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Measuring Benthic Invertebrate Community Condition in California

Bays and Estuaries

Ananda RanasingheAnandaR@sccwrp.org

Benthic Indicator Development Work Group

California SQO Science Team

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Objectives

• “Healthy Benthic Communities”– A “Sediment Quality Objective”

For California bays and estuaries

• Today’s goal:– Answer two questions:

How will SQO’s measure benthic health? How well do the tools work?

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Overview

• Why Benthos & Benthic Indices?

• SQO Benthic Indices– Five candidates

• Evaluating Index Performance– Screening-level evaluation– Classification accuracy

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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 Potentially 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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Five Candidate Indices

Acronym Name

IBI Index of Biotic Integrity

RBI Relative Benthic Index

BRI Benthic Response Index

RIVPACSRiver Invertebrate Prediction and Classification System

BQI Benthic Quality Index

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Index Approaches

• Several factors vary, including– Assumptions

Preconceived notions about relationshipsE.g., # taxa

– Measures considered Community measures

E.g., # taxa, # molluscan taxa, % sensitive species Species abundances

And pollution tolerances

– Types of sites required for development Reference only Reference and highly disturbed

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IBI: Index of Biotic Integrity

• Initially developed for freshwater streams– Several subsequent estuarine applications

• Based on community measures– Counts # values outside reference range for

SFB: # taxa, # molluscan taxa, total abundance, Capitella capitata abundance

SoCal: # taxa, # molluscan taxa, abundance of Notomastus sp., abundance of sensitive species

• Team led by Bruce Thompson (SFEI)

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RBI: Relative Benthic Index

• Developed for California estuaries– SWRCB’s BPTCP Program

• Based on community measures– Weighted sum of

• Four community measures– # taxa, # crustacean species, # crustacean individuals, # mollusc

species

• Three positive indicator species• Two negative indicator species

• Team led by Jim Oakden (Moss Landing Lab)

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BRI: Benthic Response Index

• Developed for southern California (SoCal) mainland shelf– Extended to SoCal bays and estuaries

• Abundance-weighted average pollution tolerance score (p-value)– Species p-values assigned during index development– Based on “Good” and “Bad” site information

Abundance distribution along a pollution vector in an ordination space

• SoCal benthic team led by Bob Smith

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RIVPACS: River Invertebrate Prediction and Classification System

• Developed for British freshwater streams– This is the first application in estuaries

• Compares sampled species– With expected species composition

Determined by a multivariate predictive model From assemblages at designated reference sites

• Team led by Dave Huff

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BQI: Benthic Quality Index

• Developed for Swedish west coast

• Product of– Log10 # of taxa, and

– Abundance-weighted average pollution tolerance Different than BRI pollution tolerance Based on species distribution along a richness gradient

• SoCal benthic team led by Bob Smith

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Data• All indices used the same data

– For development– And evaluation

Evaluation data were not used for development

• Polyhaline San Francisco Bay– 268 development samples– 12 evaluation samples

• Southern California Euhaline Bays– 377 development samples– 24 evaluation samples– 414 other samples

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Index Evaluation• Screening-level evaluation

– Species richness– Independence from natural gradients

• Classification accuracy– Against classification by best professional judgment

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Correlations With No. of TaxaPolyhaline San Francisco Bay

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Independence From Natural Gradients

• Benthic indices should measure habitat condition– Rather than habitat factors

• Tested by plotting benthic indices against– Depth– Percent fines– Salinity– TOC– Latitude, and– Longitude

• Conclusion– The indices are not overly sensitive to habitat factors

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Correlations with DepthPolyhaline San Francisco Bay

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Correlations with Fine SedimentsSouthern California Euhaline Bays

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Correlations with Habitat VariablesSpearman Correlation Coefficients

BQI BRI IBI RBI RIVPACs

Southern California Euhaline Bays

Depth† -0.38 -0.52 -0.02 -0.06 -0.05‡

Fines† 0.22 0.23 0.37 0.42 0.19‡

Salinity* -0.14 -0.28 -0.09 0.02 0.03

TOC† 0.15 0.19 0.36 0.29 0.24‡

Latitude† -0.05 -0.15 -0.16 -0.12 0,21‡

Longitude† 0.10 0.22 0.21 0.15 -0.15‡†: n=670; ‡: n=320; *: n=66

Polyhaline San Francisco Bay

Depth -0.29 -0.48 -0.14 -0.38 -0.32

Fines 0.53 0.56 0.25 0.61 0.49

Salinity -0.38 -0.40 -0.05 -0.42 -0.31

TOC 0.49 0.60 0.21 0.57 0.46

Latitude -0.39 -0.50 -0.05 -0.32 -0.21

Longitude 0.31 0.54 0.21 0.36 0.05n=160 for all indices other than the IBI, where n=112

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Classification Accuracy

• Index results compared to biologist BPJ– Nine benthic ecologists

• Ranked samples on condition, and• Evaluated on a four-category scale

– Reference; Low, Moderate, and High Disturbance

• 36 samples– Covering the range of conditions encountered

• On a chemical contamination gradient

• Data provided– Species abundances– Region, depth, salinity, and sediment grain size

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Advantages of BPJ Comparison

• Provides an opportunity to assess intermediate samples– Previous benthic index efforts focused on

extremes

• Quantifies classification consistency– Provides a means for assessing how well

indices are working– The commonly used 80% standard has no basis

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Evaluation Process

• Two-step evaluation

– Quantified expert performance• Condition ranks• Category concordance

– Are there “outlier” experts?

– Compared index and expert results• Condition ranks• Category concordance

– Can developer thresholds be improved?

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Condition Rank Correlations Polyhaline San Francisco Bay

n=12; p < 0.001 for all cases

C D M N O R T V

D 0.93

M 0.97 0.96

N 0.94 0.84 0.93

O 0.95 0.91 0.92 0.87

R 0.92 0.89 0.92 0.86 0.97

T 0.97 0.95 0.99 0.93 0.92 0.92

V 0.97 0.94 0.98 0.93 0.94 0.94 0.99

W 0.92 0.86 0.89 0.87 0.97 0.98 0.89 0.90

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Condition CategoriesPolyhaline San Francisco Bay

# C D M N O R T V W1 H H H H M M H H H2 R R R R R R R R L3 R R R L R R R R L4 H H H H H H H H H5 L R R M R R R R L6 M L L L L L L L L7 L R R R R R R R R8 H H H H H H H H H9 H H H H H H H H H10 M L M L L L M M L11 L R R R R R R R R12 M L M M L L M M M

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Index EvaluationCorrelation of Candidate Index Rank with Mean Rater Rank

IndexEuhaline SoCal

BaysPolyhaline San Francisco Bay

BQI 0.89 0.92

BRI 0.88 0.83

IBI 0.70 0.85

RBI 0.82 0.90

RIVPACs 0.84 0.86

Mean Rater Correlation (n=9)

0.95 0.96

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Classification Accuracy

• How well do candidate indices evaluate condition category?

• Assessed at two levels– Status (Good or Bad)– Four-category scale

• Reference; Low, Moderate, and High Disturbance

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Index Classification Accuracy

Measure

Status Classification

Accuracy

(%)

Category Classification

Accuracy

(%)

Category Bias

BQI 85.7 68.6 7

BRI 91.4 62.9 -4

IBI 75.9 55.2 -9

RBI 80.0 57.1 13

RIV 91.4 71.4 3

“Best” expert 97.1 91.4 +7, -4

“Average” expert 92.4 81.9 2.8

“Worst” expert 85.7 71.4 0

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Combined IndexClassification Accuracy

No. of Indices

Measure

Status Classification

Accuracy

(%)

Category Classification

Accuracy

(%)

Category Bias

Four BRI IBI RBI RIV 94.3 80.0 5

BQI IBI RBI RIV 88.6 71.4 6

BQI BRI RBI RIV 88.6 77.1 8

BQI BRI IBI RIV 94.3 80.0 5

BQI BRI IBI RBI 88.6 77.1 8

Five All 94.3 77.1 4

“Best” expert 97.1 91.4 +7, -4

“Average” expert 92.4 81.9 2.8

“Worst” expert 85.7 71.4 0

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Conclusion

• Experts did well– Index combinations did almost as well– Individual indices didn’t do so well

• Many index combinations worked well– Four and five generally did better than three – Three generally did better than two did better than one

• We selected a combination of four indices– Best performer (tie)– For status: Slightly better than the average expert– For categories: Slightly worse than the average expert

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Condition Rank Correlations Southern California Euhaline Bays

n=24; p < 0.0001 for all cases

C D M N O R T V

D 0.88

M 0.91 0.96

N 0.92 0.90 0.89

O 0.92 0.93 0.96 0.90

R 0.92 0.93 0.92 0.93 0.95

T 0.93 0.92 0.93 0.94 0.92 0.93

V 0.93 0.91 0.92 0.93 0.93 0.95 0.96

W 0.81 0.83 0.84 0.80 0.88 0.90 0.80 0.81

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Correlations With No. of TaxaSouthern California Euhaline Bays

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Condition CategoriesSouthern California Euhaline Bays

# C D M N O R T V W21 M L L M L L M M L22 L L L L L L L L M23 R R R R R R R R L24 L L L M L L L L L25 R R R R R R R R L26 H H H H H H M H H27 R R R R R L R R M28 H H H M H M H H H29 L R R L L L L R L30 M L L L M M M M M31 M M M L M M M M M32 M M L M L M L L M33 M L M M M M M M M34 H H H H H H M H H35 L M L L L L L L M36 H H H H H M H H M37 R R R R R R L R R38 H H H H H H M H M39 M H H H H H H H H40 R R R R R R L R R41 H M H M H M M M M42 M M M M M M M M M43 L R L L M L R L L44 R R R R L R L R R

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Polyhaline San Francisco Bay

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Southern California Euhaline Bays

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Three Step Process

• Define Habitat Strata– Identify natural assemblages and controlling habitat

factors

• Develop Candidate Indices– Apply existing index approaches to habitat-specific

data

• Evaluate Candidate Indices– With independent data

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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 AssemblagesSix in California

A Puget Sound Fine Sediments

B Puget Sound Coarse Sediments

C Southern California Euhaline Bays

D Polyhaline San Francisco Bay

E Estuaries and Wetlands

F Very Coarse Sediments

G Mesohaline San Francisco Bay

H Limnetic or Freshwater

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SalinityS

alin

ity (

psu

)

0

10

20

30

40

Assemblage

A B C D E F G H

% Fine Sediments

Fin

e s

ed

ime

nts

(%

)

0

20

40

60

80

100

Assemblage

A B C D E F G H

Depth

Bo

tto

m d

ep

th (

m)

0

50

100

150

200

Assemblage

A B C D E F G H

Latitude

La

titu

de

(d

eci

ma

l de

gre

es)

30

35

40

45

50

Assemblage

A B C D E F G H

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45

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Index Composition

Candidate Index Data

IBI Community measures

RBI Community measures

BRI Species abundances and tolerances

RIVPACS Presence/absence of multiple species

BQISpecies abundances & community measures

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Index Development Teams

Candidate Index Data

IBI Bruce Thompson (SFEI)

RBI Jim Oakden (Moss Landing)

BRI Bob Smith (SoCal benthic group)

RIVPACS Dave Huff (Univ. of Minnesota)

BQI Bob Smith (SoCal benthic group)

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Data For Benthic Index Development

Habitat# Samples

Good Bad

CSouthern California Euhaline 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 Limnetic or Freshwater 65 0