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

Slides:



Advertisements
Similar presentations
Numbers Treasure Hunt Following each question, click on the answer. If correct, the next page will load with a graphic first – these can be used to check.
Advertisements

Números.
Trend for Precision Soil Testing % Zone or Grid Samples Tested compared to Total Samples.
Trend for Precision Soil Testing % Zone or Grid Samples Tested compared to Total Samples.
AGVISE Laboratories %Zone or Grid Samples – Northwood laboratory
Trend for Precision Soil Testing % Zone or Grid Samples Tested compared to Total Samples.
AP STUDY SESSION 2.
1
EuroCondens SGB E.
Worksheets.
Copyright © 2003 Pearson Education, Inc. Slide 1 Computer Systems Organization & Architecture Chapters 8-12 John D. Carpinelli.
Addition and Subtraction Equations
Properties Use, share, or modify this drill on mathematic properties. There is too much material for a single class, so you’ll have to select for your.
Multiplication X 1 1 x 1 = 1 2 x 1 = 2 3 x 1 = 3 4 x 1 = 4 5 x 1 = 5 6 x 1 = 6 7 x 1 = 7 8 x 1 = 8 9 x 1 = 9 10 x 1 = x 1 = x 1 = 12 X 2 1.
Division ÷ 1 1 ÷ 1 = 1 2 ÷ 1 = 2 3 ÷ 1 = 3 4 ÷ 1 = 4 5 ÷ 1 = 5 6 ÷ 1 = 6 7 ÷ 1 = 7 8 ÷ 1 = 8 9 ÷ 1 = 9 10 ÷ 1 = ÷ 1 = ÷ 1 = 12 ÷ 2 2 ÷ 2 =
CHALLENGES OF USING BENTHIC ASSESSMENTS IN SAN FRANCISCO ESTUARY Bruce Thompson and Sarah Lowe San Francisco Estuary Institute.
David Burdett May 11, 2004 Package Binding for WS CDL.
CALENDAR.
1 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt BlendsDigraphsShort.
Mean, Median, Mode & Range
I can count in decimal steps from 0.01 to
1 Click here to End Presentation Software: Installation and Updates Internet Download CD release NACIS Updates.
The 5S numbers game..
突破信息检索壁垒 -SciFinder Scholar 介绍
Break Time Remaining 10:00.
This module: Telling the time
The basics for simulations
Turing Machines.
1 Heating and Cooling of Structure Observations by Thermo Imaging Camera during the Cardington Fire Test, January 16, 2003 Pašek J., Svoboda J., Wald.
Table 12.1: Cash Flows to a Cash and Carry Trading Strategy.
PP Test Review Sections 6-1 to 6-6
MM4A6c: Apply the law of sines and the law of cosines.
Look at This PowerPoint for help on you times tables
Bellwork Do the following problem on a ½ sheet of paper and turn in.
TCCI Barometer March “Establishing a reliable tool for monitoring the financial, business and social activity in the Prefecture of Thessaloniki”
Exarte Bezoek aan de Mediacampus Bachelor in de grafische en digitale media April 2014.
TCCI Barometer March “Establishing a reliable tool for monitoring the financial, business and social activity in the Prefecture of Thessaloniki”
Copyright © 2012, Elsevier Inc. All rights Reserved. 1 Chapter 7 Modeling Structure with Blocks.
1 RA III - Regional Training Seminar on CLIMAT&CLIMAT TEMP Reporting Buenos Aires, Argentina, 25 – 27 October 2006 Status of observing programmes in RA.
Progressive Aerobic Cardiovascular Endurance Run
1..
Adding Up In Chunks.
MaK_Full ahead loaded 1 Alarm Page Directory (F11)
1 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt Synthetic.
TCCI Barometer September “Establishing a reliable tool for monitoring the financial, business and social activity in the Prefecture of Thessaloniki”
2011 WINNISQUAM COMMUNITY SURVEY YOUTH RISK BEHAVIOR GRADES 9-12 STUDENTS=1021.
Before Between After.
2011 FRANKLIN COMMUNITY SURVEY YOUTH RISK BEHAVIOR GRADES 9-12 STUDENTS=332.
Foundation Stage Results CLL (6 or above) 79% 73.5%79.4%86.5% M (6 or above) 91%99%97%99% PSE (6 or above) 96%84%100%91.2%97.3% CLL.
Subtraction: Adding UP
: 3 00.
5 minutes.
Numeracy Resources for KS2
1 hi at no doifpi me be go we of at be do go hi if me no of pi we Inorder Traversal Inorder traversal. n Visit the left subtree. n Visit the node. n Visit.
Speak Up for Safety Dr. Susan Strauss Harassment & Bullying Consultant November 9, 2012.
Static Equilibrium; Elasticity and Fracture
Essential Cell Biology
Converting a Fraction to %
Resistência dos Materiais, 5ª ed.
Clock will move after 1 minute
PSSA Preparation.
Immunobiology: The Immune System in Health & Disease Sixth Edition
Physics for Scientists & Engineers, 3rd Edition
Select a time to count down from the clock above
Murach’s OS/390 and z/OS JCLChapter 16, Slide 1 © 2002, Mike Murach & Associates, Inc.
Schutzvermerk nach DIN 34 beachten 05/04/15 Seite 1 Training EPAM and CANopen Basic Solution: Password * * Level 1 Level 2 * Level 3 Password2 IP-Adr.
1 Sediment Quality Objectives for California Enclosed Bays and Estuaries Benthic Indicator Development Scientific Steering Committee 26 th July 2005.
Benthic Community Assessment Tool Development Ananda Ranasinghe (Ana) Southern California Coastal Water Research Project (SCCWRP) Sediment.
Assessment of Sediment Quality in San Francisco Estuary RMP EEWG Meeting September 6, 2007 SCCWRP Team: Bay, Ranasinghe, Ritter, Barnett, Weisberg SFEI.
Presentation transcript:

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

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 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 1990s –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. EPAs guidance for biocriteria development

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 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 –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 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 –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 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 Taxa Polyhaline 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 Depth Polyhaline San Francisco Bay

19 Correlations with Fine Sediments Southern California Euhaline Bays

20 Correlations with Habitat Variables Spearman Correlation Coefficients BQIBRIIBIRBIRIVPACs Southern California Euhaline Bays Depth Fines Salinity* TOC Latitude ,21 Longitude : n=670; : n=320; *: n=66 Polyhaline San Francisco Bay Depth Fines Salinity TOC Latitude Longitude n=160 for all indices other than the IBI, where n=112

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 Bay n=12; p < for all cases CDMNORTV D0.93 M N O R T V W

25 Condition Categories Polyhaline San Francisco Bay

26 Index Evaluation Correlation of Candidate Index Rank with Mean Rater Rank Index Euhaline SoCal Bays Polyhaline San Francisco Bay BQI BRI IBI RBI RIVPACs Mean Rater Correlation (n=9)

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 Measure Status Classification Accuracy (%) Category Classification Accuracy (%) Category Bias BQI BRI IBI RBI RIV Best expert , -4 Average expert Worst expert

29 Combined Index Classification Accuracy No. of Indices Measure Status Classification Accuracy (%) Category Classification Accuracy (%) Category Bias FourBRI IBI RBI RIV BQI IBI RBI RIV BQI BRI RBI RIV BQI BRI IBI RIV BQI BRI IBI RBI FiveAll Best expert , -4 Average expert Worst expert

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

32 Condition Rank Correlations Southern California Euhaline Bays n=24; p < for all cases CDMNORTV D0.88 M N O R T V W

33 Correlations With No. of Taxa Southern California Euhaline Bays

34 Condition Categories Southern California Euhaline Bays

35 Polyhaline San Francisco Bay

36 Southern California Euhaline Bays

37

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 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

43

44

45

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

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 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