Measuring Benthic Invertebrate Community Condition in California Bays and Estuaries

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Measuring Benthic Invertebrate Community
Condition in California Bays and Estuaries

• Ananda Ranasinghe
• AnandaR_at_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
• Todays goal
• Answer two questions
• How will SQOs 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 1990s
• 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. EPAs guidance for
  biocriteria development

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Five Candidate Indices
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Index Approaches

• Several factors vary, including
• Assumptions
• Preconceived notions about relationships
• E.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
• SWRCBs 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
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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 Bayn12 p lt 0.001 for all cases
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Condition CategoriesPolyhaline San Francisco Bay
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Index EvaluationCorrelation of Candidate Index
Rank with Mean Rater Rank
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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
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Combined IndexClassification Accuracy
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Conclusion

• Experts did well
• Index combinations did almost as well
• Individual indices didnt 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 Baysn24 p lt 0.0001 for all cases
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Correlations With No. of TaxaSouthern California
Euhaline Bays
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Condition CategoriesSouthern California Euhaline
Bays
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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
• ³v 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 gt ERM or 4 chemicals gt ERL
• Toxic to amphipods
• Located close to point sources
• DO lt 2 ppm
• 714 samples analyzed

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Identified Eight AssemblagesSix in California
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Index Composition
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Index Development Teams
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Data For Benthic Index Development