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1 Measuring Benthic Invertebrate Community Condition in California Bays and Estuaries Ananda Ranasinghe Benthic Indicator Development.

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Presentation on theme: "1 Measuring Benthic Invertebrate Community Condition in California Bays and Estuaries Ananda Ranasinghe Benthic Indicator Development."— Presentation transcript:

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

2 2 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?

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

4 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 1990s –301(h) waivers granted to dischargers –Toxic hotspot designations for the Bay Protection and Toxic Cleanup Program

5 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 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. EPAs guidance for biocriteria development

7 7 Five Candidate Indices AcronymName IBIIndex of Biotic Integrity RBIRelative Benthic Index BRIBenthic Response Index RIVPACS River Invertebrate Prediction and Classification System BQIBenthic Quality Index

8 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 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 10 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)

11 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 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 13 BQI: Benthic Quality Index Developed for Swedish west coast Product of –Log 10 # 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 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 15 Index Evaluation Screening-level evaluation –Species richness –Independence from natural gradients Classification accuracy –Against classification by best professional judgment

16 16 Correlations With No. of Taxa Polyhaline San Francisco Bay

17 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 18 Correlations with Depth Polyhaline San Francisco Bay

19 19 Correlations with Fine Sediments Southern California Euhaline Bays

20 20 Correlations with Habitat Variables Spearman Correlation Coefficients BQIBRIIBIRBIRIVPACs Southern California Euhaline Bays Depth-0.38-0.52-0.02-0.06-0.05 Fines0.220.230.370.420.19 Salinity*-0.14-0.28- TOC0.150.190.360.290.24 Latitude-0.05-0.15-0.16-0.120,21 Longitude0. : n=670; : n=320; *: n=66 Polyhaline San Francisco Bay Depth-0.29-0.48-0.14-0.38-0.32 Fines0.530.560.250.610.49 Salinity-0.38-0.40-0.05-0.42-0.31 TOC0.490.600.210.570.46 Latitude-0.39-0.50-0.05-0.32-0.21 Longitude0.310.540.210.360.05 n=160 for all indices other than the IBI, where n=112

21 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 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 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 24 Condition Rank Correlations Polyhaline San Francisco Bay n=12; p < 0.001 for all cases CDMNORTV D0.93 M0.970.96 N0.940.840.93 O0.950.910.920.87 R0.920.890.920.860.97 T 0.950.990.930.92 V0.970.940.980.930.94 0.99 W0.920.860.890.870.970.980.890.90

25 25 Condition Categories Polyhaline San Francisco Bay

26 26 Index Evaluation Correlation of Candidate Index Rank with Mean Rater Rank Index Euhaline SoCal Bays Polyhaline San Francisco Bay BQI0.890.92 BRI0.880.83 IBI0.700.85 RBI0.820.90 RIVPACs0.840.86 Mean Rater Correlation (n=9) 0.950.96

27 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 28 Index Classification Accuracy Measure Status Classification Accuracy (%) Category Classification Accuracy (%) Category Bias BQI85.768.67 BRI91.462.9-4 IBI75.955.2-9 RBI80.057.113 RIV91.471.43 Best expert97.191.4+7, -4 Average expert92.481.92.8 Worst expert85.771.40

29 29 Combined Index Classification Accuracy No. of Indices Measure Status Classification Accuracy (%) Category Classification Accuracy (%) Category Bias FourBRI IBI RBI RIV94.380.05 BQI IBI RBI RIV88.671.46 BQI BRI RBI RIV88.677.18 BQI BRI IBI RIV94.380.05 BQI BRI IBI RBI88.677.18 FiveAll94.377.14 Best expert97.191.4+7, -4 Average expert92.481.92.8 Worst expert85.771.40

30 30 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

31 31

32 32 Condition Rank Correlations Southern California Euhaline Bays n=24; p < 0.0001 for all cases CDMNORTV D0.88 M0.910.96 N0.920.900.89 O0.920.930.960.90 R0.920.930.920.930.95 T0.930.920.930.940.920.93 V 0.910.920.93 0.950.96 W0.810.830.840.800.880.900.800.81

33 33 Correlations With No. of Taxa Southern California Euhaline Bays

34 34 Condition Categories Southern California Euhaline Bays

35 35 Polyhaline San Francisco Bay

36 36 Southern California Euhaline Bays

37 37

38 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 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 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 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 42 Identified Eight Assemblages Six in California APuget Sound Fine Sediments BPuget Sound Coarse Sediments CSouthern California Euhaline Bays DPolyhaline San Francisco Bay EEstuaries and Wetlands FVery Coarse Sediments GMesohaline San Francisco Bay HLimnetic or Freshwater

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46 46 Index Composition Candidate IndexData IBICommunity measures RBICommunity measures BRISpecies abundances and tolerances RIVPACSPresence/absence of multiple species BQI Species abundances & community measures

47 47 Index Development Teams Candidate IndexData IBIBruce Thompson (SFEI) RBIJim Oakden (Moss Landing) BRIBob Smith (SoCal benthic group) RIVPACSDave Huff (Univ. of Minnesota) BQIBob Smith (SoCal benthic group)

48 48 Data For Benthic Index Development Habitat # Samples GoodBad C Southern California Euhaline Bays 8517 DPolyhaline San Francisco Bay1812 EEstuaries and Wetlands1023 FVery Coarse Sediments560 GMesohaline San Francisco Bay204 HLimnetic or Freshwater650

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