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Analysing Invertebrate data using CABIN

Analysing Invertebrate data using CABIN. Stephanie Strachan Environment Canada Columbia Basin Watershed Network Conference Panorama, BC Oct 2, 2009. Outline. Brief intro to CABIN Data Sharing Your data in CABIN How CABIN analysis works RCA model & assessment RIVPACS DEMO

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Analysing Invertebrate data using CABIN

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  1. Analysing Invertebrate data using CABIN Stephanie Strachan Environment Canada Columbia Basin Watershed Network Conference Panorama, BC Oct 2, 2009

  2. Outline • Brief intro to CABIN • Data Sharing • Your data in CABIN • How CABIN analysis works • RCA model & assessment • RIVPACS • DEMO • Analysing data without a model • Metrics • Bray-Curtis

  3. What is CABIN? • Canadian Aquatic BIomonitoring Network • Standardised biological monitoring for Canada • Assessment of aquatic health • Based on network of networks • Data Sharing & Partnerships!

  4. Goals of CABIN • To add a biological “effects” component to the national water quality monitoring program • To identifystreams where aquatic biota indicate reduced water quality • Advise and reporton status of freshwater quality in Canada with comparable, consistent and scientifically defensible data (e.g., future CESI reporting) • To providepartners with reference data and tools to apply biological monitoring

  5. CABIN Advantages • CABIN provides a scientifically defensible assessment of your site • As part of CABIN you are part of national assessment program • You are sharing reference data with other agencies therefore you are using the same benchmark as federal, provincial and municipal governments • Adds value to a WQ monitoring program (e.g. detection of non-chemical impacts, verification of assumptions of chemical guidelines, addresses cumulative effects)

  6. Why use invertebrates? • Sedentary = reflect site-specificimpacts • Long-lived (1-3 yrs) = reflect cumulativeimpacts • Diverse = respond to a wide range of stressors • Ubiquitous = can be collected everywhere • Key part of food web = ecologically important • Commonly used = protocolsare well developed

  7. CABIN Methods • Invertebrates reflect cumulative impacts therefore we measure them annuallyin the fall • Standardised collection methods of biota and habitat (for small and large rivers) • Develop watershed baselinesfor assessments using a reference condition approach • Comparepotentially impacted sites to reference conditions

  8. CABIN Tools • online resources • Database (login) • mapping tool • analytical tool • reporting tool • link with other EC websites • Online training modules and field certification http://cabin.cciw.ca

  9. Data Sharing Agreement Current policy: 4 years from sampling date

  10. Your data in CABIN Check your data first -view: site report in CABIN -export: to check your data -habitat data -benthic data

  11. Partition biological conditions into subsets Develop models for predicting biological subset from habitat Compare test site to appropriate subset RCA Overview Measure the range of desired biological conditions with habitat attributes (reference)

  12. Predictive model = Reference site A Reference site B Mid-sized streams High conductivity Deep Fast flow Headwater streams Low conductivity Shallow Low flow Understanding what is “acceptable” Define the relationship between biology and habitat

  13. Within 90% = reference Reference site 2 Test site 1 Axis 2 0 -1 90% ellipse 99% ellipse 99.9% ellipse -2 -2 -1 0 1 2 Axis 1 CABIN results Biological Condition Categories Similar to Reference Mildly Divergent Divergent Highly Divergent

  14. 3 3 3 3 3 3 3 1 1 1 1 1 1 1 Axis 2 Axis 2 Axis 2 Axis 2 Axis 2 Axis 2 Axis 2 BYR0100 BYR0100 - - - - - - - 1 1 1 1 1 1 1 - - - - - - - 3 3 3 3 3 3 3 - - - - - - - 3 3 3 3 3 3 3 - - - - - - - 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 Axis 1 Axis 1 Axis 1 Axis 1 Axis 1 Axis 1 Axis 1 3 3 3 3 3 BYR0100 1 1 1 1 1 Axis 3 Axis 3 Axis 3 Axis 3 Axis 3 - - - - - 1 1 1 1 1 - - - - - 3 3 3 3 3 - - - - - 3 3 3 3 3 - - - - - 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 Axis 1 Axis 1 Axis 1 Axis 1 Axis 1 3 3 3 3 3 BYR0100 1 1 1 1 1 Axis 3 Axis 3 Axis 3 Axis 3 Axis 3 - - - - - 1 1 1 1 1 - - - - - 3 3 3 3 3 - - - - - 3 3 3 3 3 - - - - - 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 Axis 2 Axis 2 Axis 2 Axis 2 Axis 2 CABIN Site Assessment Probability from CABIN/BEAST prediction Assessment from CABIN/BEAST software

  15. RIVPACS: Probability of taxa occurrence Probability from CABIN/BEAST prediction Probability seeing Clitellata at Test Site = sum of the frequency of occurrence in each group X probability of belonging to each group = 0.64(0.03) + 0.01(0.88) + 0.37(0.56) + 0.59(0.72) = 0.66

  16. RIVPACS: Calculating RIVPACS - example Probability from BEAST prediction • Calculate the probability of occurrence • Calculate the O:E scores (refer real data table from slide 15 for 1 site) • *Usually done at family level– order level for exercise

  17. RIVPACS: Example Results Expected taxa at P>0.70 Exp. = .9419 + .966 + .8945 + .8456 = 3.6 taxa Obs. = 4 O:E = 4/3.6 = 1.1

  18. RIVPACS: Results in CABIN Observed 4 taxa with P>0.70 Expected taxa at P>0.70 = sum prob >0.70 = (1+0.95+0.97+0.90+0.85) = 4.67 O:E ratio = 4/4.67 = 0.85 For all Fraser River reference sites O:E P>0.70 mean = 1.04 90th percentile = 1.18 10th percentile = 0.78 Sites within 0.78-1.18 are good. Sites >1.18 = Enriched or diversity hot spots? Site <0.78 - impacted

  19. Demonstration CABIN tools using Fraser River model Columbia River model currently being developed. Expected completion Spring 2010

  20. Real Data • an array of rows and columns • data points are counts for each taxon for each sample • these can be replicates, times, or treatments What does this mean? How are they similar? How are they different?

  21. Taxonomic richness – how many types of organisms? Ephemeroptera richness Plecoptera richness Trichoptera richness Composition metrics - what proportion of the community is dominated by one or few taxa? % EPT individuals % Chironomidae % non-insects % Dominance Tolerance metrics # tolerant taxa % intolerant individuals Ecological metrics % predators (other functional feeding groups) # clinger taxa Metrics Check CABIN to see how each metric is calculated Check the waterquality.ec.gc.ca website to see summary and how each responds to a perturbation

  22. Real Data Metrics

  23. Metrics Results But what do we compare this to? Upstream? Gradient? “Before” sample? Reference sites? Assessed using Target value, t-test, ANOVA

  24. Similarity among sites in a stream Which sites are most similar?

  25. Similarity Coefficient • S = 0 if two samples have no species in common • S = 100 if two samples are identical • CABIN uses Bray-Curtis Similarity Coefficient • Because…… • A scale change in measurements does not change S as all y values are multiplied by the same constant • Joint absences have no affect on S, not so for all coefficients

  26. Similarity Matrix • Calculated between every pair of samples (n(n-1)/2) comparisons • Displayed in a lower triangular matrix • Similarity matrices are the basis of most multivariate methods Site 1 vs Site 2 Site 1 vs Site 3 Site 1 vs Site 4 Site 2 vs Site 3 Site 2 vs Site 4 Site 3 vs Site 4 Site 1 Site 2 Site 3 Site 4

  27. Calculating Bray-Curtis Similarity n(n-1)/2 coefficients Thus.... 3(3-1)/2 Calculate.... 3 similarity coefficients Similarity between sites: S A1,A2 = 100*1-(1+0+0+1+0) / (17+17) = 100*(1- 0.058) = 94.1% S A1,A3 = 100*1-(7+4+1+4+2) / (17+13) = 100*(1- 0.600) = 40.0% S A2,A3 = 100*1-(6+4+1+3+2) / (17+13) = 100*(1-0.533) = 46.7%

  28. Site 1 vs Site 2 S = 100 x (1 – [ 69 / 689] ) = 100 x ( 1 – [0.100]) = 100 x (0.90) = 0.90

  29. Similarity among sites in a stream Which sites are most similar?

  30. Similarity Matrix

  31. Data Analysis Summary *Need to compare to something* What was your objective? How were your sites selected? • Metrics (target value or Index) • B-IBI calibrated Index for your region • Upstream-downstream (t-test, ANOVA) • Using metric or similarity or individual taxa counts • Gradient Analysis • Using metric or similarity or individual taxa counts • RCA • Set of reference sites; using all taxa in ordination plots • RIVPACS • Metrics compared to reference • Simplest – GRAPH IT!

  32. Simple graphs • Abundance much higher in JOS03 • EPT richness pattern follows Total Richness – report only 1 of these • Chironomidae are the dominant Dipteran and similar proportion of Dipteran at all sites

  33. Data Interpretation • CABIN is a screening tool • Tells us if there is a problem, not what the problem is • Components of the community give us clues about what the problem might be • Used to complement WQ chemical data • Can also evaluate habitat disturbance • Can be used to track changes over time • Need to do further investigation to determine the cause of the problem detected

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