Effectiveness of BenthicEffectiveness of BenthicEffectiveness of Benthic Indices of Biotic Integrity as

Effectiveness of Benthic Indices of Biotic Integrity as g yWatershed Assessment Tools

g yWatershed Assessment Tools

Caitlin Chaffee MESM Candidate

Wetland, Watershed, and Ecosystem Science

Caitlin Chaffee MESM Candidate

Wetland, Watershed, and Ecosystem Science

Presentation OutlinePresentation Outline

BackgroundIndices of Biotic Integrity Macroinvertebrates

Differing OpinionsRecent URI ResearchRecent URI ResearchFurther Study

Benthic Index of Biotic Integrity(B-IBI)(B IBI)

• Index based on macroinvertebrate samples that integrates several metrics to produce an overallintegrates several metrics to produce an overall“health score” for a given water body

Result: dose-response curves to human impact

re

Taxa richness, relative abundance of certain taxa, and feeding groups

Generalized Plot of B-IBI Scores vs. Human Impact

IBI S

corfeeding groups

Human Impact

Impervious cover, various types of land use

B-IBI Scores Measure B-IBI Scores Measure Biological IntegrityBiological Integrity

“ h bili d i i b l d“ h bili d i i b l d“the ability to support and maintain a balanced, integrated, and adaptive community of organisms having a species composition, diversity and

“the ability to support and maintain a balanced, integrated, and adaptive community of organisms having a species composition, diversity andhaving a species composition, diversity and functional organization comparable to those of natural habitats within a region" †

having a species composition, diversity and functional organization comparable to those of natural habitats within a region" †

Provides reasoning for the use of reference sites (minimally disturbed sites) as a benchmark.

Provides reasoning for the use of reference sites (minimally disturbed sites) as a benchmark.†(Karr,1981)†(Karr,1981)

Wood River

Benthic MacroinvertebratesBenthic MacroinvertebratesBenthic MacroinvertebratesBenthic Macroinvertebrates(bottom(bottom--dwelling)dwelling)

Heptageniidae sp.(Mayfly larva)

Hydropsyche sp.(Caddisfly larva)

Perlodidae sp.(Stonefly larva)

Great candidates for bioassessmentGreat candidates for bioassessment

(Mayfly larva) ( y ) (Stonefly larva)

Great candidates for bioassessment…Great candidates for bioassessment…

Macroinvertebrates as IndicatorsMacroinvertebrates as Indicators• Limited migration patterns–good indicators of localized

conditions and site-specific impactsconditions and site specific impacts

• Integrate effects of human impacts (both short-term and long-term)g )

• Sampled easily at low expense with minimal effect on resident biotaeffect on resident biota

• Easy to identify to family

B d f h bit t i t• Broad range of habitat requirements and pollution tolerances—give info. about habitat structure and water quality at aabout habitat structure and water quality at a particular site

The Tolerance Index0 - 10

0 10

most pollution sensitivee.g. Stoneflies

most pollution tolerante.g. Midges & Leechesg g g

contain hemoglobin toleraterequire high DO, clear water, rocky cobble substrate

contain hemoglobin, tolerate lower DO, prefer soft substrate, less sensitive to toxins

The River Continuum(V t t l 1980)(Vannote et al., 1980)

HEADWATERS:

•HeterotrophicCPOM

FPOMSTR

•Coarse POM

•Low ΔTmax variabilityMID-REACHES: E

AM

O

MID-REACHES:

•Seasonally autotrophic

•Finer POM

CPOMFPOM

FPOM

ORDER

•High ΔTmax variability

FPOMLARGE RIVERS:

•Heterotrophic

•Fine-Ultra fine POM

Relative Channel Width

•Fine-Ultra fine POM

•Low ΔTmax variability

Arguments for Using IBI’sg g(Karr and Chu, 2000)

• Integrate multiple dimensions of complex systems (vs. chemical tests—one parameter)

• Results easily understood by diverse audiences (single score)audiences (single score)

• Include inexpensive, simple methods

Ad t bl t di hi d• Adaptable to diverse geographies and environmental conditions

Arguments Against Using IBI’s(Suter, 2002)

• Ambiguous results: heterogeneous variables obscure causation (What is causing low scores?)obscure causation (What is causing low scores?)

• Averaging can have confounding effects (low metrics cancel out high metrics—e.g. pop. densitymetrics cancel out high metrics e.g. pop. density and disease)

• Arbitrary combining functions affect scores—littleArbitrary combining functions affect scores little rationale for choice of functions

• Based in “unreality”—no real unitsBased in unreality no real units

• Imply only one type of response to ecosystem stressto ecosystem stress

Leads us to ask the question…

To what degree should weTo what degree should weTo what degree should weTo what degree should weTo what degree should we To what degree should we rely upon IBI’s as watershed rely upon IBI’s as watershed To what degree should we To what degree should we

rely upon IBI’s as watershed rely upon IBI’s as watershed assessment tools?assessment tools?assessment tools?assessment tools?

Use of IBI’s in RI

• Results included as supplemental data in statesupplemental data in state water quality assessment reportsreports

• Used by volunteer groups y g pand watershed associations as screening toolsg

• Used in schools as d ti l t leducational tools

EPA’s Suggestions for IBI Use†EPA s Suggestions for IBI Use• Nonpoint Source Pollution Assessment• Watershed Protection• Watershed Protection• TMDL Process• NPDES Permitting• NPDES Permitting• Ecological Risk Assessment• Development of Water Quality Criteria and• Development of Water Quality Criteria and

Standards

These are suggestions…These are suggestions…Can IBI’s be Can IBI’s be successfully implemented in these programs?successfully implemented in these programs?

† Barbour et al., 1999

NRC Research Recommendations—How h ld th b dd d?should they be addressed?

• Need to consider:– Correlation between metrics and stressors

– Associated predictable errors– Associated predictable errors

– Cause/effect relationships

• Need to Improve IBI’s by:Observational data for effect of individual stressors on– Observational data for effect of individual stressors on scores with other factors remaining constant

– Including many factors relevant to biotic conditiong y

Existing URI Study on IBI’s:g yA Multimetric Approach to Assessing the

Biological Integrity of Rhode IslandBiological Integrity of Rhode Island Streams (daSilva, 2003)

Looked more closely at IBI scores, their individual metrics and their relationships to different variablesvariables

Looked at time-series trends of IBI scores over 10 i dyear period

Compared index results to watershed land use d t ( % i i i di t fdata (e.g. % impervious cover—indicator of development)

Results: Significant Time-Series Trends for Four Streams out of 41 SampledFour Streams out of 41 Sampled

HardigRBP Score for all years (1991-2001)

KickemuitRBP Score for all years (1991-2001)

50 00

y = -1.6364x + 34.364R2 = 0.3734*

n = 11

20 00

25.00

30.00

35.00

40.00

45.00

50.00y = 2.65x + 1.7833

R2 = 0.7366*n = 8

20 00

25.00

30.00

35.00

40.00

45.00

50.00

0.00

5.00

10.00

15.00

20.00

0 1 2 3 4 5 6 7 8 9 10 11 12Y ear

0.00

5.00

10.00

15.00

20.00

0 1 2 3 4 5 6 7 8 9 10 11 12Ye a r

Declining stream health

PascoagRBP Score for all years (1991-2001)

50.00

Queen'sRBP Score for all years (1991-2001)

50.00

20.00

25.00

30.00

35.00

40.00

45.00

20.00

25.00

30.00

35.00

40.00

45.00

y = 1.3909x + 21.927R2 = 0.3643*

n = 110.00

5.00

10.00

15.00

0 1 2 3 4 5 6 7 8 9 10 11 12Y ear

y = 1.8506x + 23.407R2 = 0.4525*

n = 9

0.00

5.00

10.00

15.00

0 1 2 3 4 5 6 7 8 9 10 11 12Y ear

d l l ( )da Silva, 2003 Results (cont.)

• Although only four sites showed significant trends in IBI scores 10 sites showedtrends in IBI scores, 10 sites showed significant changes over time in at least one physiochemical parameter (TSS, total P,physiochemical parameter (TSS, total P, total N, chloride, temperature)—IBI scores did not reflect these changes.did not reflect these changes.

Further StudyFurther Study::

D ti d l ti fD ti d l ti f

Further StudyFurther Study::

D ti d l ti fD ti d l ti fDo time and location of Do time and location of sampling significantly effect sampling significantly effect

Do time and location of Do time and location of sampling significantly effect sampling significantly effect p g g yp g g y

stream health scores?stream health scores?p g g yp g g y

stream health scores?stream health scores?

T l V i ti S ti l V i tiTemporal Variation Spatial Variation

• Sampled 5 streams • Samples taken from• Sampled 5 streams, once/month from June to September, 2004.

• Samples taken from Queens and Beaver River at 5 different

• Data from Queens and Beaver Rivers—May and

locations for each stream, May and June Beaver Rivers May and

June 2003, sampled by Dr. Patrick Logan and Maria Aliberti

2003.

Maria Aliberti.

Methods

• Sampled three 1m2 sections of stream• Sampled three 1m2 sections of stream reach (riffle habitat) with dip net

• Subsample size: 100 organisms• Subsample size: 100 organisms• Preserved and identified organisms in

each subsampleeach subsample• Calculated Rapid Bioassessment

Protocol (RBP) scores for eachProtocol (RBP) scores for each subsample

NEVME Project Databasejhttp://www.pse.uri.edu/museum/queens_species_search.asp

RBP MetricsRBP MetricsTaxa Richness # TaxaTaxa RichnessEPTtaxa30xpEPT30x

# Taxa

# Ephemera, Plecoptera and Trichoptera taxa

% Ephemera, Plecoptera and Trichoptera taxapEPT30xFBI30xpDom30x

% p e e a, ecopte a a d c opte a ta aFamily Biotic Index – based on tolerance values

% Dominant taxon (diversity measure)pDom30xScrapFilt30xpShred30x

% Dominant taxon (diversity measure)

Ratio of scrapers to filterers

% ShredderspShred30xCLI30xRBP Score

% Shredders

Community Loss Index – comparison to reference siteMaximum Score = 48RBP Score Maximum Score = 48

Preliminary ResultsPreliminary Results

• Greater variation among scores of• Greater variation among scores of

samples taken in different months than at

different locations along a stream

• This may imply that time of sampling is

an important factor—even within a singlean important factor even within a single

season

In Summary…

• By examining the effects of natural variation on IBI scores, we may be able , yto refine the indices to produce more meaningful, reliable results.g ,

• This would increase the degree to which we could rely upon IBI’s aswhich we could rely upon IBI s as watershed assessment and decision-making toolsmaking tools.

k l dk l dAcknowledgementsAcknowledgements

USDA CSREES New England Water Quality ProgramDr. Art GoldDr. Patrick LoganDr. Tim TyrrellMaria AlibertiSara daSilva