAPP02.01
130
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Table B.I Measurement Quality Objectives for data collected by Western Ecology Division.
Parameter
Tissue P
Shoot Length
Shoot Width
Shoot Dry wt.
Z. marina lower limit
depth
Epiphyte Biomass dry
wt.
%Plant Cover (Visual)
units
g P/g tissue
mm
mm
mg
cm
mg
% area
Expected range
0.1 - 10%
5 - 1200 mm
1-7 mm
0-1 500 mg
0-10MLLW
0-1 500 mg
0-100%
Accuracy
± 10% of
standard
±2 mm
± 1 mm
± 1%
<60cm
± 1%
10%
Precision
CV < 10%
± 1 mm
± 1 mm
±0.1 mg
n/a
±0.1 mg
10%
SOP(s) or other
QAPP02.01
QAPP 04.01
QAPP 04.01
QAPP 04.01
QAPP 04.01
QAPP 98.01
Macroalgae
% cover of algae, or
Bare Substrate (visual
estimate)
Biomass
Macroalgae nitrogen
isotope ratio (515N)
percent m"2
gdw m"2
%0
0-100%
0 -300 gdw m"2
-2 to +25
± 15%
0.1 g"1 sample
0.5 %0
0.70
o.ig'1
sample
0.5 %o
QAPP 2000.01
QAPP02.01
QAPP02.01;
QAPP 01. 02
Burrowing Shrimp Parameters
131
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Table B.I Measurement Quality Objectives for data collected by Western Ecology Division.
Parameter
Burrowing Shrimp Hole
Density
Burrowing Shrimp
Density
Benthic Invertebrate
Abundance
Burrow Surface Area
Burrow Volume
units
holes m"2
individuals m"2
individuals m"2
cm2
cm3
Expected range
0-1 500m'2
0-600 m'2
0-5000 m'2
5-1600 cm2
1-800 cm3
Accuracy
± 10%
± 10%
± 10%
±5 cm2
±5cm3
Precision
0.70
0.70
± 10%
± 1 cm2
± 1 cm3
SOP(s) or other
QAPP 2000.01
QAPP 2000.01
QAPP 2000.01
QAPP 2000.01
QAPP 2000.01
General Lab
Mass
mg
0.5-150
±5%
±5%
SOPIOP.01
Physical features
Estuary bathymetry
Ground position
CMT GPS system, HP-
GPS-L4
ft
UTM: meters
a) Easting
b) Northing
-40 to +10
MLLW
414480-428210
4933695-4942656
±0.5
± 0.5-2.5 m
NA
±1.5m
survey contract
D. Young PI
CMT GPS system, HP-
GPS-L4 Manual
132
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Table B.2 Quality Calibration and Control Checks for Instruments and Parameters.
Instrument
Calibration procedure
Quality Control Check
Frequency
Acceptance
criteria
Action if values are
unacceptable
SEE- 19 package:
Conductivity
Temperature
Depth (pressure)
PAR sensors
Seapoint turbidity
YSI
Multiparameter
Sondes
(6000 and 6600
models)
YSI Hand-held
meters
CMT-DGPS
SeaBird factory
calibration
SeaBird factory
calibration
SeaBird factory
calibration
LiCor factory
calibration- every 2
years
Seapoint factory
calibration
Factory calibrated for
depth; calibrations
performed to
manufacturer' s
specifications
Self-calibrates with
satellites ( speed of
light as the calibrant)-
every use
Solution of known conductivity
Place in water bath with NIST
traceable thermometer
In air reading compared to Fortin
type Hg barometer (Nat. Wea.
Serv. type)
Solar noon clear sky exposure
1. Laboratory 3-levels of
spherical particle solutions
2. Field total suspended solids vs
reading
All parameters checked against
factory standards upon retrieval
from deployment
All parameters checked against
factory standards
Visit type 1 USGS reference site
with multiple readings
If drift is
suspected
If drift is
suspected
If drift is
suspected
Bi-annually
1. annually
2. quarterly to
monthly
Immediately prior
to deployment
and upon retrieval
Annually
MQO
MQO
MQO
MQO
Linear R2>0.95
slope ±25% of
initial
Performed to
factory
specification,
methods and
standards
MQO
Factory return
Factory return
Factory return
Factory return
Clean and re-test
Clean and re-
calibrate or return to
factory
Clean and re-
calibrate or return to
factory
Check post
processing values
re-test
133
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LiCorLI-193SA
sensor
Balance
(5-place)
Drying oven
Fluorometer
Turner 10-AU
Microbalance
Mass
Spectrometer
Mass
Spectrometer
Mettler AT250
balance
Li-Cor factory
calibration - every 2
years
Factory
representative adjusts
on annual basis
2-point calibration
using solid secondary
standard
Check calibration
with internal weights
Annual calibration by
contractor (Quality
Control Services,
Inc.)
Solar noon clear sky exposure
Check reading of thermometer
Check solid secondary standard
reading (low and high settings)
for instrument drift.
Readings of 5 class 'S' masses
that cover expected range
Peak centering
Nitrogen isotope ratio of working
organic standard
Check accuracy with 'S' class
weights
Bi-annually
Before each
session
Before each use
At start, middle
and end of every
run
Before each
session
Before each
analysis
Once every 10
samples
With each use
MQO
Within than
class tolerance
MQO
If high setting
of secondary
standard reading
differs by ± 1%
from true value
±2mg
Center for each
of the masses
being measured
±0.2%o of long-
term measured
value
Contractor
determination
Return to factory
Contact balance
maintenance
personnel
Re-calibrate and re-
run samples
Clean weighing pan
and catch plate,
recalibrate; have
serviced
Adjust magnetic
field and source
high voltage; service
if necessary
Repack combustion
and reduction
columns, reanalyze;
service if necessary
Check cell holder
alignment, optic
cleanliness, repeat
134
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Water Quality CTD Profiles and Grab Samples
Profiles of water quality parameters were measured using the Seabird SEE 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 'Seasoff 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,
+ 3-
NOs +NC>2 , NH4 , and PC>4 ), 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.
135
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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 re-
calibrated and the previous half-set of samples were reanalyzed.
The solid secondary standard was calibrated to chlorophyll a concentrations using fresh
chlorophyll a standards provided by the manufacturer (Turner Designs). The fluorometric
chlorophyll a standards supplied by the manufacturer (Turner Designs) consisted of a high
concentration (ISljig I"1) and a low concentration (18.2 jig I"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%.
136
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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)
137
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Table B.3 Precision and accuracy (expressed as %) of phosphate, silicate, nitrate+nitrite,
ammonium, and nitrite Data.
Measure
Precision, %
Accuracy, %
PO4
97.6
(n=265)
98.2
(n=82)
Si(OH)4
98.3
(n=258)
98.2
(n=253)
NO3"+NO2"
98.2
(n=266)
96.5
(n=195)
NH4
98.0
(n=265)
96.8
(n=140)
NO2"
94.6
(n=7)
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.
138
-------
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
Precision, %
Accuracy, %
Digestion Recovery, %
Total Nitrogen
98.7
(n=39)
101.8
96.3
Total Phosphorous
96.7
(n= 36)
97.7
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.
139
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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.
Calibration Accuracy
Pre-deployment
Post Deployment
Accuracy (includes the
effect of biofouling)
Temperature
99.0
98.7
Salinity
98.2
96.5
Conductivity
100.0
97.8
Turbidity
100.0
97.1
DO
99.6
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.
140
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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
515N 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.
141
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Table B.6 Precision and Accuracy of 515N data.
Precision, %
Accuracy, %
615N
99.1 (n=23)
96.6 (n=63)
Aerial Mapping of Seagrass and Macroalgae
The remote sensing procedure used in this study to map the intertidal distribution of
eel grass 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
2. 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.
142
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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 2. 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 Arclnfo format grids. The bathymetric depth data was
interpolated onto a grid using TopoGrid in Arclnfo. 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.
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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
1998
1998-99
1999-2003
2001-2002
2002-2004
2004
Zone
1
1
1
1
1
1&2
No. of Samples
Zone 1 Zone 2
69
65
4159
2094
140
47 53
Sampling
NR
R
R
NR
NR
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.
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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.
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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 ofZ. 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^/co-
ops.nos.noaa.gov/tides05/tab2wclb.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.
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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.
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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/REOHUCvl 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/cfsl023 3 .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.htmltfcaf).
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/fsl0800.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 (C-
CAP, http://www.csc.noaa.gov/crs/lca/ccap.html) program. The more recent data were derived
from late 2000 and 2001 Landsat TM (thematic mapper) imagery. NOAA also produced a layer
from imagery collected circa 1995-1996 and the earlier dataset was used to generate a land cover
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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 (ATtlLA) 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 ATtlLA 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
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10 meter elevation data for Oregon was obtained by the USD A 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.
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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.I 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.I), 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.
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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.I 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 -
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where TV is the DIN source and (^ is a loss/uptake rate. The same value of u^ was used for all three
nitrogen sources and the value of u^ was determined by fitting total modeled DIN
(N0cean+Nriver+Nwwtf) to observations of DIN within the estuary. The best fit to observations was
found with [j, = 0. 1 d"1. Simulations were also performed with no uptake ([j, = 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
where /R,/W, and^b are the fractions of riverine, wastewater treatment facility, and oceanic DIN,
respectively, and SR, Sw, and SR 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 (Sw = 15-22%o) 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 (5R=2%o, 5w=20%o, and 5o=8.4%o) 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%o;
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.
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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 EPOl.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.
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WRS 14B.2. Standard Operating Procedure for the Determination of Total Suspended Solids
(Non- Filterable Residue). Dynamac Corporation 2005.
WRS 3 4 A. 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.
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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, overflow, 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
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size of the estuarine drainage area, estuary area and volume, overflow, 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.
157
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Table C. 1. Classifications of 14 Oregon estuaries sampled in WED Classification Study and NCA data sets (data set denoted as C and N, respectively in first
column). Lee et al. (2006) Classification based on Oregon Coastal Atlas (httrj://www.coastalatlas.net) and Bottom et al. (1979).
Estuary
Alsea
(C,N)
Columbia
(N)
Coos
(C,N)
Nehalem
(N)
Nestucca
(C,N)
Netarts
(N)
Rogue
(N)
Salmon R.
(C,N)
Siletz
(N)
Siuslaw
(N)
Tillamook
(C,N)
Umpqua
(C,N)
Yachats
(N)
Yaquina
(C,N)
NOAA
Class
River
Dominated
River
Dominated
Coastal
Embayment
River
Dominated
NA
Lagoons
River
Dominated
NA
River
Dominated
River
Dominated
River
Dominated
River
Dominated
NA
River
Dominated
Type
Straits w/
Term. Bay
Salt Wedge
V-shaped &
semi-encl.
Straits w/
Term. Bay
NA
Limited
FW Inflow
Straits w/
Term. Bay
NA
Straits w/
Term. Bay
Straits w/
Term. Bay
Straits w/
Term. Bay
Straits w/
Term. Bay
NA
Straits w/
Term. Bay
Geomorphology
Lee et al (2006)
Tidal dominated
drowned river
River dominated
drowned river
Tidal dominated
drowned river
Tidal dominated
drowned river
Tidal dominated
drowned river
Bar built
River dominated
drowned river
Bar built or
drowned river
Tidal dominated
drowned river
Tidal dominated
drowned river
Tidal dominated
drowned river
River dominated
drowned river
Tidally Restricted
coastal Creek
Tidal dominated
drowned river
Stratification
Bottom et al.
(1979)
Partially
Mixed
NA
Well Mixed
Partially
Mixed
Partially
Mixed
Well Mixed
Partially
Mixed
Partially
Mixed
Partially
Mixed
Partially
Mixed
Partially
Mixed
Partially
Mixed
NA
Partially
Mixed
Burgess
etal.
(2004)
9
1
6
9
NA
11
9
NA
9
9
9
9
NA
6
Engle et
al.
(2007)
1
5
8
8
NA
8
8
NA
8
8
8
1
NA
8
Bricker et
al. (in
prep.)
4
4
1
4
NA
1
4
NA
4
4
4
4
NA
4
Quinnet.al(1991)
Dissolved
Concentration
Potential
H
L
H
H
NA
H
M
NA
H
H
H
M
NA
H
Particle
Retention
Efficiency
L
L
L
L
NA
L
L
NA
L
L
L
L
NA
L
Predicted
Concentrations
N
M
M
M
M
NA
M
M
NA
M
M
M
M
NA
M
P
M
M
M
L
NA
L
M
NA
M
L
L
M
NA
M
Legend: NA denotes classification not available; H, M, and L represent high, medium, and low classes (respectively).
158
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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
159
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Table D.2. Summary of literature regarding the effects of macroalgae (biomass or percent cover) on
estuarine infauna, epifauna, fishes, and shorebirds.
Taxa
Shorebirds
Shorebirds
Infauna
Epi.fauna
Shorebirds
Infauna
Shorebirds
Shorebirds
Infauna/
Epi.fauna
Infauna
Infauna
Infauna
Infauna
Infauna
Infauna
Epibenthic
fauna
Juvenile
flatfish
Infauna
Decapods
Pred. Fish
Fishes
Juvenile
Flatfish
Epibenthic
fauna
Juvenile
Flatfish
Shorebirds
Type
S
S
S
S
S
F
F
F
S
F
F
S
S
S
F
F
S
L
S
S
S
Location
England
(S. coast)
England
(S. coast)
England
(S. coast)
England
(S. coast)
England
(S. coast)
Ireland
(S.W. coast)
Scotland
(E. coast)
Sweden
(S. coast)
Scotland
(E. coast)
Central
California
Scotland
(E. coast)
North Baltic
Sea
Sweden
(W. coast)
Sweden (W.
coast)
Central
California
Sweden
(W. coast)
Sweden
(W. coast)
Sweden
(W. coast)
Sweden
(W. coast)
Portugal
T
(°Q
-20
-16
14-20
4-20
10-20
14-20
9-12
14-20
Sediment
H2S or Low
Eh
Y
Y
Y
Y
Y
Y
Y
Effect*
•I abundance
Neg. correl.: areas
densest algal mats vs.
abundance
•i- infauna
t epibentkfauna
•i- shorebirds
Little effect on
zoobenthos or shorebirds
•i- abundance
(refutes Soulsby et al.,
1982)
•i- infauna
t epi.fauna
•i- amphipods
t polych. & bivalves
•i- larval settlement
Altered infaunal
composition
•i- bivalves
•i- phoronids
•i- amphipods
by -90 %
^bivalves by 73%
^epi.fauna by > 50%
•i- abundance
^ bivalves by - 70 %
t decapods
•i- foraging
•I fish biomass
•i- settling
•i- abundance
t epi.fauna
•i- epi.fauna
•i- fish sp. & foraging
No effect on feeding
Macro algal
Density
Cover: 75% in
20% of
intertidal
Cover: >25% in
40% of
intertidal
Cover: 42 %
— 300 gdw m"2
Cover: >25% in
40% of
intertidal
60 gdw m"2
143 gdw m"2
440 gdw m"2
800 gdw m"2
Cover: 100 %
200 gdw m"2
832 gdw m"2
Epiphyte cover
on eelgrass:
80%
Cover: 50%
220 gdw m"2
Cover: 100 %
Cover: 35 %
Cover: 75 %
7-225
gdw m"2
Cover: 80 %
179 gdw m"2
Cover:30-50%
Cover: 90%
Cover:30-40%
30-60 gdw m"2
Citation
Tubbs
(1977)
Tubbs &
Tubbs
(1980)
Nicholls et al.
(1981)
Soulsby etal.
(1982)
Tubbs &
Tubbs
(1983)
Thrush
(1986)
Hull
(1987)
Olafsson
(1988)
Raffaelli et al.
(1989)
Everett
(1991)
Raffaelli et al.
(1991)
Bonsdorff
(1992)
Isaksson &
Pihl (1992)
Pihl & van der
Veer (1992)
Everett
(1994)
Isaksson et al.
(1994)
Pihl et al.
(1994)
Wennhage &
Pihl
(1994)
Pihl et al.
(1995)
Murias
etal. (1996)
160
-------
Zoobenthos
Gastropod
& bivalve
Mollusks
Infaunal
bivalve
&shrimp
Zoobenthos
Zoobenthos
Infauna
Infauna
Infauna
Infauna
Zoobenthos
Amphipods
Shorebird
Black-tailed
Godwit
Infauna
Epibenthic
Mudsnail
Copepods
Infauna
Pred. birds
Zoobenthos
Infauna
Infauna
Zoobenthos
Infauna
F
F
L
S
F
L
S
F
F
S
L.
S
S
F
S
F
F
F
F
S
S
F
North Baltic
Sea
North Baltic
Sea
North Baltic
Sea
North Baltic
Sea
Scotland
(E. coast)
North Baltic
Sea
Maine,
U.S.A.
Scotland
(E. coast)
Portugal
North Baltic
Sea
Portugal
Ireland
(S. coast)
Sweden
(W. coast)
Portugal
(W. coast)
New York,
U.S.A.
Ireland
(S. coast)
Portugal
(W. coast)
Australia
(E. coast)
England (S.
coast)
NE Baltic
Sea
Netherlands
11-16
11-16
6-20
20
19
17-18
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
•i- abundance
& biomass by 87-94 %
•i- abundance & biomass
^ survival by 80%
•i- survival by 83%
Alters community
•i- abundance of invert.
prey of fishes/birds
•I juv. bivalve moll.
Shifted community
structure to detritivores
Altered infauna
t infaunal
detritivores
Altered structure.
•i- bivalve & amphipod
Population
zeroed by algae crash
•i- abundance
•i- suspension feeding
bivalves by -90 %
t abundance
•i- copepods by -85%
•i- amphipod abundance;
Black-headed gulls
avoid algal cover
t abundance mudsnail,
polych. worm
•i- polychaete, molluscan
abundance
•i- infaunal abundance
-70%
•i- infaunal biomass 70%
Minor alterations to
community structure
No major changes in
infauna
440 gdw m"2
440 gdw m"2
200 gdw m"2
440 gdw m"2
300 gdw m"2
<600
gdw m"2
Cover: 50 %
50 - 200
gdw m"2
200 gdw m"2
158 gdw m"2
181 gdw m"2
413 gdw m"2**
Cover:
40-70%
300 gdw m"2
250 gdw m"2**
100 gdw m"2
206 - 277
gdw m"2
600 gdw m"2
450 gdw m"2
Cover: 90 %
347 gdw m"2
Cover -25%
80 gdw m"2
buried in
sediment
Norkko &
Bonsdorff
(1996a)
Norkko &
Bonsdorff
(1996b)
Norkko &
Bonsdorff
(1996c)
Bonsdorff
etal. (1997)
Raffaelli et al.
(1998)
Norkko
(1998)
Thiel&
Watling
(1998)
Bolam et al.
(2000)
Lopes et al.
(2000)
Norkko et al.
(2000)
Pardal
et al. (2000)
Lewis &
Kelly (2001)
Osterling &
Pihl (2001)
Cardoso
et al. (2002)
Franz &
Friedman
(2002)
Lewis
et al. (2003)
Cardoso
et al. (2004)
Cummins et
al. (2004)
Jones & Finn
(2006)
Lauringson&
Kotta (2006)
Rossi (2006)
Type of Study: S is survey, F is field manipulation, L is lab experiment; Effects: -i- decreased; t increased;
* For surveys, negative correlation suggesting a possible effect; ** Ash free dry weight
161
-------
Appendix E: Stressor-Response Model Calibration, Input Data, and Results
250-1
200-
E) 150
CO
O
in
100-
50-
0
500 -i
Model
Data
\ \
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Figure E. 1 Stressor-Response Model Calibration for biomass and carbohydrate during 2002 in
Zostera marina. Error bars represent standard errors.
162
-------
o
o
CD
^
"00
CD
Q.
E
CD
0
2000-,
c/>
0 'w 1500-
LJ CM
X 'c
15 1000-1
LL
c
^
"o
f™
^.
Q_
O
E
^
500-
0^
Zone 1
Zone 2
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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.
163
-------
200-,
Year 1
Year 2
Year 3
Year 4
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.
164
-------
Yearl
A vA
Year 2
°
°
YearS
A .A
Year 4
Figure E.4 Case 1, Zone 2; simulations using median light attenuation and DIN values. Stable
Zoster a 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.
165
-------
T3
200-,
150-
100-
cc
I 50-1
in
400-1
300-
•o O
>^ O 200-
o w
€ z
cc 5) 100 -
o
o.
Year 1
Year 2
Year 3
Year 4
-th
Figure E.5 Case 2, Zone 1; simulations using 25 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.
166
-------
Yearl
A vA
Year 2
°
°
YearS
A .A
Year 4
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.
167
-------
200-,
Year 1
Year 2
Year 3
Year 4
-th
Figure E.7 Case 3, Zone 1; simulations using 75 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.
168
-------
Yearl
A vA
Year 2
°
°
YearS
A .A
Year 4
Figure E.8 Case 3, Zone 2; simulations using 75th percentile light attenuation and DIN values.
Zoster a 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.
169
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