DOC
EPA
United States
Department of
Commerce
National Oceanic and Atmospheric
Administration
Seattle WA 98115
United States
Environmental Protection
Agency
Office of Environmental
Engineering and Technology
Washington DC 20460
EPA-600/7-81-036
April 1981
Research and Development
Evaluation of
Existing Marine
Intertidal and
Shallow Subtidal
Biologic Data
Interagency
Energy/Environment
R&D Program
Report
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EVALUATION OF EXISTING MARINE INTERTIDAL
AND SHALLOW SUBTIDAL BIOLOGIC DATA
by
Judith E. Zeh
Mathematical Sciences Northwest, Inc.
2755 Northup Way
Bellevue, Washington 98004
Jonathan P. Houghton
and Dennis C. Lees
Dames £ Moore
155 N. E. 100th Street
Seattle, Washington 98125
Prepared for the MESA (Marine Ecosystems Analysis) Puget Sound
Project, Seattle, Washington in partial fulfillment of
EPA Interagency Agreement No. D6-E693-EN
Program Element No. EHE625-A
This study was conducted
as part of the Federal
Interagency Energy/Environment
Research and Development Program
Prepared for
OFFICE OF ENVIRONMENTAL ENGINEERING AND TECHNOLOGY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20460
July 1981
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Completion Report Submitted to
PUGET SOUND ENERGY-RELATED RESEARCH PROJECT
MARINE ECOSYSTEMS ANALYSIS PROGRAM
OFFICE OF MARINE POLLUTION ASSESSMENT
NATIONAL OCEANIC AND ATMOSPHERIC ADMINSTRATION
by
Mathematical Sciences Northwest, Inc.
2755 Northup Way
Bellevue, Washington 98004
DISCLAIMER
This work is the result of research sponsored by the Environmental
Protection Agency and administered by the National Oceanic and Atmospheric
Administration.
The National Oceanic and Atmospheric Administration (NOAA) does not
approve, recommend, or endorse any proprietary product or proprietary
material mentioned in this publication. No reference shall be made to NOAA
or to this publication furnished by NOAA in any advertising or sales
promotion which endorses any proprietary product or proprietary material
mentioned herein, or which has as its purpose an intent to cause directly or
indirectly the advertised product to be used or purchased because of this
publication.
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FOREWORD
Substantially increased petroleum tanker traffic and refining
operations are anticipated in the region of northern Puget Sound and the
Strait of Juan de Fuca as Alaskan crude oil production increases and as
pipeline deliveries of crude from Canada to the region are terminated. This
increased transport and refining activity will increase the opportunities for
spills and leaks of crude oil and refined products into the marine
environment. Recognizing the need for environmental information in the
region, the U.S. Environmental Protection Agency has supported the Puget
Sound Energy-related Project under which studies involving biological
characterizations, physical oceanography, trajectory modeling, pollutant
monitoring, and fate and effects of oil have been implemented. This Project
has been administered by NOAA's Marine Ecosystems Analysis (MESA) Puget Sound
Project office. A major part of the Project has involved a variety of
biological studies intended to provide information on the characteristics of
biological communities at risk to oil pollution in the region. This report
presents the results of a study to determine the degree of variability, and
thus, utility of existing biologic data which may be used to estimate oil
spill impacts. Intertidal and shallow subtidal benthos data collected by
investigators supported by the Project and by the Washington Department of
Ecology were studied.
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ABSTRACT
This study was initiated in order to evaluate a large set of marine
intertidal and shallow subtidal biologic data collected in two baseline study
programs in the marine waters of northwestern Washington between 1974 and
1979. These programs, sponsored by the U.S. Environmental Protection Agency
and the State of Washington Department of Ecology, shared the objective of
characterizing biologic communities which may in the future be subjected to
stresses resulting from increases in oil shipment and refining operations in
the region.
The first objective of the present study was to conduct statistical
analyses of the baseline data to assess the contributions of annual,
seasonal, tidal elevation, geographic, habitat, and between-sample variations
to overall variability in the data and to determine the predictability of
communities at future times and/or different sites from the existing data
base. In the course of these analyses, the correctness and usability of the
data tapes were also evaluated. The second objective of the study was to
recommend strategies for future research (possibly including monitoring) to
strengthen the data base.
This report summarizes and compares methodologies used by the
investigators who conducted the baseline studies and calls attention to
problems in the data base resulting from methodological differences and other
factors. Communities in three broad habitat categorizations—rocky
intertidal, soft substrate intertidal, and subtidal—were examined by means
of cluster analysis. For the intertidal habitats, numerical assemblage
parameters such as richness, biomass, and diversity were computed and
examined by means of multiple regression and analysis of variance to fulfill
the first study objective. Key populations were analyzed similarly.
Exposure, sediment characteristics, and tidal elevation proved to be
the key contributors to variability in the data. However, there were strong
site differences which could not be fully explained by these factors. In
addition, the level of replication used in the baseline studies proved to be
too low for reliable prediction and change detection. Our recommendations
for future sampling call for increasing levels of replication by focusing on
a smaller number of habitats and elevations. We also include suggestions for
streamlining and standardizing sampling methodology.
IV
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CONTENTS
Section Page
Foreword iii
Abstract iv
Figures vii
Tables ix
Acknowledgments xii
1. Introduction 1
1.1 The data base 1
1.2 Need for the present study 4
1.3 Study objectives 5
2. Conclusions 6
3. Recommendations 10
4. Discussion of the data base 13
4.1 Methods of data collection and reduction 13
4.1.1 Sampling strategies 15
4.1.2 Sampling techniques 15
4.2 Problems encountered 30
4.2.1 From field methodology 3O
4.2.2 From sample processing 31
4.2.3 From data processing 32
4.2.4 From taxonomy 35
5. General approach to objective 1 37
5.1 Our methods of resolving problems 37
5.2 Summary of statistical analyses ... 38
5.2.1 Population parameters and assemblage parameters. . . 38
5.2.2 Cluster analysis to describe assemblages 40
5.2.3 Analyses of population and assemblage parameters . . 40
5.2.4 Predictive models 41
6. Results of objective 1 analyses 43
6.1 Intertidal rocky substrates 43
6.1.1 Community analyses 43
6.1.2 Population analyses 73
6.1.3 Predictive models 76
6.1.4 Summary of the prognosis for assessing changes in
community structure at rocky intertidal sites.... 80
6.2 Intertidal soft substrates 81
6.2.1 Community analyses 81
6.2.2 Population analyses 116
6.2.3 Predictive models 121
6.2.4 Summary of the prognosis for assessing changes in com-
munity structure at soft substrate intertidal sites. 126
6 .3 Intertidal cobble substrates 128
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6.4 Subtidal substrates 129
6.4.1 Community analyses 129
6.4.2 Summary of subtidal results 143
7. Improved sampling strategies—objective 2 144
7.1 Introduction 144
7.2 Pertinent types of data 145
7.2.1 Assemblage parameters 145
7.2.2 Population parameters 148
7.3 General considerations 148
7.3.1 Investigators and taxonomy 150
7.3.2 Sampling periods and duration of study 150
7.3.3 Sampling sites and tidal elevations or depths. . . . 151
7.3.4 Replication 155
7.4 Monitoring studies 158
7.4.1 Sampling design for intertidal and subtidal rock . . 158
7.4.1 Design for intertidal and subtidal soft substrates . 162
7.5 Oil spill impact assessment 165
7.5.1 Pre-oiling assessment—phase I 166
7.5.2 Initial spill assessment—phase II 168
7.5.3 Short-term post-spill reassessment—phase III. . . . 170
7.5.4 Recovery monitoring studies—phase IV 170
8. Other possible approaches to analysis of the data base 173
Bibliography/references 174
Appendix A. Details concerning statistical methodology 178
A.I Model assumptions, data transformations, and
confidence intervals 178
A.2 Multiple regression 181
A. 3 Analysis of variance 183
A.4 Testing for significant differences 189
A.5 Cluster analysis methodology 192
Appendix B. Habitat dictionaries and rules for creating them 194
Appendix C. Animals and plants found at cobble sites 262
VI
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FIGURES
Number Page
1 Intertidal and shallow subtidal baseline study sites 3
2 Subtidal sites including those of Smith (1979) 23
3 Relationships among summer and winter rocky intertidal stations. . 44
4 Relationships among summer rocky intertidal stations 47
5 Relationships among winter rocky intertidal stations 48
6 Relationships among rocky intertidal stations from all
months, Fidalgo Head and Cantilever Pier 49
7 Relationships among rocky intertidal Strait stations, all seasons. 50
8 Predicted number of animal taxa S at Pillar Point from regression 53
ct
9 Residual versus predicted number of animal taxa S at Pillar
Point from regression 54
10 Group means from analysis of variance of Strait rocky
intertidal numerical assemblage parameters 57
11 Means of rocky intertidal assemblage parameters at
each site and elevation sampled, summer 1976 70
12 July 1976 means of log transformed counts for selected
rocky intertidal animals 75
13 Summer soft substrate intertidal station relationships 82
14 Winter soft substrate intertidal station relationships 83
15 Relationships among less exposed soft substrate
intertidal stations, mid elevations 86
16 Summer relationships among less exposed soft substrate
intertidal stations, mid elevations 87
17 Winter relationships among less exposed soft substrate
intertidal stations, mid elevations 88
VII
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18 Histograms of number of plant taxa S at protected soft
substrate sites where plants were P found 90
19 Group means from analysis of variance of numerical assemblage
parameters from exposed sand and gravel intertidal sites,
summer 91
20 Group means from analysis of variance of numerical assemblage
parameters from exposed sand and gravel intertidal sites,
winter 94
21 Group means from analysis of variance of numerical assemblage
parameters at moderately exposed sand sites, three seasons
and elevations 98
22 Group means from analysis of variance of numerical assemblage
parameters at upper intertidal exposed sand and gravel
sites, summer 101
23 Group means from analysis of variance of numerical assemblage
parameters at moderately protected intertidal sand and
gravel sites, July 1976 102
24 Numerical assemblage parameter means at protected soft
substrate sites, low to mid intertidal, all seasons 109
25 Group means from analysis of variance of numerical assemblage
parameters at protected soft substrate sites, low and mid
intertidal, summer 113
26 Group means from analysis of variance of numerical assemblage
parameters at protected soft substrate sites, low intertidal,
all years and seasons 115
27 Means of log transformed counts for selected animals from
protected soft substrate intertidal sites, low to mid
elevations, summer 1976 118
28 Relationships among shallow subtidal stations based on
50 plant and animal species or groups 133
29 Relationships among shallow subtidal stations based on
132 plant and animal species or groups 134
30 Relationships among medium-depth subtidal stations based
on 50 plant and animal species or groups 135
31 Relationships among deep subtidal stations based on 50
plant and animal species or groups 136
32 Subtidal depth-site-sediment relationships, Whidbey Island,
1978-1979 , 139
Vlll
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33 Subtidal depth-site-sediment relationships, Whidbey Island and
the Strait of Juan de Fuca, 1976-1977 141
34 Subtidal depth-site-sediment relationships, San Juan Island
and North Puget Sound 142
35 Hypothetical barnacle distribution with two alternative
sampling grids 154
IX
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TABLES
Number Page
1 Sampling dates at baseline study sites 14
2 Summary and comparison of sampling methods
in rocky intertidal surveys 16
3 Elevations for rocky intertidal stratified sampling 17
4 Sampling methods in soft substrate intertidal surveys 20
5 Elevations for soft substrate intertidal stratified sampling ... 21
6 Sampling methods in subtidal surveys 25
7 General substrate classification at subtidal
stations by date and depth 26
8 Results of regressions to partition assemblage parameter
variability, rocky intertidal sites 51
9 Contributions of site and season differences to assemblage
parameter variability, rocky Strait sites 56
10 Means, confidence intervals, and significance tests for
Strait assemblage parameters, summer and winter 65
11 Year, season, date within season, and replicate variability
at Cantilever Pier and Fidalgo Head 66
12 Site x season analysis of variance,
Cantilever Pier and Fidalgo Head 68
13 One-way analysis of variance of summer 1976 rocky
intertidal assemblage parameters, all sites 69
14 Contributions of site, year, and season differences to variability
in Strait rocky intertidal population parameters 74
15 Predictability of assemblage parameters for high elevations,
North Puget Sound rocky intertidal sites 76
16 Detectable percent changes in rocky intertidal
assemblage parameters 78
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17 Detectable percent changes in rocky intertidal
population parameters 79
18 Contributions of exposure, substrate, region, and elevation
differences to variability in summer assemblage parameters
at exposed sand and gravel intertidal sites 92
19 contributions of site and elevation differences to variability in
winter assemblage parameters at exposed sand and gravel sites. . 95
20 Contributions of site, elevation, and season differences to
assemblage parameter variability, moderately exposed sand sites. 99
21 Contributions of site and elevation differences to variability in
July 1976 moderately protected sand and gravel
assemblage parameters 103
22 Results of regressions to partition assemblage parameter
variability, protected soft substrate intertidal sites 105
23 Results of regressions over restricted ranges of elevations
and dates, Birch Bay and Fidalgo Bay 106
24 Contributions of site and season differences to variability in low
to mid intertidal protected soft substrate assemblage parameters lio
25 Percent of sediment by grain size, protected soft substrate sites. Ill
26 Contributions of site and elevation differences to variability in
protected soft substrate summer assemblage parameters,
low and mid intertidal 114
27 Contributions of year-to-year changes to variability in low
elevation protected soft substrate assemblage parameters .... 116
28 Detectable percent changes, soft substrate assemblage parameters . 123
29 Detectable percent changes in transformed population counts,
protected mud sites 125
30 Numbers of plant and animal taxa at subtidal stations 130
31 Required replication for detection of changes in numerical
assemblage parameters, rock and soft substrates 156
32 Required replication for detection of changes in density of
dominant species, rock and soft substrates 157
33 Proposed sampling program, rocky intertidal and subtidal habitats. 160
34 Recommended parameters and methodology, soft substrate sampling. . 162
xx
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A-l Analysis of variance table for multiple regression 183
A-2 One-way analysis of variance table 184
A-3 Expected mean squares for nested analysis of variance 188
B-l Taxonomic dictionary for intertidal rock substrates 196
B-2 Taxonomic dictionary for intertidal soft substrates 216
B-3 Taxonomic dictionary for subtidal substrates 231
XII
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ACKNOWLEDGMENTS
The assistance of the investigators involved in the baseline studies,
Drs. Carl Nyblade and Herbert Webber, in answering questions concerning
methodology and results has been invaluable. Ms. Alice Benedict of
Dr. Webber's staff has been particularly helpful in resolving data problems.
We would also like to thank Edward Long, our Contracting Officer's
Technical Representative, and Sid Stillwaugh of NOAA's Environmental Data and
Information Service for their assistance and support throughout the project.
Long, Nyblade, Webber, and Benedict all reviewed the draft of this report and
contributed to improvements in the final version.
Xlll
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SECTION 1
INTRODUCTION
In the past decade, a remarkable number of "baseline" or "benchmark"
surveys of littoral communities have been conducted in the marine waters of
northwest Washington and elsewhere. This activity has been spurred by the
National Environmental Policy Act (NEPA) and an increasing awareness of
potential environmental consequences of man's activities in the coastal
zone. In general, this type of survey has attempted to obtain replicated
quantitative data on species abundance and distribution as well as total
animal and/or plant density and weight (biomass), richness, and diversity.
The two primary objectives of these surveys typically have been (1) to
characterize the nature and perhaps the resource value of communities
observed and (2) to provide data that will allow testing of hypotheses
regarding factors affecting patterns in space (e.g., habitat, elevation,
location effects) or time (e.g., predisturbance/postdisturbance, seasonal,
annual effects).
The first objective has been accomplished quite adequately by a
variety of researchers (Houghton 1973; Houghton and Kyte 1978; Nyblade 1977,
1978, 1979a and b; Smith and Webber 1978; Smith 1979; Thorn 1978; Wisseman et
al. 1978; Webber 1979 and 1980). However, only infrequent attempts have been
made at statistical testing of the significance of observed patterns and the
suitability of the data obtained for detection of real differences in space
or time or for prediction of biological characteristics of assemblages in
like habitats at other locations.
The work presented in this report represents such an effort using
intertidal and shallow subtidal data obtained in two large-scale and long-
term sampling programs. The first was funded by the State of Washington
Department of Ecology (WDOE), the second by the U.S. Environmental Protection
Agency (EPA) through the Puget Sound Project Office of the Marine Ecosystems
Analysis (MESA) program of the National Oceanic and Atmospheric
Administration (NOAA). NOAA also administered the study reported in this
document.
1.1 THE DATA BASE
The WDOE North Puget Sound Baseline Studies Program (BSP) was begun in
1974 to develop, among other things, a "continuing comprehensive program of
systematic baseline studies to...use as supporting evidence of environmental
damage resulting from oil pollution..." (Gardner 1978). Specific objectives
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governing the implementation of the intertidal and shallow subtidal
(littoral) studies evaluated in this report were (Gardner 1978) to:
"Document the distribution and abundance of biological
resources and relevant oceanographic parameters in
intertidal and shallow subtidal habitats."
and
"Determine the distribution and abundance of intertidal
and shallow subtidal populations of Significant
Biological Resources which serve as major sources of
recruitment for adjacent areas."
Field studies^of intertidal and shallow subtidal biota were conducted
in North Puget Sound from the summer of 1974 through the summer of 1976.
Additional summer sampling continued at some sites through 1980. Two
different investigators performed the field investigations in two different
geographic locales: Dr. Carl Nyblade of the University of Washington
Department of Zoology worked primarily on San Juan Island, and Dr. Herbert
Webber of Western Washington University worked in the bays and islands east
of Rosario Strait and along the east shore of the Strait of Georgia.
Each investigator initially employed different sampling strategies,
with Nyblade (1977) using a stratified random design and Webber (Smith and
Webber 1978) using a gradient sampling technique. Beginning with sampling in
1975, an effort was made to standardize techniques to obtain more comparable
data from each locality.
In 1975, EPA initiated a series of nationwide environmental research
programs designed to identify the potential ecological and health impacts of
accelerated energy development. The inland waters of northwestern Washington
were selected for one of these programs as an area likely to be affected by
intensified petroleum shipping and refining operations. The NOAA/MESA Puget
Sound Project Office was selected to manage the study. The overall
objectives of this research relevant to the present study were tos
1. Characterize the major marine biological populations subject
to impact by pollution resulting from petroleum transportation and
refining activities in the Puget Sound region, and
2. Provide decision-makers with environmental and ecological
information and predictions of the effects of oil-related
activities upon the ecosystem.
*The term "North Puget Sound" as used in this report is geographically
inaccurate; the area referred to includes the San Juan Islands and the
inland waters in the approaches to Rosario Strait and adjacent to the
mainland from north of Whidbey island up to the southern end of the Strait
of Georgia. We use the term North Puget Sound (or northern Puget Sound) to
be consistent with previous studies and the guidelines for this study.
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The MESA program's intertidal and shallow subtidal baseline field
studies began in 1976. The same two investigators were contracted. General
methods used for intertidal studies were standardized, including both
gradient and stratified random measurements. Again, however, each
investigator was responsible for a separate geographic region. In addition,
the two-year sampling program on Whidbey Island began a year after the start
of the two-year program in the Strait of Juan de Fuca. Subtidal methods
varied between the researchers.
In short, the WDOE and MESA studies in the Puget Sound region were
begun in response to the same basic need. They shared the objective of
characterizing biologic communities that may in the future be subjected to
stresses resulting from expected increases in oil tanker traffic, refinery
operations, and pipeline development. While there were variations in
methodology within and between the baseline programs, an attempt was made to
standardize sample collection and laboratory analysis techniques to obtain
comparable data. The data collected comprise the data base for the present
study.
The 30 sites sampled most intensively during the WDOE and MESA studies
are shown in Figure 1. These sites represent rock, cobble, gravel, sand, mud,
and mixed habitats. Additional locations were sampled only once or a few
times.
Drayton Harbor;
Birch Bay
Guemes
Island
Mlgley Point
Legoe Bay
Point George
SAN JUAN ISLANDS
Westcott Bay
Webb Camp
Cantilever Pier
Deadman Bay
Eagle Cove
South Beach
r±
VANCOUVER
ISLAND
STRAIT
West Beac
WHIDBEnlSLAND
Dungeness
Spit
Morse
Creek
OLYMPIC
PENINSULA
Shannon
'olnt
Ebey's Landing
North Beach Sand
North Beach Cobble
ieckett Point
Figure 1. Intertidal and shallow subtidal baseline study sites.
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1.2 NEED FOR THE PRESENT STUDY
The marine waters of Washington have not yet been subjected to massive
oil spills or to the environmental problems associated with continued release
of small amounts of oil. Hence, the baseline data described above represent
an "unstressed" environment. In the event of an oil spill or other
perturbation, these data would be used to help determine changes in affected
communities.
An overall examination of the data was considered necessary to
determine the adequacy of the data base for defining the unperturbed
communities and permitting the detection of changes. If the existing data
proved inadequate, the present study was to recommend further sampling to
strengthen the data base. Events such as Canadian reductions in the amount
of crude oil piped into the United States and increases in the flow of Alaska
crude make increased petroleum shipping and refining operations in the
greater Puget Sound region in the near future a virtual certainty. Hence,
the present study was needed now to permit any further sampling determined
necessary under baseline conditions.
If a perturbation were to affect a specific site for which historical
biologic data were available, those data could be used directly in estimating
changes. If, however, areas never studied were affected, estimates of change
would have to be based upon extrapolation of existing data from nearby sites
of similar habitat type. In either case, the accuracy of estimates of change
would depend directly upon the statistical strength of the existing data
set.
The data examined in this study were archived on National
Oceanographic Data Center (NODC) intertidal/subtidal Pile 1OO format magnetic
tapes. Such tapes were produced for the NOAA/MESA studies by the
investigators under contract. The data collected under contract to WDOE
between 1974 and 1976, however, were archived in Pile 100 format only in
1979. This is the first study to attempt site comparisons and other analyses
involving both WDOE and MESA data and using the associated Pile 100 tapes.
Therefore, the present study is also important from the standpoint of
determining whether the File 10O tapes contain correct and usable data.
The present study was needed to compare sites representing the
different habitats, geographic areas, and investigators previously described
primarily on a site-by-site basis in the reports of Nyblade (1977, 1978,
1979a and b), Smith and Webber (1978) and Webber (1979, 198O). Some of the
baseline data, for example the data collected by Webber during the second
year of the WDOE study, have never been presented or discussed in reports;
therefore, this study was also needed to provide at least summary
descriptions of these data.
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1.3 STUDY OBJECTIVES
The objectives of the present study are to
1. Provide a statistical basis for assessing future changes in
community structure at any site in the study area (assuming that
identical field and laboratory methods would be used in the
future).
a. Determine the degree of variability in data for each habitat
type, where annual, seasonal, tidal elevation, and between-
sample variations are considered.
b. Determine the confidence with which site-specific data can be
used to estimate community changes at historically sampled
locations. Document trends, if any, in the relative
statistical strength of the data per habitat type.
c. Determine if biota observed at two (or more) nearby sites of
each habitat type are similar and if the data from these sites
can be used to estimate the biota at nearby unobserved sites
of similar habitat. Report on the degree of confidence that
can be associated with the estimates. Determine the
applicability of data collected from the Strait of Juan de
Fuca, Whidbey Island, San Juan Islands, and northern Puget
Sound (Bellingham-Anacortes) areas to each of the other areas
on a habitat basis; and the degree of confidence associated
with each application.
d. Determine the relative importance of tidal elevation and
habitat type upon variability.
2. Develop a sampling strategy for further monitoring, if
necessary, of previously studied and/or new sites to strengthen
the overall data base. Recommend minimum sampling frequency,
sample numbers, sample types, strata, and analyses per habitat
type. Provide a statistical basis for the recommended sampling
strategy.
In Sections 2 and 3 we summarize our conclusions and recommendations
regarding these objectives. Section 4 discusses the methods used to obtain
the data base from which our conclusions were drawn and some of the resulting
data problems. Section 5 outlines our approach to the data analyses required
to satisfy Objective 1, and Section 6 presents the detailed results of these
analyses. In Section 7 we detail our Objective 2 recommendations. Section 8
contains suggestions for additional analyses of the available baseline data
and data to be collected in the future.
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SECTION 2
CONCLUSIONS
A major conclusion of this study is that the data base is weak in
several important respects. First, many subsets of the data do not exist on
File 1OO tapes, and those that are on tape contain many errors. Second,
those subsets that were completely and correctly recorded on tape often
proved inadequate to support predictive models because of low levels of
replication and inconsistencies in sampling methodology and taxonomy.
The available data were grouped into four broad habitat categories for
purposes of analyses although more specific habitat types were considered in
the WDOE and MESA studies. Our analysis categories were rocky intertidal,
soft substrate intertidal, cobble intertidal, and subtidal. Communities were
examined using cluster analyses and analyses of numerical assemblage
parameters such as richness and diversity. Major populations were also
examined. Within each habitat there were strong site differences that could
not be fully explained by the available data on sediment size, exposure, and
other physical characteristics of the sites. Thus, the prognosis for
estimating the biota at unobserved sites from data at nearby observed sites
of similar habitat is rather poor, although exceptions will be noted below.
In the rocky intertidal habitat, tidal elevation proved to be the
dominant factor contributing to variability, with elevation effects varying
among sites. Within a given stratum of elevation the two sites in the Strait
of Juan de Fuca were relatively similar to each other and quite different
from the North Puget Sound sites. The North Sound sites were also fairly
similar to each other. The Strait sites represent a more exposed habitat
than the North Sound sites, and exposure influences the elevation at which
particular assemblages are found, accounting for the large between-region
differences.
Some seasonal and year-to-year differences were detected.in such
assemblage parameters as species richness, especially when spring data were
considered. However, seasonal effects at a given site generally accounted
for less than 5 percent and year-to-year changes less than 1O percent of the
variability in assemblage parameters, with elevation effects being much more
significant. Site and season differences made roughly the same contributions
to variability within an elevation stratum in the Strait, but site
differences dominated season differences in the northern Sound. Shorter term
(within season) variability was generally insignificant.
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Power calculations discussed in Section 6.1.3 indicate that with the
level of replication used in the Baseline Studies Program and the observed
replicate (between-sample) variability, changes in most assemblage parameters
must be of the order of 5O percent to 1OO percent or more if they are to be
reliably detected. Changes of this order in log transformed counts of some
of the most common animal species are also detectable, but changes in weights
of particular plant species are, for all practical purposes, undetectable.
In spite of the rather low probability of detecting small changes
provided by the baseline data, some significant year-to-year and site-to-site
differences were found in these parameters under baseline (unperturbed)
conditions. Hence the prognosis for cross-site prediction is poor, and even
community changes detected at historically sampled sites, seasons, and tidal
elevations cannot automatically be attributed to known perturbations such as
oil spills. Physical, chemical, and biological as well as statistical
analyses are needed to determine causes of observed changes.
Among the assemblage parameters, animal richness and diversity
appeared to be most useful for prediction. These parameters did not differ
significantly, for example, in high elevation summer data collected between
1976 and 1978 at Pidalgo Head and Cantilever Pier. Limpets, periwinkles, and
barnacles proved to be the most predictable individual organisms, with less
variability at the genus than at the species level. However, more replicates
per site/season/elevation are needed if an accurate assessment of
predictability of either assemblage parameters or particular populations in
rocky intertidal habitats is to be made.
At soft substrate intertidal sites, exposure proved to be the key
factor contributing to variability. Substrate, geographic region, and tidal
elevation influenced soft substrate assemblages as well, but their effects
were difficult to separate from exposure effects. Thus the characterization
of habitat type in terms of substrate (gravel, sand, mud) used in the
Baseline Studies Program proved to be less useful for categorizing soft
substrate sites than a characterization in terms of exposure. However,
substrate characteristics appeared to outweigh tidal elevation in importance
since the most significant "elevation" effects occurred at sites where
sediment composition changed greatly with elevation, and sites with uniform
sediment often showed no significant differences between elevations.
Our analyses pointed to a division of the baseline sites into a highly
exposed group consisting of most of the sand and gravel sites in the Strait
of Juan de Fuca and West Beach on Whidbey Island, a moderately exposed group
(the North Beach sand site in the Strait, the Ebey's Landing gravel site on
Whidbey, and the San Juan Island sand and gravel sites Eagle Cove and Deadman
Bay), a moderately protected group consisting of the North Sound sites Birch
Bay (sand) and Guemes Island South (gravel), and a protected group containing
the remaining soft substrate sites. Substrates in the latter group were mud
or mixed fine; the percent of fine sediment (silt size or smaller) is a
function of exposure. Thus, the protected group can be characterized in
terms of substrate while the more exposed groups, all consisting of sites
with sand and/or gravel sediments, cannot.
-------
At the most exposed sand and gravel sites, changes in the sparse and
extremely variable fauna cannot be reliably detected with reasonable levels
of replication. Changes over time were detected in population and assemblage
parameters in the moderately exposed and moderately protected groups, and
similarities between sites were generally too low to permit cross-site
prediction.
At the most protected mud and mixed fine sites, polychaetes, bivalves,
and amphipods occurred regularly in large numbers. However, particular
species that were found varied considerably over time and from site to site,
making reliable predictions impossible, at least with the level of
replication used in the baseline studies. Replicate variability in counts of
almost all of these animals dictated that 15 or more samples per
site/season/elevation would have to be collected to permit reliable detection
of 50 percent changes in means of log transformed counts. No plant species
were found regularly. Hence, it is unlikely that parameters of particular
plant and animal populations could be used for purposes of damage assessment
following a perturbation such as an oil spill given the present baseline
sampling methodology.
Assemblage parameters appeared to be predictable and therefore usable
for damage assessment within a well-defined habitat type and geographical
area for the protected habitats. For example, summer 1976 Webb Camp data
from low to mid elevations were usable for predicting summer 1977 and 1978
means of animal richness and diversity at low to mid elevations at Westcott
Bay. However, more data on physical parameters than are available in the
present data base would be required to permit a previously unobserved site to
be categorized by habitat type.
As in the rocky intertidal habitat, animal richness and diversity were
the most useful parameters. Changes of 50 percent or less in means of these
parameters at protected mud and mixed fine sites were detectable with
90 percent probability even with only three replicates per
site/season/elevation. Smaller changes in log transformed total animal
counts were readily detectable, but such changes occurred under baseline
conditions at soft substrate sites, particularly when samples taken two years
apart in time were compared.
Detailed analyses of cobble intertidal data were not conducted. The
complex and varying sampling techniques used in cobble habitats led to errors
and problems in the data, which made quantitative analysis difficult.
Because cobble habitats comprise only a small percent of the shoreline in the
study region, we concluded that the considerable effort involved in
collecting and analyzing cobble data could be better spent on the more common
habitats. However, it should be noted that some cobble sites were among the
most productive biologically, with very high animal density and biomass.
Further analysis of data from some of these sites might be useful if funding
is available.
Variations in sampling methodology and data errors also made analysis
of the subtidal data difficult. However, we concluded from cluster analyses
of the data that sediment characteristics and exposure are the dominant
8
-------
factors affecting variability in subtidal habitats. Depth effects appeared
to be relatively unimportant below 5 meters (m), and similarities among sites
of similar substrate were high below that depth, suggesting that the
definition of habitat in terms of substrate for predictive purposes may be
more successful subtidally than intertidally.
However, clustering by year and season in some of the subtidal
dendrograms indicates that, as in the intertidal habitats, changes in
communities occur naturally through time, so statistical analysis alone may
be inadequate to determine the effects of a perturbation such as an oil
spill. More quantitative analyses of subtidal assemblage and population
parameters are needed before final conclusions can be drawn concerning the
possibility of prediction and change detection in subtidal habitats of the
Puget Sound region.
-------
SECTION 3
RECOMMENDATIONS
A major objective of this study was to recommend sampling strategies
and methods for further baseline or monitoring programs in the Puget Sound
region. Our recommendations for baseline sampling, as well as strategy and
methodology for assessing effects of oil spills, are detailed in Section 7 of
this report. We begin this section by summarizing the recommendations of
Section 7 and conclude it with a set of recommendations for improvements
which could be made in the present baseline data set without additional
sampling.
We recommend that future sampling efforts be directed toward stations
where there are existing data, ones where risk of oil spills is great, and/or
ones which can serve as controls for impacted sites. Sites sampled should
also be those that are more protected and thus have greater vulnerability to
spills; exposed sand, gravel, and cobble have both low vulnerability and a
depauperate fauna. Areas sampled should be accessible to study, be "typical"
of as great a percentage of shoreline as possible, and offer a large expanse
of relatively uniform habitat for sampling. We also suggest that future
monitoring be preceded by a meeting of past investigators, the present study
team, and MESA and WDOE scientists to evaluate suitable sites.
Because of the naturally high variability of populations of organisms,
the level of replication used in the baseline sampling that produced the data
base analyzed in the present study was frequently inadequate. Our major
recommendation is an increase in replication to ensure a reasonable
probability of detecting changes. To make this increase possible within
constraints of time and funding, we have suggested concentrating sampling
efforts within a single intertidal elevation stratum (the mid tide range) of
the more sensitive, protected habitats and in a single subtidal depth range
(between 5 m and 10 m). To further focus available effort, sampling during
spring and fall, periods of high rates of change, should be dropped in favor
of summer and perhaps winter sampling. WDOE has wisely chosen to focus their
limited resources on summer sampling since 1976.
We recommend some departures from the techniques used in the WDOE and
NOAA/MESA baseline studies to streamline or standardize future intertidal
monitoring. For example, we recommend that more percent cover data for
plants and encrusting organisms be collected. Although the limited amount of
percent cover data available in the present baseline data set did not prove
useful for prediction and change detection, this parameter has been employed
successfully in other sampling programs (Lees et al. 1980).
10
-------
In rocky habitats we suggest identifying and enumerating only
organisms 3 millimeters (mm) or larger, in part to minimize taxonomic
problems with smaller animals. For continuity on soft substrates we suggest
maintaining the sieve sizes used in the WDOE and MESA studies. However, we
recommend using smaller core samplers to achieve higher replication for
infauna and adding large quadrats for measuring cover, density, and/or
biomass of kelps and sea grasses where they are important.
Statistical conclusions for subtidal areas were severely limited by
data errors and lack of standardization of sampling techniques. Because of
this, we recommend the use of standardized techniques for future subtidal
sampling. Subtidally, we recommend using techniques similar to those used
intertidally except that in rocky or kelp bed areas, larger quadrats should
be used to enumerate larger animals and plants. On soft sediments an airlift
sampler is recommended for the larger "live sieve" cores while the smaller
cores (1 mm sieve) can be readily taken by a diver.
As noted in Section 2, better data on physical parameters at soft
substrate intertidal and subtidal sites are needed to permit categorization
by habitat for predictive purposes. We recommend that future soft bottom
baseline sampling include at least two replicate sediment size samples taken
at the times and tidal elevations or depths at which stratified biological
samples are taken, at least until repeated sampling has shown that sediment
composition is stable at a site. Chemical parameters should also be
measured. We recommend that the File 100 Habitat Code be used to
characterize such factors as wave energy and beach gradient.
Methodologies which we propose for monitoring oil spill impacts,
discussed in Chapter 7, include a pre-oiling assessment (if time and
logistics permit), an initial spill assessment soon after oiling, short-term
post-spill reassessment, and long-term recovery monitoring.
Before another sampling program is begun we suggest one-time field
tests involving several of the conclusions and recommendations of this
analysis. These tests should include collection and analysis of 25
replicates at the mid tide level of a protected rocky, a protected mud flat,
and a protected mixed habitat. Nested box sampling should be used to
evaluate the adequacy of selected quadrat and core sizes. Subtidally, a
comparison of surface (van Veen) grab sampling and SCUBA airlift sampling
would be desirable on both sand and mud bottoms. The data collected should
be used to construct species-area curves and perform analyses of variance to
examine the stability of variance of assemblage and population parameters.
Because collection, handling, processing, and taxonomy would be uniform, such
an effort would provide a much more reliable estimate of true variability and
ability to detect change than it has been possible to.gain from the existing
baseline data set.
A number of improvements to the existing baseline data set can be made
without additional sampling. Correction of the most serious errors in the
data base (see, for example, Zeh 1980e) is of highest priority. We strongly
recommend that the data of Nyblade (1979b) be added to the File 10O data base
since they represent more recent samples than those presently on tape for
11
-------
several northern Puget Sound sites and, in addition, the only sites sampled
independently by both Nyblade and Webber. The data collected for WDOE during
the summers of 1979 and 1980 should also be archived on Pile 100 tapes.
Addition of correct habitat codes to records in which they are missing
or incorrect would facilitate the classification of sites by habitat for
predictive purposes. Uncombined rock and cobble data which have not been put
on tape could also be added to the data base to enable more complete analyses
of subsampling variability to be performed. However, these additions are
less crucial than the additions and corrections suggested in the previous
paragraph.
To avoid serious errors and omissions in data collected in future
sampling programs, several revisions to File Type 100 specifications would be
beneficial. See Section 4.2.3 and Zeh (1980a). Many problems in archived
data could be avoided by requiring timely submission of data tapes and using
the submitted tapes to perform statistical analyses as well as checking them
for obvious errors such as illegal taxonomic codes. Taxonomic code problems
could be mitigated by being sure investigators are provided with a current
NODC taxonomic code dictionary and easy mechanisms for adding new species to
this dictionary. It has been our experience that such additions often
require more than two years. It would also be helpful to include taxon name
as well as code on File 100 Species Identification records to simplify
correction of errors.
Several additional analyses of the existing data (after correction of
errors) which were impossible to complete during the present study due to
time and funding constraints should be carried out. Species-area curves
should be plotted. Nested analyses of variance should be carried out to
assess subsampling variability and the adequacy of smaller samples in those
subsets of the data base for which subsamples are available on tape, for
example, the second year soft substrate subtidal data and intertidal rock and
cobble data from the Strait. Analyses of variance and perhaps other
quantitative statistical analyses of all the subtidal data should also be
performed. These additional analyses would permit refinement of recommended
sampling methodologies before additional sampling is carried out so that
future sampling could indeed strengthen the overall data base, making it more
useful for assessing community changes caused by oil spills or other
perturbations.
12
-------
SECTION 4
DISCUSSION OP THE DATA BASE
The data base considered in the present study consists of data from
the 30 baseline study sites shown in Figure 1. The dates at which samples
were collected at these sites are shown in Table 1. The sites in this table
are categorized by habitat and region/investigator. In this and subsequent
tables and discussions the North Puget Sound sites sampled by Webber for WDOE
are labelled "NPS". Nyblade WDOE sites are denoted by "SJI"; all are on San
Juan Island except the rocky subtidal site, Point George, on Shaw Island.
"Strait" denotes Nyblade MESA sites in the Strait of Juan de Fuca, while
"Whidbey" denotes the Whidbey Island sites sampled by Webber for MESA.
The methods used by Nyblade and Webber to collect the samples yielding
the data sets examined in this document strongly influence the statistical
analyses and predictive models the data can support. Therefore, in this
section, we first describe and compare these methods. Then we discuss the
types of problems that were encountered in our analyses as a consequence of
various aspects of the studies.
4.1 METHODS OF DATA COLLECTION AND REDUCTION
Methods used to obtain data from the varied intertidal and shallow
subtidal habitats of the study areas can be categorized by habitat. The four
broad habitat types relevant to this categorization are:
a. Intertidal rock,
b. Intertidal soft substrates,
c. Intertidal cobble, and
d. Subtidal substrates.
The marked differences in substrate types and biological assemblages
dictated the use of a wide variety of sampling techniques. Furthermore,
differences in perception, experience, and interpretation among the
investigators led to varying approaches. In an attempt to facilitate
description and comparison of the strategies and methodologies employed, we
have prepared tables summarizing the methods for each substrate. These
tables have been heavily footnoted to indicate such things as differences in
sieve size and amount of replication among the investigators.
13
-------
TABLE 1. SAMPLING DATES AT BASELINE STUDY SITES
HABITAT SITE (REGION/INVESTIGATOR)
Rock Fidalgo Head (NPS)
Migley Point (NPS)
Cantilever Pier (SJI)
Point George (SJI)
Tongue Point (Strait)
Pillar Point (Strait)
Cobble Cherry Point (NPS)
Shannon Point (NPS)
South Beach (SJI)
North Beach (Strait)
Morse Creek (Strait)
Partridge Point (Whidbey)
Gravel
(NPS)
Guemes Island, South
(pebble)
Legoe Bay (NPS)
(pebble)
Webb Camp (SJI)
(protected gravel)
Deadman Bay (SJ!)
(exposed gravel)
Beckett Point (Strait)
(gravel/sand/mud)
Dungeness Spit (Strait)
(exposed gravel)
Twin Rivers (Strait)
(exposed gravel)
Ebey's Landing (Whidbey)
(gravel)
J
16
S
1974
A S 0 N D
15 29
G G
29
G
11 30
S S
27
14f 12
G G
14
G
15 31 27
S J_6S S
30 14
G G
15
1975
J F M A
22 29
G G
28 31
G G
26 31
S S
6 Y\_
23 28
G G
27 29
G G
19 26
S S
24 18
G G
8 20
G ! G G
16 16 2 29 18 29
S S S
13 16 29
S S
S S
25 27
S S
!
M J
26
G
27
G
26
S
1
14
G
24
S
19
G
16
G
25
S
13
S
J
11
G
9
S
7
G
22
G
21
G
11
S
A S 0 N D
7 4
G S
3
G
2 a
S S
5 5
G S
4
G
1
S
4 6
G S
1
G
572
S G S
4 3
S S
Sand Birch Bay (NPS)
(sand)
Eagle Cove (SJI)
(exposed sand)
North Beach (Strait)
(exposed sand)
Kydaka Beach (Strait)
(exposed sand)
West Beach (Whidbey)
(sand)
Mud Fidalgo Bay (NPS)
(mud)
Drayton Harbor (NPS)
(mud)
Padilla Bay (NPS)
(mud)
Westcott Bay (SJI)
(protected mud)
Jamestown (Strait)
(sandy mud)
17
S
31
G
12 16
S
21
G
16
S 0
1974
f
G
16
G
1
S
N
1
n
G
28
S
D
10
G
27
S
12
G.
25+
G
J
21
G
7
G
25
G
17
S
F
15
G
28
S
17
G
16
G
28
S
M A
24
G
27
S
15 25
G G
23
G
23
S
M J
10
S
10
G
8
G
J
1975
6
G
8
G
18
G
6
S
A
3
S
2
G
S
24
S
14a
-------
TABLE 1 (Continued)
HABITAT
Rock
Cobble
**
Gravel
Sand
Mud
SITE (REGION/INVESTIGATOR)
Fidalgo Head (NPS)
Migley Point (NPS)
Cantilever Pier (SJI)
Point George (SJI)
Tongue Point (Strait)
Pillar Point (Strait)
Cherry Point (NPS)
Shannon Point (NPS)
South Beach (SJI)
North Beach (Strait)
Morse Creek (Strait)
Partridge Point {Whidbey)
Guemes Island, South (NPS)
(pebble)
Legoe Bay (NPS)
(pebble)
Webb Camp (SJI)
(protected gravel)
Deadraan Bay (SJI)
(exposed gravel )
Beckett Point (Strait)
(gravel /sand/mud)
Dungeness Spit (Strait)
(exposed gravel )
Twin Rivers (Strait)
(exposed gravel )
Ebey's Landing (Whidbey)
(gravel)
Birch Bay (NPS)
(sand)
Eagle Cove (SJI)
(exposed sand)
North Beach (Strait)
(exposed sand)
Kydaka Beach (Strait)
(exposed sand)
West Beach (Whidbey)
(sand)
Fidalgo Bay (NPS)
(mud)
Drayton Harbor (NPS)
(mud)
Padilla Bay (NPS)
(mud)
Westcott Bay (SJI)
(protected mud)
Jamestown (Strait)
(sandy mud)
J F M A M
4 21" 20 13
G S S
19 15
S S
1
S
15
S
15 13 16 14
G S S
19
S
17
S
15 11 11
G 20S S
16
S
15 22 16
S S S
16
S
14
S
16
S
17 14 3 12
G S S
16 14
S S
13
S
17
s
IS 15 19 17
G S ~ S
17
S
18
S
J F M A M
1976
J J A S 0 N D
9 6 17
G S
10 2
G S
3,11 27
S,G S
39 22
S,G S
979
G S
29 25 24
~ S,G S S
3 27 23
S,G S
23 5 11
G S
12 7
S S
11 3
G S
2 12 27 19
~ S,G S S
2 25 21
~S,G S
4,1" 28 24
S,G S
12 8
G30.S
8 1
G S
2 26 25
~ S,G S
3 10 26
~ S,G S
13f 9 17
G S
11 6
S S
2 8,13 24
S,6 S
J J A S 0 N D
1976
1977
JFMAMJJASOND
26*
S
18 6 30 16
S SI 7 S S
19 5 22
S S
6 7 24
S S
17 4 7 28 13
S S S S
8,30 30 26 18 8
S S S
j
25+
7 661 19 14
S S S S S
5 7 I 27 12
S S S S
21 3 _22
s s t
7,28 1 22 17 1T
S S S
4.
24*
S
16 8 24 29 15
S S S S
20 5 29 15
S S 21 S S
6,19 ~ 2 10 15 18T
S S S
4,
27*
S
4 8 7,28 17
S S S S
JFMAMJJASOND
1977
14b
-------
TABLE 1 (Continued)
HABITAT SITE (REGION/INVESTIGATOR)*
Rock Fidalgo Head (NFS)
Migley Point (NPS)
Cantilever Pier (SOI)
Point George (SJI)
Tongue Point (Strait)
Pillar Point (Strait)
Cobble Cherry Point (NPS)
Shannon Point (NPS)
South Beach (SJI)
North Beach (Strait)
Morse Creek (Strait)
Partridge Point (Whidbey)
Gravel Guemes Island, South (NPS)
(pebble)
Legoe Bay (NPS)
(pebble)
Webb Camp (SJI)
(protected gravel)
Deadman Bay (SJI)
(exposed gravel)
Beckett Point (Strait)
(gravel/sand/mud)
Dungeness Spit (Strait)
(exposed gravel)
Twin Rivers (Strait)
(exposed gravel)
Ebey's Landing (Whidbey)
(gravel)
Sand Birch Bay (NPS)
(sand)
Eagle Cove (SJI)
(exposed sand)
North Beach (Strait)
(exposed sand)
Kydaka Beach (Strait)
(exposed sand)
West Beach (Whidbey)
(sand)
Mud Fidalgo Bay (NPS)
(mud)
Drayton Harbor (NPS)
(mud)
Padilla Bay (NPS)
(mud)
Westcott Bay (SJI)
(protected mud)
Jamestown (Strait)
(sandy mud)
1978
JFMAMJJASOND
17*
S
n
s
9
S
10
S
6
24S
13
20*
S
27 16 22
S S
19
13S
18*
S
26 8. 21
S 30S
18
12S
19*
S
18*
S
25
185
20
29S
17
IIS
21*
S
16*
S
M J
1978
1979
J F
26
8S
25
21S
J
1979
*(NPE) denotes North Puget
Sound sites sampled by
Wubber for WDOE.
(SJI) denotes San Juan
Island sites sampled by
Nyblade for WDOE.
(Strait) denotes sites
in the Strait of Juan
de Fuca sampled by
Nyblade for NOAA/MESA.
(Whidbey) denotes Whidbey
Island sites sampled by
Webber for NOAA/MESA.
Discrepancy between date
given in reports and
date appearing on File
100 tapes. The tabled
date xs the one used xn
analysis.
'Samples collected by
Nyblade (1979b) for
WDOE during the summers
of 1977 and 1978 to
extend the data base
obtained earlier in the
Baseline studies Pro-
gram. These data have
not been archived on
File 100 tapes and were
used only for model
verification in the
present study.
**We include among the
gravel sites some such
as Guemes Island, Webb
Camp, and Beckett Point
which were alternatively
characterized as "mixed
f me. " The habitat label
given xn Table 1 for all
soft substrate sites
(sand and mud as well as
gravel) is that used by
the investigator who
sampled the site in his
earliest report on the
data.
G under a date indicates
that intertidal gradient
sampling was done on
that date.
S similarly indicates
intertidal stratified
sampling. Note that
although the stratified
methodology was used
for all Whidbey Island
sampling, strata were
at 1' increments for
summer and winter
sampling, so vertical
distributions or orga-
nisms were determined.
Underlined dates are
subtidal sampling dates.
We have omitted from
Table 1 dates corres-
ponding to samples which
were not processed by
the investigators.
14c
-------
4.1.1 Sampling Strategies
The two basic strategies employed throughout these investigations were
"gradient" sampling and stratified random sampling. The primary objective of
gradient sampling, employing limited numbers of replicated samples
distributed at close intervals across the vertical elevation gradient, is to
define the vertical distribution patterns (zonation) of the major organisms
and assemblages in a study area. Hence it is useful at the beginning of
sampling in a new area, especially on soft substrates where the distribution
and composition of biological assemblages are not obvious and clearly
defined.
The main objective of stratified random sampling, employing larger
numbers of replicated samples within major assemblages, is to estimate
abundance, cover, and biomass levels of a large proportion of the organisms
in each of several predetermined, identifiable assemblages (or zones) in a
study area, and furthermore, to provide estimates of variability in these
parameters. It is the appropriate strategy for providing a data base that
permits detection of environmental change.
During the early sampling programs in North Puget Sound, Smith and
Webber (1978), primarily used the gradient sampling strategy, whereas
Nyblade (1977) used a stratified random sampling approach. Subsequently,
Nyblade (1977,1978) occasionally utilized the gradient approach at SJI and
Strait sites to provide data comparable to Webber's gradient data, thus
permitting an evaluation of the vertical distribution patterns of intertidal
biological assemblages in the inland waters of northwestern Washington.
Moreover, Smith and Webber (1978) subsequently commenced using a stratified
sampling strategy at their NPS study sites, and Webber (1979,1980) primarily
used that sampling strategy on Whidbey Island..
4.1.2 Sarqpling Techniques
Intertidal Rocky Substrates:
Long-term studies were conducted on intertidal rock habitats at five
sites in North Puget Sound and the Strait. The sites included Cantilever
Pier, San Juan Island; Migley Point, Lummi Island; Pidalgo Head, Fidalgo
Island; and Tongue Point and Pillar Point on the Olympic Peninsula.
(Figure 1 and Table 1.)
The sampling techniques used on intertidal rock habitats, detailed in
Table 2, basically fall into three categories of quadrat sampling:
1. Visually estimating the relative cover of dominant algae;
2. Manually scraping algae and small, cryptic or encrusting
invertebrates from the rock surface for identification,
weighing, and counting; and
3. Removing larger motile invertebrates from quadrats to
permit their identification and enumeration.
15
-------
North Puget Sound
Strategies and Techniques
Stratified Random Sampling
Number of Levels
Number of 0.25-m2 quadrats examined/
level
Algae cover quadrats/level
Number of 0.25-m2 algal scrapes/
0.25-m2 quadrats
Number of 0.20-m2 algal scrapes/
0.25-m2 quadrats
Number of 0.0 1-m2 algal scrapes/
0.25-m2 quadrats
Number of 0.25-m2 invertebrate
r eraova Is/quadrat*
Number of 0.20-m2 invertebrate
removals/quadrat*'
Number of 0.0 1-m2 invertebrate
removals/quadrat
Number of survey periods in which
this strategy was used
Gradient Sampling
Number of transects/site
and sampling elevations
Number of 0.25-m2 algal scrapes/
transect
Algal cover quadrats/transect
Number of 0.25-m2 algal scrapes/
0.25-m2 quadrat
Number of 0.0 1-m2 algal scrapes/
0.25-m2 quadrats
Number of 0.25-m2 invertebrate
removals/quadrat *
Number of 0.0 1-m2 invertebrate
removals/quadrat
Number of survey periods in which
this strategy was used
Minimum size of organisms identified (mm)
Before November 1975
From November 1975 on
Nyblade
1977
7/74 - 9/76
3
4
0
1
0
5
1
0
2-5
13
2 or more;
8', 7', 6',
5', 4', 3',
10
0
1
0
1
0
1
1
1
Smith and
Webber 1978
10/74-8/76 8,
3
3-5
0
1
0
5
1
0
5
4
2;
8', 7', 6',
5', 4', 3',
10
0
1
5
1
5
8
2
1
Nyblade
1979
m, 8/78
3
4
0
1
0
5
1
0
5
2
0
0
0
0
0
0
0
0
1
1
Strait
Nyblade
1978
Sp 76/W 77
3
Sp,F _S Wj[
424
424
1 1 0
0 0 1
555
110
001
555
2 1 1
2 or more;
7', 6', 5',
4', 3', 2',
8
8
1
5
1
5
1
1
1
Nyblade
1979
4/77-2/78
3
4
0
1
0
5
o
5
4
0
0
0
o
o
0
0
0
1
1
fAbbreviations for seasons: Sp = spring; S = summer; F = fall, and W = winter.
16
-------
2
The two quadrat sizes used were 0.25m (0.5 m= 1.6 ft on a side) and
O.Ol m (1O can = 3.9 in on a side). The 0.01-m quadrats were subsamples
within the 0.25-m quadrats for estimating abundance and biomass of small
abundant invertebrates or algae and within-quadrat variability.
When using the stratified random approach in intertidal rocky
habitats, both investigators routinely examined three (upper, mid, and lower
elevation) assemblages (zones). The elevations sampled varied somewhat in
all zones among investigators and geographic regions, as shown in Table 3.
However, this degree of variation is probably insignificant relative to the
expected variation in elevation of the zones from the entrance of the Strait
of Juan de Fuca to the western reaches of Puget Sound as a consequence of
differences in tidal flux and exposure to wave action. Thus, we assumed that
these differences posed no significant problems to comparative analyses of
the data among sites.
TABLE 3. ELEVATIONS FOR ROCKY INTERTIDAL STRATIFIED SAMPLING
Site Low Mid High
Elevation Elevation Elevation
Fidalgo Head (NPS) 0.0 m (O1) 0.6 m (21) 1.5 m (51)
Migley Point (NPS) — — —
Cantilever Pier (SJI) -0.3m(-l') 0.9m (3*) 1.8m(6')
#
Point George (SJI) — — —
Pillar Point (Strait) O.O m (O1) 0.9 m (31) 1.8 m (61)
Tongue Point (Strait) O.Om(O') 0.9m(3f) 1.8m(6')
No stratified sampling was done at Migley Point.
No intertidal sampling was done at Point George.
When using the gradient sampling approach in intertidal rocky
habitats, both investigators sampled at 1-ft (0.3-m) increments in elevation
along at least two transects extending across the intertidal zone between the
supralittoral and subtidal zones. Both established sampling sites from +8 ft
to -1 ft in northern Puget Sound, and Nyblade (1979a) sampled from +7 ft to
MLLW in the Strait.
2
The number of replicate 0.25-m quadrats sampled at each sampling
level varied from one or two in the gradient sampling to five on occasion in
the NPS sampling program (Smith and Webber 1978); the most commonly selected
number of replicates was four.
17
-------
A number of variations in the three basic categories of quadrat
sampling occurred within the rocky intertidal data set. Generally these
include the following.
Algal cover quadrats; The Washington Department of Ecology guidelines
for baseline methodology (revised 17 December 1975) indicate that the first
operation conducted during quadrat sampling should be to estimate relative
cover by algae (Nyblade 1977, Appendix II). However, percent plant cover was
not presented in the WDOE reports or included on the Pile 100 tapes for
either of the northern Puget Sound rock sites. Percent plant cover data are
available for most samples from the Strait.
Scrapes for algae and Small or encrusting invertebrates; Initially it
was expected that the 0.25-m scrapes would provide the data on the algal
component of the intertidal rock habitats. The main purpose of the O.Ol-m
scrapes was to quantify abundance of encrusting invertebrates and small,
motile and/or cryptic epifaunal invertebrates. At the outset, the O.Ol-m
quadrats produced little data on algal assemblages and were not an important
part of algal sampling.
However, in the Strait, Nyblade (1978,1979a) encountered a dense turf
of articulated coralline algae that required subsampling of the 0.25-m
quadrats to reduce laboratory costs. In this assemblage, the O.Ol-m
quadrats were a major source of data on algal cover and biomass. None of the
investigators attempted to quantify biomass of encrusting coralline algae.
Two sequences of scraping were utilized at rocky intertidal sites:
1. Remove all algae within the 0.25-m quadrat, bag, and
label. Remove all large invertebrates. Then scrape all
remaining algae and small invertebrates from five O.Ol-m
quadrats randomly placed within the larger quadrat, bag,
and label separately; or
2. Scrape five randomly selected O.Ol-m subquadrats clean of
algae and invertebrates. Then remove all algae from the
remainder of the quadrat, bag, and label.
The first sequence, used by Nyblade at Cantilever Pier and for the
first three quarters of sampling at the Strait sites, appears to be
redundant. If all the algae were removed from the 0.25-m quadrat first,
none should be found in the subquadrats. In practice, any algae scraped up
with the small invertebrates were combined with the algae from the O.25-m
scrape for purposes of data analysis.
Smith and Webber (1978) used the second sequence at Fidalgo Head but
combined all algae from all scrapes in a given quadrat during sample
processing. Nyblade also used the second sequence starting in the winter of
1977 in the Strait but kept the subsamples separate throughout the analysis.
The 1977-78 Strait data therefore allow for the examination of small-scale
variability (patchiness) in algal distribution. The subquadrat data are
important in these Nyblade samples, in addition, because only large
( > 1 cm ) algae removed from the remainder of the quadrat were identified
and weighed.
18
-------
Removal of larger invertebratest Larger, motile invertebrates such as
chitons and starfish were removed from the O.25-m quadrat to obtain
estimates of their density and biomass. Nyblade's criterion for "larger" was
5 mm while for Smith and Webber (1978) it was 3_cm. The removal of the
larger invertebrates occurred before the O.Ol-m subquadrat scrapes for all
samples except those taken in the Strait in 1977-78 when subsampling was done
in the field before anything else in the sampling sequence.
Intertidal Soft Substrates:
Long-term studies were conducted on intertidal soft substrates at 10
sites in northern Puget Sound, six in the Strait of Juan de Fuca and two on
the western side of Whidbey Island. The North Puget Sound sites were at
Eagle Cove, Deadman Bay, Webb Camp, and Westcott Bay on San Juan Island and
the NFS sites Birch Bay, Gueraes Island (south end), Fidalgo Bay, Drayton
Harbor, Legoe Bay, and Padilla Bay. The sites on Whidbey were at West Beach
and Ebey's Landing. The sites in the Strait were at Dungeness Spit, Beckett
Point, North Beach (sand), Jamestown, Twin Rivers, and Kydaka Beach, on the
Olympic Peninsula. (Figure 1 and Table 1. )
The sampling techniques used on intertidal soft substrates, detailed
in Table 4, basically fall within a single category of infaunal sampling,
namely, collection of "core" samples. Two sizes of "core" samples were
collected and sieved differently to obtain estimates of the density of larger
and smaller animals living in the sediment. The two sizes of "core" samples
collected were 0.25 m x 30 cm (75 1 = 2.6 ft ) and O.05 m x 15 cm (7.5 1).
When using the stratified random approach in intertidal soft substrate
habitats, both investigators routinely examined three (upper, mid and lower)
elevations, except that Smith and Webber (1978) examined only two on sand and
mud in northern Puget Sound. The low elevation was usually -0.3 m in North
Puget Sound and HLLW in the Strait and on Whidbey. The mid elevation was
most often 0.9 m and the high 1.8 m. However, both Webber and Nyblade chose
other elevations at some NPS, SJI, and Strait sites, as shown in Table 5. As
in the rocky intertidal, this degree of variation is probably insignificant
in the upper and mid zones. However, the differences may be significant in
the lower zones, where sampling elevations ranged from -0.3 m to +0.5 m.
When using the gradient sampling approach on intertidal soft
substrates, Nyblade (1978) sampled at 1-ft increments in elevation from +7 ft
to MLLW in the Strait. In northern Puget Sound, Smith and Webber (1978)
sampled at 8 equidistant points along the transects on gravel substrates and
at 15 on sand and mud, while Nyblade (1978) sampled 9 to 14 levels. On
Whidbey Island, Webber (1979) sampled at 1-ft increments in elevation from
+6 ft to -1 ft on both sand and gravel. As indicated above, transects in
gradient sampling extended perpendicularly across the beach.
The number of samples collected in stratified random sampling at each
site varied widely among sites, substrates, and surveys, ranging from 0 to 7
large cores and 2 to 10 small. For example, Nyblade (1979b) did not collect
large cores. Smith and Webber (1978) generally collected five replicate
samples on gravel and seven on sand and mud while Nyblade (1977, 1978)
19
-------
TABLE 4. SAMPLING METHODS IN SOFT SUBSTRATE INTERTIDAL SURVEYS
to
O
North Puget Sound
Strategies and Techniques
Stratified Random Sampling
Number of Levels
Sampling Seasons
No. of 0.25-m2 x 30 cm samples/level'
Condition when sieved
Sieve mesh size
0.25-m2 quadrats photographed
No. of 0.05-m2 x 15-cm cores/level
Condition when sieved
Sieve mesh size
Nyblade
1977
7/74-9/76
3
Sp,S,F,Wt
2 to 5
live
0.125"
yes
2 to 10
dead
1 mm
Smith and
Webber 1978
10/74-8/76
3 or 2*
Sp,S,F,W
5 or 7$
live
0.5"
yes
5 or 7§
dead
1 or 2 mmtt
Nyblade
1979
8/77, 8/78
3
Sp,S,F,W
3 to' 5
live
12.5 mm
yes
3 to 5
dead
1 mm
Strait Whidbey Island
Nyblade
1978
Sp 76/W 77
3
Sp,S,F,W
5 or 3*
live
12.5mm
no
5 or 3*
dead
1 mm
Nyblade
1979
4/77-2/78
3
Sp,S,F,W
5 or 2**
live
12.5 mm.
no
5 or 2**
dead
1 nua
Webber
1979
Sp 77-W 78
3
Sp,S,F,W
5
live
0.5"
no
5
dead
1' mm
No. of surveys in which this
strategy was used
Gradient Sampling
12
Number of levels
and sampling elevations
Sampling Seasons
No. of 0.25-m2 x 30-cm samples/level*
Condition when sieved
Sieve mesh size
No. of 0.05-m2 x 15-cm cores/level
Condition when sieved
Sieve mesh size
No. of surveys in which this
strategy was used
9 to 14
S or F
1 or 2
live
0.125"
1 or 2
dead
1 mm
1
8 or 15tt
Sp,S,F,W
1 on mud, sand; —
2 on gravel
live
0.5"
1 on mud, sand — -
2 on gravel
dead --
1 or 2 mm'1"1'
6
8; 7', 6' ,5',
4',3',2M',0'
S
2
live
12.5 ram
2
dead
1 mm
1
8; 6', 5', 4',
3',2',1',0',-1'
s,w
3
live
0.5"
3
dead
1 mm
2
* 3 levels on gravel and 2 on sand and mud.
t Abbreviations for seasons: Sp = spring; S « summer; F = fall; W = winter.
f Nyblade looked at all organisms retained; -Smith and Webber looked only at clams and callianassid shrimp.
§ 5 replicates on gravel; 7 replicates on sand and mud.
# 5 replicates on gravel and sand; 3 replicates on mud and mud/gravel.
**5 replicates on gravel and sand; 2 replicates split in half on protected sand and mixed sediment.
tt2 mm before 11/75; 1 mm after 11/75.
T-fs on gravel and 15 on sand and mud.
-------
TABLE 5. ELEVATIONS FOR SOFT SUBSTRATE INTERTIDAL STRATIFIED SAMPLING
Site Low Mid High
Elevation Elevation Elevation
*
Dray-ton Harbor (NPS)—mud — — —
Fidalgo Bay (NPS)—mud# 0.5 m (1.51) 1.2 m (4') —
Padilla Bay (NPS)—mud — — —
Birch Bay (NPS) — sand
Guemes South Shore (NPS) —
pebble/gravel
Legoe Bay (NPS)--
pebble/gravel
Westcott Bay (SJI) — mud
Eagle Cove (SJI) —
exposed sand
Deadman Bay (SJI) —
exposed gravel
Webb Camp (SJI) — ^
protected gravel
Jamestown (Strait) —
sandy mud
Kydaka Beach (Strait) —
exposed sand
North Beach (Strait) —
exposed sand
Dungeness Spit (Strait) —
exposed gravel
Twin Rivers (Strait) —
exposed gravel
Beckett Point (Strait) —
gravel/sand/mud
West Beach (Whidbey) — sand
Ebey's Landing (Whidbey) —
gravel
-0
-0
-0
-0
-O
-0
0
0
0
0
0
0
0
0
.3
.3
.3
.3
.3
.3
.0
.0
.0
.0
.0
.0
.0
.0
m(-l')
m(-l')
—
m(-l')
m(-l')
ra( -1 • )
m(-lf )
m (0' )
m (0* )
m (O1 )
m (0' )
m (0- )
m (0- )
m (0- )
m (0- )
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.9 m
.6 m
—
.6 m
.9 m
.9 m
.6 m
.4 m
.9 m
.6 m
.9 m
.9 m
.9 m
.9 m
.9 m
(3-)
(2«)
(2')
(3')
O1)
(2')
(1.4')
(3')
(21)
<3f)
(3')
(31)
(31)
(3')
1
1
1
1
1
1
1
1
1
1
1
1
1
.5
.7
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
—
m
—
m
m
m
m
m
m
m
m
m
m
m
m
(5')
(5.5-)
(6')
(61)
(61)
<6')
(6')
(6')
(61)
(61)
(6')
(6')
(6')
*
No stratified sampling was done at these sites.
The mid elevation at Fidalgo Bay was given as 43' in Smith and
Webber (1978) but as 1.2 m (+4') on the File 1OO tapes.
The low elevation at Birch Bay was given as +1' in Smith and
Webber (1978) but as -0.3 m (-1' ) on the File 100 tapes.
**
Webb Camp was alternatively characterized as "mixed fine" or
"gravel/sand/mud."
21
-------
usually collected five on sand and gravel, but only two or three in mud and
mixed mud habitats. Five replicates per stratum were collected on Whidbey.
In the gradient sampling programs, replication was lower, usually only
one or two samples per level. However, Webber (1979) collected three samples
per level on Whidbey.
Descriptions of the basic "core" sampling techniques used at the soft
bottom sites reveal differences among investigators and sites.
2
o. 25—m x 3O—cm cor^ sgMfflE'le.g •- These large area and volume samples
were collected in order to assess density and biomass of the larger,
uncommon, infaunal animals (such as clams, snails, and shrimp). Generally,
the samples were removed with a shovel. Smith and Webber (1978) used four
25-cm x 25-cm x 30-cm cores in a line in sand and mud. The samples obtained
by shoveling or coring were sieved in the field while the organisms were
still alive; hence they were dubbed "live sieves." The mesh size of the
sieves used to screen these samples varied from 0.125 inches (3.2 mm; Nyblade
1977) to 12.5 mm (0.5 inch; Smith and Webber 1978, Nyblade 1978 and 1979a,
Webber 1979). In the Nyblade studies all animals retained on the sieves were
examined whereas Smith and Webber generally looked at only clams and
callianassid shrimp.
2
o.os-m x 15-cm cores; These small area and volume cores were
collected in order to assess density and biomass of the smaller, more
abundant infaunal organisms. All of these samples were preserved whole by
mixing with a formalin-seawater solution and sieved later with a 1-mm or 2-mm
sieve as indicated in Table 4.
Intertidal Cobble Substrates:
Long-term studies were conducted on intertidal cobble habitats at six
NFS, SJI, strait, and Whidbey sites. The sites in northern Puget Sound were
at South Beach (SJI) and Cherry and Shannon Points (NPS). The Whidbey Island
site was at Partridge Point. The Strait sites were at Morse Creek and North
Beach (cobble) on the Olympic Peninsula. (Figure 1 and Table 1. )
The sampling techniques used on intertidal cobble habitats basically
fall into the three categories of quadrat sampling described for rocky
intertidal habitats and a single category of infaunal sampling, namely,
collection of "core" samples. Generally, the sampling methods for cobble
combined those described above for rock substrates and soft sediments. Three
quadrat sizes were used: 0.25 m , 0.05 m and 0.01 m . The smaller quadrats
were subsamples within the O.25-m quadrats for estimating abundance and
biomass of small abundant invertebrates or algae. The two core sample sizes
used were 0.25 m x 30 cm deep and 0.05 m x 15 cm deep. The specifics of
replication, quadrat and sieve sizes, sequence of collection, and sampler
placement varied considerably between investigators and surveys. For
instance, Nyblade (1977) intentionally selected an impoverished cobble site
(South Beach) on San Juan Island that lacked algal cover and abundant
invertebrates. He thus did not use quadrat sampling techniques there in
22
-------
contrast to the other cobble sites. The 0.05-m subquadrats were used at NPS
sites and the O.Ol-m subquadrats at Strait and Whidbey sites.
Because of the great differences in sampling techniques among sites
and the obvious differences in the assemblages disclosed, we have decided to
treat the cobble methods only generally. The most suitable means of
determining details of methods is to refer to the investigators' reports.
Subtidal substrates:
Surveys were conducted on subtidal habitats offshore of the intertidal
study areas at 23 sites in northern Puget Sound and the Strait" of Juan de
Fuca and on Whidbey Island (Table 1). The sites in North Puget Sound were at
Point George on Shaw Island; South Beach, Eagle Cove, Deadman Bay, Webb
Camp, and Westcott Bay on San Juan Island; and at Birch Bay, Cherry Point,
the south side of Guemes Island, Pidalgo Bay, and Fidalgo Head. The sites on
Whidbey were West Beach, Partridge Point, and Ebey's Landing. The sites in
the Strait were Morse Creek, Dungeness Spit, Twin Rivers, Kydaka and North
Beach, Jamestown, and Beckett, Tongue, and Pillar Points.
In addition, Smith (1979) examined subtidal habitats at 19 locations
in the northern and southern approaches to, and within, Rosario Strait. Each
site was examined one time at three depth levels between July 2 and
October 7, 1976. The locations are indicated in Figure 2.
Birch Polnt-lo7
Birch Bay-~2
x Nyblade and Webber sites
0 S*1th (1979) sites
Cherry Polnt-
STRAIT OF GEORGIA
Rosarlo Pt.-l
Westcott
Bay-1
Webb
Canp-1
Deadman
Bay-1
Shoal Bay-1
Cove-1
Sou'th/ / Wes
Beach-I/ / Beach-8
opez U..E-1
Willow Is.-l
VANCOUVER
ISLAND .
/•*
Partrldg
Polnt-8
Is.,N-l
loseberry Pt.-l
Portage Is.-l
lunol IS..H-I
Eliza Is.-l
Sinclair Is.-l
Guemes Is..NE-l
* imlsh Bay-1
lemes Is.,S-2
Fidalgo Head-2
'adilla Bay-1
Idalgo Bay-2
ilexander Beach-1
Whidbey Is.,N-l
OLVMPIC
PENINSULA
Beckett Polnt-2
Figure 2. Subtidal sites including those of Smith (1979). Number after
site name indicates number of sampling periods for which data
are available.
23
-------
Subtidal surveys were completed only one or two times at most sites.
More frequent sampling occurred at Point George and the three Whidbey Island
sites. In addition, quarterly subtidal samples were collected during the
first year of sampling in the Strait, but only the first quarter samples were
completely processed. Second, third, and fourth quarter samples were curated
without identifying, counting, or weighing the organisms.
The sampling techniques utilized in the subtidal surveys were
distinctly simpler 'than those employed intertidally but varied widely among
investigators, especially on soft substrates. Generally, quadrat techniques
were used on rocky substrates. These were augmented with airlift core or
grab sampling techniques on unconsolidated substrates such as cobble, gravel,
and sand. Core or grab sampling techniques were often the only sampling
techniques used on sand and mud substrates. (Table 6). Three sizes of
square quadrats—l.O, 0.25 and 0.1 m —were used to facilitate efficient
estimation of plant and animal density. Four sizes of samples were collected
to assess infaunal assemblages in soft substrates. These included two square
core samples (0.25 m x 3O cm and O.05 m x 15 cm) and samples from O.O3-m
and 0.1-m van Veen grab samplers. Smith and Webber used airlift cores while
Nyblade used the van Veen.
In the MESA studies, the investigators typically sampled at depths of
5 m (16 ft) and 1O m (33 ft), but otherwise sampling depths were not
consistent (Table 7). Nyblade (1977) sampled only at -2.5 m on San Juan
Island. In the other sampling programs there was generally at least one
depth in the 2-m to 5-m range and one in the 7-m to 10-m range.
The number of replicate samples collected was fairly consistent,
ranging from two to four regardless of substrate, etc. (Table 6). In all
cases, replication was too low for effective assessment of density or biomass
of epibenthic or infaunal organisms. In an attempt to increase replication,
in the second year of the Strait study, Nyblade (1979a) split in half each of
the two van Veen samples collected at each station, thus producing four
samples.
The basic "core" sampling techniques used in the subtidal studies are
similar to those described above for intertidal soft substrates. The major
departure is that Nyblade used a O.03-m van Veen grab sampler for his
shallow subtidal SJI samples and a 0.1-m van Veen in the Strait to collect
infaunal samples. In addition, Webber collected his infaunal samples with
the aid of an airlift sampler, which sucked up the sediments and deposited
them in a 0.7-mm mesh bag for sieving. Smith (1979) also used an airlift,
but he used a 1-mm mesh bag and, for final sieving in the laboratory, a 2-mm
sieve. Sieve sizes used for final sieving were consistently 1 mm for
subtidal samples collected by Nyblade and Webber.
The quadrat sampling techniques were similarly very like those
described above for intertidal rock substrates. However, Smith (1979)
employed replicated l.O-m quadrats to measure the density of animals with
dimensions > 10 cm. As in the case of the infaunal samples, collection of
animals and plants in scraped quadrats was facilitated by use of an airlift
sampler in the NPS, Whidbey, and Smith (1979) studies.
24
-------
TABLE 6. SAMPLING METHODS IN SOBTIDAL SURVEYS
San Juan Island- Strait of Strait of
Point George Juan de Fuca Juan de Fuca
Nyblade 1977 Nyblade 1978 Nyblade 1979a
Techniques Sp/76-W/77* Sp/77-W/78
Number of Levels 3 2§§ 2
Substrates
Rock XXX
Cobble (mixed coarse) X X
Gravel (mixed fine) X X
Sand XXX
Mud XXX
Sampling Season F,W,Sp,S S S
Rocky Substrate
(rock, cobble, and gravel)
Number of 1.0-m2 quadrats for
large invertebrates/level
Number of 0.25-m2 algal scrapes/ 244
level
Number of 0.0 1-m2 algal scrapes/ 1
0.25-m2 quadrats
Number of 0.25-m2 small 2* 4 4
invertebrate removals/level
Number of 0.01-m2 removals/ 1*
quadrat
Soft Substrates None
(cobble, gravel, sand, and mud)
Number of 0.25-m2 x 30-cm core
sampl es/1 evel
Number of 0.0 5-m2 x 15-cm core
samples/level
Number of 0.1-m2 van Veen grab 2-3* 2*'tt
samples/level
Number of 0.0 3-m2 van Veen grab 2
samples/level
Number of 0.0 5-m2 invertebrate
scrapes/level
Number of 0.25-m2 algal
scrapes/level
Whidbey N. Puget Sound N. Puget Sound
Island Rosario Strait Rosario Strait
Webber 1979 Smith 1979 Webber File 100
Sp/77-W/79 7/76-10/76 Date Tapes
33 6
X
X X
XX X
XX X
X X
Sp,S,F,W S Sp.F
None None
3*
3
3§
3**
3* 3* 2
2#
* Sp = spring, S = summer, F = fall, W = winter
t >5 cm dimension
t >10 cm dimension
§ 3< x <10 cm dimension
# Sieved through a 1-mm sieve
"Sieved through 12.5-mm sieve
ttEach grab sample was halved to increase replication
t^Not used at Fidalgo Bay
§§Samples were collected at two additional levels in summer 1976 and processed for long-term storage but not analyzed.
-------
TABLE 7. GENERAL SUBSTRATE CLASSIFICATION AT SUBTIDAL STATIONS BY DATE AND DEPTH
Site/Date
Birch Bay
Cherry Point
Pi da 1 go Bay
Fidalgo Head
Guemes Island
to
OX
Deadman Bay
Eagle Cove
Point George
South Beach
Webb Camp
Westcott Bay
Depth (m)
-1.5 -2.0 -2.5 -4.0 -5.0 -6.0 -7.5 -8.0 -10.0 -12.0 -15.0
North Puget Sound*
760303 Sf S M M M M
760316 MC MF MF MF MF MF
760319 M M M M M M
760917
760320 MC MC MC MC MC MC
760220 MC MC MC MC MC , MC
San Juan Islands^
741016 S
741016 S
741127 R R R
750206 R R R
750311 R R R
750501 R R R
741016 S
741016 M
741016 M
(continued)
-------
TABLE 7. (continued)
Site/Date
Ebey's Landing
Partridge Point
West Beach
'
770428
770822
771118
780213
780508
780630
781012
790118
770430
770822
771108
780206
780516
780710
781013
790122
770419
770810
771103
780124
780418
780629
781014
790121
-1.5
MC
MC
MF
MC
MC
MC
MC
MF
MC
MC
MF
MC
MC
MF
MF
MC
S
MC
S
S
S
S
S
S
-2.0 -2.5
MF
MF
MF
S
MC
MC
MC
S
S
S
S
Depth
-4.0 -5.0 -6
Whidbey
MF
MF
MF
MF
MF
MF
S
MF
MF
MF
MF
MF
MF
MF
MF
MF
S
S
S
S
S
S
S
S
(m)
.0 -7.5
Island§
MF
MC
MF
MF
MC
MF
MF
S
S
S
S
-8.0 -10.0 -12.0 -15.0
MF
MF
MF
MC
MF
MF
MF
MF
MF
MF
MF
MC
MF
MF
MF
MF
S
S
S
S
S
S
S
S
(continued)
-------
TABLE 7. (continued)
to
OS
Site/Date
Beckett Point
Dungeness Spit
Jamestown
Kydaka Beach
Morse Creek
North Beach
Pillar Point
Tongue Point
Twin Rivers
-1.5 -2.0
760602
770606
760602
770607
760602
770607
760603
770621
760603
770607
760602
770624
760603
760622
760702
760703
770506
770617
760614
770622
Depth (m)
-2.5 -4.0 -5.0 -6.0 -7.5 -8.0
Strait of Juan de Fuca*
S
S
MF
MF
S
S
S
S
MC
MF
S
S
S
S
R
R
R
R
MF
MF
-10.0 -12.0 -15.0
S
S
MF
MF
S
S
S
S
MC
MC
MF
MF
S
S
R
R
R
R
S
—
(continued)
-------
TABLE 7. (continued)
Upper
Site/Date
Depth (m)
Sediment
Middle
Depth (m)
Sediment
Lower
Depth (m)
Sediment
Approaches to Rosario Strait**
Alexander Beach
Buck Bay
Birch Point
Clark Island
Echo Bay
Eliza Island
Gu ernes Island, NE
Gooseberry Point
Lopez Island, E
Lummi Island, N
Lununi Island, W
Padilla Bay
Portage Island
Rosario Point
Samish Bay
Shoal Bay
Sinclair Island, N
Willow Island
Whidbey Island, N
760716
760818
760922
761005
761001
760915
760702
760803
761007
760825
760909
760924
760813
760721
760915
760728
760730
760811
760920
2.1
2.7
4.3
3.4
3.0
3.0
3.7
3.0
3.7
4.6
3.7
2.4
4.6
3.0
4.0
4.3
3.7
4.6
3.0
S
MC
MC
M
M
MF
S
S
MF
S
MF
S
S
R
M
MC
S
R
S
9.1
6.7
8.5
7.0
8.5
8.2
7.6
7.6
9.1
9.8
7.6
7.0
8.2
8.5
9.1
8.5
7.6
9.8
9.8
S
MF
S
MC
M
M
MC
M
S
MF
MF
M
M
R
M
MF
MF
R
M
15.2
13.1
14.6
13.7
15.2
15.2
16.8
13.7
14.6
15.8
13.1
14.6
13.7
16.8
15.2
12.2
16.8
14.3
15.2
S
M
S
MC
M
M
MC
M
MC
MF
MF
M
M
R
M
M
MC
R
M
* Webber, personal communication.
t Abbreviations for substrate types: M = mud,
T Nyblade 1977 and personal communication.
§ Webber 1979 and File 100 data tapes.
# Nyblade 1978, 1979.
**Smith 1979.
S = sand, MF = mixed fine, MC = mixed coarse, R = rock.
-------
4.2 PROBLEMS ENCOUNTERED
4.2.1 From Field Methodology
Levels of replication:
As noted in Section 4.1, the number of replicate samples collected at
a given site, date, and elevation varied greatly with habitat type,
investigator, and time. 'The level of replication and inconsistency in
numbers of replicates have two important consequences.
First and most important, the usual level of replication (between two
and five replicates per site/date/elevation) is too low to provide an
adequate description of the real variability in abundance and biomass for the
animal and plant populations examined. For most of the density and biomass
estimates, the range of variation within one standard deviation of the
estimated mean includes zero. Calculations in Section 6 suggest that
considerably greater replication is required to provide adequate estimates of
population parameters for even the most common species.
Next, assemblage parameters (e.g., numbers of species or individuals
and species diversity) can be compared on the basis of quadrat averages or
total (pooled) sampling effort. Because all of these parameters increase
unpredictably with an increasing number of samples, they should not generally
be compared for pooled data if replicate number varies among the sites
compared. Therefore, in our analyses, it was necessary to compare assemblage
parameters using estimates of the mean for individual samples rather than
for, say, all samples from a given site/date/elevation.
Criteria for large invertebrates:
As noted in Section 4.1, varying size criteria were used for large
invertebrate removals in the field. Different sieve sizes and criteria for
species to be examined were used for live sieve cores as well.
Estimates of densities and number of species for the large
invertebrates would be expected to be somewhat lower and more variable in the
Smith and Webber (1978) data, where only those animals over 3 cm in size were
removed from the 0.25-m quadrats, than in the other data sets where 5 mm was
the criterion. Similarly, larger estimates computed from live sieve data
would be expected in the Nyblade WDOE data where a smaller mesh was used,
although Nyblade notes that in actuality the species found in these samples
were not in the 3.2 mm to 12.5 mm range. Smaller estimates of number of
species would be expected from the Webber data where only selected species
were considered and the larger sieve size was used.
Sequence used in subsampling:
As described in Section 4.1, the sampling methodology for rock and2
cobble data involved removing algae and large invertebrates from a 0.25-m
area and scraping algae and small invertebrates from subsamples within that
area. The order in which these procedures were carried out varied with time
30
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and site during the course of the studies, thus complicating the
normalization to counts or weights per 0.25 m (Nyblade 1979a, p. 14).
Sampling area and volume:
As with variations in levels of replication, inconsistencies in areas
or volumes sampled generally invalidate comparisons of population and
assemblage parameters since these parameters do not increase linearly with
area or volume. Such inconsistencies are an especially serious problem in
the subtidal data since Nyblade, Smith, and Webber used different gear and
sampled different areas and volumes (Section 4.1.2).
4.2.2 From sample processing
Missing data from the 1-mm sieve component of the intertidal samples:
2
Before November 1975, Smith and Webber (1978) sieved the O.oi-m
subsamples from rock sites, the 0.05-m subsamples from cobble sites, and the
0.05-m x 15-cm cores from cobble and soft substrates through a 2-mm and 1-mm
sieve series. Although the 1-mm material was stored, only the 2-mm fraction
was identified, counted, and weighed. After November 1975, both fractions
were fully processed.
Although the preserved 1-mm sieve data were processed later for some
of the sites, they were not processed for Migley Point (rock), Shannon Point
(cobble), and the soft bottom sites at Drayton Harbor, Legoe Bay, and Padilla
Bay. These sites were discussed and compared with the other northern Puget
Sound sites by Smith and Webber (1978). They were not sampled after November
1975, so only data for the 2-mm fraction are available for them. Because
data for the 2-mm fraction would produce smaller estimates of numbers of
individuals and species than 1-mm data and because 1-mm sieving was done at
all other intertidal sites in both the WDOE and NOAA/MESA studies, we have
not included the sites with only 2-mm fraction data in our analyses.
Partitioning of samples in soft bottom intertidal and subtidal data:
According to Nyblade (1979a, p. 10)s
"In an effort to increase replicate number and
hopefully to decrease sample variance at Beckett Point,
Jamestown, and all soft bottom subtidal sites, the
first year quadrat size was halved in the second year
by sample partitioning. Instead of three replicates,
four half size replicates were taken."
Indeed, this procedure may have decreased sample variance in the data
set, but it had no effect on sample variance in the ecosystem. Because we
would not expect the split halves to be comparable to full-sized independent
replicates in terms of real sampling variability, we recombined the halves
into a single replicate before analysis to ensure comparability with samples
taken at other sites and times.
31
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4.2.3 From data processing
Because the data base analyzed in this study is so large
(approximately 107,300 80-character records) statistical analyses of the data
would be impossible without the aid of computers. Therefore, the data had to
be available in machine-readable form. The form chosen by NOAA/MESA was the
National Oceanographic Data Center (NODC) intertidal/subtidal File Type 100
format magnetic tapes (NOAA 1976). Most problems we encountered in data
processing resulted from discrepancies and errors in coding these File 100
tapes.
Combining samples for intertidal rock and cobble data:
Data obtained by each collection method from each quadrat at
intertidal rock and cobble sites were rescaled and combined to give a single
count and weight per 0.25 m for each species found in the quadrat in some
cases. This combining, which took place before the data were put on tape,
was done for all samples collected between 1974 and 1978 at Cantilever Pier
(SJI) and for 1976 samples from rock and cobble sites in the Strait. It is
impossible to determine which species were collected by which method or
assess subsampling variability from the combined samples. Uncombined data
for all sites are available from Nyblade, but not in File 100 format.
At Fidalgo Head, partial combining of the data was done. Data from
the five 0.01-m subsamples were added to obtain a number per O.O5 m .
Because only combined data were available at some sites and times, we
combined data from the others to enable cross-site and year-to-year
comparisons. In the cases, discussed above, where the properly normalized
counts and weights for species obtained by more than one method could not
simply be added because of the order in which collection methods were
applied, we chose the count and weight corresponding to the method that gave
the largest value of count or weight.
Data not yet available in NODC File 10O format:
We noted earlier that 1-mm fractions for several NFS sites and some
subtidal Strait data have not been processed. These data therefore do not
exist in File 10O format. In addition, some data that have been processed
and reported by the investigators who collected them have not been archived
on File 100 tapes. Hence, they are not readily available to other
investigators wishing to perform statistical analyses.
The major data sets that fall into this latter category are the
northern Puget Sound subtidal study reported by Smith (1979) and the
intertidal data of Nyblade (1979b).
Each of the 19 subtidal sites discussed by Smith was sampled only once
during summer or fall of 1976. The field and laboratory methodology used
differed from that of the subtidal sampling programs from which other File
100 data are available. For example, subtidal depth strata were defined
differently at each site instead of using the same depths at all
32
-------
sites. A l-mm mesh size bag was used for collection and a 2-ram sieve was
used in the laboratory. Hence, the Smith data, even if available on tape,
could not easily be compared with other data.
The lack of File 1OO tapes of the Nyblade <1979b) data is more
serious. These data were taken in August 1977 at Cantilever Pier, Deadman
Bay, Eagle Cove, and Westcott Bay and during the summer of 1978 at these same
San Juan Island sites and four other northern Puget Sound sites (Cherry
Point, Guemes Island, Birch Bay, and Fidalgo Bay). Hence, they represent
more recent samples than those on tape and, in addition, the only sites
sampled independently by both Nyblade and Webber.
Subsets of other data sets collected during the WDOE and NOAA/MESA
studies are also missing from the tapes. For example, no live sieve samples
are included in the northern Puget Sound data taken before 1977 except for
those from Webb Camp and Westcott Bay in the summers of 1975 and 1976 and the
fall of 1975. Other such omissions are documented in interim reports (Zeh
1980a,b,c,d,e) submitted to NOAA/MESA in the course of the present study.
Finally, data collected by Nyblade and Webber for WDOE during the
summers of 1979 and 1980 at selected baseline sites have not been archived on
File 100 tapes.
Errors and inconsistencies in tapes:
Incorrect as well as missing data presented serious problems during
the present study. Errors found in the data, many of which have been or are
being corrected, have been discussed by Zeh (1980a,b,c,d,e). We wish to
highlight here a few of the most serious problems and ways they could be
avoided in future sampling programs.
Many of the worst problems in the data stemmed from the fact that the
File 100 tapes were made several years after most of the data were
collected. Future sampling programs could avoid these problems by requiring
timely submission of data tapes by investigators. The tapes should be
checked using programs such as those being developed by Mike Crane of NOAA's
Environmental Data and Information Service (EDIS). Errors detected in
taxonomic codes, gear codes, etc., could then be corrected before the passage
of time and shifts in responsible personnel make the task difficult if not
impossible.
It should also be required that investigators involved in sampling
programs submit listings of "raw" data, for example, those included as
Appendix I in Nyblade (1978). Such listings were not available for the data
reported by Smith and Webber (1978), and consequently detection and
correction of bad data on their File 1OO tapes was extremely difficult.
Two aspects of the present File 100 specifications led to serious
problems in the data tapes. EDIS is presently modifying File 100
specifications to alleviate these problems.
33
-------
The first source of problems was the definition of the Sample Number
that appears in Record Types 3, 4, 5, and 6 as a "Unique quadrat or haul
number." The problems stemmed from the fact that several different sampling
methodologies, represented by distinct gear codes, were often used in the
same quadrat. The gear code appears on Record Type 3 (Biological Sample
Description), but not Record Type 4 (Species Identification). Therefore, in
many cases it was impossible to determine which gear (and therefore what area
or volume of substrate) had yielded a particular species and its associated
count and weight. In these cases, the data could not be correctly normalized
to count or weight per some specified sample area or volume.
The Sample Number in File 1OO specifications should be redefined so
that one or two digits specify the "Unique quadrat" within "Unique cruise
number or date" and "Station Number," which are also given on Record Types 3,
4, 5, and 6. The remaining digit or digits of the Sample Number should allow
each Type 4, 5, and 6 record to be unambiguously matched with the appropriate
Type 3 record and hence the correct Sample Description information such as
gear code. Subsamples within a quadrat should each have their own Type 3
record. A sample numbering scheme of this sort was used for some of the
Strait data.
A second weakness of the existing File 100 specifications stems from
an attempt to provide flexibility in data arrangement. The specifications
require that all records at a given station follow the Station Header
record. The other records may appear in any order as long as they have
ascending sequence numbers. Most of the baseline data was arranged with each
Sample Description record preceding the associated group of Species
Identification records. This arrangement proved to be the most convenient
for purposes of data analysis. We recommend that File 100 specifications
require, rather than suggest, such an arrangement. The Strait data, which
also met the existing specifications, were arranged with all Sample
Description records in a block followed by all Species Identification
records. This arrangement was less convenient and more error-prone. It
should be ruled out in future File 1OO data sets.
inadequate data on habitat characteristics:
We had hoped to use the File 100 Habitat Code and Sediment Size
Analysis records in defining quantitative models for the data, but data
inadequacies precluded this approach.
The Habitat Code, part of the Sample Description record, consists of
three digits. The first characterizes wave energy/beach gradient; the
second, sediment size; and the third, surface organics (for example, shell
fragments or eelgrass). It thus contains a great deal of information
critical to modelling the soft-bottom habitats. However, the Habitat Code
was missing from the SJI data. It was included in the other data sets but in
many cases did not correspond well to descriptive information provided in
reports or to the Sediment Size Analysis data.
34
-------
For example, the Habitat Code for all intertidal Sample Description
records from West Beach and Ebey's Landing in the Webber MESA data indicated
moderate wave energy and moderate beach gradient, coarse sand, and no surface
organics. However, sediment size data indicate that both sites consisted of
a gravel-sand mix. Large gravel (pebble) usually predominated at Ebey's
Landing whereas the composition at West Beach varied with time and elevation
from 18 percent sand with the remainder gravel to 99 percent sand. Webber
(1979) also indicated that the beach slope at West Beach changed dramatically
during the course of the study but was always within the File 100 definition
of low beach gradient (slope less than 15 percent).
The Habitat Code on Sample Description records should reflect observed
changes in sediment composition and beach slope if it is to be useful for
modelling. NODC may wish to consider refinements to the definition of this
code to make it more sensitive to habitat differences. However, if the
present code is used correctly by investigators it is probably adequate.
Sediment size analyses in the existing Puget Sound data set are
inadequate. No analyses were available for the NPS data. Sediment Size
Analysis records from each sampling period were included in the Whidbey data,
but there was only one replicate at each time and elevation. Thus it is
impossible to assess which apparent changes in sediment composition through
time were real and which were merely the result of sampling variability.
Sediment Size Analysis records were included in both the SJI and
Strait data. There were two replicates per elevation in most cases so
sampling variability could be assessed. However, sediment size analyses were
included for only one or two dates at each site, so temporal changes could
not be assessed.
4.2.4 From taxonomy
In any long-term sampling program, some problems in taxonomy are
inevitable. Species incorrectly identified in early samples may be correctly
identified later. However, this data set has several more systematic
problems in taxonomy that need to be pointed out.
Inconsistencies in level of identification:
Particularly in the WDOE data, some plants and animals were identified
to different levels by the different investigators at different times. For
example, amphipods were identified to genus or species by Nyblade for the
most part only in the first year of the study and by Smith and Webber only in
the second. In general, it appeared that Nyblade identified the species as
well as genus of organisms more often than Smith and Webber. Discrepancies
of this type make comparisons of such numerical assemblage parameters as
species richness and diversity across sites and times very difficult.
35
-------
Incorrect taxonomic codes:
Even when organisms were identified to species level, data were often
not available on tape because incorrect taxonomic codes were used. The NFS
data contained numerous codes that could not be unambiguously translated to
the NODC codes specified for File 100. The SJI and Strait data contained
codes corresponding to species identified by Nyblade for which NODC codes
were unavailable. For these species he used the NODC genus code and his own
code for the species digits.
36
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SECTION 5
GENERAL APPROACH TO OBJECTIVE 1
To attain the objectives of providing a statistical basis for
assessing future changes in community structure at any site in the study area
and of assessing the relative contributions to variability of factors such as
elevation, site, year, and season, it was necessary to look at data across
sites and times. Detailed descriptions of communities found at most of the
particular sites and times sampled have been given by the investigators who
collected the data and are, for the most part, outside the scope of the
present study.
Our general approach to the data base was to look for common rather
than unique characteristics of different sites and times. In addition, we
generally restricted our analyses to data available on File 100 tapes so that
other investigators using the tapes could verify or augment our results.
5.1 OUR METHODS OF RESOLVING PROBLEMS
In Section 4, we mentioned solutions to some problems encountered.
The common denominator of these solutions was the desire to ensure that
different subsets of the data could be meaningfully compared. Our approach
to taxonomic problems also was designed to eliminate systematic differences
that were due to the investigators rather than the samples.
The first step in analysis of data from each of the four major habitat
types defined in Section 4 was to extract all the data that we wished to
consider. Necessary data from File 10O Sample Description and Species
Identification records were combined to form records containing station and
sample numbers, date, elevation, gear code and quadrat area, percent plant
cover if available, and information on weight method and subsample percent as
well as taxonomic code, count, and wet weight for a plant or animal.
All taxa found in the habitat with number of samples at each site,
date, and elevation stratum were listed. The listings were examined to
determine invalid taxonomic codes, taxa that should be combined to eliminate
differences in level of identification among different sites and dates, and
key taxa to be used in clustering and other statistical analyses.
Key taxa were selected on the basis of such factors as ease of
identification of an organism, frequency of occurrence, and biological
importance as well as data-dependent considerations. Our general "lumping
rules" are given in Appendix B, which also contains the "dictionaries"
37
-------
created to associate taxonomic codes found on the File 10O tapes with those
to be used in analyses.
Statistical analysis began after the dictionaries of Appendix B were
used to correct taxonomic codes and other programs were run to correct bad
gear codes, combine samples as needed, and resolve other errors and
inconsistencies.
5.2 SUMMARY OF STATISTICAL ANALYSES
5.2.1 Population parameters and assemblage parameters
The goal of this study was to predict population parameters such as
number of individuals for animal species and biomass for plants. However,
the patchy and variable distributions of most organisms make prediction
difficult. The reports of Nyblade and Webber cited in previous sections
offer numerous examples.
The distribution of a species generally cannot be modelled well by the
usual probability distributions and, therefore, statistical methods based on
these distributions do not apply. In Appendix A, which contains detailed
descriptions of our statistical methodology, we discuss this problem and
approaches that alleviate it in some cases. No statistical manipulations can
be expected to yield predictability of counts and weights for rare or
extremely variable organisms. Therefore, we attempted to model population
parameters for only the most ubiquitous species in each habitat.
We also considered numerical assemblage parameters that characterize
the entire community in a given habitat:
S = number of animal taxa identified in a sample,
3.
S = number of plant taxa in a sample,
P
N = total count of animals in a sample,
cl
W = total plant biomass (wet weight) in a sample,
P
H' = Shannon-Weaver diversity for animals (Pielou 1966)
cL
Sa N. N.
- - z - m J:
N N
1=1 a a
where N. is the number of animals in the ith taxonomic group in the sample,
and
38
-------
H' - plant biomass diversity
Sp W. W.
= - 2 In
. , W W
1=1 p p
where W. is the weight of the ith plant taxon. Animal biomass W and animal
biomass diversity HJ, defined analogously to W and H', and percent plant
cover were considered for those subsets of the data ift which they were
available.
Our definitions of assemblage parameters are conditioned by some of
the limitations of the data set discussed in Section 4. We have already noted
that percent plant cover was not included in the WDOE data sets. Animal
weights were not consistently available in any of Nyblade's data sets because
the baseline methodology called for weighing only those species whose
individuals' aggregate weight exceeded 0.1 g. For both plants and animals
wet weights were used rather than dry weights. The latter were generally
unavailable because the sampling program mandated preservation of samples for
future reexamination if needed.
Animal and plant parameters were computed separately to provide a more
precise characterization of habitats and to avoid mixing count and weight
data.
It is important to note that our numerical assemblage parameters were
computed for each replicate rather than from pooled data including all
replicates at a given site, date, and elevation or from even larger groups of
samples. When such parameters were discussed in the reports of Smith,
Webber, and Nyblade, they were generally computed from pooled data. Hence,
larger numbers of taxa and diversities than those given in this report were
obtained.
We had two reasons for computing assemblage parameters on a sample-
by-sample basis. First, because these parameters increase unpredictably with
increasing number of samples, they cannot be compared if they are computed
from pools including different numbers of replicates. Since level of
replication varied widely in the data base, single-replicate computations
were required if different sites and times were to be compared. In addition,
we needed separate estimates for each replicate to assess sampling
variability in the parameters.
There are several motivations for concentrating on the modelling of
assemblage parameters instead of parameters for particular populations. The
first and most obvious is that the numerical assemblage parameters reduce the
often lengthy list of taxa with their counts and/or weights found in each
sample to a few simple summary statistics that at least partially
characterize the sample. A second reason for looking at assemblage
parameters is that there is a statistical basis (see Appendix A) for hoping
that the distributions of such parameters will come closer to distributions
such as the normal assumed by standard statistical methodology than those of
individual population parameters.
39
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5.2.2 Cluster analysis to deagyi'bfjt aggemblages
The numerical assemblage parameters discussed above, while providing a
concise characterization of assemblages, have the drawback that two samples
with no species in common could produce identical assemblage parameter
values. Cluster analysis, in contrast, produces a summary characterization
of a group of samples which takes into account the degree of similarity in
presence and (optionally) abundance of species found in those samples.
Cluster analysis is a technique for dividing a set of entities into
non-overlapping subsets. These subsets are defined by the requirement that
elements of a given subset are more "similar" to one another than they are to
elements of any other subset. In the normal (Q-mode) analyses of the present
study, the entities being classified were samples, and the attributes being
used to determine levels of similarity were counts of species found in the
samples. For more details concerning definitions of "similarity" and other
aspects of the cluster analysis methodology used in the present study, refer
to Appendix A.
Cluster analysis results were displayed graphically in dendrograms
that showed how small clusters of similar samples were nested within larger
less similar groups. Cluster analysis is primarily a descriptive technique,
suggesting categories and factors that can be explored quantitatively via
other statistical analyses.
5.2.3 Analyses of population and
Multiple regression and analysis of variance techniques were used for
determining variability due to annual, seasonal, and tidal elevation effects
and site differences as well as residual sampling variability. The general
procedure was to select subsets of the data within which the techniques could
appropriately be applied to population and assemblage parameters. Because of
the inadequacies in data characterizing habitats, we had to rely on cluster
analyses, descriptive information in reports, and our own experience with the
sites in constructing predictive models.
Regression analysis was used on subsets of data from single sites
because cluster analyses made it obvious that no simple available variables
could adequately represent site effects. Independent variables representing
elevation and date in our multiple regression models, described in detail in
Appendix A, allowed assessing the contributions of elevation, season, and
year effects to the overall variability in the dependent variables.
Dependent variables considered were the numerical assemblage parameters Sa,
S , H', ft", H', and percent plant cover and logarithms of N , W , and W .
TRe log transformation and its motivation are discussed in Appendix A.
Regression analysis is ideally suited to assessing variability
contributed by factors that can take on many values over some range.
Analysis of variance is more useful when dealing with factors that have a
relatively small number of discrete levels; each group in the analysis of
variance is associated with a particular level of each of the factors being
considered. For example, to assess elevation effects, regression analysis
4O
-------
was probably the best technique for data obtained by gradient sampling, while
analysis of variance was more appropriate for stratified sampling.
Analysis of variance could be applied to data from several sites
because separate sites could define separate groups in the analysis. Both
population and assemblage parameters were used in this analysis after a log
transformation of counts and weights. Analysis of variance contributes in
two ways to providing more definitive results concerning these parameters
than the annual or seasonal means at each site and elevation reported, for
example, by Nyblade (1977) and Smith and Webber (1978).
The first involves partitioning the variability. If an annual mean is
computed instead of a mean on a particular date, the variance of samples
about the annual mean will generally be larger than the variance on any
particular date, The added variance is due to season effects that cause mean
values on different dates to differ. Analysis of variance provides a
systematic breakdown of the variance into (1) that attributable to factors
such as season represented by the groups in the analysis and (2) the residual
(replicate, within-group, or sampling) variability that remains when all
factors have been accounted for. If the sampling variability is the same in
all groups, analysis of variance also provides a better estimate of its value
than the variances calculated for the individual groups.
Second, analysis of variance provides systematic ways of comparing the
means of several groups. Statistical tests with specified levels of
significance (see Appendix A) for differences among the means can be made.
Different analysis of variance models (one-way, two-way, and nested)
were used on different subsets of the data set in this study. All are
explained in detail in Appendix A, where we also discuss contrasts
(comparisons) between groups that were used extensively in the context of the
one-way analysis of variance model.
5.2.4 Predictive models
From the analyses described above, we concluded that the analysis of
variance approach yielded the most fruitful predictive models that could be
supported by the present data base. This approach uses the mean value of a
parameter computed from the most recent available samples at a given site,
season, and elevation as the predicted value for the mean of future samples
at that site, season, and elevation. (Cross-site prediction will be
discussed in a later section.)
If new samples were taken at the site, season, and elevation, the
usual test for whether the new mean was different would be a two-sample jfc-
test. Alternatively, if the estimate of sampling variability obtained from
analysis of variance was considered valid for both the old and new samples,
it could be used as the known variance for the slightly simpler normal theory
Z-test. For an example, refer to Dixon and Hassey (1969), pp. 114-116. If,
as is more likely, the assumptions of the i-test (i.e., that both samples
came from normal distributions with the same variance) were suspect, we could
41
-------
choose a nonparametric alternative such as the two-sample Mann-Whitney test
described in Appendix A.
Verification of our predictive models in the next section employs both
the two-sample ±- and Mann-Whitney tests. Samples from File 100 tapes used
in the model-building stage of the analysis were compared with samples from
Nyblade (1979b), which are not on tape, for purposes of verification. Power
of the tests to detect changes of various magnitudes in population and
numerical assemblage parameters is also discussed. The power results provide
guidelines for determining the number of replicate samples that should be
collected in future sampling programs.
42
-------
SECTION 6
RESULTS OF OBJECTIVE 1 ANALYSES
6.1 INTERTIDAL ROCKY SUBSTRATES
Of the four rocky intertidal sites included in Objective 1 analyses.
Cantilever Pier (SJI) and Tongue Point (Strait) are relatively smooth solid
rock. The Pillar Point Strait site is also solid rock, but not smooth.
Fidalgo Head (NFS) is variable, with some smooth rock shelves and some broken
areas where the rock surfaces consist of boulders. Cantilever Pier is the
least exposed of the sites and Pillar Point the most exposed.
Site locations are shown in Figure 1 of Section 1. Sampling dates and
type of sampling (gradient or stratified) are given in Table 1 of Section 4.
Samples from all tabled dates were available on File 100 tapes for analysis,
and 933 different plant and animal taxa were identified in these samples.
Brief explanations of statistical techniques and terminology used in
the analyses of this and subsequent sections are given in Section 5. Details
can be found in Appendix A.
6.1.1 Community analyses
Data from the rock sites were subjected to cluster analysis to
illustrate similarity patterns among stations (where a "station" includes
samples at a given site, date, and elevation stratum) and to facilitate
determination of factors important to these patterns. A benefit of
identifying these associations is that we can then apply our statistical
analyses to objective, moderately homogeneous station groups based on
biologic reality rather than arbitrary (and possibly faulty) groups based on
investigator biases.
The numerical assemblage parameters analyzed in this section are
defined in Section 5.2.1. Each assemblage parameter value was calculated
using data from a single 0.25-m quadrat, including appropriately normalized
and combined counts and weights from subsamples. For our analyses the low
stratum of elevation was defined as -0.3 m to +0.3 m, the middle stratum as
0.6 m to 0.9 m, and the high as 1.5 m to 1.8 m.
Similarities among all sites and elevations:
Figure 3 shows the relationships among summer and winter data for all
elevations and sites. Stations are segregated mainly on the basis of eleva-
tion and, within elevation zone, by site. The primary dichotomy is between
43
-------
Site, Region
Date Elev
FIDALGO HEAD- NFS 760789
CANTILEVER PIER, SJI 760313
CANTILEVER PIER, SJI 758126
CfiNTILEVER PIER, SJI 741133
CANTILEVER PIER, SJI 756789
PILLAR POINT, STRAIT 770119
PILLAR POINT, STRAIT 766669
FIDdLCQ HEAD. WPS 720222
CflNTILEVER PIER, SJI 7607 IB
CANTILEVER PIER, SJI 760716
CANTILEVER PIER, SJI 768319
CANTILEVER PIER, SJI 758126
CANTILEVER PIER, SJI 741120
CftNTILEVER PIER, 3JI 730783
TONGUE POINT, STRAIT 736168
TONGUE POINT, STRAIT 778118
TONGUE POINT, STRAIT 778630
TGNCUE POINT. STRAIT 7£i3711
FIDftLGO HEftD, NPS 736667
FIBflLCO HEAD, NPS 741229
FIDRLGO HEAD, NPS 730222
FIDALGO HEAD, NPS 759626
FIDfiLCO HEAD. NPS 7SQ7SS
FIDftLGO HEAD, NPS 741223
FIDALCO HEAD, NPS 766262
FIDftLGO HEAD, NPS 730222
FIDALGO HEAD, NPS 756867
FIDALGO HEAD, NPS 756626
FIDALGO MEAD, NPS 763789
FIDftLCO HEAD, NPS 766262
FIBftLGO HEftB, NPS 733626
FIDALGO HEAD, NPS 756867
TONGUE POINT, STRAIT 780108
TONGUE POINT, STRAIT 760711
TQNCUE POINT. STRAIT 77(26.20
TONGUE POINT, STRfilT 770118
PILLAR POINT, STRAIT 776119
PILLAR POINT, STRAIT 760809
PILLAR POINT, STRAIT 770119
PILLAR POINT, STRAIT 7SI32Q9
TONGUE POINT, STRftIT 770118
TONGUE POINT, STRAIT 7S8711
TONGUE POINT, STRftIT 770630
FIDALGO HEAD, NPS 756222
FID6LC.Q HEAD, NPS 741229
CANTILEVER PIER, SJI 766710
CANTILEVER PIER, SJI 756769
CANTILEVER PIER, SJI 741130
CANTILEVER PIER, SJI 750126
1-8 ] 1
l.u ' — '
^* ^ bl—
in '
8n , ,
0.9 i
0. 9 '
— i
1 1 '
. w - ' -
8f ,,„,. , ,
U. 1 " '
"0. 3 "•" •
. J '" i
8, 3
Q 9 «« , ^
e. 0
o.o • •
0.0 —
. 0
-6.3
-0.3
a -2 ———————
t
1
1
h
h
1
\
j
-i
r
a -
P
E
1-
h"
r
j
^
1
_i
I
ih-
a
1
~~l
u
I
p _
1
'i
—
h
n
a
,
1
n
B •
j
160
_L
75 58
LEVEL OF SIMILARITY
(quantitative index)
25
Figure 3. Relationships among summer and winter rocky intertidal stations,
all sites and elevations, similarity between stations is defined
by (A.5.1) of Appendix A in terms of relative abundance of the
5O plant and animal species or groups marked with stars in
Table B-l.
44
-------
-0.3 m to 0.9 m stations (group I) and 0.9 m to 1.8 m stations (group II).
All four sites are represented in each major group. This suggests that
biotic assemblages above 0.9 m on rocky intertidal habitats in the inland
waters of northwestern Washington vary considerably from those below 0.9 m.
Within both groups, the stations are segregated by both site and
elevation. For instance, group I-A includes only -0.3 m and 0.0 m stations
from Cantilever Pier and Fidalgo Head. Group I-B is more complex, comprising
a subgroup (limb) of 0.0 m stations from Tongue and Pillar Points and 0.9 m
stations from Pillar Point (limb I-B-2), as well as limbs of 0.9 m stations
from Tongue Point (limb I-B-l-b) and -O.3 m to 0.8 m stations from Fidalgo
Head (limb I-B-l-a). Within this latter limb, the lower Fidalgo Head
stations are segregated from the higher. The indication is that limb I-A
represents the most protected low intertidal rock assemblages, limb I-B-1
represents moderately exposed low intertidal assemblages, and limb I-B-2
represents more exposed low intertidal assemblages. The associations among
0.0 m stations from Tongue Point and 0.0 m and 0.9 m stations from Pillar
Point suggest that the low intertidal fauna extends higher at Pillar Point
than at Tongue Point, implying that Pillar Point is probably more exposed
than Tongue Point. Similarly, the association among the 0.9 m stations at
Tongue Point and the -0.3 m to 0.8 m stations at Fidalgo Head reinforces the
notion that low intertidal species extend higher at Tongue Point than at
Fidalgo Head. These comparisons, then, suggest a trend of increasing
exposure from Cantilever Pier (least exposed) through Fidalgo Head to Tongue
Point and Pillar Point (most exposed). They also indicate that the problems
of comparing intertidal assemblages at specified tidal elevations are severe
if the degree of exposure varies appreciably among the sites.
The patterns at the upper elevations (0.9 m to 1.8 m) are somewhat
different, possibly because the effects of desiccation become more important
above 0.9 m. The main dichotomy within this group segregated 1.5 m to 1.7 m
Fidalgo Head stations (limb II-B) from upper intertidal stations at the other
sites (limb II-A). Within limb II-A, one group (II-A-1) showed an
association between 0.9 m Cantilever Pier stations and 1.8 m Tongue Point
stations, probably as a consequence of desiccation at 0.9 m at Cantilever
Pier resulting from less wave action. The other group (II-A-2) comprises
mainly upper stations from Cantilever Pier, but also includes upper stations
from Pillar Point and Fidalgo Head. These patterns would probably be
somewhat better defined if more data were available from all sites.
Two-way analyses of variance (A.3.12) of elevation (low, mid, and
high) crossed with site (all four) indicated similar patterns in variability
of numerical assemblage parameters computed from May 1976 data. The
interaction between site and elevation was significant at the 0.001 level, an
indication of strong elevation effects which vary with site. Site
differences were also highly significant.
Seasonal patterns:
Seasonal and between-year effects are much less evident in Figure 3
than site, elevation, and exposure effects. In an attempt to clarify the
patterns within a season, we examined summer and winter data separately
45
-------
(Figures 4 and 5). Generally, the same relationships as those of Figure 3
emerged. The most noticeable difference between summer and winter was that
some mid to high elevation stations fell into group I (mainly representing
lower intertidal assemblages) in the summer while corresponding stations were
in group II (upper intertidal assemblages) in the winter. Between-year
differences appear more distinct in the summer data (Figure 4), possibly
reflecting the effects of annual differences in dominance in recruitment in
the summer. In contrast, the tendency for the rigorous conditions of winter
to increase uniformity (i.e., eliminate summer colonization experiments) is
apparent in Figure 5, especially for the Strait sites, where elevation
effects are frequently stronger than site effects. In limbs I-B-1, I-B-2-b,
and II-A-2-a-ii, for example, the Strait stations segregated across sites by
elevation.
Again, the problems of comparing data from various locations solely on
the basis of tidal elevation and without consideration of exposure are
indicated. At sites in the Strait, the biota of both lower and upper
intertidal assemblages extend to higher elevations than they do at the inner
sites. Thus, the intertidal zone is considerably compressed at the inner
sites, especially Cantilever Pier. However, it appears that this pattern of
compression may be less distinct in the winter, when the effects of
desiccation are probably not as severe at protected sites as in summer
because of storms and lower temperatures.
Elevation and site effects within region:
Finally, we examined NFS and SJI sites separately from Strait sites.
At the NPS and SJI sites (Figure 6), the primary dichotomy segregated -0.3 m
to 0.6 m stations (group I) from 0.9 m to 1.8 m stations (group II).
Unfortunately, at the interface elevations (0.6 m to O.9 m), Cantilever Pier
stations were mainly from 0.9 m with only one O.6 m station, whereas Fidalgo
Head stations were all at 0.6 m. The consequence of this difference is that
the stations from the two lower levels at Fidalgo Head were grouped with
stations from the lowest level at Cantilever Pier in group I, whereas the
stations from the upper level at Fidalgo Head were grouped with stations from
the two upper levels at Cantilever Pier in group II. Because of the
difference in levels sampled, the validity of the pattern cannot be
determined.
Generally, clustering by elevation was weaker in Figure 6 than at the
Strait sites (Figure 7), suggesting stronger vertical zonation in the
Strait. Within each elevation range in each region, within-site similarity
generally exceeded similarity between sites.
Regressions to partition assemblage parameter variability at each site:
Contributions of annual, seasonal, elevational, and between-sample
variations to overall variability at each rocky intertidal site were assessed
using the multiple regression model (A.2.1) of Appendix A with y an
assemblage parameter value. The results are summarized in Table 8.
46
-------
Site, Region
Date Elev
m
76(3866 1.5-
FIDALGO HEAD, NPS
FIDALGO HEAD, NP9 766769" 1.5
CANTILEVER PIER, SJI 7689BZ 1.8
CANTILEVER PIER, SJI 7S6716 1.5
PILLAR POINT, STRAIT 766889 1.8
CANTILEVER PIER. SJI 750709- 1.9
CANTILEVER PIER, SJI 758769 6.3
FIDALGO HEAD, HPS 756807 1.5
FIDALGO HEAD, HPS 756626 1.6
FIDftLGO HEAD, NPS 763806 0.6
FIDALCO HEAD. NPS 7£07Q9 fl. 8
CANTILEVER PIER, SJI 766962 8.9
CANTILEVER PIER, SJI 760718 8.6
FIDALGO HEAD, NPS 75666? 6.6
FIDftLGO HEAD, NPS 730626 0.6
TONGUE POINT, STRAIT 776636 1.8
TONGUE POINT, STRAIT 766711 1.8
FIBALCO HEAD. NPS 760206 Q.C
FIDALGO HEAD, NPS 766763 -8. 1
FIDALGO HErtD, NPS 759807 -0.2
FIDALCO HEAD, HPS 756626 -8. 3
CANTILEVER PIER, SJI 766716 -6.3
CANTILEVER PIER. SJI 750739 -fl.3
PILLAR POINT, STRAIT 766669 6.9
PILLAR POINT, STRAIT 760889 8.0
TONGUE POINT, STRAIT 760711 8.6
TONGUE POINT, STRAIT 773633 0.0
TOHCUE POINT, STRAIT 776636 8.9
TONGUE POINT, STRAIT 766711 6.9
II
25
LEVEL OF SIMILARITY
(quantitative index)
Figure 4. Relationships among summer rocky intertidal stations, all sites and
elevations. Similarity between stations is defined by (A. 5.1) of
Appendix A in terms of relative abundance of the 50 plant and
animal species or groups marked with stars in Table B-l.
47
-------
Site, Region
FIDALGO HEAD, NPS
CANTILEVER PIER. SJI 7£fl319
CANTILEVER PIER. SJI 751104
CANTILEVER PIER, SJI 741139
CANTILEVER PIER, SJI 756126
CANTILEVER PIER, sji 760319
CANTILEVER PIER, SJI 751164
CANTILEVER PIER, SJI 756126
CANTILEVER PIEft, SJI 741139
PILLAR POINT, STRAIT
TONGUE POINT, STRAIT 780165
TONGUE POINT, STRAIT 7F6118
FIDALGO HEAD, NPS
FIDALGO HERD, NPS
FIDALGO HEAD, NPS
FIDALGO HEAD, NPS
FIDALGO HEAD, NPS
FIDftLCO HEAD. NPS
FIDALCD HEAD. NPS
FIDALCO HEftD, HPS
FIDALCO HEUD, NPS
FIDALGO HEAD, NPS
FIDALGO HEnD, NPS
FIDALGO HEAD, HPS
PILLAR POINT, STRAIT 779119
TONGUE POINT, STRAIT 786166
TONGUE POINT, STRAIT 776118
PILLAR POINT, STRAIT
TONGUE POINT, STRAIT 77011S
FIDALGO HEAD, NPS
Pate Eley
m
76B^Q^ 1 5 • »-
751 1Q4 G 9 — —
741139 0 9
•7-3QJJC, j Q
7S6 1 65 1.3 — — —
741223 1 5
->Cfl^<-,0 « ->
T0114 0 'T
"*^1 1 Rrl IS '." ..
f 502«i2 0.0 —
766166 6. 3
i'5012o -0. 3
Tl 1 1 ''il fi 1
i | ,
199
i
1 1
i
i
75
i
-I
"~|
\
h
i
h
ll
i
a
?
F
A
c
•>
1
;a
ii -
B — ,
T -
/
LEVEL OF SIMILARITY1
(quantitative index)
Figure 5. Relationships among winter rocky intertidal stations, all sites and
elevations. Similarity between stations is defined by (A.5.1) of
Appendix A in terms of relative abundance of the 50 plant and
animal species or groups marked with stars in Table B-l.
48
-------
Site, Region
FIBALGO HEAD* NPS
FIDHLGO HEAD,
CANTILEVER PIER,
CANTILEVER PIER
CANTILEVER PIER,
CANTILEVER PIER,
CANTILEVER PIER.
CANTILEVER PIER,
CANTILEVER PIER/
CANTILEVER PIER,
FIDHLGO HEAD,
CANTILEVER PJER,
CANTILEVER PIER,
CANTILEVER PIER,
CANTILEVER PIER,
CANTILEVER PIER,
CANTILEVER PIER
CANTILEVER PIER
CANTILEVER PIER,
CANTILEVER PIER,
CANTILEVER PIER,
CANTILEVER PIER,
FIDHLGO HEAD,
FIDALGO HEAD,
FIDftLCO HEwD,
FIDALGQ HEAD,
FIBHLGO HEAD,
FIDftLGO HEAD,
FIDALGG HEAD,
FIDftLCO HEAD,
FIBALGO HEAD,
FIDHLGQ HEAD,
FIDHLGO HEAD,
FIDALGO HEAD,
FIBftLCO HEuD,
FIDALGO HEAD,
FIDALGO HEAD,
FIDHLGO HEAD,
FIDALGO HEnD,
FIDwLCO HEAD,
FIDHLGO HEAD,
FIBHLGO HEAD,
FIDHLGO HEAD,
FIBALGO HEAD,
CANTILEVER PIER,
CANTILEVER PIER,
CANTILEVER FIEF,
CANTILEVER PIER
CANTILEVER PIER
CANTILEVER PIER,
LEVEL OF SIMILARITY
(quantitative index)
Figure 6. Relationships among rocky intertidal stations from all months,
Fidalgo Head and Cantilever Pier. Similarity between stations is
defined by (A.5.1) of Appendix A in terms of relative abundance of
the 50 plant and animal species or groups marked with stars in
Table B-l.
Date Elev
m
NPS 760709 1.5 "
NPS 760114 1.3"1"1
R, 3JI 766962 1 . 8 1.
R, 8JI 751164 I 8
R, SJI 756515 1.9
ft, SJI 756902 1 8
Kt 5JI 7301iu i.o -•—-••
R, SJI 73S?69 1 . 3
R, SJ I 75U526 1.8
R, SJI 746911 1.8
Rt 5 J I 7Gt'202 0 . 0 "" ' ' i
.R, bJI .'£CUil9 0.9 "•" —
jw jJi i ji 1U4 y. j
.R. bJI .JCOC^ w.9 ' " '
.K> oJi ii%jUl1i.o U.^ '" — '
.R^ SJI 7^0!?11 9. 3 —-—•'"-"
HPS 7C0222 1.7" — ••
Nrb f jUoib 1 . b •"
IHP3 741013 l.C '"™
HP 3 736425 1 .-7 • •
HP9 760t'09 0. 9 •
NPD 760C.OZ 0.6
HP 3 730C07 0. G " '"
HPS i C0te£b 0. b
HPt 758429 O.fc
Hrb 760114 U. 0 "'
NPS 760733 -3. 1
HPS 7C1104 0. € ••'
HPS rot'oUi -t). i -
NPS 731 1B4 1.3
HPS 736222 8.6
iR, SJI 760710 -0,3 —
-R> SJI 750769 -8.3
:P, 5JI 741130 -0.3
;P., 3JI 766313 -9. 3
=.R, SJI 7561£b -6.3
1 I '
100
] '
iif-
J
i
1
n
1
i
1
I-
h
a
1
i
i
. \-
\
75
d
2
— 1
1—
b
-
— I
1
_
r
T
2
b
-
1
a
i
R
U
H
'<-
Ai
!
1
1
58
r
"i
i
— i 1
1
2b
-------
Site,
PILLAR
PILLAR
PILLAR
PILLAR
TONGUE
TONGUE
TONGUE
TONGUE
TONGUE
TONGUE
TONGUE
TONGUE
PILLAR
PILLAR
PILLAR
TONGUE
TONGUE
TONGUE
TONGUE
TONGUE
TONGUE
TONGUE
TONGUE
PILLAR
PILLAR
TONGUE
TONGUE
TONGUE
TONGUE
PILLAR
TONGUE
TONGUE
PILLAR
PILLAR
Region Date Elev
m
POINT, STRAIT 779119 1.8-
POINT, STRAIT 761122
POINT, STRAIT 768809
POINT, STRAIT 766515
POINT, STRAIT 788168
POINT, STRAIT 776118
POINT, STRAIT 771816
POINT, STRAIT 776630
POINT, STRAIT 761027
POINT, STRAIT 768711
POINT, STRAIT 770566
POINT, STRAIT 768501
POINT, STRAIT 770113
POINT, STRAIT 760569
POINT, STRAIT 766515
POINT, STRAIT 760 108
POINT, STRAIT 760711
POINT, STRAIT 761027
POINT, STRAIT 766501
POINT, STRAIT 771016
POINT, STRAIT 779638
POINL STRAIT 778118
POINT, STRAIT 770506
POINT, STRhlT 776119
POINT, STRAIT 769809
POINT, STRrilT 770118
POINT, STRAIT 761027
POINT, STRHlT 760711
POIHT, STRAIT 760581
POINT, STRAIT 761122
POINT, STRAIT 770630
POINT, STRAIT 770506
POINT, STRAIT 761122
POIHT, STRAIT 760515
1.8-
1.8-
1.8-
1.8-
1.9-
1.8-
1.3-
1.8-
1.8-
1.8-
1.8-
0.9-
0.9-
0.9-
6.9-
8.9-
0.9-
0.9-
8.9-
0. 9 •
0.9-
0 9 •
0.0-
6 0-
0. Q •
8.0-
0.0 •
0 0
0.9 •
0.0'
0.0
0.9
0.0
100
1
J-l
h
75 58
LEVEL OF SIMILARITY
(quantitative index)
25
Figure 7. Relationships among rocky intertidal Strait stations, all seasons.
Similarity between stations is defined by (A.5.1) of Appendix A in
terms of relative abundance of the 50 plant and animal species or
groups marked with stars in Table B-l.
5O
-------
TABLE 8. RESULTS OF REGRESSIONS TO PARTITION ASSEMBLAGE PARAMETER VARIABILITY, ROCKY INTERTIDAL SITES
Site
Tongue Point
Pillar Point
Cantilever
Pier
Fidalgo Head
Regression Equation Elevation
yt (standard deviations of coefficients in parentheses) (xi)
SP
Sa
logw(Na+l)
iog10(yi)
% plant cover
SP
Sa
1og10(yi)
log10(Wp+l)
% plant cover
SP
Sa
logw(V1)
Iog10(yi)
SP
Sa
log10(Na+l)
log10(Ha+l)
R2, the percentage of total
The numerical
nf (fl 9 •M ha
30.3 +
(118)
- 265
(186)
27.5 +
(8.08)
6.75 +
(8.81)
- 1418
(462)
- 328 +
(233)
541 +
(428)
11.3 +
(17.8)
25.3 +
(20.3)
723 +
(974)
- 207
(54.5)
- 282 +
(47.2)
12.7 +
(5.60)
3.68 +
(7.19)
130
(43.8)
70.8 -
(87.7)
12.4 +
(7.25)
7.82 -
(7.96)
variability
assemblage parameters S
ua Kaon nmi 1 1 c\rt in +hic -Ftik
2.25xi
(3.67)
3.73xi
(5.81)
1.46xi
(0.25)
0.16xi
(0.27)
(u'.s*1
18.4xi
(4.58)
32.3xi
(8.43)
0.46xi
(0.35)
0.40xi
(0.99)
12.9xi
(19.1)
7.04xi
(1.32)
4.50xi
(1.14)
1.32xi
(0.14)
0.39xi
(0.17)
10.2xi
(0.93)
3.95xi
(1.86)
0.05xi
(0.15)
0.14xi
(0.17)
explained
Sa, etc.
- 4.33X2 +
(1.85)
- 9.39X2 +
(2.93)
- 0.71x2 +
(0.13)
- 0.53X2 +
(0.14)
- 5.10X2 +•
(7.30)
- 14.7X2 +
(2.34)
- 23.6X2 +
(4.30)
- 0.06X2 +
(0.18)
- 0.84X2 +
(0.20)
- 18.0X2 +
(9.82)
- 0.23X2 -
(0.73)
- 5.52X2 +
(0.63)
- 0.74X2 +
(0.07)
- 0.74X2 +
(0.10)
+ 2.06X2 +
(0.39)
- 1.53X2 +
(0.78)
- 0.35X2 +
(0.06)
- 0.29x2 -
(n 07)
2.41X3 - 0.13X4
(1.65) (1.53)
7.68x3 + 4.09X4
(2.62) (2.41)
0.23X3 + 0.40X4
(0.11) (0.11)
0.42X3 + 0.12X,,
(0.12) (0.11)
9.05X3 + 19.5X4
(6.60) (5.98)
5.62X3 + 4.52X4
(2.08) (3.02)
0.88x3 - 6.61X4
(3.82) (5.56)
0.06X3 + 0.18X4
(0.16) (0.23)
0.54x3 + 0.36X4
(•0.18) (0.26)
6.31X3 - 8.50X4
(8.61) (12.7)
0.34X3 + 2.93X4
(0.93) (0.72)
O.lOxs + 3.94X4
(0.81) (0.63)
0.05X3 + 0.20X4
(0.10) (0.07)
0.08x3 - 0.02X4
(0.12) (0.10)
2.88x3 - 1.56X4
(0.65) (0.58)
1.18X3 + 1.18X4
(1.30) (1,16)
0.06X3 + 0.20X4
(0.11) (0.10)
0.07X3 + 0.14X4
(0.12) (0.11)
by the multiple regression model
used as dependent variables y,- in
25.6%
60.7
2.9
51.3
27.9
30.0
20.4
15.9
54.4
17.9
57.3
28.2
1.3
44.6
52.2
36.5
41.8
41.2
(A. 2.1)
(A. 2.1)
Contributions to R2
Elevation
Squared Season
(x2) (x3)
+ 3.7%
3.5
23.5
5.7
0.7
25.4
25.7
0.1
9.0
4.2
0.0
21.6
41.0
17.8
7.0
1.3
8.0
5.2
of Appendix
are defined
+ 2.0% +
2.1
0.1
4.2
0.0
3.5
0.3
0.0
3.7
1.2
0.6
2.5
1.3
0.1
2.5
0.6
0.5
0.0
Date
(X4)
0.0%
1.0
9.8
0.5
7.5
1.4
1.2
0.9
1.0
0.6
4.8
11.3
3.2
0.0
1.4
0.3
1.1
0.4
A, is defined by
in Section
5.2.1.
Total
R2
= 31.3%
67.3
36.3
61.7
36.1
60.3
47.6
16.9
68.1
23.9
62.7
63.6
46.8
62.5
63.1
38.7
51.4
46.8
(A.2.3).
Residual
Standard
Deviation
7.26
11.5
0.498
0.543
28.2
7.71
14.2
0.590
0.673
31.5
4.78
4.14
0.491
0.631
4.24
8.49
0.702
0.770
The subscripts j
conciseness
-------
The parameters S , S&, and N were considered at all the sites. W
was included for all sites except Fidalgo Head where the plant weight datl
were known to contain errors. W was considered at Fidalgo Head in place of
W; W could not be computed at the other sites because animal weight data
was missing from most records except at Fidalgo Head. Similarly, percent
plant cover could be considered only at the Strait sites because it was not
recorded at the others.
It should also be noted that examination of plots of residuals from
the regressions of Table 8 indicated errors in some of the data, most notably
questionable "abiotic" samples at Fidalgo Head. It also appeared that
observations at elevations less than -0.3 m and greater than 2.1 m might have
had too much influence on the fit. However, when the regressions were rerun
with questionable and extreme observations omitted there were no dramatic
changes in the results.
Table 8 indicates that elevation effects account for 3O to 6O percent
of the variability in S , 35 to 65 percent in S , 15 to 5O percent in N , and
around 60 percent of weight variability at each site. One or both
coefficients are generally significant. Elevation contributes less
significantly to variability in percent plant cover.
In all cases one or both elevation coefficients are negative,
corresponding to a decrease in parameter values at high elevations. In some
cases the decrease is linear and in others, for example S at Pillar Point,
the maximum parameter value occurs at a middle elevation rather than at the
lowest. Values of S predicted by the regression equation at Pillar Point
are plotted in Figure 8.
Seasonal effects are significant for S at Pillar Point and Fidalgo
Head, for W at both Strait sites, and for animals as well at Tongue Point.
However, thiy account for less than 5 percent of the variability in all
cases. The positive season coefficients indicate higher weights and numbers
in spring and summer than in fall and winter.
Time trends, represented by the date coefficients, generally account
for less than 10 percent of the variability in assemblage parameters.
Positive date coefficients for N indicate an increase in number of animals
over the course of the studies. &The increase is significant at the three
sites sampled both before and after the large spring 1976 barnacle
recruitment and is probably due to that event. The only other time trends
which appear to be significant are an increase in percent plant cover at
Tongue Point, increases in S and S at Cantilever Pier, and a decrease in S
at Fidalgo Head. The decreaie at Fidalgo Head may be real since a separate
regression analysis of plant weights at low elevations there also indicated a
decrease with time, but final conclusions cannot be drawn until corrected
plant weight data are available for analysis. The increase in percent plan£
cover at Tongue Point may be real or may be due to model inadequacy since R
for this parameter is low at both sites where it was computed. The increases
in number of taxa at Cantilever Pier are the most significant changes with
time. Nyblade hypothesizes that they may be due to a dense monoculture of
52
-------
Pucus which dominated the mid intertidal in the first year of the study,
leading to reduced species richness in that year.
When both year-to-year and seasonal effects were eliminated by
considering only July 1976 data at each of the three sites where gradient
samples were taken at that time, it was possible to fit quadratic equations
in elevation which generally explained 70 to 90 percent of the variability in
the assemblage parameters.
47. •«• 28
2 4
82
- 6
34. + 4
- 8 2
S '
a 8
4
8
8. +
-5.
0.00 1.00 2.00
0. 50 1. 50 2. 50
Elevation, m
Figure 8. Predicted number of animal taxa S at Pillar Point from
regression. Predictor variables in (A. 2.1) are elevation and its
square, season, and date as defined in Section A. 2 of Appendix A.
Numbers are number of data points at the position where they are
plotted.
Problems with the multiple regression model:
The regression analyses we have discussed provide useful indications
of the contributions of elevation, season, and year effects to the overall
variability in the data. However, we do not recommend the multiple
regression model as a predictive model for reasons discussed in Appendix A.
53
-------
Among the problems of the multiple regression model, one which showed
up most clearly in the rocky intertidal regressions was heterogeneity of
variances of the errors. This problem was evident in some of the plots of
residuals (observed - predicted values) versus predicted values such as
Figure 9. This figure, like Figure 8, was computed from values of S at
Pillar Point. Large positive and negative residuals tend to be associated
with large predicted values in the figure, indicating that larger error
variances are associated with larger values of S . Hence the regression
assumption that the errors e. in (A.2.1) have equal variances is violated.
2. 6+
1. 2+
Residual
S
a -0. 2+
-1. 6+
*4
*3
2*
2 2
2 2
* **
* *
* * *
* *** *
** **
* ## *
2* *
** 2*
* 2
* * *
*
* * 2 *
-3. 0+
H
-5.
*
*
8.
21.
Predicted S
47.
34.
60.
Figure 9. Residual versus predicted number of animal taxa S at Pillar Point
from regression. Predictor variables in (A.2.1) are elevation and
its square, season, and date as defined in Section A.2 of
Appendix A. Numbers are number of data points at the position
where they are plotted; * indicates a single point.
Since elevation is the dominant contributor to variability in the
numerical assemblage parameters, Figure 9 indicates that the residual
variability in these parameters may vary with elevation. Therefore, our
remaining analyses looked at low, mid, and high elevations separately.
Within one of these strata, numerical assemblage parameter values are
54
-------
relatively uniform and hence statistical models which assume homogeneity of
variances are more likely to be applicable.
Analysis of variance of assemblage parameters to assess site and season
effects within a rocky intertidal elevation stratum, Strait sites:
Since they appeared to be quite different from northern Puget Sound
sites, the Strait sites were first analyzed separately. Pour replicates per
stratum of elevation were available at each site for each season from spring
1976 to spring 1977, providing 10 groups of size n. = 4 for the one-way
analysis of variance model (A.3.1) in each stratum. Orthogonal contrasts
were used to partition variability in assemblage parameter values into
percentages due to site and season differences. Results are summarized in
Table 9. The groups and their means are shown in Figure 10.
All highly significant site differences occur in spring data. The
huge spring 1976 difference in animal counts is due to the fact that Tongue
Point was sampled before and Pillar Point after the large barnacle
recruitment. Site differences contribute more than half of the Factor SS
(see Table A-2 for definition) for S and H' at the low elevation; N , W ,
and H' at the mid elevation; and S , N , W , and H' at the high elevation.
The largest seasonal differences involve spring data in all cases but
one, a further indication that spring is the least predictable season.
Significant contrasts involving S are primarily due to larger numbers of
plant species in spring samples. Those for N are due to the Tongue Point
samples taken before the May barnacle recruitment, but the H' contrasts
appear to reflect an increase in diversity in the fall and winter resulting
from the normal attrition of juveniles that peak in density in spring and
summer.
Significant differences in percent plant cover must be interpreted
with caution for two reasons. The first is that tests for homogeneity of
variance for this parameter reflected differences in group variances
significant at the 0.01 level at both the low and high elevations. Second,
percent plant cover was missing for a few samples. Missing values were
replaced by means of the available observations in the same group in order to
maintain equal group sizes n. =4.
An arcsine transformation was tried without success for stabilizing
variances of percent plant cover. The large heterogeneous replicate
variances of this parameter remained a barrier to prediction and change
detection. Hence we will not discuss percent plant cover further.
Among the other parameters, there was some evidence of variance
heterogeneity in log (N +1) at the low elevation, log (W +1) at the mid,
and H' at both low ana mid. When all elevations were considered together,
all except S and H' exhibited differences significant at the 0.01 level, so
the separate analysis for separate elevation strata were clearly called for.
55
-------
TABLE 9. CONTRIBUTIONS OF SITE AND SEASON DIFFERENCES TO ASSEMBLAGE PARAMETER VARIABILITY, ROCKY STRAIT SITES
% OF FACTOR SS
Logic (Na + 1)
(Wp + 1)
H' Cover
LOW ELEVATION:
Site (Tongue vs. Pillar Point):
Spring 1976 14% 41%*
Summer 1976 1 3
Fall 1976 0 3
Winter 1977 02
Spring 1977 0 21
Season (averages of the two sites):
Spring 1976 vs. Summer 1976 34* 2
Fall 1976 vs. Winter 1977 14 24
Spring/Summer vs. Fall/Winter 18* 4
Spring 1976-Winter 1977 vs. Spring
1977 Jj3* p
100% 100%
MID ELEVATION:
Site (Tongue vs. Pillar Point):
Spring 1976 18* 1%
Summer 1976 68
Fall 1976 1 15
Winter 1977 12 17
Spring 1977 72
Season (averages of the two sites):
Spring vs. Summer 1 5
Fall vs. Winter 33 4
Spring/Summer vs. Fall/Winter 7 43
Spring 1976-Winter 1977 vs. Spring
1977 15 5
100% 100%
HIGH ELEVATION:
Site (Tongue vs. Pillar Point):
Spring 1976 4% 1%
Summer 1976 3 3
Fall 1976 2 1
Winter 1977 24 0
Spring 1977 24 30
Season (averages of the two sites):
Spring vs. Summer 0 2
Fall vs. Winter 10 0
Spring/Summer vs. Fall/Winter 11 45
Spring 1976-Winter 1977 vs. Spring
100% Too%
32%
5
6
0
0
22
7
11
.17
100%
12
44
1
12
1
14
13
55%*
1
3
0
6
14*
0
16*
1
27
26
100%
2
51
9
0
0
14
12
22%
2
15
8
23
_
100%
4%
24
1
1
65*
3
19
7
13
2
19
0
2
54
1
100% TOO:
14%
4
2
48
3
9 9
1 1
56* 2
15
11
2
5
12
42*
00% 100%
41 2 7
10 7 1
1 24 10
1 0 28
0 0
0 29
3 22
1002 100% T3K
1
12
27
1
49*
3
14 17 0
100% 100% TO*
The Factor SS represents the fraction of the total variability in an assemblage parameter explained by the one-way
analysis of variance model'. It is defined in Table A-2 of Appendix A, and its partitioning by means of contrasts
is explained in the discussion following that table.
M
Number of plant taxa S and the other numerical assemblage parameters included in this table are defined in
Section 5.2.1. p
Significant at the 0.001 level. Our choice of this level for testing is discussed in Section A.4 of Appendix A.
Note that the same % of Factor SS may be significant for a parameter at one elevation but not another because
the overall significance of the Factor SS is higher in the first case than in the second.
56
-------
Site
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Date Elev
m
760501 0.0
760515 0.0
760711
760809
761027
761122
770118
770119
770506
770505
Site
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Point
Point
Point
Point
Point
Point
Point
Point
Point
Point
Date
760501
760515
760711
760809
761027
761122
770118
770119
770506
770505
Elev
m
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
Site
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Point
Point
Point
Point
Point
Point
Point
Point
Point
Point
Date
760501
760515
760711
760809
761027
761122
770118
770119
770506
770505
Elev
m
1.8
1.8
1.8
1.8
1.8
1.8
1.8
1.8
1.8
1.8
Percent Plant Cover
Low Elevation
POOLED ST. DEU. =
19.8
INDIUIDUflL 95 PERCENT C. I. FOP LEUFL MERNS
fERSED ON POOLED STRNDRRD DEUIRTION3
T ~-~*- ™ "~~" I --1~IT1_ -.' . ~1T1 - - - - I J . . __ JJ,. -J_ I -„_ _IU_L-|_1 -fl .J_". l_l l_ IU __._, ..,.f._ ._ _n_^.
j AS**'*.:*'*** i #*»'»:«*:**si
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
9.
—I—
£6.
—I—
40.
—+ + +~
60. 90. 100.
—+
IcO.
Mid Elevation
£7.4
POOLED ST. DEU. =
INDIUIDUflL 95 PERCENT C. I. FOP LEUEL
lERSED ON POOLED STRNDflPD DEUIHTION3
+ + + 1
MEfiNS
—+
0.
£0.
1
40.
1
60.
80.
1
100.
1E0.
High Elevation
POOLED ST. DEU. =
£7.4
INDIUIDUflL 95 PERCENT C. I. FOR LEUEL NERNS
lERSED ON POOLED STRNDRPD DEUIflTION 3
-30.
0.
30.
e0.
1£0.
Ifd.
Figure 10. Group means from analysis of variance of Strait rocky intertidal
numerical assemblage parameters (defined in Section 5.2.1) with
individual 95 percent confidence intervals (A.1.7) based on pooled
standard deviations. The one-way analysis of variance model
(A.3.1) of Appendix A with n. - 4 in each group was used, with
separate analyses for each assemblage parameter at each
elevation.
57
-------
Number of Animal Taxa Sa
Low Elevation
FOOLED ST. IiEU. =
12. £
Site
Date
IHDIUIDUHL 95 PERCENT C. I. FOR LEUEL MEflNS
Elev 'EflSED ON POOLED STRNBRRD DEUIflTION)
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
ri i lar point
760501
760515
760711
760809
761027
761122
770118
770119
770506
//UoUb
m T
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
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10.
— 1 1 1 — 1 1
T ******** X ******** X
X ******** i *»**:*•*** x
T ******* x ******** i
I ******** x ******** i
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x ******** i ******•*• i
T ******** I ******** T
X *:****»** i ******** i
X ******$* x ******** x
X ******** x ******* x
£4. 38. -5£. 66. 30.
+
94
Site
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Date
760501
760515
760711
760809
761027
761122
770118
770119
770506
770505
Elev
m
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
Mid Elevation
POOLED ST. LEU. = 14.4
IHDIUIDUPL 95 PERCENT C. I. FOP LEUEL
iEflSED ON POOLED STflMDfiPD DEUIflTION)
j ************** j ************* j
j ************* j ************** j
T ************* I ************** J
T ************** I ************* I
I ************** J ************* I
I ************* i ****:********•** i
I ************* i ************** j
T ************** I ************** I
T ************** I ************** I
I ************** I ************* I
16.
£0.
30.
40.
50.
t~e.
High Elevation
POOLED ST. DEU. =
6.1C
INDIUIDUFIL 95 PERCENT L. I. FOP LEUEL MEfiNS
Site
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Date
760501
760515
760711
760809
761027
761122
770118
770119
770506
770505
Elev i EflSED ON POOLED STfiNDfiPD DEUIflTION)
m +
1.8
1.8
1.8
1.8
1.8
1.8
1.8
1.8
1.8
1.8
0.0
1 ********** j **:*.****** j
j ********* j ********** i
T ********** J ********* J
j ********* i ********** i
T ********** I ********* I
T ********** J ********* I
I ********** X ********* i
I ********** I ********* I
T ********* I **»******* X
T ********** I ********* I
6.8 1£.0 13.0 £4.0 30.0 36.0
Figure 10 (continued)
58
-------
Site
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Date Elev
m
760501 0.0
760515 0.0
760711
760809
761027
761122
770118
770119
770506
770505
Number of Plant Taxa Sp
Low Elevation
POOLED ST. IiEU. = 4.9£
INDIUIDUflL 95 PERCENT C. I. FOR LEUEL MEfiNS
'.EflSED OH POOLED STFiNDfiPD DEUIRTIOHJ
+ --------- + --------- + ---------- H --------- +
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
J ft***** I «»»:*•«• I
16.8
_ X ______
£4.0
?£.8
-— -- - -1- _
40.0
. 0
Site
Date Elev
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Point
Point
Point
Point
Point
Point
Point
Point
Point
Point
760501
760515
760711
760809
761027
761122
770118
770119
770506
770505
ID
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
Mid Elevation
POOLED ST. I'EU. = 7.08
INDIUIBUfiL 95 PERCENT C. I. FOR LEUEL MEFiNS
f EflSED ON POOLED STflhDflRD DEUIflTIQN)
'.0
I ^SSASiSiSIS::*: J ««****»»«** I
X o^sfcs;*;.*:** i '-tsssstssse X
X *•»»»•'*»:!«».•« x #»'***5*!«»::* i
H—
14.8
£1.0
1—
£8.8
4£.Q
49. O
Site
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Date Elev
760501
760515
760711
760809
761027
761122
770118
770119
770506
770505
High Elevation
POOLED ST. DEU. =
4.87
INDIUIDUfiL 95 PERCENT C. I. FOP LE' 'EL MEflNS
iEflSED ON POOLED STRNDflPD DEUIflTION)
m
1.8
1.8
1.8
1,
1.
1.
1.
1.
1.
1,
X « WjJi****-** I «.«-«*:*:*-*i«.<( X
y.8
X *»*«*•*«*« j »:•«•««»»:«% x
' ~T J^" — — — |^
5.0 10.0
15.0
_ I
£0.0
H
. 0
30.0
Figure 10 (continued)
59
-------
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Total Plant Weight
Low Elevation
0.396
Site
POOLED ST. DEU. =
INDIUIDUflL 95 PERCENT C. I. FOR LEUEL MEflNS
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Mid Elevation
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Point
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log,0
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760501
760515
760711
760809
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770505
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in
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.9
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0.59S
C. I. FOR LEUEL
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0. 78
4
1.40
24
£.10 £.30
125 630
3.50
3161
Figure 10 (continued) The x-axis divisions on these plots are labelled in
log units with the corresponding weights given below.
61
-------
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-------
Plant Diversity H'p
Low Elevation
Site
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Tongue
Pillar
Point
Point
Point
Point
Point
Point
Point
Point
Point
Point
Date
760501
760515
760711
760809
761027
761122
770118
770119
770506
770505
Elev
m
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
POOLED ST. DEU. = i
IHI'IUIDUflL 95 PERCENT C. I. FOP LEUEL MERHS
(BflSED ON POOLED STRNDRPD DEUIflTIQN)
4. — .T-- -I-- t H L -n i.- —
+
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0.25
0.68
0.95
1.30
1.6
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Mid Elevation
Site
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Date
760501
760515
760711
760809
761027
761122
770118
770119
770506
770505
Elev
m
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
POOLED ST. DEU. = 0.507
INDIUIDURL 95 PERCENT C. I. FOP LEUEL MEfiNS
i BflSED ON POOLED STflNDflRD DEUIPTIQH)
T **->*:*•»«**«:«:»; j %»«««»%%!«%!«:« j
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U.40
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£.48
High Elevation
POOLED ST. BEU. =
0.491
Site
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Tongue Point
Pillar Point
Date Elev
m
760501 1.8
760515 1.8
760711 1.8
760809 1.8
761027 1.8
761122 1.8
770118 1.8
770119 1.8
770506 1.8
770505 1.8
INDI'.'IDUflL 95 PERCENT C. I. FOP LEUEL MEfiNS
fEHSED ON POOLED STfiNHfiRD DEUIfiTIOW
-i ----------- 1 ----------- 1 ----------- 1 ----------- 1
T -O-*:*:-*'***-****-** I -0-^W-a:*.:*:*:***-*. J
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0.40
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Figure 10 (continued)
63
-------
Year-to-year variability within elevation stratum, rocky Strait sites:
To assess year-to-year variability of numerical assemblage parameters
we used summer 1977 and winter 1978 data from Tongue Point not used in the
analyses of variance. These data were compared first with Tongue Point data
and then with Pillar Point data from the corresponding seasons of the
previous year by means of two-sample i-tests and Mann-Whitney tests. The
results are summarized in Table 10.
Given the number of tests performed and possible violations of jt-test
assumptions, we expect some false indications of significant differences. On
the other hand, given the small number of replicates, we expect to miss some
significant differences due to lack of power of the tests.
Nevertheless, the table clearly indicates more differences between
Pillar Point and Tongue Point data than between the two years of Tongue Point
data. The only significant change in winter Tongue Point data was an
apparent decrease in plant weight from 53 g per O.25-m quadrat in the first
year to 5 g per quadrat in the second at the high elevation. More changes
were evident in summer.
Temporal variability within northern Puget Sound
rocky intertidal sites and elevations:
Bimonthly summer and winter data from Cantilever Pier and Fidalgo Head
were used to assess variability due to year, season, and date within season.
Analyses of variance of the available numerical assemblage parameters were
done separately for mid and high elevations at each site. Low elevations
were not considered because they were not sampled on some of the dates of
interest. The nested model (A.3.13) with Analysis of Variance Table A-3 was
used to obtain the results summarized in Table 11.
This table indicates that spatial patchiness, reflected in the
residual variance component, contributes more to variability in assemblage
parameters than short-term temporal change, reflected in the date-within-
season component. In addition, there is evidence that real seasonal and
year-to-year changes in numerical assemblage parameters can be expected.
Results for W and H' at Fidalgo Head are included in Table 11 only to
illustrate that bad Sata may either mask or create significant results. It
was in fact the highly significant summer versus winter difference in W
which led to the discovery of errors in Fidalgo Head plant weight data.
If we discount H' at Fidalgo Head, we are left with only one estimate
of the date-within-seasSn variance component that is significantly different
from zero. This is for S at the mid elevation at Cantilever Pier. Table 11
indicates variance heterogeneity in this parameter at this site and eleva-
tion, so the indicated significance of the date effect may be incorrect.
The significant summer versus winter and summer 1975 versus 1976
differences in animals reflect the spring 1976 barnacle recruitment as they
should. H' is less sensitive than numbers to this change. Plant parameters
3.
64
-------
TABLE 10. MEANS, CONFIDENCE INTERVALS, AND SIGNIFICANCE TESTS FOR STRAIT ASSEMBLAGE PARAMETERS. SUMMER AND WINTER
Tidal Season Assemblage
Elevation Parameter §
(meters)
C.O Summer S
p
sa
logio(Na+l)
1ogio(Wp+l)
Ha
HP
Winter No second year
0.9 Summer S
Sa
log10(Na-n)
log10(wp+D
Ha
hP
Winter S
sa
log1Q(Na+l)
Iog10(w +1)
Ha
HP
1.8 Summer S
P
sa
logio(Na+l)
logio(Wp+l)
Ha
HP
Winter S
sa
log10(Na+l)
logio(W +1)
H1
a
Hr
i p
95% CI,
Tongue Point,
Second Year *
(1.02,16.48)
(31.9,101.6)
(3.07,4.31)
(3.10,3.78)
(2.59,3.23)
(-0.13,0.44)
data
(13.25,26.75)
(39.60,65.90)
(3.97,4.71)
(2.68,3.36)
(1.11,2.92)
(0.89,1.66)
(13.10,20.90)
(17.22,54.78)
(3.07,4.13)
(1.71,3.21)
(2.07,2.71)
(0.48,1.61)
(0.60,26.90)
(14.28,31.72)
(3.59,4.10)
(1.28,3.23)
(1.21,2.26)
(0.24,2.32)
(3.83,21.17)
(10.36,15.14)
(3.03,3.85)
(0.16,1.40)
(1.55,1.93)
(1.18,2.46)
Tongue Point First Year4
Mean Significance of
Diffprencp
1-test Mann-Whitney
17.75
54.00
2.83
3.06
2.80
0.51
20.75
41.25
4.07
2.61
1.25
0.84
22.50
31.25
3.47
2.39
2.34
1.09
8.00
13.00
3.37
1.07
0.77
1.02
15.50
17.75
3.65
1.73
1.74
1.37
0.0129
ns
0.0067
ns
ns
0.0346
ns
ns
ns
0.0367
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
0.0456
ns
ns
ns
ns
ns
ns
0.0269
ns
ns
0.0304
ns
O.Q304
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
0.0304
ns
ns
Pillar Point First Year
Mean Significance of
Difference
£-test Mann-Whitney
20.00
52.00
3.51
3.21
1.89
0.67
27.50
52.50
3.38
2.93
2.38
0.99
32.50
48.00
3.65
3.01
2.51
0.57
5.00
9.25
3.60
0.77
0.98
0.78
7.00
16.75
3.64
1.07
1.60
0.47
0.0382
ns
ns
ns
ns
0.0370
0.0192
ns
0.0058
ns
ns
ns
0.0018
ns
ns
ns
ns
ns
ns
0.0038
0.0477
0.0097
0.0072
ns
ns
0.0162
ns
ns
ns
0.0027
ns
ns
ns
ns
ns
ns
0.0304
ns
0.0304
ns
ns
ns
0.0304
ns
ns
ns
ns
ns
0.0304
0.0304
ns
0.0304
0.0304
ns
ns
ns
ns
ns
ns
0.0304
Confidence intervals (CI) are defined by (A.1.6) of Appendix A.
Significance tests(see Section A.4 of Appendix A) compared second-year Tongue Point data (summer 1977 and winter 1978)
first with Tongue Point and then with Pillar Point data from the corresponding seasons of the previous year. Four
replicates were available at each year/season/site/elevation except for first year/summer/Pillar Point/0.0 m where
there were only two. Tests not significant at the 0.05 level are indicated by ns. Significance levels for the t-test
may not be exact because of variance heterogeneity and lack of normality.
Numerical assemblage parameters included in this table are defined in Section 5.2.1.
65
-------
ABUJJ^ YEAR, SEASON, DATE WITHIN SEASON, AND REPLICATE VARIABILITY AT CANTILEVER PIER AND FIDALGO HEAD.
at
SITE ELEVATION PARAMETER1
Cantilever Pier mid Sp
sa
log10(Na+l)
logJO(Hptl)
Ha
HP
high Sp
Sa
log1()(Na«)
iog10(yi)
Ha
HP
Fidalgo Head "f mid S
Sa
log10(Na+l)
iogw(yi)
Hfl
H'
P
high Sp
Sa
log10(Na+l)
log1Q(W +1)
Ha
H'
P
ESTIMATES
RESIDUAL o
14.2
9.57
0.058
0.369
0.137
0.170
1.61
9.36
0.149
0.271
0.111
0.075
10.0
71.0
0.179
0.630
0.191
0.206
1.83
10.5
0.314
0.610
0.276
0.111
OF VARIANCE COMPONENTS*
2 DATE WITHIN SEASON of.
3.55
16.1
0.019
0.026
0.044
0.005
0.00
0.00
0.00
0.00
0.012
0.018
3.93
6.75
0.00
0.363
0.055
0.061
0.320
0.00
0.00
0.00
0.00
0.094
DATE
ns
0.01
ns
•ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
0.05
LEVELS OF
SUMMER VS WINTER
ns
ns
ns
0.05
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
0.05
0.05
0.001
0.05
ns
SIGNIFICANCE"
1975 VS 1976
SUMMER WINTER
ns
ns
0.05
ns
ns
ns
ns
0.05
0.05
ns
ns
ns
ns
ns
0.01
ns
ns
ns
ns
0.05
0.01
0.01
ns
ns
--
—
—
--
—
--
—
--
—
—
—
—
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
MAX F-RATIO**
ns
0.05
ns
0.05
ns
0.01
ns
ns
ns
0.01
ns
—
ns
0.05
ns
ns
ns
ns
ns
ns
0.05
ns
ns
ns
*Differences not significant at the 0.05 level are denoted by ns. Omitted entries, denoted by --, correspond to cases where data from which
the statistics could be computed were not available.
tFour replicates at each of two sampling dates a month or two apart were available for winter 1974-75, summer 1975, and summer 1976 at each
elevation at Cantilever Pier. Hence n=4, t=2, and s=3 in Table A-3 of Appendix A for the Cantilever Pier analyses at each elevation.
JFidalgo Head samples from the same seasons as at Cantilever Pier and, in addition, winter 1976 were used, giving t=2 and s=4 in Table A-3.
Most were gradient samples, but at least three were available on each date in each elevation stratum. The first three were selected when
more than three were available to obtain n=3 in Table A-3 for the Fidalgo Head analyses.
§The numerical assemblage parameters Sp, Sa, etc. are defined in Section 5.2.1.
#The residual and date within season variance components are defined as in Table A-3.
**The maximum F-ratio test for variance heterogeneity is defined by (A.3.10).
-------
(excluding those involving bad data) exhibit less temporal variability
relative to their sampling variability than animal parameters. No
significant summer-versus-winter or year-to-year differences were detected in
SP or V
There is evidence of variance heterogeneity in log (N -t-1),
log (W +1), and H* as well as S . Hence, nonparametric tests such as the
Hann-Whltney may bi preferable to k-tests and analysis of variance for
accurately assessing change.
Finally, we note that replicate variability is larger at Fidalgo Head
than at Cantilever Pier for all the parameters except mid elevation S . This
may be due to data errors, to the fact that most of the Fidalgo Head samples
were gradient rather than stratified samples, or to site characteristics.
Relative importance of site and season, North Puget Sound:
To assess the relative importance of site and time differences at
Fidalgo Head and Cantilever Pier, the two-way analysis of variance model
(A.3.12) was used on mid and high elevation data from three seasons at the
two sites. The results are summarized in Table 12.
Residual sampling variability dominates site and season effects and
interactions for the most part. However, site differences were indicated at
the high elevation for S , H', and especially log (N +1). Numbers of taxa
and diversity were higher at Fidalgo Head while log (N +1) was higher at
Cantilever Pier. The latter difference translates into counts of 1,066 per
0.25 m at Cantilever Pier versus 122 per 0.25 m at Fidalgo Head. The
estimated variance component due to site for log (N +1) at the high
elevation is 0.41, larger than the estimated replicate variance of 0.23.
Between-site variability, all rocky intertidal sites:
Site differences between North Puget Sound and Strait sites are more
significant than those within either of these areas. These differences are
quantified in Table 13, which summarizes analyses of summer 1976 data from
all rocky intertidal sites. The between-site variance component contributes
much more significantly to variability in the data when Strait and northern
Sound sites are considered together as in Table 13 than when the latter are
considered alone as in Table 12.
Site means from the analyses of Table 13 at each elevation, plotted in
Figure 11, illustrate the fact that the large between-area differences in
numbers of taxa are due to much greater species richness in the Strait than
in the northern Sound. Between-area differences in animal counts and
diversities are less clear. Fidalgo Head appears to have larger numbers of
animals at the low elevation and smaller numbers at the high than the other
three sites while at the mid elevation Pillar Point differs most in terms of
animal numbers. Elevation effects, for example the decrease in species
richness at the high elevation, are also evident from Figure 11.
67
-------
TABLE 12. SITE x SEASON ANALYSIS OF VARIANCE, CANTILEVER PIER AND FIDALGO HEAD.
00
Site x Season
_ Interaction
Assemblage8 F=MS(«p)/MSE
Elevation Parameter (Numerator DF=2)
mid S 5.28*
Sa <1
log10(Na+l) <1
Ha 1>61
high S 3.85
Sa <1
log1Q(Na+l) 1.48
H^ 1.03
Sitet 2
F=MS(a)/MSE a =MSE
(Numerator DF=1 ) (DF=12)
<1 21.8
<1 58.6
<1 0.31
<1 0.31
5.66* 4.33
<1 5.67
17.29** 0.23
7.41* 0.19
Site Variance
Component
^2
0.00
0.00
0.00
0.00
2.24
0.00
0.41
0.13
Season?
F
(Numerator DF=2)
<1
2.67
2.48
1.40
1.13
2.03
1.17
3.56
*Significant at
-------
TABLE 13. ONE-WAY ANALYSIS OF VARIANCE OF SUMMER 1976 ROCKY INTERTIDAL ASSEMBLAGE PARAMETERS, ALL SITES.
Elevation Parameter1"
low Sn
p
S
a
log10(Na+l)
H'
a
mid S_
p
S
a
log10(Na+l)
H'
a
high Sp
s
a
Ha
Strait
Sound
F
22.6
34.6
5.04
19.5
69.0
29.7
1.12
3.32
11.3
7.02
35.0
0.14
vs. Northern
contrast
significance*
0.001
0.001
0.05
0.001
0.001
0.001
ns
ns
0.01
0.05
0.001
ns
Remainder of site
differences (within area)
F significance
0.06
4.95
3.59
2.95
5.89
0.03
4.09
3.75
0.16
0.10
11.3
1.53
ns
0.05
0.05
ns
0.01
ns
0.05
0.05
ns
ns
0.001
ns
tstlmates of
Variance Components
Between-Site Wi thin-Site
14.0
177.1
0.102
0.336
68.3
195.0
0.059
0.141
32.7
32.0
0.441
0.004
12.1
72.7
0.189
0.256
17.7
147.0
0.190
0.364
73.8
147.0
0.157
0.395
*Factors not significant at the 0.05 level are indicated by ns. Significance levels for Sp at the high elevation, Sa at the mid and high,
and log,0(N +1) at the mid elevation should be interpreted with some caution since the maximum F-statistic (A.3.10) indicated variance
heterogeneity in these parameters.
fThe assemblage parameters S , S , etc. are defined in Section 5.2.1. The analyses summarized in Table 13 are discussed in Section A.3 of
Appendix A. p
-------
Low Elevation (-0.3m to 0.3m)
Number of Plant Taxa Sp
Site, Region # of POOLED ST. I'EU. = 3.48
Samples + + + H + + +
Tongue Point, Strait 6 I*********I*********I
Pillar Point, Strait 4 I****•*:*•**:***X***********x
Cantilever Pier, SJI 6 I ********* I *********!
Fidalgo Head, NPS 7 i*******»!********i
+ + + f. + + +
9.0 1£.0 15.0 18.0 £1.8 £4.0 £7.0
Number of Animal Taxa 83
Site, Region # of POOLED ST. DE'J. = 8.53
Samples + + t- 1 -i 1 +
Tongue Point, Strait 6 I ****** I ******* I
Pillar Point, Strait 4 X********x*******x
Cantilever Pier, SJI 6 I*******!******!
Fidalgo Head, NPS 7 I ****** I *****'!
10. £0. ~&. 40. 50. 60. 70.
Total Animal Count
Site, Region # of POOLED ST. DEU. = 0.435
Samples + 1- 1- 1 H -\ 1-
Tongue Point, Strait 6 I************x***********x
Pillar Point, Strait 4 I **************ii **'«*******'*'***'* x
Cantilever Pier, SJI 6 I *********** I *********** I
Fidalgo Head, NPS 7 I *********** I ********** I
+ + + H + + +
log,Q(N +1) £.40 £.70 3.00 3.30 3.60 3.90 4.£@
Nfl 250 500 999 1994 3980 7942 15848
Animal Diversity H'a
Site, Region # of POOLED ST. DEU. = 0.506
Samples + + + + + + +
Tongue Point, Strait 6 I ******* I ******** I
Pillar Point, Strait 4 I*********I**********I
Cantilever Pier, SJI 6 I********!*******!
Fidalgo Head, NPS 7 I*******I*******I
-i 1 1 1 ^ 1 +
0.50 1.00 1.50 £.00 £.50 3.00 3.50
Figure 11. Means of rocky intertidal assemblage parameters (defined in
Section 5.2.1) at each site and elevation sampled, summer 1976,
with individual 95 percent confidence intervals (A.1.7) based on
pooled standard deviations from analysis of variance. The one-way
analysis of variance model (A.3.1) of Appendix A was used, with
separate analyses for each assemblage parameter in each elevation
stratum. All available samples were used, resulting in varying
group sizes and confidence interval lengths. Axis labels for
total animal counts are shown in untransformed as well as log
transformed units.
7O
-------
Mid Elevation (0.6m to 0.9m)
Number of Plant Taxa Sp
Site, Region # of
Samples
Tongue Point, Strait 6
Pillar Point, Strait 6
Cantilever Pier, SJI 8
Fidalgo Head, NPS 7
POOLED ST. PEU. =
-i 1
4.£0
Site, Region # of
Samples
Tongue Point, Strait 6
Pillar Point, Strait 6
Cantilever Pier, SJI
Fidalgo Head, NPS
I *#*'*». I ft*** I
+ (. + +
6.0 1£.0 18.0 £4.0
Number of Animal Taxa Sa
POOLED ST. DEU. = 1£. 1
+—
0.0
Site, Region # of
Samples
Tongue Point, Strait 6
Pillar Point, Strait 6
Cantilever Pier, SJI 8
Fidalgo Head, NPS 7
Total Animal Count
POOLED ST. DEU. = 0.436
1 --m-lr-..^, i —__L. - - ' -.. I . _._!_. _IJ. --.- +-.-I
T" —-1.-J —-y—__L _« _—. •T-___ —__- — ... .j.w
log
10l
Site, Region
# of
Samples
Tongue Point, Strait 6
Pillar Point, Strait 6
Cantilever Pier, SJI 8
Fidalgo Head, NPS 7
0.80
l.£0
1.60
£.00
j
£.40
38.0
—+
36.0
8
7 j.*;
18.
I w. ****.**« I *.ft****i«»: i
«:«%.««»:%« X ******** X
£0. 36. 40.
50.
60.
70.
+ "' ••—•—————"--—+— — —"• rrr +•"•"~ ——— - L r— — — -— -|-—.— ^,,__ -__—«—I—,--j-
.70 3.00 3.30 3.60 3.90 4.£0 4.50
500 999 1994 3980 7942 15848 31622
Animal Diversity H'a
POOLED ST. PEU. = 0.663
£.80
Figure 11 (continued)
71
-------
Site, Region
Tongue Point, Strait 6
Pillar Point, Strait 6
Cantilever Pier, SJI 8
Fidalgo Head, NPS 6
High Elevation (1.5m to 1.8m)
Number of Plant Taxa Sp
# Of POOLED ST. DEU. = 8.59
Samp! es + + + + +-
J ».#.#.#.».&#•:#'#•*:•& I I********.***'* J
I *********** J #*•»••**•*•<*»•$££ I
-6.0
£4.8
Site, Region
J *»!»***:»:*•»•*»• X *********** I
+—•————•— — f. _--•—T — _._,_| _- ,_ ____. , _ i, _.___.,, .___ .„.. f, „
0.0 6.0 t£.0 18.0
Number of Animal Taxa Sa
# of POOLED ST. DEU. = 1£.1
Sampl es + + + 1- + +—
Tongue Point, Strait 6 X***'*'*'***'****I****»*******'I
Pillar Point, Strait 6 i*»»*•***»*»[**j*»***»**»*»#j
Cantilever Pier, SJI 8 I ********** I »**#****** i
-t i 1 1 1 1—
-8.0 0.0 8.0 16.0 £4.0 3£.0
Total Animal Count
Site, Region # of POOLED ST. PEU. = 0.396
Sampl es H + + 4- + +—
Tongue Point, Strait 6 I **«•***: I ****** i
8 j :**.,**# I »•*».*•.» I
H 1 1 1 1 1
1.50 £.00 £.50 3.00 3.50 4.00
N, 31 99 315 999 3161 9999
Pillar Point, Strait
Cantilever Pier, SJI
Fidalgo Head, NPS
log10(Na+l)
30. 0
40. 0
4.50
31622
Animal Diversity H'a
Site, Region # of PuQLEB ST. PEU. = 0.6£9
Sampl es + + + + + +—
Tongue Point, Strait 6 x ************ I************ I
Pillar Point, Strait 6 I************ I #*.****•****** I
Cantilever Pier, SJI 8 I *********** I ***»****•*:.*• I
Fidalgo Head, NPS 6 I ************* I **'***»***'***ij
H ___—.———.-^ ____—^___^.^-—_—_— ^ _______ ~r~~ r '•"- —•————+——
0.00 0.40 0.80 l.£0 1.60 £.00
+
£.40
Figure 11 (continued)
72
-------
6.1.2 Population analyses
Patchiness usually precludes the use of analysis of variance or
regression analysis for population parameters. However, it was hoped that a
few key animals and plants would appear with sufficient regularity in the
rocky intertidal to permit such analyses. We considered animal counts,
available at all four sites, and plant weights, available at all sites except
Fidalgo Head.
A list of taxa to consider was compiled based on frequency of
occurrence in samples and biological importance. The plant taxa selected
were Monostroma. Enteromorpha linza. Ulva. HedophylHVfflp sessile. Alaria.
Fucug r {•}4ga'rtina- Iridaea. Endocladia muricata. Halosaccion gl andlforme. and
Rhodomela ^arly. Animals were Collisella pelta. Collisella digitalis.
Collisella strigatella. Lacuna. Littorina sitkana. Littorina scutulata.
Katharina. Mytilus edulis. CtvthtUnalus dalli. Balanus cariosus. Balanus
glandula. Idotea wosnesenskii. gammarid amphipods, Pagurus hirsutiusculus.
and Pugettia gracilis.
The Strait sites were considered first. Weights of the selected
plants and counts of animals were plotted versus sampling date and
elevation. The plots made it clear that many of these organisms exhibited
clear elevation/site preferences. For example, Littorina acutulata occurred
almost exclusively at the high elevation at Pillar Point. Distributions of
other species (for example, Ulva. Collisella pelta. and Hytilus edulis)
exhibited so much random patchiness in distribution that means of their
counts or weights were generally not significantly different from zero.
The animals and plants which occurred most regularly at each elevation
were used in analyses of variance with groups defined by sampling dates.
Fewer samples were available at the low elevation than at the mid and high,
so we will discuss only the results for the latter two strata. Site, season,
and year-to-year differences were examined using orthogonal contrasts
(Table 14).
Table 14 suggests many of the same conclusions concerning population
parameters as those drawn from analysis of numerical assemblage parameters.
There were more significant differences involving spring samples than any
other season. Winter was the least changeable season. More highly
significant site differences than year-to-year or seasonal differences are
shown, but several of these reflect the spring 1976 barnacle recruitment. In
addition, site differences may be contributing to or masking year and
seasonal differences in some cases since more Tongue Point than Pillar Point
samples are averaged into comparisons involving summer, fall, and winter.
In Figure 12 we compare Strait with North Puget Sound results. Counts
of the barnacles Chthamalus dalli and Balanus glandula were considered.
Limpets and periwinkles were used at the genus level since there were obvious
site differences at the species leveli Collisella strigatella was much more
common at Cantilever Pier than Fidalgo Head, Littorina scutulata numerous at
both these sites but nearly absent at Tongue Point. Errors in plant weight
data precluded consideration of any plants.
73
-------
TABLE 14.
CONTRIBUTIONS OF SITE, YEAR, AND SEASON DIFFERENCES TO VARIABILITY
IN STRAIT ROCKY INTERTIDAL POPULATION PARAMETERS
MID ELEVATION
(0.9 METERS)
Site (Tongue vs. Pillar Point):
Spring 1976
Summer 1976
Fall 1976
Winter 1977
Spring 1977
Year Differences:
Spring 1975 vs. 1977
Summer 1976 vs. 1977
Fall 1976 vs. 1977
Winter 1977 vs. 1978
Season Differences:
Spring vs. Summer
Fall vs. Winter
Spring/Summer vs. Fal-1/Winter
HIGH ELEVATION
(1.8 METERS)
Site (Tongue vs. Pillar Point):
Spring 1976
Summer 1976
Fall 1976
Winter 1977
Spring 1977
Year Differences:
Spring 1976 vs. 1977
Summer 1976 vs. 1977
Fall 1976 vs. 1977
Winter 1977 vs. 1978
Season Differences:
HALOSACCION
ALARIA GLANDI FORME LACUNA
% OF FACTOR SS t
KATHA-
RINA
4%
0
8
2
5
4
9
16
6
1%
3
2
12
4
0
0
40*
17
11
5
3
2
1 8 54*
6 1 5
39 12 2
100% T00% T00%
1
1
11
2
12
12
29
0
30
0
0
100%
BALANUS
CARIOSUS
6%
0
38
5
1
5
18
1
0
IDOTEA
29
3
1
1
GIGAR- ENDOCLADIA COLLISELLA COLLISELLA
TINA MURICATA DIGITALIS STRIGATELLA
5 20
13 3
8 0
T05% 100%
LITTORINA CHTHAMALUS
SITKANA DALLI
0
5
14
28
20
9
3
3
6
7
9
30
7
15
0
0
2
41
7
7
3%
0
5
0
16
0
49*
0
8
13%
19
0
1
3
5
13
6
0
51%*
21*
0
3
8
GAMMARID
AMPHIPODS
7
22
0
27*
7
12
12
1
1
2
3
100%
BALANUS
GLANDULA
28%*
23*
1
3
17*
Spring vs. Summer
Fall vs. Winter
Spring/Summer vs. Fall/Winter
0
0
_a
100%
1
1
_6_
100%
12
6
_z
100%
0
18
_1
100%
5
1
-33.
100%
0
0
_W
100%
0
0
_L&*
100%
The Factor SS represents the fraction of the total variability in an assemblage parameter explained by the one-way
analysis of variance model. It is defined in Table A-2 of Appendix A, and its partitioning by means of contrasts
is explained in the discussion following that table.
*The population parameters considered in this analysis are Iogj0(weight + 1) for the plants (Alaria, Gigartina,
Halosaccion glandiforme, and Endocladia muricata) and log...(count + -1) for the animals.
*Significant at the 0.001 level. Our choice of this level for testing is discussed in Section A.4 of Appendix A.
Note that the same % of Factor SS may be significant for a parameter at one elevation but not another because
the overall significance of the Factor SS is higher in the first case than in the second.
74
-------
Site, Region
Tongue Point, Strait
Cantilever Pier, SJI
Fidalgo Head, NPS
log,Q(count+l)
count
Site, Region
Tongue Point, Strait
Cantilever Pier, SJI
Fidalgo Head, NPS
log,0(count+l)
count
# of
Samples
6
4
4
0.
# of
Samples
6
4
4
0.
Collisella
POOLED ST. DEU. = 0.812
INDIUIDUfiL ^5 PERCENT C. I. FOP LEUEL
(BfiSED ON POOLED STRNDRPD DEUIflTION.i
HERNS
X ft****-*-*-***;*: X *».«******** X
X A*)***!*******:** I ft************* I
OO C-.60 l.£o 1.80
0 3 15 62
Littorina
POOLED ST. DEU. = 0.841
INDIUIDUfiL 95 PERCENT C. I. FOP LEUEL
(ERSED ON POOLED STRNDRRD DEUIRTION)
£.40 3.00
290 9>99
MERNS
I W****.*******! ft)********.*.:* I
X S-***:**::*1**:!***!*** X *»»•*********** X
60 l.£0 1.80 £.40
3 15 62 250
3.00 3.60
999 3980
3.60
3980
4.20
15848
Chthamalus dalli
POOLED ST. DEU. = 0.826
Site, Region
Tongue Point, Strait
Cantilever Pier, SJI
Fidalgo Head, NPS
log,Q(count+l)
count
# of
Samples
6
4
4
0.
INDIUIDUHL 95 PERCENT C.
(BfiSED ON POOLED STfiNDfiPD
00 0.80 1.60
0 5 39
I. FOP LEUEL
DEU I RT I ONi
MEfiNS
X Si*-**:*:***** A X I*-******** i* X
ft****;* I
£.40
250
3.£0
1584
4.00
9999
4.80
63095
Balanus glandula
POOLED ST. DEU. = 0.959
Site, Region
Tongue Point, Strait
Cantilever Pier, SJI
Fidalgo Head, NPS
login(count+l)
count
# of
Samples
6
4
4
-0.
INDIUIDUfiL 95 PERCENT C. I. FOR LEUEL MEfiNS
IBRSED ON POOLED STflNDRPD DEUIfiTIQN)
X *»*»*»#**$ x #.#.#». ***»:* x
X A**:.********* X **••*
X •***•.#••#.#*>.».•#•#.».#. x * ******!«)*«*** x
80 0.00 0.80 1.60 £.40
0 5 39 250
3.28
1584
I
4.00
9999
Figure 12. July 1976 means of log transformed counts for selected rocky
intertidal animals with individual 95 percent confidence intervals
(A. 1.7) based on pooled standard deviations from analysis of
variance. Axis labels are in log units with corresponding counts
given below. All available data from high elevations (1.5 m to
1.9 m) were used in the analysis.
75
-------
Site differences were significant at the 0.05 level for Chthamalus
dalli but not for the other three taxa. Thus it appears that certain key
taxonomic groups are found in predictable large numbers at all rocky sites.
Mean values of log counts from the summer 1977 and summer 1978 Cantilever
Pier data given by Nyblade (1979b) for these animals provide further
confirmation; all lie within the summer 1976 Cantilever Pier confidence
intervals except for Chthamalus dalli in 1977.
6.1.3 Pred ic t ive
We saw in Table 10 that Tongue Point means at a given elevation and
season were generally good predictors of numerical assemblage parameters at
that site, elevation, and season in the following year. S and H_ appeared
to be particularly stable. Predicting Tongue Point means from Pillar Point
data was less successful, and (Table 13 and Figure 11) Strait data on rocky
intertidal assemblages were of little use for predicting assemblage parameter
values in North Puget Sound. However, the analyses summarized in Table 14
and Figure 12 suggested that parameters of a few key populations might be
predictable .
To test site-specific and cross-site prediction of assemblage
parameter values within the northern Sound, we compared the 1976 high
intertidal Cantilever Pier and Fidalgo Head estimates of Figure 11 with
summer 1977 and 1978 Cantilever Pier values computed from Nyblade (1979b)
data. The results are summarized in Table 15.
TABLE 15. PREDICTABILITY OF ASSEMBLAGE PARAMETERS FOR HIGH ELEVATIONS, NORTH PUGET SOUND ROCKY INTERTIDAL SITES
Parameter
sn
P
sa
log10(Na+l)
H'
1976
Site
Cantilever Pier
Fidalgo Head
Cantilever Pier
Fidalgo Head
Cantilever Pier
Fidalgo Head
Cantilever Pier
Fidalgo Head
Summer
1976 Mean
2.38
1.00
9.50
6.83
3.35
2.22
1.19
1.23
Summer 1977
Mean
t-test
0.75 ns
ns
5.00 ns
ns
2.93 Q.0258
0.0226
0.70 ns
ns
Cantilever
*
Significance
Mann-Whitney
ns
ns
ns
ns
0.0508
0.0190
ns
ns
Pier
Summer 1978
Mean
t.-test
2.75 ns
ns
6.00 ns
ns
3.07 ns
Significance
Mann-Whitney
ns
ns
ns
ns
ns
0.0193 ns
1.06 ns
ns
ns
ns
Results not significant at the 0.05 level are denoted by ns. The t- and Mann-Whitney tests are described in Appendix A
Tests are based on eight samples from Cantilever Pier in summer 1976 (July and early September), four in August 1977, and
four in August 1978, and six Fidalgo Head samples from July and August 1976
76
-------
Table 15 indicates no significant changes in species richness or
diversity. However, both Cantilever Pier and Fidalgo Head means of
log (N 4-1) in 1976 were significantly different from the 1977 Cantilever
Pier vaiue. In terms of counts, the indicated difference at Cantilever Pier
translates into a decrease from 2,238 animals per 0.25 m quadrat to 850
animals per quadrat. The 1976 Fidalgo Head mean represents 165 animals per
quadrat. This Fidalgo Head value also differs from the 1978 Cantilever Pier
value of 1,174 animals per 0.25 m . As in the Strait, animal numbers appear
to be less predictable than species richness or diversity, and cross-site
prediction is less successful than site-specific prediction.
Apparent predictability of either assemblage or population parameters
can be evaluated more fully by considering the power (probability of
detecting a specified difference) of the statistical tests being used.
Powers of the two-sample £- and Mann-Whitney tests are relatively comparable,
and that of the ±-test is easily obtained as discussed in Appendix A.
In Table 16 we tabulate detectable percent changes in assemblage
parameter means as a function of numbers of replicates. We present changes
which we would have a 50 percent or 90 percent chance of detecting given that
we require the probability of incorrectly stating a change has occurred to be
5 percent or less.
Transformed animal counts and, at the lower elevations, plant weights
have the smallest percent changes with a high probability of detection.
Hence it is not surprising that many of the significant differences found in
our analyses were in these parameters. At the high elevation large replicate
variability precludes reliable detection of change in any of the parameters
except log (N +1). Changes in plant diversity H1 cannot be dependably
detected at any elevation.
A similar tabulation of detectable percent changes in population
parameters (log transformed animal counts and plant weights) is presented in
Table 17. This table indicates that patchiness of almost all plant and
animal species makes it virtually impossible to reliably detect population
changes even with considerably higher levels of replication than those used
in the WDOE and MESA studies.
Plant weights are particularly unpredictable. Even using a one-sided
test with n = n =25 the smallest change detectable with 9O percent
probability is a 60 percent change in log (weight +1) for Alaria at the low
elevation. Translated from log weight into grams, this implies a decrease to
4 g or an increase to 878 g from a value of 68 g per 0.25 m quadrat.
We fare better with animals, particularly in the relatively simple
high intertidal community. The barnacles Chthamalus dalli and Balanus
glandula are good species in terms of change detection. Limpets occur with
greatest regularity. We see that lumping to genus level increases the mean
value and decreases the variance, with the genus Collisella being the most
predictable animal taxon. Similarly among the periwinkles, smaller changes
77
-------
TABLE 16. DETECTABLE PERCENT CHANGES IN ROCKY INTERTIDAL ASSEMBLAGE PARAMETERS
Site and Elevation
Tongue Point 6.0 m
Tongue Point 0.9 m
Tongue Point 1.8 m
Parameter*
SP
'a
log10(Na+l)
iog10(VD
Ha
HP
SP
'a
log10(Na+l)
iog10(wp+D
H
«
Probability of
77%(65%)
62 (53)
39 (34)
36 (30)
36 (31)
244(207)
94 (80)
96 (82)
35 (30)
63 (54)
118(100)
H^, |l66(142)
S J168(143)
" i
S, 130(111)
d
jlog10(Na+l)
26 (22)
tlog10(Wp+l) 140(119)
Ha 143(123)
KP
Cantilever Pier* high, S
sa
log10(Na+l)
Ha
132(113)
224(190)
127(109)
21 (18)
76 (65)
61%(532)
50 (44)
31 (27)
29 (25)
29 (25)
194(170)
75 (66)
77 (67)
28 (24)
50 (44)
94 (82)
133(116)
134(117)
104 (91)
21 (18)
112 (98)
115(100)
106 (93)
178(156)
102 (89)
17 (15)
61 (53)
Detection*
ni=n2=15
35% (30%)
28 (25)
18 (16)
16 (14)
17 (15)
110 (97)
43 (38)
44 (38)
16 (14)
29 (25)
53 (47)
75 (66)
76 (67)
59 (52)
12 (10)
63 (56)
65 (57)
60 (53)
101 (89)
58 (51)
10 (8)
34 (30)
0.9
ni=n2=25
26%(24%)
21 (19)
14
12
13
84
32
33
12
22
40
57
58
45
9
48
49
46
77
44
7
26
(12)
(11)
(11)
(75)
(29)
(30)
(11)
(19)
(36)
Probability of
46% (36%)
37 (30)
24 (19)
21 (17)
22 (17)
146(116)
56 (45)
58 (46)
21 (17)
38 (30)
70 (56)
(51) J100 (79)
(52)
(40)
(8)
(43)
(44)
(41)
(69)
(39)
(7)
(23)
101 (80)
78 (62)
16 (12)
84 (67)
86 (68)
80 (63)
134(106)
76 (61)
13 (10)
46 (36)
37%(30%)
30 (24)
19 (15)
17 (14)
18 (14)
117 (95)
45 (37)
46 (38)
17 (14)
30 (25)
57 (46)
80 (65)
81 (66)
63 (51)
13 (10)
67 (55)
69 (56)
64 (52)
107 (88)
61 (50)
10 (8)
37 (30)
Detection 0
ni=n2=15
21%
17
11
10
10
66
26
26
9
17
32
45
46
35
7
38
39
36
61
35
6
HI
(17%)
(14)
(9)
(8)
(8)
(55)
(21)
(22)
(8)
(14)
(27)
(38)
(38)
(29)
(6)
(32)
(33)
(30)
(51)
(29)
(5)
(17)
5
16%(13%)
13 (11)
8 (7)
8 (6)
8 (6)
51 (42)
20 (16)
20 (17)
7 (6)
13 (11)
25 (20)
35 (29)
35 (29)
27 (23)
5 (5)
29 (24)
30 (25)
28 (23)
47 (39)
27 (22)
4 (4)
16 (13)
i The numerical assemblage parameters included in this table are defined in Section 5.2.1.
* Probabilities of detection (0.9 in the left half of the table, 0.5 in the right half) are based on the assumption that
means of the indicated numerical assemblage parameters are being compared using the two-sample t-test of (A.4.1) of
Appendix A. The level of the test is assumed to be a = 0.05. There are assumed to be ni replicates in one sample and
n2 in the other. Detectable percent changes for a two-sided test are tabulated, with values for a one-sided test in
parentheses. A parameter with a small detectable percent change is usable for estimating community changes while one
for which only large changes are detectable is less useful.
t Values of v\ in (A.4.5 ) are summer 1976 means at Tongue Point, shown in Table 10. Values of a are pooled standard
deviations from the analysis of variance of Figure 10.
f Values of yi and o for Cantilever Pier were obtained from the eight high intertidal samples collected there in summer
1976 and used in the analysis of Table 15.
78
-------
can be detected at the genus level than in particular species such as
lAttorlna ai-hkana.
It is probable that other ways of lumping species, for example into
trophic groups, would also lead to more predictable counts than those of the
individual species. However, gross differences in productivity and available
food are involved in comparing sites in different geographic areas with
widely differing amounts of exposure. The larger such physical site
differences, the less likely we are to find comparable counts and weights of
groups of organisms.
As with plant weights, the detectable percent changes in animal
populations given in Table 17 are in log units and the limits of detection
must be transformed back if we want them in counts. For Colligella, for
example, with eight replicates in both old and new samples^here is a
90 percent chance of detecting a change from 48 per 0.25 m if the new value
lies outside the interval 11 to 207 and we use a two-sided test.
TABLE 17. DETECTABLE PERCENT CHANGES IN ROCKY INTERTIDAL POPULATION PARAMETERS
Elevation and Taxon
0.0 meters
Alaria
Iridaea
Gamma rid amphipod
Pugettia gracilis
0.9 meters
Alaria
Halosaccion glandiforme
Lacuna
Katharina
Balanus cariosus
Idotea
Ganmarid amphipod
1.8 meters
Fucus
Gigartina
Endocladia muricata
Collisella
Collisella digitalis
Collisella strigatella
Li ttorina
Littorina sitkana
Chthamalus dalli
Balanus glandula
Mean
PI
1.840
0.794
2.097
0.282
1.100
0.757
1.182
0.575
1.931
1.179
2.368
0.570
0.631
0.296
1.692
1.581
0.381
2.359
2.283
2.861
2.724
S.D.
o
1.290
0.882
0.506
0.626
1.050
0.620
0.605
0.434
0.892
0.758
0.780
0.507
0.513
0.470
0.353
0.363
0.590
Q.643
0.692
0.723
0.630
Probability of
194%(165%) 123%(108%
307 (261) 194 (171)
67 (57) 42 (37)
613 (522) 388 (342)
263 (224) 167 (147)
226 (192) 143 (126)
141 (120
208 (177
127 (109
90 (79)
132 (116)
81 (71)
177 (151) 113 (99)
91 (77) 58 (51)
245 (209) 156 (137)
224 (191) 142 (125)
438 (373) 278 (245)
58 (49) 37 (32)
63 (54) 40 (35)
427 (364) 271 (238]
75 (64) 48 (42]
84 (71) 53 (47;
70 (59) 44 (39)
64 (54) 40 (36)
Detection* 0.9
) 88%
139
30
277
119
102
64
94
58
80
41
111
102
198
26
29
194
34
38
32
29
(77%)
(122)
(27)
(244)
(105)
(90)
(56)
(83)
(51)
(71)
(36)
(98)
(89)
(175)
(23)
(25)
(170)
(30)
(33)
(28)
(25)
67%(60%)
106 (94)
23 (21)
211 (189)
91 (81)
78 (70)
49 (44)
72 (64)
44 (39)
61 (55)
31 (28)
85 (76)
77 (69)
151 (135)
20 (18)
22 (20)
147 (132)
26 (23)
29 (26)
24 (21)
22 (20)
Probability of
ni=n2=4 ni=n
116% (80%) 74%
183 (127) 118
40 (28) 26
366 (253) 235
158 (109) 101
135 (93) 87
84 (58
125 (86
76 (53
54
80
49
106 (73) 68
54 (38) 35
147 (101) 94
134 (93) 86
262 (181) 168
34 (24) 22
38 (26) 24
256 (177) 164
45 (31) 29
50 (35) 32
42 (29) 27
38 (26) 25
Detection 0.5
2=8 ni=n2=15
(61%)
(97)
(21)
(193)
(83)
(71)
(45)
(66)
(40)
(56)
(29)
(77)
(71)
(138)
(18)
(20)
(135)
(24)
(26)
(22)
(20)
53% (44%)
83 (69)
18 (15)
166 (139)
72 (60)
61 (51)
38 (32)
57 (47)
35 (29)
48 (40)
25 (21)
67 (56)
61 (51)
119 (99)
16 (13)
17 (14)
116 (97)
20 (17)
23 (19)
19 (16)
17 (14)
* Probabilities of detection are based on the assumption that means of logio(weight+l) for plants and Iog18(count+l) for
animals are being compared as in Table 16. Means uj in (A.4.5 ) are from winter 1977 Tongue and Pillar Point samples.
Pooled standard deviations from analysis of variance are used for a. Detectable percent changes for a two-sided test are
tabulated, with values for a one-sided test 1n parentheses. Plants and animals with small detectable percent changes are
useful for estimating community change while those in whose populations only large changes are detectable are less useful.
79
-------
6.1.4 Summary of the prognosis for Assessing changes in
community structure at rocky inter-tidal sites
Similarity among rocky intertidal stations in terms of abundance of SO
major plants and animals was shown by cluster analysis to be 25 percent or
more in all cases. However, levels of similarity exceeding 75 percent were
almost never found between different sites or elevation strata. Taken
together with the population analyses of Section 6.1.2, these results imply
that the prognosis 'for estimating abundance of a particular species at one
site from the abundance at another is poor, even for sites as close as Tongue
Point and Pillar Point and species as common as chthamalus dalli. Cross-site
prediction at the genus level (limpets, periwinkles) appears more promising.
Analysis of numerical assemblage parameters as well as cluster
analysis pointed to elevation as the dominant factor in variability in the
rocky intertidal habitat. Elevation effects vary among the sites, probably
as a function of exposure. Within an elevation stratum, assemblage parameter
values are similar at nearby sites, particularly if sampling is done in
summer or winter rather than in the more volatile spring and fall transition
seasons. However, Strait communities are significantly different from
northern Sound communities in the same stratum of elevation, probably as a
result of exposure differences.
Analysis of variance pinpointed some seasonal and year-to-year
differences, especially in spring and summer data, but for the most part they
were less significant than site differences. Shorter term (within season)
temporal variability was generally insignificant.
The power calculations of Section 6.1.3 indicate that with the level
of replication used in the Baseline Studies Program, the probability of
detecting changes of 100 percent or more in log transformed weights of
individual plant species is less than a half. Changes in log transformed
counts of animal species must generally be 50 percent or more if they are to
be reliably detected. The situation is almost as bad for most of the
assemblage parameters. More replicates per site/season/elevation are needed
to assess which population and assemblage parameters exhibit true and which
only apparent year-to-year and/or site-to-site stability in the rocky
intertidal habitat.
In spite of the rather low probability of detecting small changes
provided by the level of replication used in the baseline program,
significant year-to-year as well as site-to-site differences were detected in
some rocky intertidal analyses (Tables 10, 11, 14, and 15) under baseline
(unperturbed) conditions. Hence even when community changes are detected at
historically sampled locations, the changes cannot be automatically
attributed to known perturbations such as oil spills. Physical, chemical,
and biological as well as statistical analyses are needed to determine causes
of observed changes.
80
-------
6.2 INTERTIDAL SOFT SUBSTRATES
A large number of diverse habitats fall into the general category of
intertidal soft substrates. All samples available on File 100 tapes from 15
sites were included in our analyses; 7O5 different plant and animal taxa were
identified in these samples. The sites are listed in Table 5 with their
stratified sampling elevations. Starred sites in this table were omitted
from our analyses since no 1-mm fraction data were available from them.
Locations of all sites are shown in Figure 1. Sampling dates and type of
sampling (gradient or stratified) are presented in Table 1.
Sites in Table 1 are arranged according to the habitats they were
chosen to represent (gravel, sand, mud). The "gravel" category includes
sites that were classified as "mixed" or "mixed fine" in some reports. Smith
and Webber (1978) classify the Guemes Island site as "pebble-gravel" while
Gardner (1978) calls it "mixed fine," for example. Gardner also applies this
label to Deadman Bay and Webb Camp, while Nyblade (1977) calls these sites
"exposed gravel" and "protected gravel," respectively.
The difficulty of appropriately categorizing some sites according to
habitat is increased by dramatic changes in substrate character with
elevation. For example, Jamestown in reality consists of a high intertidal
region of sandy gravel, a mid region of fine sand (mud), and a region of
medium sand at MLLW.
As noted in Section 4, the data base contains little usable
information on exposure. Therefore we have not attempted to tabulate
detailed exposure ratings for the sites, but it should be noted that our
analyses indicate that exposure may well be more crucial than sediment size
in defining habitats. In the following discussions of analysis results, we
attempt to fill some gaps and resolve discrepancies in habitat
characterizations of the soft-bottom intertidal sites. Our general approach
to the analysis of soft substrate intertidal habitats is the same as for
rock.
6.2.1 Community analyses
Comparison of all soft substrate sites and elevations:
To obtain an overall concept of the relationships among sites and
elevations, cluster analysis was applied to two major subsets of the data for
soft substrates, the first from the summers of 1976 through 1978 (Figure 13)
and the second winter data from 1975 through 1978 (Figure 14).
We have labelled the major groups I, II, III, IV, and V in the
figures. Relationships among these groups are weak. Separation among them
appears to be related more to degree of exposure than to geographic position,
elevation, or substrate type. Group I, the largest group in both seasons,
includes primarily protected or only moderately exposed sites. Almost all of
the group II and III sites are exposed. The substantial differences between
groups II and III probably relate to degree of exposure.
81
-------
Site, Region Date Elev
m
FIDALGO BAY, NPS 760889 1.2
VE5TCDTT BAY, SJI 760806 1.7
FIBALGO BAY, NPS 760863 0.3
JAMESTOWN, STRAIT 760769 0.6
VESTCOTT BAY, SJI 760S66 8.6
VEBB CAMP, SJI 768807 0.6
VESTCOTT BAY, SJI 760806 -0.3
VEBB CAMP, SJI 760867 -0. 3
JAMESTOWN, STRAIT 768769 8.3
BECKETT POINT, STRAI 768712 8.0
GUEMES SOUTH SHORE, 768723 1.7
GUEME5 SOUTH SHORE, 760723 0.3
GUEMES SOUTH SHORE, 768723 8.1
EAGLE COVE, SJI 760769 1.5
VEB9 CAMP, SJI 760867 1.8
BECKETT POINT, STRAI 768712 1.8
BECKETT POINT, STRAI 760712 0.3
BIRCH BAY, NFS ' 760712 8.8
BIRCH BAY, NPS 760712 8.4
BIRCH BAY, NPS 768712 -8.2
EAGLE COVE, SJI 768768 0.6
EAGLE COVE, SJI 760708 -0.3
NORTH BEACH SANB, ST 770723 8.0
NORTH BEACH SAriD, -ST 768726 8.0
NORTH BEACH SAND, ST 770729 8.6
NORTH BEACH SAND, ST 768726 0.6
VEST BEACH, WHIBBEY 770702 0.0
KYDAKA BEACH, STRflIT 768716 8.3'
NORTH BEACH SAND, ST 770729 1.9
KYDAfcfi BEACH, STRAIT 768716 1.9
KYDAKA BEACH, STRAIT 768710 8.0
"HE ABM AN BAY, SJI 760711 1.3
IEABMAN BAY, SJI 768711 8.6
JBEABMAH BAY, SJI 760711 -0.3
EBEY'S LwNDIHC, WHID 780621 8.6
EBEY'S LANDING, VHID 7S8621 1.8
BJNGEHE55 SPIT, STRA 770727 0.0
E&EY'S LANDING, WHID 780621 0.3
VEST BEACH, WHIDBEY 770762 1.9
VEST BEACH. WHIBBEY 770702 8.9
NORTH BEACH SAND, ST 768726 1.8
TWIN RIVERS, STRAIT 750725 1.8
TWIN RIVERS, STRAIT 760723 8.5
TWIN RIVERS, STRAIT 760729 8.6
IUHCENESS SPIT, STRA 768725 1.8
JAMESTOWN, STRAIT 768708 1.5
INJNGENE5S SPIT, STRA 770727 1.3
EUNGEHE33 SPIT, STRA 778727 0.3
IUNGEHESS SPIT, STRA 768725 8.9
IUNCENESS SPIT, STRA 768725 8.0
a —*•
III
109
80
60
40
20
LEVEL OF SIMILARITY
(quantitative index)
Figure 13. Summer soft substrate intertidal station relationships.
Similarity between stations is defined by (A.5.1) of Appendix A in
terms of relative abundance of the 50 plant and animal species or
groups marked with stars in Table B-2.
82
-------
Site, Region
FIDALGO SAY, NPS
FIPflLGO BAY, NPS
FIDflLGO BftY, HP3
BIRCH BAY, NPS
VESTCOTT BftV, SJI
WEBB CAMP, SJI
VE5TCOTT BftY, 5J!
WESTCOTT BftY, SJI
VEBB CAMP, GJI
JAMESTOWN, STRfilT
JAMESTOWN, STRAIT
BECKETT POINT
BECKETT POIHT
GUEME8 S. SHORE, NPS
BECKETT POINT
BECKETT POINT
BECKETT POINT
BECKETT POINT
BIRCH BAY, NPS
BIRCH BOY, HPS
EAGLE COVE, SJI
NORTH BEftCH SftND
NORTH BEACH SAND
WORTH BEACH SAND
NORTH BEfiCH SAND
JAMESTOVN, STRAIT
JftMESTCWN, STfWIT
WEBB CftMP, SJI
DEADMAN BAY, SJI
DEftDMftH BOY- SJI
KYBAKA BEACH, STRAIT
GUEMES S. SHORE, NPS
GUEMES S. SHORE, HPS
EAGLE COVE, SJI
WEST BEACH, WHIDBEY
KYDAKA BEACH, STRAIT
TWIN RIVERS, STRAIT
EBEVS LANDING
EBEY'S LANDING
EBEY'S LftNDINC
TWIN RIVERS, STRAIT
NORTH BEACH SAND
NORTH BEACH SfiND
KYDAKA BEACH, STRAIT
WEST BEACH, VHIDBEY
VEST BEACH, VHIDBEY
TWIN RIVERS, STRAIT
DUHGEHE33 SPIT
HUHGENESS SPIT
BUHCENESS SPIT
LEVEL OF SIMILARITY
(quantitative index)
Figure 14. winter soft substrate intertidal station relationships.
Similarity between stations is defined by (A.5.1) of Appendix A in
terms of relative abundance of the 50 plant and animal species or
groups marked with stars in Table B-2.
Date Ele\
m
7£Q 110 R <>•
760119 0.6
756119 6.3
7c;iooo a £
7^1 ?fl< —ft "^
770104 8. 8
78BI 11 8.0
776167 6. 6
7GA1 11 1 Q
•770 * cxj i o
78C111 8.3
778167 8. 5
"?£fl1 1? ft 4
7£ft 1 1 C. Ct Q
7S6116 8. Q
-7O£S1fl£" 1 O
760115 1.8
7£ens e. 9
77^100 1 p
7£fl1 1 1 fl 9
77Q190 fl Q
78die? 1.8
76616? '8.9
7Oft 1 ft7 fl fl
776121 0.9
780110 1.8
7S6106 1.8
fooiob y.y
770121 1.8
776163 1.3
776165 6.9
776165 8. 6
10
r
| 1
I—, i ,
.. , r '
.- - , 1 b -. „ ---
| 1
i 1
1 . A — i
1
| 1
a 1
i . .,
r
1 2 -1 i-
b
i
„, _ 1
B | 1
1 ' TU 1
— -...mi... — -l-nl IV
1 _
}
v — 1
1
1 (\ . . .
1 " n 1
•" y 1,.-^,. -,— wr- ,- - - , - ,^-
3 |"^" '^-' "" "^
III
1 1 1 1 1 1 1 1 1 1 1 1 • 1 1 • 1 1 1 1 1 1 1 1 1
0 88 60 48 20 0
83
-------
Generally, levels of similarity among stations within the major groups
are low. However, internal similarity is total (100 percent) among the
group III stations, from Dungeness Spit in both seasons, the upper level at
Jamestown in summer, and West Beach and Twin Rivers in winter. West Beach is
on Whidbey island, and the other three sites are all in the Strait of Juan de
Fuca. No NFS or SJI site is included in group III. The high level of
indicated similarity is an artifact of conventions in data analysis. The
samples from these sites either contained only oligochaetes, nematodes, or
unidentified gammarid amphipods, or contained no animals. The three general
taxa mentioned were excluded by data screening of taxonomic codes from use in
the cluster analyses because they are too unspecific to be discriminative.
However, so that sites would not be lost to the analysis, those at which no
taxa. survived the data screening were assigned an arbitrary artificial
taxonomic code, "none of the included taxa", which was subsequently used in
cluster analysis. Thus, all group III stations had that code in common and
showed 100 percent similarity. This site grouping undoubtedly comprises the
sites with the harshest environment.
Substrate type appears to be the factor second in importance in
determining groupings in the dendrograms. Muddy substrates, for instance,
only occur in subgroup (limb) A-l of group I. Group I also includes many
sand sites. The only gravel sites in group I are those alternatively
categorized as "mixed fine"; i.e., their sediments include sand or mud. In
contrast, various mixtures of gravel predominate at the sites comprising
groups II and III.
In both summer and winter, pairs of stations showing the highest level
of similarity were usually from the same site. In a few cases (North Beach,
summer; Beckett Point, winter) they were a year apart in time, indicating
considerable year-to-year stability in species composition. Site differences
usually dominated elevation differences, with subgroups often including all
elevations at a given site. Finer details of the dendrograms differ between
the two seasons.
In summer (Figure 13), group II includes approximately equal numbers
of Strait, Whidbey, and SJI stations but no NFS stations. Limb II-B includes
only Twin Rivers stations, whereas limb II-A represents five locations from
the Strait, Whidbey Island, and San Juan Island. Within limb II-A, the major
dichotomy segregates sand from gravel sites.
The primary dichotomy in group I in summer divides exposed sand sites
(limb I-B) from more protected sand, mud, and mixed fine sites (limb I-A).
Within limb I-B, Kydaka and West Beach stations are separated from North
Beach and Eagle Cove. Elevations range from -0.3 m to 1.8 m. Within
limb I-A, limb I-A-1 sites comprise the most protected mud and mixed fine
sites. Limb I-A-l-a includes mid to high elevations and limb I-A-l-b low to
mid elevations. Limb I-A-2 includes somewhat less protected sand and sandy
gravel stations; elevation, ranging from -0.2 m to 1.8 m, is not an important
consideration.
84
-------
In the winter analysis (Figure 14) the number of major dichotomies
increased from three to five and there were more individual stations that did
not fall into any of the major groups than were apparent in the summer
analysis. Groups IV and V include sand and gravel sites from all
geographical areas and exposure classifications as well as all elevation
strata. The small number of species which stations forming these groups have
in common are mostly isopods (Gnorimosph^gyQipa r Exosphaeroma) or amphipods
(Eohaua-fcorlug f Paraphoxua >. The increased number of major dichotomies may be
a reflection of a sharpening of differences by the rigors of winter.
However, the probability is just as high that it is an artifact of sampling
variability in response to typically lower abundance and numbers of species
normally encountered in winter surveys.
In winter the major dichotomy in group I separates two high-elevation
Jamestown samples a year apart (limb I-B) from limb I-A samples representing
protected or moderately exposed sites. Limb I-A-1 includes only protected
sites, with limb I-A-l-a representing NPS and limb I-A-l-b SJI and Strait
sites. Limb I-A-2-a includes four stations from Beckett Point in the Strait
and three NPS stations. Low to mid elevation samples from the moderately
exposed SJI and Strait sand sites make up limb I-A-2-b. Group II, smaller in
winter than in summer, has all Ebey's Landing stations on limb II-A and one
station each from North Beach and Twin Rivers on limb II-B.
Comparison of less exposed soft substrate sites at mid elevations;
We next partitioned out elevation and extreme exposure effects to
delineate the effects of site, season, substrate, and moderate differences in
exposure more clearly. We used data from all seasons for the middle level at
the less exposed soft substrate sites to produce the dendrogram of
Figure 15. Group I in this figure is characterized by protected mud, sand,
and mixed fine sites. Group II is characterized by moderately exposed sites
with sand. Group III consists of two anomalous NPS stations.
Within group I, segregation by substrate, site, and region is strong,
especially within limb I-A. For example, SJI sites cluster together, and
Fidalgo Bay stations form subgroup I-A-l-a. The level of similarity within
the subgroups of this limb is high. Within group II, the more exposed sand
sites (North Beach and Eagle Cove) are primarily represented in limb II-B,
whereas more protected mixed sites (Guemes Island and Beckett Point) are in
limb II-A. Segregation of stations at a site on the basis of season is
common in both groups I and II.
The analyses were further refined by partitioning summer from winter
data (Figures 16 and 17). The basic patterns are the same. The major
dichotomies are based on factors related to the degree of wave exposure, and
groups displaying the highest internal similarity comprise stations from the
same location. Two good examples in the summer analysis of Figure 16 are
limb I-A-1 (Fidalgo Bay) and I-A-2-a (Westcott Bay and Webb Camp, in Westcott
Bay). The clearest segregation by site appears in the winter analysis
(Figure 17), probably because exposure patterns are more clearly defined in
winter, and juveniles of most nonresident species that confuse distribution
patterns in summer have been eliminated by exposure factors.
85
-------
751262
731103
766768
Site, Region Date
FIDALGO BAY, NPS 760809
FIBALGO BAY, NPS 760317
FIDftLGO BAY, NPS 766613
FIDALGO BrtY, NPS 760215
FIDftLCO BAY, NPS 768119
FIDALGO BAY, NPS 751124
WESTCOTT BAY, SJI 760806
WESTCOTT BAY, SJI 731201
WESTCGTT BAY, SJI 751669
WESTCOTT 9 AY, SJI 751668
WESTCOTT BAY, SJI 760417
WESTCOTT BAY, SJI 760611
WEBB CAMP, SJI 768867
WEB9 CriMP, SJI 766612
WEBB CnHP, SJI 751667
VEBB CriMP, SJI 751087
JAMESTOWN, STRAIT 770104
JAMESTOWN, STRAIT 761624
JrtMESTQ'n'M, STRAIT 766769
JAMESTOWN. STRAIT
WEBB CAMP, SJI
BIRCH BAY, NPS
JAMESTOWN, STBAIT
BIRCH BAY. NPS
BIRCH BAY. NPS 766214
FIDALGO BAY, NPS 760119
BIRCW 3 AY, NP5 760312
GUEME3 SOUTH SHORE, 766863
GUEMES SOUTH SHORE, 766723
CUEMES SOUTH SHORE- 766511
NORTH BErtCH SAND, 3T 760726
GUEfiES SOUTH SHORE, 768H11
GUSHES SOUTH SHQSE, 760113
GUEMES SOUTH SHORE. 751166
BECKETT POINT, STPAI 776167
BECKETT POINT, STRAI 761027
BECKETT POIHT, STRAI 760712
BECKETT POINT, STRAI 760416
EAGLE COVE, SJI 766961
EAGLE COVE, SJI 766788
EAGLE COVE, SJI 760708
EAGLE COVE, SJI 760314
NORTH BEACH SAND, 3T 776116
NORTH BEnCH SAND, ST 761125
NORTH BEACH SAND, ST 766726
NORTH BENCH SAND, ST 760513
EAGLE COVE, SJI 760116
GUEMES SOUTH SHORE, 760113
BIRCH BrtS', HPS 766712
JAMESTOWN, STRAIT 766769
Elev
m
6.5-
0.3 -
0.3 -
6.5 -
0. 5 -
0.5-
0. 6 -
0. 6 -
9.7 -
0.5 -
9.6-
0. 6 -
6. 6 -
0.6 -
O.
3.
3.
0.
6.
0.
0.
0.
0.'
0.
0.
0.
as.
0.
0.
a.
0.
g.
5
4
4
4
4
6
3
6
9
9
3
3
6
9
6
3
6
S
6
9
9
g
s
9
€
9'
9
6
6 '
6
6
3
0.9 •
0.3 •
108
i
II
75 59
LEVEL OF SIMILARITY
(quantitative index)
25
Figure 15. Relationships among less exposed soft substrate intertidal
stations, mid elevations. Similarity between stations is defined
by (A.5.1) of Appendix A in terms of relative abundance of the
50 plant and animal species or groups marked with stars in
Table B-2.
86
-------
Site, region
FIBALGO BAY, NFS
FIBftLGO BAY, HPS
FIDfiLCO BAY, HPS
riDPLGO BAY, NFS
flDALGO BAY, HP9
FIDALGO BAY, NFS
FIDALGG BnY, HPS
FIDALCQ BUY. NPS
FIDALGO SAY, NP5
FIDALGO BAY, HPS
FIDALGO BAY, HPS
FIDALGO BAY, HPS
JAMESTQVN. STRAIT
WE3TCOTT BftY, SJI
VESTCOTT BAY, SJ!
VESTCOTT BAY, SJI
WE5TCOTT BftY, SJI
VEBB CAMP, SJI
WEBB CAMP, SJI
VEBB CAMP, SJI
WEBB CArlP, SJI
JAMESTOWN, STRAIT
JAMESTOWN, STRAIT
BECKETT POINT, 3TRAI
BECKETT POINT. STRAI
6ECKETT POINT, STRAI
BIRCH BAY, NPS
BIRCH BUY, hlPS
BIRCH BAY, HPS
JAMESTOWN, STRAIT
GUEMES SOUTH SHORE,
QUEUES SOUTH SHORE,
GUEMES SOUTH SHORE,
GUEMES SOUTH SHORE,
CUEMES SOUTH SHORE.
GUEMES SOUTH SHORE,
NORTH BEACH 9AHD, ST
EAGLE COVE, SJI
EAGLE COVE, SJI
EACLE COVE. SJI
EAGLE COVE, SJI
EAGLE COVE, SJI
EAGLE COVE, SJI
EAGLE COVE, SJI
NORTH BEACH SAND. ST
NORTH BEACH SAND, 3T
BIRCH BAY, NPS
BIRCH BAY, NFS
JAMESTOVN, STRAIT
Date Elev
m
760869
760613
750808
73080S
760809
760613
760613
76D613
750625
750625
750625
7SQ7QS
760806
740817
750866
75o623
750803
740816
730623
770626
750712
760712
760712
760712
768808
750606
76070S
760503
750619
760723
760723
750204
736613
760726
760901
760706
730903
740912
76D735
750710
77Q729
760726
760712
730624
760?09
1.^
. V
Q 0 ,
0.7
a. 6 1
0. 6
Q.o
0. C
ri r
U. 0
'0. 6
Q.B
6. te ~
Q. 4
0. 9
3. o
u. 9
0. Jj
0. 9
. 0
. J
0. 4
0.a
. ..'
. y
0.9 1
0 6 '
1. 2
Q 3
Q.£
0. 3
1. 2
i i i
100
h-
I
1 — '
i i
f-
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(
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h
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i
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]
i
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1 i
25
T
I
— '
i
0
LEVEL OF SIMILARITY
(quantitative index)
Figure 16. Summer relationships among less exposed soft substrate intertidal
stations, mid elevations. Similarity between stations is defined
by (A.5.1) of Appendix A in terms of relative abundance of the
50 plant and animal species or groups marked with stars in
Table B-2.
87
-------
Site, region Date
FIDALGO BAY, UPS 769215
FIDALGO BAY, NFS 760119
FIBALGO BAY, HPS 7682:13
FIDALGO BAY. NPS 769119
FIDALGO BAY* UPS 766119
FIBftLCO BflY, HPS 766119
F1BALCQ BAY, NPS 751124
FIDALGO BAY, HPS 751124
FIDALGO BAY, NFS 730Z07
FIDALGO BAY, HFS 73828?
FIDALGO BAY, HPS 759112
FIDALGO QAY, HPS 758112
FIDftLCO 9ftY, NPS 756112
FIDftLCQ BAY. NPS 750112
BIRCH BAY, NPS 763214
BIFCH EftY, NPS 750117
BIRCH BfiY, NFS 730118
BIRCH BAY, HPS 76811?
BIRCH BAY, HPS 758221
VESTCOTT QrtV, £JI 751261
VESTCOTT BAY. SJI 75Q217
VESTCOTT BHY, SJI 741228
WEBB CftMP, SJI 7'D^IS
WEBB CftMP. SJI 741225
WEBB CAMP, SJI 751262
JAMESTOVH, STPAIT 789106
J6MESTOWN- ?TPwIT 778184
BECKETT PGIUT, STPwI 78Q111
BECKETT POINT, STRAI 770187
GUEttES 5DUTH 5HORE, 760211
QUEUES SOUTH SHORE, 76911'
GUEMES 30UTH 5HOPE. 766115
GUEMCS SOUTH SHORE, 758224
CUEME9 SOUTH ?HORE, 750224
CUEMES SOUTH SHORE. 741214
EAGLE COVE, SJI 760116
EftGLE COVE, SJI 7303Z3
EftGLE COVE, SJI 739U7
EAGLE COVE, SJI 741201
hOPTH QEttCH 9AHD. ST 798118
HOPTH BEACH SiiHD, ST 7781 If
NORTH BEACH £AHD. ST 761125
Elev
m
1.2-
0.3 -
8.3-
8.5 -
8.7 -
e.s-
0.5 -
1.2-
0.4 -
8.3 -
8.9 -
8. 5 -
8.7 -
0. £ -
9.9-
0.3 -
8.3 -
8. 4 -
8. 4 -
8.6 -
a.e -
0.S-
0. 5 -
0.6 -
8.6-
8. 4 -
0.4 -
0.9-
9.9-
0.6 -
1. 1 -
8.6 -
1.9 -
9.4 -
u 5 -
9.9-
0.3 -
8.9 -
8. 9 -
8. S -
e.e -
a. 6 -
100
II
58
LEVEL OF SIMILARITY
(quantitative index)
25
Figure 17. winter relationships among less exposed soft substrate intertidal
stations, mid elevations. Similarity between stations is defined
by (A.5.1) of Appendix A in terms of relative abundance of the
50 plant and animal species or groups marked with stars in
Table B-2.
88
-------
General considerations concerning numerical assemblage parameters:
The cluster analyses described above provided guidelines for more
quantitative analyses of the soft-bottom intertidal sites. Because exposure
was the dominant factor in defining groups in the cluster analyses, we
considered exposed and protected soft-bottom sites separately. ,AH
assemblage parameters were calculated separately for each 0.05 m x 15 cm
core. We did not perform detailed analyses of the "live sieve" samples
because of numerous problems in the live sieve data (see Section 4.2).
Plants were not found in intertidal samples from exposed soft
substrate sites, but some, for example eelgrass, play an important role in
more protected communities. Nevertheless 1,084 of the 1,303 samples included
in our analyses of protected soft substrate intertidal sites contained no
plants, and only 22 contained four or more different plant species.
Histograms of S at sites where plants were found are shown in Figure 18.
Because plants occurred in such a small fraction of the samples, plant
assemblage parameters could not be examined using analysis of variance or
regression techniques. Therefore, we restricted our consideration to animal
richness S and transformed total count log (N +1) in most soft substrate
assemblage parameter analyses. Animal diversity H' was also considered at
protected sites; this parameter was generally not significantly greater than
zero at exposed sites. At NPS sites where it was consistently available,
log (W +1) was also considered.
xO ct
Analysis of variance at exposed soft substrate sites:
Evaluation of exposure, sub,iStrate. region, and elevation effects at
exposed sand and gravel sites, summer: The six Whidbey and Strait sites
which clustered in or near the "most exposed" groups II and III of Figure 14
were considered first. Five summer samples from each of the three elevation
strata were available at each of these sites. Summer 1977 data from
Dungeness Spit, Kydaka Beach, and North Beach in the Strait and West Beach on
Whidbey were used. No summer 1977 data were available on tape for Twin
Rivers in the Strait or Ebey's Landing on Whidbey, so 1976 data were used for
the former and 1978 for the latter.
Means for S and log (N +1) at each site and elevation are shown in
Figure 19. A set of orthogonal contrasts (Table 18) was used to quantify
differences, some of which are evident in Figure 19, among the groups in the
one-way analysis of variance. The overall F statistic (A.3.5) for each
assemblage parameter was highly significant (O.OOl). It was most significant
for S , which explains why 4 percent of the Factor SS is significant at the
0.00ialevel for S but not for log (N +1) in Table 18.
a 10 a
The first four contrasts indicate highly significant contributions to
variability due to differences between sand and gravel substrates and high
versus moderate wave energy. However, the possibility of confounding of
effects is present. For example, since Twin Rivers and Ebey's Landing data
are from different years, year effects could be contributing to the
"substrate" contrasts. Site differences other than sediment composition may
also be influencing the results. For instance, cluster analyses and sediment
89
-------
MIDDLE OF NUMBER OF Jamestown, Strait
I NT ERi iflL OESERUfll I ONS
0. 3?
]. 14
p. g ft**»**ftA
3. 4 *•***
4. 0
**
6.
r .
y .
9.
10.
11.
1£'.
13.
14.
15.
16.
17.
18.
1 *
0
0
0
1 *
0
f1
0
O
0
0
1 *
EflCH * REPRESENTS £' OB'
MIDDLE OF
INTERUflL
0.
1.
NUMBER OF
OBSERUfiTIONS
55 #*.<*.•*».>
3 »*
Beckett Point, Strait
b.
t .
y.
9.
1
0
1
O
c.'
EflCH * REPRESENTS
£' OBSEPUflTIONS
MIDDLE OF NUMBER OF Uestcott Bay, SJI
INTERi 'flL OBSEPUflTIONS
1.
*•*•*.*:
4.
*
*
EflCH * REPRESENTS d OBSERUflTIONS
MIDDLE OF NUMBER OF Webb CamP' SJI
INTERi iflL OESERUflTIONS
0. 7£
1. 10
£'. £ #
J> • -Z1 &&.
4. 3 »*
5. 1 »
6. 0
7. 0
8. 1 *•
EflCH * REPRESENTS 5 OESERUflTIONS
MIDDLE OF NUMBER OF Fidalgo Bay, NPS
INTEPUflL OESERUflTIONS
0. 1£7
1. 18
£. 31
.'-',. f' *
EflCH * REPRESENTS 5 OBSERUflTIONS
MIDDLE OF NUMBER OF Birch Bay, NPS
INTERUflL OBSERUflTIONS
0. 144 »*&**&»££$£$$*>&*:*£%$•*;«*•«****
£'! U3 *
3. 0
4. 1 *
EflCH * REPRESENTS 5 OESERUflTIONS
MIDDLE OF NUMBER OF Guemes Island, NPS
INTERt 'flL OBSERUflTIONS
0. 145
Figure 18.
Histograms of number of plant taxa S at protected soft substrate sites where plants
were found. The number of observations (samples) in which S (number of plant taxa)
had the "middle of interval" value is plotted. p
-------
Number of Animal Taxa Sa
POOLED ST. DEU. = l.£3
Site
Dungeness Spit, Strait
Dungeness Spit, Strait
Dungeness Spit, Strait
North Beach, Strait
North Beach, Strait
North Beach, Strait
Twin Rivers, Strait
Twin Rivers, Strait
Twin Rivers, Strait
Kydaka Beach, Strait
Kydaka Beach, Strait
Kydaka Beach, Strait
West Beach, Whidbey
West Beach, Whidbey
West Beach, Whidbey
Ebey's Landing, Whidbey
Ebey's Landing, Whidbey
Ebey's Landing, Whidbey
Date
770727
770727
770727
770729
770729
770729
760728
760728
760728
770629
770629
770629
770702
770702
770702
780621
780621
780621
Elev
rn
0.0
0.9
1.8
0.0
0.6
1.8
0.0
0.9
1.8
0.0
0.9
1.8
0.0
0.9
1.8
0.0
0.9
1.8
-
(BflSEL ON POOLED STfiNDfiRD DEUIfiTIONJi
I *•*•** I*** I
1 A'*1* I ft'*'**' I
I. ft** I Aft** I
IA**I*A**I
I AAS I Aft* I
J*A*I***I
I*AAAIA»*I
1.0 1.5 4.0 b.5 9.U 11.5
POOLED ST. BEU.
Total Animal Count
0.3SS
Site Date Elev
Dungeness Spit, Strait 770727
Dungeness Spit, Strait 770727
Dungeness Spit, Strait 770727
North Beach, Strait 770729
North Beach, Strait 770729
North Beach, Strait 770729
Twin Rivers, Strait 760728
Twin Rivers, Strait 760728
Twin Rivers, Strait 760728
Kydaka Beach, Strait 770629
Kydaka Beach, Strait 770629
Kydaka Beach, Strait 770629
West Beach, Whidbey 770702
West Beach, Whidbey 770702
West Beach, Whidbey 770702
Ebey's Landing, Whidbey 780621
Ebey's Landing, Whidbey 780621
Ebey's Landing, Whidbey 780621
m
0.0
0.9
1.8
0.0
0.6
1.8
0.0
0.9
1.8
0.0
0.9
1.8
0.0
0.9
1.8
0.0
0.9
1.8
INDIUIDUHL 95 PERCENT C. I. FOP LEUEL MEflNS
iBRSED ON POOLED STfiNDHRD DEUIflTIONJ
I ft*** I *A*A I
I »Jft.« I A*** I
I *.«*•* I ».*»;[
,(N +1) 0.00
'., a o
0.70
4
1 . 40
24
£. 10
125
£.80
630
Figure 19. Group means from analysis of variance of numerical assemblage
parameters (defined in Section 5.2.1) from exposed sand and gravel
intertidal sites, summer, with individual 95 percent confidence
intervals (A.1.7) based on pooled standard deviations. The one-
way analysis of variance model (A.3.1) of Appendix A with n. - 5
in each group was used. Axis labels for total animal count^
shown in untransfonned as well as log transformed units.
91
-------
TABLE 18. CONTRIBUTIONS OF EXPOSURE, SUBSTRATE, REGION, AND ELEVATION
DIFFERENCES TO VARIABILITY IN SUMMER ASSEMBLAGE PARAMETERS
AT EXPOSED SAND AND GRAVEL INTERTIDAL SITES
% of Factor
AVERAGES OVER ALL ELEVATIONS TO COMPARE:
Exposure:
L.| high vs. moderate wave energy gravel (?) 42* 9%*
(Dungeness Spit vs. Twin Rivers)
ly high vs. moderate wave energy sand (?) 15 * 16 *
(Kydaka Beach vs. North Beach)
Substrate:
L, Strait sand vs. gravel (?) 18 * 12 *
(Kydaka Beach/North Beach average vs.
Dungeness Spit/Twin Rivers avera'ge)
L4 Whidbey sand vs. gravel (?) 21 * 24 *
(West Beach vs. Ebey's Landing)
Geographic area:
L5 Strait vs. Whidbey (?) 2 1
(average of all four Strait sites vs.
average of both Whidbey sites)
ELEVATION:
Lg Dungeness Spit mid vs. high elevation
Ly Dungeness Spit low vs. (mid + high)
Lg North Beach mid vs. high
Lg North Beach low vs. (mid + high)
L,g Twin Rivers mid vs. high
L.JI Twin Rivers low vs. (mid + high)
L^ Kydaka Beach mid vs. high
L,^ Kydaka Beach low vs. (mid + high)
L,. West Beach mid vs. high
L,5 West Beach low vs. (mid + high)
L,, Ebey's Landing mid vs. high
L-|7 Ebey's Landing low vs. (mid + high)
0
0
3
24 *
0
0
0
0
1
0
0
12 *
100%
1
2
22 *
4
4
1
1
0
1
0
0
2
100%
t The Factor SS represents the fraction of the total variability in an assem-
blage parameter explained by the one-way analysis of variance model. It is
defined in Table A-2 of Appendix A, and its partitioning by means of con-
trasts is explained in the discussion following that table.
# The numerical assemblage parameters S= (number of animal taxa) and logjQ(Na+l)
(log transformed animal count) are defined in Section 5.2.1.
* Significant at the 0.001 level. Our choice of this level for testing is
discussed in Section A.4 of Appendix A. Note that the same % of Factor SS
may be significant for one parameter but not the other because the overall
significance of the Factor SS is higher for the one than for the other.
? Question marks indicate possible confounding of effects; see Section A.3
of Appendix A.
92
-------
size data indicated that West Beach should probably be classified as a highly
exposed mixed sand and gravel site. As noted in Section 4.2.3, sediment
composition and beach slope at West Beach varied dramatically during the
study. Hence, contrast L may be reflecting exposure rather than substrate
differences.
To assess exposure effects, only the Strait sites were considered
because the Habitat Codes assigned by Nyblade on the File 100 tapes, unlike
those of Webber for the Whidbey sites, agreed fairly well with the site
descriptions in Nyblade (1978, 1979a). Kydaka Beach and Dungeness Spit were
coded as high wave energy sites, North Beach and Twin Rivers as only moderate
wave energy, so Dungeness Spit and Twin Rivers were used to define the "high
versus moderate wave energy gravel" contrast and Kydaka and North Beach for
the corresponding contrast for sand. However, as with the substrate
contrasts, the exposure contrasts may reflect unspecified site character-
istics in addition to wave energy, and L may also involve year effects.
There are other possibilities for confounding of effect that cannot be
unraveled from the present data set. For example, the Strait versus Whidbey
dichotomy L may reflect differences between investigators as well as
geographic differences. The design of the studies that resulted in all the
Strait data being taken by Nyblade and all the Whidbey data by Webber makes
it impossible to determine whether this might be a contributing factor. The
effects of investigator bias on the number of taxa identified appear even
more likely to be a problem in the earlier WDOE data sets.
Contrasts L through L measure elevation effects at each site. The
only highly significant elevation effects were at North Beach and Ebey's
Landing. We see from Figure 19 that the low elevation at both these sites
was richer than the higher. At North Beach, total animal count was
significantly greater at the low and mid elevations than at the high. No
large elevation effects were apparent at other sites, particularly the most
exposed.
Exposed sand and gravel sites, winters We also performed a one—way
analysis of variance on winter data from the "most exposed" site group. To
eliminate any possible confounding of temporal effects with elevation and
site effects of interest, only data taken in January 1978 were used. Thus,
Kydaka Beach and Twin Rivers, which were not sampled at that time, were
eliminated. The five available samples from each of the three elevation
strata at the four remaining sites were included.
Means of S and log (N +l) for the twelve groups thus defined are
plotted in Figure 2O. As in tne summer analysis, the F statistic (A.3.5)
indicated highly significant differences among means for both parameters.
Contrasts used to pinpoint the factors leading to these differences are
presented in Table 19.
It is clear from both Figure 2O and Table 19 that differences among
the three elevations at North Beach and between North Beach and the other
sites accounted for the largest fraction of the Factor SS. The low elevation
at Ebey's Landing was also somewhat anomalous. The low and mid elevations at
93
-------
Site
Date Elev
Number of Animal Taxa Sa
POOLED 'ET. DE'J. = 0.976
INDIUIDURL 95 PERCENT C. I. FOP LE'EL MERNS
(ERSED ON POOLED sTfihDfiP.D DEUIfiTION)
Dungeness Spit, Strait
Dungeness Spit, Strait
Dungeness Spit, Strait
North Beach, Strait
North Beach, Strait
North Beach, Strait
West Beach, Whidbey
West Beach, Whidbey
West Beach, Whidbey
Ebey's Landing, Whidbey
Ebey's Landing, Whidbey
Ebey's Landing, Whidbey
780109
780109
780109
780110
780110
780110
780106
780106
780106
780107
780107
780107
m
0.0
0.9
1.8
0.0
0.6
1.8
0.0
0.9
1.8
0.0
1.8
-3.0
—I—
0.0
3.6
t..o
9.0
+
12.0
Total Animal Count
POOLED ST. DEU. =
0.375
Site
Date Elev
INniUIDUfiL 95 PERCENT C. I. FOR LEUEL MEFiNS
I.BHSED ON POOLED STRHBFIF?D BEUIRTIQNJ
Dungeness Spit, Strait 780109-
Dungeness Spit, Strait 780109
Dungeness Spit, Strait 780109
North Beach, Strait 780110
North Beach, Strait 780110
North Beach, Strait 780110
West Beach, Whidbey 780106
West Beach, Whidbey 780106
West Beach, Whidbey 780106
Ebey's Landing, Whidbey 780107
Ebey's Landing, Whidbey 780107
Ebey's Landing, Whidbey 780107
0.0
0.9
H H 1 1- r
I A*.*. I ***..* I
T »:«* T *>*:*. T
1.8 I •*.».*»• I AS** i
0.0
0.6
1.8
0.0
0.9
1.8
0.0
0.9
18 +
log,0(Na+1)
I **.*. I *!**:*; I
I #**.*! ***:*. i
I •**.*:<
J*3Mf*]
,
0.00 0.70
0 4
I*»»:«J*»**J
I* **!**** I
I ***.*. I*.*** I
1.40 £.10 £.80
24 125 630
Figure 20. Group means from analysis of variance of numerical assemblage
parameters (defined in Section 5.2.1) from exposed sand and gravel
intertidal sites, winter, with individual 95 percent confidence
intervals (A. 1.7) based on pooled standard deviations. The one-
way analysis of variance model (A.3.1) of Appendix A with n. = 5
in each group was used. Axis labels for total animal count'''are
shown in untransformed as well as log transformed units.
94
-------
TABLE 19 CONTRIBUTIONS OF SITE AND ELEVATION DIFFERENCES TO VARIABILITY IN
WINTER ASSEMBLAGE PARAMETERS AT EXPOSED SAND AND GRAVEL SITES
% of Factor SSf
Sa# lo9lO(Na+1)
SITE (comparing averages over all elevations):
L, Dungeness Spit vs. North Beach 42%* 20%*
L2 West Beach vs. Ebey's Landing 3 * 23 *
L, Strait vs. Whidbey 6 * 1
O •
ELEVATION:
L. Dungeness Spit low vs. mid elevation
L,- North Beach low vs. mid
lr West Beach low vs. mid
b
lj Ebey's Landing low vs. mid
L8 Dungeness (low + mid) vs. high
Lg North (low + mid) vs. high
L,n West (low + mid) vs. high
L^ Ebey's (low + mid) vs. high
0
16 *
0
1
0
30 *
0
2
100%
5
1
0
8 *
6 *
32 *
1
3
100%
t The Factor SS represents the fraction of the total variability in an
assemblage parameter explained by the one-way analysis of variance model.
It is defined in Table A-2 of Appendix A, and its partitioning by means
of contrasts is explained in the discussion following that table.
# The numerical assemblage parameters Sa (number of animal taxa) and
log,Q(N +1) (log transformed animal count) are defined in Section 5.2.1.
* Significant at the 0.001 level. Our choice of this level for testing is
discussed in Section A.4 of Appendix A. Note that the same % of Factor SS
may be significant for one parameter but not the other because the overall
significance of the Factor SS is higher for the one than for the other.
95
-------
North Beach and the low elevation at Ebey's Landing were richer in animals
than the other elevations and sites in winter, as was also noted in summer.
Sediment grain size analyses for the winter samples at Ebey's Landing
indicated only minimal differences in sediment composition among the three
elevations. Summer sediment data indicated the presence of cobble at the low
elevation and an increase in the fraction of sand at the others. However, as
noted in Section 4.2.3, we cannot determine the statistical significance of
these sediment shifts. Sediment size data from North Beach are lacking for
both the summer and winter sampling dates, but earlier sediment size analyses
indicate that the proportion of gravel at the North Beach site and the
variability of this proportion increase with elevation.
In short, it is likely that the "elevation" effects at these sites are
at least partially due to substrate characteristics which changed with tidal
elevation at most sites. Whatever their causes, the consistency of summer
and winter results points to the conclusion that indicated differences are
real. However, many of the highly significant differences cannot be
adequately explained even when both substrate and elevation are considered.
Dungeness Spit and Ebey's Landing were both defined as gravel habitats
and North Beach and West Beach as sand. The analysis of variance results
just discussed, like the dendrograms produced by cluster analysis, suggest
that the sand-gravel dichotomy may not produce useful habitat definitions for
predictive purposes. The sediment composition at all these sites (with the
possible exception of the low elevation at North Beach) tends to be a gravel-
sand mix that varies with time. In terms of all three assemblage parameters,
only the high elevation at North Beach "sand" was as similar to West Beach
"sand" as was the species-poor Dungeness Spit "gravel" site.
To focus on site and year effects and eliminate the anomalous lower
elevations at North Beach and Ebey's Landing as well as any more subtle
elevation differences, analyses were done on all winter upper intertidal data
from the sites previously considered and the exposed SJI sand (Eagle Cove)
and gravel (Deadman Bay) sites. Of the 80 samples included in this analysis,
30 proved to be abiotic. Therefore, the statistical assumptions of the
analysis of variance model were certainly violated, and confidence intervals
and significance tests were not meaningful. The means indicated fairly high
year-to-year and site-to-site similarity except that the SJI sites,
particularly Deadman Bay, supported a great many more animal taxa and
individuals than any of the others. Eagle Cove appeared to lie between
Deadman Bay and the other sites in richness. According to Nyblade, richness
at the SJI sites may be inflated by washed-in nonresident species.
Contributions of site, season, year, and elevation differences to
variability, moderately exposed sand and gravel sitest To further
investigate differences between the San Juan Island sites and the others
several additional analyses were performed. Contributions of elevational,
year-to-year, between-season, and within-season differences to variability
were also examined in these analyses.
96
-------
Eagle Cove and North Beach data at all elevations from the spring and
summer of 1976 and one winter data set from each of these sites were included
in one-way analyses of variance. Five replicates per elevation obtained by
stratified sampling were available at each of the selected dates except at
Eagle Cove in July 1976 where samples from -0.3 m to +0.3 m constituted the
five low elevation replicates; 0.6 m to 1.2 m, the mid; and 1.5 m to 2.1 m,
the high. The use of these gradient samples tended to increase the within-
group variability slightly on this date; the maximum P ratio statistic
(A.3.10) for log (N +1) indicated differences in group variances significant
at the 5 percent level.
Groups and their means are shown in Figure 21. Contrasts computed
from these means (Table 20) quantify the patterns evident in the figure.
Clearly, elevation effects dominate at both of these sites. Both S and N
decrease with increasing elevation. Some significant differences between the
sites at all seasons are apparent in number of animals though not in number
of taxa. A winter decrease in number of animals is indicated.
A similar analysis was performed on summer and winter data from Ebey's
Landing (1978) and Deadman Bay (1975). As at Eagle Cove and North Beach,
elevation differences accounted for more than half of the variability in
numbers of taxa and individuals. Animal counts were significantly higher at
Deadman Bay, with this difference accounting for 37 percent of the
variability. Seasonal differences in animal counts were minimal, but the
number of taxa at Ebey's Landing was significantly higher in summer than in
winter .
A separate analysis of the bi-monthly mid intertidal data taken at
Deadman Bay between July 1974 and May 1976 revealed significant year-to-year
differences in animal counts for July and March data. Months within the same
season did not differ greatly except possibly in spring, but large
differences were indicated between spring and summer and for spring/summer
versus fall/winter. S varied less with time than log (N +1). Significant
spring versus summer differences were also indicated at the low elevation at
North Beach by an analysis of the quarterly data at that site and elevation.
Si-fce and year ef fee-fas . exposed upper intertidal sand and gravel
summer i A final analysis of upper intertidal summer data from the
exposed sand and gravel sites was conducted. Deadman Bay was omitted from
this analysis because it had already been found to have much larger numbers
of animals than any of the other exposed sites, but Eagle Cove was included.
Five samples from each site, date, and elevation stratum were used. The
F-ratio (A.3.1O) indicated no significant variance heterogeneity in S or
log ( N +1 ) among the groups included in this analysis .
1O cli
The overall F-statistic (A. 3. 5) indicated significant between-group
differences in S at the 1 percent and in log (N +1) at the 5 percent
level. As expected from the results already presented, Ebey's Landing data
was the primary contributor to the between-group differences in this
analysis. The contrast between the 1978 summer mean at Ebey's Landing and
the average of the other group means (all but one, unfortunately,
representing previous years as well as other sites) accounted for a highly
97
-------
Site
Date
Number of Animal Taxa Sa
POOLED ST. DEU. = 1.54
INDIUIDLifiL 95 PERCENT C. I. FOP LEUEL MEfiMS
Elev fBflSED OH POOLED STflHDflRD DEUIflTIQM)
North Beach
North Beach
North Beach
North Beach
North Beach
North Beach
North Beach
North Beach
North Beach
Eagle Cove, SJI
Eagle Cove, SJI
Eagle Cove, SJI
Eagle Cove, SJI
Eagle Cove, SJI
Eagle Cove, SJI
Eagle Cove, SJI
Eagle Cove, SJI
Eagle Cove, SJI
Strait 760513
Strait 760513
Strait 760513
Strait 760726
Strait 760726
Strait 760726
Strait 770116
Strait 770116
Strait 770116
750127
750127
m
0.0
0.6
1.8
0.0
0.6
1.8
0.0
0.6
1.8
-0.3
0.9
1.8
-0.3
0.9
1.8
low
mid
760708 high +-
-1.0
I***** I ««»«*];
I****I****i*X
I «*•«•* I a****; I
I ft**;* I aw*** I
750127
760514
760514
760514
760708
760708
I ***** ^ ***•:* i
—i—
1.5
4.8
—+—
6.5
9.8
—+—
11.5
—+
14.0
Total Animal Count
0.240
North
North
North
North
North
North
North
North
North
Eagle
Eagle
Eagle
Eagle
Eagle
Eagle
Eagle
Eagle
Eagle
Site
Beach, Strait
Beach, Strait
Beach, Strait
Beach, Strait
Beach, Strait
Beach, Strait
Beach, Strait
Beach, Strait
Beach, Strait
Cove, SJI
Cove, SJI
Cove, SJI
Cove, SJI
Cove, SJI
Cove, SJI
Cove, SJI
Cove, SJI
Cove, SJI
Date
760513
760513
760513
760726
760726
760726
770116
770116
770116
750127
750127
750127
760514
760514
760514
760708
760708
760708
m
0.0
0.6
1.8
POOLED ST. DEU. =
INDIUIBUfiL 95 PERCENT C. I. FOP LEUEL MEfiNS
(BflSED On POOLED STflMDflPD DEUIflTION)
+ 1 H 1 1
I ***!**!
I ft** I*** I
1.8
0.0
0.6
1.8
-0.3
0.9
1.8
-0.3
X *'*!**.* I
JjitSil*:**:!
0.9
1.8
low
mid
high +
log10(Na+i;
I *»*!»•.# I
U.00
I***I**I
0.60
-i—
1 . ?0
1
1.80
62
+—
£.40
250
—+
3.00
999
Figure 21. Group means from analysis of variance of numerical assemblage
parameters (defined in Section 5.2.1) at moderately exposed sand
sites, three seasons and elevations, with individual 95 percent
confidence intervals (A.1.7) based on pooled standard deviations.
The one-way analysis of variance model (A.3.1) of Appendix A with
n. =5 in each group was used. Axis labels for total animal count
are shown in untransformed as well as log transformed units.
98
-------
TABLE 20. CONTRIBUTIONS OF SITE, ELEVATION, AND SEASON DIFFERENCES TO
ASSEMBLAGE PARAMETER VARIABILITY, MODERATELY EXPOSED SAND SITES
of Factor SS
V
EAGLE COVE VS. NORTH BEACH:
Spring 1976 low elevation 2% 0%
mid 12*
high 0 3 *
Summer 1976 low 00
mid 2 3 *
high 1 1
Winter 1975 vs. winter 1977 low 0 0
mid 0 0
high 3 10 *
SEASON (comparing averages of the two sites):
Spring vs. summer low 21
mi d 00
high 0 0
(Spring + summer) vs. winter low 0 0
mid 1 4 *
high 0 0
ELEVATION (comparing averages over sites and seasons):
Low vs. mid
(Low + mid) vs. high
22 *
66 *
100%
4 *
72 *
100%
f The Factor SS represents the fraction of the total variability in an
assemblage parameter explained by the one-way analysis of variance model.
It is defined in Table A-2 of Appendix A, and its partitioning by means
of contrasts is explained in the discussion following that table.
# The numerical assemblage parameters S. (number of animal taxa) and
log1(j(N,jH) (log transformed animal count) are defined in Section 5.2.1.
* Significant at the 0.001 level. Our choice of this level for testing is
discussed in Section A.4 of Appendix A. Note that the same % of Factor SS
may be significant for one parameter but not the other because the overall
significance of the Factor SS is higher for the one than for the other.
99
-------
significant 52 percent of the Factor SS for number of taxa and 46 percent for
number of individuals.
The group means from this analysis are shown in Figure 22. As
indicated in this figure, Ebey's Landing assemblage parameters were the only
ones significantly larger than any others according to the Newman-Keuls
procedure for comparing all means. Year-to-year differences were
insignificant at the sites for which two years of data were available.
Of course, the failure of a statistical test to detect differences is
no guarantee that none exist. For example, if we had only the 1976 summer
samples from North Beach and the 1977 summer samples from Dungeness Spit,
either a two-sample t-test or a Mann-Whitney test between the two groups of
samples would indicate significant differences in number of taxa at about the
1 percent level. We would get only a slightly less significant indication of
site difference if we included both the 1976 and 1977 summer data. This is a
site difference that most biologists would agree is real. We will confront
the issue of power of tests to detect real differences at exposed sand and
gravel sites in Section 6.2.3.
Analyses of assemblage parameter variability,
protected soft substrate sites:
Analysis of variance at moderately protected sitest Cluster analyses
indicated little similarity in species and counts of animals between the
moderately protected NFS sites, Birch Bay (sand) and Guemes Island South
(gravel), and any other baseline sites although they sometimes clustered with
Beckett Point, North Beach, and Eagle Cove. An analysis of variance which
included data from low and mid elevations at these sites showed that the NFS
sites were poorer in species and individuals than Beckett Point and more like
the moderately exposed sand sites.
We therefore compared Birch Bay and Guemes Island with the moderately
exposed SJI sites. Eagle Cove (sand) and Deadman Bay (gravel). A one-way
analysis of variance with each group consisting of July 1976 data from a
particular site and elevation stratum was performed. The groups proved to be
significantly different at the 1 percent level for all three numerical
assemblage parameters considered (Figure 23).
The contrasts used to explore these differences and the percent of
Factor SS that each explained are given in Table 21. This table reinforces
the results of cluster analyses of these sites. Like Deadman Bay, Birch Bay
and Guemes Island appear to be unique sites not much like any of the other
baseline sites. They exhibited somewhat less vertical stratification than
Eagle Cove and Deadman Bay. Guemes Island had a larger number of different
taxa but significantly fewer individuals than Deadman Bay. Numbers of
individuals at Birch Bay were low compared to Eagle Cove. The sand sites and
Guemes Island were much more diverse than Deadman Bay, perhaps because
Deadman Bay had very little sand while Guemes Island sediment had 40 to
50 percent sand mixed with its gravel and pebbles.
1OO
-------
Site
Dungeness Spit, Strait
Dungeness Spit, Strait
North Beach, Strait
North Beach, Strait
Twin Rivers, Strait
Kydaka Beach, Strait
Kydaka Beach, Strait
West Beach, Whidbey
West Beach, Whidbey
Ebey's Landing, Whidbey
Eagle Cove, SJI
Eagle Cove, SJI
Site
Dungeness Spit, Strait
Dungeness Spit, Strait
North Beach, Strait
North Beach, Strait
Twin Rivers, Strait
Kydaka Beach, Strait
Kydaka Beach, Strait
West Beach, Whidbey
West Beach, Whidbey
Ebey's Landing, Whidbey
Eagle Cove, SJI
Eagle Cove, SJI
Date
Number of Animal Taxa Sa
POOLED ST. DEU. = 1.31
IHBI'JIDUHL 95 PERCENT C. I. POP LE'O_ MEfiHS
iBfiSED OH POOLED STfiHDfiRD IiEUIfiTIOHJ
760725
760726
770729
760728
760710
770629
770702
780620
780621
750710
760708
-0
I ******* i ******** I
I ******* I ******* I
I ******* J ******** ]
j ******* I ******** j
I ******* x ******** I
X ******* i a***.**** j
j ******** i ******* j j
X ******* I ******* I •*-! •« 1 -«
I ******** I ******* I
.5 0.9 £.3 3.7 5.1 6.5
—f
Total Animal Count
POOLED ST. DEU. = 0.428
IHBI'.iIDUfiL 95 PERCENT C. I. FOP LEt.€L MEfiNS
Date (BfiSEIi OH POOLED STfiHBfiPD DEUIflTIOH)
7fin??*»
770727
760726
770729
760728
770629
770702
780620
780621
750710
760708
1og10(Na+l)
Na
I $******** j »#»*».*»» i
1 ********* i ******** i
X ********* i ********* x
X ********* i ******** x
X ******** x ********** i
I ««»*.***** x ********* x
X ******** x ********* i-»-
I ********* I ********* I
x ******** x ********* x
0.60 0.40 0.80 l.c:0 1.66 £
02 5 15 39
1
JJ
.00
99
Figure 22. Group means from analysis of variance of numerical assemblage
parameters (defined in Section 5.2.1) at upper intertidal exposed
sand and gravel sites, summer, with individual 95 percent
confidence intervals (A.1.7) based on pooled standard deviations.
The one-way analysis of variance model (A.3.1) of Appendix A with
n. = 5 in each group was used. Axis labels for total animal count
are shown in untransformed as well as log transformed units.
Arrows indicate differences which were significant at the
5 percent level according to the Newman-Keuls procedure for
comparing all means, see Section A.3 of Appendix A.
101
-------
Site
Birch Bay, NPS
Birch Bay, NPS
Eagle Cove, SJI
Eagle Cove, SJI
Deadman Bay, SJI
Deadman Bay, SJI
Guemes Island, NPS
Guemes Island, NPS
Date
760712
760712
760708
760708
760711
760711
760723
760723
Elev (
m +
low
mid
low
mid
low
mid I
low
mid
+
£.
Bl
0
Number of Animal Taxa
POOLED ST. I'EU. = £.36
INDIUIDUflL 95 PERCENT C. I. FOP LEUEL MEflNS
(BflSEH ON POOLED STflNDflRD DEUIflTIOm
j a******** j ***»•*•**** x
Site
Birch Bay, NPS
Birch Bay, NPS
Eagle Cove, SJI
Eagle Cove, SJI
Deadman Bay, SJI
Deadman Bay, SJI
Guemes Island, NPS
Guemes Island, NPS
Date
760712
760712
760708
760708
760711
760711
760723
760723
Elev
m
low
mid
low
mid
low
mid
low
mid
loo
4.0 6.0 8.0 10.0 12.0 14.0
Total Animal Count
POOLED ST. DEU. = 0.437
INDIUIDUflL 95 PERCENT C. I. FOP LEUEL MEflNS
iBflSED ON POOLED STflNDflPD DEUIfiTIOW
I .-o-w:****** I **»•*«»••*:;« j
I *'fc**»i*J*»: I V******* I
T *»*•*•»».«» j ssisjosssts.;*; j
1f.(N +1) 1.20
1 U a
j ********x ********x
+ + H
1.60 £.00
N.
15
39
99
£.40
250
1—
£.30
630
h
3.£0
1584
Site
Birch Bay, NPS
Birch Bay, NPS
Eagle Cove, SJI
Eagle Cove, SJI
Deadman Bay, SJI
Deadman Bay, SJI
Guemes Island, NPS
Guemes Island, NPS
Date
760712
760712
760708
760708
760711
760711
760723
760723
Elev
m
low
mid
low
mid
low
mid
low
mid
n
Animal Diversity H'a
POOLED ST. DEU. = 0.409
INDIUIDUflL 9S PERCENT C. I. FOP LEUEL MEflNS
IBflSED ON POOLED STflNDflRD DEUIflTION)
+ H + + + H
I **»•»%•«»•:«»%% X S!**'******* X
j ********** I ********** I
+
38
h
P. 60
I *•*•«:*«»*#!«:« x *********:* x
h --------- H ---------- H
O.90 i.£0 1.58
f
1.80
+
£.10
Figure 23. Group means from analysis of variance of numerical assemblage
parameters (defined in section 5.2.1) at moderately protected
intertidal sand and gravel sites, July 1976, with individual
95 percent confidence intervals (A. 1.7) based on pooled standard
deviations. The one-way analysis of variance model (A.3.1) of
Appendix A with n. - 6 in each group was used. The low elevation
groups include daia from -0.3 m to 0.4 m, the mid elevation groups
data from 0.5 m to 1.2 m. At Birch Bay 11 samples had been taken
in the low elevation range; the five most extreme elevations were
omitted to maintain equal group sizes for the analysis. High
elevations were not considered in this analysis because they were
not sampled at Birch Bay. Some care should be used in
interpreting these results since the maximum F ratio (A.3.10)
indicated variance heterogeneity in log1£)(Na+l) and E^.
102
-------
TABLE 21. CONTRIBUTIONS OF SITE AND ELEVATION DIFFERENCES TO VARIABILITY IN
JULY 1976 MODERATELY PROTECTED SAND AND GRAVEL ASSEMBLAGE PARAMETERS
% of Factor SS +
sa* 1o9lQ(Na+1) ^
SITE DIFFERENCES (comparing averages over
both elevations):
Birch Bay vs. Eagle Cove 1% 25 1
Deadman Bay vs. Guemes Island 17 49 * 45 *
Sand vs. gravel (Birch Bay/Eagle Cove average 40 4 48 *
vs. Guemes Island/Deadman
Bay average)
LOW ELEVATION VS. MID:
Birch Bay 412
Eagle Cove 27 15 3
Deadman Bay 9 6 1
Guemes Island 200
100% 100% 100%
t The Factor SS represents the fraction of total variability in an assemblage
parameter explained by the one-way analysis of variance model. It is defined
in Table A-2 of Appendix A, and its partitioning by means of contrasts is
explained in the discussion following that table.
# The numerical assemblage parameters Sa (number of animal taxa), log-£Q(Na+l)
(log transformed animal count), and ti'a (animal diversity)
are defined in Section 5.2.1.
* Significant at the 0.001 level. Our choice of this level for testing is
discussed in Section A.4 of Appendix A. Note that the same % of Factor SS
may be significant for one parameter but not another because the overall
significance of the Factor SS is greater for the one than for the other.
103
-------
Multiple regressions to partition variability at each site; Con~
tributions of elevation, season, and time trends to variability at each
protected soft substrate site were assessed using the multiple regression
model (A,2.1) with y. a value of S , log (N +1), or log (W +1). Results
are in Table 22. D a 10 a a
The Birch Bay analysis included all 177 available samples taken
between October 1974 and August 1976, mostly at low to mid elevations. The
multiple regression model explained only a small percentage of the
variability in assemblage parameters at Birch Bay. Sampling variability
appears to dominate other factors at this site. It is possible that there
are undetected data errors contributing to the results, but it may also be
that Birch Bay simply represents a habitat that cannot be modelled well in
terms of temporal and spatial factors.
The estimated elevation coefficients were not significantly different
from zero, but they defined curves which decrease at high elevations as we
would expect. Recall that the analysis of variance results of Table 21 had
also indicated that elevation was not an important factor at Birch Bay.
Season coefficients indicated lower numbers of animals and animal species but
higher weights in spring and summer than in fall and winter. A long-term
increase through time in all three parameters was also indicated.
The multiple regression model worked better on the 178 samples taken
at Fidalgo Bay between November 1974 and August 1976. As can be seen in
Table 22, animal weight results were much like those at Birch Bay. However,
the model explained more than 50 percent of the variability in each of the
other two parameters.
Elevation was a more significant factor at Fidalgo than at Birch Bay.
Elevations of the samples at Fidalgo Bay ranged from 0.1 m to 1.6 m with most
in the range 0.4 m to 1.2 m. The elevation coefficients for S and
log (N +1) implied decreases in these parameters with increasing elevation
up to aljout 0.9m but increases at higher elevations. The estimated season
and date coefficients, at Fidalgo Bay as at Birch Bay, were much more
significant than the elevation coefficients. Both were positive and
significant for all three assemblage parameters, indicating larger parameter
values in spring and summer than in fall and winter as well as increases over
the course of the study. Seasonal differences contributed 35 percent of the
variability in S and 23 percent in log (N +1) while the long-term time
trend accounted for 19 percent and 35 percent, respectively.
The pitfalls of a multiple regression model can be illustrated by
considering the results (Table 23) of fitting the same model to 86 Birch Bay
samples and 91 Fidalgo Bay samples taken at elevations between -0.3 m and
+1.3 m and dates between August 1975 and August 1976 inclusive. The fitted
equations and their implications sometimes differed significantly.
Webber did not identify amphipods to species level in samples taken
before August 1975. While we had lumped most gammarids in our analyses for
this reason, we had retained a few key genera such as cprophium that appeared
to be frequently identified to genus or species. We had also retained all
104
-------
TABLE 22. RESULTS OF REGRESSIONS TO PARTITION ASSEMBLAGE PARAMETER VARIABILITY, PROTECTED SOFT SUBSTRATE INTERTIDAL SITES
O
on
Site y f
Birch Bay Sa
log10(Na+l)
iog10(Ha+D
Fidalgo Bay Sfl
log10(Na+l)
iog10<«.*i>
Westcott Bay Sa
d
iog10
-------
TABLE 23. RESULTS OF REGRESSIONS OVER RESTRICTED RANGES OF ELEVATIONS AND DATES, BIRCH BAY AND FIDALGO BAY
Regression Equation
Site yf (standard deviations of coefficients in
Birch Bay S, - 284 - 1.
a (118) (1.
86xj
93)
1ogln(N+l) - 42.4 - 0.19xi
IU (10.5) (0.17)
log1n(W+l) - 8.51 + 0.
10 a (9.06) (0.
Fidalgo Bay S, 448 + 17.
a (106) (7.
logln(N +1) - 0.35 - 0.
IU a (7.52) (0.
Iog10(w+l) 29.1 + 0.
IU a (12.1) (0.
16x,
15)
7x,
21)
31xi
51)
57x!
83)
+ 0.
(2.
+ 0.
(0.
- 0.
(0.
-11.
(4.
+ 0.
(0.
- 0.
(0.
,05x2
,50)
,16X2
22)
27x2
,19)
38]
41X2
31)
31x2
.50)
- 3.
(1.
- 0.
(0.
+ 0.
(0-
+ 7.
(0.
+ 0.
(0.
+ 0.
(0.
36x3
00)
40x3
09)
Ilx3
08)
52x3
81)
35x3
06)
21x3
09)
parentheses)
+ 3,
(1.
+ 0,
(0.
+ 0.
(0,
- 5.
(1.
+ 0.
(0.
- 0.
(0.
,88x1,
,55)
,58x,,
,14)
!l2)"
,79xi,
,41)
03xi,
10)
37x,,
16)
Contributions to R2 *
» Elevation , Total Residual
Elevation Squared Season Date j,2 Standard
(xj) (x2) (x3) (x,,) Deviation
3.5% + 0.33! + 6.6% + 6.5% = 16.9% 4.08
0.4 1.8 8.0 15.9 26.1 0.364
0.2 3.6 4.9 1.1 9.8 0.314
3.9 0.2 39.0 9.3 52.4 3.44
7.9 4.2 30.8 0.1 43.0 0.244
0.1 0.6 2.3 5.7 8.7 0.393
* R2, the percentage of total variability explained by the multiple regression model (A.2.1) of Appendix A, is defined by (A.2.3).
t The numerical assemblage parameters, for example number of animal taxa S , used as dependent variables y. in (A.2.1) are defined
in Section 5.2.1. The subscripts j of (A.2.1) have been omitted in this table for conciseness. J
t Only elevations between -0.3 m and +1.3 m were included in this analysis.
5 Only dates between August 1975 and August 1976 were included 1n this analysis.
-------
caprellid amphipod species in our dictionary. We hypothesized that the
apparent significant increase in S at Fidalgo Bay was due to this
discrepancy in identification level (and perhaps others). Indeed, in the
analysis of Table 23 that does not include the data before August 1975, the
date coefficient indicates a decrease in S during the second year of the
study. Clearly the taxonomic problems discussed in Section 4.2.4 make it
difficult to use the present data base to draw meaningful conclusions about
long-term temporal variability in species richness.
We omitted the lowest elevations sampled at Birch Bay and the highest
at both sites for the analysis of Table 23 because the Minitab output
corresponding to Table 22 had indicated that these extreme elevations had
large influence on the fitted equations. As expected, the new equations
indicated a significant decrease rather than increase in S at high
elevations. The magnitude and significance of the elevation coefficients for
log (N +1) at Fidalgo Bay were also reduced in the new analysis.
The dominance of seasonal effects as a source of variability at
Fidalgo Bay was clearer in Table 23 than in the previous table. The
spring/summer increase accounted for 39 percent of the variability in S and
over 3O percent in log (N +l) in the data taken between August 1975 ana
August 1976. In most other respects, results in the two tables were
similar.
The main conclusion to be drawn from Tables 22 and 23 is that
regression results should be used only as indicators of the relative
importance of various factors. Thus, at Birch Bay neither elevation nor
temporal factors appear to be significant relative to sampling variability,
whereas at Fidalgo Bay the spring/summer increase in numbers of animal
species and individuals accounts for about a third of the variability in
these parameters. Animal weights appear to be relatively insensitive to
elevation and sampling date at both sites.
At the other four sites included in Table 22, sample elevation was by
far the most significant factor, generally accounting for 50 to 80 percent of
the variability in S and log (N +1). Since these sites were all sampled by
Nyblade, who recorded animal weignts with less regularity than Webber, we did
not examine W . Except for log (N +1) at Jamestown, for which elevation was
less significant, the fitted curves indicated decreases in S and N with
increasing elevation inside the range of elevations sampled. a
It seems likely that the negative season coefficients, mostly
insignificant, represent data anomalies rather than a real spring/summer
decline in S or N . In fact, when a similar model was fit to a subset of
data consisting of only low to mid elevation summer and winter samples, the
season coefficients indicated either insignificant seasonal changes or summer
increases in both S and N at all four sites. Decreases in S and N with
increasing elevation dominated R even over the more limited efevation
range.
107
-------
Long-term time trends are insignificant at the Nyblade sites except
possibly for the indicated decrease in S at Westcott Bay and the increase at
Jamestown. The positive value at Jamestown may be at least partly due to
improved identification of species as the MESA study progressed. The
negative estimate at Westcott Bay may be influenced by the fact that Nyblade
attempted to identify amphipods to species in the first but not the second
year of the WDOE study.
Analysis of -relative contributions of season and site differences to
YtJITTiflM11 tyt protected soft guha^hrate sitest Seasonal and site differences
were compared in an analysis of variance of fall 1975 and winter, spring, and
summer 1976 samples from Birch Bay, Webb Camp, Westcott Bay, and Fidalgo Bay
and spring, summer, and fall 1976 and winter 1977 samples from Beckett Point
and Jamestown.
Three samples at the lowest available elevations (-0.3 m to O.6 m)
were used at each selected date and site. It was realized that elevation
effects might increase replicate variability in this analysis, but samples
with identical elevations were simply hot available for cross-site
comparisons. For example, 0.5 m was the lowest regularly sampled elevation
at Fidalgo Bay while -0.3 m was the low elevation at Westcott Bay and 0.6 m
the mid elevation, so it did not seem unreasonable to include both low and
mid elevation Westcott Bay samples for purposes of comparison with Fidalgo
Bay. The maximum F-ratio test indicated no variance heterogeneity in the
assemblage parameters considered, providing a partial confirmation of our
approach.
Groups included in the analysis and their means are shown in
Figure 24. Contrasts used to quantify the obvious group differences are
presented in Table 24. Clearly, site differences far outweighed seasonal
differences at a site, accounting for 70 to 90 percent of the between-group
variability. Northern Sound sites, particularly those sampled by Webber,
were clearly different from Strait sites.
Not surprisingly, Webb Camp and Westcott Bay were the most similar of
the site pairs considered. Both sites are in fact in Westcott Bay. The
sampling dates included in this analysis were almost the same at the two
sites. Furthermore, as we will see in a moment, sediment composition at the
two sites was relatively similar, especially at the low elevation.
The NFS sites Birch Bay and Fidalgo Bay were somewhat similar to each
other though on the average Birch Bay had lower values of all three
assemblage parameters. Both were significantly poorer in species and
individuals than the other sites.
Jamestown and Beckett Point, though both are protected sites in the
eastern Strait of Juan de Fuca, were very dissimilar to each other and to the
other sites. Beckett Point exhibits an unusual fall peak in numbers of taxa
and individuals, and the spring samples were anomalously low in these
parameters, accounting for the significant seasonal as well as site contrasts
involving Beckett Point.
108
-------
Site
Birch Bay, NPS
Birch Bay, NPS
Birch Bay, NPS
Birch Bay, NPS
Beckett Point, Strait
Beckett Point, Strait
Beckett Point, Strait
Beckett Point, Strait
Jamestown, Strait
Jamestown, Strait
Jamestown, Strait
Jamestown, Strait
Webb Camp, SJI
Webb Camp, SJI
Westcott Bay, SJI
Westcott Bay, SJI
Fidalgo Bay, NPS
Fidalgo Bay, NPS
Fidalgo Bay, NPS
Fidalgo Bay, NPS
Site
Date POOLED ST.
Number of Animal Taxa Sa
DEU. = 4.96
751103
760214
760512
760808
760416
760712
761119
770107
760418
760713
761024
770104
751008
760807
751008
760806
751124
760215
760517
760809
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Total Animal Count
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Birch Bay, NPS 751103
Birch Bay, NPS 760214
Birch Bay, NPS 760512
Birch Bay, NPS 760808
Beckett Point, Strait 760416
Beckett Point, Strait 760712
Beckett Point, Strait 761119
Beckett Point, Strait 770107
Jamestown, Strait
Jamestown, Strait
Jamestown, Strait
Jamestown, Strait
Webb Camp, SJI
Webb Camp, SJI
Westcott Bay, SJI
Westcott Bay, SJI
Fidalgo Bay, NPS
Fidalgo Bay, NPS
Fidalgo Bay, NPS
Fidalgo Bay, NPS
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Birch Bay,
Site
NPS
NPS
761119
770107
760418
760713
761024
770104
751008
760807
751008
760806
751124
760215
760517
760809 +
log10(N +1)
N
a
Date POOLED
751103
760214 I******
760512
760808
760416
760712
761119
770107
760418
760713
761024
770104
751008
760807
751008
760806
751124
760215
760517
760809
1.00
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15 62 250
Animal Diversity H'a
ST. DEU. = 0.288
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Birch Bay, NPS
Birch Bay, NPS
Beckett Point, Strait 760416
Beckett Point, Strait 760712
Beckett Point, Strait 761119
Beckett Point, Strait 770107
Jamestown, Strait
Jamestown, Strait
Jamestown, Strait
Jamestown, Strait
Webb Camp, SJI
Webb Camp, SJI
Westcott Bay, SJI
Westcott Bay, SJI
Fidalgo Bay, NPS
Fidalgo Bay, NPS
Fidalgo Bay, NPS
Fidalgo Bay, NPS
Figure 24. Numerical assemblage parameter means at protected soft substrate
sites, low to mid intertidal, all seasons, with individual
95 percent confidence intervals (A. 1.7).
1O9
-------
TABLE 24. CONTRIBUTIONS OF SITE AND SEASON DIFFERENCES TO VARIABILITY IN LOW
_ TO MID INTERTIDAL PROTECTED SOFT SUBSTRATE ASSEMBLAGE PARAMETERS
% of Factor SS +
SITE (averaged over all seasons):
Birch Bay vs. Fidalgo Bay 2% 3%* 10%
Webb Camp vs. Westcott Bay 208
Beckett Point vs. Jamestown 2* 2* 23*
Birch/Fidalgo vs. Webb/Westcott 19* 55* 1
North Sound vs. Strait 64* 30* 28*
(average of Birch/Fidal go/Webb/
Westcott vs. average of Beckett/ Jamestown)
SEASONS:
Birch Bay fall (751103) vs. winter (760214)
spring (760512) vs. summer (760808)
spring/summer vs. fall/winter
Beckett Point fall (761119) vs. winter (770107)
spring (760416) vs. summer (760712)
spring/summer vs. fall/winter
Jamestown fall (761024) vs. winter (770104)
spring (760418) vs. summer (760713)
spring/summer vs. fall /winter
Webb Camp fall (751007) vs. summer (760807)
Westcott Bay fall (751008) vs. summer (760806)
Fidalgo Bay fall (751124) vs. winter (760215)
spring (760517) vs. summer (760809)
spring/summer vs. fall /winter
1
0
0
0
1
7*
0
1
0
0
0
0
0
1
100%
1
0
0
0
3*
5*
0
0
0
0
0
0
0
1
100%
9
0
1
0
9
0
3
1
2
0
0
1
3
1
100%
t The Factor SS represents the fraction of total variability in an assemblage
parameter explained by the one-way analysis of variance model. It is defined
in Table A-2 of Appendix A, and its partitioning by means of contrasts is
explained in the discussion following that table.
# The numerical assemblage parameters Sa (number of animal taxa), log1Q(Na+l)
(log transformed animal count), and Ha (animal diversity)
are defined in Section 5.2.1.
* Significant at the 0.001 level. Our choice of this level for testing is
discussed in Section A.4 of Appendix A. Note that the same % of Factor SS
may be significant for one parameter but not another because the overall
significance of the Factor SS is greater for the one than for the other.
no
-------
The analyses summarized by Table 24 and Figure 24, like those
discussed earlier, point to deficiencies in a priori habitat definitions.
The relative poverty of Birch Bay is consistent with its definition as a
moderately protected sand habitat as opposed to the other sites which were
characterized as protected mud or mixed. However, the a priori definitions
would lead us to expect the protected mud habitats Jamestown, Westcott Bay,
and Fidalgo Bay to be similar to one another and less similar to the mixed
sites, Beckett Point and Webb Camp.
The available sediment size data supplemented by the investigators'
descriptions tell a slightly different story. It is impossible to tabulate
percentage of sediment in each size class precisely because different
classification schemes were used in the different studies and replicate
samples, when available, often indicated quite different percentages. In
addition, only 1974-1975 sediment data are available at the SJI sites to go
with the 1976 biological data. Combining all the available information, we
obtain Table 25. The sites in the table are ordered roughly by percentage of
mud (fine sand to silt.) The question marks on the Birch Bay entries mean
that the sediment data available did not discriminate between fine and medium
sand. The classification as medium was based on Nyblade's (1979b)
description of the site.
We see that the low elevation of Webb Camp, in particular, is more
like muddy Westcott Bay than mixed Beckett Point. The low elevation at
Jamestown is closer to Birch Bay in sediment than to the "mud" sites, and
there is a definite gradient in the fineness of the "mud" with Jamestown
least fine, Fidalgo Bay finest, and Webb and Westcott in between.
TABLE 25. PERCENT OF SEDIMENT BY GRAIN SIZE, PROTECTED SOFT SUBSTRATE SITES
Site
Birch Bay
Beckett Point
Jamestown
Webb Camp
Westcott Bay
Fidalgo Bay
Elevation,
meters
-0.3
0.0
0.0
0.4
-0.3
0.6
-0.3
0.6
0.5
Finest sand
to silt
5*
0 to 5
0
5 to 10
35 to 40
15
60
55 to 65
95 to 100
Fine
sand
0%(7)
15 to 25
5
85
40
25 to 30
25
15
0 to 5
Medium
sand
95%(?)
35 to 50
90
5
15
5 to 10
5 to 10
10
0 to 5
Coarse
sand
0%
10 to 15
0 to 5
0 to 5
5 to 10
25 to 30
5
5 to 15
0
Gravel or
larger
0%
10 to 35
0 to 5
0 to 5
0
25 to 30
0 to 5
0
0
111
-------
In short, the "habitat" at a site may vary considerably with elevation
and date. "Habitat" definitions are clarified by sediment size data,
preferably taken concurrently with the biological data. Such data may help
to explain similarities and differences which don't make sense in terms of a
priori definitions.
Relative contributions of elevation and site differences, protected
soft substrate sites, summer: The contributions of these factors to
variability were assessed by considering all available samples at low to mid
elevations taken in summer, 1976, at Jamestown, Webb Camp, Westcott Bay, and
Fidalgo Bay. Higher elevations were omitted because they were anomalous at
Jamestown and unavailable at Fidalgo Bay. Birch Bay and Beckett Point were
eliminated because the analyses already discussed indicated that they
differed greatly from the other four sites. The groups in the analysis and
their means are plotted in Figure 25.
Figure 25 indicates that the most dramatic elevation differences
occurred at Jamestown, with the 0.6 m elevation having fewer species than the
lower ones. Elevation effects were indistinct at Fidalgo Bay, but only a
narrow range of elevations was sampled there. Differences between the June
and August samples at Webb Camp and Westcott Bay were small, indicating that,
at least in summer, within-season variability is not highly significant.
For further elucidation of the relative importance of site and
elevation, we considered a set of six orthogonal contrasts for elevation
effects and the remaining portion of the Factor SS which can be assumed to be
due largely to site effects (Table 26). A full set of orthogonal contrasts
was not constructed for this analysis because unequal group sizes made the
task too difficult. Site differences in animal count surpassed elevation
differences in importance, largely due to the low values at Fidalgo Bay.
Elevation effects dominated in the other parameters, largely due to the large
difference of the O.6 m elevation from the others at Jamestown.
Year— to— year variability, protected soft ayfryt'-'r^-he aitess A final
analysis of low-elevation data from Beckett Point and Westcott Bay was
performed to assess year-to-year variability (Figure 26 and Table 27). Two
years of quarterly data were available at Beckett Point and two years for all
seasons but fall at Westcott Bay. As in the analysis of elevation versus
site differences, we did not attempt to construct a complete set of
orthogonal contrasts due to unequal group sizes.
The only highly significant between-year difference occurred in the
spring samples at Beckett Point, one of the many examples in the data set of
greater variability in spring than in other seasons. Clearly, site
differences (which in this case could be interpreted as differences between
mixed fine and mud habitats) far outweigh year-to-year differences in
significance. In terms of animal diversity, neither site nor year
differences were highly significant .
112
-------
Number of Animal Taxa Sa
Site Date Elev # of POOLED
Jamestown, Strait 760708 0.3 2
Jamestown, Strait 760708 0.4 3
Jamestown, Strait 760708 0.6 2
Jamestown, Strait 760713 0.0 3
Webb Camp, SJI 760612 -0.3 2
Webb Camp, SJI 760612 0.6 3
Webb Camp, SJI 760807 -0.3 2
Webb Camp, SJI 760807 0.6 3
Westcott Bay, SJI 760611 -0.3 2
Westcott Bay, SJI 760611 0.6 2
Westcott Bay, SJI 760806 -0.3 2
Westcott Bay, SJI 760806 0.6 2
Fidalgo Bay, NPS 760613 0.5 5
Fidalgo Bay, NPS 760613 0.6 4
Fidalgo Bay, NPS 760613 0.7 2
Fidalgo Bay, NPS 760809 0.5 3 +
0.0
Site Date Elev i of POOLED
m Samples •*
Jamestown, Strait 760708 0.3 2
Jamestown, Strait 760708 0.4 3
Jamestown, Strait 760708 0.6 2
Jamestown, Strait 760713 0.0 3
Webb Camp, SJI 760612 -0.3 2
Webb Camp, SJI 760612 0.6 3
Webb Camp, SJI 760807 -0.3 2
Webb Camp, SJI 760807 0.6 3
Westcott Bay, SJI 760611 -0.3 2
Westcott Bay, SJI 760611 0.6 2
Westcott Bay, SJI 760806 -0.3 2
Westcott Bay, SJI 760806 0.6 2
ST. DEU. = 3.6?
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ST. DEU. = O.££t,
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N
a
Site Date Elev 1 of FOOLED
Jamestown, Strait 760708 0.3 2
Jamestown, Strait 760708 0.4 3
Jamestown, Strait 760708 0.6 2 i*».**i»
Jamestown, Strait 760713 0.0 3
Webb Camp, SJI 760612 -0.3 2
Webb Camp, SJI 760612 0.6 3
Webb Camp, SJI 760807 -0.3 2
Webb Camp, SJI 760807 0.6 3
Westcott Bay, SJI 760611 -0.3 2
Westcott Bay, SJI 760611 0.6 2
Westcott Bay, SJI 760806 -0.3 2
Westcott Bay, SJI 760806 0.6 2
Fidalgo Bay, NPS 760613 0.5 5
Fidalgo Bay, NPS 760613 0.6 4
Fidalgo Bay, NPS 760613 0.7 2
Fidalgo Bay, NPS 760809 0.5 3
6.00
11 i
£.00 £.50 3.00 3.50 4.00
99 315 - 999 3161 9999
Animal Diversity H'a
ST. DEU. = 6.186
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Figure 25. Group means from analysis of variance of numerical assemblage
parameters at protected soft substrate sites, low and mid
intertidal, summer, with individual 95 percent confidence
intervals (A.1.7) based on pooled standard deviations.
113
-------
TABLE 26. CONTRIBUTIONS OF SITE AND ELEVATION DIFFERENCES TO VARIABILITY IN
PROTECTED SOFT SUBSTRATE SUMMER ASSEMBLAGE PARAMETERS,
LOW AND MID INTERTIDAL
% of Factor SS
s/ iog10(Na+i) H;
SITE: (Percentage of Factor SS, nine
degrees of freedom, representing site
differences primarily) 42%* 86%* 41%*
ELEVATION: (Contrasts, each with one degree
of freedom)
Jamestown 0.0 vs. 0.4 meters 18 * 10 * 0
0.3 vs. 0.6 33 * 0 45 *
Webb Camp 760612 -0.3 vs. 0.6 3 0 3
760807 -0.3 vs. 0.6 2 0 10 *
Westcott 760611 -0.3 vs. 0.6 1 3 0
760806 -0.3 vs. 0.6 111*
100% 100% 100%
t The Factor SS represents the fraction of total variability in an assemblage
parameter explained by the one-way analysis of variance model. It is defined
in Table A-2 of Appendix A, and its partitioning by means of contrasts is
explained in the discussion following that table.
# The numerical assemblage parameters Sa (number of animal taxa), log1Q(Na+l)
(log transformed animal count), and Hg (animal diversity)
are defined in Section 5.2.1.
* Significant at the 0.001 level. Our choice of this level for testing is
discussed in Section A.4 of Appendix A. Note that the same % of Factor SS
may be significant for one parameter but not another because the overall
significance of the Factor SS is greater for the one than for the other.
114
-------
Site Date Elev # of
m Samples
Beckett Point, Strait 760416 0.0 3
Beckett Point, Strait 760712 0.0 3
Beckett Point, Strait 761119 0.0 3
Beckett Point, Strait 770107 0.0 3
Beckett Point, Strait 770406 0,0 2
Beckett Point, Strait 770701 0.0 2
Beckett Point, Strait 771114 0.0 2
Beckett Point, Strait 780111 0.0 2
Westcott Bay, SJI 741228 -0.3 2
Westcott Bay, SJI 750428 -0.3 2
Westcott Bay, SJI 750806 -0.3 2
Westcott Bay, SJI 751008 -0.3 2
Westcott Bay, SJI 751201 -0.3 2
Westcott Bay, SJI 760417 -0.3 2
Westcott Bay, SJI 760806 -0.3 2
Number of Animal Taxa 83
pooLef ST. DBA = 7.28
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iO. 25. 40. 55. 70. 85.
Site Date Elev # of
m Samples
Beckett Point, Strait 760416 0,0 3
Beckett Point, Strait 760712 0.0 3
Beckett Point, Strait 761119 0,0 3
Beckett Point, Strait 770107 0.0 3
Beckett Point, Strait 770406 0,0 2
Beckett Point, Strait 770701 0.0 2
Ro/-L-a-H- Dn-i v>+- Ctva-i-r- 77111/1 f\ f\ 7
DcCKcuL rOl n u, oTTol L / / I 1 \*i U. U £.
Beckett Point, Strait 780111 0.0 2
Westcott Bay, SJI 741228 -0.3 2
Westcott Bay, SJI 750428 -0.3 2
Westcott Bay, SJI 750806 -0.3 2
Westcott Bay, SJI 751008 -0.3 2
Westcott Bay, SJI 751201 -0.3 2
Westcott Bay, SJI 760417 -0.3 2
Westcott Bay, SJI 760806 -0.3 2
Tota! Animal Count
POOLED ST. EEU. = 0- 163
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10 3
Site Date Elev # of
m Samples
Beckett Point, Strait 760416 0,0 3
Beckett Point, Strait 760712 0,0 3
Beckett Point, Strait 761119 0,0 3
Beckett Point, Strait 770107 0.0 3
Beckett Point, Strait 770406 0,0 2
Beckett Point, Strait 770701 0.0 2
Beckett Point, Strait 771114 0.0 2
Beckett Point, Strait 780111 0.0 2
Westcott Bay, SJI 741228 -0.3 2
Westcott Bay, SJI 750428 .0,3 2
Westcott Bay, SJI 750806 -0.3 2
Westcott Bay, SJI 751008 -0.3 2
Westcott Bay, SJI 751201 -0.3 2
Westcott Bay, SJI 760417 -0.3 2
Westcott Bay, SJI 760806 -0.3 2
1.
N 250 630 1584 3980 9999
a
Animal Diversity H'a
POOLED ST. DEU. = 0.336
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25118
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Figure 26. Group means from analysis of variance of numerical assemblage
parameters at protected soft substrate sites, low intertidal, all
years and seasons, with individual 95 percent confidence intervals
(A. 1.7) based on pooled standard deviations.
115
-------
TABLE 27. CONTRIBUTIONS OF YEAR-TO-YEAR CHANGES TO VARIABILITY IN LOW
ELEVATION PROTECTED SOFT SUBSTRATE ASSEMBLAGE PARAMETERS
of Factor SS t
Ha
SITE AND SEASON (Percentage of Factor
SS, seven degrees of
freedom, representing
site and season
differences):
YEAR (Contrasts by site and season):
83%*
79%*
71%
Beckett Point April 1976 vs. 1977
July
November
January 1977 vs. 1978
Westcott Bay December 1974 vs. 1975
April 1975 vs. 1976
August
4
8
3
1
0
0
1
100%
16*
3
1
0
1
0
0
100%
10
0
11
1
1
0
6
100%
t The Factor SS represents the fraction of total variability in an assemblage
parameter explained by the one-way analysis of variance model. It is defined
in Table A-2 of Appendix A, and its partitioning by means of contrasts is
explained in the discussion following that table.
# The numerical assemblage parameters Sa (number of animal taxa), logjQ(Na+l)
(log transformed animal count), and Ha (animal diversity)
are defined in Section 5.2.1.
* Significant at the 0.001 level. Our choice of this level for testing is
discussed in Section A.4 of Appendix A. Note that the same % of Factor SS
may be significant for one parameter but not another because the overall
significance of the Factor SS is greater for the one than for the other.
6.2.2 Population analyses
Individual species were not examined for the exposed soft substrate
sites since even assemblage parameters were zero in too many samples to
permit unrestricted use of regression analysis or analysis of variance. The
strong clustering by site exhibited in the soft substrate dendrograms implies
that even at protected sites with similar sediment we can expect to find few
ubiquitous species.
116
-------
However, a short list of animals found quite regularly at the most
protected sites was compiled and counts of these animals were examined after
log transformation. We considered the polychaetes Eteone longaf Glycinde
picta. Pygoapio eleqans. Pseudopolydora kempi. Armandia brevia. and Capitella
capitata; the bivalves Macoma nasu-faa and Transennella tantilla; and the
gammarid amphipod genus Corophium. These animals were selected in part
because they are relatively easy to identify and were in fact identified at
some sites and times by both Nyblade and Webber. Thus it is reasonable to
assume that site differences in animal numbers uncovered by analysis of
variance are not a result of investigator bias.
An inspection of our tabulation of sites, dates, and elevations in
which these animals occurred indicated that we should consider low to mid
elevations (-0.3 m to 0.6 m) at the six sites (Birch Bay, Beckett Point,
Jamestown, Webb Camp, Westcott Bay, and Fidalgo Bay) included in the
assemblage parameter analysis of Figure 24. All available summer 1976
samples in this range of elevations were included in a one-way analysis of
variance. Groups in the analysis were defined by site and elevation, with
each group containing data from only one of the sites and only the upper or
lower half of the elevation range.
Group means with individual 95 percent confidence intervals are shown
in Figure 27. Each of the animals except Glycinde plcta was absent from at
least one group. The applicability of the analysis of variance model is
therefore questionable, and the plotted confidence intervals may be
inaccurate. Nevertheless, Figure 27 points to some clear conclusions.
First, Birch Bay has fewer animals than the other sites, accounting
for most of the zero groups. Eteone longa. Armandia brevis. Capitella
capitata. Transennella tantilla. and Corophium were not collected at Birch
Bay in these summer 1976 samples although they were found there at other
times. The remaining four species considered in this analysis occurred in
smaller numbers at Birch Bay than at the other sites. The relative poverty
of these populations at Birch Bay is consistent with the assemblage parameter
results of Figure 24 and the characterization of Birch Bay as a moderately
protected sand rather than a protected mud or mixed habitat.
Habitat definitions supplemented by the sediment data of Table 22
contribute to an understanding of other population characteristics indicated
by Figure 27. For example, Pseudopolydora k£mj£L occurs in significant
numbers only at the two finest mud sites, Westcott Bay and Fidalgo Bay.
Some geographic patterns appear evident. For example, Transennella
tantilla is most plentiful at the SJI sites and entirely absent at the NPS
sites. Macoma nasuta is also most dense at the SJI sites and is nearly
absent at Beckett Point and Jamestown in the Strait as well as at Birch Bay.
It is difficult, however, to separate effects of substrate, exposure,
geography, and other factors. For example, the Webb Camp and Westcott Bay
sites, both in Westcott Bay, are similar in terms of exposure and, especially
at the lower elevations, substrate. In addition, unlike the other sites.
117
-------
Site
Elevation # of
Samples
Birch Bay, NPS -0.3 to 0.1
Beckett Point, Strait 0.0
Jamestown, Strait 0.0
Webb Camp, SJI -0.3
Westcott Bay, SJI -0.3
Birch Bay, NPS 0.2 to 0.6
Beckett Point, Strait 0.3 to 0.6
Jamestown, Strait
Webb Camp, SJI
Westcott Bay, SJI
Fidalgo Bay, NPS
0.3 to 0.6
0.6
0.6
0.5 to 0.6
10
3
3
4
4
6
4
7
6
4
12
POOLED ST. OEV. «
I»*!»*!
I»***I*****I
Capitate capitata
O. 342
I****i
i*****i****i
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I**I*I
Iog10(count+l) O. OO 0.70 1.40 2.
count 0 4 24 125
10
2.80
630
3. 50
3161
Macoma nasuta
Site
Birch Bay, NPS
Beckett Point, Strait
Jamestown, Strait
Webb Camp, SJI
Westcott Bay, SJI
Birch Bay, NPS
Beckett Point, Strait
Jamestown, Strait
Webb Camp, SJI
Westcott Bay, SJI
Fidalgo Bay, NPS
Elevation # of POOLED ST. DEV. - O.
-0.
0.
0.
0.
0.
ID
3 to
0.0
0.0
-0.3
-0.3
2 to
3 to
3 to
0.6
0.6
5 to
0.
0.
0.
0.
0.
aamp i e b t— — «— -r- — - — — — T
1 10 I***I***I
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4
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12
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Iog10(count+l)o. OO 0.30
count 0 1
. 186
I ***** I ***** I
I ***** I ****** I
HHtI****I
I***** I ***** I
0. 6O O. 90 1. 2O 1. SO
3 7 15 31
TransenneUa tantilla
Elevation # of
m Samples
-0.3 to 0.1 10
3
Site
Birch Bay, NPS
Beckett Point, Strait 0.0
Jamestown, Strait 0.0
Webb Camp, SJI -0.3
Westcott Bay, SJI -0.3
Birch Bay, NPS 0.2 to 0.6
Beckett Point, Strait 0.3 to 0.6
Jamestown, Strait 0.3 to 0.6
Webb Camp, SJI 0.6
Westcott Bay, SJI 0.6
Fidalgo Bay, NPS 0.5 to 0.6
3
4
4
6
4
7
6
4
12
POOLED ST. DEV. «
O. 467
I****I****I
I********I********I
I********I********I
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I****I«***I
log.0(count+Bo. oo 0.60 1.20
count 0 3 15
1.80
62
2. 40
250
3. 00
999
Figure 27. Means of log transformed counts for selected animals from
protected soft substrate intertidal Bites, low to mid elevations,
summer 1976, with individual 95 percent confidence intervals
(A.1.7) based on pooled standard deviations from analysis of
variance. The model (A.3.1) of Appendix A with varying group
sizes was used, resulting in varying confidence interval lengths.
Because they are based on pooled standard deviations computed from
data at all sites, confidence intervals for absent or scarce
species at a given site extend above and below zero. Axis labels
are in log units with the corresponding counts given below.
118
-------
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-------
Pygospio elegans
Site Elevation # of
m Sample
Birch Bay, NPS -0.3 to 0.1 10
Beckett Point, Strait 0.0 3
Jamestown, Strait 0.0 3
Webb Camp, SJI -0.3 4
Westcott Bay, SJI -0.3 4
Birch Bay, NPS 0.2 to 0.6 6
Beckett Point, Strait 0.3 to 0.6 4
Jamestown, Strait 0.3 to 0.6 7
Webb Camp, SJI 0.6 6
Westcott Bay, SJI 0.6 4
Fidalgo Bay, NPS 0.5 to 0.6 12
lo<
POOLED ST. DEV. = O. 556
« <.-.._..,.„ f „, . .. ,, LJ_ 4.J. . . . j + ,, + + +
I***** I***** I
I »********» I ********** I
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I******** I ******** I
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I******* I******* I
I ********* I******** I
I****I***»I
J10(count+l)-0. 60 0.00 0.60 1.20 1.80 2.40
count - o 3 15 62 250
Pseudopolydora kempi
Site
Birch Bay, NPS
Beckett Point, Strait
Jamestown, Strait
Webb Camp, SJI
Westcott Bay, SJI
Birch Bay, NPS
Beckett Point, Strait
Jamestown, Strait
Webb Camp, SJI
Westcott Bay, SJI
Fidalgo Bay, NPS
Elevation f of
m Sample
•0.3 to 0.1
0.0
0.0
-0.3
-0.3
0.2 to 0.6
0.3 to 0.6
0.3 to 0.6
0.6
0.6
0.5 to 0.6
10
3
3
4
4
6
4
7
6
4
12
log
POOLED ST. DEV » 0 269
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9 31 99
2. 50
315
Armandia brevis
Site
Birch Bay, NPS
Beckett Point, Strait
Jamestown, Strait
Webb Camp, SJI
Westcott Bay, SJI
Birch Bay, NPS
Beckett Point, Strait
Jamestown, Strait
Webb Camp, SJI
Westcott Bay, SJI
Fidalgo Bay, NPS
Elevation # of POOLED ST. DEV. - 0.315
-0.
0.
0.
m
3 to
0.0
0.0
-0.3
-0.3
2 to
3 to
0.3 to
0.
0.6
0.6
,5 to
0,
0.
0.
0.
0,
iamp i e s -t— -—-——— r— — — 1 1
,1 10 I****I**»*I
.6
.6
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3
3
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4
6
4
7
6
4
12
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log1Q(count+l) O. 00 0. 4O
count 0 2
*I
0 SO
5
1.20 1.60
15 39
2. OO
99
Figure 27 (continued)
120
-------
they are private beaches. All of these factors may contribute to their
similarly larger populations of bivalves.
Finally, even within the limited range of elevations considered there
is some evidence of elevation effects. For example, Pseudopolydora kempi was
found more frequently in the upper part of the range at all sites. However,
site differences dominate elevation differences for all these populations.
Site differences and perhaps even some apparent elevation differences
are at least partially a reflection of the spatial patchiness of even these
most ubiquitous species. As we will see in the next section, they exhibit
temporal patchiness as well. Both sorts of patchiness make prediction of
population parameters difficult if not impossible.
6.2.3 Predictive models
As noted in earlier sections, we concluded that the analysis of
variance approach yielded the most fruitful predictive models supportable by
the existing data base. Many significant site-to-site differences were
detected by analysis of variance even within a given habitat type, and
elevation and season differences were also significant in many cases,
implying that the best predictor for assemblage parameter values at a given
site, season, and elevation would be a previously determined mean value from
the same site, season, and elevation.
Cross-site prediction within a well-defined habitat type and
geographical area sometimes appeared to be possible. For example, the
protected Westcott Bay sites were similar to each other. The moderately
exposed sand sites Eagle Cove and North Beach were similar to each other at
some seasons and elevations.
To verify predictability of assemblage parameter values at a
previously observed site from its past or from a similar nearby site, an
attempt was made to predict Eagle Cove high intertidal data for the summers
of 1977 and 1978 on numbers of taxa and individuals. These data were
available in Nyblade (1979b) and had not been used for model development.
We hypothesized that mean values of S computed from earlier summer
high intertidal samples at Eagle Cove and North Beach should be good
predictors of the 1977 and 1978 Eagle Cove values. We also tried predicting
log (N +1) although we expected it to be less predictable since among the
assemblage parameters computed at soft substrate sites, N most often
exhibited spatial and temporal variability.
The vehicle for assessing whether the indicated mean values were in
fact good predictors was a test for difference in mean or median values using
the old and new data. We used both the two-sample t-test and the Mann-
Whitney test since the latter is valid even if the old and new samples are
not normally distributed with equal variances.
121
-------
Testing at the 5 percent level, no significant differences were found
between values of 8 at Eagle Cove in either 1977 or 1978 and those computed
from either 1976 Eagle Cove data or combined 1975 and 1976 data. S computed
from either 1977 North Beach data or combined 1976 and 1977 data from that
site was also not significantly different from the 1977 or 1978 Eagle Cove
values. The means were indeed good predictors for S .
In comparing counts, the Eagle Cove data from 1976 alone did not show
significant differences from the 1977 data, but both the t- and Mann-Whitney
tests were significant at the 5 percent level when the 1975 and 1976 Eagle
Cove data combined were compared with the 1977 data. Neither the 1977 North
Beach data nor the combined 1976 and 1977 data yielded values of log (N +1)
which differed significantly from those at Eagle Cove in 1977. However,
significant differences between log (N +1) at Eagle Cove in 1978 and the
pre-1977 values at both sites were indicated. The 1977 and 1978 Eagle Cove
values did not differ significantly.
The methods used for assessing the predictability of assemblage
parameters at the moderately exposed sand sites were also applied to the
protected mud sites Westcott Bay and Fidalgo Bay. Summer 1978 data from both
sites as well as 1977 data from Westcott Bay were available in Nyblade
(1979b), The earlier samples with which they were compared were those
included in the analysis of Figure 24. This analysis had included two
replicates at -0.3 m and one at 0.6 m at both Webb Camp and Westcott Bay, so
for both 1977 and 1978 we included the three available samples at -0.3 m and
the first two at 0.6 m from Westcott Bay. At Fidalgo Bay we had three
replicates at 0.5 m in both 1976 and 1978. We tested at the 5 percent level,
so there is a high probability of one or more false rejections among the
multiple tests.
Site-specific predictions of S were possible at both Westcott Bay and
Fidalgo Bay, and the 1976 Webb Camp data were also usable for predicting S
at Westcott Bay in 1977 and 1978. Animal diversity H' was similarly
predictable. However, the i-test detected significant differences in animal
counts in the site-specific predictions of summer 1978 from 1976 data. In
fact, as we would certainly not expect from Figure 24, 1978 Westcott Bay data
were better for predicting 1978 Fidalgo Bay data than were the 1976 Fidalgo
Bay data.
Nyblade found larger numbers of species and individuals in 1978 at
Fidalgo Bay than Webber found in 1976. Several explanations for these
differences are possible. A real increase may have occurred at Fidalgo Bay
due to weather, recruitment, or other patterns. It may be that concurrent
data from a site reasonably close to Fidalgo Bay geographically and in terms
of habitat reflects these patterns better than two-year-old data from Fidalgo
Bay. It may be that undiscovered data errors or investigator biases are
contributing to the differences. It may simply be that random variability or
violation of statistical assumptions of the ±-test have led to a false
rejection of the hypothesis of year-to-year similarity at Fidalgo Bay.
122
-------
All the significant differences between the data of Figure 24 and the
westcott and Fidalgo Bay data of Nyblade (1979b) involved count data two
years apart in time. A difference between the 1976 SJI and Strait data and
the 1978 Westcott Bay data was indicated by the more generally applicable
Mann-Whitney test as well as the t-test.
We have mentioned in earlier discussions that failure of a statistical
test to detect differences is no guarantee that none exist. In order to
fully assess predictability of assemblage parameters, we must examine the
power of the tests being used to detect change in soft substrate intertidal
habitats using the techniques discussed in Appendix A and applied to rocky
intertidal data in Section 6.1.3. Table 28 gives detectable differences in
soft substrate assemblage parameters analogous to those presented for rock
data in Table 16.
TABLE 28. DETECTABLE PERCENT CHANGES, SOFT SUBSTRATE ASSEMBLAGE PARAMETERS
Habitat
Protected mud or
mixed fine, low
to mid elevations,
summer?
Exposed sand,
high elevation,
summerT
Replicates
nl "2
3
5
18
12
18
15
25
5
15
25
3
5
3
5
8
15
25
5
15
25
Sa
51%
34
31
26
21
18
14
120
63
48
Probability
(32%)
(29)
(27)
(23)
(18)
(15)
(12)
of Detection*
log10(Na+l)
21% (17%)
13 (11)
12 (11)
11 (9)
8 (7)
7 (6)
5 (5)
0.9
H
49%
33
30
25
20
17
13
a
(41%)
(28)
(26)
(22)
(18)
(15)
(12)
(105) 136 (119)
(55)
(43)
71 (62)
54 (48)
Sa
30%
21
19
16
12
10
8
76
38
29
Probability
log
(23%)
(16)
(15)
(13)
(10)
(9)
(7)
(60)
(32)
(24)
of Detection
Q(Na+l )
12% (9::)
8 (7)
7 (6)
7 (5)
5 (4)
4 (4)
3 (3)
85 (68)
42 (36)
33 (27)
0.5
H1
a
295; (22,'
19 (16)
18 (15)
16 (13)
12 (10)
10 (9)
8 (7)
§ The numerical assemblage parameters included in this table are defined in Section 5.2.1.
* Probabilities of detection (0.9 in the left half of the table, 0.5 in the right half) are based on the assumption that
means of the indicated numerical assemblage parameters are being compared using the two-sample t-test of (A'.4.1) of
Appendix A. The level of the test is assumed to be a = 0.05. There are assumed to be ni replicates in one sample and
n2 in the other. Detectable percent changes for a two-sided test are tabulated, with values for a one-sided test in
parentheses. A parameter with a small detectable percent change is usable for estimating community changes while one
for which only large changes are detectable is less useful.
+ Values of yj and a in (A.4.5) were summer 1976 means at Jamestown and pooled standard deviations from the analysis
of variance of Figure 24. Jamestown means were chosen as "typical."
t Values of m and o in (A.4.5) were summer 1977 means at the North Beach sand site, chosen as "typical," and pooled
standard deviations from the analysis of variance of Figure 22.
123
-------
The data in Table 28 indicate that in protected soft substrate
habitats, log1Q(N +1) has a smaller coefficient of variation than S and H'.
With n = n = 3 the latter two parameters must change by about 50 percent to
give a 90 percent probability of detecting the change. If n = n = 5 they
must change by about a third instead of by half to give that probability.
Relatively small changes in log (N +1) are detectable, so it is not
surprising that the significant differences were found in this parameter.
The apparent predictability of S and H' is at least partially due to
the fact that only relatively large changes in these parameters are reliably
detectable. Much power to detect change is gained by collecting five instead
of three samples. Power achievable by collecting more than five replicates
increases more slowly with n and n .
The changes detectable with 90 percent probability and n = n = 5 at
Jamestown translate into a decrease to 23 or an increase to 46 in S , a
decrease to 768 or an increase to 5,600 in N , and a decrease to 1.4 or an
increase to 2.8 in H'. a
a
The mean value of S and the value of N corresponding to the mean of
log (N +1) at the exposedasites are typified By the high intertidal North
Beach sand data used for the exposed sand calculations of Table 28. These
values are S = 3 and N =5. Diversities at the exposed sites are generally
less than one. Thus the differences between protected and exposed sites are
clearly detectable with n = n = 5. However, it is a striking feature of
Table 28 that only very large changes (50 percent or more) are reliably
detectable at the exposed sites even with 25 replicates in each of the two
samples being compared. The apparent predictability of the numerical
assemblage parameters at exposed sites is clearly due largely to high
coefficients of variation which make detection of small changes at exposed
sites improbable.
Table 29 shows detectable percent changes in population counts at
protected soft substrate sites. The animals included in the analysis of
Figure 27 were considered. Cell means and standard deviations of the 12 mid-
elevation Fidalgo Bay samples were used in the calculations for all species
except Armandia brevis and Tranaennella -bantilia, which were not found in
these Fidalgo Bay samples. The mid elevation Westcott Bay values were used
for these two species.
It is clear from Table 29 that the level of replication in the
baseline study program was inadequate for reliably detecting changes in
population densities, at least at Fidalgo Bay. As suggested by the results
for Transennella tantilla and Armandia brevis. the situation is sometimes
better and sometimes worse when we consider the other sites. We used Fidalgo
Bay values in (A.4.5) for Table 29 because the number of replicates at the
other sites was much too low to provide reasonable estimates of means and
standard deviations. In order to reliably assess the possibility of using a
particular species as an indicator of change at a. particular site, one would
need to collect 15 to 25 replicates on several occasions, estimate these
statistics, and calculate detectable percent changes for various values of n^
124
-------
and n2> It seems likely that only a few species at any site could be
monitored with a reasonable level of replication.
As mentioned in our discussion of rocky intertidal data, looking at
groups of species (for example, trophic groups) rather than individual
species might result in detectability of smaller percent changes with the
same level of replication. In addition, other population parameters such as
weight or percent cover which we did not examine due to data inadequacies
might prove to be less variable than counts and therefore more useful as
indices of population changes.
TABLE 29. DETECTABLE PERCENT CHANGES IN TRANSFORMED POPULATION COUNTS, PROTECTED MUD SITES
Probability of Detection*
n,=n,=5
Eteone Tonga ! 225% (196%)
Glycinde pi eta 106 (93)
Pygospio elegans 224 (196)
Pseudopolydora kempi 84 (73)
Armandia brevis 483 (423)
Capitella capitata
128 (112)
Macoma nasuta 196 (171)
Transennella tantilla
8 (7)
Corophium 142 (125)
n1=n2=15
117% (10350
55 (48)
117 (103)
44 (38)
252 (221)
67 (59)
102 (90)
4 (4)
74 (65)
0.9
YV25
89% (80%)
42 (37)
Probability
n,=n2=5
140% (112%)
66 (53)
89 (79) 140 (112)
33 (30)
191 (171)
51 (45)
77 (69)
52 (42)
of Detection
n1=n2=15
70% (59S)
33 (28)
70 (59)
26 (22)
302 (242) 151 (127)
80 (64)
122 (98)
3 (3) I 5 (4)
56 (50)
89 (71)
40 (34)
61 (51)
3 (2)
44 (37)
0.5
n,=n2=25
54% (45%)
26 (21)
54 (45)
20 (17)
117 (97)
31 (26)
47 (39)
2 (2)
34 (28)
* Probabilities of detection are based on the assumption that means of Iog10(count+l) for these animals are being compared
as in Table 28. Detectable percent changes for a two-sided test are tabulated, with values for a one-sided test in
parentheses. Values of yj and o in (A.4.5) were cell means and standard deviations from mid elevation summer 1976 data
included in the analysis of Figure 27. Cell means and standard deviations of the 12 mid elevation Fidalgo Bay samples
were used except for Armandia brevis and Transennella tantilla, which were not found in these Fidalgo Bay samples. The
mid elevation Westcott Bay values were used for these two species.
125
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6.2.4 Summary of the Prognosis for Assessing Changes in
Communit Structure at So'fc "ste Intertidal Sites
Seasonal and year-to-year similarities in soft substrate intertidal
communities, defined by abundance of 50 major plants and animals, were often
high for a given site and elevation. However, similarities among sites were
less than 25 percent in many cases and even stations from the same site and
elevation stratum sometimes exhibited similarities in this range.
Similarities of 50 'percent or more generally occurred only between sites with
similar substrates, although "sand" and "gravel" sites fell into the same
clusters in some cases. Elevation effects were less significant than at
rocky sites, with clusters often consisting of stations from all elevations
at a given site . Similarities of 75 percent or more involved stations from
the -same location and the same or adjacent elevation strata except for a few
predominantly exposed gravel site groupings.
The most pervasive influence on species composition in soft substrate
intertidal habitats of the inland waters of northwestern Washington appears
to be "exposure," a complex combination of factors including wave energy,
sediment stability and water retention characteristics, and seasonal wind and
current effects. Mixtures of sand and gravel are not good indicators of
exposure, expecially along a. geologically young coastline where coastal
processes have not had a sufficient period of time to rework newly exposed
sediments. Thus, mixed sediments commonly occur in both protected and
exposed areas, and "sand" and "gravel" sites which are similar in terms of
exposure have similar biological communities . However, the percent of fine
( silt size or smaller ) sediment is a function of exposure and a major
determinant of biological richness.
Analysis of variance of numerical assemblage parameters at exposed
sand and gravel sites pointed to a division between a moderately exposed
group of sites representing the eastern end of the Strait, Whidbey Island,
and San Juan Island and a. highly exposed group containing most of the Strait
sites and West Beach on Whidbey Island.
In the moderately exposed group, elevation effects were strong, with
high elevation assemblages resembling the assemblages at the more exposed
sites and the low elevations being richer. The sand sites in the group,
North Beach in the Strait and Eagle Cove on San Juan Island, were quite
similar, unlike the gravel sites, Ebey's Landing and Deadman Bay. Deadman
Bay (SJI) had more animals than Ebey's Landing (Whidbey) and showed a less
significant winter decline in richness, probably as a result of exposure.
The San Juan Island sites are probably the least exposed of the exposed sand
and gravel sites.
In the highly exposed group, elevation effects and year-to-year
differences were generally insignificant. Site differences in the assemblage
parameters were less significant than those indicated by cluster analysis
because S and N , unlike the similarity indices used for clustering, are not
affected Jay whetner the few animals found in samples at two different sites
represent the same or different species.
126
-------
There were indications of differences due to substrate among both the
moderately and highly exposed sites, but these, like geographic differences,
were difficult to separate from elevation and exposure effects.
Regression analysis and analysis of variance of numerical assemblage
parameters at protected soft substrate sites pointed to the same conclusions
as cluster analysis. Site differences, only partially explained by habitat
definition according to substrate, dominated the variability. Exposure
and/or geography as well as substrate characteristics contributed to these
site differences. For example, the moderately protected NFS sand site (Birch
Bay) and gravel site (Guemes Island) were poorer than the most protected
sites such as Westcott Bay (SJI). Birch Bay and Guemes Island, like Deadman
Bay, also appeared to be quite different from more exposed sites, pointing to
the conclusion that their use for predictions which are not site-specific is
precluded. Highly significant differences were indicated between Strait
sites and those in more protected waters (NFS, SJI).
Elevation was a highly significant factor at protected SJI and strait
sites, sometimes outweighing site differences in importance. However,
elevation was relatively unimportant at Birch Bay, Guemes Island, and Fidalgo
Bay, all NPS sites. The most significant "elevation" effects were at sites
where substrate characteristics changed greatly with elevation.
No species were found with sufficient regularity at exposed soft
substrate sites to permit population analyses. No plant species were found
consistently even at protected sites. Analysis of variance of abundances of
the few animal species (polychaetes, bivalves, and the gammarid amphipod
Corophium) found most regularly at the most protected sites indicated that
the level of replication used in the baseline study program was inadequate
for reliably detecting changes in population densities. In order to have a
90 percent probability of detecting even density changes of 50 percent or
more in most of these species, 15 to 25 replicates at a given site, season,
and elevation would be needed. The prognosis for cross-site prediction is
extremely poor since the analysis indicated obvious site differences not
explainable by available information on sediment composition and exposure.
The level of replication required for reliable detection of changes in
assemblage parameter values at exposed soft substrate intertidal sites is
comparable to that required for population parameters at protected sites—25
replicates to reliably detect changes of 50 percent in number of animal taxa
S or transformed animal count log (N +1). Nevertheless, detectable
differences in log (N +1) were observed when 1978 Eagle Cove data were
compared with pre-1977 data from Eagle Cove and North Beach, a similar
exposed sand site.
Smaller changes—around 30 percent in S or diversity H', 10 to
15 percent in log (N +1)—could be detected with five replicates at
protected mud or mixe§ sites. Differences between values of these parameters
at protected and exposed sites were clearly detectable. In addition, some
analyses indicated differences within the most protected site group,
particularly in log (N +1), even between sites which were most similar in
terms of substrate, for example the mud sites Westcott Bay and Fidalgo Bay.
127
-------
Differences in assemblage parameter values at a given protected site within
and between seasons and from one year to the next were usually insignificant,
particularly if spring samples, which exhibit more variability than data from
other seasons, were eliminated. More significant differences were detected
in samples taken two years apart.
The assemblage parameters s and H' at protected soft substrate sites
appear to be most useful for prediction and change detection. However,
cross-site prediction of these parameters requires better habitat character-
izations, especially with regard to exposure, than those of the present data
base. Cross-regional predictability (for example, prediction of parameters
at an NPS site from those at a Strait site with similar sediment and
exposure) appears problematical. The present data base does not permit the
clear separation of regional effects from differences in sediment and
exposure or investigator biases.
Heal changes in animal counts occur with time at protected as well as
exposed sites, so neither site-specific nor cross-site prediction of animal
density appears to be possible especially when it is necessary to predict
more than a year into the future. There appear to be year-to-year
dependencies in abundance, but many more years of baseline data would be
needed to determine whether there are real temporal patterns which could be
captured by predictive time series models such as the ARMA models of Box and
Jenkins (1970). As in the rocky intertidal, statistical analysis alone would
not be able to determine that an oil spill or other perturbation was
responsible for a change in counts of all or particular animal species.
6.3 INTERTIDAL COBBLE SUBSTRATES
In Appendix c we list the animals and plants found at the cobble sites
shown in Table 1. The Appendix C listing gives the number of samples in
which each plant or animal was found at each site, sampling date, and
elevation stratum.
No further analyses of intertidal cobble data were carried out due to
the problems with the data outlined in Section 4. The differences in
sampling techniques between investigators and studies were more severe in the
cobble intertidal habitat than in rocky and soft substrates, so it would have
been difficult to make appropriate comparisons of sites and times. In
addition, correction of the errors in taxonomic codes, plant weights, and
other data would have been extremely time-consuming. It was felt that the
time was better spent on analysis of the other habitats since they represent
a larger fraction of the shoreline in the inland waters of northwestern
Washington. Gardner (1978) estimates that cobble habitats make up only
20 percent of the shoreline in the SJI and NPS study regions.
128
-------
6.4 SUBTIDAL SUBSTRATES
Subtidal data from the 23 sites shown in Table 7 at the elevations
indicated in that table were available on File 100 tapes; 1,448 different
plant and animal taxa were identified in these samples. Subtidal sampling
dates at each of the sites are given in Table 1. Locations of these subtidal
sites as well as the sites sampled by Smith (1979) are shown in Figure 2.
2
As indicated in Section 4.1.2, both Nyblade and Webber sampled 0.25-m
quadrats on subtidal rock. However, sampling techniques on subtidal soft
substrates were not at all consistent. Webber employed airlift scrapes and
cores, while Nyblade used a 0.03-m van Veen grab sampler at SJI sites and a
0.1-m van Veen in the Strait. The assortment of methods used varies in
efficacy for collecting animals and plants of different sizes as well as
producing samples of differing areas and volumes. These discrepancies make
quantitative comparisons of data from the different studies extremely
difficult. In addition, there were serious errors in the subtidal data sets
on File 10O tapes. We corrected many of these errors. However, errors in
gear codes and sample numbers in the NFS subtidal data made it impossible to
assign correct counts and weights to correct sampling methods and
replicates. Quantitative analyses of the NFS data cannot be carried out
until corrected tapes are produced by the investigator.
6.4.1 Community analyses
Tabulations of plants and animals found at different sites, times, and
elevations were computed from the subtidal data. In addition, qualitative
cluster analyses were performed for various data subsets. Computation of
numerical assemblage parameters, regression analyses, and analyses of
variance could not be carried out due to the problems discussed in the
previous paragraph, and even qualitative analyses may be influenced by the
differences in subtidal sampling techniques. However, cluster analysis
produced some interesting results.
The complete subtidal taxonomic dictionary (Table B-3 of Appendix B)
was screened to two levels for cluster analysis. The subset of plants and
animals used in most of the following discussions and starred in Table B-3
comprised 50 of the more commonly encountered or representative taxa (mostly
to specific level). The longer list included 132 commonly occurring taxa;
the animals and plants added to obtain this list are marked with a plus sign
in Table B-3. As we will see below, dendrograms computed from the same
stations using the two lists did not differ dramatically.
The subtidal data base was examined from two principal viewpoints.
First, we considered all sites at fixed depth strata (shallow, defined as
above 5 m; mid, 5.0 to 7.5 m; and deep, below 7.5 m). Second, we looked at
sites within a geographic region across the depth gradient. Data on subtidal
substrates, summarized in Table 7, permit detection of segregation patterns
based on substrate type within the dendrograms. Table 30, which indicates
the number of plant and animal taxa found at each subtidal station, is also
helpful in interpreting the cluster analyses.
129
-------
TABLE 30. NUMBERS OF PLANT AND ANIMAL TAXA AT SDBTIDAL STATIONS
SITE, REGION
BIRCH BAY, NPS
BIRCH BAY, NPS
BIRCH BAY, NPS
BIRCH BAY, NPS
CHERRY POINT, NPS
CHERRY POINT, NPS
CHERRY POINT, NPS
CHERRY POINT, NPS
MORSE CREEK, STRAIT
MORSE CREEK, STRAIT
DUNGENESS SPIT, STRAIT
DUNGENESS SPIT, STRAIT
BECKETT POINT, STRAIT
BECKETT POINT, STRAIT
NORTH BEACH COBBLE
NORTH BEACH COBBLE
JAMESTOWN, STRAIT
JAMESTOWN, STRAIT
TONGUE POINT, STRAIT
TONGUE POINT, STRAIT
TONGUE POINT, STRAIT
TONGUE POINT, STRAIT
TWIN RIVERS, STRAIT
TWIN RIVERS, STRAIT
TWIN RIVERS, STRAIT
PILLAR POINT, STRAIT
PILLAR POINT, STRAIT
KYDAKA BEACH, STRAIT
KYDAKA BEACH, STRAIT
WEST BEACH, WHIDBEY
WEST BEACH, WHIDBEY
WEST BEACH, WHIDBEY
WEST BEACH, WHIDBEY
WEST BEACH, WHIDBEY
WEST BEACH, WHIDBEY
WEST BEACH, WHIDBEY
WEST BEACH, WHIDBEY
WEST BEACH, WHIDBEY
WEST BEACH, WHIDBEY
WEST BEACH, WHIDBEY
WEST BEACH, WHIDBEY
PARTRIDGE POINT WHIDBEY
DATE DEPTH # TAXA
M PLANT ANI-
MAL
760303
760303
760830
760830
760316
760316
760909
760909
760603
770607
760602
770607
760602
770606
760602
77O624
760602
770607
760702
760703
770506
770617
760604
760614
770622
760603
770622
7606O3
770621
770419
77081O
770810
771103
780124
780124
780418
780629
780629
781014
790121
790121
770430
-2.
_Q
-2.
-8.
-2.
-8.
-2.
-8.
-5.
-9.
-5.
-5.
-5.
-5.
-5.
-5.
-5.
_g
-5.
—"5 •
-5.
-5.
-9.
-5.
-5.
._r_. g
-5.
-5.
-5.
-1.
-1.
-7.
-2.
— 1
0
0
0
0
0
0
0
0
0
0
0
0
o
0
0
o
0
0
0
0
0
0
0
o
0
0
0
o
0
5
5
5
5
5
-7.5
-1.5
-1.5
-7.5
-1.5
-1.5
-7.5
-1.5
0
0
0
0
0
5
4
3
13
30
5
4
0
2
52
65
25
30
15
32
43
31
O
32
0
5
O
O
0
O
2
5
0
0
0
0
0
0
0
0
0
21
42
34
42
49
38
54
68
80
59
94
24
24
96
76
110
127
187
103
64
73
122
107
66
113
27
9O
67
49
51
17
22
62
25
18
49
12
14
59
24
9
57
55
DEPTH # TAXA DEPTH # TAXA
M PLANT ANI- M PLANT ANI
MAL MAL
-4.0
-10.0
-4.0
-10.0
-4.0
-10.0
-4.0
-10.0
-9.0
-9.0
-9
-9
-9
-nn
-9
-9
-9
-9
-9
-9
-9
-9
-5
-2
-10
-5
-2
-10
-ii-ng
-2
-10
-5
-2
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.5
.0
.0
.5
.0
.0
.5
.0
.0
.5
-10.0
-,i,_g
.0
0
0
2
0
0
2
0
1
16
8
48
0
0
24
64
27
14
37
8
0
0
6
0
0
0
0
O
8
0
0
0
7
0
0
17
44 -6.0 0
40 -12.0 0
77 -6.0 0
51
38 -6.0 0
52 -12.0 1O
70 -6.0 0
37
123
84
84
126
87
97
83
127
43
59
79
77
76
81
25 -10 . 0 O
15 -5.0 12
59
57 -10 . 0 2
32 -5.0 5
72
57 -10.0 0
32 -5.0 0
61
55 -10 . 0 0
19 -5.0 1
47
53 -10.0 19
41
37
51
50
55
66
45
49
73
40
65
48
81
48
84
(continued)
130
-------
TABLE 30 (continued)
SITE, REGION
PARTRIDGE POINT WHIDBEY
PARTRIDGE POINT WHIDBEY
PARTRIDGE POINT WHIDBEY
PARTRIDGE POINT WHIDBEY
PARTRIDGE POINT WHIDBEY
PARTRIDGE POINT WHIDBEY
PARTRIDGE POINT WHIDBEY
PARTRIDGE POINT WHIDBEY
PARTRIDGE POINT WHIDBEY
PARTRIDGE POINT WHIDBEY
EBEY'S LANDING, WHIDBEY
EBEY'S LANDING, WHIDBEY
EBEY'S LANDING, WHIDBEY
EBEY'S LANDING, WHIDBEY
EBEY'S LANDING, WHIDBEY
EBEY'S LANDING, WHIDBEY
EBEY'S LANDING, WHIDBEY
EBEY'S LANDING, WHIDBEY
EBEY'S LANDING, WHIDBEY
EBEY'S LANDING, WHIDBEY
EBEY'S LANDING, WHIDBEY
EBEY'S LANDING, WHIDBEY
SOUTH BEACH, SJI
EAGLE COVE, SJI
DEADMAN BAY, SJI
POINT GEORGE, SJI
POINT GEORGE, SJI
POINT GEORGE, SJI
POINT GEORGE, SJI
WEBB CAMP, SJI
WESTCOTT BAY, SJI
GUEMES S. SHORE, NPS
GUEMES S. SHORE, NPS
GUEMES S. SHORE, NPS
GUEMES S. SHORE, NPS
FIDALGO BAY, NPS
FIDALGO BAY, NPS
FIDALGO BAY, NPS
FIDALGO BAY, NPS
FIDALGO HEAD, NPS
FIDALGO HEAD, NPS
FIDALGO HEAD, NPS
FIDALGO HEAD, NPS
DATE DEPTH # TAXA
M PLANT ANI-
MAL
770822
771108
780206
780206
780516
7807O1
780701
781013
790122
790122
770428
77O822
770822
771118
780213
780213
780508
780630
780630
781012
790118
790118
741016
741016
741016
741127
750206
750311
750501
741O16
741016
76O220
760220
76O911
760911
760319
760319
760917
760917
760320
760320
760917
760917
-1.5
-2.5
-1.5
-7.5
-1.5
-1.5
-7.5
-1.5
-1.5
-7.5
-1.5
-1.5
-7.5
-2.5
-1.5
-7.5
-1.5
-1.5
-7.5
-1.5
-1.5
-7.5
-2.5
-2.5
-2.5
-5.0
-5.0
-5.0
-5.0
-2.5
-2.5
-2.0
-8.0
-2.0
-8.0
-2.0
-8.0
-2.0
-8.0
-2.0
-8.0
-2.0
-8.0
0
16
16
15
20
32
26
25
26
21
5
8
15
28
22
19
18
22
25
29
5
22
0
0
0
0
0
0
2
0
0
7
7
7
6
2
0
1
0
31
8
12
3
3
72
44
52
88
133
87
127
88
86
14
51
76
83
66
7O
68
61
87
81
33
76
24
23
30
16
9
10
14
21
13
45
37
45
44
38
44
42
46
22
59
69
61
DEPTH # TAXA
M PLANT ANI-
MAL
-5.0
-2.5
-10.0
-5.0
-2.5
-10.0
-5.0
-2.5
-10.0
-5.O
-2.5
-10.0
-5.0
-2.5
-10. 0
-5.O
-2.5
-10.0
-5.0
-2.5
-10.0
-10.0
-10.0
-10.0
-10.0
-4.0
-1O.O
-4.O
-10. 0
-4.O
-10.0
-4.O
-10.0
-4.0
-10.0
-4.0
-1O.O
13
16
14
26
32
29
31
17
29
2
11
13
10
16
15
17
18
26
18
0
18
0
0
0
0
3
2
5
0
1
0
5
O
27
0
13
O
66
69
82
101
117
112
85
119
92
69
64
93
80
53
91
85
122
105
76
20
95
18
14
13
15
59
34
56
38
42
41
39
33
45
68
78
2
DEPTH # TAXA
M PLANT ANI
MAL
-10
-5
-10
-5
-10
-5
-10
-5
-10
-5
-10
-5
-10
-5
-15
-15
-15
-15
-6
-6
-6
-12
-6
-6
-6
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
17
11
29
37
25
19
21
12
18
11
20
24
24
10
0
0
0
0
3
4
0
0
0
27
15
76
58
89
98
102
88
91
74
86
75
104
112
115
77
18
26
25
22
52
49
25
48
41
45
74
131
-------
Site relationships within specific depth strata:
Figures 28 through 31 show stations within specific depth strata.
Stations from Whidbey Island are numerically dominant in these figures since
the Whidbey subtidal sampling program was much more extensive than the
earlier programs.
shallow subtidal stations! The major dichotomies in the dendrogram
based on 50 taxa for the shallow depth stratum (Figure 28) appear to involve
site-related factors and substrate type. Group I in Figure 28 comprises NFS,
SJI, and Whidbey stations between -1.5 and -4 m. Limb I-A is dominated by
mixed substrates including gravel or cobble, while limb I-B includes
primarily sand and mud substrates. Group II in Figure 28 consists entirely
of West Beach (Whidbey) stations, mostly from a depth of -1.5 m with a sand
substrate. No shallow subtidal samples were collected in the Strait.
Group I-A is dominated by stations with mixed coarse substrates from
Ebey's Landing and Partridge Point on Whidbey Island and Fidalgo Head and the
south shore of Guemes Island (NPS). Within this group segregation by site is
fairly strong, but it appears that Ebey's Landing and Partridge Point support
fairly similar flora and fauna.
Group I-B-1 consists entirely of NPS stations from depths of -2 m and
-4 m with a variety of sediment types. Most of the Fidalgo Bay (mud)
stations are segregated in this group, so it probably represents the most
protected shallow subtidal sites. Group I-B-2 consists of stations from
-2.5 m or shallower depths. The predominant substrate is sand, and most
stations are from SJI or Whidbey sites.
The differences among site groups in this dendrogram are probably
related largely to the effects of substrate type and exposure on the biota.
Depth-related factors also appear to exert an influence. Group II,
comprising mainly very shallow subtidal sand stations, is characterized by
distinctly sand beach infaunal animals. Group I-B comprises a mixture of
stations with sand, mud, mixed fine, and mixed coarse sediments, and
generally they are deeper than those in group II. The infauna include
species characteristic of deeper, truly subtidal assemblages, a fact which
sets this group off from group II. In contrast, group I-A comprises stations
at which the sediments are dominated by mixtures of cobble or gravel with
silt or sand. The rock component imparts a degree of stability to the
sediment, even at the shallower stations, so that the infaunal component is
similar to that at the stations in group I-B. In addition, the rocks support
typical epibenthic organisms such as plants and limpets. These epibenthic
forms set the stations in group I-A apart from those in I-B, but the infauna
are similar, causing these groups to remain in the same major dichotomy.
Figure 29 is the dendrogram based on 132 taxa instead of 50 for the
same stations included in Figure 28. Segregation by geographic region is
clearer in Figure 29. Limbs I-A-l-a, I-A-2, and I-B consist entirely of NPS
stations. Limbs I-A-l-b and II-B include only Whidbey stations. SJI
stations comprise limb II-A.
132
-------
Site, region Date Elev
m
FIDALCO HEAD, NPS 760917 -4.0 1
FIDALGO HEAD, NPS 768917 -2.8 '
GUEMES S SHORE, HPS 768911 -2.8
prtPTRTTIPP PfiTUT UUTTtRJTV "7^ Oil 77 — 5> *\
PARTRIDGE POINT VHIDBEY 781813 -1.5 1]
PfiRTRIDGE POINT VHIIBEV 780701 -1.5 '
EBEi S LRNDIHG/ VHIDBEY 780630 —1.5 '•
PARTRIDGE POINT VHIBBEV 770438 — 1. 5
fIDALGQ BAY. HPS 768937 -4 0
CHERRV POINT.- HPS 76GE?89 -48
BIRCH BAY, NPS 766383 -4.8
BIRCH BAY, NPS 768383 -2.8
CHERRY POINT, HPS 766316 -2.0
flFOnMQH RrtY Q IT "7d1flt£" — ? ^ — — — — -
LBLT o LRHlIiNU' VHIBBtT 798118 -2.5
WEST BEACH, WHIBBEV 798121 -2 5
UJTOT Rrflifl-l UU7TIPJPV "?Gft1 *?d —9 ^
RTRCH FtoV HP^ 7G.fimS\ -^ A < — •
SOUTH BFftTH Q.IT 7dtA1£ -f? 1 — •— -
VEST BEftCH, WHIDBCY 778818 -15
VEST BEACH WHIDDd" 771183 --2 5 — •••• -
i/r*5T RFtfM UHI71RPV 7ft1B1d -1 "i -.—..- ___
VEST BErtCH, WHIDBEY 788629 -1 5
UEST BErtCHj wninnrv 7son?d -1 •? ..,_..
VEST BEACH, WHISBEV 778818 -2.5
VEST BEACH, WHIDBEY 778419 -1.5
FIDftLCO HEAD, HPD 768326 -2 6 ••
VESTCOTT BAV, SJI 741816 -2 5
WEBB CflMPj SJT 7rflft1<7 -? *i
EBEY'S LfWBIHC, VHIDBEY 77842C -1 3- — -
PARTRIDGE POIHT WMIBBEY 776S22 -1 5— — — -
1 -i i. i—
188
\-
1
1
1
(-
88
h
1
h
1
(-
~\_
h
h
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i—
i
r
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1—
!.
1 —
i
j
1 —
1
1
f-
1
68
A
1 -
2
11
I -
1 — 1
1 '
I
48 26 8
LEVEL OF SIMILARITY
Figure 28. Relationships among shallow (above -5m) subtidal stations based
on the 50 plant and animal species or groups marked with stars in
Table B-3. Similarity between stations is defined by (A.5.2) of
Appendix A in terms of presence or absence of these plants and
animals.
133
-------
Site f Reaion Date Elev
m
FIBA' GC HE£B. NP$ 7€B^7 -4 S
GUEMEb S SHORE; HPS 760911 -2 8
PfitvTft T Th~'T~ PrtTMT WU'TTfEr^-' "?QiV7(5t I -1 ^ - J
Fgr-* 'c LANIIMC WUIBDC"' ""11 IS '~2 5 - • •-
'EBEV'S LANDING, VHI3JBEV 78S213 -1.5
puj'vTFT'Tir^ pnrwT vwiTiprs-1 771 1 Ptft — ? ^ — -
PftpTJ77 nrr PHTMT ywTTiPrv TRPI^PI^ -1 ^ — -
piVTT Tnrr pnrtrr uwTipr*--- ~??oi£4"7ci — i ^ . - .—
rF«PV-'r i tWniV'ir WWTTtftFV 7^011 1^ -1 "i -
riDALCO BAY tffj'? 76951"7 -2 8 ~
r i ^ft, r n P^V SP^C "TCPTQI T1 —d fl - .
BIRTH BA1"' HT1^ ^COSO7 "4 0 - —r •••
fUTRPV Pfi TUT MP«; 7^0r3CT^ — d A
CHERRY POINT, HPS 76&3l€ -2.8 '
GUEHES S SHORE/ HFS 766226 -4 8
r ( 1FMT ^ c c UTiPJP MPC 7 £ pi*>OCi — O Q
TirLiri^t^'J RCrV Q IT 7^11 ffil^ -5 *". —
EAGLE COVb • SJ I 74 1 016 —2 . 5
^HiiTi-i ft^Lirw Q IT ^dicnir -5 ^ -
rryv^ i own TUT vuTT^Rrv ^QPIIIS -!? ^
Vr'"1" P^LiTH WHTIPP"*-' "?SPi1 ^>d. -^ S -~- -
WE1"' BEftCH/ WH1DBEY 790121 -1 5
VE^T BEftCH VMIDKTf 77GB1310 "2 5 -r •— -—•
VF^T Rrrir^ UMTTIRFV 77d5tifi — 1 ^
nnri! rfj iffr^n WP^: v^ft~'K>ici -^ fl
DESTCOTT BflY, SJI 741816 -2.5
VERB CAMP SIT "Tdlflm; -9 5
1. „., ,1 J. 1
180
1—
h
1
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u
i r
H
j
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u
I-
1 ,
75
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~1
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— i
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I
-|
B
A| 1
-
n— — J
1
B 1
2
i
25
LEVEL OF SIMILARITY'
Figure 29. Relationships among shallow (above -5 m) subtidal stations based
on the 132 plant and animal species or groups marked with stars or
plus signs in Table B-3. Similarity between stations is defined
by (A.5.2) of Appendix A in terms of presence or absence of these
plants and animals.
134
-------
Site , Region Date Elev
m
nn^ii rn WP^D MPC% "7cftQi"7 —G. &
P£if?TRTTV>F POTWT WW7TtRF"V 7ft1Pl1~? — *^ fl - .--i-r- -.-.-rrjl
PARTRIDGE POINT WHIDBEY 788781 -7.5 1 ^
PARTRIDGE POINT VHI2SBEY 788516 -5.6 H
PfiPTRTHPF PHTMT WMTRRFV 7QPl1 ?2 — "I ft *"
EBEY'S LAMBING/ VHIBBEV 788213 -5 8 '
PdPTPTTrpr Pnrwr UHTTIRFV "77Pid"7Q — =; a —
PRTV J S 1 OWft T Wr VMTTIPFV 730FiRSt -."n ft
TBPVJC t flwinur UUTRRPV 77ftQ'?'? — ^ a .....
TVTl^; PT\/PR^ ctTPATT 7CPl^1d — *i f! -«—
JAMESTOWN, STRAIT 769682 -5.8 1
NORTH BEACH COBBLE 768682 -5.8 '
P.F"Pf PTT Pn T KIT QTPft T T ~77flCCV — *i ft
PTl I £P. PntWT ^TP£lTT 7^ftft3"? • ^ n
hORSE O?CCK 3TROIT 7€OC03 5 0 ••-
rUTHr*" ^ ^HTIRF hff^*^ 7rflOl t -.fT C\ . .
£l^jPY TOINT HPS 760^3 "•£ 0
RTRTH RfrY fff1^ T^fvTfl^ -^ O
pi-Brnoy pnTWT HP^ 7/ttV^1f? -^ fl
VF*nT RFfiPHi UHTTIM^V 7QPll *?1 —7 ^ —
uc-q-r Rr-ftPH VIHTTlFtf^V 7n£Vt>^ ^ "I
VEST BEACH* WHIDBEY 760418 -5 8
VEST BEACH, WHIDBEY 788124 ~7. 5 ' 1_
VEST BEACH WHIDBEY 7Q0124 ^5 0 — •
VEST 0EACH< WHIBBCY 770010 "-7 5 — — —
urq-f RPAtTH. UHTTIRFV 77flP1O — •* fi .».*.._
VE°T BErtCH WHIDBEY ^901"l 56'
UfT^T REt'fHi WHIDRTY 7fi1IS1d -'i 0 •
VEST BEACH, WHIDDEY 780523 ™5 8 ••
tyDrtfi*! FFrtfH *tT(?A7T 7(T(VtfP n"J ft
FU3f)l_CQ BftV, HPS 760313 -"6 6 • • -
DUHCEHESS SPIT' STRftIT 770607 ^5 0
TVIH RI'vtRS, STRftIT 770622 5 0 • •••
VEST BEACH, WHIDBEY 77M19 -5 8
DUHGEHESS SPITj STRAIT 768602 ^5 8" ••
FIDffl-CO HEAD, HPS 768320 ^6 8 -
TONGUE POINT, STRAIT 778617 -5.8 ,
TONGUE POINT, STRAIT 778586 -5 8 ' —
TONGUE POINT, STRAIT 768783 -5 6
TONGUE POINT, STRAIT 768782 -5.8
POINT GEORGE, SJI 758581 -5.8 ,
POINT GEORGE, SJI 758296 -5.8^ '
POIHT GEORGE, SJI 741127 -5.8'
( i _j_ , i
188
1
}
-i
r
H
}-
\
-
r.
J.
5
-,
—
I
1
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-
b
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i
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_r
f
?
1
i\-
1
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ft
1
58
— I
I
•
j 1
II '
25 8
LEVEL OF SIMILARITY
Figure 30. Relationships among medium-depth (-5 m to -7.5 m) subtidal
stations based on the 50 plant and animal species or groups marked
with stars in Table B-3. Similarity between stations is defined
by (A.5.2) of Appendix A in terms of presence or absence of these
plants and animals.
135
-------
VEST BEACH
'WEST BEACH,
VEST BEACH,
WEST BEACH,
WEST BEACH,
CHERRY POINT,
CHERR1! POINT.
EBEY'
Site, Region
FIDALGO HEAD, NF
FIIMLGO HEAD, HP
FIDfli_GO HEAD, HF
FIDftLGO HEAD, NF
GUEMES S SHORE.
QUEUES S SHORE,
KYDAKA BEACH, ST
BECKETT POINT, S
KYDflKfi BEACH, SI
DUHGENESS SPIT,
PILLftF POINT, ST
WIN RIVERS, STR
NORTH BEACH COBBLE
BECKETT POINT,
MORSE CREEK, S
VEST BEACH, WH
BIRCH BAY- NFS
BIRCH BAV NFS
VEST BEACH,
VE!
WE'
WE:
WE'
WE
CH
CH_.
EBEV'S LAKBIHG,
ESEY'S LAHDIHG,
EBEY'S LANSING,
S LANDIHa
IDGE POINT
IDGE POINT
IDGE POINT
EBEY'S L-ANBING^
PARTRIDGE POINT
EBEY'S LANDING,
EBEY'S LANDING/
PARTRIDGE POINT
PARTRIDGE POINT
EBEY'S LANDING,
PARTRIDGE POINT
PILLAR POINT, S
NORTH BEACH COBBLE
DUNGENESS SPIT, "~
JAMESTOWN, STRAIT
MORSE CREEK,
CHERRY POINT,
CHERRY POINT,
GUEMES S. SHORE,
FIDALGO BAY, NFS
FIDALGO BAY, NPS
FIDALGO BAY
FIDALGO BAY, NPS
FIDALGO BAY, NPS
VEST BEACH, WH
CHERRY POINT, I
BIRCH BAY, NPS
BIRCH BAY, HPS
BIRCH BAY, HPS
GUENES S. SHORE,
POINT GEORGE,
POINT GEORGE,
POINT GEORGE,
POINT GEORGE,
POINT GEORGE,
POINT GEORGE,
POINT GEORGE,
POINT GEORGE,
TONGUE PQIHT,
TONGUE POIKT,
' • • 1 1 L_
LEVEL OF SIMILARITY
Figure 31. Relationships among deep (below -7.5 m) subtidal stations based on
the 50 plant and animal species or groups marked with stars in
Table B-3. Similarity between stations is defined by (A.5.2) of
Appendix A in terms of presence or absence of these plants and
animals.
Date Elev
m
S 7683213 -10.8-
RftlT 776621 -9.6-
TRAIT 778686 -9.6-
RAIT 768663 -9.8-
AIT • 768684 -9.0-
BEY 798121 —16 6-
RPV "7ft 1 P)1 4 — 1 fl ft -
RPV 7ftPld1Si — t R ft -
VHIDBEY 798138 -18.6-
WaDBEY 781812 -18 8-
WIDSEY 788588 -18.0-
tfHIDBEY 788636 -18.0-
VHIBBEV 781013 -18.8-
VHIDBEV 786516 —18. 0 •
VH2DBEV 778438 —18 8 -
r-r 77ftCCi'7 — Q fi
5 768917 -8.3
> 768319 -12.8
s 768>il9 —16.fi
768383 —12.8
HPS 7669i 1 —18. 6
Jl 758581 -15.8
JI 758311 -IB. 6
JI 741127 -18.6
JI 758286 -15.8
JI 758286 -18.0
TRAIT 778506 -9. 8
TRftIT 768783 -9.6
U
^J
nZh
t • i
»
>
h
(-
h
Z3 —
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k
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B
i
0
1
11
1
25
1 L
1
e
136
-------
As in Figure 28, group I-A is dominated by mixed coarse substrates,
while group I-B includes protected sand and mud substrates, and group II is
almost entirely sand. It is noteworthy that sediment analysis for the only
Ebey's Landing station in group II indicated that it was sand while all the
Ebey's Landing stations in group I contained cobble or gravel. Similarly,
all West Beach sand stations fell into group II-B-1 while the single West
Beach station which was mixed coarse according to sediment analysis comprises
limb II-B-2. Thus the importance of substrate is also somewhat clearer in
Figure 29.
In Figure 29 as in Figure 28, depth effects are sometimes evident,
most importantly the tendency of the shallowest West Beach stations to
cluster together. In many cases, however, most similar pairs of stations in
both figures are from the same site and/or date and different depths.
Mid-level subtidal stations; Relationships at the middle depth
stratum in the dendrogram based on 50 taxa (Figure 30) also appear to be
primarily influenced by the interactions of substrate, exposure, geographic
region, and other site-related factors. Group I includes stations from all
regions except San Juan Island, where no mid-level or deep subtidal samples
were available. Group I represents all substrates except solid rock while
group II contains the rocky stations. Both groups are partitioned clearly on
the basis of region and to a lesser extent by site. Within group I substrate
effects are also evident, with limb I-A dominated by mixed sediments and
limb I-B by sand and mud.
Group I-A-l-a consists almost entirely of mixed fine stations from
Partridge Point and Ebey's Landing on Whidbey. Group I-A-l-b has a larger
proportion of mixed fine and sand stations from the Strait. Group I-A-2 is
harder to characterize, containing sand stations from the Strait and mixed
coarse stations from the south shore of Guemes Island (NPS). Group I-B
separates into limb I-B-1, containing protected NPS stations, and limb 1-3-2,
consisting entirely of sand substrates from West Beach (Whidbey).
Within group I-A a weak tendency to segregate by season is apparent.
For instance, the survey dates for the stations in limb I-A-l-b include only
the months o£ May through August. Limb I-A-l-a contains subgroups
representing (i) fall/winter and (ii) spring/summer, each with stations from
both Partridge Point and Ebey's Landing. These seasonal effects were less
apparent and the tendency to segregate by site and region stronger in the
dendrogram based on 132 taxa, but it was otherwise very similar to
Figure 30.
Deep subtidal stations; Patterns observed in the dendrogram for
stations below -7.5 m based on 50 taxa (Figure 31) are quite similar to those
described for the medium-depth stratum. The major dichotomy is based on
substrate type, dividing soft substrate stations (group I) from rock
(group II). Segregation by substrate, site, and region within these major
groups is strong. Note that Strait stations labelled as from -9.O m in this
and subsequent dendrograms should be labelled -10.0 m; the depth was
incorrectly recorded on the File 100 tapes.
137
-------
The mixed coarse NPS stations (Fidalgo Head and Guemes Island) and
most Cherry Point stations (mixed fine, NPS) appear alone or in pairs in
isolated limbs of group I-A. The remainder of this group consists of sand
and mixed fine Strait stations
-------
Site. Reaion Date Elev
m
EBEY'S LANDING, VHI2BEY 781812 -18.0 if— I
EBEV'S LANDING, tfHIBBEY 788588 -18.8 r — ,
EBEY'S LADING, VHIDBEY 788636 -18.8 1 J 1
PARTRIDGE POINT VHIBBEY 781813 -18.8 '
PARTRIDGE POINT VHIDBCY "*0051C "15
EBEY'S LANDING^ VH3BBEY 7865^3 —1.5— • — — '
EBEYJ* L/lHDIHC- VHIUBEY 730115 1 5
totT S LflHlUHU' VHIDocr 78023o —3.5 '
VEST BEACH WilDGCY 7301^1 ••£ 5 •
VEST BEACH',' WHIBBEY 788124 -2.5 ' L
VEST BEACH, WHIDBEY 789629 -2.5 1
VEST BEACH, WHIDBEY 780418 -5.6 1 1 '
VEST BErtCH WHIDOCY 7G041S -18 fl • ' j
VEST BEDCH WHIDBEY 730121 -7 5
VEST BEACH, WHIBBEY 788124 18 0 —
VEST BEACH, WHIBBCY 7901 °1 -5 0- ••
VEST BEACH WHIDBEY 780124 "5 8^
VEST BEACH, WHIDBEY 730121 -1 5
VECT BEeTH WHIBBEY 7S18H 1 5
VEST BEACH, WHIDBEY 780418 -1.5
VEST BEACH, WHIDBEY 780629 -1.5
I j • .11
108 75
H
— i
2
B
1
2
D-i
— i
i
i —
,
— i
1
j
n
B
i
58 2,
LEVEL OF SIMILARITV
Figure 32. Subtidal depth-site-sediment relationships, Whidbey Island, 1978-
1979, based on the 50 plant and animal species or groups marked
with stars in Table B-3. Similarity between stations is defined
by (A.5.2) of Appendix A in terms of presence or absence of these
plants and animals.
139
-------
Similarly, in group I, most of the -1.5 m and -2.5 m stations are
found in limbs I-A-2 and I-B, which also include most of the mixed-coarse
stations. Nearly all of the stations at -5m, -7.5 m and -10 m are in
limb I-A-1; most of these had mixed-fine sediments. The differences in rock
size and depth strongly influence the types and amounts of algae and
epifaunal invertebrates that an area will support.
Whidbey Island and the strait r 1976—1977; in a like manner, stations
from several depths at Whidbey Island and in the Strait of Juan de Fuca were
examined to determine the relationships of these two regions. The resultant
dendrogram (Figure 33) does not exhibit a major dichotomy but instead is
characterized by extensive chaining (or "stairstep") at the basic levels.
The largest group, group A, comprises mainly -5 m and -10 m sand and
mixed fine stations from both regions. Generally, segregation within this
group is along regional lines, with limb A-l-a dominated by Whidbey stations
and limbs A-l-b and A-2 consisting entirely of Strait stations. Segregation
by depth is not strong, especially among the Strait stations where the most
similar pairs tend to be defined by site, year, and/or substrate. However,
the shallowest stations in group A all fall into group A-l-a-ii; one of these
is the only mixed coarse station in group A.
Group B comprises sand stations from West Beach and Kydaka Beach, and
group C comprises mixed coarse and mixed fine stations from Partridge Point.
The reasons these groups are set off so sharply from group A are obscure.
Group D comprises mainly the rocky subtidal sites from Tongue Point, so the
reason for its strong dissimilarity from the other sites (sharp differences
in substrate and, thus, biotic assemblages) is clear. The great disparity of
group E, comprising shallow sand stations from West Beach, is puzzling
because it shows stronger dissimilarity to groups A, B and C, all of which
support infaunal assemblages, than does group D, which only supports
epibenthic assemblages. One fairly clear pattern to emerge from this
analysis is that the subtidal soft substrate stations in the Strait are
fairly similar, i.e., they do not sort strongly by site or depth.
SJi and NPSi in a similar comparison among SJI and NPS stations
(Figure 34), we see strong segregation by site and substrate across the depth
gradient. Group II comprises all the rock stations at Point George, Shaw
Island, and is extraneous to this discussion. Group I comprises both NPS and
SJI soft substrate stations, but there are too few of the latter to permit
firm conclusions to be drawn concerning them. They cluster loosely with a
few isolated NPS stations to form small groupings outside of the major
subgroups of group I. The remaining NPS stations define two major subgroups
in group I. Limb I-A, characterized by mixed coarse sediments, includes
mostly Fidalgo Head stations. Limb I-B is larger and more diverse,
comprising mixed coarse, mixed fine, sand, and mud stations.
Limb I-B-1 consists of stations from all depths at Birch Bay, Cherry
Point and Fidalgo Bay. Mud substrates predominate. Although the three sites
frequently segregate, it seems clear that they also have strong similarities
to each other. Limb I-B-2 includes chiefly mixed coarse stations from all
depths at Guemes Island.
140
-------
Site , Region Date Elev
m
JAMESTOWN, STRAIT 768662 -5.0 1
NDPTH RF£-H <"TFBI P 7fiPi£fi? -5 fl t j
EBEY'S LANDING, VHIIBEV 770822 -5.6 1
EBEY'S LftNBJNG, tfHIDBEY 778822 -2.5 '
KyBAKfi BEftCH, STRAIT 778631 -9.8 1_
BECKETT POINT, STRAIT 778686 -9.6 >~~
BECKETT POINT, STRAIT 768602 -9.8 j f
BECKETT POINT, STRAIT 768682 -5 8 '
RrrtT"TT Prirwr CTIMTT 77o^cfcc — *n R
PILLAR POINT. STRAIT 778622 -9 8 1
PTi 1 flR PT1TMT ^TOfrTT 77ftCj^> . ^ fl J»"^«
MftPTW RCWH (TlRPf r ~?~?to£r^A — Q fl
•nt frjprKirr;C 3PTT *5TB£iTT "?7Cl£'Lfi~* -HO
IrtMTT-TnuW QTO^TT 770w£fl"'7 ...."i fl >» ..1~*~
hinpTM RForv-t rnppt r 7?pi^^d --s fl ..—...
VEST BEACH, WHIBBEY 771183 -18.0 1
VEST BEACH, WHIBBEY 771183 -5,8 >
VEST BEACH, WHIDBEY 77^16 -10.0 1
VEST BEACH, WHIBBEY 778818 -7.5 <
UfQT RraT'U VA4 T fiRPV 7?Dlffi1 Cl — *\ R - ..,—.....
CT pfTAT^ j UW T T1RFY T^Phl 1 ^ — 1 fll fi -.....— ....,,
tfVTlCif6 RPCirw 9TJW5iTT 7"?G*£Z5>\ •« »*\ fl
pftftTrfTnfT* pnrfJT wriTTtftrv "*7itfw -51 ^ •*• —
pfcRTRinrT rnrj-TT w*iT"nFirv 77Pid"w?i <.m n — -
PftRTRTTirF PfinfT UHTTtRrV 77CtdTFl • *i ft -> — •
Pftf?TF?TTinF Pnr»47 WHTTlftrV 7?ftd.Tia -1 S • •
rc.pj^r' » rtf,ITlTfr.ir UHTT^ftrV T^flfT*^ --1 "^
DLfrCEHE^'^ SPIT ^TRAIT 770C07 5 6 j ••
TWTN RT'i/TR^ ^TRftTT 77Af^*j* — "^ ft ~._«.— n
TfNHI IF PT1T4T ^TMiTT 77Pl^17 — ^ fi -r.-rr-- ---.-.--- •Tnr.-r--..-.---.- -
TONGUE POINT, STRAIT 770586 -5-8 '
Mj-vrNor- rpf-rt PTRftTT 7fiCVTfi"7 -"i A •- .
TONGUE POINT, STWtIT 768783 -50 - -,
Tf»*"3IF PflTNT, *STIM7T "?fief?FO ~*i O— —— '
TOHCUC POINTj STRflIT 778506 -9 6 -
tfEST BEftCH/ WHIBBEY 771 183 -2.5
WEST BEftCH. WHIDBEV 778419 -5.6
VEr'T BEnCH UHIDBCY T'QSIO 1 5
VEST BEACH, WHIBBEY 778818 -2.5
DUNCENE3S SPIT STRAIT 7COCC2 -30
EBEY'S LAHDIUCj VHIBBCV 778
-------
NPS
NPS
NPS
NPS
NPS
NPS
NPS
NPS
NPS
NPS
Site, Region
FIDfiLGO HEAD,
FIDflLGO HEAD,
FIEflLGO HEAD,
FIDflLGO HEAD,
FIDflLGO HEHD
FIDftLGO HEAD,
FIDftLGO HEAD.
FIDALGO HEAD,
FIDftLGO HEAD,
CHERRY POINT,
BIRCh BAV NPS
FIDALGO HEAD, NPS
GUENES S SHORE, NPS
FIDALGO BAY, NPS
FIDALGO BAY, NPS
CHERRY PC I NT, NPS
FIDALGO BAY, NPS
CHERRY POINT, NPS
CHERRY POINT, NPS
EIPCH BAY, NPS
BIRCH BfiY, NPS
CHERRY POINT, NPS
CHERRV POINT, NPS
BIRCH BAY, NPS
BIRCH BAY, NPS
BIRCH BAY. NPS
FIDALGO BAY, NPS
FIDALGO BAY, HPS
FIDALGO BAY, NPS
FIDALGC BAY, NPS
FIDALGO BAY, NPS
BIRCH BAY. NPS
BIRCH BAY, NPS
FIDALGO BAY, NPS
BIRCH BfiY, NPS
FIDAuGG BAY, NPS
CHERRY POINT, NPS
CHERRY POINT, NPS
CHERRY POINT, NPS
CHERRY POINT, NPS
S. SHORE, NPS
SHORE,
SHORE,
SHORE,
SHORE,
SHORE,
SHORE,
SHORE,
NPS
NPS
S
S.
GUEMES
GUEMES
GUEMES
GUEMES S.
GL€!1ES S
GUEMES S.
CUEMtS S.
GUEMES S
BIRCH BAY,
BIRCH BAY.
CHERRY POINT, NPS
RDftGO BAY, HPS
BEABMHH BAY, SJI
EftGLE COVE, SJI
SOJTri KftCh, SJI
GUEMES S. SHORE,
VESTCQTT BAY, SJI
VEBB C«f=> SJI
POINT GEORGE, SJI
POINT GEORGE, SJI
POINT GEORGE, SJI
POINT GEORGE, SJI
POINT GEORGE, SJI
POINT GEORGE, SJI
POINT GEORGE, SJI
POIhT GEORGE, SJI
POINT GEORGE, SJI
POIHT GEORGE, SJI
POINT GEORGE, SJI
POINT GEORGE, SJI
NPS
NPS
NPS
NPS
NPS
NPS
NPS
Date Elev
m
768917 -10 8
768917 —8 8
->^*~£ -1 ' ^- J*
760917 -2. 8
768326 -6. 0
769326 -4.8
768326 -10.0
760326 -8.8
768316 -12 0
766836 —4 8
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75 58 25
LEVEL OF SIMILARITY
Figure 34. Subtidal depth-site-sediment relationships, San Juan Island and
North Puget Sound, based on the 50 plant and animal species or
groups marked with stars in Table B-3. Similarity between
stations is defined by (A.5.2) of Appendix A in terms of presence
or absence of these plants and animals.
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The failure of the mixed coarse Fidalgo Head and Guemes island sites
to fall together in a group is somewhat puzzling. Based on the relationships
of the Guemes stations, it may be that these sites differ substantially in
terms of exposure, with Guemes being the more protected. This interpretation
seems to agree with the geographic locations of the sites. It may also be
that more precise sediment grain size data than are presently available from
the sites would explain differences in their flora and fauna.
6.4.2 Summary of aiih-Mdal results
The following conclusions seem warranted on the basis of the cluster
analyses. Sediment characteristics strongly influence relationships among
subtidal stations with rock substrates clearly distinguished from soft. On
soft substrates the presence or absence of a substantial rock component such
as cobble or gravel is important. Exposure is another significant factor but
the presence of cobble or gravel can override all but extreme exposure. Very
shallow subtidal sites (less than -2m) are often primarily characterized by
intertidal species and thus are distinctly different from deeper stations.
Depth effects become less distinct below -5 m. Mixed-coarse sediments also
are uncommon below -5 m. Clustering by site occurs frequently, often cutting
across the depth gradient.
Segregation by region is also strong. As in the intertidal data,
regional effects cannot be clearly separated from investigator biases since
all SJI and Strait samples were collected by Nyblade and all NFS and Whidbey
samples by Webber. The situation is made worse in the case of the subtidal
data by the fact that three different types of samplers were used—one for
the SJI samples, the second in the Strait, and the third in the NFS and
Whidbey sampling programs. However, neither investigator nor gear
differences contribute to the separation between NPS and Whidbey sites, so it
is likely that there are real regional differences, probably related to
exposure.
Similarities among the shallowest subtidal stations (less than -5m)
were lower than among the deeper stations, making the prognosis for either
site-specific or cross-site prediction in the shallowest depth range poor.
High similarities (mostly greater than 50 percent among stations of
similar substrate) were indicated at depths of -5 m or greater, giving a
better prognosis for prediction by habitat at these depths, especially within
a region. The lack of strong clustering by site or depth among the Strait
stations is particularly promising. It appears that the definition of
habitat in terms of sediment composition is more successful subtidally than
intertidally.
However, clustering by year and season in some of the subtidal
dendrograms indicates that, as in the intertidal habitats, changes in
communities occur naturally through time. More quantitative analyses of
subtidal assemblage and population parameters are needed before final
conclusions can be drawn concerning the possibility of prediction and change
detection in subtidal habitats of the Puget Sound region.
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SECTION 7
IMPROVED SAMPLING STRATEGIES—OBJECTIVE 2
7.1 INTRODUCTION
The second major objective of the present study was to develop a
sampling strategy for future monitoring that would provide data to complement
the existing data base, providing continuity with previous programs to the
extent possible, thus allowing more precise predictions or extrapolations to
be made for unstudied areas. Also, most importantly, the monitoring studies
proposed below should increase the statistical probability of detecting real
changes in the biota resulting from future environmental perturbations. The
numerous and diverse statistical analyses presented in Section 6, the
principal investigators' reports and recommendations, and the experience of
the writers in similar studies were used to arrive at the recommendations
contained in this section.
Section 7.2 provides a. discussion of the kinds of parameters that can
be measured or calculated to provide information about littoral benthic
assemblages and species.
Three categories of recommendations are provided in subsequent
subsections. The first group of recommendations (Section 7.3) applies
equally to all sampling programs where repeatability of techniques,
comparability of data, and ease of future data handling by persons who did
not participate in the original data collection are desired. Many of these
appear obvious and simplistic but are stated because, in some cases at least,
they were not rigorously followed in the WDOE and/or MESA studies and have
complicated the statistical testing of the data base reported in Section 6.
The second group of recommendations (Section 7.4) are those that we
feel should be implemented in subsequent baseline programs in this study
area. The third group of recommendations (Section 7.5) are those we feel
should be implemented in post-perturbation assessments of areas affected and
unaffected by some future disturbance where the goal is to statistically test
the null hypothesis of "no change" from pre-perturbation conditions. Also
provided in this section are additional recommendations of actions that could
be initiated during a spill to get baseline information on pre-spill
conditions at threatened beaches.
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7.2 PERTINENT TYPES OF DATA
Some useful types of data that may be collected in monitoring programs
contributing to the detection of real changes in the benthic biota, either
from natural causes or acute pollution insults, relate to the assemblage and
population features frequently used to describe the biota of a specific
site. Types of change that can indicate a deterioration in conditions
include reductions in species richness, species diversity, or biomass and
serious alterations in size (age) structure or average annual density of
dominant species.
The assemblage parameters include numbers of species of plants and/or
animals (S, S , or S ), number of discrete animals (N ) or plants such as
laminarian oirfucoid Jcelps or sea grasses (N ) per m2, relative cover
(percent) by plants or encrusting invertebrates, biomass of plants or animals
(W , W ), and species diversity for animals (based on abundance or biomass,
see Section 5.2.1) or plants (based on biomass).
Useful population features include many of the same parameters, namely
density (no./m ) and biomass (g/m ) of animals or macrophytes, and relative
cover (percent) of plants and encrusting invertebrates, but each of these
parameters is measured on a single-species basis. A very useful additional
parameter for many species, size (or age) structure, permits evaluation of
the degree of development of a species population, thus providing a clean,
simple, but sensitive means of detecting subtle or gross perturbations in the
environment through induced changes in survivorship curves of the species
studied (e.g., Houghton 1973 ).
It is useful to normalize all data to the same unit of area and
tabulate the data for comparison among habitats, sites, elevations, and, if
applicable, major taxa. Information required for each of these parameters,
their potential contribution to impact assessments, and situations or
habitats in which they are pertinent are described below.
A wide range of sublethal indicators of stress to individuals is also
available but is outside the scope of the baseline monitoring studies in
question.
7.2.1 Assemblage parameters
Number of plant and/or animal species (S):
The purpose of defining this parameter is to quantify species richness
of plants and/or animals, as appropriate. Generally, comparisons are
effective only when made on the basis of a standardized sampling unit or
area, such as the number of species or taxa/O.25-m quadrat. If unequal
areas have been sampled, comparisons of overall species richness between
sites are only effective if it can be demonstrated by use of species-area
curves that the sampling effort has captured most of the species present.
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This parameter should be used for some component of the biota on any
substrate examined. On rock and cobble substrates, it is useful to compile
number of species/sampling unit separately for plants and animals, as well as
a total number of species for the site. On cobble and soft substrates, it is
useful to compile number of species/sampling unit separately for epibiota and
infauna. Number of species has been examined extensively for the Puget Sound
data base in this study, but problems arose because of sampling and taxonomic
differences between investigators or regions. Only species richness values
derived from a single sampling technique and from identifications of
organisms to the same taxonomic levels are comparable (see Section 5.1).
Number of individual animals or plants (N):
The purpose of defining this parameter is to quantify density levels
for major individual animal or plant species such as snails, starfish, and
fucoid or laminarian kelps. Other types of algae and colonial or encrusting
animals (sessile epibiota) are more appropriately assessed by estimating
relative cover and thus should be excluded from this type of measurement.
The report must, then, specify which groups have been included and excluded.
This parameter should include all readily countable and identifiable
organisms above a specified size and should be used on every substrate
examined. On rock and cobble substrates, it is useful to compile
abundance/sampling unit separately for plants and animals as well as combined
counts. On cobble and soft substrates, it is useful to compile
abundance/sampling unit separately for sessile and mobile epibiota and for
infauna.
A significant amount of data on density from the MESA/WDOE data set
was lost because the order of sample collection precluded scaling-up of the
subsample data. The sequence in which subsamples are removed from sample
areas should be designed to preclude loss of data (see Section 7.4).
Relative cover (percent) by plants and encrusting animalss
The purpose of defining this parameter is to quantify the amount of
surface area covered by plants and encrusting animals, thus providing a
clearer idea of the nature of the assemblage and the identity of its dominant
taxa. independent estimates by two observers using a quadrat with a grid of
known size (in percent quadrat area) marked on the frame should be averaged
for each value recorded. Measurements are most accurately estimated in
replicated quadrats and can be safely compared among specific levels at
different sites with little concern over sample unit area. In areas of lush
algal development, multilevel assemblages are common and thus relative cover
may exceed 100 percent, even approaching 3OO percent in areas supporting a
surface canopy of kelp (i.e., Macroeyatis or Nereocystis). This method has
been used extensively in intertidal and subtidal studies in southcentral
Alaska (Lees et al. 1980). Cover estimates seldom vary by more then 5 per-
cent between experienced observers and can be assisted by providing a grid
with squares of known areas within the quadrat. It is a useful adjunct to
biomass and, in many instances, is the most practical and rapid way of
measuring the abundance of the important algae and encrusting organisms.
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This parameter should be used on rock and cobble substrates and on
soft substrates supporting appreciable macrophyte populations. Although it
was not generally useful in our analyses of the Puget Sound data base, if
sufficient replicates are collected at a site for pre- and post-spill
assessments, it can be quite useful, especially in subtidal rocky habitats.
Plant biomass:
The purpose of defining this parameter is to quantify standing stocks
of plants and, within and among study sites, permit comparisons of the
development of plant assemblages and an assessment of the relative importance
of various major plant taxa. This is a useful adjunct to the data on plant
cover. The level of detail applied to the measurement should be leavened
with practicality. For instance, a large expenditure of time measuring
biomass for a complex assemblage of small red algae is not justifiable; it is
much more practical, and is acceptable, to measure the biomass of the
aggregate, or at least separate out only the obvious dominant species.
Initially, at least, measurements of this parameter should include all
removable algae; however, it is impractical to attempt to measure biomass of
encrusting algae which can be best assessed by percent cover. Subsequently,
assessment of the data collected may indicate that only major species or
higher taxa should be sampled. Appropriate substrates are rock, cobble and
soft substrates supporting appreciable macrophyte populations. Measurements
should be compiled by species and/or major taxon.
Invertebrate biomass:
The purpose of defining this parameter is to quantify and permit
comparisons of standing stocks of invertebrates within and among study
sites. Obtaining meaningful measurements of biomass for encrusting
invertebrates and infaunal molluscs is useful but a very time-consuming task
because most of them have a proportionately large amount of shell material,
which interferes with realistic measurement of tissue weight. However,
despite this disadvantage, the parameter provides valuable insights into
energy flow, secondary productivity, and resource allocation. It is a useful
adjunct to data on relative cover for encrusting invertebrates. Average
weight of soft-bodied invertebrates (e.g., polychaetes) is also the best
indicator of their size (Nyblade, personal communication).
This parameter is most appropriately measured on rock or cobble
substrate for encrusting invertebrates, and on cobble or soft substrates for
infaunal invertebrates. Realistic measurements of infaunal biomass are often
very difficult to obtain on cobble. As in the case of plant biomass,
measurements should be compiled by species and/or major taxon, as well as by
aggregate weight.
Species diversity:
The purpose of computing species diversity is to provide a parameter
that integrates species richness, abundance, and the equitability with which
the number of individuals is distributed among the species. Comparisons are
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2
only valid when data are based on a standardized sampling unit (e.g., 0.25 m
or 1 m . )
Although it is desirable to evaluate species diversity for all
habitats, it is particularly difficult to compute a total diversity value for
rock or cobble substrates because of the varied mix of parameters that are
most appropriate to quantify the several components of the assemblage (e.g.,
percent cover, abundance, and biomass.) Biomass is probably the only common
unit of measure that will accommodate the varied types of organisms, but it
is also very time-consuming to measure for all groups. Thus, a more
practical solution is probably to compute diversity values separately for
plants, motile invertebrates, encrusting invertebrates and, in cobble and
soft substrates, infaunal invertebrates. For plants the only suitable
parameter for diversity computations is biomass, whereas for invertebrates
either biomass or abundance can be used.
7.2.2 Population parameters
Most of the useful population parameters are collected routinely to
generate the data for assemblage parameters (i.e., S, N, biomass, relative
cover, and species diversity). The assemblage parameters are, in fact, a
summary of the data for all species examined. Analyses of population
parameters mainly involve evaluating spatial and temporal changes in
abundance, biomass, or relative cover. Thus, an additional discussion of
these parameters is unnecessary.
However, the size or age structure of a population is a very useful
population parameter not considered above. Size structure data often provide
insight into age structures of populations inhabiting different locations and
are fairly sensitive to both long-term and short-term factors affecting
populations. For example, short-term perturbation of mature populations may
result in a noticeable change in the size (or age) structure from larger (or
older) to smaller (or younger) organisms. Thus, although large numbers of
recruiting juveniles may replace small numbers of adults (density increases),
the change in size structure will reveal the impact of the perturbation.
size data can be collected on most types of organisms, but good data
are difficult to collect for polychaetes and non-laminarian algae. Average
weight per individual can be used as a size indicator for these latter types
of organisms. The size of the sampling unit is not important, but the number
of measurements should be large (>30O) to reduce the effects of sampling
variability (i.e., improve the accuracy of the estimated mean).
7.3 GENERAL CONSIDERATIONS
It is evident from the discussions of the MESA/WDOE data base
(Section 4) and our statistical analyses of it (Section 6) that several
features of the two sampling programs detract from the statistical strength
of the data. The general recommendations for future sampling programs
provided in this section are directed at reducing obvious sources of
variability evident in this and other data bases; they are in no way intended
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to detract from the value of the descriptive information gathered in these
previous programs.
Two basically different types of sampling strategies are necessary to
meet the likely needs of regulatory agencies in the study area. Monitoring
studies should be conducted at strategic locations suggested by spill
trajectory analyses to provide long-term information on variability in
species composition, abundance, and standing stocks of important species in
important habitats. Impact assessment studies would be conducted at specific
impact and control sites in the event of a catastrophic oil spill. The
objective of these studies is to rapidly assess the impact of a spill. Thus,
the sampling strategy of an impact assessment is somewhat different from that
of long-term monitoring studies.
Host of the general sampling recommendations in this section apply
primarily to monitoring programs although many are equally valid for impact
assessment. Because the inadequacies of the existing data bases reduce their
comparability and usefulness for impact assessment, we have not been overly
concerned with maintaining continuity between past and proposed studies.
However, several stations previously sampled that merit continued attention
are identified.
In these types of studies, emphasis should be on obtaining good
information on assemblage parameters (e.g., S, M, and H1) and organisms
involved in major biological interactions on the specific habitat. For
example, major interactions on rock involve 1) competition for "primary"
space (i.e., rock surface for settling) among plants and sessile animals and
2) predation by limpets, snails, and starfish on space-dominating organisms
such as algae, barnacles, and mussels. With good information on these types
of organisms, investigators should be able to detect important changes in
natural conditions as well as changes following an oil spill.
It should be obvious at this point, following our analysis of the
MESA/WDOE baseline data for Puget Sound, that the collection of adequate data
is not simple; there is no quick, easy way to get good data. The sampling
replication required to "swamp out" (overcome) the natural variability (i.e.,
residual error) of intertidal assemblages is generally large, and budgetary
planning must take this into account. If the intent is to use the data as a
basis for legal action following an oil spill, the level of effort must be
great enough to insure a reasonable probability of detecting a change while
maintaining a low probability of falsely rejecting the null hypothesis that
no change has occurred. A useful feature of the data collected that became
obvious in our analyses was that smaller numbers of samples were usually
necessary to detect a given level of change in numerical assemblage
parameters than in population parameters of individual species. Thus, a
sizable economy can be achieved by conducting full analyses on a reduced
number of the replicate samples to establish estimates of assemblage
parameters and examining only selected species in the remaining samples to
provide adequate estimates of population parameters.
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it should also be recognized at the outset that field studies alone
will not establish a causal relationship though they may provide a data base
to perform correlations with the effects of oil and the changes that may be
observed following a spill. Such studies will only establish whether a
change did, in fact, occur in the areas of impact and allow quantification of
the magnitude of the change. Causal relationships can best be shown in
laboratory experiments and with hydrocarbon analyses.
7,3.1 Investigators and ^ayftpn^ny
To insure maximum comparability of sampling and analysis techniques
from site to site, particularly within a given habitat, the same
investigators should sample all sites. If this is not feasible, then at the
very least, senior investigators from each group should participate in
"hands-on" sampling and analysis by the other group early in the program so
that techniques, field conventions, and contingencies are identical.
Obviously each principal investigator must be highly experienced in the local
flora and fauna and methods of identifying, sampling, and analyzing them.
Finally, methods of coding, recording, and checking data must be identical.
The same taxonomic experts should be used by each group, and cross-
checked reference collections are mandatory. The level of taxonomic
resolution should be consistent throughout the program; i.e., if an
identification has been left at the genus level early in the program,
statistical analysis is only complicated by future identifications to the
species level unless earlier samples are re-examined, identified to species,
and the data file corrected (see Sections 4.2.4 and 5.1).
Future sampling programs should provide investigators with a current
NODC taxonomic code dictionary and easy mechanisms for adding new species to
this dictionary to ensure that species are consistently coded. The taxon
name as well as code should appear on Species Identification records to
simplify correction of errors in the code.
7.3.2 Sampling periods and duration of study
The analyses of Section 6 as well as our understanding of seasonal
changes occurring in intertidal populations strongly suggest that sampling
during the spring and fall is less useful than sampling during the summer and
winter. Spring and fall are periods of high rates of increases and
decreases, respectively, in populations of many plants and animals. Samples
taken before a major recruitment of some species in the spring or before a
major storm in the fall will yield vastly different results than samples
taken from the same place following these events. For example, a heavy
recruitment of Balanus greatly magnified the apparent differences between
Pillar Point and Tongue Point during the spring of 1976. Summer and winter
are times of less rapid changes in flora and fauna, reflecting more settled
conditions where poor competitors have been eliminated. Thus, samples
collected during these periods are more likely to indicate the real
differences in assemblages between sites or years than differences in the
timing of sampling within a given season.
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The ideal duration of a monitoring program is difficult to assess
based on the available data for this region. Under the MESA and WDOE
programs four sites (Cantilever, Deadman, Westcott, Eagle Cove) were sampled
at the same time of year for seven consecutive years. However, only three
years of data are available on tape for any site. Other quantitative field
programs in the study area (e.g., Houghton 1973, Thorn 1978, Wisseman et
al. 1978) have lasted only one or two years. Nonetheless, year-to-year
variability seen in these data bases strongly suggests that a minimum three-
year program of summer and winter sampling would be highly desirable at each
site.
Subsequent verification studies each year to monitor long-term trends
and to improve the data base such as those conducted for WDOE since 1976 are
highly desirable. These could continue to be limited to summer sampling at a
subset of the baseline sites. If there are temporal dependencies in
assemblage and population parameters as indicated by the results of Section
6.2.3, these annual samples would greatly improve the credibility of any
conclusions should a spill occur five to ten years after completion of the
initial three years of work.
7.3.3 Sampling sites and tidal elevations or depths
The analyses of Section 6 indicate substantial biological differences
among habitats that make some much more suited to monitoring studies and
impact assessment than others. In fact, the biota on exposed soft substrates
(sand, gravel) is far too variable to permit economic monitoring (Section
6.2.3; see Table 28); in addition, the productivity of such habitats is
probably too low to warrant the expenditure.
Sites selected for monitoring should have as many as possible of the
following characteristics. They should
1. be in areas with the highest risk of impact from oil
spilled under present and likely future oil
transportation scenarios (e.g., close to tanker or
pipeline routes);
2. include areas with greatest long-term sensitivity to
oil spill impacts (protected mixed, sand, and mud
habitats); lesser effort should be accorded less
sensitive areas (e.g., protected rocky habitats, see
Chan 1977); little or no effort is justifiable in
highly exposed rocky, coarse sand, gravel, cobble, or
mixed habitats where the fauna is poorly developed
and/or where wave energy is likely to rapidly purge oil
from the beaches (Gundlach et al. 1980);
3. be readily accessible yet subject to minimal human
disturbance;
4. be "typical" of as great an expanse of coastline as
possible to maximize applicability of data to other sites;
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5. offer a large expanse (>100 m laterally) of relatively
uniform habitat in the zone(s) to be sampled.
Based on application of some of these criteria, several of the
original sites examined for baseline data would be appropriate for continued
monitoring. However, because all sites have not been visited by the present
study group, we have not been able to explore all of the above criteria
(e.g., access, expanse of beach, geographic applicability) with any high
degree of reliability. Appropriate sites at risk of contamination (treatment
sites) might include Jamestown, Beckett Point, Guemes Island, Pidalgo Head,
Fidalgo Bay, Padilla Bay, Legoe Bay, and perhaps Birch Bay. Appropriate
control sites include Westcott Bay and Cantilever Pier on San Juan Island.
Note that all sites in the outer Strait of Juan de Fuca and on the west coast
of Whidbey Island are generally exposed and therefore rank low by the above
criteria. Other factors, e.g., very high risk of spill or lack of more
suitable alternatives, might dictate inclusion of these sites.
We note that historic sampling sites are lacking in extensive areas
highly susceptible to oil contamination along tanker and pipeline routes into
central Puget Sound (e.g., Admiralty Inlet) and across Whidbey Island (e.g.,
Saratoga Passage). Since the probability of oil contamination is now, or may
become, as high as it is in Rosario Strait and the Strait of Juan de Fuca, we
recommend that monitoring sites be established in sensitive habitats in these
areas. Useful historic data are available at Kiket Island in Skagit Bay
(Houghton 1973). Other new sites appear necessary, possibly along the
southern shore of Whidbey Island or the Kitsap Peninsula. We recommend a
meeting of Puget Sound MESA investigators to further evaluate potential study
sites for future monitoring.
To further improve the statistical strength of the data, we recommend
that only one intertidal and one subtidal level be sampled, thus removing an
additional variable. Sampling a single tidal level or depth would also
eliminate confusion over habitat designations at sites where the substrate
changes significantly with elevation. However, sampling at higher and lower
zones may be desirable at particular sites or at a preselected number of
sites that are particularly vulnerable to oil spills and/or contain resources
of unusual value.
Several factors suggest that the appropriate intertidal level should
be in the mid tide range. The actual elevation should be determined by
inspection at each site so that sampling falls in the zone of maximum
development for the biological assemblage characterizing that mid tide
level.
The main reasons for selecting the mid intertidal zone are that
1) probability of contamination during a spill is high, 2) the organisms here
may be somewhat more vulnerable to oil effects than at higher levels (e.g.,
less able to "shut down" activities during extended periods of unfavorable
conditions; Rice et al. 1977), and 3) the time available to work at this
level is greater than at lower tide levels. Although sensitivity and
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resource value of dominant species at lower tide levels may be greater, many
of these species are also found at the mid tide level. It is felt that the
opportunity to sample on virtually every 24-hour tide cycle is overriding.
In the WDOE and MESA sampling programs, there were several sites and times at
which planned low elevation samples could not be taken due to wave and tide
conditions. The selection is justified statistically by our analytical
results indicating that the effects of elevation on uniform soft substrates
are limited (Section 6.2.1).
The appropriate subtidal level is between 5 and 1O m below MLLW where
effects of an oil spill on subtidal algae and invertebrates would be most
acute and easily observable. Concentrations of petroleum and dispersants
would be high at this depth but the effects of wave action would be less
likely to remove the materials than at shallower depths. Our cluster
analyses (Section 6.4.2) indicated that strictly subtidal species, often more
sensitive than intertidal species (Rice et al. 1977), become common in this
range. Also, similarity among sites was higher at sites deeper than -5 m.
Moreover, diving activities are less hindered by buoyancy below -5 m and
considerably more time can be devoted to sampling at depths above -10 m.
At all sites sampled, replicate samples should be collected in a
doubly stratified random manner, where stratification is by general density
levels for dominant organisms if practically discernible within the mid
intertidal stratum (Figure 35; as suggested by Moore and McLaughlin 1978),
avoiding obvious habitat nonconformities such as boulders, crevices, ridges,
tidepools, etc. The purpose of this procedure is to eliminate as much cross-
sample and nuisance variation as possible by logical density, assemblage, or
habitat stratification and thus reduce the residual error. For example, if
quadrats are placed completely randomly as indicated in Figure 35a,
x ± s = 174 ± 218 barnacles/quadrat; obviously, with 48 percent of the
quadrats empty, s will be quite high. However, if the quadrat positions are
initially established according to general density groups (e.g., high,
moderate, and low), variance within each group would be reduced substantially
(density estimates for the groups are 44 ± 48, 194 ± 101 and 45O ± 128,
respectively, for the areas of low, mid, and high density). Pooling the data
for all areas still provides an overall density estimate of 174
barnacles/quadrat but the probability of detecting a change in any of the
given blocks is considerably higher using this technique.
Also, mid intertidal protected rocky habitats often support large,
discretely distributed populations of mussels, barnacles, and algae. To
sample all three of these major assemblages simultaneously produces high-
variance data for all three, whereas if sampling and analysis were stratified
by assemblage, within-assemblage variability would be reduced considerably,
even if replication were not increased. It should be pointed out that the
purpose of a baseline study is to provide information to permit detection of
changes, not to characterize the assemblages.
Where the substrate is sufficiently stable, the sampling area should
be well marked to permit precise relocation of the site, sampling elevation,
and quadrats. Since sample collection affects subsequent data from that
precise spot, a strong effort should be made to preclude resampling of a
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Sampling Quadrat-
Boundaries
7
Experimental Block
Boundaries
a) Regular Sampling Grid
Areas of
Moderate
Density
Sampling Quadrat
Boundaries
Area of High
Density
•
•
^
Area of Low.
^ Density
Experimental Block
Boundaries
b) Sampling Grid Blocked According to Initial Density Levels
Figure 35. Hypothetical barnacle distribution with two alternative
sampling grids. Each dot represents 100 individuals.
(After Moore and McLaughlin, 1978.)
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plot. To accomplish this, we suggest that the location of all projected
samples be determined randomly before sampling commences and that sampling
plots not be overlapping.
7.3.4 Replication
The degree of replication required to permit detection of specified
changes varies considerably by habitat, numerical parameter, and species
(Tables 16, 17, 28, and 29), but in most cases, it is fairly high. The
purpose of continued monitoring is to provide baseline data for comparisons
following an oil spill. The expected change in species richness and
diversity would be a reduction. The expected change in most algae would be
an increase whereas the invertebrates would initially decrease (e.g., Smith
1968). since we can generally predict the direction of change that each
parameter or species would take we can plan to use a one-sided test. This
serves to reduce the replication required appreciably (see Tables 16, 17, 28,
and 29).
For most parameters or species, it is probably reasonable to expect
changes in mean values of at least SO percent under natural conditions.
Therefore, if we establish a sampling design so as to have a high probability
of detecting changes of 50 percent, we will have a high probability of being
able to detect changes resulting from an oil spill or other perturbation.
Using data presented in Tables 16, 17, 28, and 29, we developed tables
showing the number of quadrats or cores that would be required to permit a
90 percent probability of detecting a 50 percent reduction in the numerical
assemblage parameters (Table 31) and in density of some of the dominant
species in rock and soft substrates (Table 32).
For assemblage parameters, the required replication is not
overwhelming except at the 1.8 m level or for species diversity. On rock,
six and nine 0.25-ra quadrats may be adequate at the 0.0 m and 0.9 m levels,
respectively, to detect reductions of 50 percent in S and log (N+l). On mud
or mixed-fine sediments, three o.05-m cores may be adequate (Table 31).
For changes in average density of selected species, the situation is
different; 77 percent of the species would require 10 or more quadrats to
permit a 90 percent chance of detecting a 50 percent reduction (one-sided
test) in density. On rock, the most favorable situation is at the 1.8 m
level where six taxa can be safely assessed with 1O or fewer replicates. At
the 0.9m and 0.0 m levels, only gammarid amphipods can be assessed with 10
or fewer quadrats and the generality of this taxon makes it of limited
significance for such purposes. All of the remaining species require 15 or
more replicates.
These statistics show the importance of the double stratification
procedure recommended above. The reduction in variance associated with
density stratification should result in a useful reduction in replication.
On soft sediments, only one species of those examined would require
less than 10 replicates, and more than half the species would require more
than 20 replicates. However, these numbers are probably somewhat exaggerated
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TABLE 31. REQUIRED REPLICATION* FOR DETECTION OF CHANGES IN NUMERICAL
ASSEMBLAGE PARAMETERS, ROCK AND SOFT SUBSTRATES
Plants Animals
Sp logiu(Wp-H) tip Sa 1og10(Na+l) Ha'
Rock Habitats
Tongue Point: U.O m 7 <4 >25 5 <4 <4
0.9 m 9 5 >25 10 <4 13
1.8 m >25 19 17 16 <4 20
Cantilever Pier - high >25 — — 15 <4 7
Soft Substrate
Protected mud or — — — <3 <3 <3
mixed fine,
low to mid-elevations
Exposed sand, -- -- -- 18 23
high elevation
*
Approximate numbers of replicates required to permit a 90 percent
probability of detecing a 50 percent reduction in the parameter are
tabled. Values are based on sampling methodology and results from
the Baseline Studies Program.
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TABLE 32. REQUIRED REPLICATION FOR DETECTION OF CHANGES IN DENSITY
OF DOMINANT SPECIES, ROCK AND SOFT SUBSTRATES
Elevation (m)
U70
Rock Substrate
Alaria sp.
Gammarid amphipods
Halosaccion glandiforme
Lacuna spp.
Katharina tunicata
Balanus cariosus
Idotea spp.
Fucus distichus
Gigartina spp.
Endocladia
Collisell a spp.
£. digitalis
£• stn'gatella
Littorina spp.
U sitkana
Chthamalus dalli
35
5
Balanus glandula
Soft Substrate
Eteone Tonga
Glycinde pi eta
Pygospio elegans
Pseudopolydora kempi
Armandia brevis
Capitella capitata
Macoma nasuta
Transenella tantilla
Corophium spp.
45
15
45
10
»50
20
47
<5
25
>50
9
48
19
41
15
30
>50
47
»50
4
5
»50
1
8
6
5
Approximate numbers of replicates required to permit a 90 percent
probability of detecting a 50 percent increase in log transformed plant
weights or a 50 percent decrease in log transformed animal counts are
tabled. Values are based on sampling methodology and results from the
Baseline Studies Program.
3 157
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because they are based on a mixture of sediment types and two lower
elevations. Thus it should be possible to improve them considerably by
restricting sampling to a specific sediment type and elevation.
In summary, we recommend that to detect reductions of the magnitude
specified in assemblage parameters at unspecified sites in the area of study,
at least nine replicates be examined on low or mid intertidal rock and at
least three on low to mid intertidal soft substrates. We further recommend
that to detect specified changes in density of abundant species, at least 20
replicates be examined initially on low or mid intertidal rock or soft
sediments. The statistics can be re-evaluated subsequent to the first
sampling period at a specific site and modified accordingly for later
surveys.
7.4 MONITORING STUDIES
7.4.1 Sampling design for intertidal and 3Ulyt\dal rock
While rocky habitats are not considered the most vulnerable to long-
term effects of spilled oil, there are situations where monitoring this
habitat is desirable (e.g., where it is a dominant in a given area or where
there are already useful data available). Several types of data must be
collected to provide useful, meaningful descriptions of intertidal and
subtidal rock assemblages. The size and density range of the organisms that
must be examined is large (from barnacles, limpets, and littorine snails to
kelps) and thus a variety of sizes of sampling units is recommended to sample
efficiently and effectively and thus provide statistically useful data points
for each parameter without excessive effort. Many larger organisms such as
starfish, urchins, and laminarian kelps, frequently of considerable
importance at lower intertidal and subtidal levels on rock, are often
distributed in large patches best sampled by relatively large quadrat sizes
(1m , 1 HI x 5 m). However, these species are of relatively less importance
at many mid tide areas or may migrate downslope making them unsuitable
baseline indicators. We therefore recommend continued use of o.25-m
quadrats as the basic unit for rocky intertidal sampling at mid tide levels.
Our recommended level of replication will allow random pooling of 0.25-m
data so that averages for larger sampling units can be used if examination of
the data indicates that this will improve normality of distributions and
result in a reduction in the range of confidence limits. A smaller subsample
(O.Ol m ) is recommended for enumerating very numerous species (e.g.,
>100/0.25 m ).
For subtidal habitats, a. certain amount of latitude is suggested
because of the great range of variability in density and biomass that will be
encountered. We also suggest that plant biomass estimates be limited to
laminarian kelps where they dominate because they are more stable and easier
to identify. Again, it is important to recognize that the data obtained in
this survey are to be used for comparisons within site rather than between
sites so that the sampling area selected can be "tailored" to the site as
long as the same area is used throughout.
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To allow practical field identification and enumeration of organisms a
minimum size of 3 mm is recommended. That is, organisms with maximum
dimension less than 3 mm should not be included in any analyses. This
minimum is recommended in order to permit estimation of densities of adult
littorines and limpets which would otherwise be mostly unsampled. This
arbitrary size limit is suggested in recognition of the necessity for some
standardized lower limit. No size limit will be agreeable to all
investigators.
A summary of methodology, sample units, and replication for each
parameter measured on rocky intertidal and subtidal habitats is given in
Table 33. For analysis, all density and biomass data should be scaled to a
per m basis whereas relative cover estimates apply generally to the study
area sampled. The density (count) and relative cover data should be obtained
directly by actual counts or visual estimates at the site.
A step-by-step breakdown of the recommended methodology for sampling
rocky sites follows:
1. Establish and permanently mark with flagged stainless steel bolts
both ends of a 100-m centerline parallel to the water line at the
elevation(s) determined as described above. Subtidally, it is useful
to mark the entire transect with a "permanent" polypropylene line to
facilitate relocation. A 50-m centerline can be used if areal extent
of the zone to be sampled is limited. Additional bolts may be placed
if needed to insure following of the beach contour. Establish
sufficient additional markers to permit relocation of the bolts.
Foot traffic should be restricted to a lane 1 m wide around the
centerline to reduce damage to the assemblages during sampling.
2. Lay out a 50- or lOO-m tape (as appropriate) along the beach contour
from bolt to bolt or along the permanent transect line. Locate
randomly pre-selected cardinal number on the measured tape. Use
randomized techniques to locate quadrats above, below, left, and
right of the cardinal numbers.
3. Photograph labeled quadrat using color film.
4. Estimate percent cover of overstory macrophytes such as laminarians.
Cut and bag all overstory species with holdfasts located within the
quadrat for density and biomass estimation. Estimate percent cover
of understory algae, cut and add to those already bagged. Field
segregation of species or major groups into different bags may save
considerable laboratory sorting time. Any animals (>3 mm) attached
to portions of the fronds lying within the quadrats should be
retained for later counts. Estimate percent cover of encrusting
algae. In some cases, subsampling of algae (e.g., articulated
corallines) may be warranted. If so. remove species to be subsampled
only from the lower left-hand O.O1 m of the larger quadrat. This
may be best accomplished after all animals have been counted.
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TABLE 33. PROPOSED SAMPLING PROGRAM, ROCKY INTERTIDAL AND SUBTIDAL HABITATS.
Organism
1. Large Macrophytes
(>3mm)
2. Large Motile Invertebrates
(>3mm)
3. Encrusting or Sessile
Invertebrates
4. Very Abundant Species
5. Key Assemblage Component
Species
Parameter
Intertidal
Percent cover
(visual estimate)
Biomassa'b
(scrape)
Subtidal
Density
(visual count)
Percent cover
(visual estimate)
Biomass
(scrape)
Intertidal
Density
(visual count)
Biomassc
[collect)
Subtidal
Density
(Visual count)
Biomassc
(collect)
Percent cover
(visual estimate)
Biomass0'
Density
and/or biomass
Size Frequency
Ctotal length,
carapace length
aperture size, etc.)
Quadrat
Size
0.25 m2
0.25 m2
1 m2 to
1 171 X 5 IT)
0.25 m2
1-m2 to
1 -m x 5 m
0.25 m2
0.25 m2
0.25m2 to
1 m _x 5 ni
0.25 m2 to
1 m x 5 m
0.25m2
0.01 IT)2
0.01 m2
Replication
20
20
20
20
20
20
20
20
20
20
(d)
20e
Use first 200-
300 individuals
collected
Unit of
Measure
Percent
g wet weight
per m2
No. per m2
Percent
g wet weight
per m2
No. per m2
g wet weight
per m2
No. per m2
g wet weight
per m2
Percent
g wet weight
per m2
a Very abundant species may be subsampled as in 4.
b Not done for encrusting plants.
c Optional depending on available time and resources.
d For biomass of species such as barnacles and mussels see methodology in the text.
e Subsample one 0.01 m2 area in the center of each of the 20-0.25-ffi2 quadrats.
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5. Count all invertebrates (>3 iran maximum dimension) within the quadrat
(see item 7, below, for variation). Species too numerous to
conveniently count (say 100 per m2 quadrat) may be subsampled by
counting only those individuals present in a O.Ol-m^ quadrat in the
lower left-hand corner of the quadrat. Estimate percent cover for
sessile and colonial species (e.g., barnacles, mussels, tunicates,
sponges, and bryozoans). It is often appropriate to measure both
abundance and cover for barnacles and mussels. If counting is too
laborious for these taxa, the following method can be used: Count
all barnacles in a O.Ol-m2 quadrat placed non-randomly in an area of
readily estimated heavy cover (e.g., 100 percent) and use this factor
to extrapolate to the number for the entire quadrat. For example, if
the entire quadrat had 75 percent cover and if 0.01 m2 of 90 percent
cover had X individuals , then the entire quadrat had an estimated
(0.75) (25) (X/0.9) individuals. Use the average of the number/percent
ratio obtained in 0.01-m2 subsamples from three randomly selected
quadrats to estimate numbers of these species represented by the
percent cover estimated in the remaining quadrat at that station.
Representative specimens of questionable species should be collected
for taxonomic resolution in the laboratory.
6. Where laminarian kelps and large invertebrates are common, count the
large plants or invertebrates in the larger (1-m2 or 1 m x 5 m)
quadrats. Density level (and water clarity subtidally) should be
considered in choosing the size of the quadrat to be employed. After
enumeration is completed, the plants can be collected for measuring
biomass or size. Mobile animals should be left in place as removal
could affect subsequent density estimates.
7. Because of the field and laboratory time required to obtain
reasonably accurate estimates of animal biomass and because density
is a defensible indicator of faunal abundance, we do not recommend
routine collection of biomass data because removal of animals during
one sampling period could influence community structure in subsequent
periods during this type of baseline program. If animal counts are
being measured, biomass for many species can be estimated in the
laboratory on the basis of size data, length-weight regressions, and
density data.
8. Take samples of five to six key species for length-frequency
analysis. Species should be pre-selected based on site
reconnaissance so that collections can begin in the first quadrat
sampled. To remove size bias in collection, the first 300 individuals
counted in the random quadrats should be retained. Three hundred is a
recommended minimum sample size for size-frequency analysis but may
not always be available. It may be possible to obtain size data for
some species from photographs taken subsequent to algal removal (e.g.,
aperture width or disc diameter of barnacles).
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7.4.2 Design for int&yticla J ^n
soft-
The three major types of data necessary to provide useful descriptions
of the biological assemblages on intertidal and subtidal soft substrates are
invertebrate abundance and biomass and size structure of important species.
The size and density range of the organisms that must be examined, although
considerable, is not as large as that observed on rocky assemblages. Thus,
the variety of sampling units that must be used to sample efficiently and
effectively is not 'as large. We recommend sampling with 0.05-m and O.OO8-m
core samplers, and 0.25-m and 1 x 5 m quadrats to provide suitable samples
for specified parameters (Table 34). As in the case of rock habitats, all
density and biomass data should be normalized to a per m basis for
comparison .
TABLE 34. RECOMMENDED PARAMETERS AND METHODOLOGY, SOFT SUBSTRATE SAMPLING
Organi sm/Parameter
Large Invertebrate Abundance
and Biomass
Small Invertebrate Abundance
and Biomass
Relative Plant Cover
Plant Abundance
Plant Biomass
Population Size Structure
Type of Sieve
Sampler Mesh (mm)
0.05-m2x 12.5
30 cm corer
0.008-m2x 1
15 cm corer
0.25-m2 quadrat
0.25-m2 quadrat
0.25-m2 quadrat
Both cores Varies
Type of
Sample
Core
Core
Visual
Estimate
Count
Removal
Cores
Final Unit
of Measure
No./m2
g wet weight/m
No./m2
g wet weight/m
%
No./m2
g wet weight/m2
—
The 0.05-m core sampler should be used to collect data on larger,
less common and deeply buried species. The sample should extend into the
sediment to a depth of 30 cm, thus yielding a 15-liter sample. Subtidally,
these samples are most easily collected with an air lift sampler from within
a 0.05-m core that has been driven into the substrate with a small sledge-
hammer . Since the purpose for this sample is to provide quantitative data on
large invertebrates, the sieve mesh size recommended to screen the samples
(12.5 mm) is the same as was used for most large core samples in the baseline
studies. It will facilitate processing the large volume of sediment
collected, eliminate the small abundant species, and retain the medium to
large size individuals of the larger species.
To allow easy sampling and adequate replication for obtaining
densities of smaller infauna we suggest using a 0.008-m corer (e.g.. Lees et
al. 198O). The O.O08-m core sampler is a readily purchased clam gun. The
sample should extend into the sediment to a depth of 15 cm, thus yielding a
1.1-liter sample. With slight modifications to the standard clam gun, these
core samples can be collected easily subtidally. The clam gun should be
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fitted with a valve to close the relief port at the closed upper end of the
sampler, thus allowing suction to be maintained easily during extraction of
the sample. Before commencing extraction, the sampler should be rotated
rapidly and worked back and forth to break the core sample loose and allow
water to flow into the hole. In addition a long cap should be fitted to the
sampler with surgical tubing thongs to use in capping the sampler to preclude
sample loss after extraction. Since the purpose of this sample is to provide
quantitative data on small animals, the sieve mesh size recommended to screen
the samples is l.O mm; most sand and mud will pass through this sieve, but it
will retain a large proportion of the species, individuals and biomass of the
sample (Reish 1959). The 1 mm size has been commonly used (as has O.5 mm) in
nearshore infaunal work. However, 1 mm will provide continuity with the
existing data base and avoid some taxonomic problems and increased time
required to process samples sieved with a finer mesh.
Some species will be collected in both the large and small core
samples. In this case, the data set providing the highest estimate of
density should be used and the other data set ignored. In no case should the
data for any particular species be pooled. However, data for total animal
density in the infaunal assemblage at any particular site will be obtained by
combining converted density data (no./m ) for species based on large core
samples with those collected in small core samples.
On many soft substrate habitats, macrophytes (algae and sea grasses)
form appreciable components. It is useful to quantify these assemblages
where they are important. The same parameters should be measured as on rock,
namely, relative plant cover, plant density, and biomass. Plant density and
biomass of large forms such as Laminaria. should be measured with a 1 m x 5 m
quadrat. A 0.25-m quadrat is quite convenient for measuring relative cover
and biomass of smaller, more abundant forms such as Zostera. Samples for
biomass measurement should be obtained by collecting and weighing all plants
with roots or holdfasts located inside the quadrat (Houghton and Kyte 1978,
Lees et al. 1980). Relative cover can be efficiently measured by visual
estimation in a 0.25-m quadrat. This size is a satisfactory compromise
between what the observer can actually comprehend in one view above and below
water and what is large enough to use for kelps.
The general sampling scheme should be similar to that described above
for rock. A measured centerline should be established on permanent station
markers to insure accurate sample collection, in this case, care should be
taken to restrict most walking and swimming to a 2-m wide traffic lane
centered on the line. To randomize the position of samples, a three-digit
random number should be used. The first two numbers determine a branch point
on the centerline. To avoid sampling in the traffic lane, 1 m is added to
the third number to determine how far away from the centerline the sample
will be taken.
The size structure of important species can be determined in two basic
ways, i.e., by measuring the size of standard skeletal components for animals
possessing them or, for animals without hard parts, by weighing them whole.
If possible, the number of animals should be at least 300, but since the
specimens are to be provided by the core samples, this may not be feasible.
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In any event, the number of specimens used to determine size structure should
be as large as is possible since this reduces the amount by which the
estimator differs from the parametric mean.
Our analyses have clearly shown the need for better characterizations
of the physical habitat (Section 6.2). Therefore, in addition to the
biological samples collected at each site, replicate samples for sediment
grain size analysi£ and measurement of organic carbon and nitrogen should be
collected at each end and near the middle of the centerline during each
survey period. Moreover, dissolved oxygen (DO) content of interstitial water
in the sediment should be measured at depths of 2, 5, 1O, 20 and 30 cm in the
sediment by a method similar to that described by Jansson (1968). This will
permit a comparison of pre- and post-spill DO levels. Replication is
necessary to reduce the effects of natural small-scale variations in sediment
parameters. Oil contamination can have a severe impact on DO levels and
microbial respiration, which in turn strongly influence the infauna. These
samples will permit a more adequate description of natural, ambient sediment
conditions and provide data for multivariate analysis.
A step-by-step breakdown of the recommended methodology for sampling
soft substrates follows s
1. Establish and permanently mark with flagged steel rods (construction
rebar) both ends of a 100-m centerline parallel to the water line at the
elevation determined and described above. Subtidally, it is useful to
mark the entire transect with a "permanent" polypropylene line. A 50-m
centerline can be used if a real extent of the zone to be sampled is
limited. Establish sufficient additional markers to permit relocation
of the bolts. Foot and swimming traffic should be restricted to a. 2-m
wide lane around the centerline to reduce damage to the assemblages
during sampling.
2. Lay out a 50-m or 100-m tape (as appropriate) along the beach contour
from rod to rod or along the permanent transect line. Locate randomly
pre-selected cardinal numbers on the measured tape. Use randomized
techniques to locate quadrats or cores above, below, left and right of
the cardinal number.
3. Estimate percent cover of macrophytes such as eelgrass or laminarians.
Cut and bag all plants with roots or holdfasts located within the
quadrat for density and biomass estimation.
4. Count all invertebrates (> 3-mm maximum dimension) within the quadrat.
Representative specimens of questionable species should be collected for
taxonomic resolution in the laboratory.
5. Where laminarian kelps and large invertebrates are common, count them in
large quadrats (ImtolxSm). General density level (and water
clarity subtidally) should be considered in choosing the size of the
quadrat to be employed. After enumeration is completed, the plants can
be collected for measuring biomass or size. Mobile animals should be
left in place as removal could affect subsequent density measurements.
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6. Where live-sieve cores and infaunal cores are collected at the same
site, the latter should be collected first from a standard location
outside of the live-sieve core, (e.g., at the lower right-hand corner).
7.5 OIL SPILL IMPACT ASSESSMENT
The intent of an oil spill impact assessment is to document the
effects of an oil spill. Because oil spills generally involve accidents and
human error or negligence, they often result in litigation or damage
settlements; and, thus, it is of paramount importance that the data collected
during impact assessments be accurate, pertinent and sufficiently sound,
statistically and biologically, to be legally defensible. Given the amount
of time usually available and the tendency for weather conditions to be quite
poor at the onset of a spill (weather is often a direct or indirect cause),
it is immediately apparent that the task is monumental but extremely
delicate. The methods employed for impact analysis, at least initially, must
be very quick and examine only the more important dominant species and the
most susceptible relationships. A high degree of flexibility on the part of
both sampling program and investigators is required. The investigators must
be able to evaluate quickly the most valuable, germane, and sensitive
resources in an area and then implement the components of the assessment
program that will permit collection of a sufficient amount of appropriate
data. It is thus highly advisable that impact assessments be conducted by
trained scientists familiar with the geographical area in which they must
operate and its ecosystems.
The time limitation dictates that priorities be established on the
order in which different habitat types and biological assemblages are
surveyed. It is important to survey the most sensitive habitats first and
most completely. Thus, protected soft substrates and cobble or mixed-coarse
habitats should be examined before protected rock habitats; exposed habitats
should not be examined until satisfactory data are available for those
above. Since it has been often stated (e.g., Gundlach et al. 1980) that
exposed rocky habitats are most tolerant to oil contamination and recover
fairly quickly (e.g., Chan 1975, 1977), there should be little concern if
time (or budgetary) limitations preclude their examination. Emphasis should
be on the more important (characteristic) animals and plants involved in the
more important biological interactions known for each specific habitat, e.g.,
competition for space, grazing, and predation. On rocky substrates,
particular attention should be given to plants and herbivores; whereas, on
soft substrates, it should be accorded to animals constructing burrows.
These particular groups exert a strong influence on the assemblages
inhabiting the respective substrates and may be severely affected by oil
spills.
Because of the time constraints surrounding an oil spill impact
assessment, it is highly advisable to establish prior arrangements with
response entities. Assessment techniques should be evaluated, tested, and
reviewed and official channels of communication and contractual arrangements
developed. Time lost in completing these details after a spill severely
reduces the probability of acquiring satisfactory data. The response
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entities should be required to maintain response kits that include all of the
field equipment and supplies necessary to move immediately to the scene of an
oil spill and be self-sufficient.
The impact assessment program we recommend has four phases, namely:
1. Pre-oiling assessment;
2. Initial spill assessment;
3. Short-term post-spill reassessment; and
4. Recovery monitoring.
These provide a rational basis for detecting effects, evaluating the
magnitude of their immediate and long-term effects, and assessing long-term
contamination and recovery rates.
The techniques suggested below were selected to permit a rapid
assessment of the biota. In some instances the data collected are
qualitative rather than quantitative. They are a modification of a
methodology developed by Davis et al, (in press) while assessing oil spill
damage at several sites in the Atlantic Ocean. This methodology combines
geomorphological, chemical, and biological observations to permit assessment
of initial and subsequent impacts and prediction of long-term impacts and
recovery rates. All but the Phase IV recovery studies are one-time surveys.
7.5.1 Pre—oiling assessment—phase I
It is generally not possible to obtain detailed information on the
biota of the sites examined before they are oiled. In some cases, however,
limited pre-spill data can be obtained at sites prior to oil coming ashore,
or sites previously not oiled may be in the probable path of a drifting oil
slick. In those instances, a strong effort should be made to collect as much
data on dominant organisms at as many sites and on as many substrates as is
possible. At this point in time, the only limitation to sample and data
collection should be the time and money available for field efforts and not
concern over existing budgetary limitations of laboratory analysis (Smith
1979). Over-sampling can be easily rectified at a later date but
undersampling of pre-spill conditions is irreversible once a habitat has been
oiled.
The purpose of a pre-oiling assessment is obviously to obtain data on
pre-spill conditions at non-oiled sites (either control sites or sites at
which oiling is projected). The goal is to determine what organisms are
dominant, how many or how much, their stage of development and appearance,
and the sediment and chemical conditions in the habitats prior to oiling.
Besides information on the biological assemblages, the survey team should
obtain abundant photographic documentation of the general appearance of each
site and adequate numbers of sediment samples for hydrocarbon analysis.
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Wherever possible, pre-spill surveys should resurvey nearby stations
that were occupied during the baseline or monitoring studies so that they may
be used to assess effects of control (unoiled) sites. As in the case of pre-
spill surveys in previously unsurveyed sites, only parameters or samples that
can be estimated or collected rapidly should be considered so as to maximize
the amount of data that can be collected in the limited time available. The
aim of resurveying old study sites is to develop an updated description of
some conditions that may be used to evaluate the degree of stability of the
biotic assemblage prior to the oil spill.
We assume that, in order to make most efficient use of time before a
spill, most travel between sampling sites will be accomplished by
helicopter. If this occurs, a useful type of data would be aerial
photographs of each station on both color and infrared film. This is most
effectively accomplished when the sunlight is from offshore, but in the
absence of sun, light should be strong. Furthermore, photographs taken at
low tide are more useful than those taken at high tide.
Upon arriving at each site, a site description sufficiently detailed
to permit relocation for subsequent surveys should be recorded and permanent
relocation stakes installed above the storm swash line. In addition,
perspective photographs should be taken in both directions along the beach
and across the beach toward the water. Construction steel ("rebar") stakes
should be installed at several points along a transect across the beach at
which sampling will be concentrated.
A beach profile should be developed along this transect indicating
elevation change related to distance from the upper permanent relocation
stake. The recommended profile method is that of Emery (1961). in
conjunction with this topographic profile, the survey team should describe
the associated geomorphology and biological assemblages, noting dominant
structures, organisms, and assemblages and prominent changes in composition.
During this procedure, numerous photographs of the biological assemblages
should be taken with color and infrared film. These photographs should
include detailed views of the specific subassemblages (e.g., mussel beds,
barnacle encrustations, or algal turfs) that dominate the various zones.
In conjunction with the general description of the biological
assemblages accomplished at each site along the profile, quantitative data
describing the level of dominance by the more important species should be
collected at three intertidal levels (low, mid, and high), if tide conditions
permit, and one subtidal level between 5 m and 10 m.
In rocky habitats, much of the data can be collected directly. Re-
types of data to be collected are relative (percent) cover, density (no./m )
and size-frequency . Cover and density data for the visually dominant
organisms should be recorded at each of three levels in about 20 0.25-m
quadrats. (This replication is based on lower Cook Inlet studies by Lees et
al. (198O) since plant cover was not uniformly recorded in the MESA/DOE
studies. ) Efforts should be limited to species covering more than 5 percent
of the rock surface or at densities greater than 10/m ; special attention
should be given to important herbivores (such as limpets, chitons,
167
-------
littorines, and sea urchins) and predators such as whelks (Nucella) and
starfish. Size data should be obtained by photography for barnacles and
collection of samples for mussels, limpets and littorines. All photographs
taken for size measurements should be close-ups with a. scale included to
facilitate measurement; the level of detail should be sufficient to measure
aperture length accurately to 1 mm. Several of these photographs should be
taken at the relocation stakes so that they can be duplicated after the spill
for comparison.
In soft substrate habitats, most of the data will be on infaunal forms
and must be determined by laboratory analysis of sediment samples. Thus,
most of the effort will involve collection of core samples with a clam gun
(0.008-m ) core sampler. Twenty core samples should be collected at each of
three levels for infaunal analysis. Each sample should be bagged and
labelled separately and preserved with a 10 percent buffered formaldehyde-
seawater solution. In addition, three smaller core samples should be
collected at each level for sediment grain size analysis. Finally, if
burrowing organisms or algae are common in the area, about twenty 0.25-m
quadrats should be measured to determine burrow density and relative cover by
plants. Lesser replication may be adequate for some parameters in some
habitats (see Tables 31 and 32).
We believe that an important indication of the short-term conditions
at a site can be determined by an examination of the shell debris and wrack
in the high-tide swash line. One would expect major changes in the
composition, condition, and volume of material in the swash line if a spill
caused appreciable damage to the biota. Therefore, we recommend that part of
any pre-spill sampling at each site be to collect all the biological material
in 25 randomly located 0.25-m quadrats in the high tide swash line, bag,
preserve, and label each sample separately, and archive these samples for
future comparisons. This effort can be accomplished during high tides and
thus need not conflict with the standard sampling that is tide-limited. A
severe storm between pre- and post-spill samplings can reduce the reliability
of results unless spatial controls are established.
It is very useful to obtain hydrocarbon baseline information at each
site to compare with existing hydrocarbon information gathered by Brown et
al. (1979). The survey team should collect sediment samples at all sites for
that purpose. An effort should be made to collect these samples from
locations where oil would collect and be retained, e.g., under rocks and in
silt pockets. It is of absolute importance that the samples be collected and
stored in chemically appropriate containers so that the samples will not be
contaminated. This requires considerable prior preparation and is another
reason for establishing commitments before an oil spill requires sampling.
7.5.2 Initial spill assessment—phase II
The initial spill assessment, often the first survey that will be
conducted at an oiled site because of the time limitations surrounding an oil
spill, is quite similar in approach to Phase I. The purpose of this study is
to determine the initial response of the assemblages to oil. This involves
documentation of the abundance of dominant organisms as well as detection of
168
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Wherever possible, pre-spill surveys should resurvey nearby stations
that were occupied during the baseline or monitoring studies so that they may
be used to assess effects of control (unoiled) sites. As in the case of pre-
spill surveys in previously unsurveyed sites, only parameters or samples that
can be estimated or collected rapidly should be considered so as to maximize
the amount of data that can be collected in the limited time available. The
aim of resurveying old study sites is to develop an updated description of
some conditions that may be used to evaluate the degree of stability of the
biotic assemblage prior to the oil spill.
We assume that, in order to make most efficient use of time before a
spill, most travel between sampling sites will be accomplished by
helicopter. If this occurs, a useful type of data would be aerial
photographs of each station on both color and infrared film. This is most
effectively accomplished when the sunlight is from offshore, but in the
absence of sun, light should be strong. Furthermore, photographs taken at
low tide are more useful than those taken at high tide.
Upon arriving at each site, a site description sufficiently detailed
to permit relocation for subsequent surveys should be recorded and permanent
relocation stakes installed above the storm swash line. In addition,
perspective photographs should be taken in both directions along the beach
and across the beach toward the water. Construction steel ("rebar") stakes
should be installed at several points along a transect across the beach at
which sampling will be concentrated.
A beach profile should be developed along this transect indicating
elevation change related to distance from the upper permanent relocation
stake. The recommended profile method is that of Emery (1961). In
conjunction with this topographic profile, the survey team should describe
the associated geomorphology and biological assemblages, noting dominant
structures, organisms, and assemblages and prominent changes in composition.
During this procedure, numerous photographs of the biological assemblages
should be taken with color and infrared film. These photographs should
include detailed views of the specific subassemblages (e.g., mussel beds,
barnacle encrustations, or algal turfs) that dominate the various zones.
In conjunction with the general description of the biological
assemblages accomplished at each site along the profile, quantitative data
describing the level of dominance by the more important species should be
collected at three intertidal levels (low, mid, and high), if tide conditions
permit, and one subtidal level between 5 m and 10 m.
In rocky habitats, much of the data can be collected directly. The
types of data to be collected are relative (percent) cover, density (no./m )
and size-frequency . Cover and density data for the visually dominant
organisms should be recorded at each of three levels in about 20 O.25-m
quadrats. (This replication is based on lower Cook Inlet studies by Lees et
al. (198O) since plant cover was not uniformly recorded in the MESA/DOE
studies. } Efforts should be limited to species covering more than 5 percent
of the rock surface or at densities greater than 10/m ; special attention
should be given to important herbivores (such as limpets, chitons,
167
-------
littorines, and sea urchins) and predators such as whelks (Nucella) and
starfish. Size data should be obtained by photography for barnacles and
collection of samples for mussels, limpets and littorines. All photographs
taken for size measurements should be close-ups with a scale included to
facilitate measurement; the level of detail should be sufficient to measure
aperture length accurately to 1 mm. Several of these photographs should be
taken at the relocation stakes so that they can be duplicated after the spill
for comparison.
In soft substrate habitats, most of the data will be on infaunal forms
and must be determined by laboratory analysis of sediment samples. Thus,
most of the effort will involve collection of core samples with a clam gun
(0.008-m ) core sampler. Twenty core samples should be collected at each of
three levels for infaunal analysis. Each sample should be bagged and
labelled separately and preserved with a 10 percent buffered formaldehyde-
seawater solution. In addition, three smaller core samples should be
collected at each level for sediment grain size analysis. Finally, if
burrowing organisms or algae are common in the area, about twenty O.25-m
quadrats should be measured to determine burrow density and relative cover by
plants. Lesser replication may be adequate for some parameters in some
habitats (see Tables 31 and 32).
We believe that an important indication of the short-term conditions
at a site can be determined by an examination of the shell debris and wrack
in the high-tide swash line. One would expect major changes in the
composition, condition, and volume of material in the swash line if a spill
caused appreciable damage to the biota. Therefore, we recommend that part of
any pre-spill sampling at each site be to collect all the biological material
in 25 randomly located 0.25-m quadrats in the high tide swash line, bag,
preserve, and label each sample separately, and archive these samples for
future comparisons. This effort can be accomplished during high tides and
thus need not conflict with the standard sampling that is tide-limited. A
severe storm between pre- and post-spill samplings can reduce the reliability
of results unless spatial controls are established.
It is very useful to obtain hydrocarbon baseline information at each
site to compare with existing hydrocarbon information gathered by Brown et
al. (1979). The survey team should collect sediment samples at all sites for
that purpose. An effort should be made to collect these samples from
locations where oil would collect and be retained, e.g., under rocks and in
silt pockets. It is of absolute importance that the samples be collected and
stored in chemically appropriate containers so that the samples will not be
contaminated. This requires considerable prior preparation and is another
reason for establishing commitments before an oil spill requires sampling.
7.5.2 Initial spill assessment—phase II
The initial spill assessment, often the first survey that will be
conducted at an oiled site because of the time limitations surrounding an oil
spill, is quite similar in approach to Phase I. The purpose of this study is
to determine the initial response of the assemblages to oil. This involves
documentation of the abundance of dominant organisms as well as detection of
168
-------
dead, moribund, or displaced organisms and behavioral changes such as altered
evasive behavior. Phases I and II surveys may be conducted concurrently at
non-oiled and oiled (control) sites, respectively, in the absence of adequate
time to conduct pre-spill surveys before oil starts grounding.
The methods of quantifying abundance of dominant organisms should be
the same as in Phase I. Also, the types of habitats and animals selected for
censusing should be basically the same. However, if organisms not previously
selected for census are abundant among the casualties of the spill, an
attempt should be made to document the abundance of the healthy population at
both oiled and non-oiled sites if feasible. The numbers of dead and moribund
organisms should be estimated with standard 0.25-m quadrat techniques, as
described above.
It may be desirable to collect numerous specimens or samples for
examination under more suitable conditions in the laboratory so as to improve
the accuracy of the taxonomic and enumeration data. As in the case of Phase I
surveys, oversampling is preferable. However, if Phase I studies were
possible before oiling, there is no need to expend valuable time in
resurveying sites at which oiling has not occurred except to search for dead
and moribund animals.
Behavioral changes in invertebrates should be measured at oiled and
non-oiled sites. This can be accomplished by measuring response time of
normal behavior, e.g., righting time of snails, escape time of crabs,
retraction time of clams or sea anemones.
Exposure to oil should be quantified by estimating the area and
thickness of oil cover in the oiled areas. Also, sediment samples should be
collected from under rocks and in areas of soft substrates. Numerous samples
should be collected. If possible, core samples should be divided into 2 cm
thick sections to determine the depth of contamination. This is particularly
important in heavily burrowed habitats such as Jamestown, where substantial
quantities of oil could be captured in ghost shrimp burrows over 30 cm deep
in the sediment.
As indicated above, liberal photographic documentation of conditions
is extremely helpful. In areas where a pre-oiling assessment was possible,
photographs should be taken at all the permanent stakes that can be relocated
to permit comparisons of pre- and post-oiling appearances.
During planning sessions for clean-up efforts in the early stages of
oil spills, it would be quite useful to establish several different zones to
which specific clean-up methods are limited, and areas in which clean-up is
not attempted. This would permit a clear design for comparing the
effectiveness and suitability of the alternate methods of clean-up as well as
natural recovery. Such experiments would be very useful in the selection and
rejection of available clean-up technology in later spills and could avoid
gross mistakes and inappropriate expenditures at later spills.
169
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7.5.3 Short-term post—apill reassessment—phase III
Two major objectives o£ this phase of the study are to: 1) document
the full impact of mortality resulting from the direct effects of an oil
spill (combining immediate and delayed mortality), and 2) detect initial
stages of recovery. Thus, the same techniques employed in Phases I and II
above should be applied at previously surveyed oiled and unoiled (control)
sites to determine the differences between initial and subsequent surveys due
to oiling, clean-up, and recovery (at the oiled sites) and natural variation
(at the control sites). A crucial component of the short-term assessment is
the examination of the shell debris and wrack in the high-tide swash line.
These surveys should not be conducted until at least one month following a
spill, but before three months have elapsed to avoid large natural changes
from seasonal effects.
7.5.4 Recovery monitoring studies—phase IV
The objectives of these studies are to: 1) document rates and
patterns of recovery in areas affected by oil and/or clean-up efforts and
2) attempt to determine the degree to which rates and patterns of recovery
are influenced by a) recruitment rates and patterns of colonizing species,
and b) residual oil and/or clean-up materials. These data would augment
information on colonization of oil-contaminated sediments developed for MESA by
Vanderhorst et al. (1979). These studies should be conducted concurrently
with on-going standard monitoring studies which will provide important
information on recruitment rates and patterns in undisturbed areas.
Furthermore, the sampling techniques for the recovery monitoring studies
should be identical to those for the standard monitoring studies, as
contrasted with the Phase I, II and III oil spill assessment studies, except
that the sites surveyed for Phase IV" should be examined at low, mid, and high
intertidal levels where these levels have been affected. Furthermore, as
many of the "traditional" monitoring sites as possible should be used for
unoiled control sites, but studies there should be augmented to provide data
from the upper and lower tide zones. These studies should be conducted
synchronously with monitoring studies, i.e., on a biannual basis, in summer
and winter.
Two different types of studies will be required to accomplish the
objectives of Phase IV studies. The standard monitoring techniques described
for the monitoring studies should provide the data necessary to document
rates and patterns of recovery. However, experimental manipulation will be
necessary to distinguish between the effects of inhibition by residual oil
and clean-up materials and natural recruitment rates and patterns on rates of
recovery. Phase IV studies should commence approximately three months
following the termination of clean-up activities to allow conditions to
stabilize and recovery to develop. The number of sites surveyed should be
limited to not more than one per treatment (untreated oiling and each major
clean-up technique) on each major habitat type. This permits adequate
concentration of sampling efforts and thus maximizes the results of
expenditures when combined with the "control" data from the standard
monitoring study. All affected and control sites studied in Phase IV should
be confined to the general geographic area of the spill since our evaluation
170
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of the baseline data indicated that it is of only limited use to extrapolate
between the major geographic regions of the WDOE and NOAA/MESA studies.
As part of both baseline and recovery monitoring surveys, a routine
hydrocarbon sampling program should be implemented to monitor hydrocarbon
levels in the dominant organisms and in the sediments. Where possible, the
organisms sampled should include members of all trophic levels. Recommended
groups and species in the intertidal zone include: 1) plants - rockweed
< Fucus distichus); 2) herbivores - acmaeids; 3) suspension feeders - mussels
fMytilus edulis). barnacles (Balanus cariosus). and clams (Protothaca
staminea or SaxidoIUMff giganteus); 4) deposit feeders — clams (Macoma). ghost
shrimp (Callianassa spp. or Upoqebia pugettensis) and the burrowing sea
cucumber (Leptosynapta clarki>.• and 5) predators - snails (Nucella spp.) and
starfish (Leptaster-las hexactis or Evasterias troachelil). Alternate species
from subtidal habitats include Laminaria saccharinar Hinnites multirugosa.
Parastichopus calif ornicus and Evasterias or Pycnopodia helianthoides.
Sediments should be analyzed to a depth of at least 30 cm, especially under
rocks in the protected rocky or cobble areas and in soft substrate habitats
that had extensive burrow systems before exposure to oil.
In addition to the Phase IV monitoring studies, we recommend that a
program be implemented to partially differentiate between the effects of
residual oil in a habitat and the vagaries in recruitment in the patterns and
rates of recovery of previously dominant species that were extirpated by oil
or clean-up operations in oiled habitats. The method of study would be to
transplant test populations of selected, previously dominant species into
oiled and control study areas and then monitor their success. Success can be
gauged by comparing growth rates as well as survival. All trophic groups
except predators should be examined.
Taxa that should be considered for transplant studies on rock habitats
include rockweed (Fucus distichus). mussels < Mytilua edulis \. barnacles
(Balanus spp. ), and limpets (Acmaeidae) and sea urchins (Strongylocentrotus
spp.), all of which are readily available for collection at undisturbed
sites. The attached taxa such as rockweed, barnacles, and mussels should be
collected on easily transportable cobbles or small boulders and transplanted
to marked locations at both the oiled and control sites. Unattached species
such as limpets and sea urchins should be removed from the rocks at
undisturbed sites and transplanted to marked rocks at control and oiled
sites.
Taxa that should be considered for transplant studies on soft
substrates include clams (e.g., Protothaca. saxidomus. and ciinocardium).
ghost shrimp and the burrowing sea cucumber lieptosynapta. The clams and sea
cucumbers should be transplanted into plastic mesh boxes buried in the
sediment so that they can be easily recovered periodically to census
survival. In addition, growth rates should be compared between control and
oiled sites. Ghost shrimp should be transported to oiled areas in which
populations were destroyed and burrows are absent. At these sites, they
should be protected until either they have established a new burrow or it is
determined that they will not dig a new one. The locations of the
171
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transplanted shrimps should be marked and the number of remaining burrows
noted on each subsequent survey.
Survival of the transplanted populations will be more of a problem to
assess at the control sites where well-established populations will already
exist than at the oiled sites where adults will be absent. However, it is
important to assess the effect of transplant activities on survival rates of
a transplant population in order to correct the observed survival rates of
the transplant populations at the oiled sites. In the latter areas, all
adults in the vicinity of transplants can be assumed to be introduced.
However, at the control sites, the transplant populations will have to be
marked in such a way as to be identifiable. In the case of rockweed and
barnacles, the rocks upon which the populations were transplanted can be
marked. The clams and sea cucumbers will be placed in marked plastic boxes
to facilitate recovery. The greatest problems are with limpets, sea urchins,
and ghost shrimp. With limpets and sea urchins the problem can be resolved
by placing the transplanted populations on isolated rocks or ledges from
which the resident population has been removed. For ghost shrimp, the
problem of identification cannot be completely resolved but the best approach
appears to be to use the collection site for the control transplant site,
thus removing a large majority of the adult shrimp and effectively destroying
the burrow systems over a large area. The transplant areas should be clearly
marked and their positions mapped so that they can be relocated. Equal
numbers of shrimp should be released in each transplant area and the numbers
of burrows in each area will be used as an index of survival.
To our knowledge, transplant studies have not been utilized in
conjunction with actual oil spill assessment. However, if properly
controlled and designed, we believe they could potentially contribute
substantially to the understanding of some of the factors influencing
recovery in oiled areas and the detection of the effects of residual oil.
Recruitment studies where the responses of larvae are measured would also
provide important data relative to recovery and community composition.
172
-------
of the baseline data indicated that it is of only limited use to extrapolate
between the major geographic regions of the WDOE and NOAA/MESA studies.
As part of both baseline and recovery monitoring surveys, a routine
hydrocarbon sampling program should be implemented to monitor hydrocarbon
levels in the dominant organisms and in the sediments. Where possible, the
organisms sampled should include members of all trophic levels. Recommended
groups and species in the intertidal zone include: 1) plants - rockweed
(Fucus diatichua) y 2) herbivores - acmaeids; 3) suspension feeders - mussels
(Mytilua edulis). barnacles (Balanus carloaus). and clams (Protothaca
staminea or saxidomus giganteus) .• 4) deposit feeders - clams (Macoma). ghost
shrimp (Callianassa spp. or Upogebia pugettensia) and the burrowing sea
cucumber (Leptosynapta clarki); and 5) predators - snails (Nucella spp.) and
starfish (Leptasterias hexactis or Evasterias troschelii). Alternate species
from subtidal habitats include Laminaria saccharins r Hinnites multirugosa.
Parastichopus calif ornicus and Evasterias or Pycnopodia helianthoides.
Sediments should be analyzed to a depth of at least 30 cm, especially under
rocks in the protected rocky or cobble areas and in soft substrate habitats
that had extensive burrow systems before exposure to oil.
In addition to the Phase IV monitoring studies, we recommend that a
program be implemented to partially differentiate between the effects of
residual oil in a habitat and the vagaries in recruitment in the patterns and
rates of recovery of previously dominant species that were extirpated by oil
or clean-up operations in oiled habitats. The method of study would be to
transplant test populations of selected, previously dominant species into
oiled and control study areas and then monitor their success. Success can be
gauged by comparing growth rates as well as survival. All trophic groups
except predators should be examined.
Taxa that should be considered for transplant studies on rock habitats
include rockweed (Fucus distichus). mussels < Mytilus edulis)f barnacles
(Balanus spp. ), and limpets (Acmaeidae) and sea urchins (strongylocentrotus
spp.), all of which are readily available for collection at undisturbed
sites. The attached taxa such as rockweed, barnacles, and mussels should be
collected on easily transportable cobbles or small boulders and transplanted
to marked locations at both the oiled and control sites. Unattached species
such as limpets and sea urchins should be removed from the rocks at
undisturbed sites and transplanted to marked rocks at control and oiled
sites.
Taxa that should be considered for transplant studies on soft
substrates include clams (e.g., Protothaca. Saxidomua. and clinocardium).
ghost shrimp and the burrowing sea cucumber lieptosynapta. The clams and sea
cucumbers should be transplanted into plastic mesh boxes buried in the
sediment so that they can be easily recovered periodically to census
survival. In addition, growth rates should be compared between control and
oiled sites. Ghost shrimp should be transported to oiled areas in which
populations were destroyed and burrows are absent. At these sites, they
should be protected until either they have established a new burrow or it is
determined that they will not dig a new one. The locations of the
171
-------
transplanted shrimps should be marked and the number of remaining burrows
noted on each subsequent survey.
Survival of the transplanted populations will be more of a problem to
assess at the control sites where well-established populations will already
exist than at the oiled sites where adults will be absent. However, it is
important to assess the effect of transplant activities on survival rates of
a transplant population in order to correct the observed survival rates of
the transplant populations at the oiled sites. In the latter areas, all
adults in the vicinity of transplants can be assumed to be introduced.
However, at the control sites, the transplant populations will have to be
marked in such a way as to be identifiable. In the case of rockweed and
barnacles, the rocks upon which the populations were transplanted can be
marked. The clams and sea cucumbers will be placed in marked plastic boxes
to facilitate recovery. The greatest problems are with limpets, sea urchins,
and ghost shrimp. With limpets and sea urchins the problem can be resolved
by placing the transplanted populations on isolated rocks or ledges from
which the resident population has been removed. For ghost shrimp, the
problem of identification cannot be completely resolved but the best approach
appears to be to use the collection site for the control transplant site,
thus removing a large majority of the adult shrimp and effectively destroying
the burrow systems over a large area. The transplant areas should be clearly
marked and their positions mapped so that they can be relocated. Equal
numbers of shrimp should be released in each transplant area and the numbers
of burrows in each area will be used as an index of survival.
To our knowledge, transplant studies have not been utilized in
conjunction with actual oil spill assessment. However, if properly
controlled and designed, we believe they could potentially contribute
substantially to the understanding of some of the factors influencing
recovery in oiled areas and the detection of the effects of residual oil.
Recruitment studies where the responses of larvae are measured would also
provide important data relative to recovery and community composition.
172
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SECTION 8
OTHER POSSIBLE APPROACHES TO ANALYSIS OF THE DATA BASE
In this section we consider other possible approaches to analysis of
the data base. These fall into three categories.
First, there are a number of analyses which could be carried out using
the present data base in order to further illuminate the effects on
variability of the diverse sampling methodologies used in the studies. For
example, subsampling variability for rock and cobble substrates could be
examined via nested analysis of variance. Assemblage parameters or key
community parameters could be considered in such an analysis. In addition,
species-area curves could be plotted to determine the adequacy of quadrat
sizes and/or number of replicates.
Second, there are analyses which could be carried out on an extended
set of baseline data. A longer time span of baseline data at one or more
sites would permit the use of predictive time series models such as the ARMA
models of Box and Jenkins (1970). Such models may be more effective than
those used in the present study for representing long-term temporal patterns
in biological assemblages.
Finally, there are a number of different approaches which could be
used to assess the effects of an event such as an oil spill if one should
occur. Sanders (1978) suggests several statistics which proved useful in
assessing the impact of an oil spill off West Falmouth, Massachusetts, on
benthic fauna in Buzzards Bay. These statistics, some of which we have
considered in the present study, include fidelity, coefficient of variation,
and discrepancy and similarity indices. Kendall's "Tau" is a particular
similarity index suggested by Ghent (1963) for examining successional changes
such as those which might be expected after an oil spill. An analysis which
takes into account the "distance" from the ecological event is suggested by
van Belle and Fisher (1977).
Like the tests for change discussed in the present study, all these
approaches are based on the availability of species-frequency lists such as
those in the present data base. It is assumed that data at the sites of
interest are collected after the event occurs. The resulting statistics for
these sites are compared with statistics calculated from control sites
sampled concurrently or earlier data from the affected sites. Certainly if a
major ecological event were to occur in Puget Sound, a variety of approaches
to assessing its effects should be considered. The results of the present
study provide some guidelines for these approaches and for additional
sampling to strengthen the baseline data which they require.
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BIBLIOGRAPHY/REFERENCES
Beyer, W.H. (Editor). 1968. Handbook of Tables for Probability and Statistics,
Second Edition. CRC Press, Inc., Boca Raton, Florida. 642 pp.
Bloom, S.A. 1977. Instruction Manual for a Package of Computer Programs for
Benthic Community Analyses. Department of Zoology, University of Florida,
Gainesville, Florida. 28 pp.
Box, G.E.P., and G.M. Jenkins. 1970. Time Series Analysis Forecasting and
Control. Holden-Day, San Francisco. 553 pp.
Brown, D.W., A. J. Friedman, D.G. Burrows, G.R. Snyder, E.G. Patten, W.E.
Ames, L.S. Ramos, P.G. Prohaska, D.D. Gennero, D.D. Dungan, M.Y. Uyeda, and
W.D. MacLeod, Jr. 1979. Investigation of Petroleum in the Marine Environs
of the Strait of Juan de Fuca and Northern Puget Sound (1979). DOC/EPA
Interagency Energy/Environment R&D Program Report EPA-600/7-79-164, U.S.
Environmental Protection Agency, Washington, D.C. 107 pp.
Chan, G.L. 1975. A Study of the Effects of the San Francisco Oil Spill on
Marine Life. Part II: Recruitment. In: Proceedings, 1975 Conference on
Prevention and Control of Oil Pollution. American Petroleum Institute,
Washington, D.C.
. 1977. The Five-Year Recruitment of Marine Life after the 1971
San Francisco Oil Spill. In: Proceedings, Oil Spill Conference, 1977,
American Petroleum Institute, API No. 4284, Washington, D.C., pp. 543-545.
Clifford, H.T., and W. Stephenson. 1975. An Introduction to Numerical
Classification. Academic Press, New York. 229 pp.
Cormack, R.M. 1971. A Review of Classification. J. Royal Stat. Soc.,
Series A, 134(3):321-367.
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Washington. 2 pp.
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APPENDIX A
DETAILS CONCERNING STATISTICAL METHODOLOGY
A.I MODEL ASSUMPTIONS, DATA TRANSFORMATIONS, AND CONFIDENCE INTERVALS
Normal parametric models
Both the multiple regression and analysis of variance models,
discussed in Section 5 and in more detail in this appendix, are examples of
parametric statistical models. They assume that a population parameter or
numerical assemblage parameter computed from a. sample is an observation of a
random variable Y which can be modelled as
Y = E(Y) + e (A.1.1)
where E(Y), the expected value or mean of Y, is a function of various
statistical parameters and e is a random error. Observations are assumed to
be uncorrelated,.and each random error e is assumed to have zero mean and the
same variance cr . The variance of e is the residual variability not ex-
plained by the model, in our case the sampling variability in the habitat.
In order to compute confidence intervals for means, perform
significance tests, etc., we must make the further assumption that the errors
are normally distributed.
Patchy dis^ri 'hutions of organisms
If the observations are counts of organisms, the patchy distribution
of most organisms leads to the violation of the assumed distribution of e.
The counts generally have a skewed rather than a normal distribution, and
large counts tend to have larger variances than small. The same is true for
weights.
A probability model often proposed for count data is the Poisson
model. A square root transformation of Poisson data results in transformed
data with a constant variance of 0.25 and a more nearly normal distribution.
Multiple regression and analysis of variance can therefore be applied to the
transformed data.
2
When Y in equation (A. 1.1) is a Poisson random variable, E(Y) = o .
If we have n observations y. of Y, we can compute
178
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1 n
- E y (A.1.2)
2
which estimates E(Y) = a and the other standard estimate
s2 = — z (y - y)2 (A.1.3)
2
of a . Then (Dixon and Massey 1969, p. 249)
x2 - (n-1) s2/ y (A.1.4)
2
has an approximate x distribution with n-1 degrees of freedom.
A test for whether particular counts have a Poisson distribution is
provided by the x statistic of (A.1.4). If the value of x computed from
data y ,...,y is too large, the Poisson model is inappropriate for these
data. This test was performed for a number of rocky intertidal animal
species. The Poisson model was rejected overwhelmingly in most cases.
Values of X /(n-1), which should be near one, were often in the tens or
hundreds.
Although other probability models for patchiness exist, as pointed out
by van Belle and Fisher (1977), there is little agreement on appropriate
statistical procedures when the Poisson model is found to be inappropriate.
For this reason we have not attempted to model counts and weights for any but
the least patchy species in a given habitat.
Coefficient of variation
Even the least patchy species do not have normal distributions with
equal variances. A simple statistic which reflects this fact is the
coefficient of variation
CV = 100 s / y (A.1.5)
where y and s are defined by (A.1.2) and (A.1.3) respectively. The
coefficient of variation expresses the standard deviation as a percentage of
the mean of the counts or weights under consideration.
If the coefficient of variation is small, the species has an even
distribution over the samples included in the computation; patchiness and
variability are low.
Log transformation
If, as is more often the case, the CV is large but relatively constant
when computed from different groups of samples, the implication is that the
standard deviation of the counts or weights is proportional to the mean. In
this case (see Dixon and Massey 1969, p. 324) it is likely that a logarithmic
179
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transformation of the data will produce transformed values which are more
nearly normal in distribution and have more nearly equal variances.
Examination of counts and weights for a number of rocky intertidal
species indicated that the CV was relatively constant. Both s and CV were
computed separately for each date and elevation stratum sampled at Tongue
Point . Four replicates were available in each group so we had n - 4 in
(A. 1.2) and (A. 1.3). The results obtained from upper intertidal samples of
Chthamalus dalli are typical. While s ranges from 3 to 1401 in the eight
groups of samples, the range of CV is only 69 to 141.
We therefore used log (count + 1) and log (weight + 1) as the data
for regression and analysis of variance in place of the untrans formed counts
or weights of an organism. We added one because log of zero does not
exist, and zero counts and weights do occur in some replicates even for the
most important species. Mean values and confidence intervals in log units
can be transformed back to counts or weights. For example, if m is a mean
of log transformed counts, the corresponding count value is 10 -1. To
express a confidence interval in the original units, both the upper and lower
limits of the interval (l,u) must be transformed back, giving the interval
(10 -1, 10 -1) in the original units.
Normalit of assemblae
Even if the log transformation stabilizes the variances of population
parameters, their normality may be open to question. The numerical
assemblage parameters defined in Section 5 are more promising in this
respect. While counts of each individual species may have distributions
which are far from normal, central-limit theorems of statistics suggest that
sums of such counts may have distributions which are more nearly normal. The
assemblage parameter N is such a sum.
cL
Similarly, S,S,W,W,Hf,ir/HI, and percent plant cover can be
viewed as sums of random variables. Hence a central-limit theorem can be
invoked to claim that they should approach normality and that regression and
analysis of variance are therefore appropriate.
in assemblat
The problem of heterogeneous error variances remains, particularly for
N , W , W , and percent plant cover. The log transformation used for
populltioR counts and weights also proved necessary for Na/ Wa/ and W . An
appropriate variance-stabilizing transformation was not found for percent
plant cover; an arcsine transformation was tried without success.
Another approach to eliminating variance heterogeneity is the
selection of appropriate data subsets to use in analyses. For example,
because values of numerical assemblage parameters vary strongly with
elevation in the rocky intertidal, separate analyses of variance were done
for the three elevation strata.
180
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Confidence I
The confidence intervals ( CI ) given in this report are based on the
normal parametric model. They have the form
(Y - _ _
_ *
for y and s given by (A. 1.2) and (A.1.3). The percentile t of the
±-distribution with n-1 degrees of freedom is obtained from a ±-table ( for
example, p. 283 of the CRC Handbook, Beyer 1968). The O.975 percentage point
is chosen to obtain a 95% CI.
If we compute many 95% CI and if the normal model is appropriate, then
in the long run 95 percent of these intervals will include the true mean
value E(Y) of (A. l.l) which we are trying to estimate.
Confidence intervals for group means under the one-way analysis of
variance model (A. 3.1) have the form
- * -1/2 - * -1/2
(Yi - tN_k(MSE/n±) ' , Yi + tN_k(MSE/ni) > (A. 1.7)
where y., N, k, MSB, and n. are as in Table A-2 of Section A. 3.
A. 2 MULTIPLE REGRESSION
Model
The general multiple regression model is
y. = Bn + B x . + •"+ B, x. + e. (A. 2.1)
*} 0 1 13 kTcu 3
where y. is the jth observation of the dependent variable being modelled. In
this study, y. was a value of a numerical assemblage parameter, for example,
S or log ( N3+l ) . The independent variables x , . . . , XL. are the
corresponding values of factors expected to influence y . . •'The constants B ,
. . . , B are the model parameters to be estimated .
The errors e . are assumed to be uncorrelated with zero means and equal
variances a . If we wish to perform significance tests or compute
confidence intervals for predicted y's or for the estimates b , . . . , b of
B , ..., B obtained in a regression analysis, we also need to assume that
the errors are normally distributed.
The independent variables x.. used in the present study represented
effects of sample elevation, season; and long-term time trends. The specific
variables considered in most of the analyses were:
x . = tidal elevation (meters)
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x = 1 for spring and summer (April - September)
= O for fall and winter (October - March)
x = date of sample = year + (month - 1 + day/31) / 12
The squared elevation x . allows fitting a curve instead of a straight line
to the dependent variable. For example, we can fit s at a site where its
maximum is at a middle elevation and it decreases at Both lower and higher
elevations.
The multiple regression model can be used for prediction as follows:
1) Compute b , ... ,b
2) Record x..,...,x, . at a new time and
place for which a prediction is desired
3) Predict the corresponding y. by
Y. = b^ + b,x, . + " ' + b. x, . (A.2.2)
3 0 1 ID k K}
Weaknesses of predictive model
There are several weaknesses in this approach to prediction in the
present study.
First, as noted in Section 4, the existing data base is deficient in
such data as sediment size, beach slope, and exposure to waves and currents
which might help to characterize site differences, so (A.2.2) could not be
used for cross-site prediction.
Second, the estimated coefficients are only valid within the ranges of
the independent variables from which they were computed. While we do not
need to predict y. for tidal elevations outside the ranges in the data base,
our goal is to predict at future times. Significant long-term time trends
detected in some parameters at some sites, for example increases in number of
taxa identified, cannot be expected to continue into the future.
Third, there is evidence, discussed in Section 6, that the assumption
of equal variances of the errors e. is violated for some parameters.
Pse of the model for assessing CP,l'rtiEJ*>VlT''r>na to variaM 1 i^y
The best use of the multiple regression model in the present context
is for assessing the relative importance of the included variables as sources
of variability. The analysis of variance Table A-l is produced by a
regression analysis. In this table "DP" stands for "degrees of freedom",
"SS" stands for "sum of squares", and "MS" stands for "mean square". The
summations are over the n observations y. of (A.2.1) included in the
analysis, y is defined by (A.1.2), and Y? is defined by (A.2.2). The
residual mean square MSB (sometimes called MS about regression or error MS)
estimates the variance ° of the errors e..
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TABLE A-l. ANALYSIS OF VARIANCE TABLE FOR MULTIPLE REGRESSION
DOE TO DP SS MS = SS/DF
n - 2
Regression k % (Y.-y)
n 2
Residual n-k-1 Z (y.-Y.) MSB
n _ 2
Total n-1 I (y.-y)
From the analysis of variance table we can compute the statistic
R2 = 100 SS(due to regression )/SS< total), (A. 2. 3)
the percentage of total variability in the data explained by the multiple
regression model. R can be tested to determine whether the percentage is
significant. It can also be partitioned into the percentage due to each of
the independent variables.
The estimated coefficients b , . . . , b give some indication of the
magnitude and direction of the effects of the independent variables. For
example, if b is positive, y. increases with x . while if b is negative,
increases in x . lead to decreases in y . . Each estimated coefficient can be
tested to determine whether it is significantly different from zero. The
estimated standard deviations of the coefficients provide a less formal
indication of their significance which does not require the assumption that
the errors e . are normally distributed .
Sd
Our multiple regression analyses were carried out using the Minitab
program of Ryan, Joiner, and Ryan (1976).
A. 3 ANALYSIS OF VARIANCE
As noted in Section 5, analysis of variance is a more natural model
than multiple regression when the factors under consideration allow the data
to be separated into a relatively small number of groups to be compared.
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One—way analysis of variance
The simplest analysis of variance model, one-way analysis of variance,
assumes that you have k groups (sometimes called "treatments" or "levels of a
factor".) You have n. observations y.. in the ith group. The model assumes
that 1 13
yij = V+ \ + eij (A.3.1)
where y is an overall mean, a. is the ith group effect, and the random
errors e.. are independent and identically distributed with mean zero and
variance a • The analysis of variance table summarizing the results of a
one-way analysis of variance is shown in Table A-2.
TABLE A-2. ONE-HAY ANALYSIS OF VARIANCE TABLE
DUE TO DP
Factor k-1
Error N-k
Total N-l
k
E
i=l
k
E
k
SS MS = SS/DP
- 2
ni
-------
2
estimates y . MSE estimates the error variance a , the within-group
sampling variability not explained by the model. The square root of MSE is a
pooled standard deviation which estimates a and can therefore be used for
calculating confidence intervals for group means, see (A. 1.7).
We can use the statistic
F = (Factor MS)/MSE (A. 3. 5)
to test whether there are any significant differences among the group means.
However, we are usually seeking more specific information about between-group
differences. Such information can be obtained by looking at contrasts
( comparisons ) among the means .
Orthoonal
Sets of orthogonal contrasts are particularly illuminating for
comparing group means because they partition the between-group variability,
represented by the Factor SS, into fractions due to the comparisons of
interest .
A linear contrast
(A.3.6)
with c + ... + c = O is orthogonal to another such contrast L if
k
2 c .c ./". = 0. (A.3.7)
pi qi i
For any one-way analysis of variance, there are one or more ways to define a
set of k-1 such contrasts for which
k-1
Z SS(due to L ) = Factor SS (A.3.8)
p=l P
where
2 * 2
SS(due to L ) = L /( Z c ±/n ) (A.3.9)
is a sum of squares with one degree of freedom. The constants c . are chosen
to define contrasts representing factors of interest.
For example, to compare the first group with the second we could set
c = 1, c = -1, and c=...=c=0. If the resulting SS(due to L )
is a large fraction of the Factor SS> we can conclude that much of the
185
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between-group variability is due to the difference between groups one and
two.
Whether or not a particular fraction of the Factor SS represents a
significant contrast depends on the level of significance of the Factor SS.
The significance of each contrast can be assessed using the F statistic
SS(due to L )/MSE. If the contrast is not significant, this statistic has an
F distribution with 1 and N-k degrees of freedom.
Confounding
Since there are usually a number of ways to construct a set of
orthogonal contrasts for a one-way analysis of variance, some subjectivity is
involved in deciding which comparisons to perform. In addition, particularly
in the data base of the present study, care must be used in interpreting
particular comparisons because of the possibility of confounding of effects.
For example, when we wish to contrast Whidbey Island sites with
similar sites from the Strait of Juan de Fuca, we average the means from the
Whidbey sites and subtract the average of the Strait means to form L .
However, any "Webber vs. Nyblade" differences will be caught in the contrast
as well as "Whidbey vs. Strait" differences since all the Whidbey data were
collected by Webber and all the Strait data by Nyblade.
Similarly, if we average data from several sand sites to contrast with
gravel sites, differences in other factors such as exposure and salinity
among the sites will affect our "sand vs. gravel" contrast. We have tried to
point out such possible confounding in our discussions of analysis of
variance results in Section 6.
Newman—Keuls procedure for comparing all means
The method of orthogonal contrasts has the disadvantage that in order
to assess significance of a contrast we must do an individual F test. We
performed many different one-way analyses of variance with a. set of ortho-
gonal contrasts for most of them. Hence, the overall probability of Type I
error is much higher than the level of each individual test. We explain
this problem and one approach we used to alleviate it in more detail in
Section A.4.
Another approach to the problem is to use a multiple comparison
procedure such as the Newman-Keuls procedure for comparing all group means.
This procedure is described in detail in standard references for analysis of
variance such as Winer (1971), pp. 191-201. Since we did not use it ex-
tensively in our analyses, we will not discuss it further in this appendix.
Random effects model
Some factors, for example season, which we use in defining groups for
an analysis of variance are "fixed" factors. There are only four seasons,
defining only four possible levels of the season factor.
186
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Other factors have an infinite number of possible levels from which we
have randomly chosen a small finite number to consider. Site can be viewed
as such a factor. The mathematical model for such "random" factors is that
the group effects a. in (A. 3.1), like the errors, are normally distributed
with zero means and equal variances . The variance a of the a . , called the
between-group variance in the random effects model, can be estimated. It is
a component of the variance of an observation; var(y. . ) = a + a under the
random effects model. 13
In some of the analyses described in Section 6 we have estimated
variance components and tested them for significance. The F statistic
(A. 3. 5) is used for this test in the one-way random effects analysis of
variance model as well as for testing for differences in means in the fixed
effects model.
Variance heterogeneity
As noted in Section A.I, equal within-group variances and normality of
errors are fundamental analysis of variance assumptions. While small
departures from these assumptions generally will not seriously compromise
results of the analysis, large departures are a matter of concern. Selection
of relatively homogeneous subsets for analysis and log transformations of
counts and weights were used to avoid serious violations of these
assumptions .
In addition, we generally performed tests for equality of variances.
Cochran's test (Winer 1971, p. 208) was used in some cases, but we more often
chose the simpler Hartley maximum F ratio test (Winer 1971, pp. 206-208). The
maximum F ratio test statistic is
F = S2 /S2. (A. 3. 10)
max max mm
2 2
where s and s . are the maximum and minimum, respectively, of the k group
-------
The two-way analysis of variance model assumes we have observations
yijk Satisfvin9
Yijk - U + a. + B. + ag.. + e..k (A.3.12)
where y and e. are the overall mean and random error respectively, a.
and 3 . are effects of the two factors, and a(3 . . is a term representing the
interaction of the two factors. 3
We have used a mixed model with the factor represented by a . the
random site factor and that represented by B. a fixed factor (season or
elevation). Expected mean squares under this^model {Winer 1971, pp. 321-329)
determine formulas for estimating the variance components a and a as well
as the significance of fixed factor effects. Under this model,
var(yijk) - a2 + <£.
Nested analsis of variance
The final analysis of variance model we have used is a nested model
llows comparing the variance component due to sa
and the error variance o . This model, used fo
parameters at a fixed site and stratum of elevation, is
which allows comparing the variance component due to sampling date within
season and the error variance o . This model, used for numerical assemblage
where y. is an individual observation at the jth date within the ith
season,3^. is the corresponding random error, y is the overall mean at the
site and elevation, a. the ith season effect, and 6-/-\ the random effect
due to date within season. If there are s seasons, t dates (times) within
each season, and n observations at each time and season, then the analysis of
variance table and formulas for variance components and F statistics are
defined by Table A-3.
TABLE A-3. EXPECTED MEAN SQUARES FOR NESTED ANALYSIS OF VARIANCE
DUE TO DP EXPECTED MS
Season
Time within season
Error
Total
s-1
s(t-l)
st( n-1 )
stn-1
a
2
a
2
a
2 nt
n°t
+ no.
Z a2
i=l X
188
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The time within season variance component is denoted by of in
Table A-3. The variance of an observation at a given2site and elevation is
var(y. ) = a + a. under this model. We estimate a by
1J Jt t «
q? = [MS(time within season) - MSE]/n (A.3.14)
2
if this expression is positive, a. = 0 otherwise. As always, the error mean
square MSE estimates a .
Programs used
One-way and two-way analyses of variance were carried out using
Hinitab (Ryan, Joiner, and Ryan 1976). Computations of contrasts and nested
analyses of variance were performed using programs written by Zen. t
A.4 TESTING FOR SIGNIFICANT DIFFERENCES
In this section we review both general concepts of hypothesis testing
and specific tests performed to obtain the results described in Section 6.
Type I and Type II errors, level, power
In the general statistical hypothesis testing situation, we have a
"null hypothesis" H of no differences among statistical parameters being
tested. A test of ?he null hypothesis may correctly accept or reject it. On
the other hand, the test results may be in error.
Two types of errors are possible. A Type I error occurs when H is in
fact true but the test incorrectly rejects it. A Type II error occurs when
H is false but the test fails to reject it.
o
The "level of significance" of a test, often denoted by the symbol a ,
is the upper bound of the probability of making a Type I error. The level of
a test is chosen prior to performing the test and determines the "critical
value" of the test statistic which tells us to reject H . If we choose a
very small value for the level and then find that the hypothesis should be
rejected, we say the indicated difference is "highly significant." This is
because the very small value of a represents the very low probability that
we have made an error in rejecting H . The level of a test can be expressed
either as a fraction (for example, a = 0.05) or as a percent (the 5% level).
The "power" of a test is the probability that we correctly reject H
when it is in fact false. In other words, power is 1 - probability of
Type II error. It can also be expressed, as we have done in Section 6, as
the percent probability of detecting a difference.
The power of a test depends on the magnitude of the true difference.
For example, if we are testing for a difference in mean values y + a. of two
groups in a one-way analysis of variance, see (A.3.1), the power of the test
is low if both groups have effects a. near zero and hence means near y.
The power is higher if, say, a. for the first group is zero but a- for the
189
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second group is large so that the difference is the large a . instead of
being near zero.
Choice of a. for tests on orthogonal contrasts
If we perform a statistical analysis which involves a single
hypothesis test and we use a stated level a for that test, then the
probability that we falsely reject H does not exceed a . If, on the other
hand, we perform many such tests in the course of the analysis, then the
probability of making a Type I error in at least one of the tests is much
larger than a .
For example, if we do five independent tests at the a = O.01 level,
then the probability of incorrectly proclaiming at least one significant
difference is 1 - 0.99 = 0.05, or 5% (Winer 1971, p. 175.) If we do twenty
such tests, the probability of at least one such error jumps to over 18%.
Because we performed many different analyses of variance with sets of
orthogonal contrasts for most of them, the probability of Type I error in
asserting significance of contrasts would have been unacceptably high if we
had used the conventional levels, a = 0.05 or a =0.01. On the other hand,
we generally did not wish to consider large numbers of a posteriori
comparisons suggested by the data, so procedures allowing all possible
comparisons seemed unnecessarily complicated and conservative. The
compromise we adopted, namely testing contrasts for significance at
the a = 0.001 level, was suggested by the discussion of Winer (1971),
pp. 172-201.
If we do 10 independent tests with « = 0.001, the probability of at
least one Type I error is 0.01 or less. We can do more than 50 such tests
without increasing the probability of at least one such error to 0.05 or
more. Hence it is unlikely that many of the significant contrasts indicated
in the tables of Section 6 are due to Type I errors.
Two— gamnT.6 t— tests, power to detect change
If an analysis of variance model such as (A. 3.1), (A. 3. 12), or
(A. 3. 13) is chosen for a population or assemblage parameter, then the
appropriate group mean is used to predict that parameter at a future time. A
two-sample i-test is generally employed if new replicate samples are
collected and we wish to determine whether a change in the parameter has
occurred. If the old group mean of the parameter is y and the new
mean -n, then the null hypothesis being tested is H : y = y .
If we have n samples y . in the old group and n new samples y then
the test statistic for the two-sample i-test is
sp
where y and y are defined by (A. 3. 3) and
J. 2
190
-------
2 ,iA,x
S - - (A. 4. 2)
nx + n2 - 2
' 22
is a pooled variance estimate with s and s defined by (A. 3. 11). The
critical value for the test is obtained from the fr-distribution with n +n -2
degrees of freedom. The POOLED T command of Minitab performs this test.
Now assume that
y2=yi+ ^ i/10° (A. 4. 3)
so that A is the percent change in the mean. Then Table A-12b of Dixon and
Massey (1969) gives values of
1/2
100 a
(1/n, + 1/n (A. 4. 4)
which can be detected at specified levels a with specified powers. The
standard deviation a in (A. 4. 4) is the square root of the assumed common
error variance of the old and new samples; s of (A. 4. 2) is an estimate of
this error variance.
To obtain percent changes in mean values detectable with specified
probabilities by a two-sample t-test of specified level, we computed
100 gd
Vn) (A.4.5)
x 2
for various values of d, n , and n at the levels and powers tabled by Oixon
and Massey. For u we used an appropriate group mean and for a the pooled
standard deviation from analysis of variance. For n = n we sometimes used
Table IV. 4 of the CRC Handbook (Beyer 1968) instead of the Dixon and Massey
table. Values of C Au) / (100 a) instead of d are given in the CRC table,
so ( l/n + 1/n ) need not be computed to get A.
If we are interested only in detecting a decrease in a population or
assemblage parameter, we use y - y in place of }y - y { in (A. 4.1) and the
critical value for a one-sided instead of a two-sided test. The alternative
to H assumed by the one-sided test is H : p > V while for the two-sided
test it is H : V f V . Our tables of detectable percent changes give the
values corresponding €o the two-sided test, with the values for the one-sided
test in parentheses.
M?nn~^hitne tests
The two-sample i-test assumes that both groups of replicates being
compared are normally distributed with variance <> . Only their mean values
191
-------
may differ. We have discussed extensively the problems with the i-test
assumptions in biological data sets.
The two-sample Mann-Whitney -test is a nonparametric alternative to the
i-test. The null hypothesis tested by the Mann-Whitney test is that the
observations y . in the old group have the same continuous probability
distribution as the y of the new group. We must assume only that the
observations in each group are independent and identically distributed.
The nonparametric null hypothesis of the Mann-Whitney test makes no
mention of group means. If, in fact, our interest is in testing for
differences in some measure of the center of the distributions such as the
mean or median, then we must add the assumption that the two distributions
have the same shape and equal variances. They need not be normal in any
case.
Several equivalent test statistics for the Mann-Whitney test exist.
The one calculated by Minitab's MANN-WHITNEY procedure and other details
concerning the test are described by Ryan, Joiner, and Ryan (1976).
Power to detect changes is harder to calculate for the Mann-Whitney
than for the i-test. According to Siegel (1956), p. 126, the power
efficiency of the Mann-Whitney test approaches 95.5 percent of that of the ±-
test when i-test assumptions are satisfied and n + n gets large. The Mann-
Whitney test may be more powerful than the i-test when the assumptions of the
latter are not satisfied.
Since normality and homogeneity of variances of population and
assemblage parameters computed from the present data base are sometimes in
question, the Mann-Whitney test should probably be used in place of or in
addition to the i-test in testing for change.
A.5 CLUSTER ANALYSIS METHODOLOGY
As noted in Section 5, the key idea of cluster analysis is the
division of a group of entities into smaller subgroups on the basis of
"similarity" with respect to a set of attributes. Entities in a given
subgroup are more similar to others in the same subgroup than to those in a
different subgroup.
Our cluster analyses were performed using a package of computer
programs for benthic community analysis by Bloom. Bloom (1977) briefly
outlines the clustering methodologies used in the programs. More details can
be found in Cormack (1971) or Clifford and Stephenson (1975). In this
section we will give only a summary of the methods applied to the analyses of
this study.
For clustering, a "station" was generally defined by pooling all
available samples at a given site, date, and stratum of elevation. We
generally used the index which Bloom (1977) calls the Czekanowski
quantitative similarity index computed from log transformed data. If we are
192
-------
clustering on S species, then the similarity between station i and station j
defined by this index is
c - 2 Z min(x ,x )/ E (x +x ) (A. 5.1)
11 k=l D k=l " DK
where x = ln(l + count of species k at station i) and x is defined
similarly. Plants were given a count of one.
For subtidal analyses we used the Czekanowski qualitative index which
defines the similarity between station i and station j as
c.. = 2a/(2a+b+c) (A. 5. 2)
where a is the number of species found at both stations, b is the number at
station i only, and c is the number at station j only.
Computing the similarity matrix which has c. . in row i and column j is
only the first step in the cluster analysis. The next step is the
application of a hierarchical classification procedure to the matrix to
produce the clusters. The technique we used was group average sorting. The
formula for similarity between group k and a group ( i j ) formed by the fusion
of groups i and j is
if group i has n . and group j n . elements . When n. =n. = n, = 1 , c. . and
c are just the1appropriate elements of the similarity matrix. The
procedure forms larger and larger groups by choosing groups to combine which
have the largest possible between-group similarity. The similarity structure
is then shown graphically in the dendrogram.
193
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APPENDIX B
HABITAT DICTIONARIES AND RULES FOR CREATING THEM
As noted in Section 5, the numerous taxonomic errors and
inconsistencies in the data base made it necessary to create dictionaries
which associate taxonomic codes found on the File 1OO tapes with the taxa to
be used in analyses. Three such dictionaries were created, representing
intertidal rock substrates, intertidal soft substrates, and subtidal
substrates. We did not create a dictionary for intertidal cobble substrates
since we did not perform detailed analyses of the cobble data.
The following general rules were used for "lumping" taxa in all three
dictionaries:
1. Truncate all subspecies to species level since few subspecies
were identified in the data set.
2. if only one species was identified in a genus and some samples
were identified only to genus level, truncate to genus level.
Use the same approach at the higher taxonomic levels; for
example, lump a single genus in a family to family level.
3. If the vast majority of organisms in a genus are identified
only to genus level, lump all species in the genus.
4. If the level to which Webber identified an organism clearly
differs from the level to which the same organism was
identified by Nyblade, lump to the lowest common level of
identification. Similarly, if the level of identification by
either investigator shows clear changes with time over the
course of the WDOE or MESA studies or between studies, truncate
to the lowest common level.
5. Truncate species coded by Nyblade with 99's (see Section 4.3.4)
to the lowest level to which the Nyblade and NODC codes
correspond.
6. Lump a species to genus level if it is unimportant and dubious
according to the above rules. For example, if there are two
species in a genus but only one or two samples of one of the
species and many identifications only to genus level, lump all
samples to genus level.
Some exceptions to these rules were dictated by biological considerations.
194
-------
One example is among gammarid amphipods. Because it was known that neither
investigator attempted to identify amphipods to species consistently
throughout the studies, all were lumped in the rocky intertidal dictionary.
However, several important amphipod genera and species appeared to be
consistently identified in soft substrate intertidal and subtidal samples, so
these were left at the lower level in the corresponding dictionaries.
Another example was Leptasterias hexactis. Although it was the only species
identified among the asteriidae in the rocky intertidal, it was considered
sufficiently important, identifiable, and unique in the family to be left at
the species level.
The rocky intertidal dictionary is given in Table B-l, the soft
substrate intertidal dictionary in Table B-2, and the subtidal dictionary in
Table B-3. The taxonomic codes found on the data tapes are given on the left
in each of these tables, and the taxa used in analyses on the right. "ER"
indicates that the taxonomic code on the tape was in error, and the
corresponding data could not be used in analyses.
195
-------
TABLE B-l. TAXONOMIC DICTIONARY FOR INTERTIDAL ROCK SUBSTRATES
03 CYANOPHYTA 03 CYANOPHYTA
07 BACILLARIOPHYTA O7 BACILLARIOPHYTA
0701 BACILLARIOPHYCEAE 07 BACILLARIOPHYTA
0703 BACILLARIOPHYCEAE PE 07 BACILLARIOPHYTA
07030501 NAVICULA 07 BACILLARIOPHYTA
08 CHLOROPHYTA 08 CHLOROPHYTA
0801 CHLOROPHYCEAE 08 CHLOROPHYTA
0805 CHLOROPHYCEAE ULOTRI 08O5 CHLOROPHYCEAE ULOTRI
08050102 ULOTHRIX 080501O2 ULOTHRIX
0805010201 ULOTHRIX PLACCA 08050102 ULOTHRIX
08050201 MONOSTROMA 08050201 MONOSTROMA
0805020105 MONOSTROMA FUSCUM 08050201 MONOSTROMA
080503 ULVACEAE 080503 ULVACEAE
08050301 BLIDINGIA 08O50301 BLIDINGIA
0805030101 BLIDINGIA MINIMA 08050301 BLIDINGIA
08050303 ENTEROMORPHA 08050303 ENTEROMORPHA
0805030302 ENTEROMORPHA COMPRES 0805030302 ENTEROMORPHA COMPRES
0805030306 ENTEROMORPHA LINZA 0805030306 ENTEROMORPHA LINZA
0805030312 ENTEROMORPHA CRUCIAT 0805030312 ENTEROMORPHA CRUCIAT
0805030317 ENTEROMORPHA INTESTI 0805030317 ENTEROMORPHA INTESTI
08050305 ULVA (CHLOROPHYCE 08O50305 ULVA (CHLOROPHYCE
0805030501 ULVA FENESTRATA 08050305 ULVA (CHLOROPHYCE
0805030502 ULVA RIGIDA 08O50305 ULVA (CHLOROPHYCE
0805030503 ULVA LACTUCA O805O305 ULVA (CHLOROPHYCE
0805030506 ULVA EXPANSA 08050305 ULVA (CHLOROPHYCE
0805030599 NAME NOT FOUND 08050305 ULVA (CHLOROPHYCE
08070102 SPONGOMORPHA 08070102 SPONGOMORPHA
0807010202 SPONGOMORPHA COALITA 0807010202 SPONGOMORPHA COALITA
0807010207 SPONGOMORPHA SPINESC 0807010207 SPONGOMORPHA SPINESC
08070103 UROSPORA 08070103 UROSPORA
0808 CHLOROPHYCEAE CLADOP 0808 CHLOROPHYCEAE CLADOP
080801 CLADOPHORACEAE O8O8O1 CLADOPHORACEAE
08080101 CHAETOMORPHA 0808O101 CHAETOMORPHA
08080102 CLADOPHORA O8080102 CLADOPHORA
0808O102O3 CLADOPHORA GRACILIS O8O8O102 CLADOPHORA
080801O3 RHIZOCLONIUM O8O8O103 RHIZOCLONIUM
0808010301 RHIZOCLONIUM IMPLEXU O8080103O1 RHIZOCLONIUM IMPLEXU
0808010302 RHIZOCLONIUM RIPARIU O8O8010302 RHIZOCLONIUM RIPARIU
0809O101O1 DERBESIA MARINA 0809O101O1 DERBESIA MARINA
08090301 CODIUM O8090301 CODIUM
10300 NAME NOT FOUND 03 CYANOPHYTA
10500 NAME NOT FOUND 16090717 CALLIARTHRON
15 PHAEOPHYTA 15 PHAEOPHYTA
1501 PHAEOPHYCEAE 15 PHAEOPHYTA
150201 ECTOCARPACEAE 150201 ECTOCARPACEAE
(continued)
Starred species or groups are important taxa which were used for
cluster analysis and, in some cases, population parameter analyses.
196
-------
TABLE B-l (continued)
15020103 ECTOCARPUS 15020103 ECTOCARPUS
15O201O3O3 ECTOCARPUS PARVUS 1502010303 ECTOCARPUS PARVUS
1502010305 ECTOCARPUS SIMULANS 1502010305 ECTOCARPUS SIMULANS
15020104 GIPPORDIA 1502O104 GIFPORDIA
1502010404 GIFFORDIA OVATA 15020104 GIPFORDIA
1502010499 NAME NOT FOUND 150201O4 GIFFORDIA
15020106 PYLAIELLA 15O201O6 PYLAIELLA
1502010601 PYLAIELLA LITTORALIS 150201O6 PYLAIELLA
1502010999 NAME NOT FOUND 150201O9 FELDMANNIA
150202 RALFSIACEAE 15O202 RALFSIACEAE
15020203 RALFSIA 150202 RALFSIACEAE
1502020303 RALFSIA PACIFICA 15O202 RALFSIACEAE
1502050301 LEATHESIA DIFFORMIS 1502050301 LEATHESIA DIFFORMIS
1502061001 HAPLOGLOIA ANDERSONI 1502061001 HAPLOGLOIA ANDERSONI
1502061101 SAUNDERSELLA SIMPLEX 1502061101 SAUNDERSELLA SIMPLEX
1502061202 ANALIPUS JAPONICUS 1502061202 ANALIPUS JAPONICUS
1503 PHAEOPHYCEAE DICTYOS 1503 PHAEOPHYCEAE DICTYOS
1503010201 STICTYOSIPHON TORTIL 1503 PHAEOPHYCEAE DICTYOS
15040102 SPHACELARIA 1504O1O2 SPHACELARIA
1504010201 SPHACELARIA RACEMOSA 15O401O2O1 SPHACELARIA RACEMOSA
1504010202 SPHACELARIA SUBFUSCA 1504010202 SPHACELARIA SUBFUSCA
1508 PHAEOPHYCEAE LAMINAR 1508 PHAEOPHYCEAE LAMINAR
150802 LAMINARIACEAE 150802 LAMINARIACEAE
15080201 LAMINARIA 15O802O1 LAMINARIA
1508020102 LAMINARIA GROENLANDI 1508020102 LAMINARIA GROENLANDI
1508020104 LAMINARIA SACCHARIN A 1508020104 LAMINARIA SACCHARINA
1508020105 LAMINARIA SETCHELLII 1508020105 LAMINARIA SETCHELLII
1508020402 AGARUM FIMBRIATUM 150802O4O2 AGARUM FIMBRIATUM
1508020501 COSTARIA COSTATA 1508020501 COSTARIA COSTATA
1508020601 CYMATHERE TRIPLICATA 1508020601 CYMATHERE TRIPLICATA
1508020701 HEDOPHYLLUM SESSILE 1508020701 HEDOPHYLLUM SESSILE *
1508020901 PLEUROPHYCUS GARDNER 1508020901 PLEUROPHYCUS GARDNER
1508021101 PHAEOSTROPHION IRREG 1508021101 PHAEOSTROPHION IRREG
15080401 ALARIA 15080401 ALARIA !*
1508040103 ALARIA MARGINATA 1508040103 ALARIA MARGINATA j
15O8O4O1O8 ALARIA TENUIFOLIA 1508040108 ALARIA TENUIFOLIA |
1508040301 EGREGIA MENZIESII 1508040301 EGREGIA MENZIESII
150902 DESMARESTIACEAE 150902 DESMARESTIACEAE
15090201 DESMARESTIA 15O9O201 DESMARESTIA
1509020101 DESMARESTIA ACULEATA 1509020101 DESMARESTIA ACULEATA
1509020102 DESMARESTIA LIGULATA 1509020102 DESMARESTIA LIGULATA
1509020103 DESMARESTIA VTRIDIS 1509020103 DESMARESTIA VIRIDIS
1509020104 DESMARESTIA INTERNED 1509020104 DESMARESTIA INTERNED
15100102 FUCUS 15100102 PUCUS \*
1510010202 PUCUS DISTICHUS 15100102 FUCUS !
1512010101 COLPOMENIA BULLOSA 1512010101 COLPOMENIA BULLOSA
1512010201 PETALONIA FASCIA 1512010201 PETALONIA FASCIA
1512010301 SCYTOSIPHON LOMENTAR 1512010301 SCYTOSIPHON LOMENTAR
(continued)
197
-------
TABLE B-l (continued)
16 RHODOPHYTA 16 RHODOPHYTA
1601 RHODOPHYCEAE 16 RHODOPHYTA
1604010199 NAME NOT POUND 16040101 GONIOTRICHUM
1605 RHODOPHYCEAE BANGIOP 1605 RHODOPHYCEAE BANGIOP
16050103 ERYTHROTRICHIA 16O5O103 ERYTHROTRICHIA
1605010304 ERYTHROTRICHIA PARKS 16050103 ERYTHROTRICHIA
1605010399 NAME NOT FOUND 1605O103 ERYTHROTRICHIA
1605010501 SMITHORA NAIADUM 1605010501 SMITHORA NAIADUM
1605020102 BANGIA PUSCOPURPUREA 1605020102 BANGIA PaSCOPURPUREA
16050202 PORPHYRA 16050202 PORPHYRA |*
1605020209 PORPHYRA PERFORATA 160502O2 PORPHYRA {
1605020211 PORPHYRA PSEUDOLANCE 1605O2O2 PORPHYRA |
1605020221 PORPHYRA SANJUANENSI 16050202 PORPHYRA i
1605020225 PORPHYRA ABBOTTAE 16050202 PORPHYRA j
1605020228 PORPHYRA SMITHII 16O50202 PORPHYRA j
1607 RHODOPHYCEAE PLORIDE 1607 RHODOPHYCEAE FLORIDE
16070101 ACROCHAETIUM 16070101 ACROCHAETIUM
1607010107 ACROCHAETIUM PACIFIC 16070101 ACROCHAETIUM
16070103 KYLINIA 16070103 KYLINIA
16070104 RHODOCHORTON 16070104 RHODOCHORTON
1607010402 RHODOCHORTON PURPURE 16070104 RHODOCHORTON
1607040102 NEMALION ELMINTHOIDE 1607040102 NEMALION ELMINTHOIDE
160801 CRUORIACEAE 160801 CRUORIACEAE
16080103 PETROCELIS 16080103 PETROCELIS
1608O1O302 PETROCELIS MIDDENDOR 16080103 PETROCELIS
1608020101 NEOAGARDHIELLA BAILE 1608020101 NEOAGARDHIELLA BAILE
16080501 PLOCAMIUM (RHODOPH 16080501 PLOCAMIUM (RHODOPH
1608050101 PLOCAMIUM TENUE 1608050101 PLOCAMIUM TENUE
1608050102 PLOCAMIUM COCCINEUM 1608O5O1O2 PLOCAMIUM COCCINEUM
1608050103 PLOCAMIUM PACIFICUM 1608050103 PLOCAMIUM PACIFICUM
1608050104 PLOCAMIUM VIOLACIUM 1608050104 PLOCAMIUM VIOLACIUM
1608O701 GRACILARIA 1608O7O1 GRACILARIA
1608070102 GRACILARIA VERRUCOSA 16080701 GRACILARIA
1608070199 NAME NOT FOUND 16O80701 GRACILARIA
1608070399 NAME NOT FOUND 16O80703 GRACILARIOPHILA
1608090101 AHNFELTIA PLICATA 1608090101 AHNFELTIA PLICATA
1608090102 AHNFELTIA GIGARTINOI 1608090102 AHNFELTIA GIGARTINOI
1608090402 GYMNOGONGRUS LEPTOPH 1608090402 GYMNOGONGRUS LEPTOPH
1608090403 GYMNOGONGRUS LINEARI 1608O904O3 GYMNOGONGRUS LINEARI
160810 GIGARTINACEAE 160810 GIGARTINACEAE
1608100102 CHONDRUS OCELLATUS 1608100102 CHONDRUS OCELLATUS
16081002 GIGARTINA 16081002 GIGARTINA !*
1608100201 GIGARTINA EXASPERATA 1608100201 GIGARTINA EXASPERATA j
1608100203 GIGARTINA PAPILLATA 16081O02O3 GIGARTINA PAPILLATA |
1608100204 GIGARTINA AGARDHII 1608100204 GIGARTINA AGARDHII j
16081003 IRIDAEA 16081003 IRIDAEA |*
1608100301 IRIDAEA CORDATA 1608100301 IRIDAEA CORDATA |
16O810O302 IRIDAEA CORNUCOPIAE 1608100302 IRIDAEA CORNUCOPIAE |
(continued)
198
-------
TABLE B-l (continued)
1608100304 IRZDAEA HETEROCARPA 1608100304 IRZDAEA HETEROCARPA
16081OO305 IRIDAEA LINEARE 16O81OO3O5 IRIDAEA LINEARE
16081004 RHODOGLOSSUM 160810O4 RHODOGLOSSUM
1608100401 RHODOGLOSSUM AFFINE 16O81OO4O1 RHODOGLOSSUM AFFINE
1608100402 RHODOGLOSSUM CALIFOR 16081OO4O2 RHODOGLOSSUM CALIFOR
160901 SQUAMARIACEAE 16O901 SQUAMARIACEAE
1609010301 PEYSSONELIA PACZFICA 160901 SQUAMARIACEAE
1609020101 DILSEA CALZFORMICA 1609O2O1O1 DILSEA CALZFORNICA
1609020201 PZKEA CALZFORNZCA 16O9O2O2O1 PZKEA CALZFORNZCA
1609020402 FARLOWIA MOLLZS 16O902O4O2 FARLOWIA MOLLIS
1609020601 CRYPTOSZPHONZA WOODI 16O9O2O6O1 CRYPTOSIPHONIA WOODI
1609050101 ENDOCLADIA MURICATA 1609O5O1O1 ENDOCLADZA MURZCATA
1609050201 GLOIOPELTIS FURCATA 16O905O201 GLOIOPELTIS FURCATA
16090601 HZLDENBRANDZA (ALG 16O906O1 HZLDENBRANDZA (ALG
1609060101 HZLDENBRANDZA OCCZDE 1609060101 HZLDENBRANDZA OCCIDE
1609060102 HZLDENBRANDZA PROTOT 16O9O6O1O2 HZLDENBRANDZA PROTOT
1609060105 NAME NOT FOUND 16O9O6O1 HZLDENBRANDZA (ALG
160907 CORALLZNACEAE 1609O7 CORALLZNACEAE
16090703 CORALLZNA 16O907O3 CORALLZNA
1609070301 CORALLZNA VANCOUVERZ 1609O7O3 CORALLZNA
1609O706 LZTHOPHYLLUM 16O9O7O6 LZTHOPHYLLUM
16090707 LZTHOTHAMNZON 16O907O7 LZTHOTHAMNZON
1609070701 LZTHOTHAMNZON CALZFO 16O9O7O7 LZTHOTHAMNZON
1609070801 MELOBESZA MEDIOCRIS 16O9O7O8 MELOBESZA
1609070899 NAME NOT FOUND 1609O708 MELOBESZA
1609O709 MESOPHYLLUM 16O9O7O9 MESOPHYLLUM
1609070901 MESOPHYLLUM LAMELLAT 16O90709O1 MESOPHYLLUM LAMELLAT
1609070962 MESOPHYLLUM CONCHATU 16O9O70902 MESOPHYLLUM CONCHATU
1609071303 CLATHROMORPHUM PARCU 1609071303 CLATHROMORPHUM PARCU
16090715 BOSSZELLA 16090715 BOSSZELLA
1609071505 BOSSZELLA PLUMOSA 16O9O715 BOSSZELLA
16090717 CALLZARTHRDN 1609O717 CALLZARTHRON
1609071701 CALLZARTHRON TUBERCU 16O9O717 CALLZARTHRON
16O9O901 CRYPTONEMZA 16O90901 CRYPTONEMZA
1609090101 CRYPTONEMIA OBOVATA 16O9O9O1O1 CRYPTONEMZA OBOVATA
1609090102 CRYPTONEMZA OVALZFOL 16O9O9O1O2 CRYPTONEHIA OVALZFOL
1609090199 NAME NOT FOUND 16O9O9O1 CRYPTONEMZA
1609090201 GRATELOUPZA DORYPHOR 16O909O2O1 GRATELOUPZA DORYPHOR
16O909O4 PRZONZTZS 16O9O9O4 PRZONZTZS
1609090401 PRZONZTZS LANCEOLATA 16O9O9O4 PRZONZTZS
16090905 HALYMENZA 1609O9O5 HALYMENZA
16O9O90501 HALYMENZA COCCZNEA 16O9O9O5 HALYMENZA
1609099999 NAME NOT FOUND 16O909 CRYPTONEMZACEAE
16091002 CALLOPHYLLZS 16O91OO2 CALLOPHYLLZS
1609100202 CALLOPHYLLZS EDENTAT 16O91O02O2 CALLOPHYLLZS EDENTAT
1609100204 CALLOPHYLLZS HAENOPH 16O91O02O4 CALLOPHYLLZS HAENOPH
1609100208 CALLOPHYLLZS FZRMA 1609100208 CALLOPHYLLZS FZRMA
16091007 ERYTHROPHYLLUM 16091OO7 ERYTHROPHYLLUM
(continued)
199
-------
TABLE B-l (continued)
1609100701 ERYTHROPHYLLUM DELES 160910O7 ERYTHROPHYLLUM
16091101 CHOREOCOLAX 16091101 CHOREOCOLAX
1609110101 CHOREOCOLAX POLYSIPH 16091101 CHOREOCOLAX
1609110201 HARVEYELLA MIRABILIS 1609110201 HARVEYELLA MIRABILIS
1609130102 CONSTANTINEA SIMPLEX 1609130102 CONSTANTINEA SIMPLEX
1610010201 LOMENTARIA BAILEYANA 161O01O201 LOMENTARIA BAILEYANA
16100202 RHODYMENIA 161O0202 RHODYMENIA
1610020202 RHODYMENIA PACIFICA 1610020202 RHODYMENIA PACIFICA
1610020203 RHODYMENIA PALMATA 1610020203 RHODYMENIA PALMATA
1610020205 RHODYMENIA STIPITATA 1610020205 RHODYMENIA STIPITATA
1610020206 RHODYMENIA CALIFORNI 1610020206 RHODYMENIA CALIFORNI
1610020301 RHODYMENIOCOLAX BOTR 1*10020301 RHODYMENIOCOLAX BOTR
1610020501 HALOSACCION GLANDIFO 1610020501 HALOSACCION GLANDIFO
1610O2O602 FAUCHEA FRYEANA 1610020602 FAUCHEA FRYEANA
1610020702 PALMARIA PALMATA 1610020702 PALMARIA PALMATA
1610020901 LEPTOFAUCHEA PACIFIC 1610020901 LEPTOFAUCHEA PACIFIC
161101 CERAMIACEAE HOM.l 161101 CERAMIACEAE HOM.l
16110101 ANTITHAMNION 161101O1 ANTITHAMNION
1611010104 ANTITHAMNION DENDROI 1611010104 ANTITHAMNION DENDROI
1611010106 ANTITHAMNION KYLINII 1611010106 ANTITHAMNION KYLINII
1611010109 ANTITHAMNION DEFECTU 1611010109 ANTITHAMNION DEFECTU
16110102 CALLITHAMNION 16110102 CALLITHAMNION
1611010207 CALLITHAMNION PIKEAN 1611010207 CALLITHAMNION PIKEAN
1611010208 CALLITHAMNION ACUTUM 1611010208 CALLITHAMNION ACUTUM
lf>110103 BORNETIA 161101O3 BORNETIA
16110104 CERAMIUM 16110104 CERAMIUM
1611010405 CERAMIUM STRICTOM 1611010405 CERAMIOM STRICTUM
1611010408 CERAMIUM PACIFICUM 1611010408 CERAMIUM PACIFICUM
1611010409 CERAMIUM CODICOLA 1611010409 CERAMIUM CODICOLA
1611010410 CERAMIUM CALIFORNICU 1611010410 CERAMIUM CALIFORNICU
1611010411 CERAMIUM GARDNERI 1611010411 CERAMIUM GARDNERI
1611010413 CERAMIUM WASHINGTONI 1611010413 CERAMIUM WASHINGTONI
1611010499 NAME NOT FOUND 16110104 CERAMIUM
16110113 MICROCLADIA 1611O113 MICROCLADIA
1611011301 MICROCLADIA BOREALIS 1611011301 MICROCLADIA BOREALIS
1611011302 MICROCLADIA COULTERI 1611011302 MICROCLADIA COULTERI
16110114 PLEONOSPORIUM 16110114 PLEONOSPORIUM
1611011403 PLEONOSPORIUM VANCOU 16110114 PLEONOSPORIUM
1611011499 NAME NOT FOUND 16110114 PLEONOSPORIUM
1611011601 PTILOTA FILICINA 1611011601 PTILOTA FILICINA
1613011602 PTILOTA PECTINATA 1611011602 PTILOTA PECTINATA
16110122 ANTITHAMNIONELLA 16110122 ANTITHAMNIONELLA
1611012201 ANTITHAMNIONELLA GLA 1611012201 ANTITHAMNIONELLA GLA
1611012202 ANTITHAMNIONELLA PAC 1611012202 ANTITHAMNIONELLA PAC
16110123 PLATYTHAMNION 16110123 PLATYTHAMNION
1611012301 PLATYTHAMNION PECTIN 1611012301 PLATYTHAMNION PECTIN
1611012302 PLATYTHAMNION VILLOS 1611012302 PLATYTHAMNION VILLOS
1611012303 PLATYTHAMNION REVERS 1611012303 PLATYTHAMNION REVERS
(continued)
2OO
-------
TABLE B-l (continued)
1611012304 PLATYTHAMNION HETERO 1611012304 PLATYTHAMNION HETERO
1611012401 NEOPTILOTA ASPLENIOI 1611012401 NEOPTILOTA ASPLENIOI
1611012402 NEOPTILOTA HYPNOIDES 1611012402 NEOPTILOTA HYPNOIDES
1611012403 NEOPTILOTA CALIFORNI 1611012403 NEOPTILOTA CALIFORNI
16110125 HOLLENBERGIA 16110125 HOLLENBERGIA
16110126 SCAGELONEMA/SCAGELIA 16110126 SCAGELONEMA/SCAGELIA
1611012601 SCAGELIA OCCIDENTALE 16110126 SCAGELONEMA/SCAGELIA
1611012701 TIFFANIELLA SNYDERAE 1611012701 TIFFANIELLA SNYDERAE
1611012801 PTILOTHAMNIONOPSIS L 1611012801 PTILOTHANIOPSIS
1611012899 NAME NOT FOUND 1611012801 PTILOTHANIOPSIS
161102 DELESSERIACEAE 161102 DELESSERIACEAE
16110206 DELESSERIA 16110206 DELESSERIA
1611020601 DELESSERIA DECIPIENS 16110206 DELESSERIA
1611020901 GONIMOPHYLLUM SKOTTS 1611020901 GONIMOPHYLLUM SKOTTS
16110211 MEMBRANOPTERA 16110211 MEMBRANOPTERA
1611021102 MEMBRANOPTERA DIMORP 1611021102 MEMBRANOPTERA DIMORP
1611021103 MEMBRANOPTERA PLATYP 1611021103 MEMBRANOPTERA PLATYP
1611021108 MEMBRANOPTERA MULTIR 1611021108 MEMBRANOPTERA MOLTIR
16110214 PHYCODRYS 16110214 PHYCODRYS
1611021404 PHYCODRYS SETCHELLII 16110214 PHYCODRYS
1611021499 NAME NOT FOUND 16110214 PHYCODRYS
1611021501 POLYNEURA LATISSIMA 1611021501 POLYNEURA LATISSIMA
1611022003 NIENBURGIA ANDERSONI 1611022003 NIENBURGIA ANDERSONI
16110224 HYMENENA 16110224 HYMENENA
1611022402 HYMENENA FLABELLIGER 16110224 HYMENENA
1611022499 NAME NOT FOUND 16110224 HYMENENA
16110227 PLATYSIPHONIA 16110227 PLATYSIPHONIA
16110302 HETEROSIPHONIA 16110302 HETEROSIPHONIA
16110401 POLYSIPHONIA 161104O1 POLYSIPHONIA |*
1611040101 POLYSIPHONIA HENDRYI 1611040101 POLYSIPHONIA HENDRYI |
1611040103 POLYSIPHONIA PACIFIC 1611O4O103 POLYSIPHONIA PACIFIC j
1611040104 POLYSIPHONIA URCEOLA 1611040104 POLYSIPHONIA URCEOLA |
1611040105 POLYSIPHONIA BRODIAE 1611040105 POLYSIPHONIA BRODIAE |
1611040115 POLYSIPHONIA TENUIST 1611040115 POLYSIPHONIA TENUIST |
16110402 PTEROSIPHONIA 16110402 PTEROSIPHONIA
1611040202 PTEROSIPHONIA BIPINN 1611040202 PTEROSIPHONIA BIPINN *
1611040203 PTEROSIPHONIA DENDRO 161104O2O3 PTEROSIPHONIA DENDRO
1611040204 PTEROSIPHONIA GARDNE 161104O2O4 PTEROSIPHONIA GARDNE
1611040401 LAURENCIA SPECTABILI 1611040401 LAURENCIA SPECTABILI
1611O405O1 RHODOMELA LARIX 161104O5O1 RHODOMELA LARIX *
1611040502 RHODOMELA LYCOPODIOI 1611040502 RHODOMELA LYCOPODIOI
16110406 ODONTHALIA 161104O6 ODONTHALIA |*
1611040603 ODONTHALIA FLOCCOSA 1611040603 ODONTHALIA FLOCCOSA |
1611040605 ODONTHALIA LYALLII 1611040605 ODONTHALIA LYALLII |
1611040606 ODONTHALIA WASHINGTO 1611040606 ODONTHALIA WASHINGTO |
1611040607 ODONTHALIA KAMTSCHAT 1611040607 ODONTHALIA KAMTSCHAT j
16110407 LOPHOSIPHONIA 161104O7 LOPHOSIPHONIA
16110412 HERPOSIPHONIA 16110412 HERPOSIPHONIA
(continued)
201
-------
TABLE B-l (continued)
1611041202
1611041203
20200
20230
2062U
2063O
20710
20950
20990
21100
21510
21620
33260103
3326010301
36
36630201
3663020102
36640708
3664070801
3665020202
3702
3704
37040102
37040104
3704040
37040502
37040503
37040504
37310101
3740
3760
376001
3760010201
3760010301
37600104
3760O1999
3760019999
39
3901
43
4303020208
4306010102
4306010603
43060501
4306050199
47
5001
500101
HERPOSIPHONIA GRANDI
HERPOSIPHONIA PLUMUL
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
PHYLLOSPADIX
PHYLLOSPADIX SCOULER
PORIFERA
HALICLONA
HALICLONA PERMOLLIS
OPHLITASPONGIA
OPHLITASPONGIA PENNA
HALICHONDRIA PANICEA
HYDROZOA HYDROIDA
HYDROZOA HYDROIDA IE
OBELIA
PHIALIDIUM
NAME NOT FOUND
SERTULARELLA
SERTULARIA
ABIETINARIA
HALICLYSTUS
ANTHOZOA
ZOANTHARIA ACTINIARI
ACTINIIDAE
ANTHOPLEURA ELEGANTI
EPIACTIS PROLIFERA
TEALIA
NAME NOT FOUND
NAME NOT FOUND
PLATYHELMINTHES
TURBELLARIA
RHYNCHOCOELA
CEREBRATULUS CALIFOR
EMPLECTONEMA GRACILE
PARANEMERTES PEREGR1
AMPHIPORUS
NAME NOT FOUND
NEMATODA
POLYCHAETA
APHRODITIDAE
1611041202
1611041203
37600104
37310101
51050105
551507
5001
6157010401
6117
65
8129030303
8831020701
33260103
33260103
36
36630201
36630201
3664O708
36640708
3665020202
3702
3704
37040102
37040104
37O404
37040502
37040503
370405O4
37310101
374O
376O
376001
376O01O201
376O0103O1
376OO104
376001
376O01
39
39
43
4303020208
4306010102
4306010603
43060501
43060501
47
5001
500101
HERPOSIPHONIA GRANDI
HERPOSIPHONIA PLUMUL
TEALIA
HALICLYSTUS
NUCELLA
ERYCINIDAE
POLYCHAETA
PANCOLUS CALIFORNIEN
COPEPODA
INSECTA IV
DIAMPHIODIA PERIERCT
CLINOCOTTUS ACUTICEP
PHYLLOSPADIX
PHYLLOSPADIX
PORIFERA
HALICLONA
HALICLONA
OPHLITASPONGIA
OPHLITASPONGIA
HALICHONDRIA PANICEA
HYDROZOA HYDROIDA
HYDROZOA HYDROIDA LE
OBELIA
PHIALIDIUM
CAMPANULINIDAE
SERTULARELLA
SERTULARIA
ABIETINARIA
HALICLYSTUS
ANTHOZOA
ZOANTHARIA ACTINIARI
ACTINIIDAE
ANTHOPLEURA ELEGANTI
EPIACTIS PROLIFERA
TEALIA
ACTINIIDAE
ACTINIIDAE
PLATYHELMINTHES
PLATYHELMINTHES
RHYNCHOCOELA
CEREBRATULUS CALIFOR
EMPLECTONEMA GRACILE
PARANEMERTES PEREGRI
AMPHIPORUS
AMPHIPORUS
NEMATODA
POLYCHAETA
APHRODITIDAE
(continued)
2O2
-------
TABLE B-l (continued)
500102 POLYNOIDAE 500102 POLYNOIDAE
5001020701 HALOSYDNA BREVISETOS 5001020701 HALOSYDNA BREVISETOS
5O010208 HARMOTHOE 500102O8 HARMOTHOE
5001020806 HARMOTHOE IMBRICATA 500102O8O6 HARMOTHOE IMBRICATA
5001020810 HARMOTHOE LUNULATA 5001020810 HARMOTHOE LUNULATA
500106 SIGALIONIDAE 500106 SIGALIONIDAE
5001060101 PHOLOE MINUTA 500106 SIGALIONIDAE
5001O8 CHRYSOPETALIDAE 500108 CHRYSOPETALIDAE
5001080101 PALEANOTUS BELLIS 500108 CHRYSOPETALIDAE
500113 PHYLLODOCIDAE 500113 PHYLLODOCIDAE
50011301 ANAITIDES/PHYLLODOCE 500113O1 ANAITIDES/PHYLLODOCE
5001130101 ANAITIDES CITRINA 5001130101 ANAITIDES CITRINA
5001130106 ANAITIDES MACULATA 5001130106 ANAITIDES MACULATA
50011302 ETEONE 50011302 ETEONE
5001130205 ETEONE LONGA 500113O2 ETEONE
50011303 EULALIA 500113O3 EULALIA
5001130301 EULALIA VIRIDIS 5001130301 EULALIA VIRIDIS
5001130302 EULALIA SANGUINEA 5001130302 EULALIA SANGUINEA
5001130304 EULALIA BILINEATA 5001130304 EULALIA BILINEATA
5001130306 EULALIA QUADRIOCULAT 500113O3O6 EULALIA QUADRIOCULAT
5001130307 EULALIA NIGRIMACULAT 5001130307 EULALIA NIGRIMACULAT
50011307 GENETYLLIS 50011307 GENETYLLIS
5001130901 HESIONURA COINEAUI 5001130901 HESIONURA COINEAUI
50011311 EUMIDA 50011311 EUMIDA
500121 HESIONIDAE 500121 HESIONIDAE
5001210401 OPHIODROMUS PUGETTEN 5001210401 OPHIODROMUS PUGETTEN
50O1210801 MICROPODARKE DUBIA 5001210801 MICROPODARKE DUBIA
500123 SYLLIDAE 500123 SYLLIDAE
50012301 AUTOLYTUS 500123O1 AUTOLYTUS
50012303 SYLLIS 50012303 SYLLIS *
50012305 TYPOSYLLIS 50012305 TYPOSYLLIS *
5001230501 TYPOSYLLIS ALTERNATA 5001230501 TYPOSYLLIS ALTERNATA
5001230505 TYPOSYLLIS PULCHRA 5001230505 TYPOSYLLIS PULCHRA
5001230506 TYPOSYLLIS STEWARTI 5001230506 TYPOSYLLIS STEWARTI
5001230507 TYPOSYLLIS FASCIATA 5001230507 TYPOSYLLIS FASCIATA
5001230509 TYPOSYLLIS ADAMANTEA 5001230509 TYPOSYLLIS ADAMANTEA
5001230511 TYPOSYLLIS HYALINA 5001230511 TYPOSYLLIS HYALINA
5001230512 TYPOSYLLIS VARIEGATA 5001230512 TYPOSYLLIS VARIEGATA
5001230601 EUSYLLIS ASSIMILIS 5OO12306O1 EUSYLLIS ASSIMILIS
50012307 EXOGONE 500123O7 EXOGONE
5001230702 EXOGONE GEMMIFERA 5001230702 EXOGONE GEMMIFERA
5001230703 EXOGONE LOUREI 5001230703 EXOGONE LOUREI
5001230706 EXOGONE VERUGERA 5001230706 EXOGONE VERUGERA
50012308 SPHAEROSYLLIS 50012308 SPHAEROSYLLIS
5001230805 SPHAEROSYLLIS PERIFE 5001230805 SPHAEROSYLLIS PERIFE
5001230806 SPHAEROSYLLIS BRANDH 5001230806 SPHAEROSYLLIS BRANDH
5001230901 BRANIA BREVIPHARYNGE 5001230901 BRANIA BREVIPHARYNGE
50012313 ODONTOSYLLIS 50012313 ODONTOSYLLIS
(continued)
203
-------
TABLE B-l (continued)
500124 NEREIDAE 5O0124 NEREIDAE
50012404 NEREIS 50012404 NEREIS |*
5001240403 NEREIS PELAGICA 5001240403 NEREIS PELAGICA |
5001240405 NEREIS VEXILLOSA 5001240405 NEREIS VEXILLOSA j
5001240406 NEREIS ZONATA 5001240406 NEREIS ZONATA |
5001240495 NAME NOT FOUND 50012404 NEREIS \
5001240501 PLATYNEREIS BICANALI 5001240501 PLATYNEREIS BICANALI *
5O0125O1 NEPHTYS 50012501 NEPHTYS
5001260201 SPHAERODOROPSIS MINU 5001260201 SPHAERODOROPSIS MINU
5001280101 GLYCINDE PICTA 5001280101 GLYCINDE PICTA
500129 ONUPHIDAE 500129 ONUPHIDAE
50012901 ONUPHIS 500129 ONUPHIDAE
5001290106 ONUPHIS STIGMATIS 500129 ONUPHIDAE
500130 EUNICIDAE 500130 EUNICIDAE
5001300102 EUNICE VALENS 500130 EUNICIDAE
50O131 LUMBRINERIDAE 500131 LUMBRINERIDAE
50013101 LUMBRINEREIS 50013101 LUMBRINEREIS *
5001310106 LUMBRINEREIS ZONATA 5001310106 LUMBRINEREIS ZONATA
5001310108 LUMBRINEREIS INFLATA 5001310108 LUMBRINEREIS INFLATA
5001310111 LUMBRINEREIS PALLIDA 5001310111 LUMBRINEREIS PALLIDA j
50013601 DORVILLEA/SCHISTOMER 50O13601 DORVILLEA/SCHISTOMER
500140 ORBINIIDAE 500140 ORBINIIDAE
50014002 NAINERIS 50014002 NAINERIS
5O0140O201 NAINERIS DENDRITICA 5001400201 NAINERIS DENDRITICA
5001400202 NAINERIS QUADRICUSPI 5001400202 NAINERIS QUADRICUSPI
50014003 SCOLOPLOS 50014003 SCOLOPLOS
5001400301 SCOLOPLOS ARMIGER 50O14003O1 SCOLOPLOS AKMIGER
5001400302 SCOLOPLOS PUGETTENSI 5001400302 SCOLOPLOS PUGETTENSI
5001410501 PARAONELLA PLATYBRAN 5001410501 PARAONELLA PLATYBRAN
500143 SPIONIDAE 50O143 SPIONIDAE
5001430201 LAONICE CIRRATA 5001430201 LAONICE CIRRATA
5O014303 NERINE 50014303 NERINE
5OO14304 POLYDORA 50O14304 POLYDORA
5001430411 POLYDORA LIGNI 5001430411 POLYDORA LIGNI
5001430412 POLYDORA WEBSTERI 5001430412 POLYDORA WEBSTERI
5001430415 POLYDORA LIMICOLA 5001430415 POLYDORA LIMICOLA
5001430417 POLYDORA PYGIDIALIS 5001430417 POLYDORA PYGIDIALIS
50014305 PRIONOSPIO 50014305 PRIONOSPIO
5001430502 PRIONOSPIO CIRRIFERA 50014305 PRIONOSPIO
50014307 SPIO 5OO143O7 SPIO
5001430701 SPIO FILICORNIS 50014307 SPIO
50O14308 BOCCARDIA 50014308 BOCCARDIA
5001430801 BOCCARDIA COLUMBIANA 5001430801 BOCCARDIA COLUMBIANA
5001430806 BOCCARDIA HAMATA 5001430806 BOCCARDIA HAMATA
5001431302 PYGOSPIO ELEGANS 5001431302 PYGOSPIO ELEGANS
50014314 MALACOCEROS 5O014314 MALACOCEROS
5001431401 MALACOCEROS GLUTAEUS 50014314 MALACOCEROS
500150 CIRRATULIDAE 500150 CIRRATULIDAE
(continued)
204
-------
TABLE B-l (continued)
50015001 CIRRATULUS 50015001
5001500101 CIRRATULUS CIKRATUS 500150O1
50015003 THARYX 500150O3
5001500302 THARYX MULTIPILIS 500150O3
50015005 DODECACERIA 50015005
5001500502 DODECACERIA FEWKESI 500150O5
5001540302 PHERUSA PLUMOSA 5001540302
5001580202 ARMANDIA BREVIS 5001580202
500160 CAPITELLIDAE 50O16O
5001600101 CAPITELLA CAPITATA 5001600101
50016004 MEDIOMASTUS 50016004
50O160O401 MEDIOMASTUS AMBISETA 50016004
500162 ARENICOLIDAE 500162
50O16201 ABARENICOLA 500162O1
5001620104 ABARENICOLA OCEANICA 500162O1
5001620301 BRANCHIOMALDANE VICE 5001620301
500163 MALDANIDAE 50O163
5001630802 AXIOTHELLA RUBROCINC 500163
50016401 OWENIA 50016401
5001640102 OWENIA FUSIFORMIS 50016401
5001650102 IDANTHYRSUS ARMATUS 5001650102
5001650201 SABELLARIA CEMENTARI 5001650201
500167 AMPHARETIDAE 500167
500168 TEREBELLIDAE 500168
5001680101 AMPHITRITE CIRRATA 5001680101
5001680201 EUPOLYMNIA HETEROBRA 5001680201
5001680601 NICOLEA ZOSTERICOLA 5001680601
5O016807 PISTA 50016807
5001680702 PISTA FASCIATA 5001680702
5001680703 PISTA ELONGATA 5001680703
50016808 POLYCIRRUS 50016808
50016810 THELEPUS 50016810
5001681001 THELEPUS CRISPUS 5O016810
50016825 STREBLOSOMA 50016825
500170 SABELLIDAE 500170
50O170O1 CHONE 500170O1
5001700105 CHONE ECAUDATA 5O0170O1
50017003 EUDISTYLIA 50017OO3
5001700303 EUDISTYLIA VANCOUVER 500170O3
50O17006 POTAMILLA 50017006
5001700602 POTAMILLA MYRIOPS 50017006
5001700699 NAME NOT FOUND 50017006
50017007 PSEUDOPOTAMILLA 50017007
5001700701 PSEUDOPOTAMILLA INTE 50017007
5001700802 SABELLA MEDIA 5001700802
5001700902 SCHIZOBRANCHIA INSIG 5001700902
50017013 FABRICIA 50017013
5001701301 FABRICIA SABELLA 5001701301
(continued)
CIRRATULUS
CIRRATULUS
THARYX
THARYX
DODECACERIA
DODECACERIA
PHERUSA PLUMOSA
ARMANDIA BREVIS
CAPITELLIDAE
CAPITELLA CAPITATA
MEDIOMASTUS
MEDIOMASTUS
ARENICOLIDAE
ABARENICOLA
ABARENICOLA
BRANCHIOMALDANE VICE
MALDANIDAE
MALDANIDAE
OWENIA
OWENIA
IDANTHYRSUS ARMATUS
SABELLARIA CEMENTARI
AMPHARETIDAE
TEREBELLIDAE
AMPHITRITE CIRRATA
EUPOLYMNIA HETEROBRA
NICOLEA ZOSTERICOLA
PISTA
PISTA FASCIATA
PISTA ELONGATA
POLYCIRRUS
THELEPUS
THELEPUS
STREBLOSOMA
SABELLIDAE
CHONE
CHONE
EUDISTYLIA
EUDISTYLIA
POTAMILLA
POTAMILLA
POTAMILLA
PSEUDOPOTAMILLA
PSEUDOPOTAMILLA
SABELLA MEDIA
SCHIZOBRANCHIA INSIG
FABRICIA
FABRICIA SABELLA
205
-------
TABLE B-l (continued)
5OO1701302 FABRICIA MINUTA 5001701302 FABRICIA MINUTA
5001701502 MANAYUNKIA 50O17015 MANAYUNKIA
5001701599 NAME NOT FOUND 50017015 MANAYUNKIA
50017O20 ORIOPSIS 50017020 ORIOPSIS
50017021 SABELLASTARTE 50O17021 SABELLASTARTE
5O0173 SERPULIDAE 500173 SERPULIDAE
5001730401 SERPULA VERMICULARIS 5001730401 SERPULA VERMICULARIS
50017305 SPIRORBIS 50017305 SPIRORBIS
5001730510 SPIRORBIS NAKAMURAI 500173O5 SPIRORBIS
5001730599 NAME NOT FOUND 50017305 SPIRORBIS
5001730602 DEXIOSPIRA SPIRILLUM 5001730602 DEXIOSPIRA SPIRILLUM
500202 PROTODRILIDAE 50O202 PROTODRILIDAE
50020501 POLYGORDIUS 50020501 POLYGORDIUS
5004 OLIGOCHAETA 5O04 OLIGOCHAETA
500501 LUMBRICULIDAE 500501 LUMBRICULIDAE
500901 ENCHYTRAEIDAE 500901 ENCHYTRAEIDAE
501 NAME NOT FOUND 6501 DIPTERA
51 GASTROPODA 51 GASTROPODA
5102040401 DIODORA ASPERA 5102040401 DIODORA ASPERA
510205 ACMAEIDAE 510205 ACMAEIDAE
51020501 TECTURA 510205O1 TECTURA
5102050103 ACMAEA MITRA 51020501 TECTURA
51020502 COLLISELLA 510205O2 COLLISELLA
5102050201 COLLISELLA PELTA 5102050201 COLLISELLA PELTA *
5102050202 COLLISELLA DIGITALIS 5102050202 COLLISELLA DIGITALIS *
5102050203 COLLISELLA OCHRACEA 5102050203 COLLISELLA OCHRACEA
5102050207 COLLISELLA STRIGATEL 5102050207 COLLISELLA STRIGATEL *
5102050301 NOTOACMAEA SCUTUM 5102050301 NOTOACMAEA SCUTUM *
5102050302 NOTOACMAEA PERSONA 5102050302 NOTOACMAEA PERSONA
5102050303 NOTOACMAEA FENESTRAT 5102050303 NOTOACMAEA FENESTRAT
5102050305 NAME NOT FOUND 5102O5O305 NOTOACMAEA SP.
5102100103 CALLIOSTOMA LIGATUM 51O21OO103 CALLIOSTOMA LIGATUM
51021003 MARGARITES/LIRULARIA 51021003 MARGARITES/LIRULARIA
5102100308 MARGARITES PUPILLUS 5102100308 MARGARITES PUPILLUS
5102100310 MARGARITES LIRULATUS 5102100310 MARGARITES LIRULATUS
5102100312 MARGARITES SUCCINCTU 51O21O0312 MARGARITES SUCCINCTU
5102100599 NAME NOT FOUND 51021005 TEGULA
51021201 HOMALOPOMA 51021201 HOMALOPOMA
5102120102 HOMALOPOMA LURIDUM 5102120102 HOMALOPOMA LURIDUM
5102120103 HOMALOPOMA BACULUM 5102120103 HOMALOPOMA BACULUM
5102120199 NAME NOT FOUND 51021201 HOMALOPOMA
51021202 MOELLERIA 51021202 MOELLERIA
510214 PHASIANELLIDAE 510214 PHASIANELLIDAE
51030903 LACUNA 510309O3 LACUNA1 j *
5103090302 LACUNA VARIEGATA 51030903 LACUNA j
51031001 LITTORINA 51031001 LITTORINA
51O31O0101 LITTORINA SITKANA 5103100101 LITTORINA SITKANA *
5103100104 LITTORINA SCUTULATA 5103100104 LITTORINA SCUTULATA *
(continued)
2O6
-------
TABLE B-l (continued)
51032001 ALVINIA 510320O1 ALVINIA
51032004 BARLEEIA 510320O4 BARLEEIA
5103200401 BARLEEIA HALIOTIPHIL 51032004 BARLEEIA
51032005 RISSOINA 510320O5 RISSOINA
5103210101 NAME NOT FOUND 510321O1 ASSIMINEA
51033599 NAME NOT FOUND 510335 VERMETIDAE
5103359999 NAME NOT FOUND 510335 VERMETIDAE
51034601 BITTIUM 510346O1 BITTIUM
5103460103 BITTIUM ESCHRICHTII 51034601 BITTIUM
51034602 CERITHIOPSIS 51034602 CERITHIOPSIS
5103620204 TRICHOTROPIS CANCELL 51034602 CERITHIOPSIS
5103640101 CALYPTRAEA FASTIGATA 5103640101 CALYPTRAEA FASTIGATA
51036402 CREPIDULA 51036402 CREPIDULA
5103640204 CREPIDULA FORNICATA 51036402 CREPIDULA
5103640298 NAME NOT FOUND 51036402 CREPIDULA
5103640301 CREPIPATELLA LINGULA 5103640301 CREPIPATELLA LINGULA
51036604 VELUTINA 51036604 VELUTINA
5103660409 VELUTINA LAEVIGATA 51036604 VELUTINA
5105010206 OCENEBRA LURIDA 5105010206 OCENEBRA LURIDA
51050105 NUCELLA 5105O105 NUCELLA
5105010501 NUCELLA CANALICULATA 5105010501 NUCELLA CANALICULATA
5105010502 NUCELLA LAMELLOSA 5105010502 NUCELLA LAMELLOSA
5105010503 NUCELLA EMARGINATA 5105010503 NUCELLA EMARGINATA
5105010802 NAME NOT FOUND 51050105 NUCELLA
5105010803 NAME NOT FOUND 510501O5 NUCELLA
5105030101 AMPHISSA COLUMBIANA 5105030101 AMPHISSA COLUMBIANA
51050302 MITKELLA 51050302 MITRELLA
5105030204 MITRELLA GOULDI 5105030204 MITRELLA GOULDI
5105030206 MITRELLA CARINATA 5105030206 MITRELLA CARINATA
5105040201 SEARLESIA DIRA 510504O2O1 SEARLESIA DIRA
5107
51080101
51080102
511004
51100402
5114020101
51140401
5127
GASTROPODA EUTHYNEUR
ODOSTOMIA
TURBONILLA
SCAPHANDRIDAE
CYLICHNA
SIPHONARIA THERSITES
PHYTIA
NUDIBRANCHIA
5107
51080101
51080102
511OO4
511004
5114020101
5114O401
5127
GASTROPODA EUTHYNEUR
ODOSTOMIA
TURBONILLA
SCAPHANDRIDAE
SCAPHANDRIDAE
SIPHONARIA THERSITES
PHYTIA
NUDIBRANCHIA
5130030301 ARCHIDORIS MONTEREYE 5130030301 ARCHIDORIS MONTEREYE
51310504 ONCHIDORIS 51310504 ONCHIDORIS
5131050401 ONCHIDORIS BILAMELLA 51310504 ONCHIDORIS
514203 AEOLIDIIDAE 514203 AEOLIDIIDAE
5143010101 ONCHIDELLA BOREALIS 5143010101 ONCHIDELLA BOREALIS
53 POLYPLACOPHORA 53 POLYPLACOPHORA
5303 NEOLORICATA ISCHNOCH 5303 NEOLORICATA ISCHNOCH
530302 ISCHNOCHITONIDAE 530302 ISCHNOCHITONIDAE
5303020101 BASILIOCHITON FLECTE 53O30201O1 BASILIOCHITON FLECTE
5303020201 CYANOPLAX DENTIENS 5303020201 CYANOPLAX DENTIENS
(continued)
207
-------
TABLE B-l (continued)
5303020601 TONICELLA INSIGNIS 53O302O601 TONICELIA INSIGNIS
5303020602 TONICELLA LINEATA 5303020602 TONICELLA LINEATA
5303020701 LEPIDOZONA MERTENSII 5303020701 LEPIDOZONA MERTENSII
5303020703 LEPIDOZONA COOPER! 5303020703 LEPIDOZONA COOPERI
5303060102 CHAETOPLEORA GEMMA 5303060102 CHAETOPLEURA GEMMA
53O30703 KATHARINA 5303O703 KATHARINA j*
5303070301 KATHARINA TUNICATA 53030703 KATHARINA !
53030704 MOPALIA 5303O704 MOPALIA
5303070401 MOPALIA CILIATA 5303070401 MOPALIA CILIATA
53O307O404 MOPALIA HINDSI 5303070404 MOPALIA HINDSI
5303070407 MOPALIA LIGNOSA 5303070407 MOPALIA LIGNOSA
5303O704O8 MOPALIA MUCOSA 5303070408 MOPALIA MUCOSA
5303070497 NAME NOT FOUND 53030704 MOPALIA
5303070499 NAME NOT FOUND 53030704 MOPALIA
5304010101 CRYPTOCHITON STELLER 5304010101 CRYPTOCHITON STELLER
55 BIVALVIA 55 BIVALVIA
5502020201 NUCULA TENUIS 5502020201 NUCULA TENUIS
5507 MYTILOIDA 5507 MYTILOIDA
550701 MYTIEIDAE 550701 MYTILIDAE
55070101 MYTILUS 55070101 MYTILUS
5507010101 MYTILUS EDULIS 5507010101 MYTILUS EDULIS *
5507010102 MYTILUS CALIFORNIANU 5507010102 MYTILUS CALIFORNIANU *
55070104 MUSCULUS 55070104 MUSCULUS
5507010401 MUSCULUS NIGER 5507010401 MUSCULUS NIGER
5507010402 MUSCULUS DISCORS 55O701O402 MUSCULUS DISCORS
5507010410 MUSCULUS PYGMAEUS 5507010410 MUSCULUS PYGMAEUS
5507010499 NAME NOT FOUND 5507O104 MUSCULUS
55070106 MODIOLUS 5507O106 MODIOLUS
5507010603 MODIOLUS RECTUS 55070106 MODIOLUS
5507010699 NAME NOT FOUND 55070106 MODIOLUS
5507011101 ADULA CALIFORNIENSIS 5507011101 ADULA CALIFORNIENSIS
55070199 NAME NOT FOUND 5507O1 MYTILIDAE
5507019999 NAME NOT FOUND 550701 MYTILIDAE
5509090103 PODODESMUS CEPIO 5509090103 PODODESMUS CEPIO
5515070101 LASAEA CISTULA 551507 ERYCINIDAE
5515079999 NAME NOT FOUND 5515O7 ERYCINIDAE
55150801 KELLIA 5515O8O1 KELLIA
5515100102 MYSELLA TUMIDA 5515100102 MYSELLA TUMIDA
5515250201 TRESUS CAPAX 5515250201 TRESUS CAPAX
5515290201 SOLEN SICARIUS 5515290201 SOLEN SICARIUS
55153101 MACOMA 55153101 MACOMA
5515310116 MACOMA BALTHICA 5515310116 MACOMA BALTHICA
5515310117 MACOMA SECTA 5515310117 MACOMA SECTA
55154701 TRANSENNELLA 55154701 TRANSENNELLA
5515470101 TRANSENNELLA TANTILL 55154701 TRANSENNELLA
5515470201 SAXIDOMUS GIGANTEA 5515470201 SAXIDOMUS GIGANTEA
5515470701 PROTOTHACA STAMINEA 5515470701 PROTOTHACA STAMINEA
5516 MYOIDA 5516 MYOIDA
(continued)
208
-------
TABLE B-l (continued)
5517060201
551706O2O3
5517060204
551801
5518010101
55180102
5518010201
5518010299
55180107
55180199
5520050101
5520050202
60
6001
6001010198
6001010199
6O0104
60010402
6001040201
6001040204
6001040299
6001040301
600106
60O1O60102
60O1O603
6001060301
6001060302
600108
6001080101
6001080102
61
6110
6110999999
6111
6117
6117999999
613O
6132010201
6134
6134010101
613402
6134O201
6134020101
6134020103
6134020104
6134020107
6134020110
6134020111
HIATELLA ARCTICA
HIATELLA GLACIANA
NAME NOT FOUND
PHOLADIDAE
ZIRFAEA PILSBURYI
PENITELLA
PENITELLA PENITA
NAME NOT FOUND
NETASTOMA
NAME NOT FOUND
ENTODESMA SAXICOLUM
LYONSIA CALIFORNICA
ARTHROPODA PYCNOGONI
PANTOPODA
NAME NOT FOUND
NAME NOT FOUND
AMMOTHEIDAE
ACHELIA
ACHELIA CHELATA
ACHELIA NUDIUSCULA
NAME NOT FOUND
AMMOTHELLA TUBERCULA
PHOXICHILIDIIDAE
PHOXICHILIDIUM FEMOR
HALOSOMA
HALOSOMA VIRIDINTEST
HALOSOMA COMPACTUM
PYCNOGONIDAE
PYCNOGONUM STEARNSI
PYCNOGONUM RIOCETTSI
ARTHROPODA MANDIBULA
OSTRACODA
NAME NOT FOUND
OSTRACODA MYODOCOPA
COPEPODA
NAME NOT FOUND
CIRRIPEDIA
POLLICIPES POLYMERUS
CIRRIPEDIA THORACICA
CHTHAMALUS DALLI
BALANIDAE
BALANUS
BALANUS BALANOIDES
BALANUS CARIOSUS
BALANUS CRENATUS
BALANUS GLANDULA
BALANUS NUBILIS
BALANUS ROSTRATUS
5517060201
5517060203
551706O2
551801
551801
55180102
551801O2
55180102
55180107
551801
5520050101
5520050202
60
6001
6001O101
600101O1
600104
60010402
6001040201
6001040204
60010402
6001040301
600106
6001060102
600106O3
6001060301
6001060302
600108
6001080101
6001080102
61
6110
6110
6111
6117
6117
6130
6132010201
6134
6134010101
613402
6134O201
6134020101
6134020103
6134020104
6134O2O107
6134020110
6134020111
HIATELLA ARCTICA
HIATELLA GLACIANA
HIATELLA
PHOLADIDAE
PHOLADIDAE
PENITELLA
PENITELLA
PENITELLA
NETASTOMA
PHOLADIDAE
ENTODESMA SAXICOLUM
LYONSIA CALIFORNICA
ARTHROPODA PYCNOGONI
PANTOPODA
NYMPHON
NYMPHON
AMMOTHEIDAE
ACHELIA
ACHELIA CHELATA
ACHELIA NUDIUSCULA
ACHELIA
AMMOTHELLA TUBERCULA
PHOXICHILIDIIDAE
PHOXICHILIDIUM FEMOR
HALOSOMA
HALOSOMA VIRIDINTEST
HALOSOMA COMPACTUM
PYCNOGONIDAE
PYCNOGONUM STEARNSI
PYCNOGONUM RICKETTSI
ARTHROPODA MANDIBULA
OSTRACODA
OSTRACODA
OSTRACODA MYODOCOPA
COPEPODA
COPEPODA
CIRRIPEDIA
POLLICIPES POLYMERUS
CIRRIPEDIA THORACICA
CHTHAMALUS DALLI
BALANIDAE
BALANUS
BALANUS BALANOIDES
BALANUS CARIOSUS
BALANUS CRENATUS
BALANUS GLANDULA
BALANUS NUBILIS
BALANUS ROSTRATUS
(continued)
2O9
-------
TABLE B-l (continued)
614501O1 NEBALIA 614501O1 NEBALIA
6154 PERACARIDA CUMACEA 6154 PERACARIDA CUMACEA
6154010104 LAMPROPS CARINATA 6154010104 LAMPROPS CARINATA
615408 NANNASTACIDAE 6154O8 NANNASTACIDAE
615408O1 CUMELLA 615408 NANNASTACIDAE
6154080102 CUMELLA VULGARIS 615408 NANNASTACIDAE
61540903 LEPTOCUMA/PSEUDOLEPT 61540903 LEPTOCUMA/PSEUDOLEPT
6157 PERACARIDA TANAIDACE 6157 PERACARIDA TANAIDACE
615701 TANAIDAE 615701 TANAIDAE
6157010301 ANATANAIS NORMANI 6157010301 ANATANAIS NORMANI
6157010401 PANCOLUS CALIFORNIEN 6157010401 PANCOLUS CALIFORNIEN
6157010501 PSEUDOTANAIS OCULATU 6157010501 PSEUDOTANAIS OCULATO
61570201 LEPTOCHELIA (TANAI 61570201 LEPTOCHELIA (TANAI
6157020101 TJ-IPTOCHELIA SAVIGNYI 6157020101 LEPTOCHELIA SAVIGNYI
6157020103 LEPTOCHELIA DUBIA 6157020103 LEPTOCHELIA DUBIA
6157020199 NAME NOT POUND 6157O201 LEPTOCHELIA (TANAI
6160010501 PARANTHURA ELEGANS 616001 ANTHURIDAE
6160019999 NAME NOT FOUND 616OO1 ANTHURIDAE
6161010101 CIROLANA KINCAIDI 6161010101 CIROLANA KINCAIDI
6161010102 CIROLANA HARFORDI 6161010102 CIROLANA HARFORDI
616102 SPHAEROMATIDAE 616102 SPHAEROMATIDAE
61610203 GNORIMOSPHAEROMA 61610203 GNORIMOSPHAEROMA
6161020301 GNORIMOSPHAEROMA ORE 616102O3 GNORIMOSPHAEROMA
61610204 EXOSPHAEROMA 6161O204 EXOSPHAEROMA
6161020401 EXOSPHAEROMA AMPLICA 6161020401 EXOSPHAEROMA AMPLICA
6161020402 EXOSPHAEROMA MEDIA 6161020402 EXOSPHAEROMA MEDIA
6161020403 EXOSPHAEROMA RHOMBUR 6161020403 EXOSPHAEROMA RHOMBUR
6161020404 EXOSPHAEROMA OCTONCU 6161020404 EXOSPHAEROMA OCTONCU
61610205 DYNAMENELLA 61610205 DYNAMENELLA
6161020501 DYNAMENELLA SHEARERI 6161020501 DYNAMENELLA SHEARERI
6161020502 DYNAMENELLA GLABRA 6161020502 DYNAMENELLA GLABRA
6161020599 NAME NOT FOUND 61610205 DYNAMENELLA
61610501 LIMNORIA 616105O1 LIMNORIA
6161050101 LIMNORIA LIGNORUM 6161050101 LIMNORIA LIGNORUM
6161050102 LIMNORIA ALGARUM 6161050102 LIMNORIA ALGARUM
6162 PERACARIDA ISOPODA V 6162 PERACARIDA ISOPODA V
61620202 SYNIDOTEA 61620202 SYNIDOTEA
6162O2O2O1 SYNIDOTEA BICUSPIDA 6162O2O201 SYNIDOTEA BICUSPIDA
6162020208 SYNIDOTEA RITTERI 6162020208 SYNIDOTEA RITTERI
6162020209 SYNIDOTEA PETTIBONEA 6162020209 SYNIDOTEA PETTIBONEA
6162020210 SYNIDOTEA ANGULATA 6162020210 SYNIDOTEA ANGOLATA
6162020296 NAME NOT FOUND 616202O2 SYNIDOTEA
61620203 IDOTEA 61620203 IDOTEA
6162020301 IDOTEA RESECATA 6162020301 IDOTEA RESECATA
6162020302 IDOTEA WOSNESENSKII 6162020302 IDOTEA WOSNESENSKII
6162020303 IDOTEA FEWKESI 6162020303 IDOTEA FEWKESI
6162020304 IDOTEA RUFESCENS 6162020304 IDOTEA RUFESCENS
6162020307 IDOTEA ACULEATA 6162020307 IDOTEA ACULEATA
(continued)
210
-------
TABLE B-l (continued)
6162020311
6162020312
6162020313
6162020396
6162020398
6162020399
61630201
6163020101
6163020102
6163020103
6163020106
6163020198
61630203
61631101
6163110101
6163110102
6163110103
61631201
6163120101
6163120102
6163120103
6163120199
6165030301
616504
6165040303
6166010101
6168
6169
6169030202
616904
61690401
6169040104
6169040118
6169040120
6169040197
6169040198
6169040298
6169060202
6169090101
6169090108
6169090199
61691202
6169120901
6169121001
61691502
6169150201
6169150208
6169150211
IDOTEA UROTOMA
IDOTEA SCHMITTI
IDOTEA MONTEKEYENSIS
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
IANIROPSIS
IANIROPSIS KINCAIDI
IANIROPSIS PUGETTENS
IANIROPSIS ANALOGA
IANIROPSIS TRIDENS
NAME NOT FOUND
JANIRALATA
JAEROPSIS
JAEROPSIS LOBATA
JAEROPSIS SETOSA
JAEROPSIS DUBIA
MUNNA
MUNNA STEPHENSENI
MUNNA CHROMATOCEPHAL
MUNNA UBIQUITA
NAME NOT FOUND
CRYPTOTHIR BALANI
BOPYRIDAE
PSEUDIONE GIARDI
LIGIA PALLASI
PERACARIDA AMPHIPODA
PERACARIDA AMPHIPODA
NAME NOT FOUND
AMPITHOIDAE
AMPHITHOE
AMPHITHOE SIMULANS
AMPHITHOE LACERTOSA
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
AOROIDES COLUMBIAE
ATYLUS TRIDENS
ATYLUS LEVIDENSUS
NAME NOT FOUND
CALLIOPIUS
OLIGOCHINUS LIGHTI
CALLIOPIELLA PRATTI
COROPHIUM
COROPHIUM ACHERUSICU
COROPHIUM BREVIS
COROPHIUM INSIDIOSUM
6162020311 IDOTEA UROTOMA
6162020312 IDOTEA SCHMITTI
6162020313 IDOTEA MONTEREYENSIS
61620203 IDOTEA
6162O2O3 IDOTEA
616202O3 IDOTEA
616302O1 IANIROPSIS
6163020101 IANIROPSIS KINCAIDI
6163020102 IANIROPSIS PUGETTENS
6163020103 IANIROPSIS ANALOGA
6163020106 IANIROPSIS TRIDENS
61630201 IANIROPSIS
61630203 JANIRALATA
61631101 JAEROPSIS
6163110101 JAEROPSIS LOBATA
6163110102 JAEROPSIS SETOSA
6163110103 JAEROPSIS DUBIA
61631201 MUNNA
6163120101 MUNNA STEPHENSENI
6163120102 MUNNA CHROMATOCEPHAL
6163120103 MUNNA UBIQUITA
61631201 MUNNA
6165030301 CRYPTOTHIR BALANI
616504 BOPYRIDAE
616504 BOPYRIDAE
6166010101 LIGIA PALLASI
6168 PERACARIDA AMPHIPODA
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
(continued)
211
-------
TABUS B-l (continued)
6169170301
6169200198
6169201O
6169201097
6169201098
61692012
6169201203
6169201204
6169201297
6169201298
6169201299
616921
6169210106
61692110
6169211005
61692201
6169230301
616924
6169240101
6169240105
6169240106
6169240107
61692402
6169240201
6169240204
6169240205
6169240207
6169240299
6169240401
61692602
6169260201
6169260210
6169260298
6169260299
6169260401
61692702
6169270202
6169270302
6169270399
61692799
6169279999
6169320199
6169342199
6169342998
61693499
6169371403
61694209
6169420928
POLYCHERIA OSBORNI
NAME NOT FOUND
PARAMOERA
NAME NOT FOUND
NAME NOT FOUND
PONTOGENEIA
PONTOGENEIA INERMIS
PONTOGENEIA INTERMED
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
GAMMARIDAE
ANISOGAMMARUS PUGETT
MELITA (AMPHIPODA
MELITA CALIFORNICA
EOHAUSTORIUS
NAJNA CONSILIORUM
HYALIDAE
ALLORCHESTES MOLEOLU
ALLORCHESTES ANGUSTU
ALLORCHESTES CAPRELL
ALLORCHESTES ANCEPS
HYALE
HYALE RUBRA
HYALE PLUMULOSA
HYALE PUGETTENSIS
HYALE GRANDICORNIS
NAME NOT FOUND
PARALLORCHESTES OCHO
PHOTIS
PHOTIS BREVIPES
PHOTIS BIFURCATA
NAME NOT FOUND
NAME NOT FOUND
GAMMAROPSIS THOMPSON
ISCHYROCERUS
ISCHYROCERUS ANGUIPE
JASSA FALCATA
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
SYNCHELIDIUM RECTIPA
PARAPHOXUS
PARAPHOXUS SPINOSUS
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
6169
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
(continued)
212
-------
TABIiE B-l (continued)
61694303 PARAPLEUSTES 6169 GAMMARID AMPHIPOD
6169430301 PARAPLEUSTES NAUTILI! 6169 GAMMARID AMPHIPOD
6169430302 PARAPLEUSTES PUGETTE 6169 GAMMARID AMPHIPOD
6169430303 PARAPLEUSTES JOHANSE 6169 GAMMARID AMPHIPOD
6169481102 STENOTHOIDES BERINGI 6169 GAMMARID AMPHIPOD
6169481599 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
616951 TALITRIDAE 6169 GAMMARID AMPHIPOD
6169999992 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999993 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999994 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999995 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999996 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999997 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999998 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999999 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6171 PERACARIDA AMPHIPODA 6171 CAPRELLID AMPHIPOD
617101 CAPRELLIDAE 617101 CAPRELLIDAE
61710101 CERCOPS 617101O1 CERCOPS
6171010102 CERCOPS COMPACTA 61710101 CERCOPS
6171010201 DEUTELLA CALIFORNICA 6171010201 DEUTELLA CALIFORNICA
6171010601 TRITELLA LAEVIS 6171010601 TRITELLA LAEVIS
6171010602 TRITELLA PILIMANA 6171010602 TRITELLA PILIMANA
61710107 CAPRELLA (AMPHIPO 61710107 CAPRELLA (AMPHIPO
6171010706 CAPRELLA DREPANOCHIR 6171010706 CAPRELLA DREPANOCHIR
6171010708 CAPRELLA IRREGULARIS 6171010708 CAPRELLA IRREGULARIS
6171010710 CAPRELLA LAEVIUSCULA 6171010710 CAPRELLA LAEVIUSCULA
6171010713 CAPRELLA INCISA 6171010713 CAPRELLA INCISA
617.1010715 CAPRELLA AUGUSTA 6171010715 CAPRELLA AUGUSTA
6171010716 CAPRELLA VERRUCOSA 6171010716 CAPRELLA VERRUCOSA
6171010721 CAPRELLA PUSTULATA 6171010721 CAPRELLA PUSTULATA
6171O1O729 CAPRELLA GREENLYI 6171010729 CAPRELLA GREENLYI
6171010799 NAME NOT FOUND 61710107 CAPRELLA (AMPHIPO
6175 EUCARIDA DECAPODA(AR 6175 EUCARIDA DECAPODA(AR
6179 EUCARIDA DECAPODA PL 6179 EUCARIDA DECAPODA PL
6179160201 SPIRONTOCARIS PRIONO 6179160201 SPIRONTOCARIS PRIONO
6179160511 HEPTACARPUS STIMPSON 6179160511 HEPTACARPUS STIMPSON
618306 PAGURIDAE 618306 PAGURIDAE
61830602 PAGURUS (DECAPODA) 6183O6O2 PAGURUS (DECAPODA)
6183060208 PAGURUS CAURINUS 6183060208 PAGURUS CAURINUS
6183O6O209 PAGURUS BERINGANUS 6183060209 PAGURUS BERINGANUS
6183060211 PAGURUS GRANOSIMANUS 6183060211 PAGURUS GRANOSIMANUS
6183060213 PAGURUS HIRSUTIUSCUL 6183060213 PAGURUS HIRSUTIUSCUL
6183060301 ELASSOCHIRUS TENUIMA 6183060301 ELASSOCHIRUS TENUIMA
618308 LITHODIDAE 618308 LITHODIDAE
6183080401 OEDIGNATHUS INERMIS 6183080401 OEDIGNATHUS INERMIS
6183081101 CRYPTOLITHODES SITCH 6183081101 CRYPTOLITHODES SITCH
6183120202 PACHYCHELES RUDIS 6183120202 PACHYCHELES RUDIS
618701 MAJIDAE 618701 MAJIDAE
(continued)
213
-------
TABLE B-l (continued)
6187010101 OREGONIA GRACILIS 6187010101 OKEGONIA GRACILIS
618701O5 PUGETTIA (DECAPODA 61870105 PUGETTIA (DECAPODA
6187010502 PUGETTIA RICHII 6187010502 PUGETTIA RICHII
6187010503 PUGETTIA GRACILIS 6187010503 PUGETTIA GRACILIS
6188020101 TELMESSUS CHEIRAGONU 6188020101 TELMESSUS CHEIRAGONU
61880301 CANCER 618803O1 CANCER
6188030101 CANCER PRODUCTUS 6188030101 CANCER PRODUCTUS
6188030105 CANCER GRACILIS 6188030105 CANCER GRACILIS
6188030106 CANCER OREGONENSIS 6188030106 CANCER OREGONENSIS
6189 EUCARIDA DECAPODA PL 6189 EUCARIDA DECAPODA PL
6189020301 FABIA SUBQUADRATA 6189O2 XANTHIDAE
61890204 NAME NOT FOUND 618902 XANTHIDAE
618906 PINNOTHERIDAE 618906 PINNOTHERIDAE
6189060299 NAME NOT FOUND 618906 PINNOTHERIDAE
61890701 HEMIGRAPSUS 6189O701 HEMIGRAPSUS
6189070101 HEMIGRAPSUS NUDUS 6189070101 HEMIGRAPSUS NUDUS
6189070102 HEMIGRAPSUS OREGONEN 6189070102 HEMIGRAPSUS OREGONEN
6208 COLLEMBOLA 62O8 COLLEMBOLA
6302 COLEOPTERA 6302 COLEOPTERA
65 INSECTA IV 65 INSECTA IV
65O1 DIPTERA 6501 DIPTERA
650508 CHIRONOMIDAE 650508 CHIRONOMIDAE
651802 DOLICHOPODIDAE 651802 DOLICHOPODIDAE
653801 EPHYDRIDAE 653801 EPHYDRIDAE
72 SIPUNCULIDA 72 SIPUNCULIDA
7200020104 GOLFINGIA PUGETTENSI 7200020104 GOLFINGIA PUGETTENSI
7200040101 PHASCOLOSOMA AGASSIZ 7200040101 PHASCOLOSOMA AGASSIZ
78 ECTOPROCTA 78 ECTOPROCTA
78030201 FLUSTRELLA 78030201 FLUSTRELLA
7809 GYMNOLAEMATA CYCLOST 7809 GYMNOLAEMATA CYCLOST
78090101 CRISIA 78090101 CRISIA
78090102 BICRISIA 78O9O1O2 BICRISIA
7809010201 BICRISIA EDWARDSIANA 78090102 BICRISIA
78090103 FILICRISIA 78090103 FILICRISIA
78120101 HETEROPORA (ECTOP 781201O1 HETEROPORA (ECTOP
7812010199 NAME NOT FOUND 78120101 HETEROPORA (ECTOP
78150401 MEMBRANIPORA 78150401 MEMBRANIPORA
7815040101 MEMBRANIPORA MEMBRAN 78150401 MEMBRANIPORA
78160201 HIPPOTHOA 781602O1 HIPPOTHOA
7816020101 HIPPOTHOA HYALINA 78160201 HIPPOTHOA
78161101 MICROPORELLA 78161101 MICROPORELLA
8104 ASTEROIDEA 8104 ASTEROIDEA
8114040105 HENRICIA LEVIUSCULA 8114040105 HENRICIA LEVIUSCULA
8117 NAME NOT FOUND 811703 ASTERIIDAE
811703 ASTERIIDAE 811703 ASTERIIDAE
8117030409 LEPTASTERIAS HEXACTI 8117030409 LEPTASTERIAS HEXACTI
8117030499 NAME NOT FOUND 81170304 LEPTASTERIAS
8129 OPHIUROIDEA OPHIURID 8129 OPHIUROIDEA OPHIURID
(continued)
214
-------
TABLE B-l (continued)
812903 AMPHIURIDAE 812903 AMPHIURIDAE
8129030299 NAME NOT FOUND 812903O2 AMPHIPHOLIS
8129030302 DIAMPHIODIA OCCIDENT 8129030302 DIAMPHIODIA OCCIDENT
8129030303 DIAMPHIODIA PERIERCT 8129030303 DIAMPHIODIA PERIERCT
81490302 STRONGYLOCENTROTUS 81490302 STRONGYLOCENTROTUS
8149030201 STRONGYLOCENTROTUS D 81490302 STRONGYLOCENTROTUS
8170 HOLOTHUROIDEA 8170 HOLOTHUROIDEA
817206 CUCUMARIIDAE 817206 CUCUMARIIDAE
8172060110 CUCUMARIA MINIATA 8172060110 CUCUMARIA MINIATA
8172060113 CUCUMARIA PSEUDOCURA 8172060113 CUCUMARIA PSEUDOCURA
8172060202 EUPENTACTA QUINQUESE 8172060202 EUPENTACTA QUINQUESE
8178010203 LEPTOSYNAPTA CLARKI 8178010203 LEPTOSYNAPTA CLARKI
84 UROCHORDATA 84 UROCHORDATA
8404040102 CHELYOSOMA PRODUCTUM 8404040102 CHELYOSOMA PRODUCTUM
8406 ASCIDIACEA PLEUROGON 8406 ASCIDIACEA PLEUROGON
840601 STYELIDAE 840601 STYELIDAE
8406020101 PYURA HAUSTOR 8406020101 PYURA HAUSTOR
8406020203 BOLTENIA VILLOSA 8406020203 BOLTENIA VILLOSA
8784010101 GOBIESOX MAEANDRICUS 8784010101 GOBIESOX MAEANDRICUS
88 GNATHOSTOMATA II 88 GNATHOSTOMATA II
8831022401 OLIGOCOTTUS MACULOSU 8831022401 OLIGOCOTTUS MACULOSU
88421221 ULVARIA 88421221 ULVARIA
884213 PHOLIDIDAE (GUNNELS) 884213 PHOLIDIDAE (GUNNELS)
999999 NAME NOT FOUND ER
215
-------
TABLE B-2. TAXONOMIC DICTIONARY FOR INTERTIDAL SOFT SUBSTRATES
07 BACIIilARIOPHYTA 07 BACILLARIOPHYTA
0701 BACILLARIOPHYCEAE 07 BACILLARIOPHYTA <•
0703 BACILLARIOPHYCEAE PE 07 BACILLARIOPHYTA
0801 CHLOROPHYCEAE 0801 CHLOROPHYCEAE
0804010103 PRASIOLA MERIDIONALI 0804010103 PRASIOLA KERIDIONALIS
0805O102 ULOTHRIX 08050102 ULOTHRIX j
08050201 MONOSTROMA 080502O1 MONOSTROMA {
0805020105 MONOSTROMA FUSCDM O8050201 MONOSTROMA |
080503 ULVACEAE 0805O3 ULVACEAE |
08O50303 ENTEROMORPHA 08050303 ENTEROMORPHA j
0805030301 ENTEROMORPHA CLATHRA 0805030301 ENTEROMORPHA CLATHRATA j
0805030302 ENTEROMORPHA COMPRES 0805030302 ENTEROMORPHA COMPRESSA j
0805030306 ENTEROMORPHA LINZA 0805030306 ENTEROMORPHA LINZA j
0805030314 ENTEROMORPHA CRINITA 0805030314 ENTEROMORPHA CRINITA j
0805030317 ENTEROMORPHA INTESTI 0805030317 ENTEROMORPHA INTESTINALIS J
0805030318 ENTEROMORPHA PROLIFE 0805030318 ENTEROMORPHA PROLIFERA |
0805030319 ENTEROMORPHA FLEXUOS 0805030319 ENTEROMORPHA FLEXUOSA |
08050305 ULVA (CHLOROPHYCE 08050305 ULVA (CHLOROPHYCEAE) |
0805030502 ULVA RIGIDA 08050305 ULVA (CHLOROPHYCEAE) |
0805030594 NAME NOT FOUND 08050305 ULVA (CHLOROPHYCEAE) j
0807010202 SPONGOMORPHA COALITA 0807010202 SPONGOMORPHA COALITA
0807010205 SPONGOMORPHA MERTENS 0807010205 SPONGOMORPHA MERTENSII
08070103 UROSPORA 08070103 UROSPORA
0807010301 UROSPORA WORMSKIOLDI 0807010301 UROSPORA WORMSKIOLDII
0807010302 UROSPORA MIRABILIS 0807010302 UROSPORA MIRABILIS
0808010101 CHAETOMORPHA CANNABI 0808010101 CHAETOMORPHA CANNABINA
08080102 CLADOPHORA O80801O2 CLADOPHORA
08080103 RHIZOCLONIUM 08080103 RHIZOCLONIUM
0808010301 RHIZOCLONIUM IMPLEXU 0808010301 RHIZOCLONIUM IMPLEXUM
O808O10302 RHIZOCLONIUM RIPARIU 0808010302 RHIZOCLONIUM RIPARIUM
0809020102 BRYOPSIS PLUMOSA 0809020102 BRYOPSIS PLUMOSA
15 PHAEOPHYTA 15 PHAEOPHYTA
15020109 FELDMANNIA 1502O109 FELDMANNIA
15080201 LAMINARIA 1508O201 LAMINARIA
15090201 DESMARESTIA 1509O201 DESMARESTIA
1510010202 FUCUS DISTICHUS 1510010202 FUCUS DISTICHUS
1510030201 CYSTOSEIRA GEMINATA 1510030201 CYSTOSEIRA GEMINATA
1512010201 PETALONIA FASCIA 1512O102O1 PETALONIA FASCIA
16 RHODOPHYTA 16 RHODOPHYTA
1601 RHODOPHYCEAE 1601 RHODOPHYCEAE
1605010501 SMITHORA NAIADUM 1605010501 SMITHORA NAIADUM
16050202 PORPHYRA 1605O202 PORPHYRA
16080501 PLOCAMIUM (RHODOPH 16080501 PLOCAMIUM (RHODOPHYTA)
16080701 GRACILARIA 16080701 GRACILARIA
1608070102 GRACILARIA VERRUCOSA 16080701 GRACILARIA
(continued)
Starred species or groups are important taxa which were used for cluster
analyses and, in some cases, population parameter analyses.
216
-------
TABLE B-2 (continued)
1608090101 AHNFELTIA PLICATA 1608090101 AHNFELTIA PLICATA
16081002 GIGARTINA 160810O2 GIGARTINA
1608100203 GIGARTINA PAPILLATA 16081OO2 GIGARTINA
16081003 IRIDAEA 16081003 IRIDAEA
1609110101 CHOREOCOLAX POLYSIPH 1609110101 CHOREOCOLAX POLYSIPHONIAE
1610020203 RHODYMENIA PALMATA 1610020203 RHODYMENIA PALMATA
16110101 ANTITHAMNION 16110101 ANTITHAMNION
16110104 CERAMIUM 16110104 CERAMIUM
1611010408 CERAMIUM PACIPICUM 1611010408 CERAMIUM PACIFICUM
1611010413 CERAMIUM WASHINGTONI 1611010413 CERAMIUM WASHINGTONIENSE
1611010489 NAME NOT FOUND 16110104 CERAMIUM
1611010495 NAME NOT FOUND 16110104 CERAMIUM
1611010499 NAME NOT FOUND 16110104 CERAMIUM
16110113 MICROCLADIA 16110113 MICROCLADIA
1611O113O1 MICROCLADIA BOREALIS 16110113 MICROCLADIA
161102 DELESSERIACEAE 161102 DELESSERIACEAE
1611020901 GONIMOPHYLLUM SKOTTS 1611020901 GONIMOPHYLLUM SKOTTSBERGII
1611021501 POLYNEURA LATISSIMA 1611021501 POLYNEURA LATISSIMA
16110224 HYMENENA 16110224 HYMENENA
16110401 POLYSIPHONIA 16110401 POLYSIPHONIA
1611040101 POLYSIPHONIA HENDRYI 1611040101 POLYSIPHONIA HENDRYI
1611040103 POLYSIPHONIA PACIFIC 1611040103 POLYSIPHONIA PACIFICA
1611040114 POLYSIPHONIA PANICUL 1611040114 POLYSIPHONIA PANICULATA
1611O402 PTEROSIPHONIA 16110402 PTEROSIPHONIA
1611040202 PTEROSIPHONIA BIPINN 1611040202 PTEROSIPHONIA BIPINNATA
1611040203 PTEROSIPHONIA DENDRO 1611040203 PTEROSIPHONIA DENDROIDEA
16110406 ODONTHALIA 16110406 ODONTHALIA
1611040603 ODONTHALIA FLOCCOSA 16110406 ODONTHALIA
16110412 HERPOSIPHONIA 1611O412 HERPOSIPHONIA
33 ANTHOPHYTA II 33 ANTHOPHYTA II
33260101 ZOSTERA 33260101 ZOSTERA j*
3326010101 ZOSTERA MARINA 33260101 ZOSTERA |
36 PORIFERA 36 PORIFERA
37 CNIDARIA 37 CNIDARIA
3701 HYDROZOA 3701 HYDROZOA
37O2 HYDROZOA HYDROIDA 3702 HYDROZOA HYDROIDA
37O30601 CORYNE 370306O1 CORYNE
37O40102 OBELIA 370401O2 OBELIA
37040711 AGLAOPHENIA 37040711 AGLAOPHENIA
3740 ANTHOZOA 3740 ANTHOZOA
3758 ZOANTHARIA ACTINIARI 3758 ZOANTHARIA ACTINIARIA
3758999999 NAME NOT FOUND 3758 ZOANTHARIA ACTINIARIA
37590401 HALCAMPA 375904 HALCAMPIDAE
3759040101 HALCAMPA DECEMTENTAC 375904 HALCAMPIDAE
3759049999 NAME NOT FOUND 375904 HALCAMPIDAE
37600102 ANTHOPLEURA 37600102 ANTHOPLEURA
3760010201 ANTHOPLEURA ELEGANTI 376001O2 ANTHOPLEURA
37600103 EPIACTIS 3760O103 EPIACTIS
(continued)
217
-------
TABLE B-2 (continued)
3760010301
3760060101
39
3901
3914020901
3914020999
3915020103
43
4302010102
4303020208
4306010102
4306010603
43060501
4306050102
4306050199
47
5001
500102
5001020402
50010205
50010208
5001020806
5001020810
5001021801
5001060101
500107
50010701
50010799
5001080101
5001100501
500113
50011301
5001130102
5001130106
5001130198
5001130199
50011302
5001130201
5001130203
5001130205
5001130206
50011303
5001130302
5001130304
5001130305
5001130306
5001130307
5001130901
EPIACTIS PROLIFERA
METRIDIUM SENILE
PLATYHELMINTHES
TURBELLARIA
ITASPIELLA ARMATA
NAME NOT FOUND
PROCERODES PACIFICA
RHYNCHOCOELA
TUBULANUS POLYMORPHU
CEREBRATULUS CALIFOR
EMPLECTONEMA GRACILE
PARANEMERTES PEREGR1
AMPHIPORUS
AMPHIPORUS BIMACULAT
NAME NOT FOUND
NEMATODA
POLYCHAETA
POLYNOIDAE
ARCTONOE VITTATA
EUNOE
HARMOTHOE
HARMOTHOE IMBRICATA
HARMOTHOE LUNULATA
LEPIDASTHEN1A BERKEL
PHOLOE MINUTA
PISIONIDAE
PISIONE
NAME NOT FOUND
PALEANOTUS BELLIS
PAREURYTHOE BOREALIS
PHYLLODOCIDAE
ANAITIDES/PHYLLODOCE
ANAITIDES GROENLANDI
ANAITIDES MACULATA
NAME NOT FOUND
NAME NOT FOUND
ETEONE
ETEONE CALIFORNICA
ETEONE PACIFICA
ETEONE LONGA
ETEONE TUBERCULATA
EULALIA
EULALIA SANGUINEA
EULALIA BILINEATA
EULALIA MACROCEROS
EULALIA QUADRIOCULAT
EULALIA NIGRIMACULAT
HESIONURA COINEAUI
37600103 EPIACTIS
3760060101 METRIDIUM SENILE
39 PLATYHELMINTHES
3901 TURBELLARIA
39140209 ITASPIELLA
39140209 ITASPIELLA
3915020103 PROCERODES PACIFICA
43 RHYNCHOCOELA
4302010102 TUBULANUS POLYMORPHUS
4303020208 CEREBRATULUS CALIFORNIENSIS
4306010102 EMPLECTONEMA GRACILE
4306010603 PARANEMERTES PEREGRINA
43060501 AMPHIPORUS
43060501 AMPHIPORUS
43060501 AMPHIPORUS
47 NEMATODA
5001 POLYCHAETA
5001O2 POLYNOIDAE
5001020402 ARCTONOE VITTATA
50010205 EUNOE
50010208 HARMOTHOE
5001020806 HARMOTHOE IMBRICATA
5001020810 HARMOTHOE LUNULATA
5001021801 LEPIDASTHENIA BERKELEYAE
5001060101 PHOLOE MINUTA
500107 PISIONIDAE
500107 PISIONIDAE
500107 PISIONIDAE
5001080101 PALEANOTUS BELLIS
5001100501 PAREURYTHOE BOREALIS
500113 PHYLLODOCIDAE
50011301 ANAITIDES/PHYLLODOCE
5001130102 ANAITIDES GROENLANDICA
5001130106 ANAITIDES MACULATA
50011301 ANAITIDES/PHYLLODOCE
50011301 ANAITIDES/PHYLLODOCE
50011302 ETEONE
5001130201 ETEONE CALIFORNICA
5001130203 ETEONE PACIFICA
5001130205 ETEONE LONGA
5001130206 ETEONE TUBERCULATA
50011303 EULALIA
5001130302 EULALIA SANGUINEA
5001130304 EULALIA BILINEATA
5001130305 EULALIA MACROCEROS
5001130306 EULALIA QUADRIOCULATA
5001130307 EULALIA NIGRIMACULATA
5001130901 HESIONURA COINEAUI
i *
(continued)
218
-------
TABLE B-2 (continued)
500121 HESIONIDAE 500121 HESIONIDAE
500121O1 GYPTIS 50012101 GYPTIS
5001210102 GYPTIS BKEVIPALPA 50012101 GYPTIS
5001210401 OPHIODROMUS PUGETTEN 5001210401 OPHIODROMUS PUGETTENSIS
5001210501 KEFERSTEINIA CIRRATA 5001210501 KEFERSTEINIA CIRRATA
5001210801 MICROPODARKE DUBIA 5001210801 MICROPODARKE DUBIA
5001219899 NAME NOT FOUND 500121 HESIONIDAE
5001219999 NAME NOT FOUND 500121 HESIONIDAE
500122 PILARGIDAE 500122 PILARGIDAE
5001220301 PILARGIS BERKELEYAE 500122 PILARGIDAE
500123 SYLLIDAE 500123 SYLLIDAE
5O012301 AUTOLYTUS 50012301 AUTOLYTUS
50012303 SYLLIS 50012303 SYLLIS
50012305 TYPOSYLLIS 50012305 TYPOSYLLIS
5001230501 TYPOSYLLIS ALTERNATA 5001230501 TYPOSYLLIS ALTERNATA
5001230502 TYPOSYLLIS ARMILLARI 5001230502 TYPOSYLLIS ARMILLARIS
5001230509 TYPOSYLLIS ADAMANTEA 5001230509 TYPOSYLLIS ADAMANTEA
5001230510 TYPOSYLLIS HARTI 5001230510 TYPOSYLLIS HARTI
5001230511 TYPOSYLLIS HYALINA 5001230511 TYPOSYLLIS HYALINA
50012307 EXOGONE 50012307 EXOGONE j *
5001230702 EXOGONE GEMMIFERA 5001230702 EXOGONE GEMMIFERA |
5001230703 EXOGONE LOUKEI 5001230703 EXOGONE LOUREI j
5001230706 EXOGONE VERUGERA 5001230706 EXOGONE VERUGERA |
50012308 SPHAEROSYLLIS 50012308 SPHAEROSYLLIS
5001230805 SPHAEROSYLLIS PERIFE 5001230805 SPHAEROSYLLIS PERIFERA
5001230806 SPHAEROSYLLIS BRANDH 5001230806 SPHAEROSYLLIS BRANDHORSTI
5001230901 BRANIA BKEVIPHARYNGE 5001230901 BRANIA BKEVIPHARYNGEA
5001231002 LANGERHANSIA HETEROC 5001231002 LANGERHANSIA HETEROCHAETA
5001231503 SYLLIDES LONGOCIRRAT 50012315 SYLLIDES
5001231599 NAME NOT FOUND 50012315 SYLLIDES
5001231604 STREPTOSYLLIS LATIPA 5001231604 STREPTOSYLLIS LATIPALPA
5001239999 NAME NOT FOUND 500123 SYLLIDAE
500124 NEREIDAE 500124 NEREIDAE
5001240101 CERATONEREIS PAUCIDE 5001240101 CERATONEREIS PAUCIDENTATA
50O12403 NEANTHES 50012403 NEANTHES
5OO1240301 NEANTHES BRANDTI 50012403 NEANTHES
50012404 NEREIS 50012404 NEREIS \*
5001240403 NEREIS PELAGICA 5001240403 NEREIS PELAGICA \
5001240404 NEREIS PROCERA 5001240404 NEREIS PROCERA
5001240405 NEREIS VEXILLOSA 5001240405 NEREIS VEXILLOSA
5001240406 NEREIS ZONATA 5001240406 NEREIS ZONATA
50O12405 PLATYNEREIS 50012405 PLATYNEREIS
5001240501 PLATYNEREIS BICANALI 5001240501 PLATYNEREIS BICANALICULATA
5001240503 PLATYNEREIS DUMERILI 5001240503 PLATYNEREIS DUMERILII
5001240701 MICRONEREIS NANAIMOE 5001240701 MICRONEREIS NANAIMOENSIS
50012501 NEPHTYS 50012501 NEPHTYS
5001250103 NEPHTYS CAECA 5001250103 NEPHTYS CAECA
5001250113 NEPHTYS CALIFORNIENS 5001250113 NEPHTYS CALIFORNIENSIS
(continued)
219
-------
TABLE B-2 (continued)
5001250119 NEPHTYS CAECOIDES 5001250119 NEPHTYS CAECOIDES
5001250199 NAME NOT FOUND 50O12501 NEPHTYS
5001260201 SPHAERODOROPSIS MINU 5001260201 SPHAERODOROPSIS MINDTA
50O127 GLYCERIDAE 500127 GLYCERIDAE
50012701 GLYCERA (POLYCHAE 50012701 GLYCERA (POLYCHAETA)
5001270103 GLYCERA TESSELATA 5001270103 GLYCERA TESSELATA
5001270104 GLYCERA AMERICANA 5001270104 GLYCERA AMERICANA
5001270201 HEMIPODUS BOREALIS 5001270201 HEMIPODUS BOREALIS *
50012801 GLYCINDE 50012801 GLYCINDE
5001280101 GLYCINDE PICTA 5001280101 GLYCINDE PICTA *
5001280103 GLYCINDE ARMIGERA 5OO1280103 GLYCINDE ARMIGERA
5001280203 GONIADA BRUNNEA 5001280203 GONIADA BRUNNEA
500129 ONUPHIDAE 500129 ONUPHIDAE
50012901 ONUPHIS 50012901 ONUPHIS
5001290101 ONUPHIS CONCHYLEGA 5001290101 ONUPHIS CONCHYLEGA
5001290.103 ONUPHIS IRIDESCENS 5001290103 ONUPHIS IRIDESCENS
5001290106 ONUPHIS STIGMATIS 5001290106 ONUPHIS STIGMATIS
5001290299 NAME NOT FOUND 50012902 DIOPATRA
500130 EUNICIDAE 500130 EUNICIDAE
500131 LUMBRINERIDAE 500131 LUMBRINERIDAE
50013101 LUMBRINEREIS 50013101 LUMBRINEREIS
5001310106 LUMBRINEREIS ZONATA 5001310106 LUMBRINEREIS ZONATA
5001310108 LUMBRINEREIS INFLATA 5001310108 LUMBRINEREIS INFLATA
5001310112 LUMBRINEREIS BREVICI 5001310112 LUMBRINEREIS BREVTCIRRA
500136 DORVILLEIDAE 500136 DORVILLEIDAE
50013601 DORVILLEA/SCHISTOMER 50013601 DORVILLEA/SCHISTOMERINGOS j*
5001360103 DORVILLEA JAPONICA 5001360103 DORVILLEA JAPONICA |
5001360104 DORVILLEA RUDOLPHI 5001360104 DORVILLEA RUDOLPHI j
5001360105 DORVILLEA ANNULATA 5001360105 DORVILLEA ANNULATA 1
5001360201 PROTODORVILLEA GRACI 5001360201 PROTODORVILLEA GRACILIS
500140 ORBINIIDAE 500140 ORBINIIDAE
50O14001O2 HAPLOSCOLOPLOS ELONG 500140O1O2 HAPLOSCOLOPLOS ELONGATUS
50014002 NAINERIS 50014002 NAINERIS
5001400201 NAINERIS DENDRITICA 5001400201 NAINERIS DENDRITICA
5001400202 NAINERIS QUADRICUSPI 5001400202 NAINERIS QUADRICUSPIDA
5001400204 NAINERIS UNCINATA 5001400204 NAINERIS UNCINATA
50014003 SCOLOPLOS 50014003 SCOLOPLOS
5001400301 SCOLOPLOS ARMIGER 5001400301 SCOLOPLOS ARMIGER *
5001400302 SCOLOPLOS PUGETTENSI 5001400302 SCOLOPLOS PUGETTENSIS *
500141 PARAONIDAE 500141 PARAONIDAE
50014102 ARICIDEA 50014102 ARICIDEA
5001410215 NAME NOT FOUND 50014102 ARICIDEA
50014103 PARAONIS 50014103 PARAONIS
5001410301 PARAONIS GRACILIS 5001410301 PARAONIS GRACILIS
5001410304 PARAONIS LYRA 5001410304 PARAONIS LYRA
5001410501 PARAONELLA PLATYBRAN 5001410501 PARAONELLA PLATYBRANCHIA *
500143 SPIONIDAE 50O143 SPIONIDAE
5001430201 LAONICE CIRRATA 5001430201 LAONICE CIRRATA
(continued)
220
-------
TABLE B-2 (continued)
50014303 NERINE 50014303 NERINE
5001430303 NERINE FOLIOSA 50014303 NERINE
50014304 POLYDORA 50014304 POLYDORA
5001430402 POLYDORA SOCIALIS 5001430402 POLYDORA SOCIALIS
5001430404 POLYDORA CAULLERYI 5001430404 POLYDORA CAULLERYI
5001430408 POLYDORA QUADRILOBAT 5001430408 POLYDORA QUADRILOBATA
5001430411 POLYDORA LIGNI 5001430411 POLYDORA LIGNI
5001430417 POLYDORA PYGIDIALIS 5001430417 POLYDORA PYGIDIALIS
5001430493 NAME NOT FOUND 50014304 POLYDORA
5001430494 NAME NOT FOUND 50014304 POLYDORA
5001430495 NAME NOT FOUND 500143O4 POLYDORA
5001430496 NAME NOT FOUND 50014304 POLYDORA
5001430497 NAME NOT FOUND 50014304 POLYDORA
50014305 PRIONOSPIO 50O14305 PRIONOSPIO
5001430502 PRIONOSPIO CIRRIFERA 5001430502 PRIONOSPIO CIRRIFERA
5001430504 PRIONOSPIO PINNATA 5001430504 PRIONOSPIO PINNATA
5001430506 PRIONOSPIO STEENSTRU 5001430506 PRIONOSPIO STEENSTRUPI
50014307 SPIO 50014307 SPIO
5001430701 SPIO FILICORNIS 5001430701 SPIO FILICORNIS
5001430703 SPIO CIRRIFERA 5001430703 SPIO CIRRIFERA
50014308 BOCCARDIA 50014308 BOCCARDIA
5O0143O801 BOCCARDIA COLUMBIANA 5001430801 BOCCARDIA COLUMBIANA
5001430803 BOCCARDIA PROBOSCIDE 5O0143O803 BOCCARDIA PROBOSCIDEA
5001430806 BOCCARDIA HAMATA 5001430806 BOCCARDIA HAMATA
50014310 SPIOPHANES 50014310 SPIOPHANES
5001431001 SPIOPHANES BOMBYX 5001431001 SPIOPHANES BOMBYX
5001431003 SPIOPHANES CIRRATA 5001431003 SPIOPHANES CIRRATA
5001431004 SPIOPHANES BERKELEYO 5001431004 SPIOPHANES BERKELEYORUM
50014313 PYGOSPIO 50014313 PYGOSPIO
5001431302 PYGOSPIO ELEGANS 50014313 PYGOSPIO
5O014314 MALACOCEROS 5O014314 MALACOCEROS
5001431401 MALACOCEROS GLUTAEUS 50014314 MALACOCEROS
5001431501 PSEUDOPOLYDORA KEMPI 5001431501 PSEUDOPOLYDORA KEMPT
5001431701 PARAPRIONOSPIO PINNA 5001431701 PARAPRIONOSPIO PINNATA
5001431801 STREBLOSPIO BENEDICT 5001431801 STREBLOSPIO BENEDICTI
50014320 SCOLELEPIS 50014320 SCOLELEPIS
5001432001 SCOLELEPIS SQUAMATA 50014320 SCOLELEPIS
5001432097 NAME NOT FOUND 50014320 SCOLELEPIS
5001432099 NAME NOT FOUND 50014320 SCOLELEPIS
5O014401 MAGELONA 50014401 MAGELONA
5001440101 MAGELONA JAPONICA 5001440101 MAGELONA JAPONICA
5001440103 MAGELONA PITELKAI 5001440103 MAGELONA PITELKAI
500149 CHAETOPTERIDAE 500149 CHAETOPTERIDAE
5001490302 SPIOCHAETOPTERUS COS 5001490302 SPIOCHAETOPTERUS COSTARUM
5001490401 MESOCHAETOPTERUS TAY 5001490401 MESOCHAETOPTERUS TAYLORI
500150 CIRRATULIDAE 500150 CIRRATULIDAE
50015001 CIRRATULUS 50015001 CIRRATULUS
5001500101 CIRRATULUS CIRRATUS 50015001 CIRRATULUS
(continued)
221
-------
TABLE B-2 (continued)
50015O03
5001500302
50015O04
5O015O04O1
5001500402
5001580202
50015803
5001580301
50015805
5001580501
50O160
50016O01
5001600101
50016O03
5001600302
5001600303
50016004
5001600401
5001609999
500162
50016201
5001620101
5001620102
5001620104
500162O3O1
500163
5001630302
5001630802
50016311
5001631101
5001640102
500166O2O2
500167
5001670201
5001670302
500168
5001680201
500168O4
5001680601
5001680701
5001680702
5001680710
500168O8
5001680898
5001680899
50016810
5001681001
5001681601
THARYX
THARYX MULTIFILIS
CHAETOZONE
CHAETOZONE SETOSA
CHAETOZONE GRACILIS
ARMANDIA BREVIS
OPHELIA
OPHELIA LIMACINA
THORACOPHELIA
THORACOPHELIA MUCRON
CAPITELLIDAE
CAPITELLA
CAPITELLA CAPITATA
NOTOMASTUS
NOTOMASTUS TENUIS
NOTOMASTUS LINEATUS
MEDIOMASTUS
MEDIOMASTUS AMBISETA
NAME NOT POUND
ARENICOLIDAE
ABARENICOLA
ABARENICOLA CLAPARED
ABARENICOLA PACIFICA
ABARENICOLA OCEANICA
BRANCHIOMALDANE VICE
MALDANIDAE
MALDANE GLEBIPEX
AXIOTHELLA RUBROCINC
EUCLYMENE
EUCLYMENE DELINEATA
OWENIA FUSIFORMIS
CISTENTDES GRANULATA
AMPHARETIDAE
AMPHARETE ARCTICA
AMPHICTEIS GLABRA
TEREBELLIDAE
EUPOLYMNIA HETEROBRA
NEOAMPHITRITE
NICOLEA ZOSTERICOLA
PISTA CRISTATA
PISTA FASCIATA
NAME NOT FOUND
POLYCIRRUS
NAME NOT FOUND
NAME NOT FOUND
THELEPUS
THELEPUS CRISPUS
LYSILLA LOVENI
50015003 THARYX
5O015O03 THARYX
50015004 CHAETOZONE
5001500401 CHAETOZONE SETOSA
5001500402 CHAETOZONE GRACILIS
5001580202 ARMANDIA BREVIS
5O015803 OPHELIA
5O0158O3 OPHELIA
50015805 THORACOPHELIA
50015805 THORACOPHELIA
500160 CAPITELLIDAE
50016001 CAPITELLA
50016001 CAPITELLA
50O16003 NOTOMASTUS
50O16O03O2 NOTOMASTUS TENUIS
5001600303 NOTOMASTUS LINEATUS
50O16004 MEDIOMASTUS
50O16004 MEDIOMASTUS
500160 CAPITELLIDAE
50O162 ARENICOLIDAE
50O16201 ABARENICOLA
50O16201O1 ABARENICOLA CLAPAREDI
5001620102 ABARENICOLA PACIFICA
5001620104 ABARENICOLA OCEANICA
5001620301 BRANCHIOMALDANE VICENTE
500163 MALDANIDAE
5001630302 MALDANE GLEBIFEX
5001630802 AXIOTHELLA RUBROCINCTA
50O16311 EUCLYMENE
50016311 EUCLYMENE
5001640102 OWENIA FUSIFORMIS
5OO1660202 CISTENIDES GRANULATA
50O167 AMPHARETIDAE
5001670201 AMPHARETE ARCTICA
5001670302 AMPHICTEIS GLABRA
50O168 TEREBELLIDAE
5001680201 EUPOLYMNIA HETEROBRANCHIA
50O16804 NEOAMPHITRITE
5001680601 NICOLEA ZOSTERICOLA
5001680701 PISTA CRISTATA
5001680702 PISTA FASCIATA
50016807 PISTA
50O16808 POLYCIRRUS
50O16808 POLYCIRRUS
50016808 POLYCIRRUS
50016810 THELEPUS
50O16810 THELEPUS
5001681601 LYSILLA LOVENI
(continued)
222
-------
TABLE B-2 (continued)
500170 SABELLIDAE 500170 SABELLIDAE
50017001 CHONE 50017001 CHONE
5001701301 FABRICIA SABELLA 5001701301 FABRICIA SABELLA
5001701501 MANAYUNKIA PACIFICA 5001701501 MANAYUNKIA PACIFICA
5001701502 MANAYUNKIA AESTUARIN 5001701502 MANAYUNKIA AESTUARINA
50017017 JASMINETRA 50017O17 JASMINEIRA
500173 SERPULIDAE 500173 SERPULIDAE
5001730401 SERPULA VERMICULARIS 5001730401 SERPULA VERMICULARIS
50017305 SPIRORBIS 50017305 SPIRORBIS
5001730602 DEXIOSPIRA SPIRILLUM 5001730602 DEXIOSPIRA SPIRILLUM
5002 ARCHIANNELIDA 5002 ARCHIANNELIDA
500202 PROTODRILIDAE 500202 PROTODRILIDAE
5002020101 PROTODRILUS FLABELLI 5O0202 PROTODRILIDAE
500204 SACCOCIRRIDAE 500204 SACCOCIRRIDAE
50O20401 SACCOCIRRUS 500204 SACCOCIRRIDAE
5002040101 SACCOCIRRUS EROTICUS 500204 SACCOCIRRIDAE
5O020501 POLYGORDIUS 50020501 POLYGORDIUS
5004 OLIGOCHAETA 5004 OLIGOCHAETA
500901 ENCHYTRAEIDAE 500901 ENCHYTRAEIDAE
50O9O2 TUBIFICIDAE 500902 TUBIFICIDAE
5012 HIRUDINEA 5012 HIRUDINEA
5085 MOLLUSCA 5O85 MOLLUSCA
51 GASTROPODA 51 GASTROPODA
5101 GASTROPODA STREPTONE 5101 GASTROPODA STREPTONEURA
510205 ACMAEIDAE 510205 ACMAEIDAE
5102050201 COLLISELLA PELTA 5102050201 COLLISELLA PELTA
5102050207 COLLISELLA STRIGATEL 5102050207 COLLISELLA STRIGATELLA
5102O503 NOTOACMAEA 51020503 NOTOACMAEA
5102050301 NOTOACMAEA SCUTUM 5102050301 NOTOACMAEA SCUTUM
5102050302 NOTOACMAEA PERSONA 5102050302 NOTOACMAEA PERSONA
51021003 MARGARITES/LIRULARIA 51021003 MARGARITES/LIRULARIA
5102100308 MARGARITES PUPILLUS 5102100308 MARGARITES PUPILLUS
5102100310 MARGARITES LIRULATUS 5102100310 MARGARITES LIRULATUS
5102100312 MARGARITES SUCCINCTU 5102100312 MARGARITES SUCCINCTUS
51030903 LACUNA 51030903 LACUNA
5103090302 LACUNA VARIEGATA 51030903 LACUNA
51031001 LITTORINA 51031001 LITTORINA
5103100101 LITTORINA SITKANA 51031OO1O1 LITTORINA SITKANA
5103100104 LITTORINA SCUTULATA 5103100104 LITTORINA SCUTULATA
51032001 ALVINIA 51032001 ALVINIA
51032004 BARLEEIA 51032O04 BARLEEIA
5103200401 BARLEEIA HALIOTIPHIL 51032O04 BARLEEIA
5103210101 NAME NOT FOUND 51032101 ASSIMINEA
5103360101 FARTULUM OCCIDENTALE 5103360101 FARTULUM OCCIDENTALE
51034602 CERITHIOPSIS 51034602 CERITHIOPSIS
5103760406 POLINICES LEWISII 5103760406 POLINICES LEWISII
5105010206 OCENEBRA LURIDA 5105010206 OCENEBRA LURIDA
51050105 NUCELLA 51O501O5 NUCELLA
(continued)
223
-------
TABLE B-2 (continued)
5105010502 NUCELLA LAMELLOSA 5105010502 NUCELLA LAMELLOSA
5105010503 NUCELLA EMARGINATA 5105010503 NUCELLA EMARGINATA
51050108 THAIS 51O50108 THAIS
5105030101 AMPHISSA COLUMBIANA 5105030101 AMPHISSA COLUMBIANA
5105030202 MITRELLA TUBEROSA 5105030202 MITRELLA TUBEROSA
51O504O201 SEARLESIA DIRA 51O504O201 SEARLESIA DIRA
5105080101 NASSARIUS MENDICUS 5105080101 NASSARIUS MENDICUS
5107 GASTROPODA EUTHYNEUR 51O7 GASTROPODA EUTHYNEURA
51080101 ODOSTOMIA 51080101 ODOSTOMIA
51100402 CYLICHNA 51100402 CYLICHNA
5110O601 AGLAJA 51100601 AGLAJA j *
511006O1O1 AGLAJA DIOMEDEUM 51100601 AGLAJA |
51101201 HAMINOEA 51101201 HAMINOEA
5110120101 HAMINOEA VESICULA 5110120101 HAMINOEA VESICULA
5110120103 HAMINOEA VIRESCENS 5110120103 HAMINOEA VIRESCENS
51140201 SIPHONARIA 51140201 SIPHONARIA
5114040101 PHYTIA MYOSOTIS 5114040101 PHYTIA MYOSOTIS
5123 SACOGLOSSA 5123 SACOGLOSSA
5124020101 PHYLLAPLYSIA TAYLORI 5124020101 PHYLLAPLYSIA TAYLORI
5127 NUDIBRANCHIA 5127 NUDIBRANCHIA
5134080101 MELIBE LEONIS 5134080101 MELIBE LEONIS
514101O1 EUBRANCHUS 51410101 EUBRANCHUS
514203 AEOLIDIIDAE 514203 AEOLIDIIDAE
5143 SOLEOLIFERA 5143 SOLEOLIFERA
53 POLYPIACOPHORA 53 POLYPLACOPHORA
5303 NEOLORICATA ISCHNOCH 53 POLYPLACOPHORA
55 BIVALVIA 55 BIVALVIA
5506060101 GLYCYMERIS SUBOBSOLE 5506060101 GLYCYMERIS SUBOBSOLETA
550701 MYTILIDAE 55O701 MYTILIDAE
55O7O1O1 MYTILUS 55070101 MYTILUS |*
5507010101 MYTILUS EDULIS 55O70101 MYTILUS |
5507010201 CRENELLA DECUSSATA 5507010201 CKENELIA DECUSSATA
5507010499 NAME NOT FOUND 55O701O4 MUSCULUS
5507010603 MODIOLUS RECTUS 5507010603 MODIOLUS RECTOS
5507011101 ADULA CALIFORNIENSIS 5507011101 ADULA CALIFORNIENSIS
5507019999 NAME NOT FOUND 55O701 MYTILIDAE
5515010101 PARVILUCINA TENUISCU 5515010101 PARVILUCINA TENUISCULPTA
5515100102 MYSELLA TUMIDA 5515100102 MYSELLA TUMIDA *
551522O1 CLINOCARDIUM 55152201 CLINOCARDIUM
5515220102 CLINOCARDIUM NUTTALL 5515220102 CLINOCARDIUM NUTTALLII *
5515220103 CLINOCARDIUM FUCANUM 5515220103 CLINOCARDIUM FUCANUM
5515250201 TRESUS CAPAX 5515250201 TRESUS CAPAX
5515250202 TRESUS NUTTALLII 5515250202 TRESUS NUTTALLII
5515290101 SILIQUA PATULA 5515290101 SILIQUA PATULA
55153101 MACOMA 55153101 MACOMA
5515310114 MACOMA NASUTA 5515310114 MACOMA NASUTA *
5515310115 MACOMA INQUINATA 5515310115 MACOMA INQUINATA
5515310116 MACOMA BALTHICA 5515310116 MACOMA BALTHICA
(continued)
224
-------
TABLE B-2 (continued)
5515310117 MACOMA SECTA 5515310117 MACOMA SECTA
55153102 TELL1NA 55153102 TELLINA
5515310203 TELLINA CAKPENTERI 5515310203 TELLINA CARPENTERI
5515310204 TELLINA MODESTA * 5515310204 TELLINA MODESTA
5515350101 SEMELE RUBROPICTA 5515350101 SEMELE RUBROPICTA
5515470101 TRANSENNELLA TANTILL 5515470101 TRANSENNELLA TANTILLA
5515470201 SAXIDOMUS GIGANTEA 5515470201 SAXIDOMUS GIGANTEA
5515470501 PSEPHIDIA LORDI 5515470501 PSEPHIDIA LORDI
5515470701 PROTOTHACA STAMINEA 5515470701 PROTOTHACA STAMINEA
5515470801 TAPES PHILIPPINARUM 5515470801 TAPES PHILIPPINARUM
5517010101 CRYPTOMYA CALIFORNIC 5517010101 CRYPTOMYA CALIFORNICA
5517010201 MYA ARENARIA 5517010201 MYA ARENARIA
5517010203 MYA TRUNCATA 5517010203 MYA TRUNCATA
551706 HIATELLIDAE 551706 HIATELLIDAE
5517060201 HIATELLA ARCTICA 5517060201 HIATELLA ARCTICA
5517060401 PANOPEA GENEROSA 5517060401 PANOPEA GENEROSA
5520050202 LYONSIA CALIFORNICA 5520050202 LYONSIA CALIFORNICA
59 ARTHROPODA CHELICERA 59 ARTHROPODA CHELICERATA ARACHNIDA
60 ARTHROPODA PYCNOGONI 60 ARTHROPODA PYCNOGONIDA
61 ARTHROPODA MANDIBULA 61 ARTHROPODA MANDIBULATA CRUSTACEA
6110 OSTRACODA 6110 OSTRACODA
61100 NAME NOT FOUND 6110 OSTRACODA
6111 OSTRACODA MYODOCOPA 6111 OSTRACODA MYODOCOPA
6117 COPEPODA 6117 COPEPODA
6118 COPEPODA CALANOIDA 6118 COPEPODA CALANOIDA
6119 COPEPODA HARPACTICOI 6119 COPEPODA HARPACTICOIDA
6122 COPEPODA MONSTRILLOI 6122 COPEPODA MONSTRILLOIDA
6130 CIRRIPEDIA 6130 CIRRIPEDIA
6134010101 CHTHAMALUS DALLI 6134010101 CHTHAMALUS DALLI
61340201 BALANUS 61340201 BALANUS j
6134020102 BALANUS BALANUS 6134020102 BALANUS BALANUS |
6134020103 BALANUS CARIOSUS 6134020103 BALANUS CARIOSUS |
6134020104 BALANUS CRENATUS 6134020104 BALANUS CRENATUS j
6134020107 BALANUS GLANDULA 6134020107 BALANUS GLANDULA j
61450101 NEBALIA 614501O1 NEBALIA
6145010102 NEBALIA PUGETTENSIS 61450101 NEBALIA
6151 PERACARIDA MYSIDACEA 6151 PERACARIDA MYSIDACEA
6153010301 ARCHAEOMYSIS GREBNIT 6153010301 ARCHAEOMYSIS GREBNITZKII
6153010901 HOLMESIELLA ANOMALA 6153010901 HOLMESIELLA ANOMALA
6153011505 NEOMYSIS MERCEDIS 6153011505 NEOMYSIS MERCEDIS
6154 PERACARIDA CUMACEA 6154 PERACARIDA CUMACEA
615401 LAMPROPIDAE 615401 LAMPROPIDAE
61540101 LAMPROPS 615401 LAMPROPIDAE
6154010104 LAMPROPS CARINATA 615401 LAMPROPIDAE
61540402 EUDORELLA 61540402 EUDORELLA
6154040203 EUDORELLA TRIDENTATA 61540402 EUDORELLA
61540501 DIASTYLIS 61540501 DIASTYLIS
61540502 DIASTYLOPSIS 6154O502 DIASTYLOPSIS
(continued)
225
-------
TABIiE B-2 ( continued )
6154050202 DIASTYLOPSIS TENUIS 61540502 DIASTYLOPSIS
6154050299 NAME NOT POUND 61540502 DIASTYI.OPSIS
61540505 COLUROSTYLIS 61540505 COLUROSTYLIS
61540801 CUMELLA 615408O1 CUMELLA
6154080102 CUMELLA VULGARIS 6154O801 CUMELLA
61540903 LEPTOCUMA/PSEUDOLEPT 61540903 LEPTOCUMA/PSEUDOLEPTOCUMA
6155 PERACARIDA TANAIDACE 6155 PERACARIDA TANAIDACEA
6157 PERACARIDA TANAIDACE 6157 PERACARIDA TANAIDACEA DIKONOPHOR
6157010301 ANATANAIS NORMANI 6157010301 ANATANAIS NORMANI
6157010401 PANCOLUS CALIPORNIEN 6157010401 PANCOLUS CALIFORNIENSIS
61570201 LEPTOCHELIA (TANAI 61570201 LEPTOCHELIA (TANAIDACEA) j*
6157020101 LEPTOCHELIA SAVIGNYI 6157020101 LEPTOCHELIA SAVIGNYI \
6157020103 LEPTOCHELIA DUBIA 6157020103 LEPTOCHELIA DUBIA j
6157020199 NAME NOT POUND 61570201 LEPTOCHELIA (TANAIDACEA) j
6161 PERACARTDA ISOPODA F 6161 PERACARIDA ISOPODA PLABELLIFERA
6161010101 CTROLANA KINCAIDI 6161010101 CIROLANA KINCAIDI *
6361010.102 CIROLANA HARFORDI 6161010102 CIROLANA HARFORDI
6161010199 NAME NOT FOUND 61610101 CIROLANA
6161020199 NAME NOT FOUND 61610201 TECTICEPS
61610203 GNORIMOSPHAEROMA 61610203 GNORIMOSPHAEROMA j *
6161020301 GNORIMOSPHAEROMA ORE 61610203 GNORIMOSPHAEROMA |
61610204 EXOSPHAEROMA 61610204 EXOSPHAEROMA
616102O4O1 EXOSPHAEROMA AMPLICA 6161020401 EXOSPHAEROMA AMPLICAUDA
6161020402 EXOSPHAEROMA MEDIA 6161020402 EXOSPHAEROMA MEDIA *
6161020501 DYNAMENELLA SHEAKERI 6161020501 DYNAMENELLA SHEARERI
61610501 LIMNORIA 61610501 LIMNORIA
6161050101 LIMNORIA LIGNORUM 61610501 LIMNORIA
6162 PERACARIDA ISOPODA V 6162 PERACARIDA ISOPODA VALVIPERA
61620202 SYNIDOTEA 61620202 SYNIDOTEA
6162020201 SYNIDOTEA BICUSPIDA 6162020201 SYNIDOTEA BICUSPIDA
6162020205 SYNIDOTEA NODULOSA 6162020205 SYNIDOTEA NODULOSA
6162020210 SYNIDOTEA ANGULATA 6162O2O21O SYNIDOTEA ANGULATA
61620203 IDOTEA 61620203 IDOTEA
6162020301 IDOTEA RESECATA 6162020301 IDOTEA RESECATA
6162020302 TDOTEA WOSNESENSKII 6162020302 IDOTEA WOSNESENSKII
6162020305 IDOTEA OCHOTENSIS 6162020305 IDOTEA OCHOTENSIS
6162020307 IDOTEA ACULEATA 6162020307 IDOTEA ACULEATA
6162020313 IDOTEA MONTEREYENSIS 6162020313 IDOTEA MONTEREYENSIS
6163020101 IANIROPSIS KINCAIDI 6163020101 IAN1ROPSIS KINCAIDI
6163069999 NAME NOT FOUND 616306 JANIRIDAE
616504 BOPYRIDAE 616504 BOPYRIDAE
6165040701 PHYLLODURUS ABDOMINA 616504 BOPYRIDAE
6166020101 ARMADILLONISCUS TUBE 6166020101 ARMADILLONISCUS TUBERCULATUS
6166030101 DETONELLA PAPILLICOR 6166030101 DETONELLA PAPILLICORNIS
6168 PERACARIDA AMPHIPODA 6168 PERACARIDA AMPHIPODA
6169 PERACARIDA AMPHIPODA 6169 GAMMARID AMPHIPOD
6169020111 AMPELISCA AGASSIZI 6169 GAMMARID AMPHIPOD
6169020114 AMPELISCA PUGETICA 6169 GAMMARID AMPHIPOD
(continued)
226
-------
TABLE B-2 (continued)
6169020197 NAME NOT POUND 6169
6169030202 NAME NOT POUND 6169
61690401 AMPHITHOE 6169
6169040116 AMPHITHOE VALIDA 6169
6169040195 NAME NOT POUND 6169
6169060202 AOROIDES COLUMBIAE 6169
6169090101 ATYLUS TRIDENS 6169
6169090105 ATYLUS COLLINGI 6169
6169090108 ATYLUS LEVIDENSUS 6169
6169120201 CALL1OPIUS LAEVIUSCU 6169
6169121001 CALLIOPIELLA PRATTI 6169
616915 COROPHIIDAE 6169
61691502 COROPHIUM 61691502
6169150201 COROPHIUM ACHERUSICU 61691502
6169150203 COROPHIUM CRASSICORN 616915O2
6169150208 COROPHIUM BREVIS 61691502
6169150209 COROPHIUM SALMONIS 61691502
6169150211 COROPHIUM INSIDIOSUM 61691502
6169200101 ACCEDOMOERA VAGOR 6169
61692010 PARAMOERA 61692010
6169201003 PARAMOERA MOHRI 61692010
6169201097 NAME NOT FOUND 61692010
6169201098 NAME NOT FOUND 61692010
61692012 PONTOGENEIA 6169
6169201208 PONTOGENEIA ROSTRATA 6169
6169201297 NAME NOT FOUND 6169
6169201299 NAME NOT POUND 6169
61692101 ANISOGAMMARUS 6169
6169210106 ANISOGAMMARUS PUGETT 6169
6169210109 ANISOGAMMARUS CONFER 6169
61692103 ELASMOPUS 6169
6169210805 MAERA DUBIA 6169
61692110 MELITA (AMPHIPODA 6169
6169211003 MELITA DENTATA 6169
6169211008 MELITA DESDICHADA 6169
6169211099 NAME NOT FOUND 6169
616922 HAUSTORIIDAE 6169
61692201 EOHAUSTORIUS 616922O1
6169220101 EOHAUSTORIUS WASHING 61692201
6169220199 NAME NOT FOUND 616922O1
6169240105 ALLORCHESTES ANGUSTU 6169
61692402 HYALE 6169
6169240201 HYALE RUBRA 6169
6169240204 HYALE PLUMULOSA 6169
6169240207 HYALE GRANDICORNIS 6169
6169240401 PARALLORCHESTES OCHO 6169
61692602 PHOTIS 6169
6169260201 PHOTIS BREVIPES 6169
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
COROPHIUM
COROPHIUM
COROPHIUM
COROPHIUM
COROPHIUM
COROPHIUM
GAMMARID AMPHIPOD
PARAMOERA
PARAMOERA
PARAMOERA
PARAMOERA
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
EOHAUSTORIUS
EOHAUSTORIUS
EOHAUSTORIUS
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
GAMMARID AMPHIPOD
i *
(continued)
227
-------
TABLE B-2 (continued)
61692603
6169260398
6169260399
61692604
6169260401
61692702
6169270202
616934
61693429
6169345201
6169371402
6169371403
6169371498
6169371499
616942
61694209
6169420901
6169420921
6169420927
6169420928
6169420930
6169420987
6169420988
6169420989
6169420999
6169440401
6169500502
616951
61695101
6169510106
6169510108
6169510199
61695104
6169510401
6169510499
6170011005
6171
6171010401
6171010602
61710107
6171010708
6171010710
6175
6179
6179140201
617916
61791605
6179160508
PROTOMEDEIA
NAME NOT FOUND
NAME NOT FOUND
GAMMAROPSIS
GAMMAROPSIS THOMPSON
ISCHYROCERUS
ISCHYROCERUS ANGUIPE
LYSIANASSIDAE
ORCHOMENE
ORCHOMENELLA MINUTA
SYNCHELIDIUM SHOEMAK
SYNCHELIDIUM RECTIPA
NAME NOT FOUND
NAME NOT FOUND
PHOXOCEPHALIDAE
PARAPHOXUS
PARAPHOXUS TRIDENTAT
PARAPHOXUS MILLERI
PARAPHOXUS EPISTOMUS
PARAPHOXUS SPINOSUS
PARAPHOXUS SIMILIS
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
PODOCERUS CRISTATUS
TIRON BIOCULATA
TALITRIDAE
ORCHESTIA
ORCHESTIA TRASKIANA
ORCHESTIA GEORGIANA
NAME NOT FOUND
ORCHESTOIDEA
ORCHESTOIDEA PUGETTE
NAME NOT FOUND
PARATHEMISTO ABYSSOR
PERACARIDA AMPHIPODA
METACAPRELLA KENNERL
TRITELLA PILIMANA
CAPRELLA (AMPHIPO
CAPRELLA IRREGULARIS
CAPRELLA LAEVIUSCULA
EUCARIDA DECAPODA(AR
EUCARIDA DECAPODA PL
BETAEUS HARRIMANI
HIPPOLYTIDAE
HEPTACARPUS
HEPTACARPUS SITCHENS
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
616942 PHOXOCEPHALIDAE
616942 PHOXOCEPHALIDAE
616942 PHOXOCEPHALIDAE
616942 PHOXOCEPHALIDAE
616942 PHOXOCEPHALIDAE
616942 PHOXOCEPHALIDAE
616942 PHOXOCEPHALIDAE
616942 PHOXOCEPHALIDAE
616942 PHOXOCEPHALIDAE
616942 PHOXOCEPHALIDAE
616942 PHOXOCEPHALIDAE
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6169 GAMMARID AMPHIPOD
6170011005 PARATHEMISTO ABYSSORUM HOM.l
6171 PERACARIDA AMPHIPODA CAPRELLIDEA
6171010401 METACAPRELLA KENNERLYI
6171010602 TRITELLA PILIMANA
61710107 CAPRELLA (AMPHIPODA)
6171010708 CAPRELLA IRREGULARIS
6171010710 CAPRELLA LAEVIUSCULA
6175 EUCARIDA DECAPODA(ARTHROPODA)
6179 EUCARIDA DECAPODA PLEOCYEMATA CA
6179140201 BETAEUS HARRIMANI
617916 HIPPOLYTIDAE
61791605 HEPTACARPUS
6179160508 HEPTACARPUS SITCHENSIS
(continued)
228
-------
TABLE B-2 (continued)
6179160510 HEPTACARPUS BKEVIROS 6179160510 HEPTACARPUS BREVTROSTRIS
6179160513 HEPTACARPUS TENUISSI 6179160513 HEPTACARPUS TENUISSIMUS
61791801 PANDALUS 617918O1 PANDALUS
61792201 CRANGON 617922O1 CRANGON
6179220101 CRANGON NIGRICAUDA 6179220101 CRANGON NIGRICAUDA
6179220107 CRANGON FRANCISCORUM 6179220107 CRANGON FRANCISCORUM
6179220111 CRANGON MUNITA 6179220111 CRANGON MUNITA
6179220115 CRANGON MUNITELLA 6179220115 CRANGON MDNITELLA
6179220202 SCLEROCRANGON ALATA 6179220202 SCLEROCRANGON ALATA
618304 CALLIANASSIDAE 618304 CALLIANASSIDAE
6183040101 UPOGEBIA PUGETTENS1S 6183040101 UPOGEBIA PUGETTENSIS
61830402 CALLIANASSA 61830402 CALLIANASSA
6183040204 CALLIANASSA CALIFORN 61830402 CALLIANASSA
618306 PAGURIDAE 618306 PAGURIDAE
61830602 PAGURUS (DECAPODA) 61830602 PAGURUS (DECAPODA)
6183060211 PAGURUS GRANOSIMANUS 6183060211 PAGURUS GRANOSIMANUS
6183060213 PAGURUS HIRSUTIUSCUL 6183060213 PAGURUS HIRSUTIUSCULUS
6184 EUCARIDA DECAPODA PL 6184 EUCARIDA DECAPODA PLEOCYEMATA BR
618701 MAJIDAE 618701 MAJIDAE
6187010101 OREGONIA GRACILIS 6187010101 OREGONIA GRACILIS
61870105 PUGETTIA (DECAPODA 61870105 PUGETTIA (DECAPODA)
6187010503 PUGETTIA GRACILIS 61870105 PUGETTIA (DECAPODA)
6188020101 TELMESSUS CHEIRAGONU 6188020101 TELMESSUS CHEIRAGONUS
61880301 CANCER 61880301 CANCER
6188030101 CANCER PRODUCTUS 6188030101 CANCER PRODUCTUS
6188030104 CANCER MAGISTER 6188030104 CANCER MAGISTER
6188030106 CANCER OREGONENSIS 6188030106 CANCER OREGONENSIS
6189020301 FABIA SUBQUADRATA 6189020301 FABIA SUBQUADRATA
618906 PINNOTHERIDAE 618906 PINNOTHERIDAE
61890604 PINNIXA 61890604 PINNIXA
6189060401 PINNIXA FABA 6189060401 PINNIXA FABA
6189060402 PINNIXA LITTORALIS 6189060402 PINNIXA LITTORALIS
6189060403 PINNIXA OCCIDENTALIS 6189060403 PINNIXA OCCIDENTALIS
61890701 HEMIGRAPSUS 61890701 HEMIGRAPSUS
6189070101 HEMIGRAPSUS NUDUS 6189070101 HEMIGRAPSUS NUDUS
6189070102 HEMIGRAPSUS OREGONEN 6189070102 HEMIGRAPSUS OREGONENSIS
6189070301 SCLEROPLAX GRANULATA 6189070301 SCLEROPLAX GRANULATA
62 INSECTA I 62 INSECTA I
6209010101 ANURIDA MARITIMA 6209010101 ANURIDA MARITIMA
6223 ODONATA 6223 ODONATA
6282 HOMOPTERA 6282 HOMOPTERA
630503 CARABIDAE 6305O3 CARABIDAE
6310 STAPHYLINOIDEA 6310 STAPHYLINOIDEA
631001 STAPHYLINIDAE 6310O1 STAPHYLINIDAE
65 INSECTA IV 65 INSECTA IV
6501 DIPTERA 6501 DIPTERA
650508 CHIRONOMIDAE 65O5O8 CHIRONOMIDAE
6518O2 DOLICHOPODIDAE 651802 DOLICHOPODIDAE
(continued)
229
-------
TABliE B-2 (continued)
654102 TACHINIDAE 654102 TACHINIDAE
65730701 CAMPONOTUS 65730701 CAMPONOTUS
66 ARTHROPODA MANDIBULA 66 ARTHROPODA MANDIBULATA CHILOPODA
72 SIPONCUIiIDA 72 SIPUNCULIDA
7200020104 GOLFINGIA PUGETTENSI 7200020104 GOLFINGIA PUGETTENSIS
7400010101 PRIAPULUS CAUDATUS 7400010101 PRIAPULUS CAUDATUS
77 PHORONIDA 77 PHORONIDA
77OO01 PHORON1DAE 770001 PHORONIDAE
77OOO1O1O2 PHORONOPSIS HARMERI 77O00101 PHORONOPSIS !*
77OO010199 NAME NOT FOUND 7700O101 PHORONOPSIS !
77000102 PHORONIS 77O00102 PHORONIS
7700010201 PHORONIS VANCOUVEREN 77000102 PHORONIS
78 ECTOPROCTA 78 ECTOPROCTA
8117030409 LEPTASTERIAS HEXACTI 8117030409 LEPTASTERIAS HEXACTIS
8120 OPHIUROIDEA 8120 OPHIUROIDEA
812701 OPHIURIDAE 812701 OPHIURIDAE
8129 OPHIUROIDEA OPHIURID 8129 OPHIUROIDEA OPHIURIDA GNATHOPHIU
812903 AMPHIURIDAE 8129O3 AMPHIURIDAE
8129030202 AMPHIPHOLIS SQUAMATA 81290302 AMPHIPHOLIS
8129030299 NAME NOT FOUND 8129O302 AMPHIPHOLIS
8129030303 DIAMPHIODIA PERIERCT 8129030303 DIAMPHIODIA PERIERCTA
81290306 OPHIOPHRAGMUS 81290306 OPHIOPHRAGMUS
8129030601 OPHIOPHRAGMUS URTICA 81290306 OPHIOPHRAGMUS
8136 ECHINOIDEA 8136 ECHINOIDEA
8155010101 DENDRASTER EXCENTRIC 8155010101 DENDRASTER EXCENTRICUS
8170 HOLOTHUROIDEA 817O HOLOTHUROIDEA
817801O2 LEPTOSYNAPTA 81780102 LEPTOSYNAPTA J*
8178010203 LEPTOSYNAPTA CLARKI 81780102 LEPTOSYNAPTA !
8406010505 STYELA GIBBSII 8406010505 STYELA GIBBSII
8717 OSTEICHTHYES 8717 OSTEICHTHYES
88 GNATHOSTOMATA II 88 GNATHOSTOMATA II
8842130206 PHOLIS ORNATA (SADDL 8842130206 PHOLIS ORNATA (SADDLEBACK GUNNEL
8847010101 CLEVELANDIA IOS 8847010101 CLEVELANDIA IDS
999900O1 NAME NOT FOUND ER
999999 NAME NOT FOUND ER
ABIOTIC NAME NOT FOUND ABIOTIC NONE OF THE ABOVE TAXA *
230
-------
TABLE B-3. TAXONOMIC DICTIONARY FOR SDBTIDAL SUBSTRATES
00 NAME NOT FOUND ER
07 BACILLARIOPHYTA 07 BACILLARIOPHYTA
0701 BACILLARIOPHYCEAE 07 BACILLARIOPHYTA
0703 BACILLARIOPHYCEAE PE 07 BACILLARIOPHYTA
070301 DIATOMACEAE 07 BACILLARIOPHYTA
07030501 NAVICULA 07 BACILLARIOPHYTA
07030515 AMPHIPLEURA 07 BACILLARIOPHYTA
08050102 ULOTHRIX 0805O1O2 ULOTHRIX
08050201 MONOSTROMA 08050201 MONOSTROMA
0805020102 MONOSTROMA OXYSPERMU 08050201 MONOSTROMA
0805020105 MONOSTROMA FUSCUM 08050201 MONOSTROMA
0805020106 MONOSTROMA GREVILLEI 08050201 MONOSTROMA
08O50303 ENTEROMORPHA 0805O303 ENTEROMORPHA
0805030306 ENTEROMORPHA LINZA 0805030306 ENTEROMORPHA LIN2A
0805030317 ENTEROMORPHA INTESTI 0805030317 ENTEROMORPHA INTESTINALIS
08050305 ULVA (CHLOROPHYCE 08050305 ULVA (CHLOROPHYCEAE)
0805030501 ULVA FENESTRATA 08050305 ULVA (CHLOROPHYCEAE)
0805030503 ULVA LACTUCA 08050305 ULVA (CHLOROPHYCEAE)
0805030505 ULVA LOBATA 08050305 ULVA (CHLOROPHYCEAE)
0806011599 NAME NOT FOUND 0806O115 ENTOCLADIA
08070102 SPONGOMORPHA 08070102 SPONGOMORPHA
0807O102O2 SPONGOMORPHA COALITA 0807010202 SPONGOMORPHA COALITA
0807010205 SPONGOMORPHA MERTENS 0807010205 SPONGOMORPHA MERTENSII
0807010207 SPONGOMORPHA SPINESC 0807010207 SPONGOMORPHA SPINESCENS
08080101 CHAETOMORPHA 08080101 CHAETOMORPHA
0808010199 NAME NOT FOUND 08080101 CHAETOMORPHA
08080102 CLADOPHORA 08080102 CLADOPHORA
0808010299 NAME NOT FOUND 08080102 CLADOPHORA
0808010302 RHIZOCLONIUM RIPARIU 08O8O10302 RHIZOCLONIUM RIPARIUM
0809010101 DERBESIA MARINA 0809010101 DERBESIA MARINA
08090201 BRYOPSIS 08O9O201 BRYOPSIS
0809020103 BRYOPSIS CORTICULANS 0809O201 BRYOPSIS
0809030201 HALICYSTIS OVALIS 0809030201 HALICYSTIS OVALIS
1501 PHAEOPHYCEAE 1501 PHAEOPHYCEAE*
150201 ECTOCARPACEAE 150201 ECTOCARPACEAE
15020103 ECTOCARPUS 15020103 ECTOCARPUS
1502010404 GIFFORDIA OVATA 1502010404 GIFFORDIA OVATA
15020106 PYLAIELLA 15O20106 PYLAIELLA
15020109 FELDMANNIA 1502O1O9 FELDMANNIA
15O2O2 RALFSIACEAE 15O2O2 RALFSIACEAE
15020203 RALFSIA 150202 RALFSIACEAE
1502020302 RALFSIA FUNGIFORMIS 1502O2 RALFSIACEAE
1502020303 RALFSIA PACIFICA 150202 RALFSIACEAE
1502020399 NAME NOT FOUND 150202 RALFSIACEAE
1502061001 HAPLOGLOIA ANDERSONI 1502061001 HAPLOGLOIA ANDERSONII
(continued)
*
Starred species or groups are important taxa used for clustering.
Plus sign denotes species or groups used only in analyses based on 132 taxa.
231
-------
TABLE B-3 (continued )
1502061202 ANALIPUS JAPONICUS 1502061202 ANALIPUS JAPONICUS
1503010201 STICTYOSIPHON TORTIL 1503010201 STICTYOSIPHON TORTILIS
150401O2 SPHACELARIA 15O40102 SPHACELARIA
1504010204 SPHACELARIA NORRISII 15040102 SPHACELARIA
1507010601 SYRINGODERMA ABYSSIC 15O70106 SYRINGODERMA
1507010699 NAME NOT FOUND 15O701O6 SYRINGODERMA
15O8 PHAEOPHYCEAE LAMINAR 15O8 PHAEOPHYCEAE LAMINARIALES
150802 LAMINARIACEAE 15O802 LAMINARIACEAE
15080201 LAMINARIA 15O80201 LAMINARIA
1508020102 LAMINARIA GROENLANDI 1508020102 LAMINARIA GROENLANDICA
1508020104 LAMINARIA SACCHARINA 1508020104 LAMINARIA SACCHARINA
1508O201O5 LAMINARIA SETCHELLII 1508020105 LAMINARIA SETCHELLII
15O802O107 LAMINARIA FARLOWII 1508020107 LAMINARIA FARLOWII
15080203 COILODESME 15080203 COILODESME
1508020401 AGARUM CRIBROSUM 1508020401 AGARUM CRIBROSUM
1508020501 COSTARIA COSTATA 1508020501 COSTARIA COSTATA
1508020601 CYMATHERE TRIPLICATA 1508020601 CYMATHERE TRIPLICATA
1508020701 HEDOPHYLLUM SESSILE 1508020701 HEDOPHYLLUM SESSILE
15080209 PLEUROPHYCUS 15080209 PLEUROPHYCUS
1508020901 PLEUROPHYCUS GARDNER 15080209 PLEUROPHYCUS
1508021101 PHAEOSTROPHION IRREG 1508021101 PHAEOSTROPHION IRREGULARE
1508030301 NEREOCYSTIS LUETKEAN 1508030301 NEREOCYSTIS LUETKEANA
15080401 ALARIA 15O804O1 ALARIA
1508040103 ALARIA MARGINATA 1508040103 ALARIA MARGINATA
1508040108 ALARIA TENUIFOLIA 1508040108 ALARIA TENUIFOLIA
1508040201 PTERYGOPHORA CALIFOR 1508040201 PTERYGOPHORA CALIFORNICA
1508040301 EGREGIA MENZIESII 1508040301 EGREGIA MENZIESII
1509020.1 DESMAKESTIA 15090201 DESMARESTIA
1509020101 DESMARESTIA ACULEATA 1509020101 DESMARESTIA ACULEATA
1509020102 DESMARESTIA LIGULATA 1509020102 DESMARESTIA LIGULATA
1509020103 DESMARESTIA VIRIDIS 1509020103 DESMARESTIA VIRIDIS
1510010202 FUCUS DISTICHUS 151O0102O2 FUCUS DISTICHUS
1510030201 CYSTOSEIRA GEMINATA 151OO30201 CYSTOSEIRA GEMINATA
1512010301 SCYTOSIPHON LOMENTAR 1512010301 SCYTOSIPHON LOMENTARIA
16 RHODOPHYTA 16 RHODOPHYTA
1601 RHODOPHYCEAE 16 RHODOPHYTA
1604010101 GONIOTRICHUM ALSIDII 16040101 GONIOTRICHUM
1604010199 NAME NOT FOUND 1604O1O1 GONIOTRICHUM
1605010501 SMITHORA NAIADUM 16O501O5O1 SMITHORA NAIADUM
1605020199 NAME NOT FOUND 16050201 BANGIA
16050202 PORPHYRA 16050202 PORPHYRA
16O502O209 PORPHYRA PERFORATA 16050202 PORPHYRA
1605020229 PORPHYRA OCCIDENTALI 16050202 PORPHYRA
16O7O101 ACROCHAETIUM 16070101 ACROCHAETIUM
1607010107 ACROCHAETIUM PACIFIC 16070101 ACROCHAETIUM
16O70104 RHODOCHORTON 1607O104 RHODOCHORTON
1607010402 RHODOCHORTON PURPURE 16070104 RHODOCHORTON
16070602 BONNEMAISONIA 160706O2 BONNEMAISONIA
(continued)
232
-------
TABLE B-3 (continued)
*
1607060299 NAME NOT FOUND 16070602 BONNEMAISONIA
1607070101 GELIDIUM CRINALE 16O7O701O1 GELIDIUM CRINALE
160801 CRUORIACEAE 160801 CRUORIACEAE
1608010199 NAME NOT FOUND 16080101 CRUORIA
1608010299 NAME NOT FOUND 16080102 CRUORIOPSIS
1608010302 PETROCELIS MIDDENDOR 1608010302 PETROCELIS MIDDENDORFFII
16080201 NEOAGARDHIELLA 16080201 NEOAGARDHIELLA |+
1608020101 NEOAGARDHIELLA BAILE 16080201 NEOAGARDHIELLA !
1608O2O201 OPUNTIELLA CALIFORNI 16O80202O1 OPUNTIELLA CALIFORNICA
1608020301 SARCODIOTHECA FURCAT 1608020301 SARCODIOTHECA FURCATA +
16080501 PLOCAMIUM (RHODOPH 16080501 PLOCAMIUM (RHODOPHYTA)
1608050101 PLOCAMIUM TENUE 1608050101 PLOCAMIUM TENUE
1608050102 PLOCAMIUM COCCINEUM 1608050102 PLOCAMIUM COCCINEUM +
1608050103 PLOCAMIUM PACIFICUM 1608050103 PLOCAMIUM PACIFICUM
1608050104 PLOCAMIUM VIOLACIUM 160805O1O4 PLOCAMIUM VIOLACIUM
1608050195 NAME NOT FOUND 16080501 PLOCAMIUM (RHODOPHYTA)
16080502 RHODOPHYLLIS/PLOCAMI 16080502 RHODOPHYLLIS/PLOCAMIOCOLAX
1608O701 GRACILARIA 16080701 GRACILARIA
1608070102 GRACILARIA VERRUCOSA 16080701 GRACILARIA
16080702 GRACILARIOPSIS 16080702 GRACILARIOPSIS j+
1608070201 GRACILARIOPSIS SJOES 16080702 GRACILARIOPSIS j
160809 PHYLLOPHORACEAE 160809 PHYLLOPHORACEAE
16080901 AHNFELTIA 160809O1 AHNFELTIA
1608090101 AHNFELTIA PLICATA 1608090101 AHNFELTIA PLICATA
1608090102 AHNFELTIA GIGARTINOI 1608090102 AHNFELTIA GIGARTINOIDES
1608090301 STENOGRAMME INTERRUP 1608090301 STENOGRAMME INTERRUPTA
16080904 GYMNOGONGRUS 16080904 GYMNOGONGRUS
1608090402 GYMNOGONGRUS LEPTOPH 16080904 GYMNOGONGRUS
16O810 GIGARTINACEAE 16081O GIGARTINACEAE
16081002 GIGARTINA 16081002 GIGARTINA
1608100203 GIGARTINA PAPILLATA 16O81OO2O3 GIGARTINA PAPILLATA
1608100204 GIGARTINA AGARDHII 1608100204 GIGARTINA AGARDHII
1608100209 GIGARTINA HARVEYANA 1608100209 GIGARTINA HARVEYANA
16081003 IRIDAEA 16O81O03 IRIDAEA
16O81O0301 IRIDAEA CORDATA 1608100301 IRIDAEA CORDATA
1608100304 IRIDAEA HETEROCARPA 1608100304 IRIDAEA HETEROCARPA
1608100305 IRIDAEA LINEAKE 16081O03O5 IRIDAEA LINEARE
16081004 RHODOGLOSSUM 16081O04 RHODOGLOSSUM
1608100401 RHODOGLOSSUM AFFINE 1608100401 RHODOGLOSSUM AFFINE
1608100402 RHODOGLOSSUM CALIFOR 1608100402 RHODOGLOSSUM CALIFORNICUM
1608100404 RHODOGLOSSUM ROSEUM 1608100404 RHODOGLOSSUM ROSEUM
16081201 SCHIZYMENIA 16081201 SCHIZYMENIA
1608120102 SCHIZYMENIA EPIPHYTI 16081201 SCHIZYMENIA
1609 RHODOPHYCEAE FLORIDE 1609 RHODOPHYCEAE FLORIDEOPHYCIDAE CR
160901 SQUAMARIACEAE 16O901 SQUAMARIACEAE
16090103 PEYSSONELIA 16090103 PEYSSONELIA
1609010301 PEYSSONELIA PACIFICA 16O901O3 PEYSSONELIA
1609020101 DILSEA CALIFORNICA 1609020101 DILSEA CALIFORNICA
(continued)
233
-------
TABLE B-3 (continued )
»
1609020202 PIKEA ROBUSTA 16090202 PIKEA
1609020299 NAME NOT FOUND 16O902O2 PIKEA
16O90204 FARLOWIA 16O90204 FAKLOWIA
16O902O701 THUKETELIiOPSIS PEGGI 1609020701 THURETELLOPSIS PEGGIANA
1609050101 ENDOCLADIA MURICATA 1609050101 ENDOCLADIA MURICATA
16090601 HILDENBRANDIA (ALG 16O906O1 HILDENBRANDIA (ALGAE)
1609060101 HILDENBRANDIA OCCIDE 16O906O1 HILDENBRANDIA (ALGAE)
160907 CORALLINACEAE 16O907 CORALLINACEAE
16O907O3 CORALLINA 16090703 CORALLINA
16090707 LITHOTHAMNION 16O90707 LITHOTHAMNION
1609070701 LITHOTHAMNION CALIFO 16090707 LITHOTHAMNION
16090709 MESOPHYLLOM 16090709 MESOPHYLLUM
1609070902 MESOPHYLLUM CONCHATU 16090709 MESOPHYLLUM
1609071303 CLATHROMOKPHUM PARCU 1609071303 CLATHROMORPHUM PARCUM
160907.15 BOSSIELLA 16090715 BOSSIELLA
1609071504 BOSSIELLA ORBIGNIANA 16O90715 BOSSIELLA
1609071505 BOSSIELLA PLUMOSA 16090715 BOSSIELLA
1609071701 CALLIARTHRON TUBERCU 1609071701 CALLIARTHRON TUBERCULOSUM
160909 CRYPTONEMIACEAE 16O909 CRYPTONEMIACEAE
16090901 CRYPTONEMIA 16090901 CRYPTONEMIA !+
1609090101 CRYPTONEMIA OBOVATA 1609090101 CRYPTONEMIA OBOVATA
1609090102 CRYPTONEMIA OVALIFOL 1609090102 CRYPTONEMIA OVALIFOLIA
1609090103 CRYPTONEMIA BOREALIS 1609090103 CRYPTONEMIA BOREALIS
160909O2 GRATELOUPIA 16090902 GRATELOUPIA
1609090201 GRATELOUPIA DORYPHOR 16090902 GRATELOUPIA
160909O4 PRIONITIS 16090904 PRIONITIS
16O9O9O401 PRIONITIS LANCEOLATA 1609090401 PRIONITIS LANCEOLATA
1609090402 PRIONITIS LYALLII 1609090402 PRIONITIS LYALLII
16090905 HALYMENIA 16090905 HALYMENIA
1609090501 HALYMENIA COCCINEA 1609090501 HALYMENIA COCCINEA
1609090502 HALYMENIA CALIFORNIC 1609090502 HALYMENIA CALIFORNICA
1609090503 HALYMENIA SCHIZYMENI 16O9O9O5O3 HALYMENIA SCHIZYMENIOIDES
1609090599 NAME NOT FOUND 16090905 HALYMENIA
16090999 NAME NOT FOUND 160909 CRYPTONEMIACEAE
1609099999 NAME NOT FOUND 160909 CRYPTONEMIACEAE
16O910 KALLYMENIACEAE 160910 KALLYMENIACEAE
16091001 CALLOCOLAX 16O91001 CALLOCOLAX
16091002 CALLOPHYLLIS 16091O02 CALLOPHYLLIS
1609100202 CALLOPHYLLIS EDENTAT 16O91O0202 CALLOPHYLLIS EDENTATA
1609100203 CALLOPHYLLIS FLABELL 1609100203 CALLOPHYLLIS FLABELLULATA *
1609100204 CALLOPHYLLIS HAENOPH 1609100204 CALLOPHYLLIS HAENOPHYLLA
1609100206 CALLOPHYLLIS PINNATA 1609100206 CALLOPHYLLIS PINNATA
1609100208 CALLOPHYLLIS FIRMA 1609100208 CALLOPHYLLIS FIRMA
1609100209 CALLOPHYLLIS THOMPSO 1609100209 CALLOPHYLLIS THOMPSONII
1609100299 NAME NOT FOUND 16091002 CALLOPHYLLIS
1609100302 EUTHORA FRUTICULOSA 1609100302 EUTHORA FRUTICULOSA
16091007 ERYTHROPHYLLUM 16091007 ERYTHROPHYLLUM
1609110101 CHOREOCOLAX POLYSIPH 1609110101 CHOREOCOLAX POLYSIPHONIAE
(continued)
234
-------
TABLE B-3 (continued)
16091301 CONSTANTINEA 16091301 CONSTANTINEA
1609130101 CONSTANTINEA ROSA-MA 1609130101 CONSTANTINEA ROSA-MARINA
1609130102 CONSTANTINEA SIMPLEX 1609130102 CONSTANTINEA SIMPLEX
1609130103 CONSTANTINEA SUBULIF 1609130103 CONSTANTINEA SUBULIFERA
16091302 WEEKSIA 16091302 WEEKSIA
1609130201 WEEKSIA RETICULATA 1609130201 WEEKSIA RETICULATA
1609130203 WEEKSIA DIGITATA 1609130203 WEEKSIA DIGITATA
16100202 RHODYMENIA 16100202 RHODYMENIA j+
1610020202 RHODYMENIA PACIFICA 1610020202 RHODYMENIA PACIFICA |
1610020203 RHODYMENIA PALMATA 1610020203 RHODYMENIA PALMATA
1610020204 RHODYMENIA PERTUSA 1610020204 RHODYMENIA PERTUSA
1610020205 RHODYMENIA STIPITATA 1610020205 RHODYMENIA STIPITATA
161OO20401 BOTRYOCLADIA PSEUDOD 1610020401 BOTRYOCLADIA PSEUDODICHOTOMA
1610020501 HALOSACCION GLANDIFO 1610020501 HALOSACCION GLANDIFORME
16100206 FAUCHEA 16100206 FAUCHEA
1610020601 FAUCHEA LACINIATA 1610020601 FAUCHEA LACINIATA
1610020602 FAUCHEA FRYEANA 1610020602 FAUCHEA FRYEANA
1610020901 LEPTOFAUCHEA PACIFIC 1610020901 LEPTOFAUCHEA PACIFICA
16100210 FRYEELLA 16100210 FRYEELLA
1611O1 CERAMIACEAE HOM.l 161101 CERAMIACEAE HOM.l
16110101 ANTITHAMNION 16110101 ANTITHAMNION +
1611010104 ANTITHAMNION DENDROI 1611010104 ANTITHAMNION DENDROIDEUM
1611010106 ANTITHAMNION KYLINII 1611010106 ANTITHAMNION KYLINII
1611010109 ANTITHAMNION DEFECTU 1611010109 ANTITHAMNION DEFECTUM
16110102 CALLITHAMNION 16110102 CALLITHAMNION
1611010205 CALLITHAMNION BISERI 1611010205 CALLITHAMNION BISERIATUM
1611010207 CALLITHAMNION PIKEAN 1611010207 CALLITHAMNION PIKEANUM
1611010208 CALLITHAMNION ACUTUM 1611010208 CALLITHAMNION ACUTUM
16110103 BORNETIA 16110103 BORNETIA
16110104 CERAMIUM 16110104 CERAMIUM
1611010404 CERAMIUM RUBRUM 1611010404 CERAMIUM RUBRUM
1611010405 CERAMIUM STRICTUM 1611010405 CERAMIUM STRICTUM
1611010410 CERAMIUM CALIFORNICU 1611010410 CERAMIUM CALIFORNICUM
1611010411 CERAMIUM GARDNERI 1611010411 CERAMIUM GARDNERI
1611010413 CERAMIUM WASHINGTONI 1611010413 CERAMIUM WASHINGTONIENSE
161101O5 GRIFFITHSIA 16110105 GRIFFITHSIA
1611010501 GRIFFITHSIA TENUIS 16110105 GRIFFITHSIA
1611010599 NAME NOT FOUND 1611O1O5 GRIFFITHSIA
1611010701 TRAILLIELLA INTRICAT 161101O7O1 TRAILLIELLA INTRICATA
16110113 MICROCLADIA 16110113 MICROCLADIA
1611011301 MICROCLADIA BOREALIS 1611011301 MICROCLADIA BOREALIS
1611011302 MICROCLADIA COULTERI 1611011302 MICROCLADIA COULTERI
16110114 PLEONOSPORIUM 16110114 PLEONOSPORIUM
1611011403 PLEONOSPORIUM VANCOU 16110114 PLEONOSPORIUM
1611011499 NAME NOT FOUND 16110114 PLEONOSPORIUM
16110116 PTILOTA 16110116 PTILOTA
1611011601 PTILOTA FILICINA 1611011601 PTILOTA FILICINA
1611011602 PTILOTA PECTINATA 1611011602 PTILOTA PECTINATA
(continued)
235
-------
TABLE B-3 (continued)
1611011603 PTILOTA TENUIS 1611011603 PTILOTA TENUIS
1611O122 ANTITHAMNIONELLA 1611O122 ANTITHAMNIONELLA
1611012201 ANTITHAMNIONELLA GLA 1611012201 ANTITHAMNIONELLA GLANDULIFERA
1611012202 ANTITHAMNIONELLA PAC 1611012202 ANTITHAMNIONELLA PACIFICA
16110123 PLATYTHAMNION 16110123 PLATYTHAMNION | +
1611012301 PLATYTHAMNION PECTIN 1611012301 PLATYTHAMNION PECTINATUM j
1611012302 PLATYTHAMNION VILLOS 1611012302 PLATYTHAMNION VILLOSUM
1611012303 PLATYTHAMNION REVERS 1611012303 PLATYTHAMNION REVERSUM
1611012304 PLATYTHAMNION HETERO 1611012304 PLATYTHAMNION HETEROMORPHOM
1611012396 NAME NOT FOUND 1611O123 PLATYTHAMNION J
16110124 NEOPTILOTA 16110124 NEOPTILOTA
1611012401 NEOPTILOTA ASPLENIOI 16110124 NEOPTILOTA
16110125 HOLLENBERGIA 16110125 HOLLENBERGIA
1611012501 HOLLENBERGIA SUBULAT 1611012501 HOLLENBERGIA SUBULATA
1611012502 HOLLENBERGIA NIGRICA 1611012502 HOLLENBERGIA NIGRICANS
16110126 SCAGELONEMA/SCAGELIA 16110126 SCAGELONEMA/SCAGELIA } +
1611012601 SCAGELIA OCCIDENTALS 16110126 SCAGELONEMA/SCAGELIA |
16110127 TIFFANTELLA 16110127 TIFFANIELLA
1611012701 TIFFANIELLA SNYDERAE 16110127 TIFFANIELLA
1611012899 NAME NOT FOUND 1611O128 PTILOTHAMNIONOPSIS
161102 DELESSERIACEAE 161102 DELESSERIACEAE
16110205 CRYPTOPLEURA 16110205 CRYPTOPLEURA
161102O5O1 CRYPTOPLEURA RUPRECH 161102O5 CRYPTOPLEURA
1611O2O502 CRYPTOPLEURA LOBULIF 16110205 CRYPTOPLEURA
1611020503 CRYPTOPLEURA VIOLACE 16110205 CRYPTOPLEURA
16110206 DELESSERIA 16110206 DELESSERIA
1611020601 DELESSERIA DECIPIENS 16110206 DELESSERIA
1611020901 GONIMOPHYLLUM SKOTTS 1611020901 GONIMOPHYLLUM SKOTTSBERGII
1611O211 MEMBRANOPTERA 16110211 MEMBRANOPTERA
1611021103 MEMBRANOPTERA PLATYP 1611021103 MEMBRANOPTERA PLATYPHYLIA
1611021108 MEMBRANOPTERA MULTIR 1611021108 MEMBRANOPTERA MULTIRAMOSA
1611021109 MEMBRANOPTERA WEEKSI 1611021109 MEMBRANOPTERA WEEKSIAE
16110212 N1TOPHYLLUM 16110212 NITOPHYLLUM
1611021299 NAME NOT FOUND 1611O212 NITOPHYLLUM
16110214 PHYCODRYS 1611O214 PHYCODRYS
1611021405 PHYCODRYS ISABELLIAE 1611O214 PHYCODRYS
16110215 POLYNEURA 16110215 POLYNEURA !*
1611021501 POLYNEURA LATISSIMA 16110215 POLYNEURA !
16110217 MYRIOGRAMME 16110217 MYRIOGRAMME
1611O22O03 NIENBURGIA ANDERSONI 1611022003 NIENBURGIA ANDERSONIANA +
1611O223 ASTEROCOLAX 16110223 ASTEROCOLAX
1611022399 NAME NOT FOUND 16110223 ASTEROCOLAX
16110224 HYMENENA 16110224 HYMENENA
1611022402 HYMENENA FLABELLIGER 16110224 HYMENENA
1611022404 HYMENENA SETCHELLII 16110224 HYMENENA
1611022499 NAME NOT FOUND 16110224 HYMENENA
16110225 BOTRYOGLOSSUM 16110225 BOTRYOGLOSSUM
1611022501 BOTRYOGLOSSUM FARLOW 16110225 BOTRYOGLOSSUM
(continued)
236
-------
TABLE B-3 (continued)
1611022799 NAME NOT FOUND 16110225 BOTRYOGLOSSUM
16110302 HETEROSIPHONIA 16110302 HETEROSIPHONIA
1611030201 HETEROSIPHONIA DENSI 16110302 HETEROSIPHONIA
16110303 RHODOPTILUM 1611O3O3 RHODOPTILUM
1611030301 RHODOPTILUM PLUMOSUM 16110303 RHODOPTILUM
16110401 POLYSIPHONIA 16110401 POLYSIPHONIA
1611040101 POLYSIPHONIA HENDRYI 1611040101 POLYSIPHONIA HENDRYI +
1611040103 POLYSIPHONIA PACIFIC 1611040103 POLYSIPHONIA PACIFICA
1611040114 POLYSIPHONIA PANICUL 1611040114 POLYSIPHONIA PANICULATA
16110402 PTEROSIPHONIA 16110402 PTEROSIPHONIA
1611040202 PTEROSIPHONIA BIPINN 1611040202 PTEROSIPHONIA BIPINNATA
1611040203 PTEROSIPHONIA DENDRO 1611040203 PTEROSIPHONIA DENDROIDEA +
1611040204 PTEROSIPHONIA GARDNE 1611040204 PTEROSIPHONIA GARDNERI
1611040205 PTEROSIPHONIA GRACIL 1611040205 PTEROSIPHONIA GRACILIS
1611040301 AMPLISIPHONIA PACIFI 1611040301 AMPLISIPHONIA PACIFICA
1611040401 LAURENCIA SPECTABILI 1611040401 LAURENCIA SPECTABILIS
16110405 RHODOMELA 16110405 RHODOMELA
1611040501 RHODOMELA LARIX 16110405 RHODOMELA
16110406 ODONTHALIA 16110406 ODONTHALIA
1611040603 ODONTHALIA FLOCCOSA 1611040603 ODONTHALIA FLOCCOSA *
1611040605 ODONTHALIA LYALLII 1611040605 ODONTHALIA LYALLII
1611040606 ODONTHALIA WASHINGTO 1611040606 ODONTHALIA WASHINGTONIENSIS
1611040607 ODONTHALIA KAMTSCHAT 1611040607 ODONTHALIA KAMTSCHATICA
16110407 LOPHOSIPHONIA 16110407 LOPHOSIPHONIA
1611040701 LOPHOSIPHONIA VILLUM 1611040701 LOPHOSIPHONIA VTLLUM
1611040702 LOPHOSIPHONIA REPTAB 1611040702 LOPHOSIPHONIA REPTABUNDA
16110412 HERPOSIPHONIA 16110412 HERPOSIPHONIA
1611041201 HERPOSIPHONIA VERTIC 1611041201 HERPOSIPHONIA VERTICILLATA
1611041202 HERPOSIPHONIA GRANDI 1611041202 HERPOSIPHONIA GRANDIS
1611041203 HERPOSIPHONIA PLUMUL 1611041203 HERPOSIPHONIA PLUMULA
16110413 PTEROCHONDRIA 16110413 PTEROCHONDRIA
1611041301 PTEROCHONDRIA WOODII 16110413 PTEROCHONDRIA
16110414 JANCZEWSKIA 16110414 JANCZEWSKIA
3326010101 ZOSTERA MARINA 3326010101 ZOSTERA MARINA *
33260103 PHYLLOSPADIX 33260103 PHYLLOSPADIX !+
3326010301 PHYLLOSPADIX SCOULER 33260103 PHYLLOSPADIX j
333101 IRIDACEAE 333101 IRIDACEAE
36 PORIFERA 36 PORIFERA
3664020801 SIGMODOCIA EDAPHUS 36 PORIFERA
37 CNIDARIA 37 CNIDARIA
3701 HYDROZOA 3701 HYDROZOA
37O2 HYDROZOA HYDROIDA 3702 HYDROZOA HYDROIDA
37O30301 CORYMORPHA 37030301 CORYMORPHA
37030302 TUBULARIA 37030302 TUBULARIA
3703060101 CORYNE TUBULOSA 3703060101 CORYNE TDBULOSA
37040101 CAMPANULARIA 37040101 CAMPANULARIA
37040102 OBELIA 37040102 OBELIA
37040404 CALICELLA 37040404 CALICELLA
(continued)
237
-------
TABLE B-3 (continued )
37O40503 SERTULARIA 37040503 SERTULARIA
370405O4 ABIETINARIA 37040504 ABIETINARIA
37040508 DIPHASIA 37O40508 DIPHASIA
37040601 HALECIUM 37040601 HALECIUM
37040701 PLUMULARIA 37040701 PLDMOLARIA
37040711 AGLAOPHENIA 37040711 AGLAOPHENIA
3730 SCYPHOZOA 3730 SCYPHOZOA
37310101 HALICLYSTUS 3731O101 HALICLYSTUS
3731010101 HALICLYSTUS AURICULA 37310101 HALICLYSTUS
3740 ANTHOZOA 3740 ANTHOZOA
3754O2O2O.1 PTILOSARCUS GURNEYI 3754020201 PTILOSARCUS GURNEYI
3758 ZOANTHARIA ACTINIARI 3758 ZOANTHARIA ACTINIARIA
3759 ZOANTHARIA ACTINIARI 3759 ZOANTHARIA ACTINIARIA NYNANTHEAE
3759O4 HALCAMPIDAE 375904 HALCAMPIDAE
37590401 HALCAMPA 375904 HALCAMPIDAE
3759040101 HALCAMPA DECEMTENTAC 375904 HALCAMPIDAE
37590499 NAME NOT FOUND 375904 HALCAMPIDAE
3759049999 NAME NOT FOUND 375904 HALCAMPIDAE
3760 ZOANTHARIA ACTINIARI 3760 ZOANTHARIA ACTINIARIA NYNANTHEAE
3760010201 ANTHOPLEURA ELEGANTI 3760010201 ANTHOPLEURA ELEGANTISSIMA
3760010301 EPIACTIS PROLIFERA 3760010301 EPIACTIS PROLIFERA
3760019799 NAME NOT FOUND 376001 ACTINIIDAE
3760060101 METRIDIUM SENILE 3760060101 METRIDIUM SENILE
3764999999 NAME NOT FOUND 3764 ZOANTHARIA SCLERACTINIA
3769010101 BALANOPHYLLIA ELEGAN 3769010101 BALANOPHYLLIA ELEGANS
39 PLATYHELMINTHES 39 PLATYHELMINTHES
3901 TURBELLARIA 39 PLATYHELMINTHES
3915030298 NAME NOT FOUND 39 PLATYHELMINTHES
43 RHYNCHOCOELA 43 RHYNCHOCOELA
4302010104 TUBULANUS PELLUCIDUS 4302010104 TUBULANUS PELLUCIDUS
43030202 CEREBRATULUS 43030202 CEREBRATULUS
43O302O2O8 CERZBRATULUS CRLIFOR 43O30202 CEKEBRATUIiUS
4306010102 EMPLECTONEMA GRACILE 4306010102 EMPLECTONEMA GRACILE
4306010603 PARANEMERTES PEREGRI 4306010603 PARANEMERTES PEREGRINA
430605O1O2 AMPHIPORUS BIMACULAT 4306050102 AMPHIPORUS BIMACULATUS
47 NEMATODA 47 NEMATODA
5001 POLYCHAETA 50O1 POLYCHAETA
50010 NAME NOT FOUND 50O1 POLYCHAETA
50O102 POLYNOIDAE 5OO1O2 POLYNOIDAE
5001020402 ARCTONOE VITTATA 5001020402 ARCTONOE VITTATA
50010205 EUNOE 50O10205 EUNOE
50O102O5O4 EUNOE SENTA 5001020504 EUNOE SENTA
5001020505 EUNOE OERSTEDI 50O1020505 EUNOE OERSTEDI
5001020606 GATTYANA TREADWELLI 5001020606 GATTYANA TREADWELLI
5001020701 HALOSYDNA BREVISETOS 5001020701 HALOSYDNA BREVISETOSA
50010208 HARMOTHOE 5001O2O8 HARMOTHOE
5001020803 HARMOTHOE EXTENUATA 5001020803 HARMOTHOE EJCTENUATA
5001020806 HARMOTHOE IMBRICATA 5001020806 HARMOTHOE IMBRICATA
(continued)
238
-------
TABLE B-3 (continued)
5001020809 HARMOTHOE MOLTISETOS 5001020809 HARMOTHOE MOLTISETOSA
5001020810 HARMOTHOE LDNULATA 5001020810 HARMOTHOE LUNULATA
5001021103 LEPIDONOTOS SQUAMATU 5001021103 LEPIDONOTUS SQOAMATUS
50010218 LEPIDASTHENIA 50010218 LEPIDASTHENIA
5001021801 LEPIDASTHENIA BERKEL 50010218 LEPIDASTHENIA
5001029999 NAME NOT FOUND 500102 POLYNOIDAE
5001030101 PEISIDICE ASPERA 5001030101 PEISIDICE ASPERA
500106 SIGALIONIDAE 5O0106 SIGALIONIDAE i *
50010601 PHOLOE 500106 SIGALIONIDAE !
5001060101 PHOLOE MINUTA 500106 SIGALIONIDAE !
5001069999 NAME NOT FOUND 5O0106 SIGALIONIDAE !
50010701 PISIONE 50O10701 PISIONE
50010801 PALEANOTUS 50O10801 PALEANOTUS
5001080101 PALEANOTUS BELLIS 50010801 PALEANOTUS
500113 PHYLLODOCIDAE 500113 PHYLLODOCIDAE
50011301 ANAITIDES/PHYLLODOCE 5O011301 ANAITIDES/PHYLLODOCE
5001130101 ANAITIDES CITRINA 5001130101 ANAITIDES CITRINA
5001130102 ANAITIDES GROENLANDI 5001130102 ANAITIDES GROENLANDICA
5001130103 ANAITIDES MEDIPAPILL 5001130103 ANAITIDES MEDIPAPILLATA
5001130104 ANAITIDES MUCOSA 5001130104 ANAITIDES MUCOSA
5001130106 ANAITIDES MACULATA 5001130106 ANAITIDES MACULATA *
5001130107 ANAITIDES MADEIRENSI 5001130107 ANAITIDES MADEIRENSIS
5001130198 NAME NOT FOUND 50O11301 ANAITIDES/PHYLLODOCE
5001130199 NAME NOT FOUND 50011301 ANAITIDES/PHYLLODOCE
50011302 ETEONE 50011302 ETEONE
5001130203 ETEONE PACIFICA 5OO1130203 ETEONE PACIFICA
5001130205 ETEONE LONGA 5001130205 ETEONE LONGA +
5001130206 ETEONE TUBERCULATA 5001130206 ETEONE TUBERCULATA
50011303 EULALIA 50011303 EULALIA !+
5001130301 EULALIA VIRIDIS 5001130301 EULALIA VIRIDIS i
5001130302 EULALIA SANGUINEA 5001130302 EULALIA SANGUINEA !
5001130304 EULALIA BILINEATA 5001130304 EULALIA BILINEATA |
5001130305 EULALIA MACROCEROS 5001130305 EULALIA MACROCEROS !
5001130306 EULALIA QUADRIOCULAT 5001130306 EULALIA QUADRIOCULATA !
5001130307 EULALIA NIGRIMACULAT 5001130307 EULALIA NIGRIMACULATA |
5001130^402 NOTOPHYLLUM IMBRICAT 5O0113O402 NOTOPHYLLUM IMBRICATUM
5001130*7 GENETYLLIS 50011307 GENETYLLIS
5001130701 GENETYLLIS CASTANEA 50011307 GENETYLLIS
5001130901 HESIONURA COINEAUI 5001130901 HESIONURA COINEAUI +
500121 HESIONIDAE 500121 HESIONIDAE
5001210102 GYPTIS BKEVIPALPA 5001210102 GYPTIS BREVIPALPA
5001210401 OPHIODROMUS PUGETTEN 5001210401 OPHIODROMUS PUGETTENSIS
5001210501 KEFERSTEINIA CIRRATA 5001210501 KEFERSTEINIA CIRRATA
5001210801 MICROPODARKE DUBIA 5001210801 MICROPODARKE DUBIA *
50012109 SYLLIDIA 50012109 SYLLIDIA
5001219899 NAME NOT FOUND 500121 HESIONIDAE
5001219999 NAME NOT FOUND 500121 HESIONIDAE
5001220201 SIGAMBRA TENTACULATA 5001220201 SIGAMBRA TENTACULATA
(continued)
239
-------
TABLE B-3 (continued )
5001220301 PILARGIS BERKELEYAE 50O12203O1 PILARGIS BEKKELEYAE
500123 SYLLIDAE 50O123 SYLLIDAE
50012301 AUTOLYTUS 50O12301 AOTOLYTUS
50O123O1O1 AUTOLYTUS CORNUTUS 5OO12301 AUTOLYTUS
50012302 PIONOSYLL1S 50O123O2 PIONOSYLLIS |+
5001230204 PIONOSYLLIS URAGA 50O12302 PIONOSYLLIS j
50012303 SYLLIS 50O12303 SYLLIS +
5001230401 TRYPANOSYLLIS GEMMIP 50O12304O1 TRYPANOSYLLIS GEMMIPARA
50012305 TYPOSYLLIS 50012305 TYPOSYLLIS !+
5O0123O5O1 TYPOSYLLIS ALTERNATA 5001230501 TYPOSYLLIS ALTERNATA j
5001230502 TYPOSYLLIS ARMILLARI 5001230502 TYPOSYLLIS ARMILLARIS
5001230506 TYPOSYLLIS STEWARTI 50O123O506 TYPOSYLLIS STEWARTI
5001230507 TYPOSYLLIS FASCIATA 5001230507 TYPOSYLLIS FASCIATA
5001230511 TYPOSYLLIS HYALINA 5001230511 TYPOSYLLIS HYALINA
5001230512 TYPOSYLLIS VARIEGATA 5001230512 TYPOSYLLIS VARIEGATA |
500123O6 EUSYLLIS 50O12306 EUSYLLIS
5001230602 EUSYLLIS BLOMSTRANDI 50012306 EUSYLLIS
5001230603 EUSYLLIS JAPONICA 50O12306 EUSYLLIS
5001230604 EUSYLLIS MAGNIFICA 50O1230.6 EUSYLLIS
500123O7 EXOGONE 50012307 EXOGONE | +
5001230702 EXOGONE GEMMIFERA 5001230702 EXOGONE GEMMIFERA j
5001230703 EXOGONE LOUREI 5001230703 EXOGONE LOUREI \
5001230704 EXOGONE MOLESTA 5001230704 EXOGONE MOLESTA j
50012308 SPHAEROSYLLIS 50O12308 SPHAEROSYLLIS
5001230805 SPHAEROSYLLIS PERIFE 5001230805 SPHAEROSYLLIS PERIFERA +
5001230806 SPHAEROSYLLIS BRANDH 5001230806 SPHAEROSYLLIS BRANDHORSTI
5001230901 BRANIA BREVIPHARYNGE 5001230901 BRANIA BREVIPHARYNGEA
5001231002 LANGERHANSIA HETEROC 5001231002 LANGERHANSIA HETEROCHAETA
50012313 ODONTOSYLLIS 50O12313 ODONTOSYLLIS
5001231302 ODONTOSYLLIS PARVA 50O12313 ODONTOSYLLIS
50012315 SYLLIDES 50012315 SYLLIDES
5001231503 SYLLIDES LONGOCIRRAT 5OO12315 SYLLIDES
5001231599 NAME NOT FOUND 50O12315 SYLLIDES
5001231604 STREPTOSYLLIS LATIPA 5001231604 STREPTOSYLLIS LATIPALPA
5001239999 NAME NOT FOUND 5OO123 SYLLIDAE
500124 NEREIDAE 500124 NEREIDAE
5001240201 CHEILONEREIS CYCLURU 5001240201 CHEILONEREIS CYCLURUS
500124O3 NEANTHES 50012403 NEANTHES
50O12403O1 NEANTHES BRANDTI 5OO12403 NEANTHES
50012404 NEREIS 50012404 NEREIS !+
5001240403 NEREIS PELAGICA 5001240403 NEREIS PELAGICA j
5001240404 NEREIS PROCERA 5001240404 NEREIS PROCERA |
5001240405 NEREIS VEXILLOSA 5001240405 NEREIS VEXILLOSA i
5001240406 NEREIS ZONATA 5001240406 NEREIS ZONATA j
5001240501 PLATYNEREIS BICANALI 5001240501 PLATYNEREIS BICANALICULATA *
5001240701 MICRONEREIS NANAIMOE 50O124O701 MICRONEREIS NANAIMOENSIS
50012501 NEPHTYS 50012501 NEPHTYS i+
5001250102 NEPHTYS CILIATA 5001250102 NEPHTYS CILIATA j
(continued)
240
-------
TABLE B-3 (continued)
5001250103 NEPHTYS CAECA 5001250103 NEPHTYS CAECA i
5001250109 NEPHTYS LONGOSETOSA 5001250109 NEPHTYS LONGOSETOSA j
5001250111 NEPHTYS FERRUGINEA 5001250111 NEPHTYS FERRUGINEA |
5001250113 NEPHTYS CALIFORNIENS 5OO125O113 NEPHTYS CALIPORNIENSIS }
5001250119 NEPHTYS CAECOIDES 5001250119 NEPHTYS CAECOIDES j
5001250199 NAME NOT FOUND 50012501 NEPHTYS j
500126 SPHAERODORIDAE 500126 SPHAERODORIDAE
5001260102 SPHAERODORUM PAPILLI 5001260102 SPHAERODOROM PAPILLIFER
5001260201 SPHAERODOROPSIS MINO 5001260201 SPHAERODOROPSIS MINUTA
5001260202 SPHAERODOROPSIS SPHA 5001260202 SPHAERODOROPSIS SPHAERULIFER
50012701 GLYCERA (POLYCHAE 50012701 GLYCERA (POLYCHAETA)
5001270101 GLYCERA CAPITATA 5001270101 GLYCERA CAPITATA
5001270103 GLYCERA TESSELATA 5001270103 GLYCERA TESSELATA
5001270104 GLYCERA AMERICANA 5001270104 GLYCERA AMERICANA
5001270201 HEMIPODUS BOREALIS 5001270201 HEMIPODUS BOREALIS *
50012801 GLYCINDE 50012801 GLYCINDE
5001280101 GLYCINDE PICTA 5001280101 GLYCINDE PICTA +
5001280103 GLYCINDE ARMIGERA 5001280103 GLYCINDE ARMIGERA
50012802 GONIADA 50012802 GONIADA
5001280202 GONIADA MACULATA 5001280202 GONIADA MACULATA
5001280203 GONIADA BRUNNEA 5001280203 GONIADA BRUNNEA
50012901 ONUPHIS 50012901 ONUPHIS j+
5001290101 ONUPHIS CONCHYLEGA 5001290101 ONUPHIS CONCHYLEGA |
5001290103 ONUPHIS IRIDESCENS 5001290103 ONUPHIS IRIDESCENS |
5001290106 ONUPHIS STIGMATIS 5001290106 ONUPHIS STIGMATIS j
5001290111 ONUPHIS ELEGANS 5001290111 ONUPHIS ELEGANS j
5001290199 NAME NOT FOUND 50012901 ONUPHIS !
5001290202 DIOPATRA ORNATA 50012902 DIOPATRA
5001290299 NAME NOT FOUND 50012902 DIOPATRA
5001300102 EUNICE VALENS 5001300102 EUNICE VALENS
50013101 LUMBRINEREIS 50013101 LUMBRINEREIS |+
5001310106 LUMBRINEREIS ZONATA 5001310106 LUMBRINEREIS ZONATA j
5001310108 LUMBRINEREIS INFLATA 5001310108 LUMBRINEREIS INFLATA |
5001310109 LUMBRINEREIS LUTI 5001310109 LUMBRINEREIS LUTI |
5001330201 ARABELLA TRICOLOR 5001330201 ARABELLA TRICOLOR
50013601 DORVILLEA/SCHISTOMER 50013601 DORVILLEA/SCHISTOMERINGOS
5001360103 DORVILLEA JAPONICA 5001360103 DORVILLEA JAPONICA
5001360104 DORVILLEA RUDOLPHI 5001360104 DORVILLEA RUDOLPHI
5001360105 DORVILLEA ANNULATA 5001360105 DORVILLEA ANNULATA
5001360201 PROTODORVILLEA GRACI 5001360201 PROTODORVILLEA GRACILIS *
5001360202 PROTODORVILLEA GASPE 5001360202 PROTODORVILLEA GASPEENSIS
500140 ORBINIIDAE 50014O ORBINIIDAE
5001400102 HAPLOSCOLOPLOS ELONG 5001400102 HAPLOSCOLOPLOS ELONGATUS
50014002 NAINERIS 50014002 NAINERIS
50014OO2O1 NAINERIS DENDRITICA 5001400201 NAINERIS DENDRITICA
5001400202 NAINERIS QUADRICUSPI 5001400202 NAINERIS QUADRICUSPIDA
5001400203 NAINERIS LAEVIGATA 5001400203 NAINERIS LAEVTGATA
500140O2O4 NAINERIS UNCINATA 5001400204 NAINERIS UNCINATA
(continued)
241
-------
TABLE B-3 (continued)
50014003 SCOLOPLOS 50O14003 SCOLOPLOS
5001400301 SCOLOPLOS ARMIGER 5001400301 SCOLOPLOS ARMIGER
5001400302 SCOLOPLOS PUGETTENSI 5001400302 SCOLOPLOS PUGETTENSIS *
5001400401 PHYLO FELIX 50O140O401 PHYLO FELIX
50014005 ORBINIA 50014005 ORBINIA
5001400501 ORBINIA MICHAELSENI 50014005 ORBINIA
500141 PARAONIDAE 50O141 PARAONIDAE
500141O2 ARICIDEA 50O14102 ARICIDEA |+
5001410201 ARICIDEA SUECICA 50014102 ARICIDEA j
5001410299 NAME NOT FOUND 50014102 ARICIDEA !
50014103 PARAONIS 50014103 PARAONIS
5001410301 PARAONIS GRACILIS 5OO141O301 PARAONIS GRACILIS
5001410304 PARAONIS LYRA 5001410304 PARAONIS LYRA
50014105 PARAONELLA 50O14105 PARAONELLA |+
5001410501 PARAONELLA PLATYBRAN 50014105 PARAONELLA j
500142O1 APISTOBRANCHUS 500142O1 APISTOBRANCHUS
5OO143 SPIONIDAE 500143 SPIONIDAE
50014302 LAONICE 50014302 LAONICE j +
5001430201 LAONICE CIRRATA 50014302 LAONICE j
5O0143O3 NERINE 50O143O3 NERINE
5001430303 NERINE FOLIOSA 50O14303 NERINE
50014304 POLYDORA 50014304 POLYDORA
5001430402 POLYDORA SOCIALIS 5001430402 POLYDORA SOCIALIS +
5001430404 POLYDORA CAULLERYI 5001430404 POLYDORA CAULLERYI
5001430408 POLYDORA QUADRILOBAT 5001430408 POLYDORA QUADRILOBATA
50O143O4O9 POLYDORA SPONGICOLA 5001430409 POLYDORA SPONGICOLA
5001430417 POLYDORA PYGIDIALIS 5001430417 POLYDORA PYGIDIALIS
5001430492 NAME NOT FOUND 5OO143O4 POLYDORA
5001430495 NAME NOT FOUND 50014304 POLYDORA
5001430496 NAME NOT FOUND 50O143O4 POLYDORA
5001430499 NAME NOT FOUND 50014304 POLYDORA
5O0143O5 PRIONOSPIO 50014305 PRIONOSPIO
5OO143O5O2 PRIONOSPIO CIRRIFERA 5001430502 PRIONOSPIO CIRRIFERA +
5001430504 PRIONOSPIO PINNATA 5001430504 PRIONOSPIO PINNATA
5001430506 PRIONOSPIO STEENSTRU 5001430506 PRIONOSPIO STEENSTRUPI *
5001430508 PRIONOSPIO CIRROBRAN 500143O5O8 PRIONOSPIO CIRROBRANCHIATA
50014307 SPIO 500143O7 SPIO
50O143O701 SPIO FILICORNIS 5001430701 SPIO FILICORNIS *
5001430703 SPIO CIRRIFERA 5001430703 SPIO CIRRIFERA
50O14308 BOCCARDIA 50O143O8 BOCCARDIA
5001430801 BOCCARDIA COLUMBIANA 5001430801 BOCCARDIA COLUMBIANA
5001430806 BOCCARDIA HAMATA 5001430806 BOCCARDIA HAMATA
50O14310 SPIOPHANES 50014310 SPIOPHANES
5001431001 SPIOPHANES BOMBYX 5001431001 SPIOPHANES BOMBYX *
5001431003 SPIOPHANES CIRRATA 5001431003 SPIOPHANES CIRRATA
5O014310O4 SPIOPHANES BERKELEYO 5001431004 SPIOPHANES BERKELEYORUM
50014312 RHYNCHOSPIO 50014312 RHYNCHOSPIO
5001431302 PYGOSPIO ELEGANS 5001431302 PYGOSPIO ELEGANS
(continued)
242
-------
!*
TABLE B-3 (continued)
50014314 MALACOCEROS 50014314 MALACOCEROS
5001431401 MALACOCEROS GLUTAEUS 50014314 MALACOCEROS i
5001431501 PSEUDOPOLYDORA KEMPI 5001431501 PSEUDOPOLYDORA KEMPI
5001431701 PARAPRIONOSPIO PINNA 5001431701 PARAPRIONOSPIO PINNATA
5001431801 STREBLOSPIO BENEDICT 5001431801 STKEBLOSPIO BENEDICTI
5001432001 SCOLELEPIS SQUAMATA 50014320 SCOLELEPIS
5001432099 NAME NOT POUND 50014320 SCOLELEPIS
50014322 AONIDES 50014322 AONIDES
500144O1 MAGELONA 500144O1 MAGELONA
5001440101 MAGELONA JAPONICA 5001440101 MAGELONA JAPONICA
5001440103 MAGELONA PITELKAI 5001440103 MAGELONA PITELKAI +
5001490202 PHYLLOCHAETOPTERUS P 50014902 PHYLLOCHAETOPTERUS
5001490299 NAME NOT FOUND 50014902 PHYLLOCBAETOPTERUS
5001490302 SPIOCHAETOPTERUS COS 5001490302 SPIOCHAETOPTERUS COSTARUM
500149O4O1 MESOCHAETOPTERUS TAY 5001490401 MESOCHAETOPTERUS TAYLORI
500150 CIRRATULIDAE 50015O CIRRATULIDAE
50015001 CIRRATULUS 50015O01 CIRRATULUS !+
5001500101 CIRRATULUS CIRRATUS 50015001 CIRRATULUS i
50015002 CAULLERIELLA 50015002 CAULLERIELLA
500150O2O2 CAULLERIELLA ALATA 5O015O0202 CAULLERIELLA ALATA
5001500203 CAULLERIELLA GRACILI 5001500203 CAULLERIELLA GRACILIS
5001500299 NAME NOT FOUND 50015002 CAULLERIELLA
50015003 THARYX 50015003 THARYX !+
5001500302 THARYX MULTIFILIS 50015003 THARYX !
50O150O4 CHAETOZONE 5O015O04 CHAETOZONE
5001500401 CHAETOZONE SETOSA 5001500401 CHAETOZONE SETOSA +
5001500402 CHAETOZONE GRACILIS 5001500402 CHAETOZONE GRACILIS
50015005 DODECACERIA 50015005 DODECACERIA
5O015O05O1 DODECACERIA CONCHARU 50015005 DODECACERIA
50015O06 CIRRIFORMIA 50O15OO6 CIRRIFORMIA
5OO151 ACROCIRRIDAE 500151 ACROCIRRIDAE
50015101 ACROCIRRUS 500151 ACROCIRRIDAE
5001510101 ACROCIRRUS HETEROCHA 500151 ACROCIRRIDAE
50015401 BRADA 5O015401 BRADA
50O154O2O1 FLABELLIGERA INFUNDI 5001540201 FLABELLIGERA INFUNDIBULARIS
5OO15402O2 FLABELLIGERA AFFINIS 5001540202 FLABELLIGERA AFFINIS
5001540302 PHERUSA PLUMOSA 5001540302 PHERUSA PLUMOSA
50O15701O1 SCALIBREGMA INFLATUM 5001570101 SCALIBREGMA INFLATUM *
5O015801 OPHELINA 50015801 OPHELINA
5O015801O1 AMMOTRYPANE AULOGAST 50015801 OPHELINA
5001580202 ARMANDIA BREVIS 5001580202 ARMANDIA BREVIS *
50015803 OPHELIA 50015803 OPHELIA
5001580301 OPHELIA LIMACINA 50015803 OPHELIA
5001580401 TRAVISIA BREVTS 5001580401 TRAVISIA BREVIS
5001580402 TRAVISIA FORBESII 5001580402 TRAVISIA FORBESII
5001580403 TRAVISIA PUPA 5001580403 TRAVISIA PUPA
50015901 STERNASPIS 50015901 STERNASPIS
5001590101 STERNASPIS SCUTATA 50015901 STERNASPIS
(continued)
243
-------
TABLE B-3 (continued)
500160 CAPITELLIDAE 500160 CAPITELLIDAE
50016001 CAPITELLA 50016O01 CAPITELLA J*
5001600101 CAPITELLA CAPITATA 500160O1 CAPITELLA J
50016003 NOTOMASTUS . 50016003 NOTOMASTUS
5001600301 NOTOMASTUS GIGANTEUS 5001600301 NOTOMASTUS GIGANTEUS
5001600302 NOTOMASTUS TENUIS 5OO16OO302 NOTOMASTUS TENUIS
5001600303 NOTOMASTUS LINEATUS 5O0160O303 NOTOMASTUS LINEATUS
5001600305 NOTOMASTUS LURIDUS 5001600305 NOTOMASTUS LURIDUS
50016004 MEDIOMASTUS 50016004 MEDIOMASTUS i*
5001600401 MEDIOMASTUS AMBISETA 50016004 MEDIOMASTUS !
50O160O5O1 DECAMASTUS GRACILIS 5001600501 DECAMASTUS GRACILIS
5001609999 NAME NOT POUND 500160 CAPITELLIDAE
50016203 BRANCHIOMALDANE 50016203 BRANCHIOMALDANE
5001620301 BRANCHIOMALDANE VICE 50016203 BRANCHIOMALDANE
500163 MALDANIDAE 500163 MALDANIDAE
50016303 MALDANE 50016303 MALDANE
5001630301 MALDANE SARSI 5001630301 MALDANE SARSI
5001630302 MALDANE GLEBIFEX 5001630302 MALDANE GLEBIFEX
50016305 NICOMACHE 50016305 NICOMACHE
5001630501 NICOMACHE LUMBRICALI 5001630501 NICOMACHE LUMBRICALIS
5001630502 NICOMACHE PERSONATA 5001630502 NICOMACHE PERSONATA *
5001630601 NOTOPROCTUS PACIFICU 5O0163O6O1 NOTOPROCTUS PACIFICUS
50O16307 PETALOPROCTUS 50016307 PETALOPROCTUS
5001630701 PETALOPROCTUS TENUIS 50016307 PETALOPROCTUS
5001630802 AXIOTHELLA RUBROCINC 5001630802 AXIOTHELLA RUBROCINCTA *
5O0163O9 PRAXILLELLA 500163O9 PRAXILLELLA
5001630901 PRAXILLELLA GRACILIS 5001630901 PRAXILLELLA GRACILIS
5001630903 PRAXILLELLA AFFINIS 5001630903 PRAXILLELLA AFFINIS
50O16311 EUCLYMENE 50016311 EUCLYMENE
5001631101 EUCLYMENE DELINEATA 50016311 EUCLYMENE
50016320 ISOCIRRUS 50016320 ISOCIKRUS
500164 OWENIIDAE 500164 OWENIIDAE
5001640102 OWENIA FUSIFORMIS 5001640102 OWENIA FUSIFORMIS *
5001640202 MYRIOCHELE OCULATA 5001640202 MYR.IOCHELE OCULATA
50O16501O2 IDANTHYRSUS ARMATUS 5001650102 IDANTHYRSUS ARMATUS
5001650201 SABELLARIA CEMENTARI 5001650201 SABELLARIA CEMENTARIUM
5001660202 CISTENIDES GRANULATA 50O16602O2 CISTENIDES GRANULATA +
50016603 PECTINARIA 50O166O3 PECTINARIA
5001660301 PECTINARIA BELGICA 5001660301 PECTINARIA BELGICA
5001660303 PECTINARIA GRANULATA 5001660303 PECTINARIA GRANULATA
500167 AMPHAKETIDAE 500167 AMPHARETIDAE
5O0167O2 AMPHARETE 50016702 AMPHARETE ! +
5001670201 AMPHARETE ARCTICA 5O016702 AMPHARETE !
50016703 AMPHICTEIS 50016703 AMPHICTEIS
5001670501 MELINNA CRISTATA 5001670501 MELINNA CRISTATA
5O0167O8 ASABELLIDES 50016708 ASABELLIDES
5001670801 ASABELLIDES SIBIRICA 5001670801 ASABELLIDES SIBIRICA
5001670803 ASABELLIDES LITTORAL 5001670803 ASABELLIDES LITTORALIS
(continued)
244
-------
TABLE B-3 (continued)
5001670804 ASABELLIDES LINEATA 5001670804 ASABELLIDES LINEATA
5001671O MELINNEXIS 5O01671O MELINNEXIS
5001671101 PSEUDOSABELLIDES LIT 5001671101 PSEUDOSABELLIDES LITTORALIS
50016714 SAMYTHA 50016714 SAMYTHA
5001671801 NAME NOT FOUND 500167 AMPHAKETIDAE
500168 TEKEBELLIDAE 5O0168 TEKEBELLIDAE
5001680201 EUPOLYMNIA HETEROBRA 5001680201 EUPOLYMNIA HETEROBRANCHIA
50016806 NICOLEA 500168O6 NICOLEA
5001680601 NICOLEA ZOSTERICOLA 50016806 NICOLEA
50016807 PISTA 50016807 PISTA
5001680701 PISTA CRISTATA 5001680701 PISTA CRISTATA
5001680702 PISTA FASCIATA 5001680702 PISTA FASCIATA
50016808 POLYCIRRUS 50016808 POLYCIRRUS j+
5001680803 POLYCIRRUS KERGUELEN 50016808 POLYCIRRUS |
5001680898 NAME NOT FOUND 50016808 POLYCIRRUS |
5001680899 NAME NOT FOUND 50016808 POLYCIRRUS j
50016810 THELEPUS 5OO16810 THELEPUS
5001681001 THELEPUS CRISPUS 5001681001 THELEPUS CRISPUS
5001681002 THELEPUS HAMATUS 5001681002 THELEPUS HAMATUS
5001681101 ARTACAMA CONIFERI 5001681101 ARTACAMA CONIFERI
5001681702 PROCLEA GRAFFII 5001681702 PROCLEA GRAFFII
5001690101 TEREBELLIDES STROEMI 5001690101 TEREBELLIDES STROEMII +
500170 SABELLIDAE 500170 SABELLIDAE
50017001 CHONE 50017O01 CHONE j +
5001700101 CHONE GRACILIS 5001700101 CHONE GRACILIS |
5001700102 CHONE INFUNDIBULIFOR 5001700102 CHONE INFUNDIBULIFORMIS j
5001700104 CHONE DUNERI 5001700104 CHONE DUNERI j
5001700105 CHONE ECAUDATA 5001700105 CHONE ECAUDATA |
5001700199 NAME NOT FOUND 50017001 CHONE j
5001700201 EUCHONE ANALIS 5001700201 EUCHONE ANALIS
5001700301 EUDISTYLIA POLYMORPH 5001700301 EUDISTYLIA POLYMORPHA
5001700303 EUDISTYLIA VANCOUVER 5OO17O0303 EUDISTYLIA VANCOUVERI
50017006 POTAMILLA 50017006 POTAMILLA
5001700601 POTAMILLA NEGLECTA 5001700601 POTAMILLA NEGLECTA
5001700602 POTAMILLA MYRIOPS 5001700602 POTAMILLA MYRIOPS
5001700698 NAME NOT FOUND 50017O06 POTAMILLA
5001700699 NAME NOT FOUND 50O17006 POTAMILLA
50017007 PSEUDOPOTAMILLA 5OO17OO7 PSEUDOPOTAMILLA
5001700702 PSEUDOPOTAMILLA OCCE 5001700702 PSEUDOPOTAMILLA OCCELATA
5001700703 PSEUDOPOTAMILLA RENI 5O017OO7O3 PSEUDOPOTAMILLA RENIFORMIS
5001700801 SABELLA CRASSICORNIS 5001700801 SABELLA CRASSICORNIS
5001700802 SABELLA MEDIA 5001700802 SABELLA MEDIA
5001700902 SCHIZOBRANCHIA INSIG 5001700902 SCHIZOBRANCHIA INSIGNIS
5001701002 BISPIRA RUGOSA 5001701002 BISPIRA RUGOSA
5001701301 FABRICIA SABELLA 5001701301 FABRICIA SABELLA
5001701302 FABRICIA MINUTA 5001701302 FABRICIA MINUTA
5O017013O3 FABRICIA PACIFICA 50O17O1303 FABRICIA PACIFICA
50017014 LAONOME 50017O14 LAONOME
(continued)
245
-------
TABLE B-3 (continued)
5001701401 LAONOME KROYERI 50017014 LAONOME
50017017 JASMINEIRA 50O17017 JASMINEIRA
5O017099 NAME NOT FOUND 50O170 SABELLIDAE
500173 SERPULIDAE 50O173 SERPULIDAE
50017301 CHITINOPOMA 5OO17301 CHITINOPOMA
5001730101 CHITINOPOMA OCCIDENT 50017301 CHITINOPOMA
5001730202 CRUCIGERA ZYGOPHORA 5001730202 CRUCIGERA ZYGOPHORA
5001730401 SEKPULA VERMICULARIS 5001730401 SERPULA VERMICULARIS
50017305 SPIRORBIS 50017305 SPIRORBIS
5001730501 SPIRORBIS QUADRANGUL 50017305 SPIRORBIS
5001730510 SPIRORBIS NAKAMURAI 50017305 SPIRORBIS
5001730598 NAME NOT FOUND 50017305 SPIRORBIS
5001730599 NAME NOT FOUND 50017305 SPIRORBIS
5001730602 DEXIOSPIRA SPIRILLUM 5001730602 DEXIOSPIRA SPIRILLUM
5002 ARCHIANNELIDA 5002 ARCHIANNELIDA
500202 PROTODRILIDAE 500202 PROTODRILIDAE
50O202O101 PROTODRILUS FLABELLI 500202 PROTODRILIDAE
5002O4 SACCOCIRRIDAE 500204 SACCOCIRRIDAE
50020401 SACCOCIRRUS 500204 SACCOCIRRIDAE
5002040101 SACCOCIRRUS EROTICUS 500204 SACCOCIRRIDAE
500205 POLYGORDIIDAE 500205 POLYGORDIIDAE
50020501 POLYGORDIUS 5O0205 POLYGORDIIDAE
5O04 OLIGOCHAETA 5004 OLIGOCHAETA
50O9O1 ENCHYTRAEIDAE 500901 ENCHYTRAEIDAE
5012 HIRUDINEA 5O12 HIRUDINEA
51 GASTROPODA 51 GASTROPODA
5102030101 HALIOTIS KAMTSCHATKA 5102030101 HALIOTIS KAMTSCHATKANA
5102040204 PUNCTURELLA CUCULLAT 510204O2O4 PUNCTURELLA CUCULLATA
5102040401 DIODORA ASPERA 5102040401 DIODORA ASPERA
510205 ACMAEIDAE 510205 ACMAEIDAE
5102050103 ACMAEA MITRA 5102050103 ACMAEA MITRA +
5102050106 ACMAEA ROSACEA 5102050106 ACMAEA ROSACEA
51020502 COLLISELLA 51020502 COLLISELLA
5102050201 COLLISELLA PELTA 5102050201 COLLISELLA PELTA
5102050202 COLLISELLA DIGITALIS 510205O2O2 COLLISELLA DIGITALIS
5102050203 COLLISELLA OCHRACEA 510205O2O3 COLLISELLA OCHRACEA
5102050301 NOTOACMAEA SCUTUM 510205O3O1 NOTOACMAEA SCUTUM
5102O701O1 CRYPTOBRANCHIA CONCE 5102070101 CRYPTOBRANCHIA CONCENTRICA
51021001 CALLIOSTOMA 51021001 CALLIOSTOMA
5102100103 CALLIOSTOMA LIGATUM 51021001 CALLIOSTOMA
51021003 MARGARITES/LIRULARIA 51021003 MARGARITES/LIRULARIA
5102100302 MARGARITES HELICINUS 5102100302 MARGARITES HELICINUS
5102100308 MARGARITES PUPILLUS 5102100308 MARGARITES PUPILLUS *
5102100310 MARGARITES LIRULATUS 5102100310 MARGARITES LIRULATUS *
51O210O4O2 SOLARIELLA OBSCURA 5102100402 SOLARIELLA OBSCURA
51021005 TEGULA 51021005 TEGULA
5102120201 MOELLERIA QUADRAE 5102120201 MOELLERIA QUADRAE
5103O903 LACUNA 51030903 LACUNA
(continued)
246
-------
TABLE B-3 (continued)
5103090301 LACUNA CARININATA 5103090301 LACUNA CARININATA
5103090302 LACUNA VARIEGATA 5103090302 LACUNA VARIEGATA *
5103100101 LITTORINA SITKANA 5103100101 LITTORINA SITKANA
5103100104 LITTORINA SCUTULATA 5103100104 LITTORINA SCUTULATA
51O32001 ALVINIA 51032O01 ALVINIA +
51032004 BARLEEIA 51032004 BARLEEIA
5103230202 VITRINELLA COLUMBIAN 5103230202 VITRINELLA COLUMBIANA
51034601 BITTIUM 51034601 BITTIUM
5103460103 BITTIUM ESCHRICHTII 51034601 BITTIUM
51O34602 CERITHIOPSIS 51034602 CERITHIOPSIS
5103460203 CERITHIOPSIS STEPHAN 51034602 CERITHIOPSIS
5103530199 NAME NOT FOUND 51035301 MELANELLA
51036202 TRICHOTROPIS 51036202 TRICHOTROPIS
5103620204 TRICHOTROPIS CANCELL 51036202 TRICHOTROPIS
510364 CALYPTRAEIDAE 510364 CALYPTRAEIDAE
5103640101 CALYPTRAEA FASTIGATA 5103640101 CALYPTRAEA FASTIGATA *
51036402 CREPIDULA 51036402 CKEPIDULA
5103640201 CREPIDULA NUMMARIA 5103640201 CKEPIDULA NUMMARIA
5103640203 CREPIDULA ADUNCA 5103640203 CREPIDULA ADUNCA
5103640298 NAME NOT FOUND 51O36402 CREPIDULA
5103640299 NAME NOT FOUND 51036402 CREPIDULA
5103640301 CREPIPATELLA LINGULA 5103640301 CREPIPATELLA LINGULATA
5103660409 VELUTINA LAEVIGATA 5103660409 VELUTINA LAEVIGATA
5103660410 VELUTINA PROLONGATA 5103660410 VELUTINA PROLONGATA
51037602 NATICA 51037602 NATICA !+
5103760201 NATICA ALEUTICA/CLAU 51037602 NATICA !
5103760402 POLINICES PALLIDA 5103760402 POLINICES PALLIDA
5103760406 POLINICES LEWISII 5103760406 POLINICES LEWISII
5103780101 FUSITRITON OREGONENS 5103780101 FUSITRITON OREGONENSIS
5105010101 CERATOSTOMA FOLIATUM 5105010101 CERATOSTOMA FOLIATUM
5105010205 OCENEBRA SCLERA 5105010205 OCENEBRA SCLERA
5105010206 OCENEBRA LURIDA 5105010206 OCENEBRA LURIDA
5105010417 TROPHONOPSIS ORPHEUS 5105010417 TROPHONOPSIS ORPHEUS
51050105 NUCELLA 51050105 NUCELLA
5105010501 NUCELLA CANALICULATA 5105010501 NUCELLA CANALICULATA
5105010502 NUCELLA LAMELLOSA 5105010502 NUCELLA LAMELLOSA
5105010503 NUCELLA EMARGINATA 5105010503 NUCELLA EMARGINATA
510503 PYRENIDAE 510503 PYRENIDAE
5105030101 AMPHISSA COLUMBIANA 5105030101 AMPHISSA COLUMBIANA *
51O5O30102 AMPHISSA RETICULATA 51O5O30102 AMPHISSA RETICULATA
5105030191 NAME NOT FOUND 51050301 AMPHISSA
51050302 MITRELLA 510503O2 MITRELLA !+
5105030202 MITRELLA TUBEROSA 5105030202 MITRELLA TUBEROSA j
5105030204 MITRELLA GOULDI 5105030204 MITRELLA GOULDI !
5105030206 MITRELLA CARINATA 5105030206 MITRELLA CARINATA |
5105040201 SEARLESIA DIRA 5105040201 SEARLESIA DIRA
51050506 MOHNIA 51050506 MOHNIA
51050509 PLICIFUSUS 51050509 PLICIFUSUS
(continued)
247
-------
TABLE B-3 (continued)
51050801 NASSA 51050801 NASSA
5105080101 NASSARIUS MENDIOJS 51050801 NASSA
5105150101 GRANULINA MARGARITUL 5105150101 GRANULINA MARGARITULA
510602 TUKRIDAE 51O602 TURRIDAE
5106020405 OENOPOTA TABULATA 510602 TURRIDAE
510801 PYRAMIDELLIDAE 510801 PYRAMIDELLIDAE
5108O101 ODOSTOMIA 51080101 ODOSTOMIA +
51080102 TURBONILLA 51080102 TURBONILLA 1+
5108010201 TURBONILLA TORQUATA 51080102 TURBONILLA |
5110 CEPHALASPIDEA 5110 CEPHALASPIDEA
51100401 ACTEOCINA 51100401 ACTEOCINA
5110O4O2 CYLICHNA 51100402 CYLICHNA
5110060101 AGLAJA DIOMEDEUM 5110060101 AGLAJA DIOMEDEUM
51100701 GASTROPTERON 51100701 GASTROPTERON
5110070101 GASTROPTERON PACIFIC 51100701 GASTROPTERON
51100901 DIAPHANA 511O0901 DIAPHANA
5110120101 HAMINOEA VESICULA 5110120101 HAMINOEA VESICULA
5110120103 HAMINOEA VIRESCENS 5110120103 HAMINOEA VIRESCENS
51101301 RETUSA 51101301 RETUSA
5124020101 PHYLLAPLYSIA TAYLORI 5124020101 PHYLLAPLYSIA TAYLORI
5127 NUDIBRANCHIA 5127 NUDIBRANCHIA
5130020301 DIAULULA SANDIEGENSI 5130020301 DIAULULA SANDIEGENSIS
5130O303 ARCHIDORIS 513O03O3 ARCHIDORIS
5131 NUDIBRANCHIA DORIDOI 5131 NUDIBRANCHIA DORIDOIDEA PHANEROB
5134O601 DENDRONOTUS 51340601 DENDRONOTUS
5134060103 DENDRONOTUS FRONDOSU 51340601 DENDRONOTUS
51340901 DOTO 51340901 DOTO
5139 NUDIBRANCHIA EOLIDOI 5139 NUDIBRANCHIA EOLIDOIDEA
51410101 EUBRANCHUS 51410101 EUBRANCHUS
514203 AEOLIDIIDAE 514203 AEOLIDIIDAE
5143010101 ONCHIDELLA BOREALIS 5143010101 ONCHIDELLA BOREALIS
53 POLYPLACOPHORA 53 POLYPLACOPHORA
5302010199 NAME NOT FOUND 5302O101 LEPTOCHITON
5302020101 HANLEYA HANLEYI 5302020101 HANLEYA HANLEYI
5303 NEOLORICATA ISCHNOCH 53O3 NEOLORICATA ISCHNOCHITONINA
530302 ISCHNOCHITONIDAE 530302 ISCHNOCHITONIDAE
5303020102 BASILIOCHITON HEATHI 5303020102 BASILIOCHITON HEATHII
5303020201 CYANOPLAX DENTIENS 53O3O202O1 CYANOPLAX DENTIENS
53030203 ISCHNOCHITON 530302O3 ISCHNOCHITON
53O3O2O303 ISCHNOCHITON INTERST 5303020303 ISCHNOCHITON INTERSTINCTUS
5303020309 ISCHNOCHITON KETIPOR 5303020309 ISCHNOCHITON RETIPOROSUS
53030206 TONICELLA 53030206 TONICELLA
5303020601 TONICELLA INSIGNIS 5303020601 TONICELLA INSIGNIS
5303020602 TONICELLA LINEATA 5303020602 TONICELLA LINEATA *
5303020603 TONICELLA MARMOREA 5303020603 TONICELLA MARMOREA
5303020701 LEPIDOZONA MERTENSII 5303020701 LEPIDOZONA MERTENSII +
5303020703 LEPIDOZONA COOPERI 5303020703 LEPIDOZONA COOPERI
530302O8O1 STENOPLAX FALLAX 5303020801 STENOPLAX FALLAX
(continued)
248
-------
TABIiE B-3 (continued)
5303060102 CHAETOPLEURA GEMMA 5303060102 CHAETOPLEORA GEMMA
5303070301 KATHARINA TUNICATA 53O307O3O1 KATHARINA TDNICATA
53030704 MOPALIA 53O3O7O4 MOPALIA
5303070401 MOPALIA CILIATA 5303070401 MOPALIA CILIATA
5303070402 MOPALIA CIRRATA 5303070402 MOPALIA CIRRATA
5303070407 MOPALIA LIGNOSA 5303070407 MOPALIA LIGNOSA
5303070408 MOPALIA MUCOSA 5303070408 MOPALIA MUCOSA
5303070498 NAME NOT FOUND 530307O4 MOPALIA
5303070499 NAME NOT FOUND 53030704 MOPALIA
55 BIVALVIA 55 BIVALVIA
5502020101 ACILA CASTRENIS 550202O1O1 ACILA CASTRENIS
5502020201 NUCULA TENUIS 5502020201 NUCULA TENUIS
5502040202 NUCULANA MINUTA 5502040202 NUCULANA MINUTA
5502040212 NUCULANA HAMATA 5502040212 NUCULANA HAMATA
5502040298 NAME NOT FOUND 55020402 NUCULANA
55020405 YOLDIA 550204O5 YOLDIA
5502040503 YOLDIA MYALIS 5502040503 YOLDIA MYALIS
5502040504 YOLDIA SCISSURATA 5502040504 YOLDIA SCISSURATA
55060601 GLYCYMERIS 5506O601 GLYCYMERIS
5506060101 GLYCYMERIS SUBOBSOLE 550606O1O1 GLYCYMERIS SUBOBSOLETA
5506060104 GLYCYMERIS SEPTENTRI 5506060104 GLYCYMERIS SEPTENTRIONALIS j
55070101 MYTILUS 550701O1 MYTILUS
5507010101 MYTILUS EDULIS 55070101 MYTILUS
5507010201 CRENELLA DECUSSATA 550701O2O1 CRENELLA DECUSSATA +
5507O104 MUSCULUS 55070104 MUSCULUS
5507010401 MUSCULUS NIGER 5507010401 MUSCULUS NIGER
5507010402 MUSCULUS DISCORS 5507010402 MUSCULUS DISCORS
5507O106 MODIOLUS 55070106 MODIOLUS !+
5507010603 MODIOLUS RECTUS 55070106 MODIOLUS !
5507010699 NAME NOT FOUND 550701O6 MODIOLUS !
5509050101 CHLAMYS HASTATA 5509050101 CHLAMYS HASTATA
5509050401 PECTEN CAURINUS 5509050401 PECTEN CAURINUS
5509090101 PODODESMUS MACROCHIS 5509O9O101 PODODESMUS MACROCHISMA
5509090103 PODODESMUS CEPIO 55O909O103 PODODESMUS CEPIO
5515 VENEROIDA 5515 VENEROIDA
55150101 PARVILUCINA 55150101 PARVILUCINA !+
5515010101 PARVILUCINA TENUISCU 55150101 PARVILUCINA !
55150102 LUCINOMA 551501O2 LUCINOMA
55150103 LUCINA 5515O103 LUCINA
5515020201 AXINOPSIDA SERRICATA 551502O2O1 AXINOPSIDA SERRICATA
5515070101 LASAEA CISTULA 5515070101 LASAEA CISTULA
5515100102 MYSELLA TUMIDA 5515100102 MYSELLA TUMIDA *
551517 CARDITIDAE 551517 CARDITIDAE
55151701 CYCLOCARDIA 55151701 CYCLOCARDIA
5515170101 CYCLOCARDIA VENTRICO 5515170101 CYCLOCARDIA VENTRICOSA
5515170102 CYCLOCARDIA CREBRICO 5515170102 CYCLOCARDIA CREBRICOSTATA
5515170103 CYCLOCARDIA UMNAKA 5515170103 CYCLOCARDIA UMNAKA
5515170105 CYCLOCARDIA CRASSIDE 5515170105 CYCLOCARDIA CRASSIDENS
(continued)
249
-------
TABLE B-3 (continued)
5515170201 MIONTODISCUS PROLONG 5515170201 MIONTODISCOS PROLONGATUS
5515170402 CAKDITA VENTRICOSA 5515170402 CARDITA VENTRICOSA
5515190102 ASTARTE ALASKENSIS 5515190102 ASTARTE ALASKENSIS
5515190105 ASTARTE COMPACTA 5515190105 ASTARTE COMPACTA
551522 CARDIIDAE 551522 CARDIIDAE
55152201 CLINOCARDIOM 55152201 CLINOCARDIUM | +
5515220101 CLINOCARDIOM CILIATO 5515220101 CLINOCARDIOM CILIATOM |
5515220102 CLINOCARDIOM NOTTALL 5515220102 CLINOCARDIOM NOTTALLII |
5515220104 CLINOCARDIOM CALIFOR 5515220104 CLINOCARDIOM CALIFORNIENSE j
5515220301 NEMOCARDIOM CENTIFOL 5515220301 NEMOCARDIOM CENTIFOLIOM
55152298 NAME NOT FOOND 551522 CARDIIDAE
5515229999 NAME NOT FOOND 551522 CARDIIDAE
551525O1 SPISOLA 55152501 SPISOIA
5515250201 TRESOS CAPAX 5515250201 TRESOS CAPAX
551529 SOLENIDAE 551529 SOLENIDAE
55152902 SOLEN 551529 SOLENIDAE
5515290201 SOLEN SICARIOS 551529 SOLENIDAE
55153101 MACOMA 55153101 MACOMA !+
5515310101 MACOMA CALCAREA 5515310101 MACOMA CALCAREA \
5515310102 MACOMA ELIMATA 5515310102 MACOMA ELIMATA |
5515310106 MACOMA OBLIQOA 5515310106 MACOMA OBLIQOA j
5515310107 MACOMA MOESTA 5515310107 MACOMA MOESTA |
5515310108 MACOMA CRASSOLA 5515310108 MACOMA CRASSOLA j
5515310111 MACOMA YOLDIFORMIS 5515310111 MACOMA YOLDIFORMIS j
5515310112 MACOMA CARLOTTENSIS 5515310112 MACOMA CARLOTTENSIS j
5515310114 MACOMA NASOTA 5515310114 MACOMA NASOTA |
5515310115 MACOMA INQOINATA 5515310115 MACOMA INQOINATA |
5515310116 MACOMA BALTHICA 5515310116 MACOMA BALTHICA j
5515310117 MACOMA SECTA 5515310117 MACOMA SECTA !
551531O2 TELLINA 55153102 TELLINA | +
5515310203 TELLINA CARPENTERI 5515310203 TELLINA CARPENTERI j
5515310204 TELLINA MODESTA 5515310204 TELLINA MODESTA |
5515350101 SEMELE ROBROPICTA 5515350101 SEMELE ROBROPICTA
551547O1 TRANSENNELLA 55154701 TRANSENNELLA | +
5515470101 TRANSENNELLA TANTILL 55154701 TRANSENNELLA !
5515470201 SAXIDOMOS GIGANTEA 5515470201 SAXIDOMOS GIGANTEA
5515470301 COMPSOMYAX SOBDIAPHA 5515470301 COMPSOMYAX SOBDIAPHANA
5515470501 PSEPHIDIA LORDI 5515470501 PSEPHIDIA LORDI *
5515470601 HOMILARIA KENNERLYI 5515470601 HOMILARIA KENNERLYI
551547O7 PROTOTHACA 55154707 PROTOTHACA
5515470701 PROTOTHACA STAMINEA 5515470701 PROTOTHACA STAMINEA *
5515470702 PROTOTHACA TENERRIMA 5515470702 PROTOTHACA TENERRIMA
5515470801 TAPES PHILIPPINAROM 5515470801 TAPES PHILIPPINAROM
5517010101 CRYPTOMYA CALIFORNIC 5517010101 CRYPTOMYA CALIFORNICA
551701O2 MYA 55170102 MYA
5517010201 MYA ARENARIA 5517010201 MYA ARENARIA +
5517010203 MYA TRUNCATA 5517010203 MYA TRONCATA
5517O1O2O5 MYA ELEGANS 5517010205 MYA ELEGANS
(continued)
250
-------
TABLE B-3 (continued)
5517060201
5517O6O401
5518010101
5520020102
552OO5O101
5520050202
5520050301
5520100103
56
6001
600101
6001010199
6001040201
6001040204
60010403
6001060102
6O010602
6001060302
61
6110
6117
6118
611801
6119
6120
612008
61340201
6134020102
6134020103
6134020104
6134020107
6134020110
6134020111
61450101
6145010102
6151
615301O1
615301O1O2
6153010107
6153010301
6153010901
6153011403
6153011509
6154
615401
61540101
6154010103
6154010104
HIATELIA ARCTICA
PANOPEA GENEROSA
ZIRFAEA PILSBURYI
PANDORA PILOSA
ENTODESMA SAXICOLOM
LYONSIA CALIFORNICA
MYTILIMERIA NUTTALLI
CARDIOMYA OLDROYDI
SCAPHOPODA
PANTOPODA
NYMPHONIDAE
NAME NOT FOUND
ACHELIA CHELATA
ACHEL1A NUDIUSCULA
AMMOTHELLA
PHOXICHILIDIUM FEMOR
ANOPLODACTYLUS
HALOSOMA COMPACTUM
ARTHROPODA MANDIBULA
OSTRACODA
COPEPODA
COPEPODA CALANOIDA
CALANIDAE
COPEPODA HARPACTICOI
COPEPODA CYCLOPOIDA
CYCLOPIDAE
BALANUS
BALANUS BALANUS
BALANUS CARIOSUS
BALANUS CRENATUS
BALANUS GLANDULA
BALANUS NUBILIS
BALANUS ROSTRATUS
NEBALIA
NEBALIA PUGETTENSIS
PERACARIDA MYSIDACEA
ACANTHOMYSIS
ACANTHOMYSIS DAVISI
ACANTHOMYSIS SCULPTA
ARCHAEOMYSIS GREBNIT
HOLMESIELLA ANOMALA
MYSIS OCULATA
NEOMYSIS INTEGER
PERACARIDA CUMACEA
LAMPROPIDAE
LAMPROPS
LAMPROPS FASCIATA
LAMPROPS CARINATA
5517060201 HIATELIA ARCTICA
5517060401 PANOPEA GENEROSA
5518010101 ZIRFAEA PILSBURYI
5520020102 PANDORA FILOSA
5520050101 ENTODESMA SAXICOLUM
5520050202 LYONSIA CALIFORNICA
5520050301 MYTILIMERIA NUTTALLII
5520100103 CARDIOMYA OLDROYDI
56 SCAPHOPODA
60O1 PANTOPODA
600101 NYMPHONIDAE
600101 NYMPHONIDAE
6001040201 ACHELIA CHELATA
6001040204 ACHELIA NUDIUSCULA
60010403 AMMOTHELLA
6001060102 PHOXICHILIDIUM FEMORATUM
60010602 ANOPLODACTYLUS
6001060302 HALOSOMA COMPACTUM
61 ARTHROPODA MANDIBULATA CRUSTACEA
6110 OSTRACODA
6117 COPEPODA
6118 COPEPODA CALANOIDA
6118 COPEPODA CALANOIDA
6119 COPEPODA HARPACTICOIDA
6120 COPEPODA CYCLOPOIDA
6120 COPEPODA CYCLOPOIDA
6134O201 BALANUS
6134020102 BALANUS BALANUS
6134020103 BALANUS CARIOSUS
6134020104 BALANUS CRENATUS
6134020107 BALANUS GLANDULA
6134020110 BALANUS NUBILIS
6134020111 BALANUS ROSTRATUS
61450101 NEBALIA |
61450101 NEBALIA i
6151 PERACARIDA MYSIDACEA
6153O101 ACANTHOMYSIS
6153010102 ACANTHOMYSIS DAVISI
6153010107 ACANTHOMYSIS SCULPTA
6153010301 ARCHAEOMYSIS GREBNITZKII
6153010901 HOLMESIELLA ANOMALA
6153011403 MYSIS OCULATA
6153O115O9 NEOMYSIS INTEGER
6154 PERACARIDA CUMACEA
615401 LAMPROPIDAE
61540101 LAMPROPS
6154010103 LAMPROPS FASCIATA
6154010104 LAMPROPS CARINATA
(continued)
251
-------
TABLE B-3 (continued)
61540102 HEMILAMPROPS 61540102 HEMILAMPROPS
61540402 EUDORELLA 61540402 EUDORELLA
615404O3 EODORELLOPS1S 61540403 EUDORELLOPSIS
61540501 DIASTYLIS 61540501 DIASTYLIS
61540502 DIASTYLOPSIS 6154O502 DIASTYLOPSIS |
6154050202 DIASTYLOPSIS TENUIS 61540502 DIASTYLOPSIS j
6154050299 NAME NOT FOUND 61540502 DIASTYLOPSIS |
615405O4 LEPTOSTYLIS 615405O4 LEPTOSTYLIS
61540505 COLUROSTYLIS 615405O5 COLOROSTYLIS
61540508 OXYOROSTYLIS 61540508 OXYUROSTYLIS
6154O7O1 CAMPYLASPIS 61540701 CAMPYLASPIS
615408O1 CUMELLA 6154O801 CUMELLA |
6154080102 CUMELLA VULGARIS 61540801 CUMELLA |
615409 BODOTRIIDAE 615409 BODOTRIIDAE
615409O3 LEPTOCUMA/PSEUDOLEPT 615409 BODOTRIIDAE
6157 PERACARIDA TANAIDACE 6157 PERACARIDA TANAIDACEA DIKONOPHOR
615701 TANAIDAE 615701 TANAIDAE
6157010301 ANATANAIS NORMANl 6157010301 ANATANAIS NORMANI
6157010401 PANCOLUS CALIFORNIEN 6157010401 PANCOLUS CALIFORNIENSIS
615702 PARATANAIDAE 615702 PARATANAIDAE
61570201 LEPTOCHELIA (TANAI 615702O1 LEPTOCHELIA (TANAIDACEA) |
6157020101 LEPTOCHELIA SAVIGNYI 6157020101 LEPTOCHELIA SAVIGNYI |
6157020103 LEPTOCHELIA DUBIA 6157020103 LEPTOCHELIA DUBIA |
6157020199 NAME NOT FOUND 615702O1 LEPTOCHELIA (TANAIDACEA) |
6158 PERACARIDA ISOPODA 6158 PERACARIDA ISOPODA
616001 ANTHURIDAE 616001 ANTHURIDAE
6160010299 NAME NOT FOUND 616001 ANTHURIDAE
6160010501 PARANTHURA ELEGANS 616001 ANTHURIDAE
6160019999 NAME NOT FOUND 616O01 ANTHURIDAE
6161 PERACARIDA ISOPODA F 6161 PERACARIDA ISOPODA FLABELLIFERA
6161010102 CIROLANA HARFORDI 6161010102 CIROLANA HARFORDI
6161010107 CIROLANA VANCOUVEREN 6161O1O1O7 CIROLANA VANCOUVERENSIS
616102 SPHAEROMATIDAE 616102 SPHAEROMATIDAE
61610201 TECTICEPS 6161O2O1 TECTICEPS
6161020301 GNORIMOSPHAEROMA ORE 6161020301 GNORIMOSPHAEROMA OREGONENSIS
616102O4 EXOSPHAEROMA 616102O4 EXOSPHAEROMA
6161020401 EXOSPHAEROMA AMPLICA 6161020401 EXOSPHAEROMA AMPLICAUDA
6161020402 EXOSPHAEROMA MEDIA 6161020402 EXOSPHAEROMA MEDIA
6161O2O403 EXOSPHAEROMA RHOMBUR 6161020403 EXOSPHAEROMA RHOMBURUM
6161020501 DYNAMENELLA SHEARERI 6161020501 DYNAMENELLA SHEARERI
6161020502 DYNAMENELLA GLABRA 6161020502 DYNAMENELLA GLABRA
616102O5O3 DYNAMENELLA DILATATA 6161020503 DYNAMENELLA DILATATA
6161050102 LIMNORIA ALGARUM 6161050102 LIMNORIA ALGARUM
6161070101 AEGA SYMMETRICA 6161070101 AEGA SYMMETRICA
61610702 ROCINELA 6161O702 ROCINELA
6162 PERACARIDA ISOPODA V 6162 PERACARIDA ISOPODA YALVIFERA
61620202 SYNIDOTEA 6162O202 SYNIDOTEA
616202O2O1 SYNIDOTEA BICUSPIDA 6162020201 SYNIDOTEA BICUSPIDA
(continued)
252
-------
TABLE B-3 (continued)
6162020205 SYNIDOTEA NODDLOSA 616202O2O5 SYNIDOTEA NODULOSA
6162020209 SYNIDOTEA PETTIBONEA 6162020209 SYNIDOTEA PETTIBONEAE
61620203 IDOTEA 61620203 IDOTEA | +
6162020301 IDOTEA RESECATA 6162020301 IDOTEA RESECATA j
6162020302 IDOTEA WOSNESENSKII 6162020302 IDOTEA WOSNESENSKII j
6162020303 IDOTEA FEWKESI 6162020303 IDOTEA FEWKESI j
6162020304 IDOTEA RUFESCENS 6162020304 IDOTEA RUFESCENS |
6162020305 IDOTEA OCHOTENSIS 6162020305 IDOTEA OCHOTENSIS \
6162020307 IDOTEA ACU1EATA 6162020307 IDOTEA ACULEATA j
6162020312 IDOTEA SCHMITTI 6162020312 IDOTEA SCHMITTI |
6162020313 IDOTEA MONTEREYENSIS 6162020313 IDOTEA MONTEREYENSIS |
6162020799 NAME NOT FOUND 61620207 EDOTEA
616302 ASELLIDAE 616302 ASELLIDAE
61630201 IANIROPSIS 61630201 IANIROPSIS
6163020101 IANIROPSIS KINCAIDI 6163020101 IANIROPSIS KINCAIDI +
6163020102 IANIROPSIS PUGETTENS 6163020102 IANIROPSIS PUGETTENSIS
6163020103 IANIROPSIS ANALOGA 6163020103 IANIROPSIS ANALOGA
6163020106 IANIROPSIS TRIDENS 6163020106 IANIROPSIS TRIDENS
6163020198 NAME NOT FOUND 61630201 IANIROPSIS
6163O2O199 NAME NOT FOUND 61630201 IANIROPSIS
6163020306 JANIRALATA OCCIDENTA 6163020306 JANIRALATA OCCIDENTALIS
61631101 JAEROPSIS 61631101 JAEROPSIS
6163110101 JAEROPSIS LOBATA 6163110101 JAEROPSIS LOBATA
6163110102 JAEROPSIS SETOSA 616311O1O2 JAEROPSIS SETOSA
6163110103 JAEROPSIS DUBIA 6163110103 JAEROPSIS DUBIA
6163110199 NAME NOT FOUND 61631101 JAEROPSIS
61631201 MUNNA 61631201 MUNNA
6163120101 MUNNA STEPHENSENI 6163120101 MUNNA STEPHENSENI
6163120102 MUNNA CHROMATOCEPHAL 6163120102 MUNNA CHROMATOCEPHALA
6163120103 MUNNA UBIQUITA 6163120103 MUNNA UBIQUITA
6163129999 NAME NOT FOUND 616312 MUNNIDAE
616504 BOPYRIDAE 616504 BOPYRIDAE
6165040201 ARGEIA PUGETTENSIS 6165040201 ARGEIA PUGETTENSIS
6165040701 PHYLLODURUS ABDOMINA 6165040701 PHYLLODURUS ABDOMINALIS
6169 PERACARIDA AMPHIPODA 6169 GAMMARID AMPHIPOD
6169O1O399 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
61690201 AMPELISCA 61690201 AMPELISCA j+
6169020101 AMPELISCA MACROCEPHA 61690201 AMPELISCA |
6169020111 AMPELISCA AGASSIZI 61690201 AMPELISCA j
6169020112 AMPELISCA CRISTATA 6169O2O1 AMPELISCA |
6169020114 AMPELISCA PUGETICA 61690201 AMPELISCA !
6169020197 NAME NOT FOUND 61690201 AMPELISCA |
6169020198 NAME NOT FOUND 61690201 AMPELISCA |
6169020199 NAME NOT FOUND 61690201 AMPELISCA |
6169020203 BYBLIS SERRATA 61690202 BYBLIS
6169020299 NAME NOT FOUND 61690202 BYBLIS
6169030202 NAME NOT FOUND 61690302 AMPHILOCHUS
6169030299 NAME NOT FOUND 6169O302 AMPHILOCHUS
(continued)
253
-------
TABLE B-3 (continued)
6169O4O1
6169040104
6169040116
6169040117
6169040118
6169O4O196
6169040197
6169040198
6169040199
6169060202
6169070101
61690901
6169090101
6169090105
6169090108
6169O9O199
616912O2
6169120901
6169121001
61691502
6169150203
6169150302
616917
6169170299
6169170301
616920
6169200199
6169201003
61692012
6169201203
6169201208
6169201297
6169201299
616921
61692101
6169210106
6169210109
61692102
6169210202
6169210299
6169210302
61692108
6169210899
61692109
6169210999
61692110
6169211OO3
6169211005
AMPHITHOE
AMPHITHOE SIMULANS
AMPHITHOE YALIDA
AMPHITHOE HUMERALIS
AMPHITHOE LACERTOSA
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
NAME NOT FOUND
AOROIDES COLUMBIAE
ARGISSA HAMATIPES
ATYLUS
ATYLUS TRIDENS
ATYLUS COLLINGI
ATYLUS LEVIDENSUS
NAME NOT FOUND
CALLIOPIUS
OLIGOCHINUS LIGHTI
CALLIOPIELLA PRATTI
COROPHIUM
COROPHIUM CRASSICORN
ERICTHONIUS BRASILIE
DEXAMINIDAE
NAME NOT FOUND
POLYCHERIA OSBORNI
EUSIRIDAE
NAME NOT FOUND
PARAMOERA MOHRI
PONTOGENEIA
PONTOGENEIA INERMIS
PONTOGENEIA ROSTRATA
NAME NOT FOUND
NAME NOT FOUND
GAMMARIDAE
ANISOGAMMARUS
ANISOGAMMARUS PUGETT
ANISOGAMMARUS CONFER
CERADOCUS
CERADOCUS SPINICAUDU
NAME NOT FOUND
ELASMOPUS ANTENNATUS
MAERA
NAME NOT FOUND
MEGALUROPUS
NAME NOT FOUND
MELITA (AMPHIPODA
MELITA DENTATA
MELITA CALIFORNICA
61690401 AMPHITHOE
61690401 AMPHITHOE
61690401 AMPHITHOE
61690401 AMPHITHOE
61690401 AMPHITHOE
61690401 AMPHITHOE
61690401 AMPHITHOE
61690401 AMPHITHOE
61690401 AMPHITHOE
6169060202 AOROIDES COLUMBIAE
6169070101 ARGISSA HAMATIPES
61690901 ATYLUS
61690901 ATYLUS
61690901 ATYLUS
616909O1 ATYLUS
61690901 ATYLUS
61691202 CALLIOPIUS
6169120901 OLIGOCHINUS LIGHTI
6169121001 CALLIOPIELLA PRATTI
61691502 COROPHIUM
61691502 COROPHIUM
6169150302 ERICTHONIUS BRASILIENSIS
616917 DEXAMINIDAE
616917 DEXAMINIDAE
616917 DEXAMINIDAE
616920 EUSIRIDAE
616920 EUSIRIDAE
6169201003 PARAMOERA MOHRI
61692012 PONTOGENEIA
61692012 PONTOGENEIA
61692012 PONTOGENEIA
61692012 PONTOGENEIA
61692012 PONTOGENEIA
616921 GAMMARIDAE
61692101 ANISOGAMMARUS
616921O1 ANISOGAMMARUS
61692101 ANISOGAMMARUS
61692102 CERADOCUS
616921O2 CERADOCUS
61692102 CERADOCUS
6169210302 ELASMOPUS ANTENNATUS
61692108 MAERA
61692108 MAERA
61692109 MEGALUROPUS
61692109 MEGALUROPUS
61692110 MELITA (AMPHIPODA)
61692110 MELITA (AMPHIPODA)
61692110 MELITA (AMPHIPODA)
1 +
(continued)
254
-------
TABLE B-3 (continued)
6169211008 MELITA DESDICHADA 61692110 MELITA (AMPHIPODA) |
6169211099 NAME NOT FOUND 61692110 MELITA (AMPHIPODA) {
616922 HAUSTORIIDAE 616922 HAUSTORIIDAE
61692201 EOHAUSTORIUS 61692201 EOHAUSTORIUS
6169220101 EOHAUSTORIUS WASHING 61692201 EOHADSTORIUS
6169220199 NAME NOT POUND 61692201 EOHAUSTORIUS
61692202 PONTOPOREIA (AMPHI 61692202 PONTOPOREIA (AMPHIPODA)
6169220201 PONTOPOREIA FEMORATA 61692202 PONTOPOREIA (AMPHIPODA)
61692303 NAJNA 61692303 NAJNA
6169230301 NAONA CONSILIORUM 61692303 NAJNA
6169240107 ALLORCHESTES ANCEPS 6169240107 ALLORCHESTES ANCEPS
61692402 HYALE 61692402 HYALE ',+
6169240201 HYALE RUBRA 61692402 HYALE i
6169240205 HYALE PUGETTENSIS 61692402 HYALE j
61692404 PARALLORCHESTES 61692404 PARALLORCHESTES
6169240401 PARALLORCHESTES OCHO 61692404 PARALLORCHESTES
616926 ISAEIDAE 616926 ISAEIDAE
61692602 PHOTIS 61692602 PHOTIS !+
6169260201 PHOTIS BREVIPES 61692602 PHOTIS
6169260205 PHOTIS FISCHMANNI 61692602 PHOTIS
6169260207 PHOTIS DENTATA 61692602 PHOTIS
6169260297 NAME NOT FOUND 61692602 PHOTIS
6169260298 NAME NOT FOUND 61692602 PHOTIS
6169260299 NAME NOT FOUND 616926O2 PHOTIS
61692603 PROTOMEDEIA 61692603 PROTOMEDEIA +
6169260399 NAME NOT FOUND 61692603 PROTOMEDEIA
61692604 GAMMAROPSIS 61692604 GAMMAROPSIS +
6169260401 GAMMAROPSIS THOMPSON 61692604 GAMMAROPSIS
6169260498 NAME NOT FOUND 61692604 GAMMAROPSIS
6169260499 NAME NOT FOUND 61692604 GAMMAROPSIS
6169260599 NAME NOT FOUND 616926 ISAEIDAE
6169269999 NAME NOT FOUND 616926 ISAEIDAE
61692702 ISCHYROCERUS 61692702 ISCHYROCERUS |+
6169270202 ISCHYROCERUS ANGUIPE 61692702 ISCHYROCERUS i
6169270302 JASSA FALCATA 6169270302 JASSA FALCATA
6169279999 NAME NOT FOUND 616927 ISCHYROCERIDAE
616934 LYSIANASSIDAE 616934 LYSIANASSIDAE
61693403 ANONYX 616934O3 ANONYX
6169340302 ANONYX NUGAX 61693403 ANONYX
6169340312 ANONYX LATICOXAE 61693403 ANONYX
6169340397 NAME NOT FOUND 61693403 ANONYX
6169340398 NAME NOT FOUND 61693403 ANONYX
61693414 HIPPOMEDON 61693414 HIPPOMEDON
6169341402 HIPPOMEDON DENTICULA 61693414 HIPPOMEDON
6169341499 NAME NOT FOUND 61693414 HIPPOMEDON
6169342199 NAME NOT FOUND 61693421 LEPIDEPECREUM
61693422 LYSIANASSA 61693422 LYSIANASSA
61693429 ORCHOMENE 61693429 ORCHOMENE j+
(continued)
255
-------
TABLE B-3 (continued)
6169342902 ORCHOMENE NANA 61693429
6169342904 ORCHOMENE PINQUIS 61693429
6169342999 NAME NOT FOUND 61693429
6169349999 NAME NOT FOUND 616934
6169370816 MONOCULODES ZERNOVI 616937O8
6169370899 NAME NOT FOUND 61693708
61693714 SYNCHELIDIUM 61693714
6169371402 SYNCHELIDIUM SHOEMAK 61693714
6169371403 SYNCHELIDIUM RECTIPA 61693714
6169371498 NAME NOT FOUND 61693714
6169371499 NAME NOT FOUND 61693714
61693715 WESTWOODILLA 61693715
6169371502 WESTWOODILLA CAECULA 61693715
616942 PHOXOCEPHALIDAE 616942
61694209 PARAPHOXUS 616942
6169420918 PARAPHOXUS ROBUSTUS 616942
6169420921 PARAPHOXUS MILLERI 616942
6169420924 PARAPHOXUS OBTUSIDEN 616942
6169420926 PARAPHOXUS VARIATUS 616942
6169420927 PARAPHOXUS EPISTOMUS 616942
6169420928 PARAPHOXUS SPINOSUS 616942
6169420997 NAME NOT FOUND 616942
6169420999 NAME NOT FOUND 616942
616943 PLEUSTIDAE 616943
61694303 PARAPLEUSTES 61694303
6169430301 PARAPLEUSTES NAUTILU 61694303
6169430302 PARAPLEUSTES PUGETTE 61694303
6169430399 NAME NOT FOUND 61694303
61694304 PLEUSTES 616943O4
6169430408 PLEUSTES DEPRESSA 61694304
6169430499 NAME NOT FOUND 61694304
61694305 PLEUSYMTES 616943O5
6169430501 PLEUSYMTES SUBGLABER 61694305
6169430599 NAME NOT FOUND 61694305
61694307 PLEUSIRUS 61694307
6169430701 PLEUSIRUS SECORRUS 61694307
6169439999 NAME NOT FOUND 616943
61694401 DULICHIA (AMPHIPO 61694401
6169440199 NAME NOT FOUND 61694401
61694404 PODOCERUS 61694404
6169440401 PODOCERUS CRISTATUS 61694404
6169440499 NAME NOT FOUND 61694404
616948 STENOTHOIDAE 616948
61694811 STENOTHOIDES 616948
6169481102 STENOTHOIDES BERINGI 616948
61695005 TIRON 61695O05
6169500502 TIRON BIOCULATA 61695O05
616951O1 ORCHESTIA 616951O1
(continued)
ORCHOMENE
ORCHOMENE
ORCHOMENE
LYSIANASSIDAE
MONOCULODES
MONOCULODES
SYNCHELIDIUM
SYNCHELIDIUM
SYNCHELIDIUM
SYNCHELIDIUM
SYNCHELIDIUM
WESTWOODILLA
WESTWOODILLA
PHOXOCEPHALIDAE
PHOXOCEPHALIDAE
PHOXOCEPHALIDAE
PHOXOCEPHALIDAE
PHOXOCEPHALIDAE
PHOXOCEPHALIDAE
PHOXOCEPHALIDAE
PHOXOCEPHALIDAE
PHOXOCEPHALIDAE
PHOXOCEPHALIDAE
PLEUSTIDAE
PARAPLEUSTES
PARAPLEUSTES
PARAPLEUSTES
PARAPLEUSTES
PLEUSTES
PLEUSTES
PLEUSTES
PLEUSYMTES
PLEUSYMTES
PLEUSYMTES
PLEUSIRUS
PLEUSIRUS
PLEUSTIDAE
DULICHIA (AMPHIPODA)
DULICHIA (AMPHIPODA)
PODOCERUS
PODOCERUS
PODOCERUS
STENOTHOIDAE
STENOTHOIDAE
STENOTHOIDAE
TIRON
TIRON
ORCHESTIA
1 +
256
-------
TABLE B-3 (continued)
6169731499 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999978 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999979 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999987 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999989 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999990 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999991 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999992 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999997 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999998 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6169999999 NAME NOT FOUND 6169 GAMMARID AMPHIPOD
6170010103 HYPERIA MEDUSARUM 6170010103 HYPERIA MEDUSARUM
6171 PERACARIDA AMPHIPODA 6171 PERACARIDA AMPHIPODA CAPRELLIDEA
617101 CAPRELLIDAE 617101 CAPRELLIDAE
6171010201 DEUTELLA CALIFORNICA 6171010201 DEUTELLA CALIFORNICA
61710104 METACAPRELLA 61710104 METACAPRELLA ! +
6171010401 METACAPRELLA KENNERL 6171010401 METACAPRELLA KENNERLYI i
6171010402 METACAPRELLA ANOMALA 6171010402 METACAPRELLA ANOMALA !
6171010601 TRITELLA LAEVIS 6171010601 TRITELLA LAEVIS
6171010602 TRITELLA PILIMANA 6171010602 TRITELLA PILIMANA +
61710107 CAPRELLA (AMPHIPO 61710107 CAPRELLA (AMPHIPODA) !+
6171010708 CAPRELLA IRREGULARIS 6171010708 CAPRELLA IRREGULARIS
6171010709 CAPRELLA GRACILIOR 6171010709 CAPRELLA GRACILIOR
6171010710 CAPRELLA LAEVIUSCULA 6171010710 CAPRELLA LAEVIUSCULA
6171010714 CAPRELLA FERREA 6171010714 CAPRELLA FERREA
6171010715 CAPRELLA AUGUSTA 6171010715 CAPRELLA AUGUSTA
6171010717 CAPRELLA CALIFORNICA 6171010717 CAPRELLA CALIFORNICA
6171010719 CAPRELLA MENDAX 6171010719 CAPRELLA MENDAX
6171010722 CAPRELLA STRIATA 6171010722 CAPRELLA STRIATA
6175 EUCARIDA DECAPODA(AR 6175 EUCARIDA DECAPODA(ARTHROPODA)
6179 EUCARIDA DECAPODA PL 6179 EUCARIDA DECAPODA PLEOCYEMATA CA
617916 HIPPOLYTIDAE 617916 HIPPOLYTIDAE
6179160102 HIPPOLYTE CLARKI 6179160102 HIPPOLYTE CLARKI
61791602 SPIRONTOCARIS 61791602 SPIRONTOCARIS
6179160201 SPIRONTOCARIS PRIONO 61791602 SPIRONTOCARIS
61791603 LEBBEUS 61791603 LEBBEUS
61791604 EUALUS 61791604 EUALUS
6179160409 EUALUS HERDMANI 61791604 EUALUS
61791605 HEPTACARPUS 61791605 HEPTACARPUS
6179160501 HEPTACARPUS DECORA 6179160501 HEPTACARPUS DECORA
6179160503 HEPTACARPUS STYLUS 6179160503 HEPTACARPUS STYLUS
6179160506 HEPTACARPUS KINCAIDI 6179160506 HEPTACARPUS KINCAIDI
6179160510 HEPTACARPUS BREVIROS 6179160510 HEPTACARPUS BREVIROSTRIS
6179160511 HEPTACARPUS STIMPSON 6179160511 HEPTACARPUS STIMPSONI
6179160512 HEPTACARPUS PALUDICO 6179160512 HEPTACARPUS PALUDICOLA
6179160517 HEPTACARPUS PALPATOR 6179160517 HEPTACARPUS PALPATOR
61791801 PANDALUS 61791801 PANDALUS
6179180104 PANDALUS MONTAGUI 6179180104 PANDALUS MONTAGUI
(continued)
257
-------
TABLE B-3 (continued)
6179180108 PANDALUS STENOLEPIS 6179180108 PANDALUS STENOLEPIS
617922 CRANGON CALIFORNIENS 617922 CRANGON CALIFORNIENSIS
61792201 CRANGON 61792201 CRANGON
617922O1O1 CRANGON NIGRICAUDA 6179220101 CRANGON NIGRICAUDA
6179220102 CRANGON ALASKENSIS 6179220102 CRANGON ALASKENSIS
6179220106 CRANGON DALLI 6179220106 CRANGON DALLI
6179220115 CRANGON MUNITELLA 6179220115 CRANGON MUNITELLA
6179220116 CRANGON RESIMA 6179220116 CRANGON RESIMA
6179220202 SCLEROCRANGON ALATA 6179220202 SCLEROCRANGON ALATA
6179220302 ARGIS DENTATA 6179220302 ARGIS DENTATA
618304 CALLIANASSIDAE 618304 CALLIANASSIDAE
6183040101 UPOGEBIA PUGETTENSIS 6183040101 UPOGEBIA PUGETTENSIS +
6183040204 CALLIANASSA CALIFORN 6183O4O204 CALLIANASSA CALIFORNIENSIS
618306 PAGURIDAE 618306 PAGURIDAE
61830601 PAGURISTES 618306O1 PAGURISTES
61830602 PAGURUS (DECAPODA) 61830602 PAGURUS (DECAPODA)
6183060203 PAGURUS ALEUTICUS 6183060203 PAGURUS ALEUTICUS
6183060205 PAGURUS CAPILLATUS 6183060205 PAGURUS CAPILLATUS
6183060206 PAGURUS SETOSUS 6183060206 PAGURUS SETOSUS
6183060207 PAGURUS KENNERLYI 6183060207 PAGURUS KENNERLYI
6183060208 PAGURUS CAURINUS 6183060208 PAGURUS CAURINUS
6183060209 PAGURUS BERINGANUS 6183060209 PAGURUS BERINGANUS *
6183060213 PAGURUS HIRSUTIUSCUL 6183060213 PAGURUS HIRSUTIUSCULUS
6183060223 PAGURUS DALLI 6183060223 PAGURUS DALLI
6183060301 ELASSOCHIRUS TENUIMA 6183060301 ELASSOCHIRUS TENUIMANUS
6183060303 ELASSOCHIRUS GILLI 6183060303 ELASSOCHIRUS GILLI
6183060401 LABIDOCHIRUS SPLENDE 6183060401 LABIDOCHIRUS SPLENDESCENS
6183060501 DISCORSOPAGURUS SCHM 6183060501 DISCORSOPAGURUS SCHMITTI
6183080202 HAPALOGASTER MERTENS 6183080202 HAPALOGASTER MERTENSII
6183080601 PHYLLOLITHODES PAPIL 6183080601 PHYLLOLITHODES PAPILLOSUS
61830811 CRYPTOLITHODES 61830811 CRYPTOLITHODES
6183081101 CRYPTOLITHODES SITCH 6183081101 CRYPTOLITHODES SITCHENSIS
6183081102 CRYPTOLITHODES TYPIC 6183081102 CRYPTOLITHODES TYPICUS
6183120101 PETROLISTHES ERIOMER 6183120101 PETROLISTHES ERIOMERUS
6183120201 PACHYCHELES PUBESCEN 6183120201 PACHYCHELES PUBESCENS
6184 EUCARIDA DECAPODA PL 6184 EUCARIDA DECAPODA PLEOCYEMATA BR
618701 MAJIDAE 618701 MAJIDAE
61870101 OREGONIA 6187O1O1 OREGONIA J+
6187010101 OREGONIA GRACILIS 61870101 OREGONIA j
6187010201 HYAS LYRATUS 618701O2O1 HYAS LYRATUS
6187010401 MIMULUS FOLIATUS 6187010401 MIMULUS FOLIATUS
61870105 PUGETTIA (DECAPODA 61870105 PUGETTIA (DECAPODA)
6187010501 PUGETTIA PRODUCTA 6187010501 PUGETTIA PRODUCTA
6187010502 PUGETTIA RICHII 6187010502 PUGETTIA RICHII *
6187010503 PUGETTIA GRACILIS 6187010503 PUGETTIA GRACILIS *
6187010701 SCYRA ACUTIFRONS 6187010701 SCYRA ACUTIFRONS
6188 EUCARIDA DECAPODA PL 6188 EUCARIDA DECAPODA PLEOCYEMATA BR
6188020101 TELMESSUS CHEIRAGONU 6188020101 TELMESSUS CHEIRAGONUS
(continued)
258
-------
TABLE B-3 (continued)
61880301 CANCER 61880301 CANCER
6188030101 CANCER PRODUCTUS 6188030101 CANCER PRODOCTUS
6188030103 CANCER BRANNER1 618803O1O3 CANCER BRANNERI
6188030104 CANCER MAGISTER 6188030104 CANCER MAGISTER
6188030105 CANCER GRACILIS 6188030105 CANCER GRACILIS
6188030106 CANCER OREGONENSIS 6188030106 CANCER OREGONENSIS
6189020101 LOPHOPANOPEUS BELLUS 6189020101 LOPHOPANOPEUS BELLUS
6189020301 FABIA SUBQUADRATA 6189020301 PABIA SUBQUADRATA
6189020403 NAME NOT FOUND 618902 XANTHIDAE
618906 PINNOTHERIDAE 618906 PINNOTHERIDAE
61890604 PINNIXA 61890604 P1NNIXA j
6189060402 PINNIXA LITTORALIS 6189060402 PINNIXA LITTORALIS }
6189060403 PINNIXA OCCIDENTALIS 6189060403 PINNIXA OCCIDENTALIS |
61890701 HEMIGRAPSUS 61890701 HEMIGRAPSUS
6189070101 HEMIGRAPSUS NUDUS 6189070101 HEMIGRAPSUS NUDUS
6189070102 HEMIGRAPSUS OREGONEN 6189070102 HEMIGRAPSUS OREGONENSIS
6189070301 SCLEROPLAX GRANULATA 6189070301 SCLEROPLAX GRANULATA
628403 CICADELLIDAE 628403 CICADELLIDAE
6501 DIPTERA 6501 DIPTERA
650508 CHIRONOMIDAE 650508 CHIRONOMIDAE
65160112 ATYLOTUS 65160112 ATYLOTUS
72 SIPUNCULIDA 72 SIPUNCULIDA
72OO NAME NOT FOUND 72 SIPUNCULIDA
72000201 GOLFINGIA 72000201 GOLFINGIA
7200020103 GOLFINGIA VULGARIS 72OOO2O103 GOLFINGIA VULGARIS
7200020104 GOLFINGIA PUGETTENSI 7200020104 GOLFINGIA PUGETTENSIS
7200040101 PHASCOLOSOMA AGASSIZ 7200040101 PHASCOLOSOMA AGASSIZII
74000101 PRIAPULUS 74000101 PRIAPULUS
7400010101 PRIAPULUS CAUDATUS 74000101 PRIAPULUS
77 PHORONIDA 77 PHORONIDA
770001 PHORONIDAE 770001 PHORONIDAE
7700010102 PHORONOPSIS HARMERI 77000101 PHORONOPSIS
7700010199 NAME NOT FOUND 77000101 PHORONOPSIS
77000102 PHORONIS 77000102 PHORONIS
7700010201 PHORONIS VANCOUVEREN 77000102 PHORONIS
78 ECTOPROCTA 78 ECTOPROCTA
7809 GYMNOLAEMATA CYCLOST 7809 GYMNOLAEMATA CYCLOSTOMATA ARTICU
7810O201 TUBULIPORA 781002O1 TUBULIPORA
78120101 HETEROPORA (ECTOP 78120101 HETEROPORA (ECTOPROCT)
7812010102 HETEROPORA PACIFICA 7812O1O1 HETEROPORA (ECTOPROCT)
7812010199 NAME NOT FOUND 78120101 HETEROPORA (ECTOPROCT)
7814 GYMNOLAEMATA CHEILOS 7814 GYMNOLAEMATA CHEILOSTOMATA
78150401 MEMBRANIPORA 78150401 MEMBRANIPORA
78150801 CALLOPORA 78150801 CALLOPORA
78152502 DENDROBEANIA 78152502 DENDROBEANIA
78161302 SMITTINA (ECTOPROC 78161302 SMITTINA (ECTOPROCTA)
80O51102O1 TEREBRATALIA TRANSVE 8005110201 TEREBRATALIA TRANSVERSA
8113010304 SOLASTER STIMPSONI 8113010304 SOLASTER STIMPSONI
(continued)
259
-------
TABLE B-3 (continued)
811403O1O1 DERMASTERIAS IMBRICA 8114030101 DERMASTERIAS IMBR1CATA
8117O3 ASTERIIDAE 811703 ASTERIIDAE
8117030409 LEPTASTERIAS HEXACTI 8117030409 LEPTASTERIAS HEXACTIS +
8117030502 PISASTER OCHRACEUS 8117030502 PISASTER OCHRACEUS
8117031001 ORTHASTERIAS KOEHLER 8117031001 ORTHASTERIAS KOEHLERI
8120 OPHIUROIDEA 8120 OPHIUROIDEA
8127O1 OPH1URIDAE 812701 OPHIURIDAE
8129 OPHIUROIDEA OPHIURID 8129 OPHIUROIDEA OPHIURIDA GNATHOPHIU
8129020101 OPHIOPHOLIS ACULEATA 8129020101 OPHIOPHOLIS ACULEATA
812903 AMPHIURIDAE 812903 AMPHIURIDAE
8129O301 AMPHIODIA 81290301 AMPHIODIA
81290302 AXIOGNATHUS 8129O302 AXIOGNATHUS
8129030299 NAME NOT FOUND 812903O2 AXIOGNATHUS
8136 ECHINOIDEA 8136 ECHINOIDEA
81490302 STRONGYLOCENTROTUS 81490302 STRONGYLOCENTROTUS
8149030201 STRONGYLOCENTROTUS D 8149030201 STRONGYLOCENTROTUS DROEBACHIENSI
8149030202 STRONGYLOCENTROTUS F 8149030202 STRONGYLOCENTROTUS FRANCISCANUS
8149030203 STRONGYLOCENTROTUS P 8149030203 STRONGYLOCENTROTUS PALLIDUS
8149030204 STRONGYLOCENTROTUS P 8149030204 STRONGYLOCENTROTUS PURPURATUS
8155010101 DENDRASTER EXCENTRIC 8155010101 DENDRASTER EXCENTRICUS
8170 HOLOTHUROIDEA 817O HOLOTHUROIDEA
8172 HOLOTHUROIDEA DENDRO 8172 HOLOTHUROIDEA DENDROCHIROTACEA D
8172O302O1 PSOLUS CHITINOIDES 8172030201 PSOLUS CHITINOIDES
817206 CUCUMARIIDAE 817206 CUCUMARIIDAE
81720601 CUCUMARIA 81720601 CUCUMARIA
8172060109 CUCUMARIA LUBRICATA 8172060109 CUCUMARIA LUBRICATA
8172060110 CUCUMARIA MINIATA 8172060110 CUCUMARIA MINIATA
81720602 EUPENTACTA 81720602 EUPENTACTA
8172060201 EUPENTACTA PSEUDOQUI 8172060201 EUPENTACTA PSEUDOQUINQUESEMITA
8172O6O202 EUPENTACTA QUINQUESE 8172060202 EUPENTACTA QUINQUESEMITA
81720603 PENTAMERA 81720603 PENTAMERA
8172060599 NAME NOT FOUND 81720605 THYONE
8175020101 PARASTICHOPUS CALIFO 8175020101 PARASTICHOPUS CALIFORNICUS
81780102 LEPTOSYNAPTA 81780102 LEPTOSYNAPTA j *
8178010203 LEPTOSYNAPTA CLARKI 81780102 LEPTOSYNAPTA j
8179 HOLOTHUROIDEA APODAC 8179 HOLOTHUROIDEA APODACEA MOLPADIID
817901 MOLPADIIDAE 8179 HOLOTHUROIDEA APODACEA MOLPADIID
8179O101O1 MOLPADIA INTERMEDIA 8179 HOLOTHUROIDEA APODACEA MOLPADIID
8201 ENTEROPNEUSTA 82O1 ENTEROPNEUSTA
830OO003O3 SAGITTA ELEGANS 8300000303 SAGITTA ELEGANS
84 UROCHORDATA 84 UROCHORDATA
8401 ASCIDIACEA 8401 ASCIDIACEA
8403010401 ARCHIOISTOMA RITTERI 8403010401 ARCHIOISTOMA RITTERI
8404040102 CHELYOSOMA PRODUCTUM 8404040102 CHELYOSOMA PRODUCTUM
8404040202 COKELLA WILLMERIANA 8404040202 CORELLA WILLMERIANA
8406010201 METANDROCARPA DURA 8406010201 METANDROCARPA DURA
84O6O10302 CNEMIDOCARPA FINMARK 8406010302 CNEMIDOCARPA FINMARKIENSIS
8406010505 STYELA GIBBSII 8406010505 STYELA GIBBSII
(continued)
260
-------
TABLE B-3 (continued)
8406020101 PYURA HAUSTOR 8406020101 PYURA HAUSTOR
8406020203 BOLTENIA VILLOSA 84O602O203 BOLTENIA VILLOSA
8717 OSTEICHTHYES 8717 OSTEICHTHYES
8784010101 GOBIESOX MAEANDRICUS 8784010101 GOBIESOX MAEANDRICUS (NORTHERN C
8831070101 PSYCHROLUTES PARADOX 8831070101 PSYCHROLUTES PARADOXUS (TADPOLE
8831090803 LIPARIS CALLYODON (S 8831090803 LIPARIS CALLYODON (SPOTTED SNAIL
88421302 PHOLIS 88421302 PHOLIS
8842130205 PHOLIS LAETA (CRESCE 884213O2 PHOLIS
99990001 NAME NOT POUND ER
999999 NAME NOT POUND ER
ABIOTIC NAME NOT FOUND ABIOTIC NONE OP THESE TAXA *
261
-------
APPENDIX C
ANIMALS AND PLANTS FOUND AT COBBLE SITES
The tabulation which comprises this appendix includes animals and
plants found at cobble sites. The total number of samples in which each
occurred and the number at each site, date, and elevation stratum are
tabulated.
The elevation strata for this tabulation are defined as low, -1 m to
+0.4 m; mid, +0.5 m to +1.4 m; and high, greater than +1.4 m. The station
codes used in the tabulation are
1012 Cherry Point (NFS),
2O16 Morse Creek (Strait),
2050 North Beach (Strait),
2063 Partridge Point (Whidbey), and
3064 South Beach (SJI).
Shannon Point is not included because it was one of the sites where only
gradient sampling during the first year of the NPS study was done and only
2-mm fractions were fully processed. Live sieve data are also omitted since
they are not available for_Cherry Point.
(Pages 263-310 microfiched)
262
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