Ontario Benthos Biomonitoring Network Participants’ Training

Updated April 2006

Clear Lake Inflow, 22-May-2005Longitude: -74.7° Latitude: 45.0°

Sampled by: Jones & Dmytrow

Test Site AtypicalHypothesis-test Results Index Contributions

D F Pnon-central

7.52 94.31 0.03

Index Contributions*CA1(abundance) CA2(abundance) Richness % Chironomidae % EPT

D 6.52 3.38 1.53 5.04 4.22F 110.28 100.73 2.3 65.81 6.4P 0.015 0.014 0.177 0.038 0.032

*values in bold typeface are beyond 2 st. dev. from the reference-site mean

Summary StatisticsReference Sites (nref=15) Test Site*

mean St. Dev. meanCA1(abundance) -0.84 0.43 0.09CA2(abundance) -0.34 0.41 2.74Richness 13 4.16 13% Chironomidae 34 12.31 33% EPT 62 8.90 46

*values in bold typeface are beyond 2 st. dev. from the reference-site mean

Stream reference sites with test-site like collection method, gear type, mesh size, collection season, and flow permanence were selected based on similarity (Euclidean distance) to the following test-site habitat features: dominant substrate, elevation, latitude, longitude, and catchment area. Euclidean distances for reference sites ranged from 5 to 72. Total Euclidean distance for 15 reference sites and 5 attributes was 494

Standard Report (OBBN Vision)

InstructorsChris Jones, Ministry of Environment, Benthic Biomonitoring Scientist and

OBBN coordinator (Lead Instructor)

Nicole Dmytrow, Saugeen Conservation, OBBN Assistant Coordinator (Sampling, Benthos Identification)

Ron Reid, Ministry of Environment, Benthos Scientist (Sampling, Benthos Identification)

Michelle Bowman, University of Toronto (RCA bioassessment calculations: Test Site Analysis)

Desired OutcomeParticipants understand the purpose and

administration of the OBBN, and demonstrate competence with its methods.

This course is part of OBBN’s quality assurance plan: certification is one way of protecting the credibility of the

Network.

The OBBN is part of the Canada-wide Canadian Aquatic Biomonitoring Network (CABIN). We are working on

standard training and certification requirements for CABIN.

Participants’ Certification• 2 types of certificates (Participant, Trainer)• To be certified, participants must:

– Pass a general multiple-choice test– Correctly identify 40 of 44 benthos specimens to the coarse

OBBN 27-group level• In addition to above, trainers must:

– Assist with teaching the course– List at least 2 diagnostic characters for each specimen on the

benthos identification test (without consulting references)

Student Instructors

Rebecca Crockford, District of Muskoka

Lynette Dawson, Quinte Conservation

Gerry Sullivan, Otonabee Region Conservation Authority

Angela Wallace, Gartner Lee

Biomonitoring Knowledge vs. Degree of OBBN InvolvementDegree of Network involvement vs. OBBN Knowledgea

y = 1.3454x + 6.8247R2 = 0.274

y = -1.2307x + 6.5219R2 = 0.2557

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6

Ordinal Degree of involvement

# of

Ans

wer

s

Correct

Don't Know

Equation R2 C = 6.82 + 1.35(ODI) 0.27 DK = 6.52 - 1.23(ODI) 0.26 C = 10.5 + 0.072(MI) 0.11

DK = 3.29 - 0.0716(MI) 0.12 Regression results (C = number of correct answers, DK = number of questions

answered ‘Don’t Know’, ODI = ordinal degree of OBBN involvement; all listed comparisons are significant at the =0.05 level)

Agenda: Day 1Welcome to the Course PurposeBackground (Need for Biomonitoring; Benthos as Indicators; Benthos facts; Complementarity of Stressor- and Effect-based Monitoring; OBBN Components, Principles, and Status Update)

Chris Jones, Gerry Sullivan

The Reference Condition Approach(RCA Overview; Definition of Reference Site; OBBN Reference Site Sampling Strategy; Criteria for minimally impacted; Spot the reference site; Example of RCA Bioassessment)

Chris Jones,Angela Wallace

Protocol(Standardization vs. Flexibility, Collection Procedures For Lakes, Streams, Wetlands, Processing Methods, Archiving,Habitat Characterization)

Chris Jones,Lisa Campbell

Sampling: Kennisis River and Lake of Bays(Student trainers as group leaders)

Nicole Dmytrow, Chris Jones, Ron Reid, Gerry Sullivan, Angela Wallace, Lisa Campbell, Lynette Dawson, Rebecca Crockford

Sieve Samples Nicole Dmytrow

Agenda: Day 2

Benthos Picking (random sub-sampling to obtain ~100-count sample)

Nicole Dmytrow, Chris Jones, Ron ReidGerry Sullivan, Angela Wallace, Lisa Campbell, Lynette Dawson, Rebecca Crockford

Benthos Identification (OBBN 27-group Level)- Diagnostic features of each group (slide show)- Examples from the DESC reference collection

(demonstration)- Practice using specimens collected yesterday (hands-on)

Chris Jones, Nicole Dmytrow,Rebecca Crockford, Lynette Dawson

Practice identification skills Chris Jones, Nicole Dmytrow

Students to identify specimens in front of class (microscope projection), highlighting diagnostic characters

Chris Jones

Agenda: Day 3

Assessment: Is Test Site Within Normal Range?-Summary Metrics-Hypothesis Testing (TSA)

Michelle Bowman, Chris Jones

Review Gerry Sullivan, Angela Wallace, Lisa Campbell, Lynette Dawson, Rebecca CrockfordChris Jones, Nicole Dmytrow

Certification Test (Optional) Chris Jones, Nicole Dmytrow

Take-up test, general discussion, and wrap-up Chris Jones

Biomonitoring Rationale • Legislation & policy stress protection of biota

– Biological definitions of impairment and adverse impact in Ontario

– “biological integrity” in U.S. Water Pollution Control Act

– The EU Water Framework Directive requires both “good ecological status” and “good chemical status” of surface water

• Management stresses protection/rehabilitation of biota:– Target setting– Performance evaluation

(Roux et al. 1999, Jones et al. 2005b, Jones 2006)

Biomonitoring Rationale II

“Biomonitoring is required … because the consequences of environmental stress can only be determined by an appraisal of the

biota”. Wright (2000)

What are Benthos?

• Bottom-dwelling aquatic invertebrates

• Include animals like insects, worms, mollusks, crustaceans, and mites Caddisfly of the

family Helicopsychidae

Mayfly of the family Ephemerellidae.

Why Use Benthos As Bioindicators?

• Abundant and widespread • Nobody cares• Easily and inexpensively sampled• Sedentary (unlike fish)• Long lived (months to years)• Many species with different

tolerances • Respond to both water and

sediment chemistry• Readily archived for future reference• Provide early-warning

Stream benthos collection in the Raisin River watershed

Benthos are excellent indicators of aquatic ecosystem health.

(Rosenberg & Resh 1993, 1996; Mackie 2001)

Complementarity of Stressor- and Effect-based Monitoring

Stressor-based Approach Effect-based Approach

Monitoring focus

Stressors causing environmental change, i.e., chemical and physical inputs

Effects (responses) of natural and/or anthropogenic disturbances, e.g., changes in the structure and function of biological communities

Management focus

Water quality regulation: controlling stressors through regulations

Aquatic ecosystem protection: managing ecological integrity

Primary indicators

Chemical and physical habitat variables, e.g., pH, dissolved oxygen, copper concentration

Structural and functional biological attributes (e.g., relative taxa abundances, frequency of deformities)

Assessment end points

Degree of compliance with a set criterion or discharge standard

Degree of deviation from a benchmark or desired biological condition

Adapted from Roux et al. (1999)

Stressor and Effect-based Approaches are Complementary

Phosphorus Data: 1997 - 2001

0

0.02

0.04

0.06

0.08

0.1

Pretty River @ hwy. 26, Collingwood

mg/

L

Zinc Data: 1997 - 2001

0

5

10

15

20

25

Pretty River @ Hwy. 26, Collingwood

ug/l

Biology

Chemistry

Benthos data, Pretty River, October 1996; reference site data, 1997-2000

= Ontario Water Quality Objective

Pretty River, Highway 26,

Collingwood, Ontario

Pretty

Mad R.Noisy Nottawasaga

Pine 1

Pine 2

Sheldon

CA1

CA

2

95% confidence ellipse

Technical Issues

• Unlike water chemistry, no guidelines or “biocriteria”exist

• Complex; many confounding factors: biota respond to things other than stressor of interest

• No standard sampling protocol• Taxonomy requires special

expertise• Experts disagree on

hypothesis-testing procedures and interpretation

• Cost

The application of benthos biomonitoring has been limited by a number of technical issues.

OBBN BackgroundOBBN: a collaborative lake-, stream-, and wetland-bioassessment network

Leads: Ontario Ministry of Environment and Environment Canada (EMAN), but part of national CABIN program

1. Evaluate aquatic ecosystem condition

2. Measure effectiveness of programs

3. Provide biological complement to Provincial Water Quality Monitoring Program

4. Support development of biocriteria for aquatic ecosystem condition

Purposes

Aquatic mite

Barriers to Biomonitoring in Ontario

Standard Protocol

Data Sharing

Training

ImplementationStatus

Protocol

Research Analytical Software

Database

Training

OBBN

• Train-the-trainer • Integration with North American Benthological Society Taxonomic Certification Program (NABS TCP)

• Query tool, data exporter, automated bioassessment-hypothesis test, reporting module

• spring 2006 release date

• Collaborative projects required to develop OBBN products

• Current focus is on understanding sources of variance and evaluating methods

• On-line• Printed

manual subject to Ministry approval

• National integration• Launched 31 Oct. 2005• ~30 organizations have

accounts

http://obbn.eman-rese.ca

OBBN Partners

OBBN Leads

• Ontario Ministry of Environment

• Environment Canada’s Ecological Monitoring

and Assessment Network

Technical Advisory Committee

• Universities • Conservation Authorities

• Ontario’s Ministries of Environment and Natural

Resources• Environment Canada

• Trout Unlimited• District of Muskoka

Certified Participants

• All Sectors

OBBN Partner Roles

Partners

• Sampling (for their own purposes and to collaborate on regional, provincial, and national reporting)

• Data-sharing• Research

OBBN Leads

• Coordinate 5 program components

• Provide technical advice and equipment

• Research

Technical Advisory Committee

• Technical guidance and review

• Research• Program Priorities• Problem Solving

Data-sharing Agreement

I understand and accept that as a partner in the Canadian Aquatic Biomonitoring Network, data entered into this system is freely shared among all Network participants.

I further understand and accept that CABIN and its partners put no restrictions on, and do not regulate, how data is used by network members.

Although I have made every attempt to ensure the quality of the data I enter into the database, I make no guarantee about the accuracy of that data, and assume no liability associated with its use.

OBBN Socio-economics and Demography

Vocational Sector (n=38)c

0%5%

10%15%20%25%30%35%

PS Gov CA Acad Ed NGO

Age (n=37)

0%

10%

20%

30%

40%

20-29 30-39 40-49 50-59 60-69

Perc

ent o

f Res

pons

es

Highest Level of Education Achieved (n=38)a

0%10%20%30%40%50%

CD UUG UGD

Employment Status (n=38)b

0%

20%

40%

60%

80%

100%

U R PT FT Other

Perc

ento

f Res

pons

es

OBBN participants’ socio-economic status and demography (aCD = college diploma; UUG = university undergrad. degree; UGD = university grad.degree; bU = unemployed; R = retired; PT = part-time; FT = full-time; cPS = private sector; Gov = government; CA = conservation authority; Acad = academic; Ed = education; NGO = non-government or non-profit organization

Motives of Participation

0% 20% 40% 60% 80% 100%

TE (n=37)

AEC (n=39)

AMD (n=38)

GE (n=34)

GRR (n=37)

PE (n=38)

MO (n=37)

R (n=36)

Percent of Responses

VeryImportant

SomewhatImportant

NotImportant

Motives of OBBN participation (R = research; MO = meeting others with common interests; PE = performance evaluation (i.e., evaluating performance of water management programs; GRR = guiding rehabilitation or restoration; GE = Guiding enforcement; AMD = Assessing or managing biodiversity; AEC = Assessing/managing ecological condition; TE = Training/education)

*

Perspectives on Network

Implementation (I)

0% 10% 20% 30% 40% 50% 60% 70%

Choice of sampling sites (n=35)

Choice of data shared (n=34)

Developing and refining methods (n=32)

Analysis and interpretation (n=32)

Follow-up action (n=29)

Percent of Responses

Full control (5)

4

3

2

No Control (1)

Relationship Type (based on degree of participant control)a Control Partnership Collaboration Co-optation Who determines monitoring protocol? Participants Shared Shared Government Who selects sites to be monitored? Participants Participants Shared Government Who determines analytical methods, interpretation, and data distribution?

Participants Participants Shared Government

Who determines follow-up action? Participants Participants, then government

Shared Government

Types of government-participant relationships in monitoring programs (adapted from Savan et al. 2004).

Participants’ perceived control or influence over components of the OBBN

• 88% categorized participant-government relationship type as partnership or collaboration

**

Benthos: From Snot Globules to Jewelry

Caddisfly larva (Hydropsychidae)

Anterior view of water-boatman head (Corixidae)

Mayflies

True Flies

Black Flies

Caddisflies

Leeches

Dragonflies & Damselflies

Biocriteria and the Reference Condition Approach

Biocriteria“Healthy is Variable.”

–Dr. Robert Bailey, University of Western Ontario

(Kilgour et al. 1998, Bowman & Somers 2005)

StreamSampleDatePartnerHYDRACARINATrhypochthoniidae 2 1EPHEMEROPTERABaetidae 81 49Ephemerellidae 1 2PLECOPTERALeuctridae 1 1Capniidae 1 0Perlodidae 6 5Chloroperlidae 0 1TRICHOPTERARhyacophilidae 2 1Hydropsychidae 2 3COLEOPTERAElmidae 11 20DIPTERAChironomidae 20 29Ceratopogonidae 3 2Tipulidae 4 6Simulidae 0 2Empididae 1 0

Total: 135 122

• 2 equally healthy sites may have different biological assemblages

• Need to determine what normal is• Biomonitoring conundrum: Is an

observed difference greater than expected by chance? Is it biologically meaningful?

• Biocriteria are critical values for hypothesis tests

• The “normal range” is a pragmatic biocriterion

Baxter BaxterRiffle 1 Riffle 2

16-Aug-04 16-Aug-04ORCA ORCA

Biocriteria“Healthy is Variable.”

–Dr. Robert Bailey, University of Western Ontario

(Kilgour et al. 1998, Bowman & Somers 2005)

• 2 equally healthy sites may have different biological assemblages

• Need to determine what normal is• Biomonitoring conundrum: Is an

observed difference greater than expected by chance? Is it biologically meaningful?

• Biocriteria are critical values for hypothesis tests

• The “normal range” is a pragmatic biocriterion

StreamSampleDatePartnerHYDRACARINATrhypochthoniidae 2 1EPHEMEROPTERABaetidae 81 49Ephemerellidae 1 2PLECOPTERALeuctridae 1 1Capniidae 1 0Perlodidae 6 5Chloroperlidae 0 1TRICHOPTERARhyacophilidae 2 1Hydropsychidae 2 3COLEOPTERAElmidae 11 20DIPTERAChironomidae 20 29Ceratopogonidae 3 2Tipulidae 4 6Simulidae 0 2Empididae 1 0

Total: 135 122

Experimental Designs for Bioassessments

Monitoring for WhereNo

Monitoring for WhenYesNo

Temporal (Before-After)No

Optimal Impact Study (BACI)YesYesNo

Modern Analog ApproachNo

Reference Condition ApproachYesNo

Impact from Spatial PatternNo

Spatial Study (Control-Impact)YesYesYes

Experimental Design NameIs there a control area?

Is when and where known?

Has the impact occurred?

Monitoring for WhereNo

Monitoring for WhenYesNo

Temporal (Before-After)No

Optimal Impact Study (BACI)YesYesNo

Modern Analog ApproachNo

Reference Condition ApproachYesNo

Impact from Spatial PatternNo

Spatial Study (Control-Impact)YesYesYes

Experimental Design NameIs there a control area?

Is when and where known?

Has the impact occurred?

(Adapted from Green 1979 [Bowman and Somers 2005]; see also Underwood 1997)

History of the RCA• A product of researchers working on the common

challenge of studying an environment where an impact had (or was likely to have) occurred, but when and where the impact occurred were not known

• UK: RivPACS, Australia: AusRivAS, Canada: BEAST

• U.S.: Rapid-Bioassessment Procedures

(Wright et al. 2000, Bailey et al. 2004, Barbour et al. 1999, Bowman and Somers 2005)

Reference Condition Approach (RCA)

“Long-term monitoring programs…provide the measures of normal (reference data) against which the abnormal is judged. It is impossible to convince a court that something is wrong if ‘right’ is not defined.” – MOEE Biomonitoring Review Committee, 1994

Reference site

Test site

Multiple, minimally impacted control sites define the normal range of biological conditions to be expected at a test site

RCA StepsThe RCA has the following 5 steps (Bailey et al. 2004):

1. Minimally impacted reference sites are randomly selected and their biological communities and habitats are characterized.

2. Reference sites are grouped according to the similarity of their biological assemblages and/or habitats (depending on the approach used, a model that predicts a test site’s reference-state assemblage type, hence its reference-site group membership, may be built using a set of natural-habitat or physiographic attributes that are known to distinguish assemblage types).

3. A test site is sampled to characterize its biological community and habitat.

4. Appropriate reference sites are selected to define the normal or expected test-site condition.

5. Statistically test the bioassessment null hypothesis (i.e., that the test site is in reference condition).

Sample benthos and habitat at a variety ofrandomly selected, minimally impacted reference sites

Summarize the biological condition of reference sites. Group reference sites having similar biological communities.

Build a statistical model that predicts group membership based “niche variables” (physiographic variables that account for separation between groups)

Sample the biological community of a test site and characterize its niche attributes. Summarize biological condition using a set of metrics

Use physiographic model to predict test site to a reference group.

No

NoYesSite likely unimpaired. Resample periodically and confirm reference

group selection

Site may be impaired. Confirm reference group selection and resample.

If same result, investigate for causes of

impairment

Establish normal range of biological condition for test site using appropriate reference site group ( ref±2SD)

Suitable reference site group available?

Yes

Biological condition of test site is within normal range?

RCA Steps

RCA Messiness

-Different definitions of minimal impact, reference site classification methods, summarization and hypothesis-testing procedures (e.g., Wright et al. 2000, Linke et al. 2005).

-Different researchers have different approaches to each step (Bowman and Somers 2005)

RCA Step-1 Challenges : Reference Sites and Minimal Impact

1. Minimally impacted reference sites are randomly selected and their biological communities and habitats are characterized.

• “Sites that are not disturbed by human activities are ideal reference sites; however, land-use practices and atmospheric pollution have so altered the landscape and quality of water resources … that truly undisturbed sites are rarely available (Barbour et al. 1996). ”

• Standard criteria for minimal impact don’t exist• It is particularly difficult to find reference sites for large waterbodies and for any

waterbodies in areas where climate and geography favour agriculture or urban development

• randomly selecting reference sites may be difficult because of their restricted and aggregated spatial distribution, and because of their remote location and difficult access (Hughes 1995).

Reference Site Criteria: Wyoming

(U.S. EPA 1996)

Different weights for different attributesDifferent thresholds for different eco-regions

OBBN: Qualitative Definition of Minimal Impact

CRITERIA FOR “MINIMALLY IMPACTED” Well downstream of significant point sources Minimal regulation of water level (minimal affect from dams and impoundments) Extensive naturally vegetated buffer Well forested catchment Minimal development or urban land use in catchment Minimal agricultural land use in catchment Minimal impervious cover and artificial drainage in catchment Minimal anthropogenic acidification (i.e. pH matches expectation based on local geology) Water chemistry better than regulatory guidelines, e.g. Ontario Ministry of Environment PWQO’s (REF PWQO)

(Jones et al. 2004)

RCA Step-1 Challenges: What is a reference site?

X

OBBN Approaches to RCA Step 1• Sample a wide range of sites (but also ensure relevance to test sites)

• Reserve at least 10% of annual sampling effort for reference site re-sampling (same sites each year, or different sites in different years, or a combination of the two strategies)

• Ideally, sample enough reference sites to adequately describe the normal ranges of different types of waterbodies (~30 sites per group; Bowman and Somers 2005)

• Where insufficient reference sites exist, estimate normal range using best available sites, modeling, and applying best professional judgment.

Remember: • We don’t know how many assemblage types there are

• Try to sample some unusual sites (e.g. large rivers, clay plain streams)

OBBN Approaches to RCA Step 1

• Standard methods required: location, taxa counts, habitat data

• OBBN Coordinators provide QC checks on reference-site samples; confirmed taxa enumerations and physiographic data returned to collector

• Depending on question, impacted sites may be used in bioassessments; however, minimally impacted sites are always useful for determining relative condition

Use of Impacted Control Sites

CA1

CA2

Urban mine-impacted test site

Urban control site

Minimally impacted reference site

(e.g., Reynoldson et al. 2005)

? ?

(Hypothetical Data)

Use of Impacted Control Sites

CA1

CA2

Urban mine-impacted test site

Urban control site

Minimally impacted reference site

(e.g., Reynoldson et al. 2005)

!

(Hypothetical Data)

Send Reference

Site Samples(But Not

Like This)

RCA Challenges, Steps 2-4 : Sampling Methods, Classification and Prediction

2. Reference sites are grouped according to the similarity of their biological assemblages and/or habitats (depending on the approach used, a model that predicts a test site’s reference-state assemblage type, hence its reference-site group membership, may be built using a set of natural-habitat or physiographic attributes that are known to distinguish assemblage types).

3. A test site is sampled to characterize its biological community and habitat.

4. Appropriate reference sites are selected to define the normal or expected test-site condition.

RCA Challenges, Steps 2-4• No agreement on sampling methods (collection, sample processing,

taxonomic resolution)

• No agreement on data summarization (multivariate, multi-metric, hybrid)

• Difficult to know a priori which habitat attributes (and scale) to measure

• Numerous questions about classification: – Method (a priori vs a posteriori, statistical methods)? – # of groups? – # of sites per group? – Habitat measures to match ref and test sites?

Grouping reduces residual variation among reference sites and increases power of assessment BUT:• It goes against our knowledge that communities change continuously across environmental gradients• How many groups are there? CA1

CA

2Reference Gp. 1Reference Gp. 2Reference Gp. 3Test

(Gerritsen et al. 2000)

ReferenceSites

Test Site

Why Classify?

Grouping reduces residual variation among reference sites and increases power of assessment BUT:• It goes against our knowledge that communities change continuously across environmental gradients• How many groups are there? CA1

CA

2Reference Gp. 1Reference Gp. 2Reference Gp. 3Test

(Gerritsen et al. 2000)

ReferenceSites

Test Site

Why Classify?

2 main ways to group sites: a priori and a posteriori

a priori a posterioriGrouping method

Groups based on assumptions about factors that determine community composition (e.g., ecoregion); May under- or over-estimate # of groups because assumptions about deterministic factors may be incorrect; within- and between-group variance may not be optimal

Biological community composition dictates group; # of groups tends to make more biological sense

Prediction Easy; if you know the habitat attributes you know the group

Can be tricky because not all between-group variation can be explained and because deterministic factors may not be adequately measured

Different Approaches to Classification

Messiness in Classification

Different reference-site classification methods will result in different models of reference condition (e.g., Wright et al. 2000, Bowman and Somers 2005)

A1A2A3A4

A5

A6

A7

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B1

B2B3

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C1

C2C3C4

C5C6C7C8

C9

D1D2

D3

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D5D6

D7D8

D9

CA

A9

A8

A7

A6

A5

A4A3A2

A1

D8

D4C4

B9

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C1B4

B1D2

D1

C9

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C7C6C5

C3C2

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TWINSPATWINSPANC

A9

A8

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A5

A4A3A2

A1

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B5B3B2

B1

C9C7C6C5

C4C3C2

C1B4

D3

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D8 D7D6

D5

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D2D1

C8

UPGMAD

A9

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A5

A4A3A2

A1

C3

B9

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B5B3B2

B1

D3C9

C7C6C5

C4C2

C1B4

D9

D8 D7D6

D5

D4

D2D1

C8

Ward'sE

A8

A6

A5

A4A3A2

A1

B9

B8

B7

B6

B5

B4

B3B2

B1

C3D3

D7D6

C8

C7C6C5

C4C2

C1

D9

D8 D5

D4

D2D1

C9

A9

A7

K-meansF

A1

A2A3A4

A5

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B1

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C1

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D1 D2

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NMDSBA

PCO1

PCO

2

PCO1

PCO

2

Further Messiness in Classification

A 2-axis Principle Coordinates Analysis ordination plot showing a seemingly appropriate set of 22 reference sites defining an assemblage type (left), and an alternate classification (right) of two groups of 20 sites that results from adding additional data for an assemblage type that was under-represented in the solution shown at left. Ellipses represent 90% confidence bounds for each assemblage type. Hypothetical data: Group 1 sites (diamond symbols) were simulated as randomly distributed variables (mean PCO1 = 1, mean PCO2 = 3.5); group 2 (squares) had mean PCO1 = 4 and PCO2 = 1. The standard deviations for PCO1 and PCO2 values was 1 for both groups.

OBBN Approach, Steps 2-4• Balance standardization with flexibility• Classification-free reference-site matching: Nearest Neighbour• Sampling is more than just collecting bugs: in data-driven approach, niche

variables used to select reference sites for test sites• Habitat characterized with site-, reach-, and catchment-scale measures• To summarize biotic composition, a variety of indices should be used,

because each summarizes and emphasizes different patterns in the assemblage. Further guidance may be given as we learn more about responses to stressors in different parts of the province

• Analytical software defaults will reflect current knowledge and recommendations

• Selecting reference sites will be automated by OBBN/CABIN database• Refining models is a research priority

Classification vs. Nearest Neighbour

Predictor 1

Pred

icto

r 2

ClassificationApproach

Nearest-Neighbour orClassification-free Approach

Predictor 1

Pred

icto

r 2

(Simulated Data)

RCA Challenges, Step 55. Statistically test the bioassessment null hypothesis

(i.e., that the test site is in reference condition).

• How much deviation from normal is ecologically significant? What level of confidence is required?

• Hypothesis-testing methods differ in the way they implicitly define “health” or biological integrity, in their assumptions, in their manner of quantifying biological condition and effects, in the format of their outputs, and in the predictability of their response to stress (Norris and Hawkins 2000)– U.K. and Australia: Ratio of expected-to-observed taxa richness, (e.g., Davies

2000 and Moss 2000)– U.S.: Multi-metric scores, with biocriteria set using regional reference sites (e.g.,

Barbour and Yoder 2000); – Canada: Ordination-axis-scores compared against confidence ellipses for

reference sites (e.g., Reynoldson et al. 2000).

Ecologically Significant Effect

• When testing bioassessment hypotheses (H0: test site normal), critical effect size must be defined a priori

• Central test (H0: no difference) not biologically meaningful or management-relevant

• OBBN-recommended: 95% of reference site distribution …but need to consider Type I (false positive) & Type II (false negative) error rates and their consequences

(Bowman and Somers 2005, Jones et al. 2004)

Biocriteria Messiness: Error

Rate and Effect Size Considerations

Null Hypothesis

Decision True FalseReject H0 Type I error

(false positive, α)

Correct decision

Accept H0 Correct decision

Type II error(false

negative, ) (From Bailey et al. 2004)

Biocriteria: Summary of Key Points• Biocriteria: critical values for testing bioassessment null hypothesis (H0:

test site normal)• Confidence in bioassessment decision (i.e., pass or fail) depends on how

well we model normal range, and therefore how well we estimate probabilities of false positives and false negatives

• Setting biocriteria means trade-offs between Type-I and Type-II error rates: consider the consequences of these errors (management responses and costs)

• There is no magic α-level• Determining Type-II error rate requires a set of observations that are

known to deviate from normal by a specified effect size (this requires simulated data)

(Bailey et al. 2004, Jones et al. 2004, Bowman and Somers 2005)

OBBN Approach, RCA Step 5• Use data from the same season• Test Site Analysis (TSA; Bowman and Somers 2005,

2006a, and 2006b) is recommended method for testing bioassessment null hypothesis; Represents a convergence of multivariate and multi-metric methods:– Multiple indices are used to summarize composition – A non-central multivariate equivalence test (e.g., McBride 1993)

is calculated using all indices and considering redundancies among the summary indices (test statistics include D, F, and p)

– Why not just count-up individual passes and fails?– If the site fails, a discriminant analysis is done to describe the

effect size associated with each of the indices used in the equivalence test thereby characterizing the test-site’s response signature.

OBBN Approach, RCA Step 5

• OBBN recommends 95th percentile of reference-site distribution as biocriterion (but need to consider error rates and power appropriate for specific studies

• This step will ultimately be automated by OBBN database

Summary Index 1

Sum

mar

y In

dex

2

ReferenceTestCentroid

(Simulated Data)

Does our Site Pass?Cumulative Probability

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Bray-Curtis Distance

Perc

entil

e

(Simulated Data)

Walker's

Keast

Black

Sheldon 2SheldonPine

Nott2

Noisy

NottCentre

Silver

Misc. Diptera

SimulidaeTipulidae

Ceratopogonidae

Chironomidae

Gastropoda

Coleoptera

Trichoptera

Megaloptera

PlecopteraAnisopteraEphemeroptera

Mites

Decapoda

Amphipoda

IsopodaHirudinea

OligochaetaTurbellaria

-8

-6

-4

-2

0

2

4

6

8

10

-4 -3 -2 -1 0 1 2 3 4

columns

rows

50% Ellipse

75% Ellipse

95% Ellipse

99.9 Ellipse

RCA Bioassessment Example

RCA Bioassessment Example

WillowPine Everett

Pine River (Mulmur)

mad River

Boyne River

Teesw ater RiverNorth Saugeen River

Penetangore North

Penetangore South

Misc. DipteraSimulidae

Tipulidae

Ceratopogonidae

Tabanidae

Chironomidae

Gastropoda

ColeopteraLepidoptera Trichoptera

Megaloptera

HemipteraPlecoptera

ZygopteraAnisoptera

Ephemeroptera

Mites

DecapodaAmphipoda

Pelecypoda

Isopoda Hirudinea

Oligochaeta

Turbellaria

-4

-3

-2

-1

0

1

2

3

-4 -3 -2 -1 0 1 2 3 4

columns

row s

50% Ellipse

75% Ellipse

95% Ellipse

Sampling Methods

Sampling Protocols

Standardization vs. FlexibilityBiomonitoring

Component Recommendation

Study Design Reference Condition Approach Benthos Collection Method

Travelling-Kick-and-Sweep (where possible); replication in lakes and wetlands, sub-sampling in streams

Mesh Size 500 m Time of Year Any season; assessment comparisons use data from the same season Picking In lab (preferred) or in field (optional); preserved (preferred) or live

(optional), microscope (preferred) or visually unaided (optional); random sub-sampling using Marchant Box (preferred) or Bucket Method (optional) to provide a minimum 100-animal count per sample

Taxonomic Level Mix of 27 Phyla, Classes, Orders and Families (minimum); Family (preferred); Genus/Species (optional, recommended for reference sites)1

Analysis (Bioassessment Hypothesis Testing)

Test Site Analysis (TSA; see Appendix 9): Mahalanobis distance (e.g., Legendre and Legendre 1998) calculated across selected summary metrics; non-central significance test to determine if biological distance between test site and reference site group mean is larger than a specified effect size; if the null hypothesis (H0: │Dtest – Dreference mean │≤ critical effect size) is rejected, use discriminant function analysis to identify metrics contributing most to the separation between the test site and reference condition

Protocol Instruction Format1. Sampling unit/inference 2. Replication3. Benthos collection methods

General Comments:1. Some protocols require evaluation and may be updated2. There may be situations in which protocols will not work as

written. In this case, adapt as necessary3. If time or property access limit ability to apply techniques, collect

what you can. Some information is better than none4. Obtain landowner permission5. Avoid sensitive times (e.g., fish spawning) and sensitive habitats6. Adjust sampling effort if experience shows a habitat to have

exceptionally high or low benthos densities

(Excerpt from Protocol Manual)

Sub-sampling vs. Replication• Sub-sampling: “In some experimental situations, several

observations may be made within the experimental unit … such observations are made on sub-samples of sampling units. Differences among sub-samples within an experimental unit are observational differences rather than experimental unit differences”

• Replication: “When a treatment appears more than once in an experiment, it is said to be replicated.”

(Steel and Torrie 1980)

Lakes • Sampling Unit• Replication• Collection method

Replicate #1

Replicate #2

Replicate #3

Transect1 m depth contour

Lake Segment (sampling unit) • Sampling unit is

“lake segment”• 10 minute

traveling kick and sweep along transects

• 3 replicates collected

Streams• Sampling unit• Alternate

definitions (pg. 21)

A

B

Cross Section A-B

A B

Top of both banks approximately same height from water surface

Channel Mid Line

Thalweg

Cross-over Point

Sampling Reach Boundary

Flow Direction

Streams • Replication & collection methods

Transect Traveling Kick and Sweep

Flow

Optional Transect

Sampling Location

Sampling ReachBoundary

• Samling unit encompasses 2 riffles and 1 pool (often meander sequence)

• 2 transect subsamples in riffles, one in pool

• ~ 3 minute, 10 m kick

Pool

Riffle or

cross-over

Riffle or

cross-over

Riffle or

cross-over

Pool

Applying Traveling Kick and Sweep in Large or Small Streams

Flow

TransectSampled portion of transect

Current Speed Distribution1 2 3 4 5

Stratum boundary

Flow

TransectSupplementary

Transect

Pool

Riffle

Riffle

Streams: Grab Sampling

Ekman, Ponar or other grab sample

Sampling ReachBoundary

Flow

OptionalTransect

• Sampling unit encompasses 2 riffles and one pool (meander sequence)

• 2 transects in riffles, 1 transect in pool• Each subsample is a composite of 3 (or more) grabs

Pool

Riffle or

cross-over

Riffle or

cross-over

Riffle or

cross-over

Pool

Traveling Kick Transect

Stovepipe Core Sample

Jab and Sweep Sample

1 m depth contour

2 m depth contour

Wetland Segment (replicate)

• Sampling Unit• Replication• Collection Methods

Wetlands

Wetlands: Selecting Collection Method

Water Depth

Substrate Type

Plant Density

Recommended Gear

Recommended Technique

0.15-1 m Stable (e.g., sand/gravel)

Low D-net Traveling kick and Sweep

0.05-1 m Soft (e.g., organic, muck)

moderate D-net Jab and Sweep

<0.05 m or saturated soils

Soft to moderately stable

Any Stovepipe Corer Core

Summary of Collection MethodsCollection Method Streams Lakes Wetlands

Traveling kick and sweep; standard method for wadeable habitats Grab samples (Ekman Dredge, Ponar Grab, or similar); option for deep water sites O OJab and Sweep; option for wadeable, sparsely vegetated, soft sediments OCoring; option for deep or very shallow water (especially in shallow wetland soils) O OArtificial substrate; option for atypical habitats or special studies O O O

Sampling Groups

1 2 3Gerry Sullivan Angela Wallace Lisa Campbell

Christine Spedalieri Nancy Harrtrup Robin TapleyChris Brown Rebecca Scobie Valerie StevensonTrevor Middel John Haselmayer Scott Parker

Ben Jewiss Suzanne Partridge Liisa KearneyCassandra Borm Alana Nunn Julie Hordowick

4 5Lynette Dawson Rebecca Crockford

Beth Gilbert Rajesh BejankiwarMarnie Guindon Erin McGauley

Diana Tyner Sara KellyDebbie DePasquale Carolyn Paterson

Vince D'Elia Josh Hevenor

Sample Processing

• Sieve• Sub-sample

– Marchant Box (preferred)– Bucket method

• Sort carefully (Optional: microscope or magnifier)

• Identify and tally (taxonomic level matches training)

• 100 count (minimum)• Preserve and archive sample

Sample Processing: Transporting to Lab

• Sieve in net in field• Release non-benthos • Keep live samples cool• Label transport containers inside and out

(date, location, sample number, etc.)

Sample Processing: Sieving

• Must be done to remove fines• Preliminary done in field, thorough done in

lab• 0.5 mm mesh sieve• Remove large pieces (rocks, wood)

Sample Processing: Sub-sampling & Picking

• Need random sub-samples• 100-count but sort entire last sub-sample• Consider suction device if using Marchant Box• If using Bucket Method, estimate portion picked

by weight or volume• A bit of soap will sink floaters• Screen for fast moving• Sort thoroughly

Benthos ID: 27 Group Level

Coelenterata(Hydras)

Turbellaria(Flatworms)

Nematoda(Roundworms)

Oligochaeta(Aquatic Earthworms)

Hirudinea(Leeches)

Isopoda(Sow Bugs)

Decapoda (Crayfish)

Trombidiformes-Hydracarina(Mites)

Ephemeroptera(Mayflies)

Anisoptera (Dragonflies)

Zygoptera(Damselflies)

Amphipoda(Scuds)

Plecoptera(Stoneflies)

Hemiptera (True Bugs)

Megaloptera (Fishflies, Alderflies)

Trichoptera (Caddisflies)

Lepidoptera(Aquatic Moths)

Coleoptera (Beetles)

Gastropoda (Snails, limpets)

Pelecypoda (Clams)

Chironomidae(Midges)

Tabanidae (Horse and Deer Flies)

Culicidae(Mosquitos)

Ceratopogonidae(No-see-ums)

Tipulidae (Crane Flies)

Simuliidae(Black Flies)

Misc. Diptera (Misc. True Flies)

Version 1.0, revised March 2004Ontario Benthos Biomonitoring Network

Water Body Name: _________________________ Site #: ____________ Replicate #: ______ Date (mm/dd/yyyy) and Time: _________________________

Organization: _____________________________ Department_______________________ Address:_____________________________________________

Contact: ________________ Phone: _________________ E-mail: _____________________________ % picked for 100-count ______ # of vials: _________

Circle Method: (Sub-sampling) Marchant Box / Teaspoon (Location) Field / Lab (Preservation) Live / Preserved (Magnification) Microscope / Unaided

Sample Processing: Preservation

• Formalin or Alcohol can be used• Small volumes can be discharged to septic

system or municipal sewage system• Safe storage• Avoid poisonous denatured alcohols• Replace formalin with alcohol after a

couple of days

Habitat CharacterizationDone for 2 reasons:

1. Niche Attributes2. Diagnosis

diagnostic useful in determining cause (often of biological impairment)

niche variable a natural habitat (often physiographic) variable that accounts for a significant portion of the difference in biological condition between reference site groups

Habitat Characterization (Table 10, Pg. 37)

Measured at site Measured remotely (GIS)

Location (latitude & longitude)

Organic matter, areal coverage

Elevation Riparian vegetation

Water temperature Canopy cover (%)

Dissolved oxygen, pH, conductivity, alkalinity

Aquatic macrophytes and algae

Maximum Depth Bank full width (m)

Maximum hydraulic head

Instantaneous discharge (m3/s)

Wetted width Perennial or intermittent(presence of standing water)

Dominant substrate classes

Drainage area

Base Flow Index

Basin relief

Mean annual lake evaporationLength of main channel Mean annual precipitationMean Annual Run-offMean Annual SnowfallMaximum Watershed ElevationMean ElevationMaximum Flow Distance

Minimum Watershed ElevationMean Slope of Watershed Catchment PerimeterShape factorSlope of main channelTributary densityCatchment land cover (areal proportions of 28 land cover types) OrderAspectAreaPerimeterFetch

TSA

Insert TSA Section: Michelle Bowman

General Discussion/Review

Certification Test• Test is optional• Passing grade for both multiple-choice and benthos identification tests is

90%• For benthos identification test:

– Participants can use references– Trainers are not permitted to use references, and a correct answer

includes both the taxonomic group and at least 2 diagnostic characters

• Students cannot be immediately certified without a passing grade, but arrangements can be made for a re-test (you do not have to redo the course to re-take the test)