Measuring Benthic Invertebrate Community Condition in California Bays and Estuaries

1. Measuring Benthic Invertebrate Community Condition in California Bays and Estuaries Ananda Ranasinghe AnandaR@sccwrp.org Benthic Indicator Development Work Group California SQO Science Team

2. 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?

3. Overview • Why Benthos & Benthic Indices? • SQO Benthic Indices • Five candidates • Evaluating Index Performance • Screening-level evaluation • Classification accuracy

4. 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

5. 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

6. 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

7. Five Candidate Indices

8. 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

9. 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)

10. 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)

11. 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

12. 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

13. 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

14. 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

15. Index Evaluation • Screening-level evaluation • Species richness • Independence from natural gradients • Classification accuracy • Against classification by best professional judgment

16. Correlations With No. of TaxaPolyhaline San Francisco Bay

17. 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

18. Correlations with DepthPolyhaline San Francisco Bay

19. Correlations with Fine SedimentsSouthern California Euhaline Bays

20. Correlations with Habitat VariablesSpearman Correlation Coefficients

21. 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

22. 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

23. 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?

24. Condition Rank Correlations Polyhaline San Francisco Bayn=12; p < 0.001 for all cases

25. Condition CategoriesPolyhaline San Francisco Bay

26. Index EvaluationCorrelation of Candidate Index Rank with Mean Rater Rank

27. 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

28. Index Classification Accuracy

29. Combined IndexClassification Accuracy

30. 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

32. Condition Rank Correlations Southern California Euhaline Baysn=24; p < 0.0001 for all cases

33. Correlations With No. of TaxaSouthern California Euhaline Bays

34. Condition CategoriesSouthern California Euhaline Bays

35. Polyhaline San Francisco Bay

36. Southern California Euhaline Bays

38. 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

39. 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

40. 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

41. 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

42. Identified Eight AssemblagesSix in California

46. Index Composition

47. Index Development Teams

48. Data For Benthic Index Development