evaluating the noaa coastal ecology

7/28/2019 Evaluating the NOAA coastal ecology

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Evaluating the NOAA Coastal and Marine EcologicalClassification Standard in estuarine systems: A Columbia River

Estuary case study

Matthew L. Keefer a,*, Christopher A. Peery a, Nancy Wright a, William R. Daigle a,Christopher C. Caudill a, Tami S. Clabough a, David W. Griffith a, Mark A. Zacharias b

a Fish Ecology Research Laboratory, Department of Fish and Wildlife Resources, University of Idaho, Moscow, ID 83844-1141, USAb Department of Geography, University of Victoria, P.O. Box 3050 STN CSC, Victoria, BC V8W 3P5, Canada

Received 17 January 2007; accepted 19 November 2007

Available online 26 December 2007

Abstract

A common first step in conservation planning and resource management is to identify and classify habitat types, and this has led to a pro-

liferation of habitat classification systems. Ideally, classifications should be scientifically and conceptually rigorous, with broad applicability

across spatial and temporal scales. Successful systems will also be flexible and adaptable, with a framework and supporting lexicon accessible

to users from a variety of disciplines and locations. A new, continental-scale classification system for coastal and marine habitats dthe Coastal

and Marine Ecological Classification Standard (CMECS)dis currently being developed for North America by NatureServe and the National

Oceanic and Atmospheric Administration (NOAA). CMECS is a nested, hierarchical framework that applies a uniform set of rules and

terminology across multiple habitat scales using a combination of oceanographic (e.g. salinity, temperature), physiographic (e.g. depth, substra-

tum), and biological (e.g. community type) criteria. Estuaries are arguably the most difficult marine environments to classify due to large spatio-

temporal variability resulting in rapidly shifting benthic and water column conditions. We simultaneously collected data at eleven subtidal sitesin the Columbia River Estuary (CRE) in fall 2004 to evaluate whether the estuarine component of CMECS could adequately classify habitats

across several scales for representative sites within the estuary spanning a range of conditions. Using outputs from an acoustic Doppler current

profiler (ADCP), CTD (conductivity, temperature, depth) sensor, and PONAR (benthic dredge) we concluded that the CMECS hierarchy

provided a spatially explicit framework in which to integrate multiple parameters to define macro-habitats at the 100 m2 to >1000 m2 scales,

or across several tiers of the CMECS system. The classifications strengths lie in its nested, hierarchical structure and in the development of

a standardized, yet flexible classification lexicon. The application of the CMECS to other estuaries in North America should therefore identify

similar habitat types at similar scales as we identified in the CRE. We also suggest that the CMECS could be improved by refining classification

thresholds to better reflect ecological processes, by direct integration of temporal variability, and by more explicitly linking physical and

biological processes with habitat patterns.

2007 Elsevier Ltd. All rights reserved.

Keywords: classification systems; current measurement; acoustic Doppler current profiler (ADCP); estuaries; United States; Columbia River Estuary

1. Introduction

Estuaries are spatially and temporally dynamic transition

zones spanning multiple spatial and temporal scales. The

boundaries of estuarine habitats are structured over time and

space primarily as a result of daily tidal cycles, seasonal and

inter-annual variations in river discharge and temperature,

* Corresponding author.

E-mail addresses: [email protected] (M.L. Keefer), nwright@uidaho.

edu (N. Wright), [email protected] (W.R. Daigle), [email protected] (C.C.

Caudill), [email protected] (T.S. Clabough), [email protected].

mil (D.W. Griffith), [email protected] (M.A. Zacharias).

0272-7714/$ - see front matter 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ecss.2007.11.020

Available online at www.sciencedirect.com

Estuarine, Coastal and Shelf Science 78 (2008) 89e106www.elsevier.com/locate/ecss
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://www.elsevier.com/locate/ecsshttp://www.elsevier.com/locate/ecssmailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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and long-term shifts in bedform and sediment distribution

(Sherwood and Creager, 1990; Dyer, 1997; Reed et al.,

2004). Classification of estuarine habitats is therefore both

scale dependent (Attrill and Rundle, 2002) and strongly

context driven (Elliott and McLusky, 2002), meaning the scale

of habitat partitions must appropriately correspond with the

physical or biological processes being investigated and thepartitions may vary depending on scale. For example,

ecological boundaries in estuaries may be much more spatially

restricted for benthic plants and animals than are those for

more mobile organisms that can migrate as physical habitats

change (Bottom and Jones, 1990; Colloty et al., 2002). For

the most motile and euryhaline species, estuaries might be

considered relatively uniform mixed freshwater-seawater

habitats, versus a complex continuum of interconnected habi-

tats or physical gradients for more sedentary, stenohaline, or

otherwise ecologically-restricted organisms (Neira et al.,

1992; Harris et al., 2001). Importantly, the complex interac-

tions over time and space between abiotic and biotic processes

often result in ecological boundaries that do not necessarilycorrespond with boundaries derived using physical or

chemical surrogates.

The challenges of scale and context in estuaries have long

been recognized and highlight the need for spatially and

temporally explicit classification systems (Hansen and Rattray,

1966; Cowardin et al., 1979; Jay et al., 2000; Elliott and

McLusky, 2002). Many estuarine, coastal and marine classifi-

cations have been developed in recent decades, using a variety

of conceptual and methodological approaches (e.g., Boyd

et al., 1992; Iba~nez et al., 1997; Buzzelli et al., 1999;

Whitfield, 1999; Edgar et al., 2000; Bricker et al., 2003;

Kenny et al., 2003; Roff et al., 2003). These systems havebeen generated to address an assortment of management and

conservation objectives, and as such have arisen from multiple

disciplines and were developed mostly at local or regional

scales. A number of these efforts have demonstrated both

recurring and persistent associations of marine biological

communities with oceanographic and physiographic structures

to a degree that the spatial locations of communities can be

mapped with measures of certainty (e.g. Doyle et al., 1993;

Auster et al., 2001). However, few have attempted to compre-

hensively reflect underlying ecological pattern and process at

multiple scales, which would be broadly useful for facilitating

local, regional and global comparisons. One attempt, the

Coastal and Marine Ecological Classification Standard

(CMECS), is being developed as a potential single classifica-

tion standard for North America (Madden et al., 2005). The

intent of CMECS is to provide a framework for systematically

inventorying, describing, characterizing, and predicting

habitats and, where applicable, their constituent species and

communities. The framework consists of six, nested levels

linking broad marine environment types (e.g. intertidal,

oceanic) downward to biotopes, which are discrete habitat

units that support a unique and recognizable community

type. The overall objective is that CMECS will be a flexible

and evolving classification framework, designed to be widely

applicable across all coastal and marine habitat types from

freshwater-marine and marine-terrestrial interfaces to the

open ocean (Madden et al., 2005).

While the conceptual framework has been defined and

successfully applied (mapped) at the higher levels, it is as

yet unknown whether many components of the classification

correspond with real-world habitats that can be spatially

mapped using existing inventory methods. In this paper, weattempted to apply the CMECS estuarine definitions at various

levels using several commonly-collected data types acquired

at representative sites spanning a range of ecotypes occurring

in the Columbia River Estuary (CRE, Washington-Oregon).

We focused on parameters describing energy inputs (e.g.,

current velocity), physicochemical characteristics of the water

column, and benthic substrate rather than biotic distributions

because the CMECS approach is structured by physicochemi-

cal processes at larger spatial scales and because many

management uses of classifications aim to identify suitable

habitat for species currently depressed or absent. Specific

study objectives included: (1) testing whether existing

available sampling approaches (e.g., acoustic Doppler currentprofilers [ADCP] and benthic and water column sampling

devices) can provide the underlying physical and chemical

data necessary to classify estuarine habitats at one or more

scales; (2) evaluating the sensitivity of CMECS to spatial

and temporal variability in both vertical (e.g., water column)

and horizontal gradients in environmental conditions (e.g.,

across zones of freshwater influence); (3) examining the

relationship between CMECS classification levels and the pro-

cesses underlying habitat development and persistence; and

(4) evaluating the potential for CRE habitat mapping at the

various CMECS classification levels for hypothetical mobile

(e.g., salmon) and sessile (e.g., benthic infauna) organisms.Our primary aim was to evaluate CMECS as a classification

tool and it was beyond the scope of the study to produce

a definitive classification of the CRE.

2. Materials and methods

2.1. Columbia River Estuary: Background

The Columbia River drains approximately 660,000 km2 of

the U.S. Pacific Northwest and British Columbia, and has

among the highest annual discharge of all North American

rivers (mean annual flow w7000 m3 s1) (Simenstad et al.,

1990; Naik and Jay, 2005). The estuary is characterized by

strong tidal currents and highly variable freshwater inputs

(Hamilton, 1990; Jay and Smith, 1990; Baptista et al.,

2005). Within-year river discharge near the mouth can vary

by an order of magnitude, from low flows near 2000 m3 s1

in fall to levels exceeding 20,000 m3 s1 during summer

runoff peaks (Bottom et al., 2005).

The upstream extent of the salt-fresh mixing zone and the

presence or absence of a salt wedge in the CRE is strongly

linked to hydrologic conditions and spring-neap tidal cycles

(Hamilton, 1990; Jay and Smith, 1990). In contrast to many

large estuaries, high river discharge through the CRE results

in generally low salinity and rapid flushing times, and the

90 M.L. Keefer et al. / Estuarine, Coastal and Shelf Science 78 (2008) 89e106


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degree of vertical stratification varies with freshwater input

(Giese and Jay, 1989). The spatial extent of saltwater intru-

sions is also topographically controlled (Fox et al., 1984).

Two major channels, a series of minor channels, mid-estuary

sandbanks, and peripheral tidal flats, marshes, and bays shape

circulation patterns, with the greatest salinity intrusion in the

deep channels (Hughes and Rattray, 1980; Hamilton, 1990;Jay and Smith, 1990; Johnson et al., 2003).

Tidal and wave energy, combined with river currents and

large-scale circulation patterns have shaped the bedforms

and sedimentary geology of the CRE. Because the size and

distribution of benthic sediments are important determinants

of habitat type and stability, considerable effort has been

invested in CRE sediment mapping and modeling. In

a multi-year study, Sherwood and Creager (1990) analyzed

more than 2000 sediment samples and concluded that the

CRE is dominated by a narrow range of sands and finer

sediments. The sampling study and an associated energetics

study (Jay et al., 1990) found that CRE sediments were largely

riverine in origin, tended to decrease in size closer to theocean, and were mobile on daily and seasonal time scales,

particularly in areas with greater energy inputs. Local current

velocities and physical energy inputs appear to be critical fac-

tors shaping sediment distribution processes and consequently

the complex mosaic of benthic habitats in the CRE. This

suggests that spatial and temporal variability in energy inputs

will be particularly important to any habitat classification

effort in this system.

The broad-based understanding of the physical processes at

work in the CRE has been complimented by many ecological

studies, driven in part by the major decline in anadromous

Pacific salmonids (Oncorhynchus spp.). The Columbia Riverwas historically one of the worlds most productive salmon

rivers (Chapman, 1986; Augerot, 2005), but overfishing, water

withdrawal, and habitat loss and alteration decimated runs

(National Research Council, 1996; Ruckelshaus et al., 2002;

McClure et al., 2003). The construction of dozens of upriver

hydroelectric and water storage dams, combined with

estuarine diking and dredging, significantly altered hydrologic

regimes, sediment and carbon inputs, and subsequently CRE

habitats and food webs (Bottom et al., 2005). Salmonid

declines have precipitated a massive recovery effort (National

Marine Fisheries Service, 2000) and as a result, patterns of sal-

monid habitat use and mortality in the CRE have been studied

for decades (e.g., Sims and Johnsen, 1974; Ledgerwood et al.,

1991; Collis et al., 2002; Bottom et al., 2005; Schreck et al.,

2006). Non-salmonid flora and fauna in the CRE have also

been well-studied, including distribution, abundance, and

ecological relationships for fish (Haertel and Osterberg,

1967; Misitano, 1977; McCabe et al., 1983), benthic inverte-

brates (Simenstad et al., 1981; Emmett and Durkin, 1985;

Bottom and Jones, 1990; Jones et al., 1990), and phytoplank-

ton and zooplankton (Haertel et al., 1969; Frey et al., 1981;

Amspoker and McIntire, 1986; Morgan et al., 1997). A large

multidisciplinary effort (Columbia River Estuary Task Force)

has integrated much of the CRE research in a series of publi-

cations and habitat atlases (many available at http://www.

orednet.org/wcrest/index.html), providing a good backdrop

for evaluating classifications derived using CMECS. However,

these data sets were not used in the test of CMECS in the CRE

because we could not efficiently resolve temporal and spatial

scale incompatibilities between existing datasets. Specifically,

the CMECS evaluation required data collected simultaneously

across multiple scales at each location while much of thehistoric data could not be merged for single locations across

multiple scales at adequate resolutions.

2.2. Study sampling protocols

We selected 11 subtidal CRE study sites along the w25 km

reach between Astoria and Woody Island, WA (Fig. 1, Table 1).

In site selection, we aimed to capture a wide range of physical

and hydrological variability. The sampled sites included large

natural and dredged channels, sand flats, the relic channels of

Cathlemet Bay, and sloughs surrounding the islands south of

the dredged main channel. Sites were also selected so that avariety of substrates were sampled, ranging from the dominant

sand and fine sediment habitats of the CRE, to sites with

potential large woody debris or boulder fields. The sampling

design was further intended to capture gradients in salinity,

tidal influence, and water velocity.

Sites at Fort Columbia and Cliff Point (Fig. 1, sites 1 and 2)

were in the natural (non-dredged) North Channel, east and

west of the deep trough that runs from Ellice Point on the

Washington State coast to Desdemona Sands in mid-estuary

(south of sites 1 and 2). These sites include deep holes and

troughs (>15 m), subtidal (5e15 m), and shallow subtidal

(2e

5 m) habitats; neither the artificial shoreline (rock rein-forcement) nor the intertidal ribbon at the edge of Desdemona

Sands was sampled. Two other study sites, Grays Point and

Portuguese Point (Fig. 1, sites 3 and 4) were influenced by

fresh water, sediment, and nutrients from Grays River and

Deep River. These inputs move westward across the mud flats

of Grays Bay and add to the continual flux in the North

Channel; these combine to influence physical and biological

processes at these survey sites. Greys Point and Portuguese

Point sites also included the full range of CRE estuarine depth

zones. With the exception of the Tongue Point site (Fig. 1, site

6) which included a portion of the dredged shipping lane, the

remaining study sites (Fig. 1, sites 5, 7e11) were located in

the southern sloughs and natural channels from Cathlemet

Bay eastward to Woody Island. These sites were primarily

subtidal or shallow subtidal habitats with reduced flow. Shore-

line and intertidal habitats adjacent to the study sites included

a mix of freshwater and tidal marshes, mud and sandflats, tidal

drainage networks, and diked areas.

All sampling took place between 24 September and 20

October, 2004 to coincide with fall salmonid migrations and

an associated salmonid tagging study. During this time, tides

measured at Astoria ranged from approximately 1.7e3.1 m

relative to mean chart datum (NOAA station #9439040,

4612.50 N, 12346.00 W) and Columbia River discharge

measured upstream near Quincy, OR was variable but near

91M.L. Keefer et al. / Estuarine, Coastal and Shelf Science 78 (2008) 89e106
http://www.orednet.org/%223C%3Bcrest/index.htmlhttp://www.orednet.org/%223C%3Bcrest/index.htmlhttp://www.orednet.org/%223C%3Bcrest/index.htmlhttp://www.orednet.org/%223C%3Bcrest/index.htmlhttp://www.orednet.org/%223C%3Bcrest/index.htmlhttp://www.orednet.org/%223C%3Bcrest/index.html


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the annual low (mean 3,850 m3 s1, SD 575 m3 s1;

USGS station #14246900, 4610.90 N, 12310.80 W).

All sampling took place from a 6.1 m inboard jetboat. Aninstrument platform for this boat included a pair of pivoting

booms to deploy an ADCP, sidescan sonar and acoustic and

radio telemetry devices for fish tracking. Other sampling

equipment included a conductivity-temperature-depth (CTD)

probe run from a transom-mounted downrigger, and a benthic

dredge (PONAR) operated with a hand line. Results of the

salmon telemetry study were reported in (Griffith, 2007).

2.3. Current velocity surveys

Velocity profiles were collected using a boat-mounted1200 kHz ADCP (Teledyne RD Instruments Inc., San Diego,

CA). The instrument, a Workhorse Rio Grande model, was

mounted to a boom and when lowered was approximately

0.3 m below the surface. Velocity accuracy for this ADCP

was 2.5 cm s1 and the near-bed region missed was typically

about 1 m. In addition to automatically filtering ambiguous

velocity data near the bottom, the ADCP checked velocity

Fig. 1. Map of Columbia River Estuary (CRE) showing depths of intertidal (high tide to 2 m), shallow subtidal (2 e5 m), subtidal (6e15 m) and deep (>15 m). The

locations show 11 sites where ADCP, CTD, and/or benthic dredge samples were collected during SeptembereOctober, 2004. Sites: 1 Fort Columbia; 2 Cliff

Point; 3 Grays Point; 4 Portuguese Point; 5 Mott Island West; 6 Tongue Point Channel; 7 Cathlamet Bay North; 8 McGregor Island; 9 South

Channel; 10 Prairie Channel; 11 Woody Island Channel.

Table 1

Names, locations and lengths of sampling sites where benthic sediment (PONAR), water velocity (ADCP), and physiochemical (CTD) data were collected. Dashes

indicate data were not collected

Site Number & Name Latitude Longitude Transect length PONAR ADCP CTD(m) Ebb Flood Ebb Flood

1. Fort Columbia 4613.90 12354.20 2,080 x x x x x

2. Cliff Point 4615.70 12350.20 450 x x x x x

3. Grays Point 4616.10 12347.00 900 x x x x x

4. Portuguese Point 4616.60 12345.30 270 x x x x x

5. Mott Island West 4611.80 12345.00 800 x x x x x

6. Tongue Point Channel 4613.2 12345.10 1,530 e x x x e

7. Cathlamet Bay Northa 4612.60 12343.60 560 x x x x x

8. McGregor Islanda 4612.20 12341.80 490 x x x x x

9. South Channel 4610.30 12342.40 180 x x x x e

10. Prairie Channelb 4610.70 12339.40 270 x x x e e

11. Woody Is. Channel 4614.60 12333.80 730 x x x x x

a Located in the North Channel of Cathlamet Bay.

b Only 2 of 6 ebb-tide and 4 of 6 flood-tide ADCP transects collected.



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data at each bin (see below), and only bins containing valid

data were retained (Gordon, 1996). The ADCP was integrated

with DGPS technology (Trimble Navigation Ltd., Sunnyvale,

CA; model DSM 132) to provide navigational positions and

facilitate transect replication. An integrated sonar feature

allowed bottom-tracking, which adjusted the ADCP velocity

measurements for boat speed relative to the ground. Real-time data presentation during the surveys was via an onboard

computer, using the Windows-based software WinRiver (v.

1.06, RD Instruments).

Twelve ADCP profiles were collected for most transects,

which included two replicates of six different tidal periods

(early ebb and flood, middle ebb and flood, and late ebb and

flood). All transects were perpendicular to the prevailing

current and transect length varied from 180e2080 m (mean

750 m) depending on site configuration. Because tidal timing

differs by location within the estuary, transects were run based

on estimated timing from the Astoria tidal gage, and the mid-

dle periods approximated maximum predicted currents.

Transect replicates were spread across 2 to 3 days at eachsite and therefore provided velocity snapshots rather than

continuous profiles. Transects were collected only during

fall, and results may or may not represent conditions present

during other seasons or discharge/tidal conditions.

The mechanics of ADCP velocity calculations have been

described in detail in Wewetzer et al. (1999), Simpson

(2001), and Dinehart and Burau (2005). In essence, ADCP

uses the Doppler Effect to determine water velocity in three

dimensions (east-west, north-south, and vertical) across the

entire water column. The ADCP collects all velocity measure-

ments, or vectors, simultaneously in a vertical profile (depth

bins 0.5 m) using a 4-beam transducer approximately onceper second. Each vertical profile is called a velocity ensemble,

and in our study the ADCP automatically averaged 5 ensem-

bles internally to compress data. This resulted in 200e800

ensembles per transect. Because velocities in adjacent ensem-

bles were typically highly correlated, we further averaged

ADCP data at 10-ensemble intervals using Matlab (v. 7.0.0)

to simplify some data presentation. Within tidal stage, transect

replicates were generally very similar.

2.4. CTD surveys

Eight CTD profiles, four each during ebb and flood tides,

were collected at the deepest point along each sampled

transect using a Hydrolab Minisonde (Hach Co., Loveland,

Colorado) (Table 1). In addition to conductivity (salinity),

depth, and temperature, the Hydrolab also collected dissolved

oxygen data for each profile. Dissolved oxygen data were

strongly correlated with temperature and salinity across sites,

but we considered the data quality for oxygen to be lower than

for other variables because of potential calibration errors.

Therefore, we present oxygen results primarily to demonstrate

oxygens utility as a classification variable. CTD data were

collected at 5 s intervals, at approximately 0.5 m increments

starting at the bottom of the water column. Within tidal cycle,

the four profiles were collected sequentially, with time gaps

between replicates typically


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The second CMECS level (Formation) is based on major

geomorphic or hydrographic features. Examples of formations

include islands, peninsulas, trenches, seamounts, reefs,

lagoons, embayments, oceanic currents, and coastal fronts.

At the time of writing, CMECS had identified 28 estuarine

formations (http://www.natureserve.org/getData/CMECS/).

Many types of CMECS estuarine regime formations are found

in the CRE study.

The third level (Zone) is a vertical classification ranging

from the supratidal (e.g., the surf or splash zone) to the deepocean bottom. Relevant CMECS classification zones across

the littoral, water column and bottom occur in the CRE with

spatial scales ranging from 100 m2 to 10,000 km2. Because

our sampling sites were all subtidal (i.e., not in the littoral

zone), we only classified water column and bottom zones.

Nested within zones are macrohabitats (the 4th level),

which structure the distributions of ecological communities

along physical or chemical gradients. Macrohabitats occur at

scales of 100 m2 to several thousand square meters and are

repeatable and persistent physical environments with relatively

homogenous geomorphology, hydrology and vegetative struc-

tures but multiple distinct biological associations (Madden

et al., 2005). Level five (Habitat) is at the scale directly

used by biota (e.g., for food, shelter, spawning, or refuge)

and is typically modified or shaped by several specific environ-

mental variables. Habitats occur at smaller spatial scales

within formations and macrohabitats, and may be similar to

or smaller than macrohabitats in spatial extent. Often, multiple

habitats are contained within a macrohabitat, and therefore

both macrohabitat and habitat scales potentially overlappedthe scale of our sampling. Level six (Biotope) is defined

by specific faunal and/or floral relationships, where species

are physically linked to the habitat (e.g., plants or organisms

with very high site fidelity) and therefore help define it (e.g.,

seagrass beds). Classification at the biotope level was beyond

the study scope.

A critical aspect of the CMECS classification approach is

the use of attribute-based descriptors, which can be physico-

chemical, physical, geomorphologic, biological, anthropo-

genic, or biogeographic (Table 2). Descriptors can be

Regime

Littoral Water column Benthic

Zone

Macrohabitat MacrohabitatMacrohabitat

HabitatHabitatHabitat

Biotope Biotope Biotope

Geoform/Hydroform

L6: Biotope

L5: Habitat

L4: Macrohabitat

L3: Zone

L2: Formation

L1: Regime

10 km2 - >1,000 km2

10,000 m2 - 100 km2

10 m2 10,000 km2

100 m2 1000s m2

1 m2 - 100 m2

1 m2 - 100 m2

Fig. 2. Example structure of the Coastal and Marine Ecological Classification Standard (CMECS) classification, modified from Madden et al. (2005). In the full

hierarchy, Levels 2e

5 are nested within five regimes: Estuarine, Freshwater-influenced, Nearshore Marine, Neritic, and Oceanic. Descriptors and Classifiers areused to further describe and categorize ecosystem and environmental conditions at each level (see Table 2).

Table 2Descriptors, defined in Madden et al. (2005) as environmental characteristics that provide insight to a classification unit. At the higher levels of the classification

descriptors are used to differentiate broad physical and hydrologic regimes and formations. At lower levels of the classification, finer subsets of each descriptor are

used as classifiers for the identification of discrete habitats. Items in italics were not evaluated

Physico-Chemical Physical Geomorphologic Biological

Salinity Energy Type Slope Cover Type

Oxygen Energy Intensity Large Scale Relief Cover Class

Temperature Energy Direction Substrate Type Trophic Status

Turbidity Class Primary Water Source Substrate Size

Turbidity Type Enclosure Substrate Composition Anthropogenic

Turbidity Provenance Depth Class Relief Anthropogenic Impact

Tide Range Profile

Spatial Patterns Temporal Persistence Biogeographic

Photic Quality Ecological Region

Subzone

http://www.natureserve.org/getData/CMECS/http://www.natureserve.org/getData/CMECS/


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applied to all six CMECS levels, if the scale and characteris-

tics of the classification unit are appropriate. Examples include

salinity, temperature, depth, oxygen and turbidity levels,

energy type and intensity, tide range, water source, slope,

substrate composition, trophic status, temporal persistence,

and degree of human impact. The final classification compo-

nents are classifiers, which are used to more precisely defineand categorize descriptors. For example, the descriptor salinity

can be classified using practical salinity units (psu) as fresh

(0 psu), oligohaline (0e5 psu), mesohaline (5e18 psu), poly-

haline (18e30 psu), euhaline (30e40 psu), marine (35 psu),

hyperaline (>40 psu), or freshwater-influenced (< 30 psu

for at least 2 months) (Madden et al., 2005). Quantification

and precise definitions of the CMECS descriptors and

classifiers are an evolving part of the classification.

3. Results

3.1. Velocity profiles

Mean mid-tide water velocities at all study sites were

between 0.16 and 0.80 m s1 (Table 3). However, velocities

varied considerably within individual transects in response to

tidal stage, stratification, and underlying bathymetry. At sites

with substantial saltwater intrusion (e.g., Fort Columbia and

Cliff Point), velocity profiles were often vertically and/or

horizontally stratified, with the magnitude of the velocity gra-

dient related to tidal stage. During the mid-ebb tide at the Fort

Columbia site, for example, velocities were


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3.2. CTD profiles

Vertical salinity, temperature, and dissolved oxygen profiles

largely delineated the range of saltwater intrusion. The Fort

Columbia and Cliff Point sites had salt wedge characteristics

during both ebb and flood tides, with clearly stratified salinity

and temperature profiles (Table 3, Fig. 6). Salinity levels in

surface waters at these sites were mostly 4e6 psu, while salt

wedge values ranged from 16e30 psu. The Grays Point and

Portuguese Point sites were weakly stratified, with higher

salinity during flood tides and maximum salinity about

5 psu. Temperature profiles at these four sites showed patterns

that paralleled the salinity measures. In contrast, the remaining

sites were all vertically well-mixed, with no physiochemical

stratification. These sites were tidally influenced, but

maximum salinity levels did not exceed 1 psu (Table 3,

Fig. 6) indicating local tidal exchange of freshwater.

3.3. Benthic sampling

On average, dredge samples were predominantly sand with

finer silts and muds as the secondary substrate type. The

exception was the Mott Island West site, where samples

were largely silt/mud and organic detritus (Fig. 7). Coarser

substrate (e.g., pebbles and small rocks) and organic detritus

made up small percentages of the averaged samples at most

sites, while plants were collected only at the Mott Island

West (mean 3%) and South Channel (mean 10%) sites.

Across sites, substrate composition was significantly asso-

ciated with water velocity (Fig. 8). Mean percent sand was

positively correlated with mean water velocity during the

mid-ebb tide (P 0.036, r2 0.49, linear regression) and

a corresponding negative relationship was seen for the mean

percentage of fines (P 0.049, r2 0.45). Substitution of

mean mid-flood velocity produced less significant correlations,

probably because velocities during mid-ebb were typically the

highest among the measured tidal stages. Substrate at the Mott

Island West site was the most unique (3% sand, 83% fines),

and excluding this point reduced regression fit slightly

(r2 0.43 for sand and 0.31 for fines). Excluding the Fort Co-

lumbia site, where benthic samples were not collected along

the entire ADCP transect had little effect on regression results.

Variability in benthic sample composition within most sites

was quite high, indicating more fine-scale benthic structuring

than we anticipated. Some within-site variability appeared to

be due to velocity differences between channel and adjacent

Fig. 3. ADCP velocity profiles for thew2080 m Fort Columbia study transect during mid-ebb (top) and mid-flood (bottom) tides in October, 2004. Colors bar units

are in m s1 and range from 0.0 (magenta) to 2.5 (red). ADCP bins are 0.5 m in depth. The thick black line is the estuary bottom and white areas represent censored

data due to velocity ambiguity or interference (near-bottom) or ADCP blanking distance and boat movement (near-surface). Flow direction was downstream during

the ebb tide and upstream during the flood. Fig. is oriented so reader is looking downstream.



9/18

depositional habitats. However, testing this empirically would

be difficult using the sampling scheme we adopted for this test

study. A sampling design that retained randomness but

allowed direct comparison of velocity estimates and benthic

composition would probably allow association of some of

the finer-scale variability we observed with within-transect

differences in energy/current regime, such as potential differ-

ences between channels and adjacent flats.

Bivalves, amphipods, fine shell debris, mollusks, snails,

worms and/or worm casings were found in many samples.

However, the presence and abundance of soft sediment benthic

invertebrates was highly variable among samples within and

among transects and we did not observe consistent benthic

assemblages or associations with physical habitat features.

Such associations probably did occur at finer spatial scales

than we sampled.

3.4. CMECS classification testing

We used the above data types to classify the study sites

using CMECS (Table 4). The combined ADCP, CTD, and

benthic sampling data provided classification information for

about half of the nearly 30 CMECS descriptors (Table 2)

with potential relevance to the CRE study sites. Several

descriptors could be applied across all classification levels

we considered, from regime (Level 1) to habitat (Level 5).

For example, all study sites were in the ecological region

called the Columbian Pacific, the degree of enclosure was

partial (50e75% encircled by land), and during the sampling

month the temperature class was temperate (10e20 C) and

the tide range was moderate (1e5 m). Energy type at all sites

included wind, current, and tide. The energy direction

was a mix of horizontal and seaward, substrate composition

was mixed, and large scale relief was flat (height:width

ratio w0). The depth class at most sites included all of the

subtidal classifications for this descriptor: very shallow

(0e5 m), shallow (5e15 m), and deep (>15 m).

All sites were partially-enclosed and freshwater-influenced,

placing them in the Estuarine Regime (CMECS Level 1, code

A). We found classifying the CRE using the CMECS Level 2

(Formation) more difficult. At this range of scales (10,000 m2

to 100 km2) several geoforms and hydroforms appeared

applicable. For example, study sites in the peripheral bays

(e.g., South Channel, Mott Island West, Grays Point) had char-

acteristics of embayments (Level 2, code A02) but also could

have been characterized as subsurface channel (Level 2, code

A08) formations. Subsurface channel also seemed the most

appropriate formation classification for the Fort Columbia

Fig. 4. ADCP velocity profiles for thew730 m Woody Island Channel study transect during late-ebb (top) and late-flood (bottom) tides in October, 2004. Colors

bar units are in m s1 and range from 0.0 (magenta) to 2.5 (red). ADCP bins are 0.5 m in depth. The thick black line is the estuary bottom and white areas represent

censored data due to velocity ambiguity or interference (near-bottom) or ADCP blanking distance and boat movement (near-surface). Flow direction was

downstream during the ebb tide and upstream during the flood. Fig. is oriented so reader is looking downstream.



10/18

and Tongue Point Channel sites, each of which had deep

channels >1 km wide. Surface channel (Level 2, code

A25) may also have been appropriate for the more constricted

South Channel and Prairie Channel sites. At the higher end of

the formation scale (w100 km2

), however, both surface and

subsurface channels could be viewed as part of a larger

complex of channels and subtidal flats extending for 100s of

km2 throughout the CRE. To us, this suggested a repeating

channelized subtidal flat formation, an option not currently

included in CMECS. A distinction between natural and

Fig. 5. Examples of water velocity (m s1) and direction (right East) during early, middle, and late portions of ebb and flood tides at four Columbia River Estuary

study sites, generated from ADCP transect data. Colors represent velocity in the top 5 m of the water column (ebb pink; flood green), from 5e10 m deep

(ebb red; flood blue), and at >10 m (ebb maroon; flood black). Plotted points represent 10-ensemble averages at 0.5 m depth intervals from the

ADCP profiles. Concentric rings represent 0.2 m s1 intervals, with the outer ring of each plot 1.0 m s1.



11/18

dredged channels may also be appropriate, as both types were

widely distributed in the CRE. Finally, given that the CRE is

dominated by large freshwater inputs and net seaward flow,

the current (Level 2, code A13) hydroform may have been

a reasonable formation selection.

Classification of vertical zones (Level 3) and water column

macrohabitats (Level 4) nested within the zones was more

straightforward. Only the Fort Columbia and Cliff Point sites

showed consistent vertical stratification of the water column

zone. At the Fort Columbia site, the upper water column

macrohabitat (above the pyncnocline) was mesohaline, oxic

to oxygen saturated, and with low to moderate tidal and cur-

rent energy (Table 4). The upper water column macrohabitat

at the Cliff Point site was low energy, oligohaline, and oxic.

The lower water column macrohabitatdthe salt wedgedat

both Fort Columbia and Cliff Point was low energy,

polyhaline, and oxic. The remaining nine sites were largely

unstratified, suggesting a single well-mixed water column

macrohabitat. During the sampling period all nine sites had

low energy inputs. Those closer to the estuary mouth were

oligohaline with a mix of river and estuarine water exchange,

while those further upstream were largely tidally-exchangedfreshwater sites. Water column zones and their macrohabitats

were relatively stable during the studied tidal cycles, but these

classifications and their descriptors and classifiers would

Temperature (C)

10 12 14 16 18 20

Depth(m)

0

2

4

6

8

10

12

14

16

18

Salinity (psu)

0 5 10 15 20 25 30 35

0

2

4

6

8

10

12

14

16

18

Dissolved oxygen (mg l-1)

4 6 8 10 12 14 16

0

2

4

6

8

10

12

14

16

18

Fig. 6. Examples of vertical salinity (psu), temperature (C), and dissolved

oxygen (mg L1) profiles for four Columbia River Estuary study sites during

ebb (open symbols) and flood (closed symbols) tides. Circles Cliff Point

site; triangles Mott Island West site; squares McGregor Island site;

diamonds Woody Island Channel site.

Mean water velocity (m s-1)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Percent(%)

0

20

40

60

80

100

FC

FC

Fig. 8. Relationships between mean water velocity (v) during mid-ebb tide and

mean substrate percentages (C sand; B fines [mud and silt]) from 10

ponar grabs randomly selected near each Columbia River Estuary study tran-

sect. Linear regression results: percent sand 80.8(v) 28.1 (P 0.036,

r2 0.49) and percent fines75.6(v) 60.7 (P 0.049, r2 0.45).

FC Fort Columbia site.

Fig. 7. Mean substrate composition percentages from 10 benthic dredge grabsat each study site. Gray sands; black fines (silts and muds); white peb-

ble; vertical lines rock; hash marks organics; diagonal lines plants.



12/18

Table 4

Classification grid for the 11 CRE study sites based on the collected physical data. All sites were in the CMECS Estuarine Regime (level 1). The sampling scale

was most appropriate for classifying levels 2 (Formation), 3 (Zone), and 4 (Macrohabitat). Example classifications for level 5 (habitat) are included in the text.

Gray cells indicate habitat conditions occurred at the site. Vertical lines indicate data was not collected due to depth and current conditions (site 6), and dashes

indicate missing or incomplete data

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13/18

almost certainly change with fluctuations in river discharge ortidal range.

High variability among samples within site and the

limitations related to our sampling design made classifying

bottom zone macrohabitats across all scales problematic. At

the low end of the macrohabitat scale (100 m2), each random

benthic sample at a site may have been adequate to character-

ize a bottom macrohabitat, and several recognizable macroha-

bitat types may have been present at each study site given the

apparent patchiness of fine and coarse sediments within sites.

This was seen at South Channel where a primarily sandy

substrate was mixed with rock at one sample site and vegeta-

tion at another. (Tables 4 and 5, Fig. 7). At the upper end of the

macrohabitat classification level (1000 m2, approximately the

scale of study transects), an average of several or all of

the benthic samples at a site or a classification category reflect-

ing the heterogeneity in substrate types may have been more

appropriate. In contrast, macrohabitat classification was straight-

forward when substrates were relatively homogeneous. This was

especially true at McGregor, where sand alone comprised 97% of

the sample. Clearly, different spatial scales could have pro-

duced different benthic macrohabitat classifications for most

study sites. Importantly, the scale of habitat patchiness (and

sampling) may strongly affect classification outcome and care-

fully selecting appropriate macrohabitat categories (including

mixed categories) and sampling scale will be critical to

accurately capturing habitat features at this biologically im-portant scale. Of the currently defined CMECS bottom macro-

habitats, subtidal flat, softbottom, and mixed coarse

sediments appeared to most typify the study area, though

the currently undefined estuarine unconsolidated sediments

may also be appropriate. A sample classification of three study

sites using CMECS at its current level of development (Table 5)

omits specific macrohabitat types while relying on descriptors

and classifiers to describe macrohabitat characteristics for both

the bottom and the water column. The ambiguity in our clas-

sification as it moved lower in the classification hierarchy

would likely have been reduced had our a priori emphasis

been for an individual species or ecological community and

had we adjusted our sampling intensity to match the distribu-

tion of the target organisms.

As we did not have a focal species or community, specific

habitat classifications (Level 5) were not a goal of the evalua-

tion. However, some general habitat classification examples

from the CRE sites could include pyncnocline, turbidity max-

imum, salt wedge, oligohaline surface water, and oxic bottom

water (water column habitats) as well as bare sandy, organic

mud, mud-shell hash, vegetated softbottom, mixed coarse

sediments, unconsolidated sediments, and sand wave (bottom

habitats). Beyond this, our sampling resolution was insuffi-

cient to recognize the small-scale associations between biota,

substrate, and physiochemical environment.

Table 5

Sample site classifications using the CMECS structure and terminology

Site 1: Fort Columbia (23.4 km2) The Fort Columbia site has two geohydrologic formations and consistent vertical stratification:

Formation 1: A natural (non-dredged) subsurface channel formation with

Zone 1: lower water column (>15 m depth) with

Macrohabitat 1 (hydrologic): a low energy, polyhaline, and oxic environment (salt wedge); and

Zone 2: bottom (benthic) with

Macrohabitat 1 (geomorphic): a flat to sloping (0e

30

) trough bottom, no vegetative cover,and unconsolidated sediments consisting primarily of sand and fines (mud and silt); and

Formation 2: A natural (non-dredged) current formation with

Zone 1: upper water column (6e15 m depth) with

Macrohabitat 1 (hydrologic): a mesohaline, oxic to oxygen saturated environment with low to

moderate tidal and current energy in mixed flow directions; and

Zone 2: bottom (benthic) with

Macrohabitat 1 (geomorphic): flat to sloping (0e30) bottom with unconsolidated sediments

consisting primarily of sands, fines (mud and silt) with some evidence of pebbles and plants from

a narrow rock ledge and a wedge of sand from the corner of Baker Bay

Site 8. McGregor Island (4.08 km2) The McGregor Island site has a single geohydrologic formation:

Formation 1: A natural (non-dredged) current formation; with

Zone 1: non-stratified, water column (0e15 m depth) with

Macrohabitat 1 (hydrologic): a fresh-water environment receiving flow from the watershed

(as opposed to estuarine exchange), with oxic to oxygen saturated conditions and low energy intensity; andZone 2: bottom (benthic) with

Macrohabitat 1 (geomorphic): flat to sloping (0e30) bottom comprised almost

exclusively of sand with occasional pebble inclusions and no evidence of vegetation

Site 9. South Channel (8.04 km2) The South Channel site has a single geohydrologic formation:

Formation 1: A slough formation; with

Zone 1: shallow, non-stratified subtidal (0e5 m depth) with

Macrohabitat 1 (hydrologic): a fresh-water environment receiving flow from the watershed

(as opposed to estuarine exchange), with oxic conditions and low energy intensity; and

Macrohabitat 2 (geomorphic): flat to sloping (0e30) bottom with unconsolidated sediments

including sand, fines (mud and silt), rocks, organics, and patches of full, sparse or no vegetation



14/18

4. Discussion

A persistent challenge in habitat classification is the tension

between defining habitats by their physical attributes versus

defining them by biological requirements, community

structure or ecological functionality (Diaz et al., 2004; Kurtz

et al., 2006). In practice, most ecological classifications blendabiotic and biotic criteria, though the relative weight given

each category varies widely (Edgar et al., 2000; Colloty

et al., 2002). Depending on the context, each approach has

clear advantages. Abiotic classifications are useful in that

they can be relatively easily quantified, data are often available

at multiple spatial and temporal scales, and abiotic variables

may be used as surrogates for biotic composition and struc-

ture. Classifications that explicitly incorporate biologically

meaningful information, such as habitat use, species relation-

ships or limiting factors, may be more likely to accurately

reflect ecological patterns (Van Dolah et al., 1999; Jay et al.,

2000). The degree to which a classification system can be

applied to address a research or management question dependson the degree of overlap between the question and the

classification components. A primarily abiotic classification,

for example, may poorly predict the distribution of individual

biota or community composition, while a more biologically-

based classification may not be easily scalable or transferable

across ecosystem boundaries. Importantly, the question of

scale may help resolve this tension because it may be that

physiochemical attributes are adequate for classification at

coarser resolution, while biotic associations and interactions

become increasingly important at finer resolution (e.g., Poff,

1997). The CMECS attempts to bridge this gap in part by

implicitly recognizing that processes affecting habitatformation occur at multiple, hierarchical scales.

In this research, we evaluated the CMECS with a set of

abiotic data types without the aim of developing a classification

for a specific ecological or management question. This testing

approach is not unlike real-world scenarios where manage-

ment or resource agencies attempt to classify habitats using

existing or routinely-collected monitoring data. Selecting

one of the CREs specific benthic or epibenthic assemblages

(e.g., Jones et al., 1990), for example, would have reduced

ambiguity surrounding the correct scale for identifying forma-

tions or macrohabitats. A much different spatial scale may

have been appropriate had our focus been on mobile fauna

like migrating salmon, for which larger-scale processes likely

have a greater effect on behavior and distribution. This con-

text-driven ambiguity regarding scale appears to be a CMECS

weakness, because classifications of the same site could

substantially diverge for users with different target species or

communities. However, a hierarchical, multi-scale approach

such as CMECS has the potential to successfully synthesize

classifications from context-specific studies conducted at

different scales. For instance, habitats recognized in surveys

for adult salmonids at larger spatial scales may be integrated

with finer scale surveys of habitats that identify biotic commu-

nities such as oyster beds. Such explicit recognition of scale

dependence among study questions, sampling designs and

underlying ecological pattern and process among surveys is

necessary to achieving an integrated and holistic classification.

The CMECSs use of hierarchical, nested spatial scales,

including the vertical component, is a conceptual strength

of the CMECS framework and largely accommodated the

spatial complexity and ambiguity of habitats within the

CRE (e.g., Johnson et al., 2003). Importantly, the CMECSclassification categories identified for the CRE study sites

aligned well with the habitats defined in the much more spa-

tially extensive studies of the Columbia River Estuary Task

Force. For example, CMECS bottom habitat classifications

were consistent with the types and distributions of sediments

described in Fox et al. (1984) and Sherwood and Creager

(1990). Similarly, salinity intrusion and energy patterns

were similar to those in Jay et al. (1990) and Hamilton

(1990) despite our temporally restricted sampling regime.

However, two types of spatial complexity could perhaps be

improved within the CMECS. First, a mechanism is

necessary to describe hybrid or transitory habitats within

a classification level, which are those where more than oneof the predefined classification options appeared appropriate

at a given scale. Second, as described above, we recognize

that the operational thresholds we used within CMECS to de-

fine the borders between habitat types were somewhat arbi-

trary. While the broad ranges within each classification

level allowed considerable flexibility in defining habitats,

clear correspondence between spatial thresholds and some

measure of ecological functionality was not necessarily

evident. Such ambiguity in drawing distinctions in scale

within and across units may frustrate efforts to map discrete

habitats or to compare habitats at regional or global scales.

One potential analytical approach to examining this issue isto perform sensitivity analyses whereby the threshold values

are varied across a range of plausible values and the affect

on the classification and resulting maps are compared. Such

an analysis was beyond the scope of this study.

In contrast to its relatively explicit handling of spatial

variability, using CMECS to capture temporal variability is

more challenging. A single physical descriptordtemporal

persistencedwas largely qualitative (e.g., permanent, vari-

able, stochastic, low persistence, high persistence; Madden

et al., 2005), which may at times be inadequate in highly

dynamic estuarine systems like the CRE. The historical inter-

disciplinary CRE studies identified predictable and quantifi-

able daily and seasonal events that clearly shape ecological

processes (e.g., Simenstad et al., 1990; Prahl et al., 1998)

and animal behaviors (Bottom and Jones, 1990; Morgan

et al., 1997), but CMECS lacks a suitable framework to

capture and classify this type of predictable variability. One

approach may be to incorporate a hierarchy of appropriate

temporal scales for averaging environmental conditions in

predictable, but varying habitats. For instance, in the CRE it

may be most appropriate to average local salinity and/or water

velocity within and across tidal cycles (hours), within seasons

(days to weeks), or across seasons or years (months to years),

depending on the ecological scale of interest. Overall, we

suspect that long-term averaging is appropriate in many cases,



15/18

but not in others where stochastic or episodic events may have

strong organizing effects (e.g., brief periods of anoxia).

Addressing temporal variability in any estuarine classifica-

tion effort is critical because temporally restricted data collec-

tion methods run the risk of misrepresenting aspects of

estuarine ecological composition, structure, and function.

The snapshot approach to data collectiond

point-grab ben-thic samples, haphazard CTD or velocity profiles, or seasonal

biological samplingdis common in habitat surveys and in

existing databases, but classifications based on these data

types are inherently incomplete. The ADCP data we collected,

for example, illustrated predictable patterns of tidal effects and

current velocity gradients, all within a fairly narrow range of

energy input. What we did not capture, however, were shifts

in tidal phase and amplitude or seasonal order-of-magnitude

changes in CRE freshwater input (Jay et al., 1990; Naik and

Jay, 2005), any of which could alter classification conclusions

from the temporally restricted data. Had our sampling effort

been during a high-discharge spring flood event, for example,

velocity, salinity, and temperature profiles could have beensubstantially different and some classification decisions would

need to be reconsidered. Strong spatial-temporal coupling

between physiochemical conditions (e.g., saltwater intrusion

distance, oxygen availability) and some biological events

(e.g., animal aggregations or migrations, primary production

cycles, etc.) suggests that temporal variability should be

more fully integrated into CMECS, perhaps as an additional

category, nested within each hierarchical level. The collection

of time-series data to populate the temporal category could be

focused on the physical and hydrological characteristics of the

classes identified at each level. Adding clear temporal resolu-

tion to a highly structured two-dimensional framework may bedifficult, but should result in much greater flexibility for many

classification scenarios.

Considerable recent attention has also been given to how

physical and biochemical processes structure estuarine

environments (Uncles, 2002; Thain et al., 2004; Deloffre

et al., 2005; Ralston and Stacey, 2005). Some processes,

such as erosion and deposition cycles (Dyers et al., 2000) or

tidal forcing of water column stratification and mixing

(Uncles, 2002), directly affect habitat creation and persistence.

Therefore, understanding mechanisms that shape and trans-

form habitats has clear value for habitat classification. In the

CRE, the combination of river, tidal and wind energy affect

habitats by their influence on large-scale morphology,

bedforms, sediment distribution, and the limits of salinity

intrusion (Giese and Jay, 1989; Jay et al., 1990). On a small

scale, our ADCP data also suggested a CRE benthic habitat

gradient structured by energy input. CMECS most explicitly

addresses this type of hydrodynamic process using the

physical descriptors energy type, energy intensity and energy

direction. Among these, we believe energy intensity should

perhaps be further classified. Although our study sites had sig-

nificantly different benthic habitats, apparently as a function of

energy inputs, all but one were classified as low energy using

the CMECS intensity scale (none, low, moderate, high),

suggesting that this scale was too coarse for differentiating

estuarine habitats that structure in response to subtle but crit-

ical energy differences. It is also possible that low frequency

but high energy inputs sort benthic sediments and that our

sampling period was not suitably scaled to capture these events.

Integrating process-based details into any habitat classifica-

tion is a conceptual challenge because process-based models

tend to be data intensive (e.g., Buzzelli et al., 1999; Monteet al., 2006; Simionato et al., 2004), while the intention of

most classifications is to simplify information and reduce

data inputs (e.g., Roy et al., 2001). Conceptually, CMECS

frequently linked classification to ecological process, referring

to reciprocal relationships between biological and ecological

processes and habitat morphology and structure at various

scales (Madden et al., 2005). However, the framework

currently uses descriptors and/or spatial scale as proxies for

ecological processes, which differs from explicitly allowing

users to incorporate specific processes into classification

decisions. Striking a balance between the rigor of mechanistic

approaches and the convenience of descriptive approaches

may be achievable by critically selecting parameters that arereliable indicators for controlling ecological processes. How-

ever, this selection process must be transparent and explicit,

with clear explanations of how classification parameters

substitute for specific processes. Continued testing and

refinement of CMECS, perhaps including procedures for users

to add and refine parameters, may help identify and develop

more precise process surrogates.

One of our goals in this evaluation was to test how well

existing (and relatively low cost) technology could be used

to help populate the various CMECS tiers. In general, the

combined ADCP, CTD and PONAR data collection types

were useful for mid-level classification units (zones andmacrohabitats), and each has obvious potential for application

at the finer habitat and biotope scales. The ADCP was a partic-

ularly versatile tool, providing physical and geomorphologic

data including basic bathymetry and relief and detailed

measures of energy direction and intensity. In other studies,

repeated ADCP measurements have been used to understand

hydrographic structure over varying time scales (Reed et al.,

2004; Dinehart and Burau, 2005), and such an approach would

likely have improved our test classification of CRE habitats. It

is also possible to use backscatter data from ADCP profiles to

make inferences about the type and volume of suspended

sediments and plankton (e.g., Holdaway et al., 1999; Gartner,

2004), which may be helpful for some classification questions.

The point grab nature of both the CTD and PONAR data

suggested to us that additional sampling intensity with these

instruments would probably be necessary to clearly delineate

boundaries between habitat types, particularly at mid-level

and low-level CMECS tiers. Co-location of ADCP, CTD and

benthic grabs would have provided the ability to better test

for associations between parameters at finer scales. We also

briefly tested a sidescan sonar during the preparation for the

CMECS evaluation, and this instrument provided high

resolution data on sand wave bedforms, rock bars, and other

estuarine bottom morphology. Sidescan sonar has been used

to map a variety of marine habitat types (e.g., Kenny et al.,



16/18

2003; Allen et al., 2005) and has obvious potential for use as

a habitat classification tool. Finally, the CMECS descriptors we

could not apply in our classification test (e.g., turbidity, photic

quality, trophic status) could have been included by collecting

a handful of additional data types (e.g., Secchi depth, turbidity,

and/or chlorophyll a) that require only basic instrumentation.

Though still a work in progress, the CMECS frameworkshows promise as a classification standard. The approach is

general and therefore applicable to a wide range of coastal

and marine habitat types. The supporting terminology,

combined with the nested hierarchical design, appears to

provide a good conceptual foundation for generating classifi-

cations that address a spectrum of research and management

objectives. As the classification moves towards greater quanti-

fication and more precise definitions, however, it will be

essential for CMECS to maintain an open and adaptive stance.

To provide ecologically relevant information and value to

a wide range of potential end users, the classification must

be flexible enough to accommodate an array of data types at

multiple spatial and temporal scales. Classification inputs forlocalized estuary studies or species management (e.g., Harris

et al., 2001; Johnson et al., 2003) will differ greatly from those

for continental-scale studies of estuarine fish habitats (e.g.,

Harrison and Whitfield, 2006), regional studies of intertidal

diversity (e.g., Zacharias and Roff, 2001), or broad efforts to

classify and protect marine biodiversity (e.g., Ward et al.,

1999; Zacharias and Roff, 2000; Halpern, 2003). Such diverse

objectives will be accompanied by specialized, discipline-

oriented data and vocabulary. To be widely adopted, CMECS

must accommodate these existing knowledge systems while

simultaneously encouraging a disciplined, standardized

classification framework and lexicon.

Acknowledgements

The authors would like to thank P. Wilbur (NOAA National

Marine Fisheries Service) for vision, direction and funding;

K. Nielsen (NOAA National Ocean Service) for excellent da-

tabase design and GIS support; C. Simenstad (University of

Washington) for guidance in site selection; A. Baptista and

M. Wilkin (Oregon Health & Science University) for project

guidance and technical assistance; and the Fish Ecology

Research Laboratory, University of Idaho, who supported

this project from conception to completion.

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