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Sediment Quality Objectivesfor California Enclosed Bays and Estuaries

Benthic Indicator Development

Scientific Steering Committee26th July 2005

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Overview

• Why Benthos and Benthic Indices?• The Index Development Process

– Define Habitat Strata– Calibrate Candidate Benthic Indices– Validate and Evaluate Candidate Indices

• Proposed Next Steps

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Why Benthos?• Benthic organisms are living resources

– Direct measure of what legislation intends to protect

• They are good indicators– Sensitive, limited mobility, high exposure, integrate impacts, integrate

over time

• Already being used to make regulatory and sediment management decisions– Santa Monica Bay removed from 303(d) list

• Listed for metals in the early 1990’s– 301(h) waivers granted to dischargers– Toxic hotspot designations for the Bay Protection and Toxic Cleanup

Program

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Benthic Assessments Pose Several Challenges

• Interpreting species abundances is difficult– Samples may have tens of species and hundreds of organisms

• Benthic species and abundances vary naturally with habitat– Different assemblages occur in different habitats– Comparisons to determine altered states should vary accordingly

• Sampling methods vary– Gear, sampling area and sieve size affect species and individuals

captured

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Benthic Indices Meet These Challenges

• Benthic Indices– Remove much of the subjectivity associated with data

interpretation– Account for habitat differences– Are single values – Provide simple means of

• Communicating complex information to managers• Tracking trends over time• Correlating benthic responses with stressor data

– Are included in the U.S. EPA’s guidance for biocriteria development

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Overview

• Why Benthos and Benthic Indices?• The Index Development Process

– Define Habitat Strata– Calibrate Candidate Benthic Indices– Validate and Evaluate Candidate Indices

• Proposed Next Steps

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Define Habitat Strata

• Rationale– Species and abundances vary naturally from

habitat to habitat• Benthic indicators and definitions of reference

condition should vary accordingly

• Objectives– Identify naturally occurring benthic

assemblages, and– The habitat factors that structure them

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Approach

• Identify assemblages by cluster analysis– Standard choices

• Species in ≥ 2 samples• ³√ transform, species mean standardization• Bray Curtis dissimilarity with step-across adjustment• Flexible sorting ß=-0.25

• Evaluate habitat differences between assemblages– Salinity, % fines, depth, latitude, longitude, TOC– Using Mann-Whitney tests

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Data• EMAP data enhanced by regional data sets

– Comparable methods• Sampling, measurements, taxonomy

– OR and WA data included• Potential to increase amount of data for index development

– 1164 samples in database• Eliminated potentially contaminated sites

– ≥ 1 chemical > ERM or ≥ 4 chemicals > ERL– Toxic to amphipods– Located close to point sources– DO < 2 ppm

• 714 samples analyzed

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Identified Eight Assemblages

A Puget Sound Fine SedimentsB Puget Sound Coarse SedimentsC Southern California Euhaline BaysD Polyhaline San Francisco BayE Estuaries and WetlandsF Very Coarse SedimentsG Mesohaline San Francisco BayH Limnetic or Freshwater

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SalinityS

alin

ity (p

su)

0

10

20

30

40

AssemblageA B C D E F G H

% Fine Sediments

Fine

sed

imen

ts (%

)

0

20

40

60

80

100

AssemblageA B C D E F G H

Depth

Bot

tom

dep

th (m

)

0

50

100

150

200

AssemblageA B C D E F G H

Latitude

Latit

ude

(dec

imal

deg

rees

)

30

35

40

45

50

AssemblageA B C D E F G H

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Overview

• Why Benthos and Benthic Indices?• The Index Development Process

– Define Habitat Strata– Calibrate Candidate Benthic Indices– Validate and Evaluate Candidate Indices

• Proposed Next Steps

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Six Candidate Indices

Acronym Name

IBI Index of Biotic Integrity

RBI Relative Benthic Index

BRI* Benthic Response Index

RIVPACS River Invertebrate Prediction and Classification System

BQI Benthic Quality Index*: Two variations

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Candidate IndicesComponents

Candidate Index Data

IBI Community measures

RBI Community measures

BRI-TC Species abundances

BRI-MNDF Species abundances

RIVPACS Presence/absence of multiple species

BQI Species abundances & community measures

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Index Development Teams

Candidate Index Index Leader Reference

IBI Bruce Thompson Thompson and Lowe (2004)

RBI Jim Oakden Hunt et al. (2001)

BRI* Bob Smith Smith et al. (2001, 2003)

RIVPACS David Huff Wright et al. 1993

BQI Bob Smith Rosenberg et al. (2004)

*: Two variations

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Common Definitions

• A common set of definitions were established– For “Good” and “Bad” sites

• Used in two ways– Identify data to be withheld from index development

• Subsequently used to validate index• Goal: A set of clearly affected or reference sites to evaluate

index performance– “A Gold Standard”

– Identify reference and degraded condition for index calibration

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Common Criteria

“Good” (Reference) Sites• Meet all the following criteria:

– Far from known point sources– Data available for sediment chemistry and at least one

amphipod toxicity test– No ERM* exceedences– No more than 3 ERL* exceedences– No toxicity

• Amphipod survival > 83%– Species abundance list does not indicate bad biology (In

progress)

*: As, Cd, Cu, Pb, Hg, Ag, Zn, Hmw(8) & Lmw(11) PAH, Total PCB

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Common Criteria

“Bad” (Degraded) Sites• Meet both of the following criteria

– 1 or more ERM exceedences, or3 or more ERL exceedences, and

– >50% mortality in an acute amphipod test

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National vs. CA dataSouthNorth

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Data For Benthic Index Development

Habitat# Samples

Good BadC Euhaline California Bays 85 17

D Polyhaline San Francisco Bay 18 12

E Estuaries and Wetlands 102 3

F Very Coarse Sediments 56 0

G Mesohaline San Francisco Bay 20 4

H Tidal Freshwater 65 0

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Data For Benthic Index DevelopmentNumbers of samples

HabitatCalibration Validation

G B G BC Euhaline California Bays 75 9 10 8

D Polyhaline San Francisco Bay 9 6 11 6

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The Calibration Process

• Identify habitats with sufficient data– “Good” and “Bad” sites– For index calibration and validation

• Distribute calibration data– Teams calibrate candidate indices

• Distribute independent data for validation– Teams apply candidates to data

• Results compiled for evaluation

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Overview

• Why Benthos and Benthic Indices?• The Index Development Process

– Define Habitat Strata– Calibrate Candidate Benthic Indices– Validate and Evaluate Candidate Indices

• Proposed Next Steps

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Index Validation Approaches• Classification accuracy

– Chemistry and toxicity– Biologist best professional judgment

• Repeatability– Same day– Same site on different days

• Independence from natural gradients• Correlations with other information

– Species richness– Other indices

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Overall Classification AccuracyValidation Data (%)

Index Overall(n=35)

RIVPACS 83BRI-TC 77IBI 70BRI-MNDF 63BQI 63RBI 51

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Habitat Classification Accuracy Validation Data (%)

IndexSouthern California

(n=18)

San Francisco Bay(n=17)

RIVPACS 72 94BRI-TC 72 82IBI 67 73BRI-MNDF 56 71BQI 50 76RBI 22 82

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Status Classification Accuracy Validation Data (%)

Index“Good”

Sites(n=21)

“Bad”Sites(n=14)

RIVPACS 86 79BRI-TC 81 71IBI 100 29BRI-MNDF 67 57BQI 81 36RBI 52 50

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Potential Reasons for Low Classification Accuracy

• Do threshold and scaling problems exist?– Does an index correlate well with condition,

but an incorrect threshold lead to the wrong interpretation?

• Are chemistry-toxicity “bad” definitions inadequate?– Chemistry criteria were less stringent than

many other benthic index efforts

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RIVPACS vs Amphipod Mortality - San Francisco BayA

mph

ipod

Mor

talit

y (%

)

-20

0

20

40

60

80

100

RIVPACS Score-1.5 -1.0 -0.5 0.0

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RBI vs Amphipod Mortality - San Francisco BayA

mph

ipod

Mor

talit

y (%

)

-20

0

20

40

60

80

100

RBI Score-1.0 -0.8 -0.6 -0.4 -0.2 0.0

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BQI vs Amphipod Mortality - Southern CaliforniaA

mph

ipod

Mor

talit

y (%

)

-20

0

20

40

60

80

100

BQI Score-20 -15 -10 -5 0

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Are Validation Sites Misclassified?

• Is our “Gold Standard” correct?– Are multiple indices disagreeing?– How do index disagreements relate to biology?

• Samples with multiple disagreements evaluated– Using biologist best professional judgment

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Disagreements with Status Designations

Number of Candidates Disagreeing

N(Σ=35)

0 8

1 9

2 5

3 6

4 4

5 2

6 1

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Biology Comparison

• For six of seven samples– Biologists agreed that the chemistry-toxicity

status was incorrect• All four biologists agreed for four samples• 75% agreement for other two

• “Gold Standard” is tarnished

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Effect of Status Changeon Overall Classification Accuracy

Index Original After Change

RIVPACS 83 83

BRI-TC 77 89

IBI 70 76

BRI-MNDF 63 74

BQI 63 80

RBI 51 63

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Overview

• Why Benthos and Benthic Indices?• The Index Development Process

– Define Habitat Strata– Calibrate Candidate Benthic Indices– Validate and Evaluate Candidate Indices

• Proposed Next Steps

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Complete the Index Validation Process

• Classification accuracy– Chemistry and toxicity– Biologist best professional judgment

• Repeatability– Same day– Same site on different days

• Independence from natural gradients• Correlations with other information

– Species richness– Other indices

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Biology Classification

• Panel of six external experts– Evaluate 20-25 samples– Samples where 5 of 6 experts agree will

establish a new “Gold Standard”• To be used in the same way as the chemistry-

toxicity classification

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Repeatability

• Identify sites where– Multiple samples were collected on the same

visit– Multiple visits to the same site

• Evaluate candidate index stability

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Summary

• We will be able to develop benthic indices for two habitats– Some indices validating well

• Validation rates with sediment toxicity and chemistry data are low– Need to re-visit our scaling methods for some indices– Need to establishing biology-based good and bad criteria

• Best professional judgment of an independent panel of experts

• Have more validation steps to complete before making final selections