Effectiveness of Benthic Indices of Biotic Integrity as Watershed Assessment Tools

Transcription

1 Effectiveness of Benthic Indices of Biotic Integrity as Watershed Assessment Tools Caitlin Chaffee MESM Candidate Wetland, Watershed, and Ecosystem Science

2 Presentation Outline Background Indices of Biotic Integrity Macroinvertebrates Differing Opinions Recent URI Research Further Study

3 Benthic Index of Biotic Integrity (B-IBI) Index based on macroinvertebrate samples that integrates several metrics to produce an overall health score for a given water body Result: dose-response curves to human impact Taxa richness, relative abundance of certain taxa, and feeding groups IBI Scor re Generalized Plot of B-IBI Scores vs. Human Impact Human Impact Impervious cover, various types of land use

4 B-IBI Scores Measure Biological Integrity the ability to support and maintain i a balanced, integrated, and adaptive community of organisms 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. (Karr,1981)

5 Wood River

6 Benthic Macroinvertebrates (bottom-dwelling) Heptageniidae sp. Hydropsyche sp. Perlodidae sp. (Mayfly larva) (Caddisfly larva) (Stonefly larva) Great candidates for bioassessment

7 Macroinvertebrates as Indicators Limited migration patterns good indicators of localized conditions and site-specificspecific impacts Integrate effects of human impacts (both short-term and long-term) Sampled easily at low expense with minimal effect on resident biota Easy to identify to family Broad range of habitat t requirements and pollution tolerances give info. about habitat structure and water quality at a particular site

8 The Tolerance Index most pollution sensitive most pollution tolerant e.g. Stoneflies e.g. Midges & Leeches require high DO, clear water, rocky cobble substrate contain hemoglobin, tolerate lower DO, prefer soft substrate, less sensitive to toxins

9 The River Continuum (Vannote et al., 1980) HEADWATERS: Heterotrophic Coarse POM Low ΔTmax variability MID-REACHES: Seasonally autotrophic Finer POM High ΔTmax variability LARGE RIVERS: S T R E A M O R D E R FPOM CPOM FPOM FPOM CPOM Heterotrophic Fine-Ultra fine POM Low ΔTmax variability Relative Channel Width

10 Arguments for Using IBI s (Karr and Chu, 2000) Integrate multiple dimensions of complex systems (vs. chemical tests one parameter) Results easily understood by diverse audiences (single score) Include inexpensive, simple methods Ad t bl t di hi d Adaptable to diverse geographies and environmental conditions

11 Arguments Against Using IBI s (Suter, 2002) Ambiguous results: heterogeneous variables obscure causation (What is causing low scores?) Averaging can have confounding effects (low metrics cancel out high metrics e.g. e.g. pop. density and disease) Arbitrary combining functions affect scores little rationale for choice of functions Based in unreality no no real units Imply only one type of response to ecosystem stress Leads us to ask the question

12 To what degree should we rely upon IBI s as watershed assessment tools?

13 Use of IBI s in RI Results included as supplemental data in state water quality assessment reports Used by volunteer groups and watershed associations as screening tools Used in schools as educational tools

14 EPA s Suggestions for IBI Use Nonpoint Source Pollution Assessment Watershed Protection TMDL Process NPDES Permitting Ecological Risk Assessment Development of Water Quality Criteria and Standards These are suggestions Can IBI s be successfully implemented in these programs? Barbour et al., 1999

15 NRC Research Recommendations How should they be addressed? d? Need to consider: Correlation between metrics and stressors Associated predictable errors Cause/effect relationships Need to Improve IBI s by: Observational data for effect of individual stressors on scores with other factors remaining constant Including many factors relevant to biotic condition

16 Existing URI Study on IBI s: A Multimetric Approach to Assessing the Biological Integrity of Rhode Island Streams (dasilva, 2003) Looked more closely at IBI scores, their individual metrics and their relationships to different variables Looked at time-series trends of IBI scores over 10 year period Compared index results to watershed land use data (e.g. % impervious i cover indicator of development)

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18 Results: Significant Time-Series Trends for Four Streams out of 41 Sampled Declining stream health Hardig RBP Score for all years ( ) y = x R 2 = * n = Year Kickemuit RBP Score for all years ( ) y = 2.65x R 2 = * n = Year Pascoag RBP Score for all years ( ) y = x R 2 = * n = Y ear Queen's RBP Score for all years ( ) y = x R 2 = * n = Y ear

19 da Silva, 2003 Results (cont.) Although only four sites showed significant trends in IBI scores, 10 sites showed significant changes over time in at least one physiochemical parameter (TSS, total P, total N, chloride, temperature) IBI scores did not reflect these changes.

20 Further Study: Do time and location of sampling significantly effect stream health scores?

21 Temporal Variation Spatial Variation Sampled 5 streams, once/month from June to September, Data from Queens and Beaver Rivers May and June 2003, sampled by Dr. Patrick Logan and Maria Aliberti. Samples taken from Queens and Beaver River at 5 different locations for each stream, May and June 2003.

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23 Methods Sampled three 1m 2 sections of stream reach (riffle habitat) with dip net Subsample size: 100 organisms Preserved and identified organisms in each subsample Calculated Rapid Bioassessment Protocol (RBP) scores for each subsample

24 NEVME Project Database j

25 RBP Metrics Taxa Richness # Taxa EPTtaxa30x pept30x FBI30x pdom30x ScrapFilt30x pshred30x % Shredders CLI30x RBP Score Maximum Score = 48 # Ephemera, Plecoptera and Trichoptera taxa % Ephemera, e Plecoptera and Trichoptera taxaa Family Biotic Index based on tolerance values % Dominant taxon (diversity measure) Ratio of scrapers to filterers Community Loss Index comparison to reference site

26 Preliminary Results 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 single season

27 In Summary By examining the effects of natural variation on IBI scores, we may be able to refine the indices to produce more meaningful, reliable results. This would increase the degree to which we could rely upon IBI s as watershed assessment and decision- making tools.

28 Acknowledgements USDA CSREES New England Water Quality Program Dr. Art Gold Dr. Patrick Logan Dr. Tim Tyrrell Maria Aliberti Sara dasilva