Analysing Invertebrate data using CABIN

Stephanie StrachanEnvironment Canada

Columbia Basin Watershed Network ConferencePanorama, BCOct 2, 2009

DRAFT – Page 2 – April 21, 2023

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

DRAFT – Page 3 – April 21, 2023

What is CABIN?

• Canadian Aquatic BIomonitoring Network

• Standardised biological monitoring for Canada

• Assessment of aquatic health

• Based on network of networks

• Data Sharing & Partnerships!

DRAFT – Page 4 – April 21, 2023

Goals of CABIN

• To add a biological “effects” component to the national water quality monitoring program

• To identify streams where aquatic biota indicate reduced water quality

• Advise and report on status of freshwater quality in Canada with comparable, consistent and scientifically defensible data (e.g., future CESI reporting)

• To provide partners with reference data and tools to apply biological monitoring

DRAFT – Page 5 – April 21, 2023

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)

DRAFT – Page 6 – April 21, 2023

Why use invertebrates?

• Sedentary = reflect site-specific impacts

• Long-lived (1-3 yrs) = reflect cumulative impacts

• Diverse = respond to a wide range of stressors

• Ubiquitous = can be collected everywhere

• Key part of food web = ecologically important

• Commonly used = protocols are well developed

DRAFT – Page 7 – April 21, 2023

CABIN Methods

• Invertebrates reflect cumulative impacts therefore we measure them annually in the fall

• Standardised collection methods of biota and habitat (for small and large rivers)

• Develop watershed baselines for assessments using a reference condition approach

• Compare potentially impacted sites to reference conditions

DRAFT – Page 8 – April 21, 2023

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

DRAFT – Page 9 – April 21, 2023

Data Sharing Agreement

Current policy:4 years from sampling date

Your data in CABINCheck your data first

-view: site report in CABIN

-export: to check your data

-habitat data

-benthic data

DRAFT – Page 11 – April 21, 2023

RCA Overview

Measure the range of

desiredbiological

conditions with habitat

attributes(reference)

Partition biologicalconditions

into subsets

Compare test site to

appropriate subset

Develop models for predicting

biological subset from habitat

DRAFT – Page 12 – April 21, 2023

Understanding what is “acceptable”

Define the relationship between biology and habitat

Reference site BReference site A

Predictive model

=

Headwater streamsHeadwater streamsLow conductivityLow conductivityShallowShallowLow flowLow flow

Mid-sized streamsMid-sized streamsHigh conductivityHigh conductivityDeepDeepFast flowFast flow

DRAFT – Page 13 – April 21, 2023

Similar to ReferenceSimilar to Reference

Mildly DivergentMildly Divergent

DivergentDivergent

Highly DivergentHighly Divergent

Biological Condition CategoriesBiological Condition Categories

Ax

is 2

-2 -1 0 1 2

Axis 1

-2

-1

0

1

2

Within 90% = reference

Reference site

Test site

90% ellipse

99% ellipse

99.9% ellipse

CABIN results

DRAFT – Page 14 – April 21, 2023

CABIN Site Assessment

-3 -1 1 3Axis 1

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BYR0100

Grp1 Grp2 Grp3 Grp4

Test site

0.03 0.01 0.37 0.59

Probability from CABIN/BEAST

prediction

Axis 1 v 2 Axis 1 v 3 Axis 2 v 3 Overall

Similar to Reference

Mildly Divergent

Mildly Divergent

Mildly Divergent

Assessment from CABIN/BEAST

software

DRAFT – Page 15 – April 21, 2023

RIVPACS: Probability of taxa occurrence

FREQUENCY grp1 grp2 grp3 grp4

Clitellata 0.64 0.88 0.56 0.72

Ephemeroptera 0.98 0.93 0.99 0.91

Plecoptera 1.00 0.7 0.98 0.96

Trichoptera 0.92 0.88 0.9 0.89

Diptera (other) 0.25 0.3 0.41 0.27

Chironomidae 1.00 1.00 1.00 1.00

Arachnida (mites) 0.74 0.67 0.82 0.87

Grp1 Grp2 Grp3 Grp4

Test site

0.03 0.01 0.37 0.59

Probability from CABIN/BEAST prediction

Probability seeing Clitellata at Test Site

= 0.64(0.03) + 0.01(0.88) + 0.37(0.56) + 0.59(0.72)

= sum of the frequency of occurrence in each group X probability of belonging to each group

= 0.66

DRAFT – Page 16 – April 21, 2023

RIVPACS: Calculating RIVPACS - example

Grp1 Grp2 Grp3 Grp4

Test site

0.03 0.01 0.37 0.59

Probability from BEAST prediction

FREQUENCY grp1 grp2 grp3 grp4

Clitellata 0.64 0.88 0.56 0.72

Ephemeroptera 0.98 0.93 0.99 0.91

Plecoptera 1.00 0.7 0.98 0.96

Trichoptera 0.92 0.88 0.9 0.89

Diptera (other) 0.25 0.3 0.41 0.27

Chironomidae 1.00 1.00 1.00 1.00

Arachnida (mites) 0.74 0.67 0.82 0.87

1. Calculate the probability of occurrence2. Calculate the O:E scores (refer real data table from slide 15 for 1 site)

*Usually done at family level – order level for exercise

DRAFT – Page 17 – April 21, 2023

RIVPACS: Example Results

Count Prob

Clitellata 100 0.66

Ephemeroptera 27 0.9419

Plecoptera 1 0.966

Trichoptera 35 0.8945

Diptera (other) 27 0.3215

Chironomidae 134 1.00

Arachnida (mites) 47 0.8456

Expected taxa at P>0.70Exp. = .9419 + .966 + .8945 + .8456 = 3.6 taxaObs. = 4

O:E = 4/3.6 = 1.1

DRAFT – Page 18 – April 21, 2023

RIVPACS: Results in CABIN

Taxon Count Prob

Chironomids 42 1.00

Ephemeroptera 31 0.95

Plecoptera 6 0.97

Trichoptera 17 0.90

Mites 0 0.85

Annelida 21 0.66

Coleoptera 0 0.32

Other non-insect 0 0.26

Collembola 0 0.14

other insects 10 0.01

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 = 1.1810th percentile = 0.78

Sites within 0.78-1.18 are good. Sites >1.18 = Enriched or diversity hot spots?Site <0.78 - impacted

DRAFT – Page 19 – April 21, 2023

Demonstration

CABIN tools using Fraser River model

Columbia River model currently being developed. Expected completion Spring 2010

DRAFT – Page 20 – April 21, 2023

What does this mean?

How are they similar?

How are they different?

• an array of rows and columns

• data points are counts for each taxon for each sample

• these can be replicates, times, or treatments

Real Data

Order/Class Family Site 1 Site 2 Site 3

Arachnida 47 55 18

Clitellata 100 89 21

Diptera Chironomidae 134 121 58

Diptera Tipulidae 4 7 11

Diptera Simuliidae 12 0 2

Diptera Empididae 11 8 12

Trichoptera Glossosomatdiae 14 5 0

Trichoptera Hyrdopsychidae 21 18 20

Ephemeroptera Heptageniidae 18 5 18

Ephemeroptera Baetidae 9 9 72

Ephemeroptera Ephemerellidae 0 0 1

Ephemeroptera Leptophlebiidae 0 0 2

Plecoptera Perlidae 0 0 1

Plecoptera Nemouridae 1 1 38

Plecoptera Perlodidae 0 0 1

Plecoptera Chloroperlidae 0 0 4

Plecoptera Capniidae 0 0 4

DRAFT – Page 21 – April 21, 2023

Metrics

• 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

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

DRAFT – Page 22 – April 21, 2023

Real Data Metrics

Site 1 Site 2 Site 3

Abundance 371 318 283

Richness 11 10 16

# EPT taxa 5 5 10

# Ephemeroptera 2 2 4

# Plecoptera 1 1 5

# Trichoptera 2 2 1

% Chironomidae 36% 38% 20%

% EPT 17% 12% 57%

% dominance (top 3) 76% 83% 59%

DRAFT – Page 23 – April 21, 2023

Metrics Results

Family level metrics Test site Reference

Abundance 937 111-2788

Total Richness 12 7-28

EPT Richness 4 2-17

% EPT 64.5 9.2-98.5

% Dominance (top 3 taxa) 84.5 44.4-96.9

% Chironomidae 18.7 0.3-87.1

% non-insects 19.6 0-22.4

# Ephemeroptera taxa 1 0-6

# Plecoptera taxa 2 2-7

# Trichoptera taxa 1 0-6

But what do we

compare this to?

Upstream?Gradient?

“Before” sample?Reference sites?

Assessed usingTarget value, t-test,

ANOVA

DRAFT – Page 24 – April 21, 2023

Similarity among sites in a stream

Which sites are most similar?

Site 1 Site 2 Site 3 Site 4

Arachnida 47 55 18 5

Clitellata 100 89 21 88

Diptera (Chironomidae) 134 121 58 126

Diptera (0ther) 27 15 25 16

Trichoptera 35 23 20 8

Ephemeroptera 27 14 93 41

Plecoptera 1 1 48 22

DRAFT – Page 25 – April 21, 2023

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

DRAFT – Page 26 – April 21, 2023

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 1Site 2Site 3Site 4

Site 1 vs Site 2Site 1 vs Site 3Site 1 vs Site 4Site 2 vs Site 3Site 2 vs Site 4Site 3 vs Site 4

Site 1 Site 2 Site 3 Site 4

Site 1 100 - - -

Site 2 S12 100 - -

Site 3 S13 S23 100 -

Site 4 S14 S24 S34 100

DRAFT – Page 27 – April 21, 2023

Calculating Bray-Curtis Similarity

}1{1001

1

ikijpi

ikijpi

jk yy

yyS

Species/Site

S 1 S 2 S 3 S 4 S 5 S1,S5

A1 2 5 2 5 3 17

A2 3 5 2 4 3 17

A3 9 1 1 1 1 13

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%

n(n-1)/2 coefficients

Thus.... 3(3-1)/2

Calculate....3 similarity coefficients

DRAFT – Page 28 – April 21, 2023

Site 1 vs Site 2

  Arach Clitell ChironOther

Dip Trichop Ephem Plec Total

Site 1 47 100 134 27 35 27 1 371

Site 2 55 89 121 15 23 14 1 318

Sum 102 189 255 42 58 41 2 689

|Diff| 8 11 13 12 12 13 0 69

S = 100 x (1 – [ 69 / 689] ) = 100 x ( 1 – [0.100]) = 100 x (0.90) = 0.90

DRAFT – Page 29 – April 21, 2023

Similarity among sites in a stream

Which sites are most similar?

Site 1 Site 2 Site 3 Site 4

Arachnida 47 55 18 5

Clitellata 100 89 21 88

Diptera (Chironomidae) 134 121 58 126

Diptera (0ther) 27 15 25 16

Trichoptera 35 23 20 8

Ephemeroptera 27 14 93 41

Plecoptera 1 1 48 22

DRAFT – Page 30 – April 21, 2023

Similarity Matrix

Site 1 Site 2 Site 3 Site 4

Site 1 100 - - -

Site 2 90 100 - -

Site 3 52.0 48.9 100 -

Site 4 80.1 80.8 58.1 100

DRAFT – Page 31 – April 21, 2023

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!

DRAFT – Page 32 – April 21, 2023

Simple graphs

Abundance

0

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JOS01-08

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JOS02-08

JOS03-07

JOS03-08

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JOS01-07

JOS01-08

JOS02-07

JOS02-08

JOS03-07

JOS03-08

Richness EPT Richness

• 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

00.05

0.10.15

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0.40.45

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JOS01-07

JOS01-08

JOS02-07

JOS02-08

JOS03-07

JOS03-08

% Diptera %Chironomidae

DRAFT – Page 33 – April 21, 2023

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