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Estuarine, Coastal and Shelf Science 62 (2005) 253–270
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The consequences of scale: assessing the distribution
of benthic populations in a complex estuarine fjord
Megan N. Dethiera,), G. Carl Schochb
a
Friday Harbor Labs and Department of Biology, University of Washington, 620 University Road, Friday Harbor, WA 98250, USA
b
Prince William Sound Science Center, 300 Breakwater Avenue, Cordova, AK 99574, USA
Received 28 February 2003; accepted 25 August 2004
Abstract
Evidence suggests that patterns of benthic community structure are functionally linked to estuarine processes and physical
characteristics of the benthos. To assess these linkages for coarse-sediment shorelines, we used a spatially nested sampling design to
quantify patterns of distribution and abundance of both macroinfauna and macroepibiota. We examined replicate beach segments
within a site (w1 km), sites within areas of relatively uniform salinity and temperature (w10 km), and areas (w100 km) in the two
major basins of Puget Sound, Washington. Because slight variations in physical characteristics of a beach can lead to significant
alterations in biota, we minimized confounding physical influences by working only in the predominant shoreline habitat type in
Puget Sound, a mixture of sand, pebbles and cobbles. Species richness decreased steadily from north to south along gradients of
declining wave energy, increasing temperature and decreasing salinity. A few taxa were confined to the South Basin, but many more
were found in the North. Most of the variability in population abundance was captured at the smaller spatial scales. Physical
conditions tend to become increasingly different with distance among sites. Communities became more different from north to south
as species intolerant of more estuarine conditions dropped out. There was significant spatial autocorrelation among populations on
neighboring beach segments for 73 of the 172 species sampled. Populations of these benthic species may be connected via dispersal
on scales of at least km in Puget Sound. Our results strengthen prior conclusions about the strong linkages between the biota and
physical patterns and processes in estuaries. It is important for monitoring and impact-detection studies to account for natural
variation of physical gradients across the sampling scales used. Nested, replicated sampling designs can facilitate the detection of
environmental change at spatial scales ranging from global (e.g., warming or El Niño), to regional (e.g., estuary-wide changes in
salinity patterns), to local (e.g., from development at a site).
Ó 2004 Elsevier Ltd. All rights reserved.
Keywords: estuarine coasts; benthic organisms; estuarine hydrodynamics; sampling designs; salinity gradient; temperature gradient; wave energy;
Puget Sound
1. Introduction
Processes known to affect the ecology of nearshore
benthic marine organisms include a broad range of
physical (e.g., wave action, substrate type, upwelling
) Corresponding author.
E-mail addresses: [email protected] (M.N. Dethier),
[email protected] (G.C. Schoch).
0272-7714/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ecss.2004.08.021
conditions) and biological (e.g., predation, competition,
recruitment) factors. In estuaries, recent research has
focused on subtidal benthic communities in soft
sediment, and on the effects of pollutants and other
human impacts. Key physical processes controlling the
distributions of benthic populations in estuaries include
salinity, wave action, and sediment grain size. Salinity is
thought to play a primary role (reviewed by Carriker,
1967; Tenore, 1972; Bulger et al., 1993; Christensen
et al., 1997; Constable, 1999; Smith and Witman, 1999);
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M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
reduced or more-variable salinity has a direct negative
effect on species diversity, to the extent that one
estuarine index of ‘health’ incorporated the expected
species diversity ‘‘adjusted to remove the effects of
salinity’’ (Engle and Summers, 1999). In addition, lower
salinity generally increases lethal and sublethal impacts
of other stressors such as pollutants or high temperatures (Carriker, 1967; Vernberg and Vernberg, 1974).
However, salinity may be a proxy for other variables
that directly affect organisms, e.g., substrate types or
water column turbidity. In some cases it is not the mean
salinity that is critical but the variation (e.g., Montague
and Ley, 1993).
Elucidation of critical physical forcing functions in
estuaries has been slowed by two major factors. First,
most sampling regimes involve bottom grabs or cores
taken at a variety of stations, often seasonally and
sometimes over a number of years (references below),
but replication at the per-site level is often low, e.g., two
(Service and Feller, 1992) or three samples (Flint and
Kalke, 1985; Rakocinski et al., 1997). Because softsediment populations are patchy, it is difficult to
separate within-site variability from among-site or
among-time variability (Morrisey et al., 1992). Second,
studies have seldom been able to separate the physical
variables or gradients that are so characteristic of
estuaries, e.g., substrate type vs. salinity vs. temperature
vs. turbidity. These variables are often inextricably
linked, for example sediment type covaries with wave
action, salinity often fluctuates with temperature, and
contaminants may be linked to both (Clarke and Green,
1988). The research effort described here focuses on one
sediment type and uses a sampling design tailored to
avoid these artifacts as fully as possible.
Attempts to separate the effects of salinity from those
of other estuarine forcing functions such as sediment
type or wave action have usually involved multivariate
analyses of data collected from many sites. For example,
Flint and Kalke (1985) sampled repeatedly from four
sites along an estuarine gradient, and then used
discriminant analysis to suggest that the key variables
affecting the benthos were variance in the bottom water
salinity, and secondarily differences in sediment structure. Mannino and Montagna (1997) sampled each of
three sediment types in each of four salinity regions in
a randomized, partially hierarchical design. They found
that both sediment grain size and salinity critically
influence diversity, abundance, and biomass of benthic
species.Not all species responded in the same way to the
parameters, however. Holland et al. (1987) found that
groups of benthic species tended to ‘‘correspond’’ to
particular substrates and depths in estuaries, but that
abundances varied highly from year to year, driven in
part by salinity shifts. Similar broad effects of sediment
type and salinity (with individual species showing
different patterns) have been noted by many other
authors (e.g., Chester et al., 1983; Constable, 1999;
Estacio et al., 1999; Ysebaert et al., 2002; Freeman and
Rogers, 2003). EMAP (Environmental Monitoring and
Assessment Program) studies in the Gulf of Mexico
(e.g., Rakocinski et al., 1997, 1998; Engel and Summers,
1999; Brown et al., 2000) have used stratified random
sampling over contaminated and uncontaminated sites
from different estuarine habitats; they illustrate both the
importance of these physical forcing functions and the
ways that natural variation in benthic communities can
obscure anthropogenic impacts.
Recently, numerous authors (e.g., Schneider, 1994;
Bell et al., 1997; Edgar and Barrett, 2002) have noted
that ecological studies are often done at the scale of
organisms (!1 m), whereas key processes are examined
in oceanographic studies at the scale of geographic
features (10–100 km), creating a mismatch and difficulty
in linking these scales. Recognizing that many physical
processes (e.g., local wave action, bathymetry) act at
intermediate or mesoscales (1–10 km), current oceanographic research is focused on resolving smaller scale
features and the effect these have on biological processes. Local sedentary biota integrate physical (e.g.,
hydrodynamic) and biotic (e.g., predation) events
operating on a variety of temporal and spatial scales
(Thrush et al., 1997). The appropriate scale of research
(Schneider, 1994) is debated; intense local replication
(e.g., many cores per beach) can give us confidence in
local results, but does not provide generality. Broad
sampling can give generality but if it comes at the
expense of low local replication, the confidence in results
is reduced. Very recently, several estuarine studies have
begun working across these scales, with replication at
many levels. Edgar and Barrett (2002) studied benthic
fauna at scales ranging from within-site to amongestuaries. Ysebaert and Herman (2002) replicated
samples from the within-site to the among-region scales
in one estuary. Scales of maximum variation differed
among species and among parameters (e.g., abundance
vs. biomass), but in each study the hierarchical design
provided unique information about links between the
environment and biota.
In the early summer of 1999, we surveyed the spatial
distribution of nearshore biota from physically similar
beach segments in the south and central basins of Puget
Sound, Washington. In order to hold one key physical
variable (sediment type) constant, we sampled in the
most common beach type, a mixed substrate consisting
of pebbles and cobbles embedded in sand. Thus our
results have broad applicability to understanding intertidal habitats of this estuary. With the assumption
that larger spatial scales are more likely to encompass
greater physical differences, we addressed the question:
what are the significant spatial scales of variation for the
intertidal populations and communities of Puget Sound
gravel beaches? Our data allow a statistical examination
M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
of the effects of substrate grain size, salinity and wave
action on the shoreline biota of Puget Sound. We paid
particular attention to the level of replication used at all
scales in the sampling design, thus avoiding the difficulty
of not distinguishing patchiness at small scales from real
variation at larger scales.
2. Methods
2.1. Study area
Puget Sound is an estuarine embayment connected to
the Pacific Ocean by the Juan de Fuca Strait and is
navigable by large ships for about 140 km south to the
city of Olympia (Fig. 1). The Sound has a shoreline
length of about 3700 km and an area of about 2600 km2,
surrounded by the most densely populated areas of
Washington including the principal cites of Seattle and
Tacoma. Freshwater runoff from rivers has a yearly
mean of about 1200 m3 sÿ1, ranging seasonally between
a peak of about 10,400 m3 sÿ1 in the winter and
a minimum of about 400 m3 sÿ1 in the summer (Cannon
et al., 1979).
255
Like many estuarine fjords (e.g., Rasmussen, 1973;
Gray et al., 1988; Rippeth et al., 1995), Puget Sound
consists of a series of underwater basins separated by
ridges or sills, and flushed daily by two unequal tides.
The Sound has an average depth of 137 m with
a maximum of 283 m just north of Seattle. A relatively
shallow sill separates the waters of the Juan de Fuca
Strait from the deep central Puget Sound basin, and
greatly influences the exchange of water between them.
Surface stratified lower-salinity water moving seaward
from the central basin is drawn down and mixed with
the deeper higher-salinity water flowing from the Strait.
The central basin is separated from the south basin by
a shallow sill at the Tacoma Narrows (Fig. 1). This sill
forces inflowing deeper water to upwell or move towards
the surface. In many of the shallow, semi-enclosed bays
of the south basin, water moves slowly and retention
time is higher than in the central basin (Barnes and
Ebbesmeyer, 1978). These estuarine circulation patterns
also affect the millions of tons of sediment, pollutants,
and other materials transported to or resuspended in the
Sound. However, unlike the waters that eventually move
seaward, most sediment particles are permanently
trapped in the basins. In the central basin for example,
only a small fraction of the particles initially present in
the surface water enter the Juan de Fuca Strait.
2.2. Physical characterization
Fig. 1. Locations of study Areas (numbered and outlined), sample sites
(labeled), and beach Segment (dots) of the nested design, with three
replicate Segments within a site, three sites in an Area. The south
Puget Sound basin is represented by Area 1, and the central basin by
Areas 2–5.
Characterizing all the physical properties of the ocean
and atmosphere and how they can affect intertidal
benthic organisms is beyond the scope of this paper, yet
some properties seem intrinsically linked to readily
observed patterns. For example, the tidal cycle exposes
the shore twice per day to atmospheric and oceanic
conditions. The intermediate spatial scale properties we
characterized as a proxy for this effect were air and
water temperatures, and precipitation. To identify
basin scale climatological differences we compared the
monthly air temperature and precipitation for one year
prior to our field sampling from data provided by the
National Weather Service Western Regional Climate
Center (http://www.wrcc.dri.edu) for regional airports
distributed along the axis of Puget Sound. Monthly
water temperatures and salinities between June 1998 and
June 1999 from the Washington Department of Ecology
were compared among 10 water quality monitoring
stations also positioned along the axis of Puget Sound
(http://www.ecy.wa.gov).
Finer-scale physical measurements were made at 45
pebble beach segments in the central and south basins
using a nested sampling design (Fig. 1). We characterized the physical attributes of three replicate beach segments per site (w1 km), three sites per Area (w10 km),
and five Areas in two basins within Puget Sound
(w100 km) for a 3 ! 3 ! 5 design (Tables 1 and 2).
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M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
For each of these 45 beach segments, we measured pore
water salinity and temperature, slope angle, grain size,
and calculated wave energy. The water percolating out
of a beach at low tide may sustain species otherwise
intolerant of local thermal or haline stress. The amount
of water retained by a beach during the ebb and flow of
the tide depends on the permeability of the beach
sediments. Low slope angle, fine sediment beaches
generally release pore water at a slower rate than
steeper, coarse-sediment beaches. The differences between properties of pore water and the adjacent marine
water are a function of retention time within the beach
sediments and the volume of ground water seeping into
the beach from the uplands. Pore water temperature
and conductivity were measured in situ, from three
randomly spaced 15 cm holes excavated in the beach, on
a 50 m horizontal transect line established at the mean
lower low water (MLLW) tidal datum (0 m) with
a surveyor’s level. We then used a rapid assessment
method to quantify the sediment grain size distribution.
Three replicate digital 0.25 m2 photo-quadrats with
a 10 cm grid were taken at random intervals along the
transect. In the laboratory, grain size was measured at
16 grid intersections using computer software (SigmaScan Pro v5.0 1999) to quantify the length of the longest
visible grain axis. Sediment grain sizes were categorized
based on the Wentworth scale (Pettijohn, 1949) and
relativized by the total number of grid intersections
sampled (48) to give an estimate of percent cover for
each sediment size category. Slope was measured in the
field with a hand-held digital inclinometer. Wave energy,
or more accurately wave power, was calculated for each
beach segment based on mean significant wave heights
derived from fetch, wind velocity, and wind duration
according to Komar (1998). An application of these
calculations is fully described in Schoch and Dethier
(1996).
2.3. Biological characterization
Biological sampling was done using transects at each
of the 45 beach segments described above (Fig. 1,
Table 2
Nested sampling design with sample locations from south to north
(sites shown in Fig. 1)
Region Basin
Puget
Sound
South
Area Site
1
Central 2
3
Table 1
The biological sampling and physical scaling terminology used for this
study
Biological sample units
Quadrat
0.25 m2
Core
0.001 m3
Transect
50 m
Physical scaling units
Zone
MLLW
Segment 50–100 m
Site
w1 km
Area
w10 km
Basin
10–100 km
Epibiota sample unit
consisting of a 0.25 m2 frame
Infauna sample unit consisting
of a 10 cm diameter ! 15 cm excavation
A biological sample consisting of
10 quadrats randomly positioned
horizontally along a 50 m
line positioned exactly on the tidal datum
within a segment (see below)
The low zone within the intertidal shoreline
A section of shoreline that is physically
uniform for a horizontal distance
greater than 50 m
Three replicate shore segments within a
distance of 1 km
Three sites within a distance of about
10 km
One or more Areas representing an
oceanographic region of Puget Sound
4
5
Budd Inlet
Transect Position
BD1
BD2
BD3
Case Inlet
CS1
CS2
CS3
Carr Inlet
CR1
CR2
CR3
Browns
BP1
Point
BP2
BP3
Redondo
RE1
RE2
RE3
Normandy NO1
NO2
NO3
Seahurst
SE1
SE2
SE3
Brace
BR1
BR2
BR3
Alki
AL1
AL2
AL3
West
WP1
Point
WP2
WP3
Carkeek
CK1
CK2
CK3
Wells Point WE1
WE2
WE3
Edmonds
ED1
ED2
ED3
Possession PO1
PO2
PO3
Double
DO1
Bluff
DO2
DO3
Lat.
Long.
47.1135
47.1285
47.1407
47.285
47.2682
47.25
47.2932
47.3367
47.3455
47.3042
47.3068
47.3108
47.3505
47.3532
47.35
47.4191
47.43
47.4322
47.4623
47.8867
47.5008
47.5054
47.5113
47.5277
47.5429
47.5711
47.5709
47.6693
47.6941
47.6971
47.7167
47.7232
47.737
47.7568
47.75
47.7786
47.8291
47.8306
47.842
47.9045
47.9096
47.9136
47.9744
47.9721
47.9671
ÿ122.9218
ÿ122.9240
ÿ122.9243
ÿ122.8590
ÿ122.8627
ÿ122.8583
ÿ122.7498
ÿ122.7304
ÿ122.7075
ÿ122.4443
ÿ122.4390
ÿ122.4327
ÿ122.3241
ÿ122.3244
ÿ122.3254
ÿ122.3499
ÿ122.3510
ÿ122.3520
ÿ122.3687
ÿ122.3675
ÿ122.3845
ÿ122.3898
ÿ122.3949
ÿ122.3981
ÿ122.3984
ÿ122.4126
ÿ122.4126
ÿ122.4192
ÿ122.4070
ÿ122.4036
ÿ122.3767
ÿ122.3763
ÿ122.3762
ÿ122.3797
ÿ122.3871
ÿ122.3972
ÿ122.3657
ÿ122.3638
ÿ122.3510
ÿ122.3785
ÿ122.4136
ÿ122.4329
ÿ122.5168
ÿ122.5219
ÿ122.5446
M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
Table 1). We sampled during spring tides in May and
June of 1999 in the lower intertidal zone (at MLLW, the
same level as the physical characterization). Even
though the tidal range varies along the axis of Puget
Sound, we stratified the sampling elevation at the 0 m
level because here the biota are submersed 90% of the
time everywhere (on the U.S. west coast). This design
potentially allows for detection of oceanic vs. atmospheric effects over small and large spatial scales,
because the marine signal is stronger at MLLW than
at higher intertidal levels which are subject to longer
atmospheric exposure times (Helmuth et al., 2002).
Since we were interested in the community structure
characterizing the low zone of an entire beach segment,
our samples consisted of the mean species abundances
from 10 randomly spaced sample units along 50 m
horizontal transects. Pilot studies showed that 10 sample
units accounted for 95% of the diversity per transect,
with approximately 80% accounted for by the first six
sample units. Therefore, additional sample units would
not appreciably increase the estimates of diversity. Each
sample unit consisted of a 0.25 m2 quadrat to quantify
abundance of surface macroflora and epifauna, plus
a 10 cm diameter ! 15 cm deep core for macroinfauna.
Percent cover was estimated for all sessile taxa in the
quadrats, and all motile epifauna were counted. Core
samples were washed through 4 and 2 mm mesh sieves
and taxa were counted. We used 2 mm mesh sieves
because for this general survey we were more interested
in adult macroinfauna rather than juveniles and
meiofauna, and because this pebbly–sandy sediment
would clog smaller sieve sizes. All organisms not
identifiable to the species level in the field were placed
in formalin and identified in the lab. Taxonomic
references were Kozloff (1996) and Blake et al. (1997)
for invertebrates, and Gabrielson et al. (2000) for
macroalgae. Species were classified into different trophic
categories using Fauchald and Jumars (1979) and
Kozloff (1983).
2.4. Data analysis
The Moran test for spatial autocorrelation was used
to evaluate whether spatial modeling was appropriate
for analyzing relationships among populations and
communities (Legendre, 1993). The covariance structure
used to analyze overall spatial autocorrelation among
samples was calculated using the nearest neighbor
algorithm and the Euclidian distance between all
sampled locations based on the geographical coordinates. We log-transformed the species abundances to
improve normality, then modeled the data using a local
regression fit (LOESS), with longitude and latitude as
predictors to examine the spatial trends in two directions
(Kaluzny and Vega, 1997). Nested ANOVAs were run
for each organism to assess how much variability was
257
added at each spatial increment from transect scale
samples to sites to Areas using the fully nested model of
Sokal and Rohlf (1995). The multivariate analyses
methods of Clarke and Warwick (1994) and Primer
software (Clarke and Gorley, 2001) were used to detect
patterns in the spatial distribution of community
structure. The data matrix of taxon abundances was
fourth-root transformed to improve the assumptions of
multivariate normality and we used the ordination
technique of non-metric multidimensional scaling
(MDS) to group communities based on the Bray–Curtis
similarity metric. Graphical plots of ordination results
for the two axes explaining the greatest proportion of
the variance were examined for obvious sample groupings. Analyses of similarity (ANOSIM) tested the
significance of hypothesized differences among sample
groups. Spearman’s rank correlations were used to
examine relationships between environmental variables
and the ordination scores (BIO-ENV), and joint plots
were produced in PC-ORD (McCune et al., 2002) to
visualize these relationships. This technique was also
used to examine the relationship between the ordination
scores and the taxa that best explained the observed
pattern of community similarity.
3. Results
3.1. Physical characteristics
The mean monthly air temperature and precipitation
gradients between June 1998 and June 1999 for Puget
Sound are shown in Fig. 2A and B. During the summer
months (April–September), the mean air temperature
from the regional array of airports does not increase
from north to south (Table 3), although precipitation
increases slightly. During the winter months (October–
March) the mean air temperatures are also not
significantly different (Table 3), but the mean precipitation is significantly higher in the south. There is
a strong gradient in the mean annual sea surface
temperature (at 2 m depth), increasing in magnitude
and variability from north to south (Fig. 3A, Table 3),
and a similarly strong gradient in the mean annual
salinity, decreasing in magnitude but increasing in
variability from north to south (Fig. 3B, Table 3).
Measurements from our low zone beach segments
showed that pore water temperature decreased slightly
from north to south with a coincident decrease in
variability (Fig. 4A, Table 3), and pore water salinity
was highly variable within and among beaches with even
less of an axial (north–south) gradient (Fig. 4B). The
mean beach slope angle was also variable and showed
no regional trend (Fig. 4C). The mean tide range for the
sampled beach segments was estimated from local tide
stations and is shown in Fig. 5A. The calculated wave
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M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
this gradient in wave energy, with generally larger grain
sizes to the north and an increasing abundance of sand
(based on % cover at the surface) to the south (Fig. 5C).
Wave energy was regressed against the abundance of
sand and not surprisingly, the two parameters are
negatively correlated (linear regression r2 Z ÿ0.69,
F1,14 Z 26.31, p ! 0.001). Larger waves create more
turbulence keeping sand in suspension, therefore there is
less sand in high wave energy environments.
3.2. Biological characteristics
The low zone biota of pebble beach segments in the
south and central basins of Puget Sound were sampled
with 45 transects (450 quadrats C cores) (360 in the
Central Basin and 90 in the South Basin). Of the 166 taxa
found, we identified 134 taxa to the species level, 23 to
the genus level, and 10 were grouped into complexes
(Appendix A). About 30% of these taxa were observed in
fewer than 10 sample units. There were 26 primary
producers, 139 invertebrates, and 1 vertebrate (fish). In
the producer group, we found 7 phaeophytes (3 bladed,
3 branched, 1 tube), 2 chlorophytes (1 bladed,
1 branched), 16 rhodophytes (3 bladed, 11 branched,
Fig. 2. Coastal climatic gradients for the one-year period preceding
field sampling: (A) mean monthly air temperature and (B) precipitation. All error bars are 1 SD. Port Angeles is in Juan de Fuca
Strait, Olympia is in the south (see Fig. 1).
power parameter showed a strong along-axis gradient,
illustrating the higher wave energies in the north where
exposure to longer fetches results in larger waves
(Fig. 5B, Table 3). The distribution of sediments reflects
Table 3
Results of statistical analyses of gradients and variation in physical
variables along the north–south axis of Puget Sound
LOESS
ANOVA
r2
df
p Value
F
Regional parameters
Summer air temperature
Summer precipitation
Winter air temperature
Winter precipitation
Sea surface temperature
Sea surface salinity
0.36
0.38
0.16
0.94
1.00
0.98
3.20
3.20
3.20
3.20
1.21
1.21
0.44
3.21
0.20
8.97
30.68
4.32
0.727
0.045
0.898
!0.001
!0.001
0.050
Beach parameters
Pore water temperature
Pore water salinity
Beach slope angle
Calculated wave power
Sand abundance
Pebble abundance
Cobble abundance
0.052
0.001
0.001
0.59
0.78
0.04
0.21
14.30
14.30
14.30
14.30
14.30
14.30
14.30
4.62
4.82
2.89
10.43
3.19
2.83
3.68
!0.001
!0.001
0.007
!0.001
0.004
0.008
0.001
Fig. 3. (A) Mean monthly sea surface temperature (2 m depth), and (B)
mean annual salinity. All error bars are 1 SD. Locations are ordered
from north to south (see Fig. 1).
M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
Fig. 4. Measured beach Segment parameters: (A) pore water
temperature, (B) pore water salinity, (C) mean slope angle. All error
bars are 1 SD among the three beach segments per site.
1 epiphyte, 1 fleshy crust), and an angiosperm (eelgrass).
Of the invertebrates, we found 29 carnivores, 10 herbivores, 13 omnivores, 21 suspension feeders, 41 deposit
feeders, 7 commensals, and 18 scavengers. Interestingly,
herbivores are not well represented. In terms of phyla,
85% of the observed taxa were represented by annelids,
molluscs, arthropods, and rhodophytes.
We compared taxon-sampling curves for each Area
(Fig. 6). While it was beyond the scope of this study to
exhaustively sample each Area to its richness asymptote,
a comparison among curves was useful to assess relative
taxon density (richness per area) and the rate of
accumulation with respect to the number of transects
sampled (Gotelli and Colwell, 2001). These data were
plotted without compromising the spatial integrity of
the transects, therefore, these curves show that most
taxa are accounted for within the first three transects
sampled (i.e. the first site). For each Area, the first
sampled site contributed a mean of 68% (SD Z 6.8) of
the taxa, the second site accounted for an additional
259
19% (SD Z 4.6), while the third site contributed the
remaining 13% (SD Z 3.2). Cumulative taxon richness
ranged from 121 taxa found in the northern Area 5 to 72
in the southern Area 1.
A regional plot of diversity per transect along the
north–south axis of Puget Sound is a simple means of
comparing among different spatial scales (Fig. 7). The
Shannon’s H# diversity index (Magurran, 1988) was
calculated by combining richness with relative abundance. The decreasing north to south trend in taxa
diversity is minimized by this measure, and the amongtransect variability is high and spatially correlated
(Fig. 7A, Table 4). The plots of taxa richness, however,
show an overall decrease from north to south with high
variability among transects, high spatial correlation, and
consistent relative contributions of cores and quadrats
to overall transect diversity (Fig. 7B, Table 4). The
transects at Wells had distinctly lower richness than
surrounding transects to the north or south; these 3
beaches have highly variable wave climates (Fig. 5B)
and very low salinity (Fig. 4B). The richness distribution
for 5 trophic groups is shown in Fig. 7C, with each
bar partitioned to show the relative contributions of
each group. Most of this spatial gradient is explained
(Table 4) by the numbers of different deposit feeders and
carnivores. Contributing a lesser amount to the overall
gradient was suspension feeders and herbivores, and
algae contributed the least (Table 4).
Many species (41) were geographically distributed
throughout the Sound (Appendix A). Some were
abundant everywhere (e.g., barnacles, grapsid crabs,
ulvoids), others were never abundant but appeared at
many sites (e.g., the crab Lophopanopeus). In some
cases, this apparent broad distribution may be the result
of lumping of taxa; for example, the species of
‘‘encrusting red algae’’ or ‘‘Pagurus spp.’’ might change
from south to north, but our sampling method did not
differentiate species in these taxonomic groups. A few
taxa (11) appeared exclusively or more frequently in our
South Sound samples. Some of these (e.g., Crepidula
fornicata) are associated locally with taxa (oysters)
cultured largely in this region and not to the north.
Interestingly, we found no C. fornicata at more northern
sites despite its having a pelagic larval stage lasting at
least 30 days (Pechenik, 1984). Some taxa (e.g., the
burrowing anemone Edwardsia spp.) occur mostly in
reduced salinities. Twenty taxa show a patchy distribution, i.e. are found in abundance in only some areas, and
with no apparent geographic trend. Some of these are
fairly narrow in their substrate requirements (Kozloff,
1983); examples include the sand dollar Dendraster
(prefers sand with no pebbles) and the mud shrimps
Neotrypaea and Upogebia (prefer beaches with more
fines). Dendraster also settles in an aggregative manner,
increasing its tendency to be patchy (Highsmith, 1982).
Large and motile taxa, such as some predators, are
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Fig. 5. (A) The mean tidal range in Puget Sound showing the four main datums. MHW Z mean high water, MSL Z mean sea level, MLW Z mean
low water, MLLW Z mean lower low water. The range at the southernmost beach Segments is twice the range at the northernmost. Measured beach
Segment parameters: (B) wave power; error bars are 1 SD among the three beach Segments per site; note that some sets of Segments were better
matched for wave power (no visible error bars) than others; (C) substrate size distribution.
patchily distributed depending on their prey base;
examples include Cancer crabs and moon snails. Still
others are poor dispersers (no pelagic propagules) such
as the small seastar Leptasterias. The polychaete
Mediomastus californiensis has been described as opportunistic (e.g., Rakocinski et al., 2000) and it may
benefit from the greater wave disturbance to the north.
The spatial distribution and variability of each
species’ abundance was visually assessed as shown by
selected examples in Fig. 8. Every beach segment is
represented with either a column indicating relative
abundance or a horizontal line indicating that the species
was not present. The replicate beach segment values are
shown grouped by site. The barnacle Balanus glandula
and fleshy encrusting red algae were present in all sites
but had consistently higher abundances in the north
(Fig. 8A and B); 53 species showed this distribution
pattern (Appendix A). The distribution of the snail Alia
gausapata was generally limited to the southern basin of
Puget Sound (Fig. 8C), as were 14 other species
(Appendix A). The capitellid Notomastus tenuis was
observed in most transects but was highly variable within
and among sites with no particularly dominant spatial
pattern (Fig. 8D). Many other taxa (52) showed similar
broad distributions. Another capitellid Mediomastus
californiensis was observed in high numbers only north
of Brace (Fig. 8E), and the snail Lacuna vincta was not
observed in the south basin (Fig. 8F), along with 27 other
species only found in the north.
Of the 166 taxa sampled, 73 (43%) showed significant
spatial autocorrelation. Spatial autocorrelation may
predict the outcome for each sample based partially on
its dependence on nearby or neighboring samples. If two
samples are neighbors then seemingly random processes
measured at these regions might be spatially correlated.
Five general patterns emerged from examining the
plotted abundances of species (or complexes); those
that: (1) decrease from north to south, (2) decrease from
south to north, (3) are abundant in the north and south
but not in the center, (4) are abundant in the center but
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M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
Fig. 6. Taxon–area curves showing cumulative taxon richness in each
of five Areas. Each line represents the accumulation of taxa in an Area
with 90 quadrats and cores, or nine transects at three sites. The vertical
dotted lines show the partitioned contribution of the first and second
site. The numbers refer to the total taxon richness for each Area. For
Area numbers, see Fig. 1.
not in the north or south, and (5) no pattern (Appendix
A). The results of the nested ANOVAs on species
abundance by aggregation level (Transects within sites,
sites within Areas, among Areas) were binned into these
distribution categories and are summarized in Table 5.
For 60 species, abundance varied significantly only at
the transect level (among 3 transects). Far fewer species
(11) varied only at the site level (among 3 sites), and only
3 species varied just at the Area level (among 5 Areas).
Many species (49) were not significantly variable at any
level, although these are mostly uncommon taxa.
Combinations of Transect level and site (30 species),
Transect level and Area (11 species), and all 3 levels (2)
suggests that the Transect level sampling accounts for
most of the species variability. We did not analyze
within segment variability (i.e. among sample units
within a transect) because we were interested in
quantifying the characteristic low zone populations
and community structure of entire beach segments,
and not the inherent physical and biological heterogeneity of mixed sediments at the scale of the sample unit.
Fig. 7. Among-transect variability in taxon diversity, as: (A) Shannon’s
H#, (B) Taxon richness, and (C) Trophic categories. Transects are
ordered from north to south (left to right). Two relatively taxondepauperate sites are represented by transects at Wells and Case.
This characteristic high variability in abundances of
individual species at most spatial scales makes discerning
ecological patterns difficult. However, some trends are
clear at the community level, using multivariate analyses.
Comparisons of community similarity were made on the
low zone Transect level data (45 samples ! 166 taxa) to
Table 4
Results of statistical analyses of spatial correlation and gradients in taxon diversity along the north–south suite of sampled beaches
Moran
Shannon’s H#
Overall richness
Core richness
Quadrat richness
Deposit feeder richness
Carnivore richness
Suspension feeder richness
Herbivore richness
Algal richness
LOESS (all df 2,42)
2
ANOVA (all df 1,43)
Value
p Value
r
F
p Value
F
p Value
0.43
0.78
0.72
0.62
0.72
0.59
0.57
0.58
0.42
!0.001
!0.001
!0.001
!0.001
!0.001
!0.001
!0.001
!0.001
!0.001
0.42
0.66
0.56
0.59
0.62
0.54
0.36
0.36
0.30
15.05
41.10
27.06
30.38
34.72
24.70
11.99
11.60
8.95
!0.001
!0.001
!0.001
!0.001
!0.001
!0.001
!0.001
!0.001
!0.001
12.84
78.85
58.85
49.29
79.04
33.19
10.75
17.20
15.03
!0.001
!0.001
!0.001
!0.001
!0.001
!0.001
0.002
!0.001
!0.001
262
M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
Fig. 8. Population distributions are shown for taxa that represent the principle spatial patterns of abundance (A–F). Each column represents
a different species, and each bar indicates the abundance (percent cover or counts), per sampled transect, relativized by the highest observed value for
each species. Each row of bars represents a site labeled on the y-axis. Sites are grouped by Area indicated by dashed horizontal lines. See text for
further explanations.
determine: (1) whether groups of communities of algae
and invertebrates can be explained by geographic
location; (2) which physical variables were correlated
with community patterns; and (3) which taxa were
statistically driving the ordination patterns. The plots on
Fig. 9 show the 2-dimensional solution of the MDS
ordination. Each data point represents a sampled
transect (i.e. the mean abundance and frequency of all
taxa on a transect). The plots simply show that transects
closer together have communities more similar than
transects farther apart. To illustrate this, the polygons in
Fig 9A each encircle data points representing communities from the 3 transects in Budd, Case, and Carr Inlets of
the south basin. The communities at the sampled
transects in Budd Inlet are more similar (grouped tighter
together) than those sampled in Case and Carr Inlets, but
Case and Carr Inlet communities are more similar (more
overlap) than those in Budd Inlet. The significance of this
difference was tested using a one-way analysis of
similarity (ANOSIM). Because only 10 permutations
are available, the power of this test is low. Nonetheless,
the results suggest that Case Inlet and Carr Inlet
communities do not differ (R Z 0.159, p Z 0.30; maximal
separation when R Z 1). Budd Inlet and Case Inlet, and
Budd Inlet and Carr Inlet are more dissimilar (R Z 0.815
and R Z 0.593, respectively), but the differences are still
not significant ( p Z 0.10 for both).
To evaluate the relationship between community
structure and regional geographic scales, we tested the
community similarity among Areas defined a priori
M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
Table 5
Summary of the nested ANOVA analyses for individual organisms at
the transect (T), site (S) and Area (A) scales
Levels
T
S
A
T&S
T&A
T&S&A
Ø
Totals
Category
1
2
3
4
5
High N
High S
High
N&S
Low
N&S
No pattern
25
5
1
17
6
1
13
68
10
3
0
6
2
0
7
28
0
0
0
0
0
0
1
1
10
0
1
1
0
0
11
23
15
3
1
6
3
1
17
46
Total
60
11
3
30
11
2
49
166
Spatial distribution categories for organism abundances (from Appendix A) are: (1) high abundance in the north and low in the south, (2) high
in the south and low in the north, (3) high in the north and south and low
in the middle, (4) low in the north and south and high in the middle, and
(5) no pattern. Values are the numbers of taxa with significantly different
abundances at each level (and combinations) in each group. The null (Ø)
row indicates the number of taxa not significantly different at any level.
No S&A significant differences were found.
(Fig. 9B). Low zone communities in the South Sound
(Area 1) are clearly distinguished from all other Areas in
the Central Sound (R Z 0.958, 999 permutations,
p Z 0.001). Comparisons among Areas in the Central
Sound showed some similarity (R Z 0.49), but in all
263
pairwise comparisons there were significant differences.
Furthermore, as expected, the differences among Areas
increased with separation distance (i.e. R-values increased from north to south; Area comparisons: 5–4,
R Z 0.244, p Z 0.005; 5–3, R Z 0.538, p Z 0.001; 5–2,
R Z 0.92, p Z 0.001; 5–1, R Z 0.95, p Z 0.001). Pairwise comparisons among the other Areas showed similar
results for the R statistic.
We used Spearman’s ranked correlation coefficients
to qualitatively explore the biological and physical
variables that best explain the observed ordination
patterns (Fig. 9C and D). The angle and length of the
radiating lines in each joint plot relate to the direction
and strength of the Spearman’s correlation. In Fig. 9C,
the strongest correlation coefficients are represented by
the highlighted taxa. These taxa account for 87% of the
variation in the observed ordination pattern (r Z 0.87),
with relative contributions as follows: Littorina (periwinkles), r Z 0.45; Lottia (limpets), r Z 0.44; Acrosiphonia (green algae), r Z 0.34; Mastocarpus (red algae),
r Z 0.29; Mediomastus (worms), r Z 0.28; and Crepidula
(introduced snails), r Z 0.19. Driving the south–north
trend in diversity are about 58 taxa that become steadily
more abundant towards the more marine and waveexposed areas, such as the snail Lacuna vincta.
The best combination of physical variables explaining
the observed pattern are shown in Fig. 9D. This subset
accounts for a combined correlation of r Z 0.81, with
Fig. 9. The two-dimensional solution for the non-metric multidimensional scaling ordination (final stress Z 0.14). The 45 data points represent
communities sampled on each transect, grouped by site (A) and Area (B). The separation between South (Area 1) and Central (Areas 2–5) basin
communities is evident. The latitudinal gradient of community structure along the axis of Puget Sound, and particularly in the central basin, is also
clearly evident by the near linear arrangement of Area level samples. The subset of taxa that best explain the observed patterns are shown with
vectors (C) representing the scaled Spearman’s ranked correlation to the community patterns. The physical variables that best explain the observed
pattern and the correlation vectors are also shown (D).
264
M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
relative contributions of water temperature, r Z 0.58;
precipitation, r Z 0.55; wave energy, r Z 0.32; air temperature, r Z 0.33; and salinity, r Z 0.28. Although these
correlations are not supported by statistical tests of
significance, they do provide guidance for further studies.
For example, by using stepwise regression we found that
the gradient in Puget Sound species richness was best
explained by a combination of wave energy, water
temperature and salinity (r2 Z 0.71, F6,38 Z 14.98,
p ! 0.001).
4. Discussion
Detection of static patterns in space and dynamic
patterns in time both require consideration of scales of
variation. Understanding patterns in estuarine systems
is difficult because variation in both physical forcing
functions and biota occurs at all scales. Previous studies
have noted that sample-to-sample variability is very
high (e.g., Ferraro et al., 1989), statistically significant
(Morrisey et al., 1992; James and Fairweather, 1996), or
even the greatest source of variation among all samples
(Service and Feller, 1992), making detection of patterns
at (larger) scales of interest problematic. As in other
studies where replicate sample units were deliberately
pooled (e.g., Boesch, 1973), we were interested in
characterizing the low zone communities at the scale
of beach segments (not small sample units), and then
examining variability at larger scales to understand the
primary drivers of ecosystem structure. Our nested
sampling design (Table 1) enabled us to test variation
across a wide range of spatial scales, from the amongbeach scale (transects), to the among-site scale, to the
among-Area scale, and also to examine among-basin
differences along the extensive Puget Sound shoreline.
We have shown that the waters of Puget Sound vary
in temperature, salinity, and wave energy along the
north–south axis (Fig. 3), although less dramatically
than in many estuaries. Our sampling design allowed us
to examine population and community variation along
these quantified gradients and conclude that the low
intertidal gravel beach biota of Puget Sound are tightly
linked to physical properties of the benthos and
nearshore ocean. Communities from beach segments
within a site are very similar, sites within an Area are
more different, and variation among Areas is still greater
(Fig. 9). And, as anticipated, there are substantial
differences in biota between the south and central
Sound basins. The differences in species richness
between gravel beach segments in the south vs. central
basin are striking; there is a clear, almost unidirectional
trend from south to north in the numbers of species
found per beach (Fig. 7). The trend is visible both in the
epibiota and in the infauna. When the taxa are broken
down by trophic mode, the south–north trend is still
visible in virtually all the groups (Fig. 7C). This differs
from results in other, perhaps more contaminated
estuaries, where trophic diversity declines with species
richness (e.g., Rakocinski et al., 2000). The taxa found
throughout the Sound are presumably tolerant of the
lowered salinities and other physical conditions characteristic of the southern sites, but are generalists in terms
of their physical requirements. There are few taxa found
primarily in South Sound (Appendix A). Some of these
are aquaculture-related, and others (e.g., the periwinkle
Littorina scutulata) can be found abundantly in central
and northern sites, but only at tidal levels above our low
shore samples; this probably reflects the increased wave
action in the more northerly sites (Fig. 5B), which tends
to result in many species living higher on the shore.
By far the most taxa tended to be found exclusively or
most commonly in the north–central sites (Appendix A).
The richness in these northern samples (Figs. 6 and 7)
parallels many other estuarine studies that find the
greatest benthic diversity in areas near the mouth of
estuaries, where salinities and temperatures tend to be
the least variable (and most marine), wave action
highest, turbidity and sedimentation lowest, and pollution the least (references in Section 1 also Edgar and
Barrett, 2002; Ysebaert and Herman, 2002; Ysebaert
et al., 2003). By stratifying our sampling to one substrate
type and thus eliminating several ‘‘nuisance’’ variables
(Clarke and Green, 1988), we were able to detect these
patterns even though the range of values of key physical
variables is relatively small in Puget Sound compared
with other coastal plain and fjordal estuaries (e.g., mean
salinity varies by less than 4 psu, Fig. 3). Low-salinity
stress may exclude many species from the South Sound
(Fig. 3), although we could find no literature on
experimental salinity studies for the local flora and
fauna. Higher sedimentation of fines in the south
(Fig. 5C) may exclude some taxa, for instance suspension feeding phoronid worms, and these tube-builders in
turn provide habitat for a variety of associated infauna
(Rader, 1980).
The spatial autocorrelation analyses showed that the
abundances of almost half of the taxa were not
independent of those at neighboring beach segments.
These results suggest that there is significant connectedness among beach segments in Puget Sound, presumably
via pelagic larval and spore dispersal processes, affected
by the basin- and sill-dominated hydrodynamic circulation of this fjordal estuary (see Section 2.1). Thus for
many or perhaps most benthic species in this system, the
populations at one beach may be part of a metapopulation comprising much of Puget Sound. It is likely that
there is also mixing with populations outside the Sound
(e.g., in the Juan de Fuca Strait) where many of these
species are found, but this may occur at time scales
dictated by the rates and magnitudes of water exchange
over the sill separating the central basin and the Strait.
M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
Mixing should especially occur in taxa (e.g., clams or
barnacles: Strathmann et al., 1981) with long pelagic
larval stages, but gradual genetic mixing should be
possible even for species with shorter dispersal distances
(e.g., most algae, Santelices, 1990). Unfortunately, data
on rates of exchange of individuals among populations
in marine systems are rare (Caley et al., 1996). The
prevalence of beaches with similar physical structure,
and the hydrodynamic circulation patterns in Puget
Sound, undoubtedly contribute to the observed connectedness and thus, spatial autocorrelation. If the
sampled beaches were exact replicates, then we would
expect lower variance among beaches in addition to
highly autocorrelated population abundances. However,
in apparent contradiction to this observation, we found
high variance among replicate transects within each site
(Table 5). Even though within-site transects were
considered replicates, this is a statistical construct and
not realistic because exact physical replicates are
unlikely to exist in nature. Therefore, the biological
variance we observed is most likely a function of slight
differences in the physical structure of each sampled
beach, compounded by species interactions, even though
the general trend in abundances is still dominated by
spatial autocorrelation.
Benthic organisms within estuarine and marine
nearshore ecosystems are sensitive to environmental
gradients and may serve as indicators of changes
occurring in the coastal ocean (e.g., Warwick and
Clarke, 1993; Rakocinski et al., 2000; Warwick et al.,
2002). These organisms may have life spans ranging
from days to seasons or years, and they frequently occur
in large numbers, thus providing an attractive baseline
for statistical analyses (Clarke and Green, 1988). For
these reasons, and because of logistical accessibility,
detecting change in nearshore biological communities is
a key component of experimental ecological research
and applied monitoring programs. Our data provide
evidence that pebble beach communities have a high
degree of fidelity to specific physical conditions of the
habitat (‘‘habitat envelopes’’, Freeman and Rogers,
2003). These habitat conditions result from a combination of small and large spatial scale physical forcings. By
matching the spatial scales of these forcing functions to
the scale of the biological response, we improve our
understanding of the significance of any observed
environmental change. While other studies have shown
that estuarine diversity is a function of salinity and
temperature, our analysis suggests that in Puget Sound,
these parameters may combine with wave energy
(forcing grain size distribution) to directly or indirectly
affect gravel beach community structure and ultimately
benthic diversity.
Our sampling methodology is labor-intensive, but the
stratified design and large number of replicates provide
good spatial coverage and characterization of the
265
pebble-sand beach habitat type (the most common
shore type in this estuary). By accounting for ecologically important geophysical gradients such as temperature, salinity, and wave energy, we are able to establish
correlations with a biological response, although the
mechanisms by which these gradients affect the biota
require experimental testing. Any change in estuarine
gradients should cause a detectable change in the biota.
For example, any further development of Puget Sound’s
shorelines (e.g., construction of more seawalls) that
changes sediment distribution on the beaches is likely to
change the biotic community. Climate change either
from a global scale effect, an El Niño, or a regional
effect can change the estuarine gradients. For example,
higher rainfall will increase the slope of the salinity
gradient by decreasing the salinity in the southern basin;
we predict this would reduce the species richness there
and increase the slope of the richness gradient we
observed. Thus our sampling design can allow ecologists
and managers to detect a biological response from
natural or anthropogenic change at a global, soundwide, or local scale.
Acknowledgements
We gratefully acknowledge funding, logistical support, and stimulating companionship from Program
Manager Helen Berry, and field assistance from the staff
of the Nearshore Habitat Program, Washington Department of Natural Resources, especially Betty Bookheim, Amy Sewell, Blain Reeves, and Tom Mumford.
MND thanks the director and staff of the Friday
Harbor Laboratories for space and support, and L.
Harris and C. Staude for taxonomic assistance. Comments from three anonymous reviewers greatly improved earlier versions of the manuscript.
Appendix A
List of all species found in the study, along with their
phylum and trophic category (Prim Z primary producer, Carn Z carnivore, Scav Z scavenger, Dep Z
deposit feeder, Dep/Susp Z facultative deposit or suspension feeder, Susp Z suspension feeder, Comm Z
commensal, Herb Z herbivore, Omni Z omnivore. Distrib. Z qualitative trend towards being more abundant
in the north (N), in the south (S), in the center of the
region (C), at the edges of the region (E), or with no
discernable pattern (O). The three columns to the right
give p values for nested ANOVAs indicating significant
variation for that species at that spatial scale; significant
values are in bold.
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M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
Appendix A
Species name
Phylum
Trophic
Distrib.
Transect
Site
Area
Acrosiphonia spp.
Alaria sp. (unident.)
Alia gausapata
Allorchestes angusta
Amphiodia periercta
Amphiodia urtica
Amphipholis squamata
Ampithoe dalli
Ampithoe lacertosa
Anisogammarus pugettensis
Anthopleura elegantissima
Aphelochaeta multifilis
Armandia brevis
Axiothella rubrocincta
Balanus glandula
Boccardiella hamata
Calliostoma sp.
Cancer sp.
Capitella capitata
Caulleriella ?pacifica
Ceramium sp.
Cirratulus cingulatus
Cirratulus multioculatus
Cirratulus robustus
Cirriformia sp.A
clam siphons (unident.)
Clinocardium nuttallii
Crassostrea gigas
Crepidula dorsata
Crepidula fornicata
Cryptomya californica
Dendraster excentricus
Desmarestia viridis
Dorvillea annulata
Edwardsia spp.
Encrusting red algae
Eogammarus oclairi
Eteone pacifica
Euclymene sp.A
Euclymene sp.B (cf. zonalis)
Eulalia parvoseta
Eupolymnia sp. A
Exosphaeroma inornata
Fabia subquadrata
Flatworm (unident.)
Fucus gardneri
Gelidium spp.
Glycera americana
Glycinde picta
Gnorimosphaeroma oregonense
Gracilaria pacifica
Gunnel (unident.)
Hemigrapsus nudus
Hemigrapsus oregonensis
Hemipodus borealis
Hermissenda crassicornis
Hesionid sp. (unident.)
Hyale frequens
Idotea sp.
Kefersteinia sp.
Lacuna vincta
Laminaria saccharina
Leitoscoloplos pugettensis
Leptasterias hexactis
Chlorophyta
Phaeophyta
Mollusca
Arthropoda
Echinodermata
Echinodermata
Echinodermata
Arthropoda
Arthropoda
Arthropoda
Cnidaria
Annelida
Annelida
Annelida
Arthropoda
Annelida
Mollusca
Arthropoda
Annelida
Annelida
Rhodophyta
Annelida
Annelida
Annelida
Annelida
Mollusca
Mollusca
Mollusca
Mollusca
Mollusca
Mollusca
Echinodermata
Phaeophyta
Annelida
Cnidaria
Rhodophyta
Arthropoda
Annelida
Annelida
Annelida
Annelida
Annelida
Arthropoda
Arthropoda
Platyhelminthes
Phaeophyta
Rhodophyta
Annelida
Annelida
Arthropoda
Rhodophyta
Chordata
Arthropoda
Arthropoda
Annelida
Mollusca
Annelida
Arthropoda
Arthropoda
Annelida
Mollusca
Phaeophyta
Annelida
Echinodermata
Prim
Prim
Carn
Scav
Scav
Scav
Scav
Scav
Scav
Scav
Carn
Dep
Dep
Dep
Prim
Dep/Susp
Herb
Carn
Dep
Dep
Prim
Dep
Dep
Dep
Dep
Susp
Susp
Susp
Susp
Susp
Comm
Susp
Prim
Carn
Susp
Prim
Scav
Carn
Dep
Dep
Carn
Dep
Scav
Comm
Carn
Prim
Prim
Carn
Carn
Scav
Prim
Carn
Scav
Scav
Carn
Carn
Omni
Scav
Herb
Omni
Herb
Prim
Dep
Carn
N
C
S
O
N
C
N
E
S
N
N
S
N
E
N
O
N
N
N
N
C
N
N
O
S
O
N
S
S
S
S
S
N
C
S
N
S
O
C
O
O
S
N
N
N
N
N
C
N
N
S
N
S
S
C
N
N
O
C
C
N
C
O
N
0
0.469
0
0
0
0.469
0
0
0
0
0
0
0
0.767
0
0.469
0.469
0.794
0
0.957
0.469
0
0
0
0
0.469
0
0.912
0
0
0.469
0.083
0.469
0.469
0.469
0
0
0.469
0
0
0.469
0.469
0
0
0
0
0
0.089
0.015
0
0.001
0
0
0
0
0
0
0.912
0
0.469
0
0
0.005
0
0.144
0.879
0.198
0
0.602
0.465
0.254
0.362
0
0.003
0.452
0.465
0
0.188
0.004
0.465
0.465
0.023
0.993
0
0.465
0.465
0.465
0.002
0.465
0.465
0.084
0.026
0.301
0
0.465
0.453
0.879
0.465
0.465
0.848
0.465
0.465
0.4
0.465
0.465
0.465
0
0.781
0.235
0.421
0.36
0.188
0.029
0.049
0.762
0.341
0.465
0.002
0.183
0.143
0.002
0.026
0.526
0.465
0
0.257
0.442
0.465
0.189
0.034
0.006
0.497
0.094
0.452
0.705
0.05
0.455
0.349
0.042
0.452
0.366
0.534
0.042
0.452
0.58
0.132
0.002
0.466
0.452
0.452
0.574
0.452
0.452
0.452
0.266
0.552
0.622
0.413
0.552
0.499
0.034
0.452
0.452
0
0.452
0.58
0.076
0.452
0.452
0.452
0.229
0.059
0.244
0.58
0.08
0.355
0.193
0.319
0.179
0.159
0.452
0.094
0.573
0.601
0.335
0.525
0.061
0.452
0.141
0.434
0.499
0.49
267
M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
Appendix A (continued)
Species name
Phylum
Trophic
Distrib.
Transect
Site
Area
Leptochelia dubia
Leptosynapta clarki
Lirularia sp.
Littorina scutulata
Lophopanopeus bellus bellus
Lottia pelta
Lottia strigatella
Lucina tenuisculpta
Lumbrineris zonata
Macoma balthica
Macoma inquinata
Macoma nasuta
Macoma secta
Magelona hobsonae
Malacoceros glutaeus
Maldanid (unident.)
Malmgreniella nigralba
Mastocarpus papillatus
Mazzaella heterocarpa/oregona
Mazzaella splendens
Mediomastus californiensis
Megalorchestia pugettensis
Metridium sp.
Microcladia borealis
Micropodarke dubia
Mopalia lignosa
Mysella tumida
Mytilus trossulus
Nassarius mendicus
Nemertean (unident.)
Neoamphitrite robusta
Neorhodomela oregona
Neotrypaea californiensis
Nephtys caeca
Nephtys caecoides
Nephtys ferruginea
Nephtys longosetosa
Nephtys sp. (unident.)
Nereis limnicola
Nereis procera
Nereis vexillosa
Nicolea zostericola (?)
Nicomache ?personata
Notomastus lineatus
Notomastus tenuis
Nucella canaliculata
Nucella lamellosa
Odonthalia floccosa
Odostomia sp. (unident.)
Onchidoris bilamellata
Onuphis iridescens
Ostreola conchaphila
Owenia fusiformis
Pagurus spp.
Pectinaria granulata
Pherusa plumosa
Pholoe minuta
Phoronopsis harmeri
Pinnixia faba
Pinnixia schmitti/occidentalis
Pinnotherid sp. (unident.)
Platynereis bicanaliculata
Podarke pugettensis
Arthropoda
Echinodermata
Mollusca
Mollusca
Arthropoda
Mollusca
Mollusca
Mollusca
Annelida
Mollusca
Mollusca
Mollusca
Mollusca
Annelida
Annelida
Annelida
Annelida
Rhodophyta
Rhodophyta
Rhodophyta
Annelida
Arthropoda
Cnidaria
Rhodophyta
Annelida
Mollusca
Mollusca
Mollusca
Mollusca
Nemertea
Annelida
Rhodophyta
Arthropoda
Annelida
Annelida
Annelida
Annelida
Annelida
Annelida
Annelida
Annelida
Annelida
Annelida
Annelida
Annelida
Mollusca
Mollusca
Rhodophyta
Mollusca
Mollusca
Annelida
Mollusca
Annelida
Arthropoda
Annelida
Annelida
Annelida
Phoronida
Arthropoda
Arthropoda
Arthropoda
Annelida
Annelida
Carn
Dep
Herb
Herb
Carn
Herb
Herb
Susp
Omni
Dep
Dep
Dep
Dep
Dep
Dep/Susp
Dep
Comm
Prim
Prim
Prim
Dep
Scav
Susp
Prim
Omni
Herb
Susp
Susp
Scav
Carn
Dep
Prim
Dep
Carn
Carn
Carn
Carn
Carn
Omni
Omni
Omni
Dep
Dep
Dep
Dep
Carn
Carn
Prim
Carn
Carn
Omni
Susp
Dep
Scav
Dep
Dep
Dep
Susp
Comm
Comm
Comm
Omni
Omni
N
S
O
S
S
C
O
C
N
O
N
N
S
S
C
E
S
N
O
O
N
N
C
C
N
O
O
O
S
O
O
O
S
O
O
O
N
N
C
C
S
O
O
S
S
O
N
N
N
N
O
N
N
O
O
N
N
N
O
O
O
N
N
0.106
0
0.085
0
0
0
0
0
0
0.469
0
0.027
0.385
0.469
0
0.469
0.469
0.022
0
0.55
0
0
0.008
0.469
0.912
0
0.007
0
0
0.037
0.007
0
0
0.089
0.003
0.469
0.469
0
0.469
0
0.3
0.469
0.469
0
0
0
0
0.015
0.988
0.005
0.469
0.469
0.435
0
0
0
0
0
0.469
0.345
0.877
0.469
0.043
0.01
0.063
0.439
0.006
0.093
0
0.022
0.053
0.002
0.465
0.729
0.927
0.026
0.465
0.755
0.746
0.465
0
0.575
0.162
0.02
0.465
0.428
0.465
0.188
0.062
0.465
0.49
0.004
0.027
0.465
0.465
0.009
0.566
0.704
0.465
0.465
0.853
0.465
0.442
0.002
0.465
0.465
0.906
0.129
0.417
0
0.242
0.002
0
0.465
0.465
0.405
0.17
0.05
0.465
0.566
0.523
0.465
0.321
0.022
0.465
0.209
0.452
0.032
0.546
0.148
0.641
0.101
0.021
0.3
0.464
0.452
0.02
0.022
0.525
0.452
0.102
0.171
0.452
0.267
0.021
0.211
0.22
0.452
0.553
0.58
0.072
0.933
0.525
0.302
0.312
0.113
0.525
0.452
0.387
0.737
0.388
0.737
0.452
0.072
0.452
0.431
0.452
0.452
0.552
0.054
0.316
0.152
0.358
0.543
0.452
0.097
0.58
0.452
0.061
0.722
0.473
0.452
0.284
0.364
0.737
0.683
0.559
0.58
0.439
(continued on next page)
268
M.N. Dethier, G.C. Schoch / Estuarine, Coastal and Shelf Science 62 (2005) 253–270
Appendix A (continued)
Species name
Phylum
Trophic
Distrib.
Transect
Site
Area
Podarkeopsis glabrus
Pododesmus cepio
Polinices lewisii
Polydora columbiana
Polynoid (unident., in quadrat)
Polysiphonia sp. (unident.)
Pontogeneia ivanovi
Porphyra sp.
Prionitis sp. (unident.)
Prionospio multibranchiata
Prionospio steenstrupi
Protothaca staminea
Pugettia gracilis
Punctaria expansa
Sabellid (unident.)
Sarcodiotheca sp. (unident.)
Sargassum muticum
Saxidomus giganteus
Scleroplax granulata
Scolelepis squamata
Scytosiphon simplicissimus
Semibalanus cariosus
Serpulid sp. (unident.)
Smithora naiadum
Spio filicornis
Spiochaetopterus costarum tubes
Spiophanes bombyx
Stronglyocentrotus droebachiensis
Syllis stewarti
Syllis heterochaeta
Tectura scutum
Tellina modesta
Tellina nuculoides
Terebellides californica
Tonicella lineata
Transennella tantilla
Tresus capax
Ulvoids (unident.)
Zostera marina
Annelida
Mollusca
Mollusca
Annelida
Annelida
Rhodophyta
Arthropoda
Rhodophyta
Rhodophyta
Annelida
Annelida
Mollusca
Arthropoda
Phaeophyta
Annelida
Rhodophyta
Phaeophyta
Mollusca
Arthropoda
Annelida
Phaeophyta
Arthropoda
Annelida
Rhodophyta
Annelida
Annelida
Annelida
Echinodermata
Annelida
Annelida
Mollusca
Mollusca
Mollusca
Annelida
Mollusca
Mollusca
Mollusca
Chlorophyta
Anthophyta
Omni
Susp
Carn
Dep/Susp
Carn
Prim
Scav
Prim
Prim
Dep/Susp
Dep/Susp
Susp
Scav
Prim
Susp
Prim
Prim
Susp
Comm
Dep/Susp
Prim
Susp
Susp
Prim
Dep/Susp
Susp
Dep/Susp
Herb
Carn
Carn
Carn
Dep
Dep
Dep
Herb
Susp
Susp
Prim
Prim
N
C
N
O
O
N
N
O
N
N
N
O
N
O
O
N
C
N
S
N
S
O
N
C
N
O
N
N
O
N
O
C
N
O
C
C
N
O
C
0.015
0.012
0.469
0.469
0.637
0
0
0
0.666
0.469
0
0
0.001
0
0.312
0
0.007
0.017
0.469
0.469
0
0
0.469
0.469
0.016
0
0.001
0
0.469
0.469
0.96
0
0.469
0
0.469
0.469
0.169
0
0.469
0.693
0.074
0.465
0.465
0
0.632
0.003
0.557
0.438
0.465
0.18
0.143
0.035
0.404
0.704
0.188
0.562
0.213
0.465
0.465
0.123
0.057
0.465
0.465
0.279
0.007
0.039
0.017
0.465
0.465
0.465
0.529
0.465
0.003
0.465
0.465
0.028
0.002
0.465
0.191
0.202
0.737
0.452
0.428
0.115
0.545
0.041
0.239
0.452
0.406
0.905
0.208
0.076
0.187
0.548
0.397
0.287
0.58
0.58
0.11
0.041
0.452
0.452
0.008
0.212
0.452
0.091
0.452
0.452
0.001
0.256
0.452
0.541
0.452
0.552
0.223
0.252
0.452
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